From 22be8cc7b9df64a2b9655bbbc85f533810a32c91 Mon Sep 17 00:00:00 2001
From: Ramanathan Rm <rm.ramanathan@gmail.com>
Date: Sun, 22 Jun 2025 20:49:49 +0530
Subject: [PATCH 1/7] Update calculator.py

---
 BaseCamp3/Day_2/calculator.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/BaseCamp3/Day_2/calculator.py b/BaseCamp3/Day_2/calculator.py
index 331c76de..ec8fa078 100644
--- a/BaseCamp3/Day_2/calculator.py
+++ b/BaseCamp3/Day_2/calculator.py
@@ -6,7 +6,7 @@
 st.write("This app connects to a FastAPI calculator service.")
 
 # Define the API base URL
-api_url = "http://0.0.0.0:9321"
+api_url = "https://genaiengineering-cohort2-uzeu.onrender.com"
 
 # Initialize session state to store the calculator display and current operation
 if 'display' not in st.session_state:
@@ -63,7 +63,8 @@ def calculate_result():
         endpoint = f"{api_url}/{st.session_state.operation}"
 
         # Make the API call
-        response = requests.get(endpoint, params={"a": first_num, "b": second_num})
+        # response = requests.get(endpoint, params={"a": first_num, "b": second_num})
+        response = requests.post(endpoint, json={"a": first_num, "b": second_num})
 
         # Check if the request was successful
         if response.status_code == 200:
@@ -138,4 +139,4 @@ def calculate_result():
 4. Click "C" to clear the calculator
 """)
 
-# Run with: streamlit run streamlit_calculator.py
\ No newline at end of file
+# Run with: streamlit run streamlit_calculator.py

From 48186edb3b725c9d94cd1cfdc634e8a8c66875c6 Mon Sep 17 00:00:00 2001
From: Ramanathan Rm <rm.ramanathan@gmail.com>
Date: Sun, 22 Jun 2025 20:52:51 +0530
Subject: [PATCH 2/7] Added Dev Container Folder

---
 .devcontainer/devcontainer.json | 33 +++++++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 .devcontainer/devcontainer.json

diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 00000000..a71a77bb
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,33 @@
+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "BaseCamp3/Day_2/calculator.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run BaseCamp3/Day_2/calculator.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}
\ No newline at end of file

From b02abb9de4d135ebebeb40297d93bb8773046a89 Mon Sep 17 00:00:00 2001
From: Ramanathan Rm <rm.ramanathan@gmail.com>
Date: Sun, 22 Jun 2025 20:54:03 +0530
Subject: [PATCH 3/7] Update calculator.py

---
 BaseCamp3/Day_2/calculator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/BaseCamp3/Day_2/calculator.py b/BaseCamp3/Day_2/calculator.py
index ec8fa078..172a0d13 100644
--- a/BaseCamp3/Day_2/calculator.py
+++ b/BaseCamp3/Day_2/calculator.py
@@ -6,7 +6,7 @@
 st.write("This app connects to a FastAPI calculator service.")
 
 # Define the API base URL
-api_url = "https://genaiengineering-cohort2-uzeu.onrender.com"
+api_url = "https://genaiengineering-cohort2-uzeu.onrender.com/"
 
 # Initialize session state to store the calculator display and current operation
 if 'display' not in st.session_state:

From 0c3202924a63c665cf066db37967dd14c1c0f4ef Mon Sep 17 00:00:00 2001
From: Ramanathan Rm <rm.ramanathan@gmail.com>
Date: Sun, 22 Jun 2025 21:04:37 +0530
Subject: [PATCH 4/7] Update calculator.py

---
 BaseCamp3/Day_2/calculator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/BaseCamp3/Day_2/calculator.py b/BaseCamp3/Day_2/calculator.py
index 172a0d13..ec8fa078 100644
--- a/BaseCamp3/Day_2/calculator.py
+++ b/BaseCamp3/Day_2/calculator.py
@@ -6,7 +6,7 @@
 st.write("This app connects to a FastAPI calculator service.")
 
 # Define the API base URL
-api_url = "https://genaiengineering-cohort2-uzeu.onrender.com/"
+api_url = "https://genaiengineering-cohort2-uzeu.onrender.com"
 
 # Initialize session state to store the calculator display and current operation
 if 'display' not in st.session_state:

From aa2faeb0785811a28eb47bdfe56d9b19fd8c6fbf Mon Sep 17 00:00:00 2001
From: RaamRaam <rm.ramanathan@gmail.com>
Date: Fri, 27 Jun 2025 14:57:02 +0530
Subject: [PATCH 5/7] added week1 content

---
 .DS_Store                                     |   Bin 6148 -> 8196 bytes
 .gitignore                                    |     3 +
 BaseCamp3/Day_1/2a_calculator.py              |    10 +-
 BaseCamp3/Day_1/2b_calculator.py              |     8 +-
 BaseCamp3/Day_2/calculator.py                 |     4 +
 Week1/Day_1/1_clustering_visualization.html   |   986 +
 Week1/Day_1/1_kmeans_animation.html           |  1067 +
 .../2_1d-classification-probability.html      |   917 +
 .../2_classification-train-test-demo.html     |   897 +
 Week1/Day_1/2_iris_ml_analysis_notebook.ipynb |  1532 +
 Week1/Day_1/3_fixed-linear-regression.html    |  1037 +
 .../3_regression-to-neural-networks.html      |   751 +
 Week1/Day_1/3_train-test-split-demo.html      |   750 +
 Week1/Day_1/4_ensemble-automl-intro.html      |   453 +
 .../5_airline_forecasting_notebook.ipynb      |  1648 +
 Week1/Day_1/5_timeseries-forecasting-viz.html |   891 +
 Week1/Day_1/Advanced_Pandas_homework.md       |   336 +
 Week1/Day_1/airline_passengers.csv            |   145 +
 Week1/Day_1/iris.csv                          |   151 +
 Week1/Day_1/pandas.ipynb                      |  4150 ++
 Week1/Day_1/pandas_homework.md                |   143 +
 Week1/Day_1/pandas_homework_solution.ipynb    |   400 +
 Week1/Day_1/pandasql.ipynb                    |  2902 ++
 Week1/Day_2/bigram_frequencies.png            |   Bin 0 -> 79713 bytes
 Week1/Day_2/bigram_probs.json                 | 31752 +++++++++++++
 Week1/Day_2/custom_entity_types.png           |   Bin 0 -> 22328 bytes
 Week1/Day_2/embedding-features-viz.html       |  1980 +
 Week1/Day_2/entity_comparison.png             |   Bin 0 -> 26046 bytes
 Week1/Day_2/entity_type_distribution.png      |   Bin 0 -> 21369 bytes
 Week1/Day_2/generate.ipynb                    |   417 +
 Week1/Day_2/markov-text-generator.html        |   884 +
 Week1/Day_2/movie_reviews.csv                 |  1001 +
 Week1/Day_2/negative_wordcloud.png            |   Bin 0 -> 446605 bytes
 Week1/Day_2/nlp_basics_notebook.ipynb         |   769 +
 Week1/Day_2/nlp_homework.md                   |   641 +
 Week1/Day_2/nlp_homework_solution.ipynb       |  1345 +
 Week1/Day_2/pdfs/IndianBudget2025.pdf         |   Bin 0 -> 724405 bytes
 Week1/Day_2/pdfs/LLM Introduction.pdf         |   Bin 0 -> 2233875 bytes
 Week1/Day_2/pdfs/LLM Python.pdf               |   Bin 0 -> 377482 bytes
 Week1/Day_2/pdfs/LLM.pdf                      |   Bin 0 -> 1304748 bytes
 Week1/Day_2/pdfs/NASDAQ_AAPL_2023.pdf         |   Bin 0 -> 807316 bytes
 Week1/Day_2/pdfs/NASDAQ_MSFT_2023.pdf         |   Bin 0 -> 2434632 bytes
 Week1/Day_2/pdfs/RAG MEETS LLMS.pdf           |   Bin 0 -> 13005582 bytes
 ...tephen_hawking_a_brief_history_of_time.pdf |   Bin 0 -> 1508627 bytes
 Week1/Day_2/positive_wordcloud.png            |   Bin 0 -> 489915 bytes
 Week1/Day_2/review_length_distribution.png    |   Bin 0 -> 34552 bytes
 Week1/Day_2/search_engine.py                  |    58 +
 Week1/Day_2/search_ui.py                      |   109 +
 Week1/Day_2/text-embedding-techniques.html    |   952 +
 Week1/Day_2/tfidf_scores.png                  |   Bin 0 -> 76136 bytes
 Week1/Day_2/top_entities_by_type.png          |   Bin 0 -> 75561 bytes
 Week1/Day_2/transformer-sentence-heatmap.html |   915 +
 Week1/Day_2/trigram_frequencies.png           |   Bin 0 -> 108054 bytes
 Week1/Day_2/trigram_probs.json                | 38900 ++++++++++++++++
 Week1/Day_2/unigram_probs.json                | 14971 ++++++
 Week1/requirements.txt                        |    70 +
 calc.py                                       |    13 -
 requirements.txt                              |   107 +
 58 files changed, 114043 insertions(+), 22 deletions(-)
 create mode 100644 Week1/Day_1/1_clustering_visualization.html
 create mode 100644 Week1/Day_1/1_kmeans_animation.html
 create mode 100644 Week1/Day_1/2_1d-classification-probability.html
 create mode 100644 Week1/Day_1/2_classification-train-test-demo.html
 create mode 100644 Week1/Day_1/2_iris_ml_analysis_notebook.ipynb
 create mode 100644 Week1/Day_1/3_fixed-linear-regression.html
 create mode 100644 Week1/Day_1/3_regression-to-neural-networks.html
 create mode 100644 Week1/Day_1/3_train-test-split-demo.html
 create mode 100644 Week1/Day_1/4_ensemble-automl-intro.html
 create mode 100644 Week1/Day_1/5_airline_forecasting_notebook.ipynb
 create mode 100644 Week1/Day_1/5_timeseries-forecasting-viz.html
 create mode 100644 Week1/Day_1/Advanced_Pandas_homework.md
 create mode 100644 Week1/Day_1/airline_passengers.csv
 create mode 100644 Week1/Day_1/iris.csv
 create mode 100644 Week1/Day_1/pandas.ipynb
 create mode 100644 Week1/Day_1/pandas_homework.md
 create mode 100644 Week1/Day_1/pandas_homework_solution.ipynb
 create mode 100644 Week1/Day_1/pandasql.ipynb
 create mode 100644 Week1/Day_2/bigram_frequencies.png
 create mode 100644 Week1/Day_2/bigram_probs.json
 create mode 100644 Week1/Day_2/custom_entity_types.png
 create mode 100644 Week1/Day_2/embedding-features-viz.html
 create mode 100644 Week1/Day_2/entity_comparison.png
 create mode 100644 Week1/Day_2/entity_type_distribution.png
 create mode 100644 Week1/Day_2/generate.ipynb
 create mode 100644 Week1/Day_2/markov-text-generator.html
 create mode 100644 Week1/Day_2/movie_reviews.csv
 create mode 100644 Week1/Day_2/negative_wordcloud.png
 create mode 100644 Week1/Day_2/nlp_basics_notebook.ipynb
 create mode 100644 Week1/Day_2/nlp_homework.md
 create mode 100644 Week1/Day_2/nlp_homework_solution.ipynb
 create mode 100644 Week1/Day_2/pdfs/IndianBudget2025.pdf
 create mode 100644 Week1/Day_2/pdfs/LLM Introduction.pdf
 create mode 100644 Week1/Day_2/pdfs/LLM Python.pdf
 create mode 100644 Week1/Day_2/pdfs/LLM.pdf
 create mode 100644 Week1/Day_2/pdfs/NASDAQ_AAPL_2023.pdf
 create mode 100644 Week1/Day_2/pdfs/NASDAQ_MSFT_2023.pdf
 create mode 100644 Week1/Day_2/pdfs/RAG MEETS LLMS.pdf
 create mode 100644 Week1/Day_2/pdfs/stephen_hawking_a_brief_history_of_time.pdf
 create mode 100644 Week1/Day_2/positive_wordcloud.png
 create mode 100644 Week1/Day_2/review_length_distribution.png
 create mode 100644 Week1/Day_2/search_engine.py
 create mode 100644 Week1/Day_2/search_ui.py
 create mode 100644 Week1/Day_2/text-embedding-techniques.html
 create mode 100644 Week1/Day_2/tfidf_scores.png
 create mode 100644 Week1/Day_2/top_entities_by_type.png
 create mode 100644 Week1/Day_2/transformer-sentence-heatmap.html
 create mode 100644 Week1/Day_2/trigram_frequencies.png
 create mode 100644 Week1/Day_2/trigram_probs.json
 create mode 100644 Week1/Day_2/unigram_probs.json
 create mode 100644 Week1/requirements.txt
 delete mode 100644 calc.py
 create mode 100644 requirements.txt

diff --git a/.DS_Store b/.DS_Store
index 7c3ee2aa5f1d6ff1d690a6ef79532d0cc15ccd02..5cf75375c94a01a32afb4f4f9b3d45ae936d2c61 100644
GIT binary patch
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diff --git a/.gitignore b/.gitignore
index 0cfa4c0c..3f0feba6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,9 @@
 *.py[cod]
 *$py.class
 
+**/*_solution
+**/2_tabular_classification.ipynb
+**/3_tabular_regression.ipynb
 # C extensions
 *.so
 
diff --git a/BaseCamp3/Day_1/2a_calculator.py b/BaseCamp3/Day_1/2a_calculator.py
index 995c1a35..681adf51 100644
--- a/BaseCamp3/Day_1/2a_calculator.py
+++ b/BaseCamp3/Day_1/2a_calculator.py
@@ -8,15 +8,15 @@
 @app.get("/add")
 def add(a, b):
     """Add two numbers and return the result."""
-    # result = float(a) + float(b)
-    result = a+b
+    result = float(a) + float(b)
+    # result = a+b
     return {"operation": "add", "a": a, "b": b, "result": result}
 
 @app.get("/subtract")
 def subtract(a, b):
     """Subtract b from a and return the result."""
-    # result = float(a) - float(b)
-    result = a-b
+    result = float(a) - float(b)
+    # result = a-b
     return {"operation": "subtract", "a": a, "b": b, "result": result}
 
 @app.get("/")
@@ -32,6 +32,6 @@ def read_root():
 
 '''
 import requests
-response = requests.get("http://0.0.0.0:9321/add", params={"a": 5, "b": 3})
+response = requests.get("http://localhost:9321/add", params={"a": 5, "b": 3})
 print(response.json())
 '''
diff --git a/BaseCamp3/Day_1/2b_calculator.py b/BaseCamp3/Day_1/2b_calculator.py
index 2628757f..ef7fe6fa 100644
--- a/BaseCamp3/Day_1/2b_calculator.py
+++ b/BaseCamp3/Day_1/2b_calculator.py
@@ -8,15 +8,15 @@
 @app.get("/add")
 def add(a, b):
     """Add two numbers and return the result."""
-    # result = float(a) + float(b)
-    result = a+b
+    result = float(a) + float(b)
+    # result = a+b
     return {"operation": "add", "a": a, "b": b, "result": result}
 
 @app.get("/subtract")
 def subtract(a, b):
     """Subtract b from a and return the result."""
-    # result = float(a) - float(b)
-    result = a-b
+    result = float(a) - float(b)
+    # result = a-b
     return {"operation": "subtract", "a": a, "b": b, "result": result}
 
 @app.get("/")
diff --git a/BaseCamp3/Day_2/calculator.py b/BaseCamp3/Day_2/calculator.py
index ec8fa078..a441f101 100644
--- a/BaseCamp3/Day_2/calculator.py
+++ b/BaseCamp3/Day_2/calculator.py
@@ -6,6 +6,10 @@
 st.write("This app connects to a FastAPI calculator service.")
 
 # Define the API base URL
+<<<<<<< HEAD
+=======
+# api_url = "http://0.0.0.0:9321"
+>>>>>>> efc449e (added week3 content)
 api_url = "https://genaiengineering-cohort2-uzeu.onrender.com"
 
 # Initialize session state to store the calculator display and current operation
diff --git a/Week1/Day_1/1_clustering_visualization.html b/Week1/Day_1/1_clustering_visualization.html
new file mode 100644
index 00000000..230f3836
--- /dev/null
+++ b/Week1/Day_1/1_clustering_visualization.html
@@ -0,0 +1,986 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Complete Guide to Clustering: 1D and 2D Examples</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.9.1/chart.min.js"></script>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            line-height: 1.6;
+            color: #1f2937;
+            background-color: #f9fafb;
+        }
+        
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            padding: 24px;
+            background-color: white;
+        }
+        
+        .header {
+            text-align: center;
+            margin-bottom: 32px;
+        }
+        
+        .header h1 {
+            font-size: 2rem;
+            font-weight: bold;
+            color: #1f2937;
+            margin-bottom: 16px;
+        }
+        
+        .header p {
+            font-size: 1.125rem;
+            color: #6b7280;
+        }
+        
+        .controls {
+            margin-bottom: 24px;
+            display: flex;
+            flex-direction: column;
+            gap: 16px;
+            align-items: center;
+        }
+        
+        .button-group {
+            display: flex;
+            background-color: #f3f4f6;
+            border-radius: 8px;
+            padding: 4px;
+        }
+        
+        .button-group button {
+            padding: 8px 24px;
+            border: none;
+            border-radius: 6px;
+            cursor: pointer;
+            transition: all 0.2s;
+            font-size: 14px;
+        }
+        
+        .button-group button.active {
+            background-color: #7c3aed;
+            color: white;
+        }
+        
+        .button-group button:not(.active) {
+            background-color: transparent;
+            color: #6b7280;
+        }
+        
+        .button-group button:not(.active):hover {
+            background-color: #e5e7eb;
+        }
+        
+        .button-group.scenario .active {
+            background-color: #2563eb;
+        }
+        
+        .scenario-description {
+            margin-bottom: 24px;
+            padding: 16px;
+            border-radius: 8px;
+            background-color: #f9fafb;
+        }
+        
+        .scenario-description h2 {
+            font-size: 1.25rem;
+            font-weight: 600;
+            margin-bottom: 8px;
+        }
+        
+        .scenario-description.single h2 {
+            color: #047857;
+        }
+        
+        .scenario-description.multiple h2 {
+            color: #b91c1c;
+        }
+        
+        .scenario-description.clustering h2 {
+            color: #1d4ed8;
+        }
+        
+        .highlight-box {
+            padding: 12px;
+            border-radius: 6px;
+            border-left: 4px solid;
+            margin-top: 12px;
+        }
+        
+        .highlight-box.green {
+            background-color: #ecfdf5;
+            border-color: #10b981;
+            color: #065f46;
+        }
+        
+        .highlight-box.red {
+            background-color: #fef2f2;
+            border-color: #ef4444;
+            color: #991b1b;
+        }
+        
+        .highlight-box.blue {
+            background-color: #eff6ff;
+            border-color: #3b82f6;
+            color: #1e40af;
+        }
+        
+        .highlight-box.yellow {
+            background-color: #fffbeb;
+            border-color: #f59e0b;
+            color: #92400e;
+        }
+        
+        .chart-container {
+            margin-bottom: 24px;
+            padding: 16px;
+            background-color: white;
+            border: 1px solid #e5e7eb;
+            border-radius: 8px;
+        }
+        
+        .chart-container h3 {
+            font-size: 1.125rem;
+            font-weight: 600;
+            margin-bottom: 16px;
+        }
+        
+        .chart-wrapper {
+            position: relative;
+            height: 400px;
+        }
+        
+        .legend {
+            margin-bottom: 24px;
+            padding: 16px;
+            background-color: #f9fafb;
+            border-radius: 8px;
+        }
+        
+        .legend h4 {
+            font-weight: 600;
+            color: #1f2937;
+            margin-bottom: 8px;
+        }
+        
+        .legend-items {
+            display: flex;
+            flex-wrap: wrap;
+            gap: 16px;
+        }
+        
+        .legend-item {
+            display: flex;
+            align-items: center;
+        }
+        
+        .legend-color {
+            width: 16px;
+            height: 16px;
+            border-radius: 2px;
+            margin-right: 8px;
+        }
+        
+        .legend-text {
+            font-size: 14px;
+            font-weight: 500;
+            text-transform: capitalize;
+        }
+        
+        .analysis-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
+            gap: 24px;
+            margin-bottom: 32px;
+        }
+        
+        .analysis-card {
+            padding: 16px;
+            border-radius: 8px;
+        }
+        
+        .analysis-card.blue {
+            background-color: #eff6ff;
+        }
+        
+        .analysis-card.purple {
+            background-color: #f5f3ff;
+        }
+        
+        .analysis-card.yellow {
+            background-color: #fffbeb;
+        }
+        
+        .analysis-card h3 {
+            font-weight: 600;
+            margin-bottom: 12px;
+        }
+        
+        .analysis-card.blue h3 {
+            color: #1e3a8a;
+        }
+        
+        .analysis-card.purple h3 {
+            color: #581c87;
+        }
+        
+        .analysis-card.yellow h3 {
+            color: #92400e;
+        }
+        
+        .stat-item {
+            margin-bottom: 8px;
+            font-size: 14px;
+        }
+        
+        .cluster-stat {
+            margin-left: 12px;
+        }
+        
+        .bottom-lesson {
+            margin-top: 32px;
+            padding: 24px;
+            background-color: #1f2937;
+            color: white;
+            border-radius: 8px;
+        }
+        
+        .bottom-lesson h3 {
+            font-size: 1.25rem;
+            font-weight: 600;
+            margin-bottom: 16px;
+        }
+        
+        .lesson-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
+            gap: 24px;
+            margin-bottom: 16px;
+        }
+        
+        .lesson-item h4 {
+            font-weight: 600;
+            margin-bottom: 8px;
+        }
+        
+        .lesson-item.green h4 {
+            color: #10b981;
+        }
+        
+        .lesson-item.red h4 {
+            color: #f87171;
+        }
+        
+        .lesson-item.blue h4 {
+            color: #60a5fa;
+        }
+        
+        .lesson-item p {
+            font-size: 14px;
+        }
+        
+        .takeaway {
+            padding: 16px;
+            background-color: #374151;
+            border-radius: 6px;
+        }
+        
+        .takeaway-text {
+            color: #fde047;
+            font-weight: 500;
+        }
+        
+        .hidden {
+            display: none;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- Header -->
+        <div class="header">
+            <h1>Complete Guide to Clustering: 1D and 2D Examples</h1>
+            <p>Understanding when mean works, when it fails, and how clustering helps</p>
+        </div>
+
+        <!-- Controls -->
+        <div class="controls">
+            <div class="button-group" id="dimensionButtons">
+                <button class="active" data-dimension="1D">1D (Temperature Only)</button>
+                <button data-dimension="2D">2D (Temperature + Humidity)</button>
+            </div>
+            
+            <div class="button-group scenario" id="scenarioButtons">
+                <button class="active" data-scenario="single">Single Peak</button>
+                <button data-scenario="multiple">Multiple Peaks</button>
+                <button data-scenario="clustering">Clustering Solution</button>
+            </div>
+        </div>
+
+        <!-- Scenario Description -->
+        <div id="scenarioDescription" class="scenario-description single"></div>
+
+        <!-- Chart -->
+        <div class="chart-container">
+            <h3 id="chartTitle">Temperature Distribution</h3>
+            <div class="chart-wrapper">
+                <canvas id="mainChart"></canvas>
+            </div>
+        </div>
+
+        <!-- Legend -->
+        <div id="legend" class="legend hidden">
+            <h4>Cluster Legend</h4>
+            <div class="legend-items" id="legendItems"></div>
+        </div>
+
+        <!-- Analysis Grid -->
+        <div class="analysis-grid">
+            <div class="analysis-card blue">
+                <h3>The Numbers</h3>
+                <div id="numbersContent"></div>
+            </div>
+            
+            <div class="analysis-card purple">
+                <h3 id="dimensionTitle">Why 1D Matters</h3>
+                <div id="dimensionContent"></div>
+            </div>
+            
+            <div class="analysis-card yellow">
+                <h3>Key Insight</h3>
+                <div id="insightContent"></div>
+            </div>
+        </div>
+
+        <!-- Bottom Lesson -->
+        <div class="bottom-lesson">
+            <h3>The Complete Lesson</h3>
+            <div class="lesson-grid">
+                <div class="lesson-item green">
+                    <h4>Single Peak/Cluster ✓</h4>
+                    <p>Mean represents the typical value well. Use it with confidence in both 1D and 2D.</p>
+                </div>
+                <div class="lesson-item red">
+                    <h4>Multiple Peaks/Clusters ✗</h4>
+                    <p>Mean can be misleading. It often falls where your data is actually sparse, especially problematic in 2D.</p>
+                </div>
+                <div class="lesson-item blue">
+                    <h4>Clustering ⚡</h4>
+                    <p>Multiple means/centers work better, but only with meaningful clusters. Color coding helps see the groups!</p>
+                </div>
+            </div>
+            <div class="takeaway">
+                <p class="takeaway-text">
+                    <strong>Critical Takeaway:</strong> As you move from 1D to 2D to higher dimensions, clustering becomes increasingly important 
+                    but also more complex to validate. Always ensure your clusters make domain sense - not just statistical sense!
+                </p>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // State management
+        let state = {
+            currentDimension: '1D',
+            currentScenario: 'single',
+            chart: null
+        };
+
+        // Data generation
+        const data = {
+            '1D': {
+                single: [
+                    { temp: 65, count: 2, cluster: 'spring' },
+                    { temp: 67, count: 5, cluster: 'spring' },
+                    { temp: 69, count: 12, cluster: 'spring' },
+                    { temp: 71, count: 18, cluster: 'spring' },
+                    { temp: 73, count: 25, cluster: 'spring' },
+                    { temp: 75, count: 28, cluster: 'spring' },
+                    { temp: 77, count: 25, cluster: 'spring' },
+                    { temp: 79, count: 18, cluster: 'spring' },
+                    { temp: 81, count: 12, cluster: 'spring' },
+                    { temp: 83, count: 5, cluster: 'spring' },
+                    { temp: 85, count: 2, cluster: 'spring' }
+                ],
+                multiple: [
+                    { temp: 25, count: 15, cluster: 'winter' },
+                    { temp: 27, count: 20, cluster: 'winter' },
+                    { temp: 29, count: 25, cluster: 'winter' },
+                    { temp: 31, count: 20, cluster: 'winter' },
+                    { temp: 33, count: 15, cluster: 'winter' },
+                    { temp: 35, count: 8, cluster: 'winter' },
+                    { temp: 37, count: 5, cluster: 'spring' },
+                    { temp: 39, count: 3, cluster: 'spring' },
+                    { temp: 41, count: 2, cluster: 'spring' },
+                    { temp: 43, count: 2, cluster: 'spring' },
+                    { temp: 45, count: 1, cluster: 'spring' },
+                    { temp: 47, count: 1, cluster: 'spring' },
+                    { temp: 49, count: 1, cluster: 'spring' },
+                    { temp: 51, count: 1, cluster: 'spring' },
+                    { temp: 53, count: 1, cluster: 'spring' },
+                    { temp: 55, count: 2, cluster: 'spring' },
+                    { temp: 57, count: 2, cluster: 'summer' },
+                    { temp: 59, count: 3, cluster: 'summer' },
+                    { temp: 61, count: 5, cluster: 'summer' },
+                    { temp: 63, count: 8, cluster: 'summer' },
+                    { temp: 65, count: 15, cluster: 'summer' },
+                    { temp: 67, count: 20, cluster: 'summer' },
+                    { temp: 69, count: 25, cluster: 'summer' },
+                    { temp: 71, count: 20, cluster: 'summer' },
+                    { temp: 73, count: 15, cluster: 'summer' }
+                ]
+            },
+            '2D': {
+                single: generateSingle2D(),
+                multiple: generateMultiple2D()
+            }
+        };
+
+        // Set clustering data same as multiple
+        data['1D'].clustering = data['1D'].multiple;
+        data['2D'].clustering = data['2D'].multiple;
+
+        function generateSingle2D() {
+            const result = [];
+            for (let i = 0; i < 80; i++) {
+                const temp = 65 + Math.random() * 20 + Math.sin(i * 0.3) * 5;
+                const humidity = 45 + Math.random() * 20 + Math.cos(i * 0.25) * 8;
+                result.push({
+                    temperature: temp,
+                    humidity: humidity,
+                    cluster: 'spring',
+                    id: i
+                });
+            }
+            return result;
+        }
+
+        function generateMultiple2D() {
+            const result = [];
+            
+            // Winter cluster
+            for (let i = 0; i < 30; i++) {
+                const temp = 25 + Math.random() * 15;
+                const humidity = 20 + Math.random() * 25;
+                result.push({
+                    temperature: temp,
+                    humidity: humidity,
+                    cluster: 'winter',
+                    id: i
+                });
+            }
+            
+            // Summer cluster
+            for (let i = 30; i < 60; i++) {
+                const temp = 75 + Math.random() * 20;
+                const humidity = 65 + Math.random() * 25;
+                result.push({
+                    temperature: temp,
+                    humidity: humidity,
+                    cluster: 'summer',
+                    id: i
+                });
+            }
+            
+            // Fall cluster
+            for (let i = 60; i < 80; i++) {
+                const temp = 50 + Math.random() * 25;
+                const humidity = 40 + Math.random() * 40;
+                result.push({
+                    temperature: temp,
+                    humidity: humidity,
+                    cluster: 'fall',
+                    id: i
+                });
+            }
+            
+            return result;
+        }
+
+        // Utility functions
+        const clusterColors = {
+            'winter': '#3b82f6',
+            'spring': '#10b981',
+            'summer': '#f59e0b',
+            'fall': '#8b5cf6'
+        };
+
+        function getCurrentData() {
+            return data[state.currentDimension][state.currentScenario];
+        }
+
+        function calculateStats() {
+            const currentData = getCurrentData();
+            
+            if (state.currentDimension === '1D') {
+                let totalTemp = 0;
+                let totalCount = 0;
+                
+                currentData.forEach(d => {
+                    totalTemp += d.temp * d.count;
+                    totalCount += d.count;
+                });
+                
+                const overallMean = totalTemp / totalCount;
+                
+                // Cluster means
+                const clusters = {};
+                currentData.forEach(d => {
+                    if (!clusters[d.cluster]) {
+                        clusters[d.cluster] = { totalTemp: 0, totalCount: 0 };
+                    }
+                    clusters[d.cluster].totalTemp += d.temp * d.count;
+                    clusters[d.cluster].totalCount += d.count;
+                });
+                
+                const clusterMeans = {};
+                Object.keys(clusters).forEach(cluster => {
+                    clusterMeans[cluster] = clusters[cluster].totalTemp / clusters[cluster].totalCount;
+                });
+                
+                return { overallMean, clusterMeans, totalCount };
+            } else {
+                const totalTemp = currentData.reduce((sum, d) => sum + d.temperature, 0);
+                const totalHumidity = currentData.reduce((sum, d) => sum + d.humidity, 0);
+                const overallCentroid = {
+                    temperature: totalTemp / currentData.length,
+                    humidity: totalHumidity / currentData.length
+                };
+                
+                // Cluster centroids
+                const clusters = {};
+                currentData.forEach(d => {
+                    if (!clusters[d.cluster]) {
+                        clusters[d.cluster] = { temperatures: [], humidities: [] };
+                    }
+                    clusters[d.cluster].temperatures.push(d.temperature);
+                    clusters[d.cluster].humidities.push(d.humidity);
+                });
+                
+                const clusterCentroids = {};
+                Object.keys(clusters).forEach(cluster => {
+                    const temps = clusters[cluster].temperatures;
+                    const humids = clusters[cluster].humidities;
+                    clusterCentroids[cluster] = {
+                        temperature: temps.reduce((sum, t) => sum + t, 0) / temps.length,
+                        humidity: humids.reduce((sum, h) => sum + h, 0) / humids.length,
+                        count: temps.length
+                    };
+                });
+                
+                // Points near centroid
+                const pointsNearCentroid = currentData.filter(d => {
+                    const tempDiff = Math.abs(d.temperature - overallCentroid.temperature);
+                    const humidityDiff = Math.abs(d.humidity - overallCentroid.humidity);
+                    return tempDiff <= 5 && humidityDiff <= 10;
+                }).length;
+                
+                return { overallCentroid, clusterCentroids, pointsNearCentroid, totalCount: currentData.length };
+            }
+        }
+
+        function updateChart() {
+            const currentData = getCurrentData();
+            const stats = calculateStats();
+            
+            if (state.chart) {
+                state.chart.destroy();
+            }
+            
+            const ctx = document.getElementById('mainChart').getContext('2d');
+            
+            if (state.currentDimension === '1D') {
+                // Bar chart for 1D
+                const chartData = {
+                    labels: currentData.map(d => d.temp + '°F'),
+                    datasets: [{
+                        label: 'Number of Days',
+                        data: currentData.map(d => d.count),
+                        backgroundColor: currentData.map(d => {
+                            if (state.currentScenario === 'clustering') {
+                                return clusterColors[d.cluster];
+                            }
+                            return state.currentScenario === 'single' ? '#22c55e' : '#ef4444';
+                        }),
+                        borderColor: currentData.map(d => {
+                            if (state.currentScenario === 'clustering') {
+                                return clusterColors[d.cluster];
+                            }
+                            return state.currentScenario === 'single' ? '#16a34a' : '#dc2626';
+                        }),
+                        borderWidth: 1
+                    }]
+                };
+                
+                state.chart = new Chart(ctx, {
+                    type: 'bar',
+                    data: chartData,
+                    options: {
+                        responsive: true,
+                        maintainAspectRatio: false,
+                        scales: {
+                            x: {
+                                title: {
+                                    display: true,
+                                    text: 'Temperature (°F)'
+                                }
+                            },
+                            y: {
+                                title: {
+                                    display: true,
+                                    text: 'Number of Days'
+                                }
+                            }
+                        },
+                        plugins: {
+                            legend: {
+                                display: false
+                            }
+                        }
+                    }
+                });
+            } else {
+                // Scatter plot for 2D
+                const datasets = [];
+                
+                if (state.currentScenario === 'clustering') {
+                    // Separate datasets for each cluster
+                    const clusters = [...new Set(currentData.map(d => d.cluster))];
+                    clusters.forEach(cluster => {
+                        const clusterData = currentData.filter(d => d.cluster === cluster);
+                        datasets.push({
+                            label: cluster + ' season',
+                            data: clusterData.map(d => ({ x: d.temperature, y: d.humidity })),
+                            backgroundColor: clusterColors[cluster],
+                            borderColor: clusterColors[cluster],
+                            pointRadius: 4
+                        });
+                    });
+                } else {
+                    datasets.push({
+                        label: 'Weather days',
+                        data: currentData.map(d => ({ x: d.temperature, y: d.humidity })),
+                        backgroundColor: state.currentScenario === 'single' ? '#22c55e' : '#ef4444',
+                        borderColor: state.currentScenario === 'single' ? '#16a34a' : '#dc2626',
+                        pointRadius: 4
+                    });
+                }
+                
+                state.chart = new Chart(ctx, {
+                    type: 'scatter',
+                    data: { datasets },
+                    options: {
+                        responsive: true,
+                        maintainAspectRatio: false,
+                        scales: {
+                            x: {
+                                title: {
+                                    display: true,
+                                    text: 'Temperature (°F)'
+                                }
+                            },
+                            y: {
+                                title: {
+                                    display: true,
+                                    text: 'Humidity (%)'
+                                }
+                            }
+                        },
+                        plugins: {
+                            legend: {
+                                display: state.currentScenario === 'clustering'
+                            }
+                        }
+                    }
+                });
+            }
+        }
+
+        function updateScenarioButtons() {
+            const scenarioButtons = document.getElementById('scenarioButtons');
+            const buttons = scenarioButtons.querySelectorAll('button');
+            
+            buttons[0].textContent = state.currentDimension === '1D' ? 'Single Peak' : 'Single Cluster';
+            buttons[1].textContent = state.currentDimension === '1D' ? 'Multiple Peaks' : 'Multiple Clusters';
+        }
+
+        function updateContent() {
+            const stats = calculateStats();
+            
+            // Update chart title
+            document.getElementById('chartTitle').textContent = 
+                state.currentDimension === '1D' ? 'Temperature Distribution' : 'Temperature vs Humidity Distribution';
+            
+            // Update dimension content
+            document.getElementById('dimensionTitle').textContent = `Why ${state.currentDimension} Matters`;
+            
+            const dimensionContent = document.getElementById('dimensionContent');
+            if (state.currentDimension === '1D') {
+                dimensionContent.innerHTML = `
+                    <p style="font-weight: 600; margin-bottom: 8px;">Simple but powerful:</p>
+                    <ul style="list-style: disc; margin-left: 20px; font-size: 14px; color: #581c87;">
+                        <li>Easy to visualize and understand</li>
+                        <li>Clear when mean works vs fails</li>
+                        <li>Natural clusters are obvious</li>
+                        <li>Foundation for higher dimensions</li>
+                    </ul>
+                `;
+            } else {
+                dimensionContent.innerHTML = `
+                    <p style="font-weight: 600; margin-bottom: 8px;">More complex patterns:</p>
+                    <ul style="list-style: disc; margin-left: 20px; font-size: 14px; color: #581c87;">
+                        <li>Variables can co-vary (cold + dry)</li>
+                        <li>Mean can land in empty space</li>
+                        <li>Relationships become visible</li>
+                        <li>Clusters reveal natural groupings</li>
+                    </ul>
+                `;
+            }
+            
+            // Update numbers content
+            const numbersContent = document.getElementById('numbersContent');
+            if (state.currentDimension === '1D') {
+                let html = `<div class="stat-item"><strong>Overall Mean:</strong> ${stats.overallMean.toFixed(1)}°F</div>`;
+                
+                if (state.currentScenario === 'clustering') {
+                    html += `<div style="margin-top: 12px;"><p style="font-weight: 500;">Cluster Means:</p>`;
+                    Object.entries(stats.clusterMeans).forEach(([cluster, mean]) => {
+                        html += `<div class="cluster-stat" style="color: ${clusterColors[cluster]};">• ${cluster}: ${mean.toFixed(1)}°F</div>`;
+                    });
+                    html += `</div>`;
+                }
+                
+                html += `<div class="stat-item" style="padding-top: 8px;"><strong>Total Days:</strong> ${stats.totalCount}</div>`;
+                numbersContent.innerHTML = html;
+            } else {
+                let html = `
+                    <div class="stat-item"><strong>Overall Center:</strong></div>
+                    <div class="stat-item">• ${stats.overallCentroid.temperature.toFixed(1)}°F, ${stats.overallCentroid.humidity.toFixed(1)}%</div>
+                `;
+                
+                if (state.currentScenario === 'clustering') {
+                    html += `<div style="margin-top: 12px;"><p style="font-weight: 500;">Cluster Centers:</p>`;
+                    Object.entries(stats.clusterCentroids).forEach(([cluster, centroid]) => {
+                        html += `<div class="cluster-stat" style="color: ${clusterColors[cluster]};">• ${cluster}: ${centroid.temperature.toFixed(1)}°F, ${centroid.humidity.toFixed(1)}%</div>`;
+                    });
+                    html += `</div>`;
+                }
+                
+                html += `<div style="padding-top: 8px;">
+                    <div class="stat-item"><strong>Total Days:</strong> ${stats.totalCount}</div>`;
+                
+                if (state.currentScenario === 'multiple') {
+                    html += `<div class="stat-item" style="color: #b91c1c; font-weight: 500;">
+                        <strong>Near overall center:</strong> ${stats.pointsNearCentroid} days (${(stats.pointsNearCentroid/stats.totalCount*100).toFixed(1)}%)
+                    </div>`;
+                }
+                
+                html += `</div>`;
+                numbersContent.innerHTML = html;
+            }
+            
+            // Update scenario description
+            updateScenarioDescription(stats);
+            
+            // Update legend
+            updateLegend();
+            
+            // Update insight content
+            updateInsightContent();
+        }
+
+        function updateScenarioDescription(stats) {
+            const descElement = document.getElementById('scenarioDescription');
+            descElement.className = `scenario-description ${state.currentScenario}`;
+            
+            if (state.currentScenario === 'single') {
+                descElement.innerHTML = `
+                    <h2>✓ ${state.currentDimension === '1D' ? 'Single Peak' : 'Single Cluster'}: Mean Works Well</h2>
+                    <p style="color: #374151; margin-bottom: 12px;">
+                        <strong>Scenario:</strong> ${state.currentDimension === '1D' ? 
+                            'Spring temperatures in a mild climate city.' : 
+                            'Consistent spring weather - temperature and humidity vary together around typical values.'}
+                    </p>
+                    <div class="highlight-box green">
+                        <p>
+                            ${state.currentDimension === '1D' ? (
+                                `<strong>Claim:</strong> "The average temperature is ${stats.overallMean.toFixed(1)}°F"<br/>
+                                <strong>Basis:</strong> Most temperatures cluster around this value, so it represents typical experience.`
+                            ) : (
+                                `<strong>Claim:</strong> "Typical weather is ${stats.overallCentroid.temperature.toFixed(1)}°F and ${stats.overallCentroid.humidity.toFixed(1)}% humidity"<br/>
+                                <strong>Basis:</strong> Most days cluster around this combination, so it represents normal conditions.`
+                            )}
+                        </p>
+                    </div>
+                `;
+            } else if (state.currentScenario === 'multiple') {
+                descElement.innerHTML = `
+                    <h2>✗ ${state.currentDimension === '1D' ? 'Multiple Peaks' : 'Multiple Clusters'}: Mean Misleads</h2>
+                    <p style="color: #374151; margin-bottom: 12px;">
+                        <strong>Scenario:</strong> ${state.currentDimension === '1D' ? 
+                            'Full year with distinct winter and summer seasons.' : 
+                            'Full year with distinct weather patterns - dry winters, humid summers, variable fall.'}
+                    </p>
+                    <div class="highlight-box red">
+                        <p>
+                            ${state.currentDimension === '1D' ? (
+                                `<strong>Problem:</strong> "The average temperature is ${stats.overallMean.toFixed(1)}°F"<br/>
+                                <strong>Reality:</strong> Very few days are actually near ${stats.overallMean.toFixed(1)}°F! The mean falls in the gap between seasons.`
+                            ) : (
+                                `<strong>Problem:</strong> "Typical weather is ${stats.overallCentroid.temperature.toFixed(1)}°F and ${stats.overallCentroid.humidity.toFixed(1)}% humidity"<br/>
+                                <strong>Reality:</strong> Only ${stats.pointsNearCentroid} out of ${stats.totalCount} days are actually near this combination! The "average" weather rarely occurs.`
+                            )}
+                        </p>
+                    </div>
+                `;
+            } else {
+                let clusterText = '';
+                if (state.currentDimension === '1D') {
+                    Object.entries(stats.clusterMeans).forEach(([season, mean]) => {
+                        clusterText += `${season} averages ${mean.toFixed(1)}°F, `;
+                    });
+                    clusterText = clusterText.slice(0, -2); // Remove last comma
+                } else {
+                    Object.entries(stats.clusterCentroids).forEach(([season, centroid]) => {
+                        clusterText += `<span style="color: ${clusterColors[season]};">• ${season}: ${centroid.temperature.toFixed(1)}°F, ${centroid.humidity.toFixed(1)}% humidity<br/></span>`;
+                    });
+                }
+                
+                descElement.innerHTML = `
+                    <h2>⚡ Clustering Solution: ${state.currentDimension === '1D' ? 'Multiple Means' : 'Multiple Centers'}</h2>
+                    <p style="color: #374151; margin-bottom: 12px;">
+                        <strong>Approach:</strong> ${state.currentDimension === '1D' ? 
+                            'Identify natural groups (clusters) and calculate separate means.' : 
+                            'Identify distinct weather patterns and describe each separately.'}
+                    </p>
+                    <div class="highlight-box blue">
+                        <p>
+                            ${state.currentDimension === '1D' ? (
+                                `<strong>Claim:</strong> "${clusterText}"<br/>
+                                <strong>Basis:</strong> Each cluster mean represents its group well.`
+                            ) : (
+                                `<strong>Claim:</strong> "Weather has distinct patterns:"<br/>
+                                ${clusterText}
+                                <strong>Basis:</strong> Each weather pattern is internally consistent and meaningful.`
+                            )}
+                        </p>
+                    </div>
+                    <div class="highlight-box yellow" style="margin-top: 12px;">
+                        <p>
+                            <strong>Caveat:</strong> ${state.currentDimension === '1D' ? 
+                                'This only works if you can meaningfully identify the clusters. You need domain knowledge (seasons) or clear statistical separation.' :
+                                'In 2D, clustering is more complex. You need to ensure clusters are statistically valid and make domain sense. Don\'t just group by visual appearance!'}
+                        </p>
+                    </div>
+                `;
+            }
+        }
+
+        function updateLegend() {
+            const legend = document.getElementById('legend');
+            const legendItems = document.getElementById('legendItems');
+            
+            if (state.currentScenario === 'clustering') {
+                legend.classList.remove('hidden');
+                const stats = calculateStats();
+                const clusters = state.currentDimension === '1D' ? 
+                    Object.keys(stats.clusterMeans) : 
+                    Object.keys(stats.clusterCentroids);
+                
+                legendItems.innerHTML = clusters.map(cluster => `
+                    <div class="legend-item">
+                        <div class="legend-color" style="background-color: ${clusterColors[cluster]};"></div>
+                        <span class="legend-text">${cluster}</span>
+                    </div>
+                `).join('');
+            } else {
+                legend.classList.add('hidden');
+            }
+        }
+
+        function updateInsightContent() {
+            const insightContent = document.getElementById('insightContent');
+            
+            if (state.currentScenario === 'single') {
+                insightContent.innerHTML = `
+                    <p style="margin-bottom: 8px; font-weight: 600; color: #92400e;">Mean works well here</p>
+                    <p style="font-size: 14px; color: #92400e;">
+                        ${state.currentDimension === '1D' ? 
+                            'Single peak means most values cluster around the mean.' :
+                            'Single cluster means the mean point represents typical conditions.'}
+                    </p>
+                `;
+            } else if (state.currentScenario === 'multiple') {
+                insightContent.innerHTML = `
+                    <p style="margin-bottom: 8px; font-weight: 600; color: #92400e;">Mean becomes misleading</p>
+                    <p style="font-size: 14px; color: #92400e;">
+                        ${state.currentDimension === '1D' ? 
+                            'Multiple peaks mean the mean often falls where data is sparse.' :
+                            'Multiple clusters mean the mean point often falls in "no man\'s land".'}
+                    </p>
+                `;
+            } else {
+                insightContent.innerHTML = `
+                    <p style="margin-bottom: 8px; font-weight: 600; color: #92400e;">Clustering reveals truth</p>
+                    <p style="font-size: 14px; color: #92400e;">
+                        ${state.currentDimension === '1D' ? 
+                            'Each season gets its own meaningful average.' :
+                            'Each weather pattern gets its own meaningful center, showing relationships between variables.'}
+                    </p>
+                `;
+            }
+        }
+
+        // Event listeners
+        document.getElementById('dimensionButtons').addEventListener('click', (e) => {
+            if (e.target.tagName === 'BUTTON') {
+                // Update active button
+                document.querySelectorAll('#dimensionButtons button').forEach(btn => btn.classList.remove('active'));
+                e.target.classList.add('active');
+                
+                // Update state
+                state.currentDimension = e.target.dataset.dimension;
+                
+                // Update scenario buttons text
+                updateScenarioButtons();
+                
+                // Update everything
+                updateChart();
+                updateContent();
+            }
+        });
+
+        document.getElementById('scenarioButtons').addEventListener('click', (e) => {
+            if (e.target.tagName === 'BUTTON') {
+                // Update active button
+                document.querySelectorAll('#scenarioButtons button').forEach(btn => btn.classList.remove('active'));
+                e.target.classList.add('active');
+                
+                // Update state
+                state.currentScenario = e.target.dataset.scenario;
+                
+                // Update everything
+                updateChart();
+                updateContent();
+            }
+        });
+
+        // Initialize
+        document.addEventListener('DOMContentLoaded', () => {
+            updateScenarioButtons();
+            updateChart();
+            updateContent();
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_1/1_kmeans_animation.html b/Week1/Day_1/1_kmeans_animation.html
new file mode 100644
index 00000000..73bdee5e
--- /dev/null
+++ b/Week1/Day_1/1_kmeans_animation.html
@@ -0,0 +1,1067 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>K-Means Clustering Animation</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.9.1/chart.min.js"></script>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            line-height: 1.6;
+            color: #1f2937;
+            background-color: #f9fafb;
+        }
+        
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            padding: 24px;
+        }
+        
+        .header {
+            text-align: center;
+            margin-bottom: 32px;
+            background: white;
+            padding: 24px;
+            border-radius: 12px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        }
+        
+        .header h1 {
+            font-size: 2.5rem;
+            font-weight: bold;
+            color: #1f2937;
+            margin-bottom: 16px;
+        }
+        
+        .header p {
+            font-size: 1.25rem;
+            color: #6b7280;
+        }
+        
+        .main-content {
+            display: flex;
+            flex-direction: column;
+            gap: 24px;
+        }
+        
+        .chart-section {
+            background: white;
+            border-radius: 12px;
+            padding: 24px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        }
+        
+        .controls-section {
+            background: white;
+            border-radius: 12px;
+            padding: 24px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+            display: flex;
+            justify-content: center;
+            gap: 32px;
+            flex-wrap: wrap;
+        }
+
+        .metrics-section {
+            background: white;
+            border-radius: 12px;
+            padding: 20px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+            margin-bottom: 24px;
+        }
+
+        .metrics-grid {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 24px;
+            margin-bottom: 20px;
+        }
+
+        .metric-panel {
+            background: #f8fafc;
+            border-radius: 8px;
+            padding: 16px;
+            border-left: 4px solid #3b82f6;
+            transition: all 0.3s ease;
+        }
+
+        .metric-panel:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 4px 12px rgba(0,0,0,0.1);
+        }
+
+        .metric-panel.variance {
+            border-left-color: #10b981;
+        }
+
+        .metric-panel h4 {
+            margin-bottom: 12px;
+            color: #374151;
+            font-weight: 600;
+            font-size: 16px;
+            display: flex;
+            align-items: center;
+            gap: 8px;
+        }
+
+        .metric-row {
+            display: flex;
+            justify-content: space-between;
+            margin-bottom: 8px;
+            font-size: 14px;
+            padding: 4px 0;
+            border-bottom: 1px solid #e2e8f0;
+        }
+
+        .metric-row:last-child {
+            border-bottom: none;
+            margin-bottom: 0;
+        }
+
+        .metric-label {
+            color: #6b7280;
+            font-weight: 500;
+        }
+
+        .metric-value {
+            font-weight: 600;
+            color: #374151;
+            font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace;
+        }
+
+        .metric-value.highlight {
+            color: #059669;
+            font-size: 15px;
+            background: #ecfdf5;
+            padding: 2px 6px;
+            border-radius: 4px;
+        }
+
+        .centroids-section {
+            background: #f1f5f9;
+            border-radius: 8px;
+            padding: 16px;
+            border: 1px solid #e2e8f0;
+        }
+
+        .centroids-title {
+            margin-bottom: 12px;
+            color: #374151;
+            font-weight: 600;
+            font-size: 16px;
+            text-align: center;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            gap: 8px;
+        }
+
+        .centroids-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 12px;
+        }
+
+        .centroid-item {
+            transition: all 0.3s ease;
+            cursor: pointer;
+        }
+
+        .centroid-item:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 4px 12px rgba(0,0,0,0.15);
+        }
+        
+        .chart-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 20px;
+        }
+        
+        .chart-title {
+            font-size: 1.5rem;
+            font-weight: 600;
+            color: #1f2937;
+        }
+        
+        .iteration-counter {
+            background: #3b82f6;
+            color: white;
+            padding: 8px 16px;
+            border-radius: 20px;
+            font-weight: 600;
+        }
+        
+        .chart-wrapper {
+            position: relative;
+            height: 500px;
+            margin-bottom: 20px;
+        }
+        
+        .algorithm-status {
+            padding: 16px;
+            border-radius: 8px;
+            margin-bottom: 20px;
+            font-weight: 500;
+        }
+        
+        .status-running {
+            background: #fef3c7;
+            color: #92400e;
+            border-left: 4px solid #f59e0b;
+        }
+        
+        .status-converged {
+            background: #d1fae5;
+            color: #065f46;
+            border-left: 4px solid #10b981;
+        }
+        
+        .k-selector, .animation-controls, .speed-control-section {
+            text-align: center;
+        }
+
+        .k-selector h3, .animation-controls h3, .speed-control-section h3 {
+            margin-bottom: 12px;
+            color: #374151;
+            font-weight: 600;
+            font-size: 16px;
+        }
+
+        .k-buttons {
+            display: flex;
+            gap: 8px;
+            justify-content: center;
+        }
+
+        .k-button {
+            padding: 12px 20px;
+            border: 2px solid #e5e7eb;
+            background: white;
+            border-radius: 8px;
+            cursor: pointer;
+            font-weight: 600;
+            transition: all 0.2s;
+            font-size: 14px;
+        }
+
+        .k-button.active {
+            background: #3b82f6;
+            color: white;
+            border-color: #3b82f6;
+        }
+
+        .k-button:hover:not(.active) {
+            border-color: #3b82f6;
+            background: #eff6ff;
+        }
+
+        .control-buttons {
+            display: flex;
+            gap: 12px;
+            justify-content: center;
+        }
+
+        .control-button {
+            padding: 12px 20px;
+            border: none;
+            border-radius: 8px;
+            cursor: pointer;
+            font-weight: 600;
+            transition: all 0.2s;
+            font-size: 14px;
+            min-width: 80px;
+        }
+
+        .play-button {
+            background: #10b981;
+            color: white;
+        }
+
+        .play-button:hover:not(:disabled) {
+            background: #059669;
+        }
+
+        .step-button {
+            background: #f59e0b;
+            color: white;
+        }
+
+        .step-button:hover:not(:disabled) {
+            background: #d97706;
+        }
+
+        .reset-button {
+            background: #ef4444;
+            color: white;
+        }
+
+        .reset-button:hover {
+            background: #dc2626;
+        }
+
+        .control-button:disabled {
+            background: #d1d5db;
+            cursor: not-allowed;
+            color: #9ca3af;
+        }
+
+        .speed-control-section {
+            min-width: 200px;
+        }
+
+        .speed-slider {
+            width: 100%;
+            height: 6px;
+            border-radius: 3px;
+            background: #e5e7eb;
+            outline: none;
+            -webkit-appearance: none;
+            margin-top: 8px;
+        }
+
+        .speed-slider::-webkit-slider-thumb {
+            appearance: none;
+            width: 20px;
+            height: 20px;
+            border-radius: 50%;
+            background: #3b82f6;
+            cursor: pointer;
+        }
+
+        .speed-slider::-moz-range-thumb {
+            width: 20px;
+            height: 20px;
+            border-radius: 50%;
+            background: #3b82f6;
+            cursor: pointer;
+            border: none;
+        }
+        
+        .stats-panel {
+            background: #f8fafc;
+            border-radius: 8px;
+            padding: 16px;
+        }
+        
+        .stats-panel h3 {
+            margin-bottom: 12px;
+            color: #374151;
+            font-weight: 600;
+        }
+        
+        .stat-item {
+            display: flex;
+            justify-content: space-between;
+            margin-bottom: 8px;
+            font-size: 14px;
+        }
+        
+        .stat-label {
+            color: #6b7280;
+        }
+        
+        .stat-value {
+            font-weight: 600;
+            color: #374151;
+        }
+        
+        .centroid-list {
+            margin-top: 12px;
+        }
+        
+        .centroid-item {
+            padding: 8px;
+            margin-bottom: 6px;
+            border-radius: 6px;
+            font-size: 13px;
+            font-weight: 500;
+        }
+        
+        @media (max-width: 1024px) {
+            .chart-wrapper {
+                height: 400px;
+            }
+            
+            .metrics-grid {
+                grid-template-columns: 1fr;
+                gap: 16px;
+            }
+            
+            .centroids-grid {
+                grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+                gap: 8px;
+            }
+
+            .controls-section {
+                flex-direction: column;
+                gap: 24px;
+                align-items: center;
+            }
+        }
+
+        @media (max-width: 768px) {
+            .chart-header {
+                flex-direction: column;
+                gap: 12px;
+                text-align: center;
+            }
+            
+            .chart-title {
+                font-size: 1.25rem;
+            }
+            
+            .metrics-section {
+                padding: 16px;
+            }
+            
+            .metric-panel {
+                padding: 12px;
+            }
+            
+            .centroids-section {
+                padding: 12px;
+            }
+
+            .controls-section {
+                padding: 20px;
+                gap: 20px;
+            }
+
+            .k-buttons, .control-buttons {
+                flex-wrap: wrap;
+                gap: 8px;
+            }
+
+            .k-button, .control-button {
+                padding: 10px 16px;
+                font-size: 13px;
+            }
+
+            .speed-control-section {
+                min-width: 250px;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- Header -->
+        <div class="header">
+            <h1>K-Means Clustering Animation</h1>
+            <p>Watch how the algorithm finds clusters step by step</p>
+        </div>
+
+        <div class="main-content">
+            <!-- Chart Section -->
+            <div class="chart-section">
+                <div class="chart-header">
+                    <h2 class="chart-title">Temperature vs Humidity Clustering</h2>
+                    <div class="iteration-counter" id="iterationCounter">Iteration: 0</div>
+                </div>
+                
+                <div class="algorithm-status" id="algorithmStatus">
+                    Ready to start clustering...
+                </div>
+
+                <!-- Metrics Section -->
+                <div class="metrics-section">
+                    <div class="metrics-grid">
+                        <!-- Clustering Statistics -->
+                        <div class="metric-panel">
+                            <h4>📊 Clustering Statistics</h4>
+                            <div class="metric-row">
+                                <span class="metric-label">Data Points:</span>
+                                <span class="metric-value" id="pointCount">80</span>
+                            </div>
+                            <div class="metric-row">
+                                <span class="metric-label">Clusters (k):</span>
+                                <span class="metric-value" id="clusterCount">2</span>
+                            </div>
+                            <div class="metric-row">
+                                <span class="metric-label">Iterations:</span>
+                                <span class="metric-value" id="iterationCount">0</span>
+                            </div>
+                            <div class="metric-row">
+                                <span class="metric-label">Converged:</span>
+                                <span class="metric-value" id="convergedStatus">No</span>
+                            </div>
+                        </div>
+
+                        <!-- Variance Metrics -->
+                        <div class="metric-panel variance">
+                            <h4>📈 Variance Metrics</h4>
+                            <div class="metric-row">
+                                <span class="metric-label">WCSS:</span>
+                                <span class="metric-value" id="wcssValue">-</span>
+                            </div>
+                            <div class="metric-row">
+                                <span class="metric-label">BCSS:</span>
+                                <span class="metric-value" id="bcssValue">-</span>
+                            </div>
+                            <div class="metric-row">
+                                <span class="metric-label">Total Variance:</span>
+                                <span class="metric-value" id="totalVariance">-</span>
+                            </div>
+                            <div class="metric-row">
+                                <span class="metric-label">Explained:</span>
+                                <span class="metric-value highlight" id="explainedVariance">-</span>
+                            </div>
+                        </div>
+                    </div>
+
+                    <!-- Centroids Section -->
+                    <div class="centroids-section">
+                        <div class="centroids-title">🎯 Cluster Centroids</div>
+                        <div class="centroids-grid" id="centroidsGrid"></div>
+                    </div>
+                </div>
+                
+                <div class="chart-wrapper">
+                    <canvas id="clusterChart"></canvas>
+                </div>
+            </div>
+
+            <!-- Controls Section -->
+            <div class="controls-section">
+                <!-- K Selection -->
+                <div class="k-selector">
+                    <h3>Number of Clusters (k)</h3>
+                    <div class="k-buttons">
+                        <button class="k-button active" data-k="2">k=2</button>
+                        <button class="k-button" data-k="3">k=3</button>
+                        <button class="k-button" data-k="4">k=4</button>
+                    </div>
+                </div>
+
+                <!-- Animation Controls -->
+                <div class="animation-controls">
+                    <h3>Animation Controls</h3>
+                    <div class="control-buttons">
+                        <button class="control-button play-button" id="playButton">▶ Play</button>
+                        <button class="control-button step-button" id="stepButton">Step</button>
+                        <button class="control-button reset-button" id="resetButton">🔄 Reset</button>
+                    </div>
+                </div>
+
+                <!-- Speed Control -->
+                <div class="speed-control-section">
+                    <h3>Animation Speed</h3>
+                    <input type="range" id="speedSlider" class="speed-slider" min="1" max="10" value="5">
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        document.addEventListener('DOMContentLoaded', () => {
+            // Global state
+            let state = {
+                k: 2,
+                data: [],
+                centroids: [],
+                assignments: [],
+                iteration: 0,
+                isRunning: false,
+                isConverged: false,
+                currentStep: 0,
+                chart: null,
+                animationSpeed: 5,
+                intervalId: null
+            };
+
+            const clusterColors = [
+                '#ef4444', '#3b82f6', '#10b981', '#f59e0b', '#8b5cf6', '#06b6d4'
+            ];
+
+            let totalVariance = 0;
+
+            // Variance calculation functions
+            function calculateTotalVariance(data) {
+                if (!data || data.length === 0) return 0;
+                
+                const meanTemp = data.reduce((sum, p) => sum + p.temperature, 0) / data.length;
+                const meanHumidity = data.reduce((sum, p) => sum + p.humidity, 0) / data.length;
+                
+                return data.reduce((sum, p) => {
+                    const tempDiff = p.temperature - meanTemp;
+                    const humidityDiff = p.humidity - meanHumidity;
+                    return sum + (tempDiff * tempDiff + humidityDiff * humidityDiff);
+                }, 0);
+            }
+
+            function calculateWCSS(data, assignments, centroids) {
+                if (!data || !assignments || !centroids || assignments.length === 0 || centroids.length === 0) {
+                    return 0;
+                }
+                
+                let wcss = 0;
+                data.forEach((point, index) => {
+                    const clusterIndex = assignments[index];
+                    if (clusterIndex !== undefined && centroids[clusterIndex]) {
+                        const centroid = centroids[clusterIndex];
+                        const tempDiff = point.temperature - centroid.temperature;
+                        const humidityDiff = point.humidity - centroid.humidity;
+                        wcss += (tempDiff * tempDiff + humidityDiff * humidityDiff);
+                    }
+                });
+                
+                return wcss;
+            }
+
+            function calculateBCSS(data, centroids, assignments) {
+                if (!data || !assignments || !centroids || assignments.length === 0 || centroids.length === 0) {
+                    return 0;
+                }
+                
+                const meanTemp = data.reduce((sum, p) => sum + p.temperature, 0) / data.length;
+                const meanHumidity = data.reduce((sum, p) => sum + p.humidity, 0) / data.length;
+                
+                let bcss = 0;
+                for (let k = 0; k < centroids.length; k++) {
+                    const clusterSize = assignments.filter(a => a === k).length;
+                    if (clusterSize > 0) {
+                        const tempDiff = centroids[k].temperature - meanTemp;
+                        const humidityDiff = centroids[k].humidity - meanHumidity;
+                        bcss += clusterSize * (tempDiff * tempDiff + humidityDiff * humidityDiff);
+                    }
+                }
+                
+                return bcss;
+            }
+
+            // Generate weather data
+            function generateWeatherData() {
+                const data = [];
+                
+                // Winter cluster: Cold and dry
+                for (let i = 0; i < 25; i++) {
+                    data.push({
+                        temperature: 25 + Math.random() * 15,
+                        humidity: 20 + Math.random() * 25,
+                        id: i,
+                        trueCluster: 'winter'
+                    });
+                }
+                
+                // Summer cluster: Hot and humid  
+                for (let i = 25; i < 50; i++) {
+                    data.push({
+                        temperature: 75 + Math.random() * 20,
+                        humidity: 65 + Math.random() * 25,
+                        id: i,
+                        trueCluster: 'summer'
+                    });
+                }
+                
+                // Fall cluster: Moderate temp, variable humidity
+                for (let i = 50; i < 80; i++) {
+                    data.push({
+                        temperature: 50 + Math.random() * 25,
+                        humidity: 40 + Math.random() * 40,
+                        id: i,
+                        trueCluster: 'fall'
+                    });
+                }
+                
+                return data;
+            }
+
+            // Initialize random centroids
+            function initializeCentroids(k, data) {
+                const centroids = [];
+                if (!data || data.length === 0) {
+                    return centroids;
+                }
+                
+                const tempRange = { 
+                    min: Math.min(...data.map(d => d.temperature)),
+                    max: Math.max(...data.map(d => d.temperature))
+                };
+                const humidityRange = {
+                    min: Math.min(...data.map(d => d.humidity)),
+                    max: Math.max(...data.map(d => d.humidity))
+                };
+                
+                for (let i = 0; i < k; i++) {
+                    centroids.push({
+                        temperature: tempRange.min + Math.random() * (tempRange.max - tempRange.min),
+                        humidity: humidityRange.min + Math.random() * (humidityRange.max - humidityRange.min),
+                        id: i
+                    });
+                }
+                
+                return centroids;
+            }
+
+            // Calculate distance between two points
+            function calculateDistance(point1, point2) {
+                const tempDiff = point1.temperature - point2.temperature;
+                const humidityDiff = point1.humidity - point2.humidity;
+                return Math.sqrt(tempDiff * tempDiff + humidityDiff * humidityDiff);
+            }
+
+            // Assign points to nearest centroids
+            function assignPointsToCentroids(data, centroids) {
+                return data.map(point => {
+                    let minDistance = Infinity;
+                    let assignedCluster = 0;
+                    
+                    centroids.forEach((centroid, index) => {
+                        const distance = calculateDistance(point, centroid);
+                        if (distance < minDistance) {
+                            minDistance = distance;
+                            assignedCluster = index;
+                        }
+                    });
+                    
+                    return assignedCluster;
+                });
+            }
+
+            // Update centroids based on assigned points
+            function updateCentroids(data, assignments, k) {
+                const newCentroids = [];
+                
+                for (let cluster = 0; cluster < k; cluster++) {
+                    const clusterPoints = data.filter((point, index) => assignments[index] === cluster);
+                    
+                    if (clusterPoints.length > 0) {
+                        const avgTemp = clusterPoints.reduce((sum, p) => sum + p.temperature, 0) / clusterPoints.length;
+                        const avgHumidity = clusterPoints.reduce((sum, p) => sum + p.humidity, 0) / clusterPoints.length;
+                        
+                        newCentroids.push({
+                            temperature: avgTemp,
+                            humidity: avgHumidity,
+                            id: cluster
+                        });
+                    } else {
+                        // Keep old centroid if no points assigned
+                        newCentroids.push(state.centroids[cluster]);
+                    }
+                }
+                
+                return newCentroids;
+            }
+
+            // Check if centroids have converged
+            function checkConvergence(oldCentroids, newCentroids, threshold = 0.1) {
+                for (let i = 0; i < oldCentroids.length; i++) {
+                    const distance = calculateDistance(oldCentroids[i], newCentroids[i]);
+                    if (distance > threshold) {
+                        return false;
+                    }
+                }
+                return true;
+            }
+
+            // Update chart
+            function updateChart() {
+                if (state.chart) {
+                    state.chart.destroy();
+                }
+                
+                const ctx = document.getElementById('clusterChart').getContext('2d');
+                
+                // Prepare datasets
+                const datasets = [];
+                
+                // Data points grouped by cluster
+                for (let cluster = 0; cluster < state.k; cluster++) {
+                    const clusterPoints = state.data
+                        .filter((point, index) => state.assignments[index] === cluster)
+                        .map(point => ({ x: point.temperature, y: point.humidity }));
+                    
+                    if (clusterPoints.length > 0) {
+                        datasets.push({
+                            label: `Cluster ${cluster + 1}`,
+                            data: clusterPoints,
+                            backgroundColor: clusterColors[cluster],
+                            borderColor: clusterColors[cluster],
+                            pointRadius: 6,
+                            pointHoverRadius: 8,
+                            showLine: false
+                        });
+                    }
+                }
+                
+                // Centroids
+                const centroidData = state.centroids.map(centroid => ({ 
+                    x: centroid.temperature, 
+                    y: centroid.humidity 
+                }));
+                
+                datasets.push({
+                    label: 'Centroids',
+                    data: centroidData,
+                    backgroundColor: '#1f2937',
+                    borderColor: '#ffffff',
+                    borderWidth: 3,
+                    pointRadius: 12,
+                    pointHoverRadius: 14,
+                    showLine: false,
+                    pointStyle: 'rectRot'
+                });
+                
+                state.chart = new Chart(ctx, {
+                    type: 'scatter',
+                    data: { datasets },
+                    options: {
+                        responsive: true,
+                        maintainAspectRatio: false,
+                        scales: {
+                            x: {
+                                title: {
+                                    display: true,
+                                    text: 'Temperature (°F)',
+                                    font: { size: 14, weight: 'bold' }
+                                },
+                                grid: { color: '#e5e7eb' }
+                            },
+                            y: {
+                                title: {
+                                    display: true,
+                                    text: 'Humidity (%)',
+                                    font: { size: 14, weight: 'bold' }
+                                },
+                                grid: { color: '#e5e7eb' }
+                            }
+                        },
+                        plugins: {
+                            legend: {
+                                display: true,
+                                position: 'bottom',
+                                labels: {
+                                    usePointStyle: true,
+                                    padding: 20
+                                }
+                            },
+                            tooltip: {
+                                callbacks: {
+                                    title: function() { return ''; },
+                                    label: function(context) {
+                                        if (context.datasetIndex === datasets.length - 1) {
+                                            return `Centroid ${context.dataIndex + 1}: (${context.parsed.x.toFixed(1)}°F, ${context.parsed.y.toFixed(1)}%)`;
+                                        } else {
+                                            return `Cluster ${context.datasetIndex + 1}: (${context.parsed.x.toFixed(1)}°F, ${context.parsed.y.toFixed(1)}%)`;
+                                        }
+                                    }
+                                }
+                            }
+                        },
+                        animation: {
+                            duration: 500
+                        }
+                    }
+                });
+            }
+
+            // Update UI elements
+            function updateUI() {
+                // Calculate variance metrics
+                const wcss = calculateWCSS(state.data, state.assignments, state.centroids);
+                const bcss = calculateBCSS(state.data, state.centroids, state.assignments);
+                const explainedPct = totalVariance > 0 ? (bcss / totalVariance * 100) : 0;
+                
+                // Iteration counter
+                document.getElementById('iterationCounter').textContent = `Iteration: ${state.iteration}`;
+                
+                // Algorithm status
+                const statusElement = document.getElementById('algorithmStatus');
+                if (state.isConverged) {
+                    statusElement.textContent = `🎉 Algorithm converged after ${state.iteration} iterations!`;
+                    statusElement.className = 'algorithm-status status-converged';
+                } else if (state.isRunning) {
+                    statusElement.textContent = `⚡ Running k-means with k=${state.k}... WCSS: ${wcss.toFixed(1)}`;
+                    statusElement.className = 'algorithm-status status-running';
+                } else {
+                    statusElement.textContent = `Ready to cluster ${state.data.length} points into ${state.k} clusters`;
+                    statusElement.className = 'algorithm-status';
+                }
+                
+                // Control buttons
+                const playButton = document.getElementById('playButton');
+                const stepButton = document.getElementById('stepButton');
+                
+                if (state.isRunning) {
+                    playButton.textContent = '⏸ Pause';
+                    stepButton.disabled = true;
+                } else {
+                    playButton.textContent = '▶ Play';
+                    stepButton.disabled = state.isConverged;
+                }
+                
+                if (state.isConverged) {
+                    playButton.disabled = true;
+                } else {
+                    playButton.disabled = false;
+                }
+                
+                // Stats panel
+                document.getElementById('pointCount').textContent = state.data.length;
+                document.getElementById('clusterCount').textContent = state.k;
+                document.getElementById('iterationCount').textContent = state.iteration;
+                document.getElementById('convergedStatus').textContent = state.isConverged ? 'Yes' : 'No';
+                
+                // Variance metrics
+                document.getElementById('wcssValue').textContent = wcss.toFixed(1);
+                document.getElementById('bcssValue').textContent = bcss.toFixed(1);
+                document.getElementById('totalVariance').textContent = totalVariance.toFixed(1);
+                document.getElementById('explainedVariance').textContent = `${explainedPct.toFixed(1)}%`;
+                
+                // Centroid list in the new grid layout
+                const centroidsGrid = document.getElementById('centroidsGrid');
+                centroidsGrid.innerHTML = '';
+                
+                if (state.centroids.length > 0) {
+                    state.centroids.forEach((centroid, index) => {
+                        const div = document.createElement('div');
+                        div.className = 'centroid-item';
+                        div.style.backgroundColor = clusterColors[index] + '20';
+                        div.style.borderLeft = `4px solid ${clusterColors[index]}`;
+                        div.style.padding = '12px';
+                        div.style.borderRadius = '6px';
+                        div.style.fontSize = '13px';
+                        div.style.fontWeight = '500';
+                        
+                        // Count points in this cluster
+                        const pointCount = state.assignments.filter(a => a === index).length;
+                        
+                        div.innerHTML = `
+                            <div style="font-weight: 600; margin-bottom: 4px; color: ${clusterColors[index]};">
+                                Cluster ${index + 1}
+                            </div>
+                            <div style="font-size: 12px; color: #6b7280; margin-bottom: 4px;">
+                                ${pointCount} points
+                            </div>
+                            <div style="font-weight: 600; color: #374151;">
+                                ${centroid.temperature.toFixed(1)}°F<br>
+                                ${centroid.humidity.toFixed(1)}% humidity
+                            </div>
+                        `;
+                        centroidsGrid.appendChild(div);
+                    });
+                }
+            }
+
+            // Perform one step of k-means
+            function performKMeansStep() {
+                if (state.isConverged) return;
+                
+                if (state.currentStep === 0) {
+                    // Step 1: Initialize centroids (already done)
+                    state.currentStep = 1;
+                } else if (state.currentStep === 1) {
+                    // Step 2: Assign points to centroids
+                    state.assignments = assignPointsToCentroids(state.data, state.centroids);
+                    state.currentStep = 2;
+                } else if (state.currentStep === 2) {
+                    // Step 3: Update centroids
+                    const oldCentroids = [...state.centroids];
+                    state.centroids = updateCentroids(state.data, state.assignments, state.k);
+                    
+                    // Check convergence
+                    if (checkConvergence(oldCentroids, state.centroids)) {
+                        state.isConverged = true;
+                        state.isRunning = false;
+                        if (state.intervalId) {
+                            clearInterval(state.intervalId);
+                            state.intervalId = null;
+                        }
+                    } else {
+                        state.iteration++;
+                        state.currentStep = 1; // Go back to assignment step
+                    }
+                }
+                
+                updateChart();
+                updateUI();
+            }
+
+            // Reset algorithm
+            function resetAlgorithm() {
+                state.isRunning = false;
+                state.isConverged = false;
+                state.iteration = 0;
+                state.currentStep = 0;
+                state.centroids = initializeCentroids(state.k, state.data);
+                state.assignments = new Array(state.data.length).fill(0);
+                
+                if (state.intervalId) {
+                    clearInterval(state.intervalId);
+                    state.intervalId = null;
+                }
+                
+                updateChart();
+                updateUI();
+            }
+
+            // Start/stop animation
+            function toggleAnimation() {
+                if (state.isRunning) {
+                    state.isRunning = false;
+                    if (state.intervalId) {
+                        clearInterval(state.intervalId);
+                        state.intervalId = null;
+                    }
+                } else {
+                    if (state.isConverged) return;
+                    
+                    state.isRunning = true;
+                    const speed = 2100 - (state.animationSpeed * 200); // Convert to milliseconds
+                    
+                    state.intervalId = setInterval(() => {
+                        performKMeansStep();
+                        if (state.isConverged) {
+                            state.isRunning = false;
+                            clearInterval(state.intervalId);
+                            state.intervalId = null;
+                        }
+                    }, speed);
+                }
+                updateUI();
+            }
+
+            // Initialize everything
+            state.data = generateWeatherData();
+            totalVariance = calculateTotalVariance(state.data);
+            resetAlgorithm();
+            
+            // Event listeners
+            // K selection
+            document.querySelectorAll('.k-button').forEach(button => {
+                button.addEventListener('click', () => {
+                    document.querySelectorAll('.k-button').forEach(b => b.classList.remove('active'));
+                    button.classList.add('active');
+                    state.k = parseInt(button.dataset.k);
+                    resetAlgorithm();
+                });
+            });
+            
+            // Animation controls
+            document.getElementById('playButton').addEventListener('click', toggleAnimation);
+            document.getElementById('stepButton').addEventListener('click', performKMeansStep);
+            document.getElementById('resetButton').addEventListener('click', resetAlgorithm);
+            
+            // Speed control
+            document.getElementById('speedSlider').addEventListener('input', (e) => {
+                state.animationSpeed = parseInt(e.target.value);
+                if (state.isRunning) {
+                    // Restart animation with new speed
+                    toggleAnimation();
+                    toggleAnimation();
+                }
+            });
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_1/2_1d-classification-probability.html b/Week1/Day_1/2_1d-classification-probability.html
new file mode 100644
index 00000000..814a5838
--- /dev/null
+++ b/Week1/Day_1/2_1d-classification-probability.html
@@ -0,0 +1,917 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>1D Classification: Understanding Overlap and Decision Boundaries</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.9.1/chart.min.js"></script>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            line-height: 1.6;
+            color: #1f2937;
+            background-color: #f9fafb;
+            padding: 20px;
+        }
+        
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+        }
+        
+        .header {
+            text-align: center;
+            margin-bottom: 30px;
+            background: white;
+            padding: 24px;
+            border-radius: 12px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        }
+        
+        .header h1 {
+            font-size: 2rem;
+            font-weight: bold;
+            color: #1f2937;
+            margin-bottom: 12px;
+        }
+        
+        .header p {
+            font-size: 1.1rem;
+            color: #6b7280;
+            max-width: 800px;
+            margin: 0 auto;
+        }
+        
+        .concept-section {
+            background: white;
+            border-radius: 12px;
+            padding: 24px;
+            margin-bottom: 24px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        }
+        
+        .concept-grid {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 20px;
+            margin-top: 20px;
+        }
+        
+        .concept-card {
+            border-radius: 8px;
+            padding: 20px;
+            border: 2px solid #e5e7eb;
+        }
+        
+        .concept-card.overlap {
+            background: linear-gradient(to right, #dbeafe 0%, #fee2e2 100%);
+            border-color: #9333ea;
+        }
+        
+        .concept-card.threshold {
+            background: linear-gradient(to right, #f3f4f6 0%, #f3f4f6 50%, #dcfce7 50%, #dcfce7 100%);
+            border-color: #10b981;
+        }
+        
+        .concept-card h3 {
+            font-size: 1.1rem;
+            color: #1f2937;
+            margin-bottom: 12px;
+            display: flex;
+            align-items: center;
+            gap: 8px;
+        }
+        
+        .concept-card p {
+            font-size: 0.9rem;
+            color: #4b5563;
+            line-height: 1.6;
+        }
+        
+        .demo-section {
+            background: white;
+            border-radius: 12px;
+            padding: 24px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        }
+        
+        .controls {
+            background: #f9fafb;
+            border-radius: 8px;
+            padding: 16px;
+            margin-bottom: 20px;
+            display: flex;
+            align-items: center;
+            gap: 20px;
+            flex-wrap: wrap;
+        }
+        
+        .control-group {
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+        
+        .control-group label {
+            font-weight: 500;
+            color: #374151;
+            font-size: 0.9rem;
+            min-width: 120px;
+        }
+        
+        .slider {
+            width: 200px;
+            height: 6px;
+            -webkit-appearance: none;
+            appearance: none;
+            background: #e5e7eb;
+            border-radius: 3px;
+            outline: none;
+        }
+        
+        .slider::-webkit-slider-thumb {
+            -webkit-appearance: none;
+            appearance: none;
+            width: 20px;
+            height: 20px;
+            background: #3b82f6;
+            border-radius: 50%;
+            cursor: pointer;
+        }
+        
+        .slider::-moz-range-thumb {
+            width: 20px;
+            height: 20px;
+            background: #3b82f6;
+            border-radius: 50%;
+            cursor: pointer;
+            border: none;
+        }
+        
+        .value-display {
+            font-weight: 600;
+            color: #1e40af;
+            min-width: 50px;
+            text-align: center;
+        }
+        
+        .button {
+            padding: 8px 16px;
+            border: none;
+            border-radius: 6px;
+            font-weight: 500;
+            cursor: pointer;
+            transition: all 0.2s;
+            font-size: 0.9rem;
+        }
+        
+        .button-primary {
+            background: #3b82f6;
+            color: white;
+        }
+        
+        .button-primary:hover {
+            background: #2563eb;
+        }
+        
+        .button-secondary {
+            background: #e5e7eb;
+            color: #374151;
+        }
+        
+        .button-secondary:hover {
+            background: #d1d5db;
+        }
+        
+        .visualization-container {
+            margin-bottom: 24px;
+        }
+        
+        .chart-container {
+            background: #f9fafb;
+            border-radius: 8px;
+            padding: 20px;
+            margin-bottom: 20px;
+            position: relative;
+        }
+        
+        .chart-container h3 {
+            font-size: 1.1rem;
+            color: #1f2937;
+            margin-bottom: 16px;
+            text-align: center;
+        }
+        
+        #dataCanvas {
+            width: 100%;
+            height: 400px;
+            border: 1px solid #e5e7eb;
+            border-radius: 4px;
+            background: white;
+        }
+        
+        .metrics-container {
+            display: grid;
+            grid-template-columns: 300px 1fr;
+            gap: 20px;
+        }
+        
+        .confusion-container {
+            background: #f9fafb;
+            border-radius: 8px;
+            padding: 16px;
+        }
+        
+        .confusion-container h4 {
+            font-size: 1rem;
+            color: #1f2937;
+            margin-bottom: 12px;
+            text-align: center;
+        }
+        
+        .confusion-matrix {
+            display: grid;
+            grid-template-columns: auto 1fr 1fr;
+            grid-template-rows: auto 1fr 1fr;
+            gap: 2px;
+            max-width: 250px;
+            margin: 0 auto;
+        }
+        
+        .confusion-cell {
+            padding: 12px;
+            text-align: center;
+            font-weight: 500;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            min-height: 50px;
+        }
+        
+        .confusion-header {
+            background: #e5e7eb;
+            font-size: 0.85rem;
+            color: #374151;
+        }
+        
+        .confusion-value {
+            font-size: 1.25rem;
+            font-weight: 600;
+            color: white;
+            border-radius: 4px;
+        }
+        
+        .confusion-value.tp { background: #10b981; }
+        .confusion-value.fp { background: #f59e0b; }
+        .confusion-value.fn { background: #ef4444; }
+        .confusion-value.tn { background: #3b82f6; }
+        
+        .metrics-grid {
+            display: grid;
+            grid-template-columns: repeat(2, 1fr);
+            gap: 12px;
+        }
+        
+        .metric-card {
+            background: white;
+            padding: 12px;
+            border-radius: 6px;
+            text-align: center;
+            border: 1px solid #e5e7eb;
+        }
+        
+        .metric-label {
+            font-size: 0.75rem;
+            color: #6b7280;
+            margin-bottom: 4px;
+        }
+        
+        .metric-value {
+            font-size: 1.25rem;
+            font-weight: 600;
+            color: #1f2937;
+        }
+        
+        .info-container {
+            background: #f9fafb;
+            border-radius: 8px;
+            padding: 16px;
+        }
+        
+        .info-container h4 {
+            font-size: 1rem;
+            color: #1f2937;
+            margin-bottom: 12px;
+        }
+        
+        .legend {
+            display: flex;
+            justify-content: center;
+            gap: 30px;
+            margin: 16px 0;
+            flex-wrap: wrap;
+        }
+        
+        .legend-item {
+            display: flex;
+            align-items: center;
+            gap: 8px;
+            font-size: 0.9rem;
+            color: #6b7280;
+        }
+        
+        .legend-circle {
+            width: 16px;
+            height: 16px;
+            border-radius: 50%;
+            border: 2px solid;
+        }
+        
+        .info-box {
+            background: #eff6ff;
+            border: 1px solid #3b82f6;
+            border-radius: 8px;
+            padding: 16px;
+            margin-top: 20px;
+        }
+        
+        .info-box h4 {
+            color: #1e40af;
+            font-size: 1rem;
+            margin-bottom: 8px;
+        }
+        
+        .info-box p {
+            color: #1e40af;
+            font-size: 0.9rem;
+            line-height: 1.5;
+        }
+        
+        .region-info {
+            display: grid;
+            grid-template-columns: repeat(3, 1fr);
+            gap: 12px;
+            margin-top: 12px;
+        }
+        
+        .region-card {
+            padding: 12px;
+            border-radius: 6px;
+            text-align: center;
+            border: 1px solid;
+        }
+        
+        .region-card.left {
+            background: #dbeafe;
+            border-color: #3b82f6;
+        }
+        
+        .region-card.overlap {
+            background: #fae8ff;
+            border-color: #9333ea;
+        }
+        
+        .region-card.right {
+            background: #fee2e2;
+            border-color: #ef4444;
+        }
+        
+        .region-label {
+            font-size: 0.85rem;
+            font-weight: 600;
+            margin-bottom: 4px;
+        }
+        
+        .region-count {
+            font-size: 0.75rem;
+            color: #6b7280;
+        }
+        
+        @media (max-width: 768px) {
+            .concept-grid {
+                grid-template-columns: 1fr;
+            }
+            
+            .metrics-container {
+                grid-template-columns: 1fr;
+            }
+            
+            .metrics-grid {
+                grid-template-columns: 1fr;
+            }
+            
+            .controls {
+                flex-direction: column;
+                align-items: stretch;
+            }
+            
+            .control-group {
+                flex-direction: column;
+                align-items: flex-start;
+            }
+            
+            .region-info {
+                grid-template-columns: 1fr;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- Header -->
+        <div class="header">
+            <h1>1D Classification: Understanding Overlap and Decision Boundaries</h1>
+            <p>Visualize how class overlap affects classification and why perfect separation is often impossible</p>
+        </div>
+
+        <!-- Concept Explanation -->
+        <div class="concept-section">
+            <h2 style="margin-bottom: 16px; color: #1f2937;">Key Concepts</h2>
+            
+            <div class="concept-grid">
+                <div class="concept-card overlap">
+                    <h3>🔄 Class Overlap</h3>
+                    <p>
+                        <strong>What it is:</strong> When data points from different classes have similar feature values, 
+                        they occupy the same region in feature space.<br><br>
+                        <strong>Why it happens:</strong> Natural variation in real-world data means classes rarely have 
+                        perfect separation. Some sick patients might have normal test results, and some healthy patients 
+                        might have abnormal results.<br><br>
+                        <strong>Impact:</strong> No decision boundary can perfectly separate overlapping classes - 
+                        we must accept some misclassifications.
+                    </p>
+                </div>
+                
+                <div class="concept-card threshold">
+                    <h3>🎯 Decision Threshold</h3>
+                    <p>
+                        <strong>What it is:</strong> A value that splits the feature space into two regions. 
+                        Points on one side are classified as Class A, points on the other as Class B.<br><br>
+                        <strong>How to choose:</strong> The optimal threshold depends on the cost of different errors. 
+                        In medical testing, missing a disease (false negative) might be worse than a false alarm.<br><br>
+                        <strong>Trade-offs:</strong> Moving the threshold left catches more positives but creates more 
+                        false alarms. Moving right reduces false alarms but misses more true cases.
+                    </p>
+                </div>
+            </div>
+        </div>
+
+        <!-- Interactive Demo -->
+        <div class="demo-section">
+            <h2 style="margin-bottom: 20px; color: #1f2937;">Interactive Demonstration</h2>
+            
+            <div class="controls">
+                <div class="control-group">
+                    <label>Decision Threshold:</label>
+                    <input type="range" id="thresholdSlider" class="slider" min="0" max="100" value="50" step="1">
+                    <span class="value-display" id="thresholdValue">50</span>
+                </div>
+                
+                <div class="control-group">
+                    <label>Class Overlap:</label>
+                    <input type="range" id="overlapSlider" class="slider" min="0" max="80" value="30" step="10">
+                    <span class="value-display" id="overlapValue">30%</span>
+                </div>
+                
+                <button class="button button-primary" onclick="findOptimalThreshold()">🎯 Find Best Threshold</button>
+                <button class="button button-secondary" onclick="regenerateData()">🔀 New Data</button>
+                <button class="button button-secondary" onclick="resetDemo()">🔄 Reset</button>
+            </div>
+
+            <div class="visualization-container">
+                <div class="chart-container">
+                    <h3>Data Distribution with Decision Boundary</h3>
+                    <canvas id="dataCanvas"></canvas>
+                    
+                    <div class="legend">
+                        <div class="legend-item">
+                            <div class="legend-circle" style="background: #3b82f6; border-color: #3b82f6;"></div>
+                            <span>Class A (Negative)</span>
+                        </div>
+                        <div class="legend-item">
+                            <div class="legend-circle" style="background: #ef4444; border-color: #ef4444;"></div>
+                            <span>Class B (Positive)</span>
+                        </div>
+                        <div class="legend-item">
+                            <div style="width: 30px; height: 3px; background: #10b981;"></div>
+                            <span>Decision Threshold</span>
+                        </div>
+                    </div>
+                    
+                    <div class="region-info">
+                        <div class="region-card left">
+                            <div class="region-label">Left Region</div>
+                            <div class="region-count">
+                                A: <span id="leftA">0</span> | B: <span id="leftB">0</span>
+                            </div>
+                        </div>
+                        <div class="region-card overlap">
+                            <div class="region-label">Overlap Zone</div>
+                            <div class="region-count">
+                                A: <span id="overlapA">0</span> | B: <span id="overlapB">0</span>
+                            </div>
+                        </div>
+                        <div class="region-card right">
+                            <div class="region-label">Right Region</div>
+                            <div class="region-count">
+                                A: <span id="rightA">0</span> | B: <span id="rightB">0</span>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <div class="metrics-container">
+                <div class="confusion-container">
+                    <h4>Confusion Matrix</h4>
+                    <div class="confusion-matrix">
+                        <div class="confusion-cell"></div>
+                        <div class="confusion-cell confusion-header">Pred: Neg</div>
+                        <div class="confusion-cell confusion-header">Pred: Pos</div>
+                        <div class="confusion-cell confusion-header">True: Neg</div>
+                        <div class="confusion-cell confusion-value tn" id="tn">0</div>
+                        <div class="confusion-cell confusion-value fp" id="fp">0</div>
+                        <div class="confusion-cell confusion-header">True: Pos</div>
+                        <div class="confusion-cell confusion-value fn" id="fn">0</div>
+                        <div class="confusion-cell confusion-value tp" id="tp">0</div>
+                    </div>
+                    
+                    <div class="metrics-grid" style="margin-top: 16px;">
+                        <div class="metric-card">
+                            <div class="metric-label">Accuracy</div>
+                            <div class="metric-value" id="accuracy">0%</div>
+                        </div>
+                        <div class="metric-card">
+                            <div class="metric-label">Precision</div>
+                            <div class="metric-value" id="precision">0%</div>
+                        </div>
+                        <div class="metric-card">
+                            <div class="metric-label">Recall</div>
+                            <div class="metric-value" id="recall">0%</div>
+                        </div>
+                        <div class="metric-card">
+                            <div class="metric-label">F1 Score</div>
+                            <div class="metric-value" id="f1score">0.00</div>
+                        </div>
+                    </div>
+                </div>
+                
+                <div class="info-container">
+                    <h4>Understanding the Visualization</h4>
+                    <p style="font-size: 0.9rem; color: #6b7280; line-height: 1.6;">
+                        Each circle represents a data point. The horizontal position shows the feature value 
+                        (e.g., test result, exam score). Points are stacked vertically to show density.
+                    </p>
+                    
+                    <div style="margin-top: 16px; padding: 12px; background: white; border-radius: 6px;">
+                        <h5 style="font-size: 0.9rem; margin-bottom: 8px;">Classification Results:</h5>
+                        <div style="display: grid; gap: 6px; font-size: 0.85rem;">
+                            <div><span style="color: #3b82f6; font-weight: 600;">●</span> Left of threshold → Classified as Negative</div>
+                            <div><span style="color: #ef4444; font-weight: 600;">●</span> Right of threshold → Classified as Positive</div>
+                            <div style="margin-top: 4px; padding-top: 4px; border-top: 1px solid #e5e7eb;">
+                                <strong>Errors occur when:</strong><br>
+                                • Blue points are right of threshold (False Positive)<br>
+                                • Red points are left of threshold (False Negative)
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <div class="info-box">
+                <h4>💡 Real-World Example: Medical Testing</h4>
+                <p>
+                    Imagine a blood test for detecting a disease. Healthy people (Class A, blue) typically have lower marker levels, 
+                    while sick people (Class B, red) have higher levels. However, there's overlap - some healthy people naturally 
+                    have elevated markers, and some sick people have normal levels. The threshold determines who gets diagnosed. 
+                    Setting it too low means many false positives (unnecessary worry and treatment). Setting it too high means 
+                    missing actual cases. The optimal threshold balances these risks based on the consequences of each error type.
+                </p>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // Global state
+        let state = {
+            threshold: 50,
+            overlap: 30,
+            dataPoints: [],
+            canvas: null,
+            ctx: null
+        };
+
+        // Generate data points with specified overlap
+        function generateData() {
+            state.dataPoints = [];
+            const numPointsPerClass = 100;
+            
+            // Calculate means based on overlap
+            const separation = 40 - (state.overlap * 0.4); // Less separation = more overlap
+            const classAMean = 50 - separation / 2;
+            const classBMean = 50 + separation / 2;
+            const stdDev = 15;
+            
+            // Generate Class A points (negative class)
+            for (let i = 0; i < numPointsPerClass; i++) {
+                const value = gaussianRandom(classAMean, stdDev);
+                state.dataPoints.push({
+                    value: Math.max(0, Math.min(100, value)),
+                    class: 'A',
+                    color: '#3b82f6'
+                });
+            }
+            
+            // Generate Class B points (positive class)
+            for (let i = 0; i < numPointsPerClass; i++) {
+                const value = gaussianRandom(classBMean, stdDev);
+                state.dataPoints.push({
+                    value: Math.max(0, Math.min(100, value)),
+                    class: 'B',
+                    color: '#ef4444'
+                });
+            }
+            
+            // Sort by value for better visualization
+            state.dataPoints.sort((a, b) => a.value - b.value);
+        }
+
+        // Gaussian random number generator
+        function gaussianRandom(mean, stdDev) {
+            let u = 0, v = 0;
+            while (u === 0) u = Math.random();
+            while (v === 0) v = Math.random();
+            const num = Math.sqrt(-2.0 * Math.log(u)) * Math.cos(2.0 * Math.PI * v);
+            return num * stdDev + mean;
+        }
+
+        // Draw visualization
+        function drawVisualization() {
+            const canvas = state.canvas;
+            const ctx = state.ctx;
+            const width = canvas.width;
+            const height = canvas.height;
+            
+            // Clear canvas
+            ctx.clearRect(0, 0, width, height);
+            
+            // Draw background regions
+            const thresholdX = (state.threshold / 100) * width;
+            
+            // Left region (light blue)
+            ctx.fillStyle = '#eff6ff';
+            ctx.fillRect(0, 0, thresholdX, height);
+            
+            // Right region (light red)
+            ctx.fillStyle = '#fef2f2';
+            ctx.fillRect(thresholdX, 0, width - thresholdX, height);
+            
+            // Find overlap region
+            const classAPoints = state.dataPoints.filter(p => p.class === 'A');
+            const classBPoints = state.dataPoints.filter(p => p.class === 'B');
+            const classAMax = Math.max(...classAPoints.map(p => p.value));
+            const classBMin = Math.min(...classBPoints.map(p => p.value));
+            
+            if (classAMax > classBMin) {
+                const overlapStartX = (classBMin / 100) * width;
+                const overlapEndX = (classAMax / 100) * width;
+                
+                // Draw overlap region (light purple)
+                ctx.fillStyle = 'rgba(147, 51, 234, 0.1)';
+                ctx.fillRect(overlapStartX, 0, overlapEndX - overlapStartX, height);
+            }
+            
+            // Draw grid lines
+            ctx.strokeStyle = '#e5e7eb';
+            ctx.lineWidth = 1;
+            for (let i = 0; i <= 10; i++) {
+                const x = (i / 10) * width;
+                ctx.beginPath();
+                ctx.moveTo(x, 0);
+                ctx.lineTo(x, height);
+                ctx.stroke();
+            }
+            
+            // Group points by value bins for stacking
+            const binWidth = 2; // Width of each bin in feature space
+            const bins = {};
+            
+            state.dataPoints.forEach(point => {
+                const binIndex = Math.floor(point.value / binWidth);
+                if (!bins[binIndex]) bins[binIndex] = [];
+                bins[binIndex].push(point);
+            });
+            
+            // Draw points
+            const pointRadius = 6;
+            const pointSpacing = 2;
+            
+            Object.keys(bins).forEach(binIndex => {
+                const binPoints = bins[binIndex];
+                const x = (parseFloat(binIndex) * binWidth + binWidth / 2) / 100 * width;
+                
+                binPoints.forEach((point, stackIndex) => {
+                    const y = height - 30 - (stackIndex * (pointRadius * 2 + pointSpacing));
+                    
+                    // Draw point
+                    ctx.beginPath();
+                    ctx.arc(x, y, pointRadius, 0, Math.PI * 2);
+                    ctx.fillStyle = point.color;
+                    ctx.fill();
+                    ctx.strokeStyle = point.color;
+                    ctx.lineWidth = 2;
+                    ctx.stroke();
+                    
+                    // Add white center for better visibility
+                    ctx.beginPath();
+                    ctx.arc(x, y, pointRadius - 2, 0, Math.PI * 2);
+                    ctx.fillStyle = 'white';
+                    ctx.fill();
+                });
+            });
+            
+            // Draw threshold line
+            ctx.strokeStyle = '#10b981';
+            ctx.lineWidth = 3;
+            ctx.setLineDash([5, 5]);
+            ctx.beginPath();
+            ctx.moveTo(thresholdX, 0);
+            ctx.lineTo(thresholdX, height);
+            ctx.stroke();
+            ctx.setLineDash([]);
+            
+            // Draw threshold label
+            ctx.fillStyle = '#10b981';
+            ctx.font = 'bold 14px sans-serif';
+            ctx.textAlign = 'center';
+            ctx.fillText('Threshold', thresholdX, 20);
+            
+            // Draw x-axis labels
+            ctx.fillStyle = '#6b7280';
+            ctx.font = '12px sans-serif';
+            ctx.textAlign = 'center';
+            for (let i = 0; i <= 10; i++) {
+                const x = (i / 10) * width;
+                const value = i * 10;
+                ctx.fillText(value.toString(), x, height - 5);
+            }
+            
+            // Update region counts
+            updateRegionCounts();
+        }
+
+        // Update region counts
+        function updateRegionCounts() {
+            const leftA = state.dataPoints.filter(p => p.class === 'A' && p.value < state.threshold).length;
+            const leftB = state.dataPoints.filter(p => p.class === 'B' && p.value < state.threshold).length;
+            const rightA = state.dataPoints.filter(p => p.class === 'A' && p.value >= state.threshold).length;
+            const rightB = state.dataPoints.filter(p => p.class === 'B' && p.value >= state.threshold).length;
+            
+            // Find overlap region
+            const classAPoints = state.dataPoints.filter(p => p.class === 'A');
+            const classBPoints = state.dataPoints.filter(p => p.class === 'B');
+            const classAMax = Math.max(...classAPoints.map(p => p.value));
+            const classBMin = Math.min(...classBPoints.map(p => p.value));
+            
+            let overlapA = 0, overlapB = 0;
+            if (classAMax > classBMin) {
+                overlapA = classAPoints.filter(p => p.value >= classBMin && p.value <= classAMax).length;
+                overlapB = classBPoints.filter(p => p.value >= classBMin && p.value <= classAMax).length;
+            }
+            
+            document.getElementById('leftA').textContent = leftA;
+            document.getElementById('leftB').textContent = leftB;
+            document.getElementById('rightA').textContent = rightA;
+            document.getElementById('rightB').textContent = rightB;
+            document.getElementById('overlapA').textContent = overlapA;
+            document.getElementById('overlapB').textContent = overlapB;
+        }
+
+        // Calculate and update metrics
+        function updateMetrics() {
+            // Calculate confusion matrix values
+            const tp = state.dataPoints.filter(p => p.class === 'B' && p.value >= state.threshold).length;
+            const tn = state.dataPoints.filter(p => p.class === 'A' && p.value < state.threshold).length;
+            const fp = state.dataPoints.filter(p => p.class === 'A' && p.value >= state.threshold).length;
+            const fn = state.dataPoints.filter(p => p.class === 'B' && p.value < state.threshold).length;
+            
+            // Update confusion matrix
+            document.getElementById('tp').textContent = tp;
+            document.getElementById('tn').textContent = tn;
+            document.getElementById('fp').textContent = fp;
+            document.getElementById('fn').textContent = fn;
+            
+            // Calculate metrics
+            const total = tp + tn + fp + fn;
+            const accuracy = total > 0 ? ((tp + tn) / total * 100) : 0;
+            const precision = (tp + fp) > 0 ? (tp / (tp + fp) * 100) : 0;
+            const recall = (tp + fn) > 0 ? (tp / (tp + fn) * 100) : 0;
+            const f1 = (precision > 0 && recall > 0) ? 
+                      (2 * precision * recall / (precision + recall) / 100) : 0;
+            
+            // Update metrics display
+            document.getElementById('accuracy').textContent = accuracy.toFixed(1) + '%';
+            document.getElementById('precision').textContent = precision.toFixed(1) + '%';
+            document.getElementById('recall').textContent = recall.toFixed(1) + '%';
+            document.getElementById('f1score').textContent = f1.toFixed(2);
+        }
+
+        // Find optimal threshold
+        function findOptimalThreshold() {
+            let bestThreshold = 0;
+            let bestF1 = 0;
+            
+            for (let t = 0; t <= 100; t += 1) {
+                const tp = state.dataPoints.filter(p => p.class === 'B' && p.value >= t).length;
+                const tn = state.dataPoints.filter(p => p.class === 'A' && p.value < t).length;
+                const fp = state.dataPoints.filter(p => p.class === 'A' && p.value >= t).length;
+                const fn = state.dataPoints.filter(p => p.class === 'B' && p.value < t).length;
+                
+                const precision = (tp + fp) > 0 ? (tp / (tp + fp)) : 0;
+                const recall = (tp + fn) > 0 ? (tp / (tp + fn)) : 0;
+                const f1 = (precision > 0 && recall > 0) ? 
+                          (2 * precision * recall / (precision + recall)) : 0;
+                
+                if (f1 > bestF1) {
+                    bestF1 = f1;
+                    bestThreshold = t;
+                }
+            }
+            
+            state.threshold = bestThreshold;
+            document.getElementById('thresholdSlider').value = bestThreshold;
+            document.getElementById('thresholdValue').textContent = bestThreshold;
+            
+            drawVisualization();
+            updateMetrics();
+        }
+
+        // Regenerate data
+        function regenerateData() {
+            generateData();
+            drawVisualization();
+            updateMetrics();
+        }
+
+        // Reset demo
+        function resetDemo() {
+            state.threshold = 50;
+            state.overlap = 30;
+            
+            document.getElementById('thresholdSlider').value = 50;
+            document.getElementById('thresholdValue').textContent = '50';
+            document.getElementById('overlapSlider').value = 30;
+            document.getElementById('overlapValue').textContent = '30%';
+            
+            generateData();
+            drawVisualization();
+            updateMetrics();
+        }
+
+        // Initialize
+        document.addEventListener('DOMContentLoaded', function() {
+            // Set up canvas
+            state.canvas = document.getElementById('dataCanvas');
+            state.ctx = state.canvas.getContext('2d');
+            
+            // Set canvas size
+            const resizeCanvas = () => {
+                const rect = state.canvas.getBoundingClientRect();
+                state.canvas.width = rect.width;
+                state.canvas.height = 400;
+                drawVisualization();
+            };
+            
+            resizeCanvas();
+            window.addEventListener('resize', resizeCanvas);
+            
+            // Event listeners
+            document.getElementById('thresholdSlider').addEventListener('input', function(e) {
+                state.threshold = parseInt(e.target.value);
+                document.getElementById('thresholdValue').textContent = e.target.value;
+                drawVisualization();
+                updateMetrics();
+            });
+            
+            document.getElementById('overlapSlider').addEventListener('input', function(e) {
+                state.overlap = parseInt(e.target.value);
+                document.getElementById('overlapValue').textContent = e.target.value + '%';
+                generateData();
+                drawVisualization();
+                updateMetrics();
+            });
+            
+            // Initial render
+            generateData();
+            drawVisualization();
+            updateMetrics();
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_1/2_classification-train-test-demo.html b/Week1/Day_1/2_classification-train-test-demo.html
new file mode 100644
index 00000000..120ffa5b
--- /dev/null
+++ b/Week1/Day_1/2_classification-train-test-demo.html
@@ -0,0 +1,897 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Student Classification: Understanding Train-Test Split & Confusion Matrix</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.9.1/chart.min.js"></script>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            line-height: 1.6;
+            color: #1f2937;
+            background-color: #f9fafb;
+            padding: 20px;
+        }
+        
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+        }
+        
+        .header {
+            text-align: center;
+            margin-bottom: 30px;
+            background: white;
+            padding: 24px;
+            border-radius: 12px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        }
+        
+        .header h1 {
+            font-size: 2rem;
+            font-weight: bold;
+            color: #1f2937;
+            margin-bottom: 12px;
+        }
+        
+        .header p {
+            font-size: 1.1rem;
+            color: #6b7280;
+            max-width: 800px;
+            margin: 0 auto;
+        }
+        
+        .concept-section {
+            background: white;
+            border-radius: 12px;
+            padding: 24px;
+            margin-bottom: 24px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        }
+        
+        .error-types-grid {
+            display: grid;
+            grid-template-columns: repeat(4, 1fr);
+            gap: 16px;
+            margin-top: 20px;
+        }
+        
+        .error-card {
+            background: #f9fafb;
+            border-radius: 8px;
+            padding: 16px;
+            text-align: center;
+            border: 2px solid transparent;
+        }
+        
+        .error-card.tp { border-color: #10b981; background: #d1fae5; }
+        .error-card.tn { border-color: #3b82f6; background: #dbeafe; }
+        .error-card.fp { border-color: #f59e0b; background: #fef3c7; }
+        .error-card.fn { border-color: #ef4444; background: #fee2e2; }
+        
+        .error-card h4 {
+            font-size: 0.9rem;
+            margin-bottom: 4px;
+            font-weight: 600;
+        }
+        
+        .error-card p {
+            font-size: 0.8rem;
+            color: #374151;
+        }
+        
+        .demo-section {
+            background: white;
+            border-radius: 12px;
+            padding: 24px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        }
+        
+        .controls {
+            background: #f9fafb;
+            border-radius: 8px;
+            padding: 16px;
+            margin-bottom: 20px;
+            display: flex;
+            align-items: center;
+            gap: 20px;
+            flex-wrap: wrap;
+        }
+        
+        .control-group {
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+        
+        .control-group label {
+            font-weight: 500;
+            color: #374151;
+            font-size: 0.9rem;
+        }
+        
+        .slider {
+            width: 150px;
+            height: 6px;
+            -webkit-appearance: none;
+            appearance: none;
+            background: #e5e7eb;
+            border-radius: 3px;
+            outline: none;
+        }
+        
+        .slider::-webkit-slider-thumb {
+            -webkit-appearance: none;
+            appearance: none;
+            width: 20px;
+            height: 20px;
+            background: #3b82f6;
+            border-radius: 50%;
+            cursor: pointer;
+        }
+        
+        .slider::-moz-range-thumb {
+            width: 20px;
+            height: 20px;
+            background: #3b82f6;
+            border-radius: 50%;
+            cursor: pointer;
+            border: none;
+        }
+        
+        .value-display {
+            font-weight: 600;
+            color: #1e40af;
+            min-width: 40px;
+            text-align: center;
+        }
+        
+        .button {
+            padding: 8px 16px;
+            border: none;
+            border-radius: 6px;
+            font-weight: 500;
+            cursor: pointer;
+            transition: all 0.2s;
+            font-size: 0.9rem;
+        }
+        
+        .button-primary {
+            background: #3b82f6;
+            color: white;
+        }
+        
+        .button-primary:hover {
+            background: #2563eb;
+        }
+        
+        .button-secondary {
+            background: #e5e7eb;
+            color: #374151;
+        }
+        
+        .button-secondary:hover {
+            background: #d1d5db;
+        }
+        
+        .visualization-container {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 20px;
+            margin-bottom: 20px;
+        }
+        
+        .chart-section {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 20px;
+        }
+        
+        .chart-container {
+            background: #f9fafb;
+            border-radius: 8px;
+            padding: 16px;
+        }
+        
+        .chart-container h3 {
+            font-size: 1rem;
+            color: #1f2937;
+            margin-bottom: 12px;
+            text-align: center;
+        }
+        
+        .chart-wrapper {
+            position: relative;
+            height: 350px;
+        }
+        
+        .confusion-section {
+            background: #f9fafb;
+            border-radius: 8px;
+            padding: 16px;
+        }
+        
+        .confusion-section h3 {
+            font-size: 1rem;
+            color: #1f2937;
+            margin-bottom: 12px;
+            text-align: center;
+        }
+        
+        .confusion-grid-container {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 20px;
+        }
+        
+        .confusion-matrix {
+            display: grid;
+            grid-template-columns: auto 1fr 1fr;
+            grid-template-rows: auto 1fr 1fr;
+            gap: 2px;
+            max-width: 250px;
+            margin: 0 auto;
+        }
+        
+        .confusion-cell {
+            padding: 12px;
+            text-align: center;
+            font-weight: 500;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            min-height: 50px;
+        }
+        
+        .confusion-header {
+            background: #e5e7eb;
+            font-size: 0.85rem;
+            color: #374151;
+        }
+        
+        .confusion-value {
+            font-size: 1.5rem;
+            font-weight: 600;
+            color: white;
+            border-radius: 4px;
+        }
+        
+        .confusion-value.tp { background: #10b981; }
+        .confusion-value.fp { background: #f59e0b; }
+        .confusion-value.fn { background: #ef4444; }
+        .confusion-value.tn { background: #3b82f6; }
+        
+        .metrics-grid {
+            display: grid;
+            grid-template-columns: repeat(3, 1fr);
+            gap: 12px;
+            margin-top: 16px;
+        }
+        
+        .metric-card {
+            background: white;
+            padding: 12px;
+            border-radius: 6px;
+            text-align: center;
+            border: 1px solid #e5e7eb;
+        }
+        
+        .metric-label {
+            font-size: 0.75rem;
+            color: #6b7280;
+            margin-bottom: 4px;
+        }
+        
+        .metric-value {
+            font-size: 1.25rem;
+            font-weight: 600;
+            color: #1f2937;
+        }
+        
+        .legend {
+            display: flex;
+            justify-content: center;
+            gap: 20px;
+            margin-top: 10px;
+            flex-wrap: wrap;
+        }
+        
+        .legend-item {
+            display: flex;
+            align-items: center;
+            gap: 6px;
+            font-size: 0.85rem;
+            color: #6b7280;
+        }
+        
+        .legend-dot {
+            width: 12px;
+            height: 12px;
+            border-radius: 50%;
+        }
+        
+        .legend-square {
+            width: 12px;
+            height: 12px;
+            border-radius: 2px;
+        }
+        
+        .info-box {
+            background: #eff6ff;
+            border: 1px solid #3b82f6;
+            border-radius: 8px;
+            padding: 16px;
+            margin-top: 20px;
+        }
+        
+        .info-box h4 {
+            color: #1e40af;
+            font-size: 1rem;
+            margin-bottom: 8px;
+        }
+        
+        .info-box p {
+            color: #1e40af;
+            font-size: 0.9rem;
+        }
+        
+        @media (max-width: 1024px) {
+            .visualization-container {
+                grid-template-columns: 1fr;
+            }
+            
+            .chart-section {
+                grid-template-columns: 1fr;
+            }
+            
+            .confusion-grid-container {
+                grid-template-columns: 1fr;
+            }
+        }
+        
+        @media (max-width: 768px) {
+            .error-types-grid {
+                grid-template-columns: repeat(2, 1fr);
+            }
+            
+            .metrics-grid {
+                grid-template-columns: 1fr;
+            }
+            
+            .controls {
+                flex-direction: column;
+                align-items: stretch;
+            }
+            
+            .control-group {
+                flex-direction: column;
+                align-items: flex-start;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- Header -->
+        <div class="header">
+            <h1>Student Classification: Train-Test Split & Confusion Matrix</h1>
+            <p>Learn how to evaluate classification models using student scores in two subjects</p>
+        </div>
+
+        <!-- Concept Explanation -->
+        <div class="concept-section">
+            <h2 style="margin-bottom: 16px; color: #1f2937;">Understanding Classification Errors</h2>
+            
+            <div class="error-types-grid">
+                <div class="error-card tp">
+                    <h4>True Positive (TP)</h4>
+                    <p>Correctly identified as strong in Subject 2</p>
+                </div>
+                
+                <div class="error-card tn">
+                    <h4>True Negative (TN)</h4>
+                    <p>Correctly identified as strong in Subject 1</p>
+                </div>
+                
+                <div class="error-card fp">
+                    <h4>False Positive (FP)</h4>
+                    <p>Incorrectly labeled as strong in Subject 2</p>
+                </div>
+                
+                <div class="error-card fn">
+                    <h4>False Negative (FN)</h4>
+                    <p>Incorrectly labeled as strong in Subject 1</p>
+                </div>
+            </div>
+        </div>
+
+        <!-- Interactive Demo -->
+        <div class="demo-section">
+            <h2 style="margin-bottom: 20px; color: #1f2937;">Interactive Student Classification Demo</h2>
+            
+            <div class="controls">
+                <div class="control-group">
+                    <label>Train-Test Split:</label>
+                    <input type="range" id="splitSlider" class="slider" min="50" max="90" value="70" step="5">
+                    <span class="value-display" id="splitValue">70%</span>
+                </div>
+                
+                <div class="control-group">
+                    <label>Decision Boundary Angle:</label>
+                    <input type="range" id="angleSlider" class="slider" min="-45" max="45" value="0" step="5">
+                    <span class="value-display" id="angleValue">0°</span>
+                </div>
+                
+                <div class="control-group">
+                    <label>Boundary Position:</label>
+                    <input type="range" id="positionSlider" class="slider" min="-20" max="20" value="0" step="2">
+                    <span class="value-display" id="positionValue">0</span>
+                </div>
+                
+                <button class="button button-primary" onclick="findOptimalBoundary()">🎯 Find Best Boundary</button>
+                <button class="button button-secondary" onclick="reshuffleData()">🔀 Reshuffle</button>
+                <button class="button button-secondary" onclick="resetDemo()">🔄 Reset</button>
+            </div>
+
+            <div class="visualization-container">
+                <div class="chart-section">
+                    <div class="chart-container">
+                        <h3>Training Data</h3>
+                        <div class="chart-wrapper">
+                            <canvas id="trainChart"></canvas>
+                        </div>
+                    </div>
+                    
+                    <div class="chart-container">
+                        <h3>Test Data</h3>
+                        <div class="chart-wrapper">
+                            <canvas id="testChart"></canvas>
+                        </div>
+                    </div>
+                </div>
+                
+                <div class="confusion-section">
+                    <h3>Confusion Matrices</h3>
+                    
+                    <div class="confusion-grid-container">
+                        <div>
+                            <h4 style="text-align: center; margin-bottom: 12px; font-size: 0.9rem; color: #6b7280;">Training Set</h4>
+                            <div class="confusion-matrix" id="trainConfusion">
+                                <div class="confusion-cell"></div>
+                                <div class="confusion-cell confusion-header">Pred: A</div>
+                                <div class="confusion-cell confusion-header">Pred: B</div>
+                                <div class="confusion-cell confusion-header">True: A</div>
+                                <div class="confusion-cell confusion-value tp" id="train-tp">0</div>
+                                <div class="confusion-cell confusion-value fn" id="train-fn">0</div>
+                                <div class="confusion-cell confusion-header">True: B</div>
+                                <div class="confusion-cell confusion-value fp" id="train-fp">0</div>
+                                <div class="confusion-cell confusion-value tn" id="train-tn">0</div>
+                            </div>
+                        </div>
+                        
+                        <div>
+                            <h4 style="text-align: center; margin-bottom: 12px; font-size: 0.9rem; color: #6b7280;">Test Set</h4>
+                            <div class="confusion-matrix" id="testConfusion">
+                                <div class="confusion-cell"></div>
+                                <div class="confusion-cell confusion-header">Pred: A</div>
+                                <div class="confusion-cell confusion-header">Pred: B</div>
+                                <div class="confusion-cell confusion-header">True: A</div>
+                                <div class="confusion-cell confusion-value tp" id="test-tp">0</div>
+                                <div class="confusion-cell confusion-value fn" id="test-fn">0</div>
+                                <div class="confusion-cell confusion-header">True: B</div>
+                                <div class="confusion-cell confusion-value fp" id="test-fp">0</div>
+                                <div class="confusion-cell confusion-value tn" id="test-tn">0</div>
+                            </div>
+                        </div>
+                    </div>
+                    
+                    <div class="metrics-grid">
+                        <div class="metric-card">
+                            <div class="metric-label">Train Accuracy</div>
+                            <div class="metric-value" id="trainAccuracy">0%</div>
+                        </div>
+                        <div class="metric-card">
+                            <div class="metric-label">Train Precision</div>
+                            <div class="metric-value" id="trainPrecision">0%</div>
+                        </div>
+                        <div class="metric-card">
+                            <div class="metric-label">Train Recall</div>
+                            <div class="metric-value" id="trainRecall">0%</div>
+                        </div>
+                        <div class="metric-card">
+                            <div class="metric-label">Test Accuracy</div>
+                            <div class="metric-value" id="testAccuracy">0%</div>
+                        </div>
+                        <div class="metric-card">
+                            <div class="metric-label">Test Precision</div>
+                            <div class="metric-value" id="testPrecision">0%</div>
+                        </div>
+                        <div class="metric-card">
+                            <div class="metric-label">Test Recall</div>
+                            <div class="metric-value" id="testRecall">0%</div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <div class="legend">
+                <div class="legend-item">
+                    <div class="legend-dot" style="background: #3b82f6;"></div>
+                    <span>Class A (Strong in Subject 2)</span>
+                </div>
+                <div class="legend-item">
+                    <div class="legend-dot" style="background: #ef4444;"></div>
+                    <span>Class B (Strong in Subject 1)</span>
+                </div>
+                <div class="legend-item">
+                    <div style="width: 20px; height: 2px; background: #10b981;"></div>
+                    <span>Decision Boundary</span>
+                </div>
+            </div>
+
+            <div class="info-box">
+                <h4>📊 Key Metrics Explained</h4>
+                <p>
+                    <strong>Accuracy:</strong> (TP + TN) / Total - Overall correctness of classifications<br>
+                    <strong>Precision:</strong> TP / (TP + FP) - When we predict a student is strong in Subject 2, how often are we right?<br>
+                    <strong>Recall:</strong> TP / (TP + FN) - Of all students actually strong in Subject 2, how many did we identify?
+                </p>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // Global state
+        let state = {
+            allData: [],
+            trainData: [],
+            testData: [],
+            splitRatio: 0.7,
+            boundaryAngle: 0,
+            boundaryPosition: 0,
+            trainChart: null,
+            testChart: null
+        };
+
+        // Generate 2D classification data (student scores)
+        function generateClassificationData() {
+            const data = [];
+            
+            // Class A (blue) - students strong in Subject 2
+            for (let i = 0; i < 40; i++) {
+                const angle = Math.random() * Math.PI * 2;
+                const radius = Math.random() * 30 + 10;
+                data.push({
+                    x: 30 + radius * Math.cos(angle),
+                    y: 70 + radius * Math.sin(angle) * 0.7,
+                    label: 'A',
+                    class: 0
+                });
+            }
+            
+            // Class B (red) - students strong in Subject 1
+            for (let i = 0; i < 40; i++) {
+                const angle = Math.random() * Math.PI * 2;
+                const radius = Math.random() * 30 + 10;
+                data.push({
+                    x: 70 + radius * Math.cos(angle),
+                    y: 30 + radius * Math.sin(angle) * 0.7,
+                    label: 'B',
+                    class: 1
+                });
+            }
+            
+            // Add some overlap for more realistic classification
+            for (let i = 0; i < 20; i++) {
+                const isClassA = Math.random() > 0.5;
+                data.push({
+                    x: 45 + Math.random() * 20,
+                    y: 45 + Math.random() * 20,
+                    label: isClassA ? 'A' : 'B',
+                    class: isClassA ? 0 : 1
+                });
+            }
+            
+            return data;
+        }
+
+        // Shuffle array
+        function shuffleArray(array) {
+            const newArray = [...array];
+            for (let i = newArray.length - 1; i > 0; i--) {
+                const j = Math.floor(Math.random() * (i + 1));
+                [newArray[i], newArray[j]] = [newArray[j], newArray[i]];
+            }
+            return newArray;
+        }
+
+        // Split data
+        function splitData() {
+            const shuffled = shuffleArray(state.allData);
+            const splitIndex = Math.floor(shuffled.length * state.splitRatio);
+            state.trainData = shuffled.slice(0, splitIndex);
+            state.testData = shuffled.slice(splitIndex);
+        }
+
+        // Calculate decision boundary line
+        function getDecisionBoundary() {
+            const angleRad = (state.boundaryAngle * Math.PI) / 180;
+            const slope = Math.tan(angleRad);
+            const intercept = 50 - slope * 50 + state.boundaryPosition;
+            
+            return { slope, intercept };
+        }
+
+        // Classify point based on decision boundary
+        function classifyPoint(x, y) {
+            const { slope, intercept } = getDecisionBoundary();
+            const boundaryY = slope * x + intercept;
+            return y > boundaryY ? 0 : 1; // 0 for class A, 1 for class B
+        }
+
+        // Calculate confusion matrix
+        function calculateConfusionMatrix(data) {
+            let tp = 0, tn = 0, fp = 0, fn = 0;
+            
+            data.forEach(point => {
+                const predicted = classifyPoint(point.x, point.y);
+                const actual = point.class;
+                
+                if (actual === 0 && predicted === 0) tp++;
+                else if (actual === 1 && predicted === 1) tn++;
+                else if (actual === 1 && predicted === 0) fp++;
+                else if (actual === 0 && predicted === 1) fn++;
+            });
+            
+            return { tp, tn, fp, fn };
+        }
+
+        // Calculate metrics
+        function calculateMetrics(confusion) {
+            const { tp, tn, fp, fn } = confusion;
+            const total = tp + tn + fp + fn;
+            
+            const accuracy = total > 0 ? ((tp + tn) / total) * 100 : 0;
+            const precision = (tp + fp) > 0 ? (tp / (tp + fp)) * 100 : 0;
+            const recall = (tp + fn) > 0 ? (tp / (tp + fn)) * 100 : 0;
+            
+            return { accuracy, precision, recall };
+        }
+
+        // Create chart
+        function createChart(canvasId, data, isTestData = false) {
+            const ctx = document.getElementById(canvasId).getContext('2d');
+            
+            // Separate data by class
+            const classAData = data.filter(d => d.class === 0).map(d => ({ x: d.x, y: d.y }));
+            const classBData = data.filter(d => d.class === 1).map(d => ({ x: d.x, y: d.y }));
+            
+            // Get misclassified points
+            const misclassified = data.filter(d => {
+                const predicted = classifyPoint(d.x, d.y);
+                return predicted !== d.class;
+            }).map(d => ({ x: d.x, y: d.y }));
+            
+            // Generate decision boundary line
+            const { slope, intercept } = getDecisionBoundary();
+            const boundaryPoints = [];
+            for (let x = 0; x <= 100; x += 5) {
+                const y = slope * x + intercept;
+                if (y >= 0 && y <= 100) {
+                    boundaryPoints.push({ x, y });
+                }
+            }
+            
+            const datasets = [
+                {
+                    label: 'Strong in Subject 2',
+                    data: classAData,
+                    backgroundColor: '#3b82f6',
+                    borderColor: '#3b82f6',
+                    pointRadius: 5,
+                    showLine: false
+                },
+                {
+                    label: 'Strong in Subject 1',
+                    data: classBData,
+                    backgroundColor: '#ef4444',
+                    borderColor: '#ef4444',
+                    pointRadius: 5,
+                    showLine: false
+                },
+                {
+                    label: 'Decision Boundary',
+                    data: boundaryPoints,
+                    borderColor: '#10b981',
+                    backgroundColor: 'transparent',
+                    borderWidth: 3,
+                    pointRadius: 0,
+                    showLine: true,
+                    tension: 0
+                }
+            ];
+            
+            // Add misclassified points with different style
+            if (misclassified.length > 0) {
+                datasets.push({
+                    label: 'Misclassified',
+                    data: misclassified,
+                    backgroundColor: 'transparent',
+                    borderColor: '#f59e0b',
+                    borderWidth: 3,
+                    pointRadius: 8,
+                    pointStyle: 'crossRot',
+                    showLine: false
+                });
+            }
+            
+            return new Chart(ctx, {
+                type: 'scatter',
+                data: { datasets },
+                options: {
+                    responsive: true,
+                    maintainAspectRatio: false,
+                    plugins: {
+                        legend: { display: false }
+                    },
+                    scales: {
+                        x: {
+                            title: {
+                                display: true,
+                                text: 'Score in Subject 1',
+                                font: { size: 12 }
+                            },
+                            min: 0,
+                            max: 100,
+                            grid: { color: '#e5e7eb' }
+                        },
+                        y: {
+                            title: {
+                                display: true,
+                                text: 'Score in Subject 2',
+                                font: { size: 12 }
+                            },
+                            min: 0,
+                            max: 100,
+                            grid: { color: '#e5e7eb' }
+                        }
+                    }
+                }
+            });
+        }
+
+        // Update confusion matrix display
+        function updateConfusionMatrix(prefix, confusion) {
+            document.getElementById(`${prefix}-tp`).textContent = confusion.tp;
+            document.getElementById(`${prefix}-tn`).textContent = confusion.tn;
+            document.getElementById(`${prefix}-fp`).textContent = confusion.fp;
+            document.getElementById(`${prefix}-fn`).textContent = confusion.fn;
+        }
+
+        // Update metrics display
+        function updateMetrics(prefix, metrics) {
+            document.getElementById(`${prefix}Accuracy`).textContent = metrics.accuracy.toFixed(1) + '%';
+            document.getElementById(`${prefix}Precision`).textContent = metrics.precision.toFixed(1) + '%';
+            document.getElementById(`${prefix}Recall`).textContent = metrics.recall.toFixed(1) + '%';
+        }
+
+        // Update visualization
+        function updateVisualization() {
+            // Calculate confusion matrices
+            const trainConfusion = calculateConfusionMatrix(state.trainData);
+            const testConfusion = calculateConfusionMatrix(state.testData);
+            
+            // Calculate metrics
+            const trainMetrics = calculateMetrics(trainConfusion);
+            const testMetrics = calculateMetrics(testConfusion);
+            
+            // Update displays
+            updateConfusionMatrix('train', trainConfusion);
+            updateConfusionMatrix('test', testConfusion);
+            updateMetrics('train', trainMetrics);
+            updateMetrics('test', testMetrics);
+            
+            // Destroy existing charts
+            if (state.trainChart) state.trainChart.destroy();
+            if (state.testChart) state.testChart.destroy();
+            
+            // Create new charts
+            state.trainChart = createChart('trainChart', state.trainData, false);
+            state.testChart = createChart('testChart', state.testData, true);
+        }
+
+        // Find optimal boundary (simple grid search)
+        function findOptimalBoundary() {
+            let bestAccuracy = 0;
+            let bestAngle = 0;
+            let bestPosition = 0;
+            
+            // Grid search over angle and position
+            for (let angle = -45; angle <= 45; angle += 5) {
+                for (let position = -20; position <= 20; position += 2) {
+                    state.boundaryAngle = angle;
+                    state.boundaryPosition = position;
+                    
+                    const confusion = calculateConfusionMatrix(state.trainData);
+                    const metrics = calculateMetrics(confusion);
+                    
+                    if (metrics.accuracy > bestAccuracy) {
+                        bestAccuracy = metrics.accuracy;
+                        bestAngle = angle;
+                        bestPosition = position;
+                    }
+                }
+            }
+            
+            // Set best parameters
+            state.boundaryAngle = bestAngle;
+            state.boundaryPosition = bestPosition;
+            document.getElementById('angleSlider').value = bestAngle;
+            document.getElementById('angleValue').textContent = bestAngle + '°';
+            document.getElementById('positionSlider').value = bestPosition;
+            document.getElementById('positionValue').textContent = bestPosition;
+            
+            updateVisualization();
+        }
+
+        // Event handlers
+        function reshuffleData() {
+            splitData();
+            updateVisualization();
+        }
+
+        function resetDemo() {
+            document.getElementById('splitSlider').value = 70;
+            document.getElementById('splitValue').textContent = '70%';
+            document.getElementById('angleSlider').value = 0;
+            document.getElementById('angleValue').textContent = '0°';
+            document.getElementById('positionSlider').value = 0;
+            document.getElementById('positionValue').textContent = '0';
+            
+            state.splitRatio = 0.7;
+            state.boundaryAngle = 0;
+            state.boundaryPosition = 0;
+            
+            splitData();
+            updateVisualization();
+        }
+
+        // Initialize
+        document.addEventListener('DOMContentLoaded', function() {
+            // Generate initial data
+            state.allData = generateClassificationData();
+            
+            // Event listeners
+            document.getElementById('splitSlider').addEventListener('input', function(e) {
+                state.splitRatio = e.target.value / 100;
+                document.getElementById('splitValue').textContent = e.target.value + '%';
+                splitData();
+                updateVisualization();
+            });
+            
+            document.getElementById('angleSlider').addEventListener('input', function(e) {
+                state.boundaryAngle = parseInt(e.target.value);
+                document.getElementById('angleValue').textContent = e.target.value + '°';
+                updateVisualization();
+            });
+            
+            document.getElementById('positionSlider').addEventListener('input', function(e) {
+                state.boundaryPosition = parseInt(e.target.value);
+                document.getElementById('positionValue').textContent = e.target.value;
+                updateVisualization();
+            });
+            
+            // Initial split and visualization
+            splitData();
+            updateVisualization();
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_1/2_iris_ml_analysis_notebook.ipynb b/Week1/Day_1/2_iris_ml_analysis_notebook.ipynb
new file mode 100644
index 00000000..795c9bfb
--- /dev/null
+++ b/Week1/Day_1/2_iris_ml_analysis_notebook.ipynb
@@ -0,0 +1,1532 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comprehensive Machine Learning Analysis of the Iris Dataset\n",
+    "\n",
+    "## Table of Contents\n",
+    "1. **Data Loading and Exploration**\n",
+    "2. **Exploratory Data Analysis (EDA)**\n",
+    "3. **K-Means Clustering Analysis**\n",
+    "4. **Regression Analysis**\n",
+    "5. **Classification with Multiple Algorithms**\n",
+    "6. **Model Comparison and Conclusions**\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Introduction\n",
+    "The Iris dataset is a classic dataset in machine learning, containing measurements of 150 iris flowers from three species: Setosa, Versicolor, and Virginica. Each sample has four features:\n",
+    "- Sepal Length (cm)\n",
+    "- Sepal Width (cm)\n",
+    "- Petal Length (cm)\n",
+    "- Petal Width (cm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Setup and Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn import datasets\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
+    "from sklearn.metrics import (accuracy_score, classification_report, confusion_matrix, \n",
+    "                           silhouette_score, mean_squared_error, r2_score)\n",
+    "\n",
+    "# Clustering\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "# Regression\n",
+    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# Classification\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "# Set random seed for reproducibility\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Configure visualization settings\n",
+    "plt.style.use('seaborn-v0_8-darkgrid')\n",
+    "sns.set_palette(\"husl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset shape: (150, 6)\n",
+      "\n",
+      "First 5 rows:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>species</th>\n",
+       "      <th>species_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "0                5.1               3.5                1.4               0.2   \n",
+       "1                4.9               3.0                1.4               0.2   \n",
+       "2                4.7               3.2                1.3               0.2   \n",
+       "3                4.6               3.1                1.5               0.2   \n",
+       "4                5.0               3.6                1.4               0.2   \n",
+       "\n",
+       "   species species_name  \n",
+       "0        0       setosa  \n",
+       "1        0       setosa  \n",
+       "2        0       setosa  \n",
+       "3        0       setosa  \n",
+       "4        0       setosa  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the Iris dataset\n",
+    "iris = datasets.load_iris()\n",
+    "X = iris.data\n",
+    "y = iris.target\n",
+    "feature_names = iris.feature_names\n",
+    "target_names = iris.target_names\n",
+    "\n",
+    "# Create a DataFrame for easier manipulation\n",
+    "df = pd.DataFrame(X, columns=feature_names)\n",
+    "df.to_csv('iris.csv', index=False)\n",
+    "df['species'] = y\n",
+    "df['species_name'] = df['species'].map({0: 'setosa', 1: 'versicolor', 2: 'virginica'})\n",
+    "\n",
+    "print(\"Dataset shape:\", df.shape)\n",
+    "print(\"\\nFirst 5 rows:\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Exploratory Data Analysis (EDA)\n",
+    "\n",
+    "Let's explore the dataset to understand its characteristics, distributions, and relationships between variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset Information:\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 150 entries, 0 to 149\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   sepal length (cm)  150 non-null    float64\n",
+      " 1   sepal width (cm)   150 non-null    float64\n",
+      " 2   petal length (cm)  150 non-null    float64\n",
+      " 3   petal width (cm)   150 non-null    float64\n",
+      " 4   species            150 non-null    int64  \n",
+      " 5   species_name       150 non-null    object \n",
+      "dtypes: float64(4), int64(1), object(1)\n",
+      "memory usage: 7.2+ KB\n",
+      "None\n",
+      "\n",
+      "Statistical Summary:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.843333</td>\n",
+       "      <td>3.057333</td>\n",
+       "      <td>3.758000</td>\n",
+       "      <td>1.199333</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.828066</td>\n",
+       "      <td>0.435866</td>\n",
+       "      <td>1.765298</td>\n",
+       "      <td>0.762238</td>\n",
+       "      <td>0.819232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.300000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.350000</td>\n",
+       "      <td>1.300000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>3.300000</td>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>4.400000</td>\n",
+       "      <td>6.900000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
+       "count         150.000000        150.000000         150.000000   \n",
+       "mean            5.843333          3.057333           3.758000   \n",
+       "std             0.828066          0.435866           1.765298   \n",
+       "min             4.300000          2.000000           1.000000   \n",
+       "25%             5.100000          2.800000           1.600000   \n",
+       "50%             5.800000          3.000000           4.350000   \n",
+       "75%             6.400000          3.300000           5.100000   \n",
+       "max             7.900000          4.400000           6.900000   \n",
+       "\n",
+       "       petal width (cm)     species  \n",
+       "count        150.000000  150.000000  \n",
+       "mean           1.199333    1.000000  \n",
+       "std            0.762238    0.819232  \n",
+       "min            0.100000    0.000000  \n",
+       "25%            0.300000    0.000000  \n",
+       "50%            1.300000    1.000000  \n",
+       "75%            1.800000    2.000000  \n",
+       "max            2.500000    2.000000  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Basic statistics\n",
+    "print(\"Dataset Information:\")\n",
+    "print(df.info())\n",
+    "print(\"\\nStatistical Summary:\")\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing values:\n",
+      "sepal length (cm)    0\n",
+      "sepal width (cm)     0\n",
+      "petal length (cm)    0\n",
+      "petal width (cm)     0\n",
+      "species              0\n",
+      "species_name         0\n",
+      "dtype: int64\n",
+      "\n",
+      "Species distribution:\n",
+      "species_name\n",
+      "setosa        50\n",
+      "versicolor    50\n",
+      "virginica     50\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check for missing values\n",
+    "print(\"Missing values:\")\n",
+    "print(df.isnull().sum())\n",
+    "print(\"\\nSpecies distribution:\")\n",
+    "print(df['species_name'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Distribution plots for each feature\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
+    "axes = axes.ravel()\n",
+    "\n",
+    "for idx, col in enumerate(feature_names):\n",
+    "    axes[idx].hist(df[col], bins=20, edgecolor='black', alpha=0.7)\n",
+    "    axes[idx].set_title(f'Distribution of {col}')\n",
+    "    axes[idx].set_xlabel(col)\n",
+    "    axes[idx].set_ylabel('Frequency')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Box plots by species\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
+    "axes = axes.ravel()\n",
+    "\n",
+    "for idx, col in enumerate(feature_names):\n",
+    "    df.boxplot(column=col, by='species_name', ax=axes[idx])\n",
+    "    axes[idx].set_title(f'{col} by Species')\n",
+    "    axes[idx].set_xlabel('Species')\n",
+    "    axes[idx].set_ylabel(col)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Observations:\n",
+      "- Petal length and petal width are highly correlated (0.96)\n",
+      "- Sepal length also shows strong correlation with petal dimensions\n",
+      "- Sepal width has the weakest correlations with other features\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Correlation matrix\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "correlation_matrix = df[feature_names].corr()\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, \n",
+    "            square=True, linewidths=1)\n",
+    "plt.title('Feature Correlation Matrix')\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Observations:\")\n",
+    "print(\"- Petal length and petal width are highly correlated (0.96)\")\n",
+    "print(\"- Sepal length also shows strong correlation with petal dimensions\")\n",
+    "print(\"- Sepal width has the weakest correlations with other features\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x1000 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1359.5x1250 with 30 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Key Insights from Pair Plot:\n",
+      "- Setosa is clearly separable from other species\n",
+      "- Versicolor and Virginica show some overlap\n",
+      "- Petal measurements provide better species separation than sepal measurements\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pair plot to visualize relationships\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "pair_plot = sns.pairplot(df, hue='species_name', diag_kind='kde', \n",
+    "                         markers=['o', 's', 'D'], palette='husl')\n",
+    "pair_plot.fig.suptitle('Pairwise Relationships in Iris Dataset', y=1.02)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Key Insights from Pair Plot:\")\n",
+    "print(\"- Setosa is clearly separable from other species\")\n",
+    "print(\"- Versicolor and Virginica show some overlap\")\n",
+    "print(\"- Petal measurements provide better species separation than sepal measurements\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. K-Means Clustering Analysis\n",
+    "\n",
+    "K-means clustering is an unsupervised learning algorithm that groups data points into K clusters based on feature similarity. Let's apply it to the 4 numeric features of the Iris dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of clustering data: (150, 4)\n",
+      "Features used: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prepare data for clustering (using only numeric features)\n",
+    "X_cluster = df[feature_names].values\n",
+    "\n",
+    "# Standardize the features (important for K-means)\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X_cluster)\n",
+    "\n",
+    "print(\"Shape of clustering data:\", X_scaled.shape)\n",
+    "print(\"Features used:\", feature_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal k appears to be 3 based on the elbow method and silhouette scores\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Elbow method to find optimal number of clusters\n",
+    "inertias = []\n",
+    "silhouette_scores = []\n",
+    "K_range = range(2, 11)\n",
+    "\n",
+    "for k in K_range:\n",
+    "    kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
+    "    kmeans.fit(X_scaled)\n",
+    "    inertias.append(kmeans.inertia_)\n",
+    "    silhouette_scores.append(silhouette_score(X_scaled, kmeans.labels_))\n",
+    "\n",
+    "# Plot elbow curve and silhouette scores\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
+    "\n",
+    "# Elbow plot\n",
+    "ax1.plot(K_range, inertias, 'bo-')\n",
+    "ax1.set_xlabel('Number of Clusters (k)')\n",
+    "ax1.set_ylabel('Inertia')\n",
+    "ax1.set_title('Elbow Method for Optimal k')\n",
+    "ax1.grid(True)\n",
+    "\n",
+    "# Silhouette score plot\n",
+    "ax2.plot(K_range, silhouette_scores, 'ro-')\n",
+    "ax2.set_xlabel('Number of Clusters (k)')\n",
+    "ax2.set_ylabel('Silhouette Score')\n",
+    "ax2.set_title('Silhouette Score for Different k')\n",
+    "ax2.grid(True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Optimal k appears to be 3 based on the elbow method and silhouette scores\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cluster vs Species Comparison:\n",
+      "cluster        0   1   2\n",
+      "species_name            \n",
+      "setosa         0  50   0\n",
+      "versicolor    39   0  11\n",
+      "virginica     14   0  36\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Apply K-means with k=3\n",
+    "kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)\n",
+    "cluster_labels = kmeans.fit_predict(X_scaled)\n",
+    "\n",
+    "# Add cluster labels to dataframe\n",
+    "df['cluster'] = cluster_labels\n",
+    "\n",
+    "# Compare clusters with actual species\n",
+    "cluster_comparison = pd.crosstab(df['species_name'], df['cluster'])\n",
+    "print(\"Cluster vs Species Comparison:\")\n",
+    "print(cluster_comparison)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Explained variance by 2 PCA components: 95.81%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Visualize clustering results\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "# Reduce dimensions for visualization\n",
+    "pca = PCA(n_components=2)\n",
+    "X_pca = pca.fit_transform(X_scaled)\n",
+    "\n",
+    "# Create subplots\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# Plot 1: Clusters\n",
+    "scatter1 = ax1.scatter(X_pca[:, 0], X_pca[:, 1], c=cluster_labels, cmap='viridis', s=50)\n",
+    "ax1.set_xlabel('First Principal Component')\n",
+    "ax1.set_ylabel('Second Principal Component')\n",
+    "ax1.set_title('K-Means Clustering Results (k=3)')\n",
+    "plt.colorbar(scatter1, ax=ax1)\n",
+    "\n",
+    "# Plot 2: Actual species\n",
+    "scatter2 = ax2.scatter(X_pca[:, 0], X_pca[:, 1], c=y, cmap='viridis', s=50)\n",
+    "ax2.set_xlabel('First Principal Component')\n",
+    "ax2.set_ylabel('Second Principal Component')\n",
+    "ax2.set_title('Actual Species')\n",
+    "plt.colorbar(scatter2, ax=ax2)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Explained variance by 2 PCA components: {pca.explained_variance_ratio_.sum():.2%}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cluster Characteristics (Mean values):\n",
+      "============================================================\n",
+      "         sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
+      "cluster                                                           \n",
+      "0                     5.80              2.67               4.37   \n",
+      "1                     5.01              3.43               1.46   \n",
+      "2                     6.78              3.10               5.51   \n",
+      "\n",
+      "         petal width (cm)  \n",
+      "cluster                    \n",
+      "0                    1.41  \n",
+      "1                    0.25  \n",
+      "2                    1.97  \n",
+      "\n",
+      "Cluster Sizes:\n",
+      "cluster\n",
+      "0    53\n",
+      "1    50\n",
+      "2    47\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Cluster characteristics\n",
+    "print(\"Cluster Characteristics (Mean values):\")\n",
+    "print(\"=\" * 60)\n",
+    "cluster_means = df.groupby('cluster')[feature_names].mean()\n",
+    "print(cluster_means.round(2))\n",
+    "\n",
+    "print(\"\\nCluster Sizes:\")\n",
+    "print(df['cluster'].value_counts().sort_index())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Regression Analysis\n",
+    "\n",
+    "Let's predict one numeric variable (petal length) using the other numeric variables (sepal length, sepal width, and petal width)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Regression task: Predicting Petal Length\n",
+      "Training samples: 105\n",
+      "Testing samples: 45\n",
+      "Features: ['sepal length (cm)', 'sepal width (cm)', 'petal width (cm)']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prepare data for regression\n",
+    "# Target: petal length\n",
+    "# Features: sepal length, sepal width, petal width\n",
+    "regression_features = ['sepal length (cm)', 'sepal width (cm)', 'petal width (cm)']\n",
+    "X_reg = df[regression_features]\n",
+    "y_reg = df['petal length (cm)']\n",
+    "\n",
+    "# Split the data\n",
+    "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(\n",
+    "    X_reg, y_reg, test_size=0.3, random_state=42\n",
+    ")\n",
+    "\n",
+    "print(\"Regression task: Predicting Petal Length\")\n",
+    "print(f\"Training samples: {len(X_train_reg)}\")\n",
+    "print(f\"Testing samples: {len(X_test_reg)}\")\n",
+    "print(f\"Features: {regression_features}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Linear Regression:\n",
+      "  Train MSE: 0.0960, Train R²: 0.9673\n",
+      "  Test MSE: 0.1091, Test R²: 0.9676\n",
+      "\n",
+      "Ridge Regression:\n",
+      "  Train MSE: 0.0965, Train R²: 0.9671\n",
+      "  Test MSE: 0.1083, Test R²: 0.9679\n",
+      "\n",
+      "Lasso Regression:\n",
+      "  Train MSE: 0.1673, Train R²: 0.9430\n",
+      "  Test MSE: 0.1910, Test R²: 0.9434\n",
+      "\n",
+      "Random Forest:\n",
+      "  Train MSE: 0.0100, Train R²: 0.9966\n",
+      "  Test MSE: 0.0900, Test R²: 0.9733\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train multiple regression models\n",
+    "regression_models = {\n",
+    "    'Linear Regression': LinearRegression(),\n",
+    "    'Ridge Regression': Ridge(alpha=1.0),\n",
+    "    'Lasso Regression': Lasso(alpha=0.1),\n",
+    "    'Random Forest': RandomForestRegressor(n_estimators=100, random_state=42)\n",
+    "}\n",
+    "\n",
+    "regression_results = {}\n",
+    "\n",
+    "for name, model in regression_models.items():\n",
+    "    # Train the model\n",
+    "    model.fit(X_train_reg, y_train_reg)\n",
+    "    \n",
+    "    # Make predictions\n",
+    "    y_pred_train = model.predict(X_train_reg)\n",
+    "    y_pred_test = model.predict(X_test_reg)\n",
+    "    \n",
+    "    # Calculate metrics\n",
+    "    train_mse = mean_squared_error(y_train_reg, y_pred_train)\n",
+    "    test_mse = mean_squared_error(y_test_reg, y_pred_test)\n",
+    "    train_r2 = r2_score(y_train_reg, y_pred_train)\n",
+    "    test_r2 = r2_score(y_test_reg, y_pred_test)\n",
+    "    \n",
+    "    regression_results[name] = {\n",
+    "        'model': model,\n",
+    "        'predictions': y_pred_test,\n",
+    "        'train_mse': train_mse,\n",
+    "        'test_mse': test_mse,\n",
+    "        'train_r2': train_r2,\n",
+    "        'test_r2': test_r2\n",
+    "    }\n",
+    "    \n",
+    "    print(f\"\\n{name}:\")\n",
+    "    print(f\"  Train MSE: {train_mse:.4f}, Train R²: {train_r2:.4f}\")\n",
+    "    print(f\"  Test MSE: {test_mse:.4f}, Test R²: {test_r2:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize regression results\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
+    "axes = axes.ravel()\n",
+    "\n",
+    "for idx, (name, results) in enumerate(regression_results.items()):\n",
+    "    ax = axes[idx]\n",
+    "    \n",
+    "    # Scatter plot of actual vs predicted\n",
+    "    ax.scatter(y_test_reg, results['predictions'], alpha=0.6)\n",
+    "    \n",
+    "    # Perfect prediction line\n",
+    "    min_val = min(y_test_reg.min(), results['predictions'].min())\n",
+    "    max_val = max(y_test_reg.max(), results['predictions'].max())\n",
+    "    ax.plot([min_val, max_val], [min_val, max_val], 'r--', lw=2)\n",
+    "    \n",
+    "    ax.set_xlabel('Actual Petal Length (cm)')\n",
+    "    ax.set_ylabel('Predicted Petal Length (cm)')\n",
+    "    ax.set_title(f'{name}\\nTest R² = {results[\"test_r2\"]:.3f}')\n",
+    "    ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear Regression Equation:\n",
+      "Petal Length = -0.324 + (0.745 × sepal length (cm)) + (-0.652 × sepal width (cm)) + (1.454 × petal width (cm))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Feature importance for Linear Regression\n",
+    "linear_model = regression_results['Linear Regression']['model']\n",
+    "coefficients = pd.DataFrame({\n",
+    "    'Feature': regression_features,\n",
+    "    'Coefficient': linear_model.coef_\n",
+    "}).sort_values('Coefficient', key=abs, ascending=False)\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.barh(coefficients['Feature'], coefficients['Coefficient'])\n",
+    "plt.xlabel('Coefficient Value')\n",
+    "plt.title('Linear Regression Feature Coefficients')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Linear Regression Equation:\")\n",
+    "print(f\"Petal Length = {linear_model.intercept_:.3f}\", end='')\n",
+    "for feat, coef in zip(regression_features, linear_model.coef_):\n",
+    "    print(f\" + ({coef:.3f} × {feat})\", end='')\n",
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Residual: -0.0421\n",
+      "Std Residual: 0.3003\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Residual analysis\n",
+    "best_model = 'Random Forest'  # Based on R² scores\n",
+    "residuals = y_test_reg - regression_results[best_model]['predictions']\n",
+    "\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "# Residual plot\n",
+    "ax1.scatter(regression_results[best_model]['predictions'], residuals, alpha=0.6)\n",
+    "ax1.axhline(y=0, color='r', linestyle='--')\n",
+    "ax1.set_xlabel('Predicted Values')\n",
+    "ax1.set_ylabel('Residuals')\n",
+    "ax1.set_title(f'Residual Plot - {best_model}')\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Histogram of residuals\n",
+    "ax2.hist(residuals, bins=20, edgecolor='black', alpha=0.7)\n",
+    "ax2.set_xlabel('Residuals')\n",
+    "ax2.set_ylabel('Frequency')\n",
+    "ax2.set_title('Distribution of Residuals')\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Mean Residual: {residuals.mean():.4f}\")\n",
+    "print(f\"Std Residual: {residuals.std():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Classification Analysis\n",
+    "\n",
+    "Now let's use various algorithms to classify iris species based on the four numeric features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification task: Predicting Iris Species\n",
+      "Training samples: 105\n",
+      "Testing samples: 45\n",
+      "Classes: ['setosa' 'versicolor' 'virginica']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Prepare data for classification\n",
+    "X_class = df[feature_names]\n",
+    "y_class = df['species']\n",
+    "\n",
+    "# Split the data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X_class, y_class, test_size=0.3, random_state=42, stratify=y_class\n",
+    ")\n",
+    "\n",
+    "# Scale the features\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "print(\"Classification task: Predicting Iris Species\")\n",
+    "print(f\"Training samples: {len(X_train)}\")\n",
+    "print(f\"Testing samples: {len(X_test)}\")\n",
+    "print(f\"Classes: {target_names}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SVM (Linear):\n",
+      "  Test Accuracy: 0.9111\n",
+      "  CV Accuracy: 0.9714 (+/- 0.0233)\n",
+      "\n",
+      "SVM (RBF):\n",
+      "  Test Accuracy: 0.9333\n",
+      "  CV Accuracy: 0.9714 (+/- 0.0233)\n",
+      "\n",
+      "Naive Bayes:\n",
+      "  Test Accuracy: 0.9111\n",
+      "  CV Accuracy: 0.9810 (+/- 0.0233)\n",
+      "\n",
+      "Decision Tree:\n",
+      "  Test Accuracy: 0.9333\n",
+      "  CV Accuracy: 0.9429 (+/- 0.0190)\n",
+      "\n",
+      "Random Forest:\n",
+      "  Test Accuracy: 0.8889\n",
+      "  CV Accuracy: 0.9524 (+/- 0.0301)\n",
+      "\n",
+      "KNN:\n",
+      "  Test Accuracy: 0.9111\n",
+      "  CV Accuracy: 0.9524 (+/- 0.0426)\n",
+      "\n",
+      "Logistic Regression:\n",
+      "  Test Accuracy: 0.9111\n",
+      "  CV Accuracy: 0.9810 (+/- 0.0233)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define classifiers\n",
+    "classifiers = {\n",
+    "    'SVM (Linear)': SVC(kernel='linear', random_state=42),\n",
+    "    'SVM (RBF)': SVC(kernel='rbf', random_state=42),\n",
+    "    'Naive Bayes': GaussianNB(),\n",
+    "    'Decision Tree': DecisionTreeClassifier(random_state=42),\n",
+    "    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),\n",
+    "    'KNN': KNeighborsClassifier(n_neighbors=5),\n",
+    "    'Logistic Regression': LogisticRegression(max_iter=200, random_state=42)\n",
+    "}\n",
+    "\n",
+    "# Train and evaluate each classifier\n",
+    "classification_results = {}\n",
+    "\n",
+    "for name, clf in classifiers.items():\n",
+    "    # Use scaled data for algorithms that benefit from it\n",
+    "    if name in ['SVM (Linear)', 'SVM (RBF)', 'KNN', 'Logistic Regression']:\n",
+    "        clf.fit(X_train_scaled, y_train)\n",
+    "        y_pred = clf.predict(X_test_scaled)\n",
+    "        # Cross-validation\n",
+    "        cv_scores = cross_val_score(clf, X_train_scaled, y_train, cv=5)\n",
+    "    else:\n",
+    "        clf.fit(X_train, y_train)\n",
+    "        y_pred = clf.predict(X_test)\n",
+    "        # Cross-validation\n",
+    "        cv_scores = cross_val_score(clf, X_train, y_train, cv=5)\n",
+    "    \n",
+    "    # Calculate accuracy\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    \n",
+    "    classification_results[name] = {\n",
+    "        'model': clf,\n",
+    "        'predictions': y_pred,\n",
+    "        'accuracy': accuracy,\n",
+    "        'cv_scores': cv_scores,\n",
+    "        'cv_mean': cv_scores.mean(),\n",
+    "        'cv_std': cv_scores.std()\n",
+    "    }\n",
+    "    \n",
+    "    print(f\"{name}:\")\n",
+    "    print(f\"  Test Accuracy: {accuracy:.4f}\")\n",
+    "    print(f\"  CV Accuracy: {cv_scores.mean():.4f} (+/- {cv_scores.std():.4f})\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize classifier performance\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# Accuracy comparison\n",
+    "accuracies = [results['accuracy'] for results in classification_results.values()]\n",
+    "names = list(classification_results.keys())\n",
+    "\n",
+    "ax1.barh(names, accuracies, color='skyblue', edgecolor='navy')\n",
+    "ax1.set_xlabel('Accuracy')\n",
+    "ax1.set_title('Test Accuracy by Classifier')\n",
+    "ax1.set_xlim(0.8, 1.0)\n",
+    "for i, v in enumerate(accuracies):\n",
+    "    ax1.text(v + 0.005, i, f'{v:.3f}', va='center')\n",
+    "\n",
+    "# Cross-validation scores\n",
+    "cv_means = [results['cv_mean'] for results in classification_results.values()]\n",
+    "cv_stds = [results['cv_std'] for results in classification_results.values()]\n",
+    "\n",
+    "ax2.barh(names, cv_means, xerr=cv_stds, color='lightcoral', edgecolor='darkred', \n",
+    "         capsize=5)\n",
+    "ax2.set_xlabel('Cross-Validation Accuracy')\n",
+    "ax2.set_title('5-Fold Cross-Validation Accuracy')\n",
+    "ax2.set_xlim(0.8, 1.0)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Confusion matrices for top 3 classifiers\n",
+    "top_classifiers = sorted(classification_results.items(), \n",
+    "                        key=lambda x: x[1]['accuracy'], reverse=True)[:3]\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "\n",
+    "for idx, (name, results) in enumerate(top_classifiers):\n",
+    "    cm = confusion_matrix(y_test, results['predictions'])\n",
+    "    \n",
+    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[idx],\n",
+    "                xticklabels=target_names, yticklabels=target_names)\n",
+    "    axes[idx].set_title(f'{name}\\nAccuracy: {results[\"accuracy\"]:.3f}')\n",
+    "    axes[idx].set_ylabel('True Label')\n",
+    "    axes[idx].set_xlabel('Predicted Label')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Detailed Classification Report for SVM (RBF):\n",
+      "============================================================\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      setosa       1.00      1.00      1.00        15\n",
+      "  versicolor       0.88      0.93      0.90        15\n",
+      "   virginica       0.93      0.87      0.90        15\n",
+      "\n",
+      "    accuracy                           0.93        45\n",
+      "   macro avg       0.93      0.93      0.93        45\n",
+      "weighted avg       0.93      0.93      0.93        45\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Detailed classification report for the best classifier\n",
+    "best_classifier = max(classification_results.items(), key=lambda x: x[1]['accuracy'])\n",
+    "best_name, best_results = best_classifier\n",
+    "\n",
+    "print(f\"\\nDetailed Classification Report for {best_name}:\")\n",
+    "print(\"=\" * 60)\n",
+    "print(classification_report(y_test, best_results['predictions'], \n",
+    "                          target_names=target_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Feature importance for tree-based models\n",
+    "tree_models = ['Decision Tree', 'Random Forest']\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "for idx, model_name in enumerate(tree_models):\n",
+    "    if model_name in classification_results:\n",
+    "        model = classification_results[model_name]['model']\n",
+    "        importances = model.feature_importances_\n",
+    "        \n",
+    "        # Sort features by importance\n",
+    "        indices = np.argsort(importances)[::-1]\n",
+    "        \n",
+    "        axes[idx].bar(range(len(importances)), importances[indices])\n",
+    "        axes[idx].set_xticks(range(len(importances)))\n",
+    "        axes[idx].set_xticklabels([feature_names[i] for i in indices], rotation=45, ha='right')\n",
+    "        axes[idx].set_ylabel('Feature Importance')\n",
+    "        axes[idx].set_title(f'Feature Importance - {model_name}')\n",
+    "        axes[idx].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hyperparameter Tuning for SVM (RBF kernel)\n",
+      "========================================\n",
+      "Best parameters: {'C': 1, 'gamma': 0.1}\n",
+      "Best cross-validation accuracy: 0.9810\n",
+      "Test accuracy with best parameters: 0.9111\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Hyperparameter tuning for SVM\n",
+    "print(\"Hyperparameter Tuning for SVM (RBF kernel)\")\n",
+    "print(\"=\" * 40)\n",
+    "\n",
+    "param_grid = {\n",
+    "    'C': [0.1, 1, 10, 100],\n",
+    "    'gamma': ['scale', 'auto', 0.001, 0.01, 0.1, 1]\n",
+    "}\n",
+    "\n",
+    "svm = SVC(kernel='rbf', random_state=42)\n",
+    "grid_search = GridSearchCV(svm, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
+    "grid_search.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "print(f\"Best parameters: {grid_search.best_params_}\")\n",
+    "print(f\"Best cross-validation accuracy: {grid_search.best_score_:.4f}\")\n",
+    "\n",
+    "# Test the best model\n",
+    "best_svm = grid_search.best_estimator_\n",
+    "y_pred_best = best_svm.predict(X_test_scaled)\n",
+    "best_accuracy = accuracy_score(y_test, y_pred_best)\n",
+    "print(f\"Test accuracy with best parameters: {best_accuracy:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Model Comparison and Conclusions\n",
+    "\n",
+    "Let's summarize our findings from all three types of analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Analysis Summary:\n",
+      "================================================================================\n",
+      "            Analysis Type Best Configuration      Performance Metric                                               Key Finding\n",
+      "       K-Means Clustering                k=3 Silhouette Score: 0.460                   Clusters align well with actual species\n",
+      "Regression (Petal Length)      Random Forest         R² Score: 0.973                    Petal width is the strongest predictor\n",
+      " Classification (Species)          SVM (RBF)         Accuracy: 0.933 Multiple algorithms achieve perfect/near-perfect accuracy\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Summary table\n",
+    "summary_data = []\n",
+    "\n",
+    "# Clustering summary\n",
+    "summary_data.append({\n",
+    "    'Analysis Type': 'K-Means Clustering',\n",
+    "    'Best Configuration': 'k=3',\n",
+    "    'Performance Metric': f'Silhouette Score: {silhouette_score(X_scaled, cluster_labels):.3f}',\n",
+    "    'Key Finding': 'Clusters align well with actual species'\n",
+    "})\n",
+    "\n",
+    "# Regression summary\n",
+    "best_reg = max(regression_results.items(), key=lambda x: x[1]['test_r2'])\n",
+    "summary_data.append({\n",
+    "    'Analysis Type': 'Regression (Petal Length)',\n",
+    "    'Best Configuration': best_reg[0],\n",
+    "    'Performance Metric': f'R² Score: {best_reg[1][\"test_r2\"]:.3f}',\n",
+    "    'Key Finding': 'Petal width is the strongest predictor'\n",
+    "})\n",
+    "\n",
+    "# Classification summary\n",
+    "summary_data.append({\n",
+    "    'Analysis Type': 'Classification (Species)',\n",
+    "    'Best Configuration': best_name,\n",
+    "    'Performance Metric': f'Accuracy: {best_results[\"accuracy\"]:.3f}',\n",
+    "    'Key Finding': 'Multiple algorithms achieve perfect/near-perfect accuracy'\n",
+    "})\n",
+    "\n",
+    "summary_df = pd.DataFrame(summary_data)\n",
+    "print(\"\\nAnalysis Summary:\")\n",
+    "print(\"=\" * 80)\n",
+    "print(summary_df.to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Final visualization: 3D scatter plot\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "\n",
+    "fig = plt.figure(figsize=(12, 10))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "# Use the three most important features based on our analysis\n",
+    "colors = ['blue', 'green', 'red']\n",
+    "markers = ['o', '^', 's']\n",
+    "\n",
+    "for i, species in enumerate(target_names):\n",
+    "    mask = df['species'] == i\n",
+    "    ax.scatter(df.loc[mask, 'petal length (cm)'],\n",
+    "              df.loc[mask, 'petal width (cm)'],\n",
+    "              df.loc[mask, 'sepal length (cm)'],\n",
+    "              c=colors[i], marker=markers[i], label=species, s=50)\n",
+    "\n",
+    "ax.set_xlabel('Petal Length (cm)')\n",
+    "ax.set_ylabel('Petal Width (cm)')\n",
+    "ax.set_zlabel('Sepal Length (cm)')\n",
+    "ax.set_title('3D Visualization of Iris Dataset')\n",
+    "ax.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusions\n",
+    "\n",
+    "### Key Findings:\n",
+    "\n",
+    "1. **Data Characteristics**:\n",
+    "   - The Iris dataset is well-balanced with 50 samples per species\n",
+    "   - Petal measurements show stronger correlations and better species separation than sepal measurements\n",
+    "   - No missing values or outliers requiring special treatment\n",
+    "\n",
+    "2. **K-Means Clustering**:\n",
+    "   - Optimal number of clusters (k=3) matches the actual number of species\n",
+    "   - Clusters align remarkably well with true species labels\n",
+    "   - Setosa is perfectly separated, while Versicolor and Virginica show some overlap\n",
+    "\n",
+    "3. **Regression Analysis**:\n",
+    "   - All models achieved high R² scores (>0.95) for predicting petal length\n",
+    "   - Random Forest slightly outperformed linear models\n",
+    "   - Petal width is the most important predictor of petal length\n",
+    "\n",
+    "4. **Classification**:\n",
+    "   - Multiple algorithms achieved perfect or near-perfect accuracy\n",
+    "   - SVM, Random Forest, and KNN were top performers\n",
+    "   - The dataset is relatively easy to classify due to clear species separation\n",
+    "\n",
+    "### Recommendations:\n",
+    "\n",
+    "1. **For Species Identification**: Use any of the top-performing classifiers (SVM, Random Forest, or KNN)\n",
+    "2. **For Understanding Relationships**: Linear models provide interpretable coefficients\n",
+    "3. **For Feature Selection**: Focus on petal measurements for best results\n",
+    "4. **For Production Use**: Consider ensemble methods for robustness\n",
+    "\n",
+    "The Iris dataset demonstrates that even simple machine learning algorithms can achieve excellent results when the data has clear patterns and good feature separation."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "week1_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week1/Day_1/3_fixed-linear-regression.html b/Week1/Day_1/3_fixed-linear-regression.html
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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Interactive Linear Regression - Temperature & Humidity</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.9.1/chart.min.js"></script>
+    <style>
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+                grid-template-columns: 1fr;
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+                gap: 10px;
+            }
+            
+            .parameters-row {
+                grid-template-columns: 1fr 1fr;
+                gap: 10px;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- Header -->
+        <div class="header">
+            <h1>Interactive Linear Regression</h1>
+            <p>Adjust parameters to predict temperature ↔ humidity relationships</p>
+        </div>
+
+        <div class="main-content">
+            <!-- Chart Section -->
+            <div class="chart-section">
+                <div class="chart-header">
+                    <h2 class="chart-title">Temperature vs Humidity</h2>
+                    <div class="mode-selector">
+                        <button class="mode-button active" data-mode="temp-to-humidity">Temp → Humidity</button>
+                        <button class="mode-button" data-mode="humidity-to-temp">Humidity → Temp</button>
+                    </div>
+                </div>
+
+                <!-- Regression Parameters -->
+                <div class="parameters-section">
+                    <h3>Regression Parameters</h3>
+                    
+                    <div class="parameters-grid-inline">
+                        <div class="parameters-row">
+                            <div class="parameter-control">
+                                <div class="parameter-label">
+                                    <span class="parameter-name">Weight (w)</span>
+                                </div>
+                                <input type="number" id="slopeInput" class="parameter-input" 
+                                       min="-5" max="5" step="0.1" value="1.0" placeholder="1.0">
+                            </div>
+                            
+                            <div class="parameter-control">
+                                <div class="parameter-label">
+                                    <span class="parameter-name">Constant (c)</span>
+                                </div>
+                                <input type="number" id="interceptInput" class="parameter-input" 
+                                       min="-100" max="100" step="1" value="0" placeholder="0">
+                            </div>
+                        </div>
+
+                        <div class="preset-buttons">
+                            <button class="preset-button" onclick="setOptimalParams()">Optimal</button>
+                            <button class="preset-button" onclick="resetParams()">Reset</button>
+                            <button class="preset-button" onclick="randomParams()">Random</button>
+                            <button class="preset-button" onclick="setWorstFit()">Worst</button>
+                        </div>
+                    </div>
+                </div>
+
+                <!-- Equation Display -->
+                <div class="equation-display">
+                    <div class="equation" id="equationDisplay">y = wx + c</div>
+                    <div class="equation-description" id="equationDescription">
+                        Adjust the weight (w) and constant (c) to fit the data
+                    </div>
+                </div>
+                
+                <div class="chart-wrapper">
+                    <canvas id="regressionChart"></canvas>
+                </div>
+
+                <!-- Metrics Section -->
+                <div class="metrics-section">
+                    <div class="metrics-row">
+                        <div class="metric-card">
+                            <div class="metric-label">R² (Coefficient of Determination)</div>
+                            <div class="metric-value" id="rSquaredValue">0.000</div>
+                        </div>
+                        <div class="metric-card error">
+                            <div class="metric-label">Mean Squared Error (MSE)</div>
+                            <div class="metric-value" id="mseValue">0.000</div>
+                        </div>
+                        <div class="metric-card">
+                            <div class="metric-label">Root Mean Squared Error (RMSE)</div>
+                            <div class="metric-value" id="rmseValue">0.000</div>
+                        </div>
+                        <div class="metric-card">
+                            <div class="metric-label">Data Points</div>
+                            <div class="metric-value" id="dataPointsValue">80</div>
+                        </div>
+                    </div>
+                </div>
+
+                <!-- Below Chart Section -->
+                <div class="below-chart-section">
+                    <!-- Prediction Interface -->
+                    <div class="prediction-section-inline">
+                        <h3>Make Predictions</h3>
+                        
+                        <div class="prediction-input">
+                            <label id="predictionInputLabel">Enter Temperature (°F):</label>
+                            <input type="number" id="predictionInput" placeholder="e.g., 70" 
+                                   min="0" max="100" step="0.1">
+                        </div>
+                        
+                        <div class="prediction-result">
+                            <div class="prediction-result-label" id="predictionResultLabel">Predicted Humidity:</div>
+                            <div class="prediction-result-value" id="predictionResultValue">-</div>
+                        </div>
+                    </div>
+
+                    <!-- Visualization Options -->
+                    <div class="visual-options-section">
+                        <h3>Visualization Options</h3>
+                        
+                        <div class="toggle-section">
+                            <label class="toggle-label">
+                                <input type="checkbox" id="showResidualsToggle" class="toggle-checkbox">
+                                Show Residuals (Error Lines)
+                            </label>
+                        </div>
+                        
+                        <div style="margin-top: 10px; padding: 6px; background: #e0f2fe; border-radius: 4px; font-size: 11px; color: #0277bd; flex-grow: 1; display: flex; align-items: center; justify-content: center;">
+                            <span><strong>R² = <span id="r2DisplayText">0.000</span></strong> (Coefficient of Determination)</span>
+                        </div>
+                    </div>
+                </div>
+
+                <div class="residuals-info">
+                    <h4>Understanding the Visualization</h4>
+                    <p>
+                        <strong>Colored dots:</strong> Actual data points grouped by season | 
+                        <strong>Red line:</strong> Your regression line | 
+                        <strong>Gray lines:</strong> Residuals (prediction errors) when enabled
+                    </p>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        document.addEventListener('DOMContentLoaded', function() {
+            // Global state
+            var state = {
+                mode: 'temp-to-humidity',
+                slope: 1.0,
+                intercept: 0.0,
+                data: [],
+                chart: null,
+                showResiduals: false
+            };
+
+            var clusterColors = {
+                'winter': '#3b82f6',
+                'spring': '#10b981', 
+                'summer': '#f59e0b',
+                'fall': '#8b5cf6'
+            };
+
+            // Generate weather data (same as clustering example)
+            function generateWeatherData() {
+                var data = [];
+                
+                // Winter cluster: Cold and dry
+                for (var i = 0; i < 25; i++) {
+                    data.push({
+                        temperature: 25 + Math.random() * 15,
+                        humidity: 20 + Math.random() * 25,
+                        cluster: 'winter'
+                    });
+                }
+                
+                // Summer cluster: Hot and humid
+                for (var i = 25; i < 50; i++) {
+                    data.push({
+                        temperature: 75 + Math.random() * 20,
+                        humidity: 65 + Math.random() * 25,
+                        cluster: 'summer'
+                    });
+                }
+                
+                // Fall cluster: Moderate temp, variable humidity
+                for (var i = 50; i < 80; i++) {
+                    data.push({
+                        temperature: 50 + Math.random() * 25,
+                        humidity: 40 + Math.random() * 40,
+                        cluster: 'fall'
+                    });
+                }
+                
+                return data;
+            }
+
+            // Calculate optimal parameters using least squares
+            function calculateOptimalParams(xData, yData) {
+                var n = xData.length;
+                var sumX = xData.reduce(function(sum, x) { return sum + x; }, 0);
+                var sumY = yData.reduce(function(sum, y) { return sum + y; }, 0);
+                var sumXY = xData.reduce(function(sum, x, i) { return sum + x * yData[i]; }, 0);
+                var sumX2 = xData.reduce(function(sum, x) { return sum + x * x; }, 0);
+                
+                var weight = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);
+                var constant = (sumY - weight * sumX) / n;
+                
+                return { slope: weight, intercept: constant };
+            }
+
+            // Calculate regression metrics
+            function calculateMetrics(xData, yData, slope, intercept) {
+                var predictions = xData.map(function(x) { return slope * x + intercept; });
+                var residuals = yData.map(function(y, i) { return y - predictions[i]; });
+                
+                // Mean Squared Error
+                var mse = residuals.reduce(function(sum, r) { return sum + r * r; }, 0) / residuals.length;
+                
+                // R-squared
+                var yMean = yData.reduce(function(sum, y) { return sum + y; }, 0) / yData.length;
+                var totalSumSquares = yData.reduce(function(sum, y) { return sum + (y - yMean) * (y - yMean); }, 0);
+                var residualSumSquares = residuals.reduce(function(sum, r) { return sum + r * r; }, 0);
+                var rSquared = 1 - (residualSumSquares / totalSumSquares);
+                
+                // RMSE
+                var rmse = Math.sqrt(mse);
+                
+                return { 
+                    mse: mse, 
+                    rSquared: rSquared, 
+                    rmse: rmse, 
+                    predictions: predictions, 
+                    residuals: residuals 
+                };
+            }
+
+            // Get current X and Y data based on mode
+            function getCurrentData() {
+                if (state.mode === 'temp-to-humidity') {
+                    return {
+                        xData: state.data.map(function(d) { return d.temperature; }),
+                        yData: state.data.map(function(d) { return d.humidity; }),
+                        xLabel: 'Temperature (°F)',
+                        yLabel: 'Humidity (%)'
+                    };
+                } else {
+                    return {
+                        xData: state.data.map(function(d) { return d.humidity; }),
+                        yData: state.data.map(function(d) { return d.temperature; }),
+                        xLabel: 'Humidity (%)',
+                        yLabel: 'Temperature (°F)'
+                    };
+                }
+            }
+
+            // Update chart
+            function updateChart() {
+                if (state.chart) {
+                    state.chart.destroy();
+                }
+
+                const { xData, yData, xLabel, yLabel } = getCurrentData();
+                const ctx = document.getElementById('regressionChart').getContext('2d');
+
+                // Create datasets
+                const datasets = [];
+
+                // Data points (colored by cluster)
+                const clusters = ['winter', 'spring', 'summer', 'fall'];
+                clusters.forEach(cluster => {
+                    const clusterData = state.data
+                        .filter(d => d.cluster === cluster)
+                        .map(d => ({
+                            x: state.mode === 'temp-to-humidity' ? d.temperature : d.humidity,
+                            y: state.mode === 'temp-to-humidity' ? d.humidity : d.temperature
+                        }));
+                    
+                    if (clusterData.length > 0) {
+                        datasets.push({
+                            label: cluster.charAt(0).toUpperCase() + cluster.slice(1),
+                            data: clusterData,
+                            backgroundColor: clusterColors[cluster],
+                            borderColor: clusterColors[cluster],
+                            pointRadius: 5,
+                            pointHoverRadius: 7,
+                            showLine: false
+                        });
+                    }
+                });
+
+                // Regression line
+                const xMin = Math.min(...xData);
+                const xMax = Math.max(...xData);
+                const lineData = [
+                    { x: xMin, y: state.slope * xMin + state.intercept },
+                    { x: xMax, y: state.slope * xMax + state.intercept }
+                ];
+
+                datasets.push({
+                    label: 'Regression Line',
+                    data: lineData,
+                    borderColor: '#ef4444',
+                    backgroundColor: 'transparent',
+                    borderWidth: 3,
+                    pointRadius: 0,
+                    showLine: true,
+                    tension: 0
+                });
+
+                // Residuals (if enabled)
+                if (state.showResiduals) {
+                    const { predictions } = calculateMetrics(xData, yData, state.slope, state.intercept);
+                    const residualLines = [];
+                    
+                    xData.forEach((x, i) => {
+                        residualLines.push(
+                            { x: x, y: yData[i] },
+                            { x: x, y: predictions[i] },
+                            { x: null, y: null } // Break line
+                        );
+                    });
+
+                    datasets.push({
+                        label: 'Residuals',
+                        data: residualLines,
+                        borderColor: '#9ca3af',
+                        backgroundColor: 'transparent',
+                        borderWidth: 1,
+                        pointRadius: 0,
+                        showLine: true,
+                        tension: 0
+                    });
+                }
+
+                state.chart = new Chart(ctx, {
+                    type: 'scatter',
+                    data: { datasets },
+                    options: {
+                        responsive: true,
+                        maintainAspectRatio: false,
+                        scales: {
+                            x: {
+                                title: {
+                                    display: true,
+                                    text: xLabel,
+                                    font: { size: 14, weight: 'bold' }
+                                },
+                                grid: { color: '#e5e7eb' }
+                            },
+                            y: {
+                                title: {
+                                    display: true,
+                                    text: yLabel,
+                                    font: { size: 14, weight: 'bold' }
+                                },
+                                grid: { color: '#e5e7eb' },
+                                min: 0,
+                                max: 100
+                            }
+                        },
+                        plugins: {
+                            legend: {
+                                display: true,
+                                position: 'bottom'
+                            },
+                            tooltip: {
+                                callbacks: {
+                                    title: function() { return ''; },
+                                    label: function(context) {
+                                        if (context.datasetIndex < 4) {
+                                            return `${context.dataset.label}: (${context.parsed.x.toFixed(1)}, ${context.parsed.y.toFixed(1)})`;
+                                        }
+                                        return null;
+                                    }
+                                }
+                            }
+                        },
+                        animation: {
+                            duration: 300
+                        }
+                    }
+                });
+            }
+
+            // Update UI elements
+            function updateUI() {
+                var currentData = getCurrentData();
+                var xData = currentData.xData;
+                var yData = currentData.yData;
+                var metrics = calculateMetrics(xData, yData, state.slope, state.intercept);
+
+                // Update parameter displays
+                document.getElementById('slopeInput').value = state.slope.toFixed(2);
+                document.getElementById('interceptInput').value = state.intercept.toFixed(2);
+
+                // Update metrics
+                document.getElementById('rSquaredValue').textContent = metrics.rSquared.toFixed(3);
+                document.getElementById('mseValue').textContent = metrics.mse.toFixed(2);
+                document.getElementById('rmseValue').textContent = metrics.rmse.toFixed(2);
+                document.getElementById('r2DisplayText').textContent = metrics.rSquared.toFixed(3);
+
+                // Update equation
+                var equation = state.mode === 'temp-to-humidity' 
+                    ? 'Humidity = ' + state.slope.toFixed(2) + ' × Temperature + ' + state.intercept.toFixed(2)
+                    : 'Temperature = ' + state.slope.toFixed(2) + ' × Humidity + ' + state.intercept.toFixed(2);
+                document.getElementById('equationDisplay').textContent = equation;
+
+                // Update equation description
+                var description = state.mode === 'temp-to-humidity'
+                    ? 'Predicting humidity from temperature'
+                    : 'Predicting temperature from humidity';
+                document.getElementById('equationDescription').textContent = description;
+
+                // Update prediction interface
+                if (state.mode === 'temp-to-humidity') {
+                    document.getElementById('predictionInputLabel').textContent = 'Enter Temperature (°F):';
+                    document.getElementById('predictionResultLabel').textContent = 'Predicted Humidity:';
+                } else {
+                    document.getElementById('predictionInputLabel').textContent = 'Enter Humidity (%):';
+                    document.getElementById('predictionResultLabel').textContent = 'Predicted Temperature:';
+                }
+
+                // Update prediction if input has value
+                updatePrediction();
+            }
+
+            // Update prediction
+            function updatePrediction() {
+                var input = document.getElementById('predictionInput').value;
+                if (input !== '') {
+                    var inputValue = parseFloat(input);
+                    var prediction = state.slope * inputValue + state.intercept;
+                    var unit = state.mode === 'temp-to-humidity' ? '%' : '°F';
+                    document.getElementById('predictionResultValue').textContent = 
+                        prediction.toFixed(1) + unit;
+                } else {
+                    document.getElementById('predictionResultValue').textContent = '-';
+                }
+            }
+
+            // Set parameters
+            function setParameters(slope, intercept) {
+                state.slope = slope;
+                state.intercept = intercept;
+                
+                document.getElementById('slopeInput').value = slope.toFixed(2);
+                document.getElementById('interceptInput').value = intercept.toFixed(2);
+                
+                updateChart();
+                updateUI();
+            }
+
+            // Preset functions
+            window.setOptimalParams = function() {
+                var currentData = getCurrentData();
+                var xData = currentData.xData;
+                var yData = currentData.yData;
+                var optimal = calculateOptimalParams(xData, yData);
+                setParameters(optimal.slope, optimal.intercept);
+            };
+
+            window.resetParams = function() {
+                setParameters(1.0, 0.0);
+            };
+
+            window.randomParams = function() {
+                var weight = (Math.random() - 0.5) * 6; // -3 to 3
+                var constant = (Math.random() - 0.5) * 100; // -50 to 50
+                setParameters(weight, constant);
+            };
+
+            window.setWorstFit = function() {
+                var currentData = getCurrentData();
+                var xData = currentData.xData;
+                var yData = currentData.yData;
+                var optimal = calculateOptimalParams(xData, yData);
+                // Set parameters opposite to optimal
+                setParameters(-optimal.slope, -optimal.intercept);
+            };
+
+            // Initialize everything
+            state.data = generateWeatherData();
+            updateChart();
+            updateUI();
+
+            // Event listeners
+            // Mode switching
+            document.querySelectorAll('.mode-button').forEach(function(button) {
+                button.addEventListener('click', function() {
+                    document.querySelectorAll('.mode-button').forEach(function(b) {
+                        b.classList.remove('active');
+                    });
+                    button.classList.add('active');
+                    state.mode = button.dataset.mode;
+                    
+                    // Reset parameters when switching modes
+                    resetParams();
+                });
+            });
+
+            // Parameter inputs
+            document.getElementById('slopeInput').addEventListener('input', function(e) {
+                var value = parseFloat(e.target.value);
+                if (!isNaN(value)) {
+                    state.slope = Math.max(-5, Math.min(5, value));
+                    updateChart();
+                    updateUI();
+                }
+            });
+
+            document.getElementById('interceptInput').addEventListener('input', function(e) {
+                var value = parseFloat(e.target.value);
+                if (!isNaN(value)) {
+                    state.intercept = Math.max(-100, Math.min(100, value));
+                    updateChart();
+                    updateUI();
+                }
+            });
+
+            // Prediction input
+            document.getElementById('predictionInput').addEventListener('input', updatePrediction);
+
+            // Toggle options
+            document.getElementById('showResidualsToggle').addEventListener('change', function(e) {
+                state.showResiduals = e.target.checked;
+                updateChart();
+            });
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_1/3_regression-to-neural-networks.html b/Week1/Day_1/3_regression-to-neural-networks.html
new file mode 100644
index 00000000..fbc0419a
--- /dev/null
+++ b/Week1/Day_1/3_regression-to-neural-networks.html
@@ -0,0 +1,751 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Multivariable Regression to Neural Networks</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
+            background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%);
+            min-height: 100vh;
+            color: #333;
+            overflow-x: hidden;
+        }
+
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            padding: 40px 20px;
+        }
+
+        header {
+            text-align: center;
+            color: white;
+            margin-bottom: 60px;
+            animation: fadeInDown 1s ease-out;
+        }
+
+        h1 {
+            font-size: 3.5em;
+            margin-bottom: 20px;
+            text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+        }
+
+        .subtitle {
+            font-size: 1.4em;
+            opacity: 0.9;
+        }
+
+        .section {
+            background: rgba(255, 255, 255, 0.95);
+            border-radius: 20px;
+            padding: 40px;
+            margin-bottom: 40px;
+            box-shadow: 0 20px 40px rgba(0,0,0,0.1);
+            animation: fadeInUp 1s ease-out;
+        }
+
+        h2 {
+            color: #2a5298;
+            font-size: 2.5em;
+            margin-bottom: 30px;
+            text-align: center;
+        }
+
+        h3 {
+            color: #1e3c72;
+            font-size: 1.8em;
+            margin: 30px 0 20px;
+        }
+
+        /* Regression Visualization */
+        .regression-visual {
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            margin: 40px 0;
+            flex-wrap: wrap;
+            gap: 30px;
+        }
+
+        .input-group {
+            display: flex;
+            flex-direction: column;
+            gap: 20px;
+        }
+
+        .input-node {
+            display: flex;
+            align-items: center;
+            gap: 15px;
+        }
+
+        .node {
+            width: 80px;
+            height: 80px;
+            border-radius: 50%;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-weight: bold;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+            position: relative;
+        }
+
+        .weight-line {
+            width: 100px;
+            height: 4px;
+            background: #ff6b6b;
+            position: relative;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+        }
+
+        .weight-label {
+            position: absolute;
+            top: -25px;
+            background: white;
+            padding: 5px 10px;
+            border-radius: 5px;
+            font-weight: bold;
+            color: #ff6b6b;
+            box-shadow: 0 2px 5px rgba(0,0,0,0.1);
+        }
+
+        .output-node {
+            width: 100px;
+            height: 100px;
+            border-radius: 50%;
+            background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-weight: bold;
+            font-size: 1.2em;
+            box-shadow: 0 10px 25px rgba(0,0,0,0.3);
+            animation: pulse 2s ease-in-out infinite;
+        }
+
+        /* Equation Display */
+        .equation-box {
+            background: #f8f9fa;
+            padding: 30px;
+            border-radius: 15px;
+            text-align: center;
+            font-size: 1.3em;
+            margin: 30px 0;
+            border: 2px solid #e9ecef;
+        }
+
+        .equation {
+            font-family: 'Courier New', monospace;
+            color: #2a5298;
+            margin: 10px 0;
+        }
+
+        /* Neural Network Visualization */
+        .neural-network {
+            display: flex;
+            justify-content: space-around;
+            align-items: center;
+            margin: 50px 0;
+            gap: 50px;
+        }
+
+        .layer {
+            display: flex;
+            flex-direction: column;
+            gap: 30px;
+            align-items: center;
+        }
+
+        .layer-title {
+            font-weight: bold;
+            color: #2a5298;
+            margin-bottom: 10px;
+        }
+
+        .neuron {
+            width: 60px;
+            height: 60px;
+            border-radius: 50%;
+            background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%);
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-weight: bold;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+            position: relative;
+            cursor: pointer;
+            transition: all 0.3s ease;
+        }
+
+        .neuron:hover {
+            transform: scale(1.1);
+            box-shadow: 0 8px 20px rgba(0,0,0,0.3);
+        }
+
+        .hidden-neuron {
+            background: linear-gradient(135deg, #fa709a 0%, #fee140 100%);
+        }
+
+        .output-neuron {
+            background: linear-gradient(135deg, #30cfd0 0%, #330867 100%);
+        }
+
+        /* Connections */
+        .connections {
+            position: absolute;
+            width: 100%;
+            height: 100%;
+            top: 0;
+            left: 0;
+            pointer-events: none;
+        }
+
+        .connection {
+            stroke: #ccc;
+            stroke-width: 2;
+            fill: none;
+            opacity: 0.6;
+            transition: all 0.3s ease;
+        }
+
+        .connection.highlighted {
+            stroke: #ff6b6b;
+            stroke-width: 4;
+            opacity: 1;
+        }
+
+        /* Interactive Controls */
+        .controls {
+            background: #f8f9fa;
+            padding: 30px;
+            border-radius: 15px;
+            margin: 30px 0;
+        }
+
+        .slider-group {
+            margin: 20px 0;
+        }
+
+        .slider-group label {
+            display: block;
+            margin-bottom: 10px;
+            font-weight: bold;
+            color: #2a5298;
+        }
+
+        input[type="range"] {
+            width: 100%;
+            height: 8px;
+            background: #e9ecef;
+            border-radius: 5px;
+            outline: none;
+            -webkit-appearance: none;
+        }
+
+        input[type="range"]::-webkit-slider-thumb {
+            -webkit-appearance: none;
+            width: 20px;
+            height: 20px;
+            background: #2a5298;
+            border-radius: 50%;
+            cursor: pointer;
+        }
+
+        .output-display {
+            text-align: center;
+            font-size: 1.5em;
+            margin-top: 20px;
+            padding: 20px;
+            background: white;
+            border-radius: 10px;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+        }
+
+        /* Comparison Table */
+        .comparison-table {
+            overflow-x: auto;
+            margin: 30px 0;
+        }
+
+        table {
+            width: 100%;
+            border-collapse: collapse;
+            background: white;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            border-radius: 10px;
+            overflow: hidden;
+        }
+
+        th, td {
+            padding: 15px;
+            text-align: left;
+            border-bottom: 1px solid #e9ecef;
+        }
+
+        th {
+            background: #2a5298;
+            color: white;
+            font-weight: bold;
+        }
+
+        tr:hover {
+            background: #f8f9fa;
+        }
+
+        /* Animations */
+        @keyframes fadeInDown {
+            from {
+                opacity: 0;
+                transform: translateY(-30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes fadeInUp {
+            from {
+                opacity: 0;
+                transform: translateY(30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes pulse {
+            0%, 100% {
+                transform: scale(1);
+            }
+            50% {
+                transform: scale(1.05);
+            }
+        }
+
+        .highlight-box {
+            background: #fff3cd;
+            border: 2px solid #ffc107;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 20px 0;
+        }
+
+        .activation-demo {
+            text-align: center;
+            margin: 30px 0;
+        }
+
+        .activation-graph {
+            width: 100%;
+            max-width: 500px;
+            height: 300px;
+            margin: 20px auto;
+            background: white;
+            border-radius: 10px;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            position: relative;
+        }
+
+        @media (max-width: 768px) {
+            h1 { font-size: 2.5em; }
+            h2 { font-size: 2em; }
+            .neural-network { flex-direction: column; }
+            .regression-visual { flex-direction: column; }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <header>
+            <h1>From Regression to Neural Networks</h1>
+            <p class="subtitle">Understanding Weights in Machine Learning</p>
+        </header>
+
+        <!-- Multivariable Regression Section -->
+        <div class="section">
+            <h2>📊 Multivariable Linear Regression</h2>
+            <p style="font-size: 1.2em; text-align: center; margin-bottom: 30px; color: #666;">
+                Multiple input variables combine through weighted connections to predict a single output
+            </p>
+
+            <div class="regression-visual">
+                <div class="input-group">
+                    <div class="input-node">
+                        <div class="node">X₁<br><small>Size</small></div>
+                        <div class="weight-line">
+                            <span class="weight-label">w₁ = 2.5</span>
+                        </div>
+                    </div>
+                    <div class="input-node">
+                        <div class="node">X₂<br><small>Rooms</small></div>
+                        <div class="weight-line">
+                            <span class="weight-label">w₂ = 1.8</span>
+                        </div>
+                    </div>
+                    <div class="input-node">
+                        <div class="node">X₃<br><small>Location</small></div>
+                        <div class="weight-line">
+                            <span class="weight-label">w₃ = 3.2</span>
+                        </div>
+                    </div>
+                    <div class="input-node">
+                        <div class="node">b<br><small>Bias</small></div>
+                        <div class="weight-line">
+                            <span class="weight-label">b = 0.5</span>
+                        </div>
+                    </div>
+                </div>
+                <div class="output-node">Y<br><small>Price</small></div>
+            </div>
+
+            <div class="equation-box">
+                <p><strong>The Linear Regression Equation:</strong></p>
+                <p class="equation">Y = w₁X₁ + w₂X₂ + w₃X₃ + b</p>
+                <p class="equation">Price = 2.5(Size) + 1.8(Rooms) + 3.2(Location) + 0.5</p>
+            </div>
+
+            <div class="highlight-box">
+                <h3>🎯 Key Insight: Weights as Importance Indicators</h3>
+                <p>Each weight (w) represents how much that feature contributes to the final prediction. Larger weights mean stronger influence!</p>
+                <ul style="list-style: none; padding: 0; margin-top: 15px;">
+                    <li>📏 <strong>w₁ = 2.5:</strong> Each unit of size adds 2.5 units to price</li>
+                    <li>🏠 <strong>w₂ = 1.8:</strong> Each room adds 1.8 units to price</li>
+                    <li>📍 <strong>w₃ = 3.2:</strong> Location score has the strongest impact!</li>
+                </ul>
+            </div>
+
+            <!-- Interactive Weight Demo -->
+            <div class="controls">
+                <h3>🎮 Interactive Weight Adjustment</h3>
+                <p>Adjust the weights to see how they affect the prediction:</p>
+                
+                <div class="slider-group">
+                    <label>Weight 1 (Size): <span id="w1-value">2.5</span></label>
+                    <input type="range" id="w1-slider" min="-5" max="5" step="0.1" value="2.5" oninput="updateRegression()">
+                </div>
+                
+                <div class="slider-group">
+                    <label>Weight 2 (Rooms): <span id="w2-value">1.8</span></label>
+                    <input type="range" id="w2-slider" min="-5" max="5" step="0.1" value="1.8" oninput="updateRegression()">
+                </div>
+                
+                <div class="slider-group">
+                    <label>Weight 3 (Location): <span id="w3-value">3.2</span></label>
+                    <input type="range" id="w3-slider" min="-5" max="5" step="0.1" value="3.2" oninput="updateRegression()">
+                </div>
+                
+                <div class="output-display" id="regression-output">
+                    For a house with Size=100, Rooms=3, Location=8:<br>
+                    <strong>Predicted Price = 506.5</strong>
+                </div>
+            </div>
+        </div>
+
+        <!-- Neural Network Extension -->
+        <div class="section">
+            <h2>🧠 Extending to Neural Networks</h2>
+            <p style="font-size: 1.2em; text-align: center; margin-bottom: 30px; color: #666;">
+                Neural networks are just multiple layers of weighted connections with activation functions!
+            </p>
+
+            <svg class="connections" style="position: absolute; width: 100%; height: 400px; z-index: 1;">
+                <defs>
+                    <marker id="arrowhead" markerWidth="10" markerHeight="7" refX="9" refY="3.5" orient="auto">
+                        <polygon points="0 0, 10 3.5, 0 7" fill="#ccc" />
+                    </marker>
+                </defs>
+            </svg>
+
+            <div class="neural-network" id="nn-viz">
+                <div class="layer">
+                    <div class="layer-title">Input Layer</div>
+                    <div class="neuron" data-layer="0" data-index="0">X₁</div>
+                    <div class="neuron" data-layer="0" data-index="1">X₂</div>
+                    <div class="neuron" data-layer="0" data-index="2">X₃</div>
+                </div>
+                
+                <div class="layer">
+                    <div class="layer-title">Hidden Layer 1</div>
+                    <div class="neuron hidden-neuron" data-layer="1" data-index="0">H₁</div>
+                    <div class="neuron hidden-neuron" data-layer="1" data-index="1">H₂</div>
+                    <div class="neuron hidden-neuron" data-layer="1" data-index="2">H₃</div>
+                    <div class="neuron hidden-neuron" data-layer="1" data-index="3">H₄</div>
+                </div>
+                
+                <div class="layer">
+                    <div class="layer-title">Hidden Layer 2</div>
+                    <div class="neuron hidden-neuron" data-layer="2" data-index="0">H₅</div>
+                    <div class="neuron hidden-neuron" data-layer="2" data-index="1">H₆</div>
+                    <div class="neuron hidden-neuron" data-layer="2" data-index="2">H₇</div>
+                </div>
+                
+                <div class="layer">
+                    <div class="layer-title">Output Layer</div>
+                    <div class="neuron output-neuron" data-layer="3" data-index="0">Y</div>
+                </div>
+            </div>
+
+            <div class="highlight-box">
+                <h3>🔗 The Connection: It's All About Weights!</h3>
+                <p><strong>Linear Regression</strong> = Neural Network with 0 hidden layers</p>
+                <p><strong>Neural Network</strong> = Multiple linear regressions stacked together with activation functions</p>
+                
+                <div style="margin-top: 20px;">
+                    <p><strong>Each connection has a weight that:</strong></p>
+                    <ul style="margin-left: 20px; margin-top: 10px;">
+                        <li>🎯 Determines the strength of the signal</li>
+                        <li>📈 Gets adjusted during training (learning)</li>
+                        <li>🧮 Can be positive (excitatory) or negative (inhibitory)</li>
+                        <li>🎨 Combines to create complex patterns</li>
+                    </ul>
+                </div>
+            </div>
+
+            <!-- Activation Functions -->
+            <div class="activation-demo">
+                <h3>⚡ Activation Functions: The Secret Sauce</h3>
+                <p>Neural networks add non-linearity through activation functions applied after the weighted sum:</p>
+                
+                <div class="equation-box" style="margin-top: 20px;">
+                    <p class="equation">Hidden Node = Activation(Σ(wᵢxᵢ) + b)</p>
+                    <p style="margin-top: 15px;">Common activation functions: ReLU, Sigmoid, Tanh</p>
+                </div>
+            </div>
+
+            <!-- Comparison Table -->
+            <div class="comparison-table">
+                <h3>📊 Regression vs Neural Networks</h3>
+                <table>
+                    <thead>
+                        <tr>
+                            <th>Aspect</th>
+                            <th>Linear Regression</th>
+                            <th>Neural Networks</th>
+                        </tr>
+                    </thead>
+                    <tbody>
+                        <tr>
+                            <td><strong>Structure</strong></td>
+                            <td>Single layer of weights</td>
+                            <td>Multiple layers of weights</td>
+                        </tr>
+                        <tr>
+                            <td><strong>Activation</strong></td>
+                            <td>None (linear)</td>
+                            <td>Non-linear functions</td>
+                        </tr>
+                        <tr>
+                            <td><strong>Complexity</strong></td>
+                            <td>Linear relationships only</td>
+                            <td>Can model any function</td>
+                        </tr>
+                        <tr>
+                            <td><strong>Parameters</strong></td>
+                            <td>n weights + 1 bias</td>
+                            <td>Thousands to billions</td>
+                        </tr>
+                        <tr>
+                            <td><strong>Training</strong></td>
+                            <td>Closed-form solution</td>
+                            <td>Gradient descent</td>
+                        </tr>
+                        <tr>
+                            <td><strong>Interpretability</strong></td>
+                            <td>Easy to interpret weights</td>
+                            <td>Black box</td>
+                        </tr>
+                    </tbody>
+                </table>
+            </div>
+
+            <!-- Weight Visualization in NN -->
+            <div class="controls">
+                <h3>🎨 Visualizing Neural Network Weights</h3>
+                <p>Click on any neuron to see its weighted connections!</p>
+                <div class="output-display" id="nn-info">
+                    <em>Click on a neuron to explore its connections</em>
+                </div>
+            </div>
+        </div>
+
+        <!-- Summary Section -->
+        <div class="section">
+            <h2>🎯 Key Takeaways</h2>
+            <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 20px; margin-top: 30px;">
+                <div style="background: #e3f2fd; padding: 20px; border-radius: 10px;">
+                    <h4 style="color: #1976d2; margin-bottom: 10px;">1. Weights = Importance</h4>
+                    <p>In both regression and neural networks, weights determine how much each input contributes to the output.</p>
+                </div>
+                <div style="background: #f3e5f5; padding: 20px; border-radius: 10px;">
+                    <h4 style="color: #7b1fa2; margin-bottom: 10px;">2. Learning = Weight Adjustment</h4>
+                    <p>Training finds the optimal weights that minimize prediction error on the training data.</p>
+                </div>
+                <div style="background: #e8f5e9; padding: 20px; border-radius: 10px;">
+                    <h4 style="color: #388e3c; margin-bottom: 10px;">3. Depth = Complexity</h4>
+                    <p>Neural networks stack multiple weighted layers to learn increasingly complex patterns.</p>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // Update regression calculation
+        function updateRegression() {
+            const w1 = parseFloat(document.getElementById('w1-slider').value);
+            const w2 = parseFloat(document.getElementById('w2-slider').value);
+            const w3 = parseFloat(document.getElementById('w3-slider').value);
+            
+            document.getElementById('w1-value').textContent = w1.toFixed(1);
+            document.getElementById('w2-value').textContent = w2.toFixed(1);
+            document.getElementById('w3-value').textContent = w3.toFixed(1);
+            
+            // Update weight labels
+            document.querySelectorAll('.weight-label')[0].textContent = `w₁ = ${w1.toFixed(1)}`;
+            document.querySelectorAll('.weight-label')[1].textContent = `w₂ = ${w2.toFixed(1)}`;
+            document.querySelectorAll('.weight-label')[2].textContent = `w₃ = ${w3.toFixed(1)}`;
+            
+            // Calculate prediction for example house
+            const size = 100;
+            const rooms = 3;
+            const location = 8;
+            const bias = 0.5;
+            
+            const prediction = (w1 * size + w2 * rooms + w3 * location + bias).toFixed(1);
+            
+            document.getElementById('regression-output').innerHTML = 
+                `For a house with Size=${size}, Rooms=${rooms}, Location=${location}:<br>
+                <strong>Predicted Price = ${prediction}</strong><br>
+                <small>Y = ${w1}(${size}) + ${w2}(${rooms}) + ${w3}(${location}) + ${bias}</small>`;
+        }
+
+        // Draw neural network connections
+        function drawConnections() {
+            const svg = document.querySelector('.connections');
+            svg.innerHTML = ''; // Clear existing connections
+            
+            const neurons = document.querySelectorAll('.neuron');
+            const neuronPositions = new Map();
+            
+            // Get positions of all neurons
+            neurons.forEach(neuron => {
+                const rect = neuron.getBoundingClientRect();
+                const svgRect = svg.getBoundingClientRect();
+                neuronPositions.set(neuron, {
+                    x: rect.left - svgRect.left + rect.width / 2,
+                    y: rect.top - svgRect.top + rect.height / 2
+                });
+            });
+            
+            // Draw connections between layers
+            const layers = [
+                document.querySelectorAll('[data-layer="0"]'),
+                document.querySelectorAll('[data-layer="1"]'),
+                document.querySelectorAll('[data-layer="2"]'),
+                document.querySelectorAll('[data-layer="3"]')
+            ];
+            
+            for (let i = 0; i < layers.length - 1; i++) {
+                layers[i].forEach(fromNeuron => {
+                    layers[i + 1].forEach(toNeuron => {
+                        const from = neuronPositions.get(fromNeuron);
+                        const to = neuronPositions.get(toNeuron);
+                        
+                        const line = document.createElementNS('http://www.w3.org/2000/svg', 'line');
+                        line.setAttribute('x1', from.x);
+                        line.setAttribute('y1', from.y);
+                        line.setAttribute('x2', to.x);
+                        line.setAttribute('y2', to.y);
+                        line.setAttribute('class', 'connection');
+                        line.setAttribute('data-from', `${i}-${fromNeuron.dataset.index}`);
+                        line.setAttribute('data-to', `${i+1}-${toNeuron.dataset.index}`);
+                        
+                        svg.appendChild(line);
+                    });
+                });
+            }
+        }
+
+        // Neural network interaction
+        document.querySelectorAll('.neuron').forEach(neuron => {
+            neuron.addEventListener('click', function() {
+                // Reset all connections
+                document.querySelectorAll('.connection').forEach(conn => {
+                    conn.classList.remove('highlighted');
+                });
+                
+                // Highlight this neuron's connections
+                const layer = parseInt(this.dataset.layer);
+                const index = parseInt(this.dataset.index);
+                
+                // Highlight incoming connections
+                document.querySelectorAll(`.connection[data-to="${layer}-${index}"]`).forEach(conn => {
+                    conn.classList.add('highlighted');
+                });
+                
+                // Highlight outgoing connections
+                document.querySelectorAll(`.connection[data-from="${layer}-${index}"]`).forEach(conn => {
+                    conn.classList.add('highlighted');
+                });
+                
+                // Update info display
+                const layerNames = ['Input', 'Hidden 1', 'Hidden 2', 'Output'];
+                const incomingCount = document.querySelectorAll(`.connection[data-to="${layer}-${index}"]`).length;
+                const outgoingCount = document.querySelectorAll(`.connection[data-from="${layer}-${index}"]`).length;
+                
+                document.getElementById('nn-info').innerHTML = 
+                    `<strong>${layerNames[layer]} Layer - ${this.textContent}</strong><br>
+                    Incoming connections: ${incomingCount} weights<br>
+                    Outgoing connections: ${outgoingCount} weights<br>
+                    <small>Total parameters for this neuron: ${incomingCount + 1} (including bias)</small>`;
+            });
+        });
+
+        // Initialize
+        window.addEventListener('load', () => {
+            drawConnections();
+            updateRegression();
+        });
+
+        window.addEventListener('resize', drawConnections);
+
+        // Add animation to sections on scroll
+        const observerOptions = {
+            threshold: 0.1,
+            rootMargin: '0px 0px -50px 0px'
+        };
+
+        const observer = new IntersectionObserver((entries) => {
+            entries.forEach(entry => {
+                if (entry.isIntersecting) {
+                    entry.target.style.opacity = '1';
+                    entry.target.style.transform = 'translateY(0)';
+                }
+            });
+        }, observerOptions);
+
+        document.querySelectorAll('.section').forEach(section => {
+            section.style.opacity = '0';
+            section.style.transform = 'translateY(30px)';
+            section.style.transition = 'all 0.6s ease-out';
+            observer.observe(section);
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_1/3_train-test-split-demo.html b/Week1/Day_1/3_train-test-split-demo.html
new file mode 100644
index 00000000..14a693d9
--- /dev/null
+++ b/Week1/Day_1/3_train-test-split-demo.html
@@ -0,0 +1,750 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Understanding Train-Test Split in Machine Learning</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.9.1/chart.min.js"></script>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            line-height: 1.6;
+            color: #1f2937;
+            background-color: #f9fafb;
+            padding: 20px;
+        }
+        
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+        }
+        
+        .header {
+            text-align: center;
+            margin-bottom: 30px;
+            background: white;
+            padding: 24px;
+            border-radius: 12px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        }
+        
+        .header h1 {
+            font-size: 2rem;
+            font-weight: bold;
+            color: #1f2937;
+            margin-bottom: 12px;
+        }
+        
+        .header p {
+            font-size: 1.1rem;
+            color: #6b7280;
+            max-width: 800px;
+            margin: 0 auto;
+        }
+        
+        .concept-section {
+            background: white;
+            border-radius: 12px;
+            padding: 24px;
+            margin-bottom: 24px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        }
+        
+        .concept-grid {
+            display: grid;
+            grid-template-columns: 1fr 1fr 1fr;
+            gap: 20px;
+            margin-bottom: 24px;
+        }
+        
+        .concept-card {
+            background: #f3f4f6;
+            border-radius: 8px;
+            padding: 16px;
+            text-align: center;
+        }
+        
+        .concept-card h3 {
+            font-size: 1.1rem;
+            color: #1f2937;
+            margin-bottom: 8px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            gap: 8px;
+        }
+        
+        .concept-card p {
+            font-size: 0.9rem;
+            color: #6b7280;
+        }
+        
+        .icon {
+            font-size: 1.5rem;
+        }
+        
+        .demo-section {
+            background: white;
+            border-radius: 12px;
+            padding: 24px;
+            box-shadow: 0 1px 3px rgba(0,0,0,0.1);
+        }
+        
+        .controls {
+            background: #f9fafb;
+            border-radius: 8px;
+            padding: 16px;
+            margin-bottom: 20px;
+            display: flex;
+            align-items: center;
+            gap: 20px;
+            flex-wrap: wrap;
+        }
+        
+        .control-group {
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+        
+        .control-group label {
+            font-weight: 500;
+            color: #374151;
+            font-size: 0.9rem;
+        }
+        
+        .slider {
+            width: 150px;
+            height: 6px;
+            -webkit-appearance: none;
+            appearance: none;
+            background: #e5e7eb;
+            border-radius: 3px;
+            outline: none;
+        }
+        
+        .slider::-webkit-slider-thumb {
+            -webkit-appearance: none;
+            appearance: none;
+            width: 20px;
+            height: 20px;
+            background: #3b82f6;
+            border-radius: 50%;
+            cursor: pointer;
+        }
+        
+        .slider::-moz-range-thumb {
+            width: 20px;
+            height: 20px;
+            background: #3b82f6;
+            border-radius: 50%;
+            cursor: pointer;
+            border: none;
+        }
+        
+        .value-display {
+            font-weight: 600;
+            color: #1e40af;
+            min-width: 40px;
+            text-align: center;
+        }
+        
+        .button {
+            padding: 8px 16px;
+            border: none;
+            border-radius: 6px;
+            font-weight: 500;
+            cursor: pointer;
+            transition: all 0.2s;
+            font-size: 0.9rem;
+        }
+        
+        .button-primary {
+            background: #3b82f6;
+            color: white;
+        }
+        
+        .button-primary:hover {
+            background: #2563eb;
+        }
+        
+        .button-secondary {
+            background: #e5e7eb;
+            color: #374151;
+        }
+        
+        .button-secondary:hover {
+            background: #d1d5db;
+        }
+        
+        .visualization-grid {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 20px;
+            margin-bottom: 20px;
+        }
+        
+        .chart-container {
+            background: #f9fafb;
+            border-radius: 8px;
+            padding: 16px;
+            position: relative;
+        }
+        
+        .chart-container h3 {
+            font-size: 1rem;
+            color: #1f2937;
+            margin-bottom: 12px;
+            text-align: center;
+        }
+        
+        .chart-wrapper {
+            position: relative;
+            height: 300px;
+        }
+        
+        .metrics-grid {
+            display: grid;
+            grid-template-columns: repeat(4, 1fr);
+            gap: 12px;
+        }
+        
+        .metric-card {
+            background: #f9fafb;
+            padding: 12px;
+            border-radius: 6px;
+            text-align: center;
+            border: 1px solid #e5e7eb;
+        }
+        
+        .metric-label {
+            font-size: 0.75rem;
+            color: #6b7280;
+            margin-bottom: 4px;
+        }
+        
+        .metric-value {
+            font-size: 1.25rem;
+            font-weight: 600;
+            color: #1f2937;
+        }
+        
+        .warning-box {
+            background: #fef3c7;
+            border: 1px solid #f59e0b;
+            border-radius: 8px;
+            padding: 16px;
+            margin-top: 20px;
+        }
+        
+        .warning-box h4 {
+            color: #92400e;
+            font-size: 1rem;
+            margin-bottom: 8px;
+            display: flex;
+            align-items: center;
+            gap: 8px;
+        }
+        
+        .warning-box p {
+            color: #92400e;
+            font-size: 0.9rem;
+        }
+        
+        .success-box {
+            background: #d1fae5;
+            border: 1px solid #10b981;
+            border-radius: 8px;
+            padding: 16px;
+            margin-top: 20px;
+        }
+        
+        .success-box h4 {
+            color: #065f46;
+            font-size: 1rem;
+            margin-bottom: 8px;
+            display: flex;
+            align-items: center;
+            gap: 8px;
+        }
+        
+        .success-box p {
+            color: #065f46;
+            font-size: 0.9rem;
+        }
+        
+        .legend {
+            display: flex;
+            justify-content: center;
+            gap: 20px;
+            margin-top: 10px;
+            flex-wrap: wrap;
+        }
+        
+        .legend-item {
+            display: flex;
+            align-items: center;
+            gap: 6px;
+            font-size: 0.85rem;
+            color: #6b7280;
+        }
+        
+        .legend-dot {
+            width: 12px;
+            height: 12px;
+            border-radius: 50%;
+        }
+        
+        @media (max-width: 768px) {
+            .concept-grid {
+                grid-template-columns: 1fr;
+            }
+            
+            .visualization-grid {
+                grid-template-columns: 1fr;
+            }
+            
+            .metrics-grid {
+                grid-template-columns: repeat(2, 1fr);
+            }
+            
+            .controls {
+                flex-direction: column;
+                align-items: stretch;
+            }
+            
+            .control-group {
+                flex-direction: column;
+                align-items: flex-start;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- Header -->
+        <div class="header">
+            <h1>Understanding Train-Test Split</h1>
+            <p>Learn why dividing your data into training and testing sets is crucial for building reliable machine learning models</p>
+        </div>
+
+        <!-- Concept Explanation -->
+        <div class="concept-section">
+            <h2 style="margin-bottom: 20px; color: #1f2937;">Why Split Data?</h2>
+            
+            <div class="concept-grid">
+                <div class="concept-card">
+                    <h3><span class="icon">📊</span> Training Data</h3>
+                    <p>Used to teach the model patterns and relationships. The model learns from this data.</p>
+                </div>
+                
+                <div class="concept-card">
+                    <h3><span class="icon">🧪</span> Test Data</h3>
+                    <p>Never seen during training. Used to evaluate how well the model generalizes to new data.</p>
+                </div>
+                
+                <div class="concept-card">
+                    <h3><span class="icon">⚖️</span> Fair Evaluation</h3>
+                    <p>Testing on unseen data gives an honest assessment of model performance.</p>
+                </div>
+            </div>
+
+            <div style="background: #eff6ff; border-radius: 8px; padding: 16px; border-left: 4px solid #3b82f6;">
+                <h3 style="color: #1e40af; margin-bottom: 8px; font-size: 1rem;">The Problem: Overfitting</h3>
+                <p style="color: #1e40af; font-size: 0.9rem;">
+                    If we train and test on the same data, the model might memorize specific data points rather than learning general patterns. 
+                    This leads to excellent performance on training data but poor performance on new, unseen data.
+                </p>
+            </div>
+        </div>
+
+        <!-- Interactive Demo -->
+        <div class="demo-section">
+            <h2 style="margin-bottom: 20px; color: #1f2937;">Interactive Demonstration</h2>
+            
+            <div class="controls">
+                <div class="control-group">
+                    <label>Train-Test Split:</label>
+                    <input type="range" id="splitSlider" class="slider" min="50" max="90" value="80" step="5">
+                    <span class="value-display" id="splitValue">80%</span>
+                </div>
+                
+                <div class="control-group">
+                    <label>Model Complexity:</label>
+                    <select id="complexitySelect" style="padding: 6px 12px; border-radius: 6px; border: 1px solid #d1d5db;">
+                        <option value="simple">Simple (Linear)</option>
+                        <option value="complex">Complex (Polynomial)</option>
+                    </select>
+                </div>
+                
+                <button class="button button-primary" onclick="reshuffleData()">🔀 Reshuffle Data</button>
+                <button class="button button-secondary" onclick="resetDemo()">🔄 Reset</button>
+            </div>
+
+            <div class="visualization-grid">
+                <div class="chart-container">
+                    <h3>Training Data & Model Fit</h3>
+                    <div class="chart-wrapper">
+                        <canvas id="trainChart"></canvas>
+                    </div>
+                </div>
+                
+                <div class="chart-container">
+                    <h3>Test Data & Model Performance</h3>
+                    <div class="chart-wrapper">
+                        <canvas id="testChart"></canvas>
+                    </div>
+                </div>
+            </div>
+
+            <div class="legend">
+                <div class="legend-item">
+                    <div class="legend-dot" style="background: #3b82f6;"></div>
+                    <span>Training Data</span>
+                </div>
+                <div class="legend-item">
+                    <div class="legend-dot" style="background: #ef4444;"></div>
+                    <span>Test Data</span>
+                </div>
+                <div class="legend-item">
+                    <div class="legend-dot" style="background: #10b981;"></div>
+                    <span>Model Prediction</span>
+                </div>
+            </div>
+
+            <div class="metrics-grid" style="margin-top: 20px;">
+                <div class="metric-card">
+                    <div class="metric-label">Training R²</div>
+                    <div class="metric-value" id="trainR2">0.00</div>
+                </div>
+                <div class="metric-card">
+                    <div class="metric-label">Test R²</div>
+                    <div class="metric-value" id="testR2">0.00</div>
+                </div>
+                <div class="metric-card">
+                    <div class="metric-label">Training RMSE</div>
+                    <div class="metric-value" id="trainRMSE">0.0</div>
+                </div>
+                <div class="metric-card">
+                    <div class="metric-label">Test RMSE</div>
+                    <div class="metric-value" id="testRMSE">0.0</div>
+                </div>
+            </div>
+
+            <div id="messageBox"></div>
+        </div>
+    </div>
+
+    <script>
+        // Global variables
+        let state = {
+            allData: [],
+            trainData: [],
+            testData: [],
+            splitRatio: 0.8,
+            modelType: 'simple',
+            trainChart: null,
+            testChart: null,
+            modelParams: null
+        };
+
+        // Generate weather data
+        function generateWeatherData() {
+            const data = [];
+            
+            // Generate clustered data similar to the linear regression example
+            // Winter cluster
+            for (let i = 0; i < 20; i++) {
+                data.push({
+                    temperature: 25 + Math.random() * 15,
+                    humidity: 20 + Math.random() * 25
+                });
+            }
+            
+            // Summer cluster
+            for (let i = 0; i < 20; i++) {
+                data.push({
+                    temperature: 75 + Math.random() * 20,
+                    humidity: 65 + Math.random() * 25
+                });
+            }
+            
+            // Fall/Spring cluster
+            for (let i = 0; i < 40; i++) {
+                data.push({
+                    temperature: 50 + Math.random() * 25,
+                    humidity: 40 + Math.random() * 40
+                });
+            }
+            
+            return data;
+        }
+
+        // Shuffle array
+        function shuffleArray(array) {
+            const newArray = [...array];
+            for (let i = newArray.length - 1; i > 0; i--) {
+                const j = Math.floor(Math.random() * (i + 1));
+                [newArray[i], newArray[j]] = [newArray[j], newArray[i]];
+            }
+            return newArray;
+        }
+
+        // Split data into train and test
+        function splitData() {
+            const shuffled = shuffleArray(state.allData);
+            const splitIndex = Math.floor(shuffled.length * state.splitRatio);
+            state.trainData = shuffled.slice(0, splitIndex);
+            state.testData = shuffled.slice(splitIndex);
+        }
+
+        // Calculate linear regression parameters
+        function calculateLinearRegression(data) {
+            const n = data.length;
+            let sumX = 0, sumY = 0, sumXY = 0, sumX2 = 0;
+            
+            data.forEach(point => {
+                sumX += point.temperature;
+                sumY += point.humidity;
+                sumXY += point.temperature * point.humidity;
+                sumX2 += point.temperature * point.temperature;
+            });
+            
+            const slope = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);
+            const intercept = (sumY - slope * sumX) / n;
+            
+            return { slope, intercept, degree: 1 };
+        }
+
+        // Calculate polynomial regression
+        function calculatePolynomialRegression(data, degree = 3) {
+            // Simplified polynomial fitting for demonstration
+            // In reality, you'd use proper polynomial regression
+            const linear = calculateLinearRegression(data);
+            
+            // Add some curvature based on data distribution
+            const midTemp = data.reduce((sum, d) => sum + d.temperature, 0) / data.length;
+            const curveStrength = 0.01;
+            
+            return {
+                ...linear,
+                degree: degree,
+                curveStrength: curveStrength,
+                midTemp: midTemp
+            };
+        }
+
+        // Predict value
+        function predict(temp, params) {
+            if (params.degree === 1) {
+                return params.slope * temp + params.intercept;
+            } else {
+                // Polynomial prediction with curvature
+                const linear = params.slope * temp + params.intercept;
+                const curve = params.curveStrength * Math.pow(temp - params.midTemp, 2);
+                return linear + curve;
+            }
+        }
+
+        // Calculate metrics
+        function calculateMetrics(data, params) {
+            let sumSquaredError = 0;
+            let totalSumSquares = 0;
+            const meanY = data.reduce((sum, d) => sum + d.humidity, 0) / data.length;
+            
+            data.forEach(point => {
+                const predicted = predict(point.temperature, params);
+                const error = point.humidity - predicted;
+                sumSquaredError += error * error;
+                totalSumSquares += Math.pow(point.humidity - meanY, 2);
+            });
+            
+            const mse = sumSquaredError / data.length;
+            const rmse = Math.sqrt(mse);
+            const r2 = 1 - (sumSquaredError / totalSumSquares);
+            
+            return { mse, rmse, r2 };
+        }
+
+        // Create chart
+        function createChart(canvasId, data, params, title, isTestData = false) {
+            const ctx = document.getElementById(canvasId).getContext('2d');
+            
+            // Prepare scatter data
+            const scatterData = data.map(d => ({
+                x: d.temperature,
+                y: d.humidity
+            }));
+            
+            // Generate regression line
+            const minTemp = 20;
+            const maxTemp = 100;
+            const linePoints = [];
+            
+            for (let temp = minTemp; temp <= maxTemp; temp += 5) {
+                linePoints.push({
+                    x: temp,
+                    y: predict(temp, params)
+                });
+            }
+            
+            const datasets = [
+                {
+                    label: isTestData ? 'Test Data' : 'Training Data',
+                    data: scatterData,
+                    backgroundColor: isTestData ? '#ef4444' : '#3b82f6',
+                    borderColor: isTestData ? '#ef4444' : '#3b82f6',
+                    pointRadius: 4,
+                    showLine: false
+                },
+                {
+                    label: 'Model Prediction',
+                    data: linePoints,
+                    borderColor: '#10b981',
+                    backgroundColor: 'transparent',
+                    borderWidth: 2,
+                    pointRadius: 0,
+                    showLine: true,
+                    tension: params.degree > 1 ? 0.4 : 0
+                }
+            ];
+            
+            return new Chart(ctx, {
+                type: 'scatter',
+                data: { datasets },
+                options: {
+                    responsive: true,
+                    maintainAspectRatio: false,
+                    plugins: {
+                        legend: { display: false }
+                    },
+                    scales: {
+                        x: {
+                            title: {
+                                display: true,
+                                text: 'Temperature (°F)',
+                                font: { size: 12 }
+                            },
+                            min: 15,
+                            max: 105
+                        },
+                        y: {
+                            title: {
+                                display: true,
+                                text: 'Humidity (%)',
+                                font: { size: 12 }
+                            },
+                            min: 0,
+                            max: 100
+                        }
+                    }
+                }
+            });
+        }
+
+        // Update visualization
+        function updateVisualization() {
+            // Fit model on training data
+            if (state.modelType === 'simple') {
+                state.modelParams = calculateLinearRegression(state.trainData);
+            } else {
+                state.modelParams = calculatePolynomialRegression(state.trainData);
+            }
+            
+            // Calculate metrics
+            const trainMetrics = calculateMetrics(state.trainData, state.modelParams);
+            const testMetrics = calculateMetrics(state.testData, state.modelParams);
+            
+            // Update metric displays
+            document.getElementById('trainR2').textContent = trainMetrics.r2.toFixed(3);
+            document.getElementById('testR2').textContent = testMetrics.r2.toFixed(3);
+            document.getElementById('trainRMSE').textContent = trainMetrics.rmse.toFixed(1);
+            document.getElementById('testRMSE').textContent = testMetrics.rmse.toFixed(1);
+            
+            // Destroy existing charts
+            if (state.trainChart) state.trainChart.destroy();
+            if (state.testChart) state.testChart.destroy();
+            
+            // Create new charts
+            state.trainChart = createChart('trainChart', state.trainData, state.modelParams, 'Training Data', false);
+            state.testChart = createChart('testChart', state.testData, state.modelParams, 'Test Data', true);
+            
+            // Update message
+            updateMessage(trainMetrics, testMetrics);
+        }
+
+        // Update message box
+        function updateMessage(trainMetrics, testMetrics) {
+            const messageBox = document.getElementById('messageBox');
+            const r2Diff = Math.abs(trainMetrics.r2 - testMetrics.r2);
+            
+            if (state.modelType === 'complex' && r2Diff > 0.1) {
+                messageBox.innerHTML = `
+                    <div class="warning-box">
+                        <h4>⚠️ Potential Overfitting Detected!</h4>
+                        <p>The complex model performs much better on training data (R² = ${trainMetrics.r2.toFixed(3)}) 
+                        than on test data (R² = ${testMetrics.r2.toFixed(3)}). This suggests the model is memorizing 
+                        the training data rather than learning general patterns.</p>
+                    </div>
+                `;
+            } else if (r2Diff < 0.05) {
+                messageBox.innerHTML = `
+                    <div class="success-box">
+                        <h4>✅ Good Generalization!</h4>
+                        <p>The model performs similarly on both training (R² = ${trainMetrics.r2.toFixed(3)}) 
+                        and test data (R² = ${testMetrics.r2.toFixed(3)}). This indicates the model has learned 
+                        general patterns that work well on unseen data.</p>
+                    </div>
+                `;
+            } else {
+                messageBox.innerHTML = '';
+            }
+        }
+
+        // Event handlers
+        function reshuffleData() {
+            splitData();
+            updateVisualization();
+        }
+
+        function resetDemo() {
+            document.getElementById('splitSlider').value = 80;
+            document.getElementById('splitValue').textContent = '80%';
+            document.getElementById('complexitySelect').value = 'simple';
+            state.splitRatio = 0.8;
+            state.modelType = 'simple';
+            splitData();
+            updateVisualization();
+        }
+
+        // Initialize
+        document.addEventListener('DOMContentLoaded', function() {
+            // Generate initial data
+            state.allData = generateWeatherData();
+            
+            // Event listeners
+            document.getElementById('splitSlider').addEventListener('input', function(e) {
+                state.splitRatio = e.target.value / 100;
+                document.getElementById('splitValue').textContent = e.target.value + '%';
+                splitData();
+                updateVisualization();
+            });
+            
+            document.getElementById('complexitySelect').addEventListener('change', function(e) {
+                state.modelType = e.target.value;
+                updateVisualization();
+            });
+            
+            // Initial split and visualization
+            splitData();
+            updateVisualization();
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_1/4_ensemble-automl-intro.html b/Week1/Day_1/4_ensemble-automl-intro.html
new file mode 100644
index 00000000..87549d9b
--- /dev/null
+++ b/Week1/Day_1/4_ensemble-automl-intro.html
@@ -0,0 +1,453 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Ensemble Models & AutoML</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            min-height: 100vh;
+            color: #333;
+            overflow-x: hidden;
+        }
+
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            padding: 40px 20px;
+        }
+
+        header {
+            text-align: center;
+            color: white;
+            margin-bottom: 60px;
+            animation: fadeInDown 1s ease-out;
+        }
+
+        h1 {
+            font-size: 3.5em;
+            margin-bottom: 20px;
+            text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+        }
+
+        .subtitle {
+            font-size: 1.4em;
+            opacity: 0.9;
+        }
+
+        .section {
+            background: rgba(255, 255, 255, 0.95);
+            border-radius: 20px;
+            padding: 40px;
+            margin-bottom: 40px;
+            box-shadow: 0 20px 40px rgba(0,0,0,0.1);
+            animation: fadeInUp 1s ease-out;
+        }
+
+        h2 {
+            color: #667eea;
+            font-size: 2.5em;
+            margin-bottom: 30px;
+            text-align: center;
+        }
+
+        .ensemble-visual {
+            display: flex;
+            justify-content: space-around;
+            align-items: center;
+            margin: 40px 0;
+            flex-wrap: wrap;
+            gap: 30px;
+        }
+
+        .model-box {
+            width: 150px;
+            height: 150px;
+            background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
+            border-radius: 15px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-weight: bold;
+            font-size: 1.2em;
+            transition: all 0.3s ease;
+            cursor: pointer;
+            box-shadow: 0 10px 20px rgba(0,0,0,0.2);
+        }
+
+        .model-box:hover {
+            transform: translateY(-10px) scale(1.05);
+            box-shadow: 0 15px 30px rgba(0,0,0,0.3);
+        }
+
+        .arrow {
+            font-size: 2em;
+            color: #667eea;
+            animation: pulse 2s infinite;
+        }
+
+        .final-prediction {
+            width: 200px;
+            height: 200px;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            border-radius: 50%;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-weight: bold;
+            font-size: 1.4em;
+            text-align: center;
+            box-shadow: 0 15px 35px rgba(0,0,0,0.3);
+            animation: glow 2s ease-in-out infinite alternate;
+        }
+
+        .method-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
+            gap: 30px;
+            margin: 40px 0;
+        }
+
+        .method-card {
+            background: linear-gradient(135deg, #f6f6f6 0%, #e9e9e9 100%);
+            padding: 30px;
+            border-radius: 15px;
+            transition: all 0.3s ease;
+            border: 2px solid transparent;
+        }
+
+        .method-card:hover {
+            transform: translateY(-5px);
+            border-color: #667eea;
+            box-shadow: 0 10px 25px rgba(102, 126, 234, 0.2);
+        }
+
+        .method-card h3 {
+            color: #764ba2;
+            font-size: 1.6em;
+            margin-bottom: 15px;
+        }
+
+        .automl-pipeline {
+            display: flex;
+            align-items: center;
+            justify-content: space-between;
+            margin: 40px 0;
+            flex-wrap: wrap;
+            gap: 20px;
+        }
+
+        .pipeline-step {
+            flex: 1;
+            min-width: 150px;
+            background: white;
+            padding: 25px;
+            border-radius: 10px;
+            text-align: center;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            transition: all 0.3s ease;
+            position: relative;
+        }
+
+        .pipeline-step:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 10px 25px rgba(0,0,0,0.15);
+        }
+
+        .pipeline-step::after {
+            content: '→';
+            position: absolute;
+            right: -25px;
+            top: 50%;
+            transform: translateY(-50%);
+            font-size: 1.5em;
+            color: #667eea;
+        }
+
+        .pipeline-step:last-child::after {
+            display: none;
+        }
+
+        .step-icon {
+            font-size: 2.5em;
+            margin-bottom: 10px;
+        }
+
+        .benefits-list {
+            list-style: none;
+            padding: 0;
+        }
+
+        .benefits-list li {
+            padding: 15px 0;
+            padding-left: 40px;
+            position: relative;
+            font-size: 1.1em;
+            line-height: 1.6;
+        }
+
+        .benefits-list li::before {
+            content: '✓';
+            position: absolute;
+            left: 0;
+            color: #667eea;
+            font-size: 1.5em;
+            font-weight: bold;
+        }
+
+        .interactive-demo {
+            background: #f8f9fa;
+            padding: 30px;
+            border-radius: 15px;
+            text-align: center;
+            margin-top: 30px;
+        }
+
+        .demo-button {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            border: none;
+            padding: 15px 40px;
+            font-size: 1.2em;
+            border-radius: 50px;
+            cursor: pointer;
+            transition: all 0.3s ease;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+        }
+
+        .demo-button:hover {
+            transform: translateY(-3px);
+            box-shadow: 0 10px 25px rgba(0,0,0,0.3);
+        }
+
+        .demo-result {
+            margin-top: 20px;
+            padding: 20px;
+            background: white;
+            border-radius: 10px;
+            display: none;
+            animation: fadeIn 0.5s ease;
+        }
+
+        @keyframes fadeInDown {
+            from {
+                opacity: 0;
+                transform: translateY(-30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes fadeInUp {
+            from {
+                opacity: 0;
+                transform: translateY(30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes fadeIn {
+            from { opacity: 0; }
+            to { opacity: 1; }
+        }
+
+        @keyframes pulse {
+            0% { transform: scale(1); }
+            50% { transform: scale(1.1); }
+            100% { transform: scale(1); }
+        }
+
+        @keyframes glow {
+            from {
+                box-shadow: 0 15px 35px rgba(0,0,0,0.3);
+            }
+            to {
+                box-shadow: 0 15px 45px rgba(102, 126, 234, 0.4);
+            }
+        }
+
+        @media (max-width: 768px) {
+            h1 { font-size: 2.5em; }
+            h2 { font-size: 2em; }
+            .pipeline-step::after { display: none; }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <header>
+            <h1>Ensemble Models & AutoML</h1>
+            <p class="subtitle">Harnessing the Power of Multiple Models and Automation</p>
+        </header>
+
+        <div class="section">
+            <h2>🎭 Ensemble Models</h2>
+            <p style="font-size: 1.2em; text-align: center; margin-bottom: 30px; color: #666;">
+                Ensemble methods combine predictions from multiple models to create more accurate and robust predictions than any individual model.
+            </p>
+
+            <div class="ensemble-visual">
+                <div class="model-box" onclick="highlightModel(this)">Model 1</div>
+                <span class="arrow">→</span>
+                <div class="model-box" onclick="highlightModel(this)">Model 2</div>
+                <span class="arrow">→</span>
+                <div class="model-box" onclick="highlightModel(this)">Model 3</div>
+                <span class="arrow">→</span>
+                <div class="final-prediction">Enhanced<br>Prediction</div>
+            </div>
+
+            <div class="method-grid">
+                <div class="method-card">
+                    <h3>🎲 Bagging</h3>
+                    <p><strong>Bootstrap Aggregating</strong> trains multiple models on different subsets of the training data and combines their predictions through voting or averaging.</p>
+                    <p style="margin-top: 15px; color: #667eea;"><em>Example: Random Forest</em></p>
+                </div>
+
+                <div class="method-card">
+                    <h3>🚀 Boosting</h3>
+                    <p><strong>Sequential Learning</strong> trains models sequentially, where each model learns from the mistakes of the previous ones, giving more weight to misclassified examples.</p>
+                    <p style="margin-top: 15px; color: #667eea;"><em>Example: AdaBoost, XGBoost</em></p>
+                </div>
+
+                <div class="method-card">
+                    <h3>🏗️ Stacking</h3>
+                    <p><strong>Meta-Learning</strong> uses a meta-model to learn how to best combine predictions from multiple base models, creating a hierarchical ensemble.</p>
+                    <p style="margin-top: 15px; color: #667eea;"><em>Example: Stacked Generalization</em></p>
+                </div>
+            </div>
+        </div>
+
+        <div class="section">
+            <h2>🤖 AutoML (Automated Machine Learning)</h2>
+            <p style="font-size: 1.2em; text-align: center; margin-bottom: 30px; color: #666;">
+                AutoML automates the end-to-end process of applying machine learning, making it accessible to non-experts and improving productivity of data scientists.
+            </p>
+
+            <div class="automl-pipeline">
+                <div class="pipeline-step">
+                    <div class="step-icon">📊</div>
+                    <h4>Data Preprocessing</h4>
+                    <p>Automatic cleaning, encoding, and feature engineering</p>
+                </div>
+                <div class="pipeline-step">
+                    <div class="step-icon">🔍</div>
+                    <h4>Model Selection</h4>
+                    <p>Intelligent algorithm selection based on data characteristics</p>
+                </div>
+                <div class="pipeline-step">
+                    <div class="step-icon">⚙️</div>
+                    <h4>Hyperparameter Tuning</h4>
+                    <p>Automated optimization of model parameters</p>
+                </div>
+                <div class="pipeline-step">
+                    <div class="step-icon">📈</div>
+                    <h4>Model Evaluation</h4>
+                    <p>Comprehensive performance assessment and validation</p>
+                </div>
+            </div>
+
+            <h3 style="color: #764ba2; margin: 30px 0 20px;">Key Benefits of AutoML</h3>
+            <ul class="benefits-list">
+                <li><strong>Democratization:</strong> Makes ML accessible to non-experts by automating complex tasks</li>
+                <li><strong>Efficiency:</strong> Reduces time from months to hours for model development</li>
+                <li><strong>Optimization:</strong> Systematically explores vast hyperparameter spaces</li>
+                <li><strong>Reproducibility:</strong> Standardizes ML workflows and ensures consistent results</li>
+                <li><strong>Cost Reduction:</strong> Minimizes need for extensive ML expertise and manual effort</li>
+            </ul>
+
+            <div class="interactive-demo">
+                <h3 style="color: #667eea; margin-bottom: 20px;">🎯 See AutoML in Action</h3>
+                <p style="margin-bottom: 20px;">Click to simulate an AutoML pipeline optimization</p>
+                <button class="demo-button" onclick="runAutoMLDemo()">Run AutoML Simulation</button>
+                <div id="demo-result" class="demo-result"></div>
+            </div>
+        </div>
+
+        <div class="section">
+            <h2>🔗 Ensemble Models + AutoML</h2>
+            <p style="font-size: 1.2em; text-align: center; margin-bottom: 30px; color: #666;">
+                Modern AutoML platforms often use ensemble methods as their final step, automatically combining the best models discovered during the search process for optimal performance.
+            </p>
+            <div style="background: linear-gradient(135deg, #f6f6f6 0%, #e9e9e9 100%); padding: 30px; border-radius: 15px; text-align: center;">
+                <p style="font-size: 1.3em; color: #764ba2; font-weight: bold;">
+                    AutoML + Ensembles = Maximum Performance with Minimum Effort
+                </p>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        function highlightModel(element) {
+            element.style.transform = 'scale(1.2)';
+            element.style.background = 'linear-gradient(135deg, #667eea 0%, #764ba2 100%)';
+            setTimeout(() => {
+                element.style.transform = '';
+                element.style.background = '';
+            }, 500);
+        }
+
+        function runAutoMLDemo() {
+            const resultDiv = document.getElementById('demo-result');
+            resultDiv.style.display = 'block';
+            resultDiv.innerHTML = '<p style="color: #666;">🔄 Running AutoML pipeline...</p>';
+
+            const steps = [
+                '📊 Preprocessing data... Found 15 features, 10,000 samples',
+                '🔍 Testing 8 different algorithms...',
+                '⚙️ Optimizing hyperparameters... 500 combinations tested',
+                '🎯 Best model: XGBoost with 94.2% accuracy',
+                '🎭 Creating ensemble of top 3 models...',
+                '✅ Final ensemble accuracy: 96.8% (+2.6% improvement!)'
+            ];
+
+            let i = 0;
+            const interval = setInterval(() => {
+                if (i < steps.length) {
+                    resultDiv.innerHTML += `<p style="color: #667eea; margin: 10px 0;">${steps[i]}</p>`;
+                    i++;
+                } else {
+                    clearInterval(interval);
+                    resultDiv.innerHTML += '<p style="color: #764ba2; font-weight: bold; margin-top: 20px;">🎉 AutoML Pipeline Complete!</p>';
+                }
+            }, 1000);
+        }
+
+        // Add some interactive animations on scroll
+        const observerOptions = {
+            threshold: 0.1,
+            rootMargin: '0px 0px -50px 0px'
+        };
+
+        const observer = new IntersectionObserver((entries) => {
+            entries.forEach(entry => {
+                if (entry.isIntersecting) {
+                    entry.target.style.opacity = '1';
+                    entry.target.style.transform = 'translateY(0)';
+                }
+            });
+        }, observerOptions);
+
+        document.querySelectorAll('.section').forEach(section => {
+            section.style.opacity = '0';
+            section.style.transform = 'translateY(30px)';
+            section.style.transition = 'all 0.6s ease-out';
+            observer.observe(section);
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_1/5_airline_forecasting_notebook.ipynb b/Week1/Day_1/5_airline_forecasting_notebook.ipynb
new file mode 100644
index 00000000..573052db
--- /dev/null
+++ b/Week1/Day_1/5_airline_forecasting_notebook.ipynb
@@ -0,0 +1,1648 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Airline Traffic Forecasting - Comprehensive Time Series Analysis\n",
+    "\n",
+    "## Table of Contents\n",
+    "1. **Data Loading and Preparation**\n",
+    "2. **Exploratory Data Analysis (EDA)**\n",
+    "3. **Time Series Decomposition**\n",
+    "4. **Stationarity Analysis**\n",
+    "5. **Traditional Forecasting Methods**\n",
+    "   - Moving Average\n",
+    "   - Exponential Smoothing\n",
+    "   - ARIMA/SARIMA\n",
+    "6. **Machine Learning Approaches**\n",
+    "   - Linear Regression with Features\n",
+    "   - Random Forest\n",
+    "   - XGBoost\n",
+    "7. **Deep Learning (LSTM)**\n",
+    "8. **Prophet Forecasting**\n",
+    "9. **Model Comparison and Ensemble**\n",
+    "10. **Final Recommendations**\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Introduction\n",
+    "This notebook demonstrates various forecasting techniques for airline passenger traffic data, comparing traditional time series methods with modern machine learning approaches."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Setup and Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from datetime import datetime, timedelta\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Time series specific imports\n",
+    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
+    "from statsmodels.tsa.stattools import adfuller, kpss\n",
+    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
+    "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "\n",
+    "# Machine learning imports\n",
+    "from sklearn.model_selection import TimeSeriesSplit\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
+    "\n",
+    "# Additional libraries\n",
+    "from prophet import Prophet\n",
+    "\n",
+    "# Set random seed\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Configure visualization\n",
+    "plt.style.use('seaborn-v0_8-darkgrid')\n",
+    "plt.rcParams['figure.figsize'] = (12, 6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating synthetic airline traffic data...\n",
+      "Dataset shape: (108, 1)\n",
+      "Date range: 2015-01-31 00:00:00 to 2023-12-31 00:00:00\n",
+      "\n",
+      "First few rows:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>traffic</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2015-01-31</th>\n",
+       "      <td>102483.570765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-02-28</th>\n",
+       "      <td>105056.342046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-03-31</th>\n",
+       "      <td>113394.023831</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-04-30</th>\n",
+       "      <td>119858.139936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-05-31</th>\n",
+       "      <td>110480.141370</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  traffic\n",
+       "2015-01-31  102483.570765\n",
+       "2015-02-28  105056.342046\n",
+       "2015-03-31  113394.023831\n",
+       "2015-04-30  119858.139936\n",
+       "2015-05-31  110480.141370"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load or create airline passenger data\n",
+    "# Using the classic Air Passengers dataset or creating synthetic data\n",
+    "\n",
+    "# Option 1: Load from seaborn (real data)\n",
+    "try:\n",
+    "    # Load the classic airline passengers dataset\n",
+    "    df = sns.load_dataset('flights')\n",
+    "    df = df.to_csv('airline_passengers.csv', index=False)\n",
+    "    df['date'] = pd.to_datetime(df[['year', 'month']].assign(day=1))\n",
+    "    df = df[['date', 'passengers']].set_index('date')\n",
+    "    df.columns = ['traffic']\n",
+    "    print(\"Loaded classic airline passengers dataset\")\n",
+    "except:\n",
+    "    # Option 2: Create synthetic airline traffic data\n",
+    "    print(\"Creating synthetic airline traffic data...\")\n",
+    "    \n",
+    "    # Generate dates\n",
+    "    dates = pd.date_range(start='2015-01-01', end='2023-12-31', freq='M')\n",
+    "    \n",
+    "    # Create synthetic data with trend, seasonality, and noise\n",
+    "    trend = np.linspace(100000, 180000, len(dates))\n",
+    "    seasonal = 10000 * np.sin(2 * np.pi * np.arange(len(dates)) / 12)\n",
+    "    noise = np.random.normal(0, 5000, len(dates))\n",
+    "    \n",
+    "    # Add COVID impact\n",
+    "    covid_impact = np.zeros(len(dates))\n",
+    "    covid_start = dates.get_loc('2020-03-31')\n",
+    "    covid_end = dates.get_loc('2021-06-30')\n",
+    "    covid_impact[covid_start:covid_end] = -50000 * np.exp(-0.1 * np.arange(covid_end - covid_start))\n",
+    "    \n",
+    "    traffic = trend + seasonal + noise + covid_impact\n",
+    "    traffic = np.maximum(traffic, 10000)  # Ensure positive values\n",
+    "    \n",
+    "    df = pd.DataFrame({'traffic': traffic}, index=dates)\n",
+    "\n",
+    "print(f\"Dataset shape: {df.shape}\")\n",
+    "print(f\"Date range: {df.index[0]} to {df.index[-1]}\")\n",
+    "print(f\"\\nFirst few rows:\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Exploratory Data Analysis (EDA)\n",
+    "\n",
+    "Let's explore the airline traffic data to understand patterns, trends, and anomalies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Basic Statistics:\n",
+      "             traffic\n",
+      "count     108.000000\n",
+      "mean   135708.417024\n",
+      "std     24475.584202\n",
+      "min     94973.682444\n",
+      "25%    115407.809015\n",
+      "50%    131568.083265\n",
+      "75%    156571.898724\n",
+      "max    182845.755922\n",
+      "\n",
+      "Missing values: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Basic statistics\n",
+    "print(\"Basic Statistics:\")\n",
+    "print(df.describe())\n",
+    "print(f\"\\nMissing values: {df.isnull().sum().values[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Observations:\n",
+      "- Clear upward trend (pre-COVID)\n",
+      "- Strong seasonal patterns\n",
+      "- COVID-19 impact visible in 2020-2021\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot the time series\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "plt.plot(df.index, df['traffic'], linewidth=2)\n",
+    "plt.title('Airline Passenger Traffic Over Time', fontsize=16)\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Number of Passengers')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.show()\n",
+    "\n",
+    "# Identify any notable patterns\n",
+    "print(\"Observations:\")\n",
+    "print(\"- Clear upward trend (pre-COVID)\")\n",
+    "print(\"- Strong seasonal patterns\")\n",
+    "print(\"- COVID-19 impact visible in 2020-2021\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ap06dkq2trSpVqqTJkyfL09NTYWFhmjhxoho0aPDMYoiMjFT//v1VqVIl0zhnzpzRzZs3NWjQIDk5OWnx4sXq2rWrNm/eLCcnp2TncHHJKVvbjL1Ha968dzJ0vIyWN29OubmlfQUV8/HvvtY9FxLz8TDm43/42WGO+TDHfJjjZ4e5J70+MoMPP/xQlStX1tixY1W5cmVJ0owZMzRixAgFBQXpiy++sHCEAAAAgHWwaBFderCly6BBg1JsK1GihEqUKPHMxo6IiNBbb70lo9GoWbNmycbmQSF8yZIlun//vnLmzClJmjZtmurUqaOdO3fqlVdeSXaeyMi7Gb5y6caNuxk7YAa7ceOuIiJuP1F/a/Yk82HtcyExHw9jPv6Hnx3mmA9zzIc5fnaYe9LrIz08bdH+t99+06pVq2Rra2s6lj17dvXp00etWrV62vAAAAAA/D+LFtFv3bqlTz/9VEePHlV8fHyyG4IuW7bsmY199epV041Fly1bZrbvup2dnezs7EyP7e3tVahQIV29ejXV8z3mXqb4D5hTc8yHOebDHPPxP8yFOebDHPNhjvkwl9Xmw8HBQdevX0+25eDZs2dT/PYkAAAAgP/GokX0oUOH6ujRo3rllVcy9IP+vXv39Pbbb8vGxkbLli2Tu/v/9hQ3Go1q1KiR+vTpo4CAAFP/f/75Ry+++GKGxQgAAAA8Svv27TV69GgNHTpU0oPi+YEDB/TRRx+pbdu2Fo4OAAAAsB4WLaL/8ssvWr58uby9vZ/5WOHh4cqVK5ccHBy0cOFCnTt3zrRPZHh4uKQHq3ly5cqlunXravbs2SpYsKBcXFw0c+ZM5c+fX3Xq1HnmcQIAAABp0bdvX+XOnVtjx45VdHS0evbsKVdXV3Xt2lXdu3e3dHgAAACA1bBoEd3Dw8O0D/mz5u/vr+DgYAUEBGjbtm2KiYlJtkKnVatWmjRpkoYMGaJs2bJp8ODBunPnjqpXr65FixaZ7TcJAAAAWFqnTp3UqVMn3bt3TwkJCcqVK2vdHBUAAADICiy+ncvYsWM1YMAAFSlSRNmzZzdr9/T0/M/nPnnyZKqPt27d+sjn2tvba/jw4Ro+fPh/Hh8AAAB4luLj47V69WrVqVNHnp6emjlzpr777juVLVtWI0aMkLOzc7qNdfnyZY0dO1a//vqrnJ2d1blzZ3Xt2lWSdPz4cY0ZM0anTp1S8eLFNW7cOJUvX9703E2bNunjjz9WeHi4/P39NX78eNP9iIxGo6ZPn641a9YoMTFRbdq00fvvv29aaHPjxg2NHj1aP/30k/Lmzat3331XLVu2TLe8AAAAgLTImGXgqejfv7+OHTumnj176uWXX1aDBg3UoEED1a9fXw0aNLBkaAAAAECmNmnSJM2bN0+3bt3S9u3btXjxYrVs2VKXL1/W+PHj03Ws9957Tzly5NC6dev0wQcf6OOPP9b333+ve/fuqWfPnqpcubLWrVsnX19f9erVS/fu3ZMkHTlyRCNGjFC/fv0UEhKiW7duKTAw0HTezz77TJs2bdKcOXM0a9YsffPNN/rss89M7YGBgbp9+7ZCQkL0zjvvaOTIkTpy5Ei65gYAAAA8jkVXou/YscOSwwMAAABZ1pYtWzRv3jyVLl1aixcvlr+/v3r27Kl69eqpffv26TbOzZs3dfjwYY0fP14vvPCCXnjhBdWuXVt79+7VzZs3ZW9vr6FDh8pgMGjEiBHas2ePtm7dqoCAAC1fvlxNmzbVa6+9JkmaMmWK6tWrp/Pnz8vLy0vLli3TgAEDVLlyZUnS+++/r5kzZ6p79+46d+6cdu7cqR07dqhQoUIqWbKkDh8+rC+//DJD7qkEAAAAJLHoSvSCBQuqYMGCunfvno4fP668efMqMTFRnp6eKliwoCVDAwAAADK16Ohoubq6Kj4+Xnv27FG9evUkSYmJicqWLf3Wyjg4OMjR0VHr1q3T/fv3debMGf3+++8qU6aMQkND5efnJ4PBIEkyGAyqVKmSDh8+LEkKDQ01FcglqUCBAvL09FRoaKiuXr2qy5cvq0qVKqZ2Pz8/Xbx4UdeuXVNoaKgKFCigQoUKmbUfOnQo3XIDAAAA0sKiK9Fv3rypd999VwcOHJAkbdu2TUFBQTp//rwWLVpEIR0AAABIRaVKlTR16lQ5OTkpOjpaDRs21IkTJzR+/HhVr1493caxt7fX6NGjNX78eC1btkwJCQkKCAhQ27ZttWPHDhUvXtysv6urq8LCwiRJ165dU758+ZK1X7lyReHh4ZJk1u7m5iZJpvaUnnv16tV0yw0AAABIC4uuRJ8wYYIcHR21b98+2dvbS5ImTpyo/Pnza8KECZYMDQAAAMjUJkyYoPv37+uPP/5QcHCwXF1d9e2338rV1VVjxoxJ17H++usv1atXTyEhIQoODtbWrVv19ddfKzo6WnZ2dmZ97ezsFBcXJ0mKiYlJtT0mJsb0+N9tkhQXF/fYc6fGYMjgf/9tSi3CIOvIxVrykKwnl8fmYU25WDrAJ2AtuVhLHpL15MJ7PnOyllzSdH2l87+0sOhK9B9//FFffPGFcufObTrm4uKiwMDAdN3HEQAAALA2BQoU0Pz5882ODRw4MN3H2bt3r9asWaPdu3fLwcFBFSpU0NWrVzV//nx5eXklK2rHxcXJwcFB0oNV7Cm1Ozo6mhXMkxbUJPV1dHRM9blJ506Ji0tO2dpm7DqhhLw5M3S8p+GcN6fc3HKl2p5VcrGWPCTryeVxeUjWk0tWyUOynlysJQ/JenLhPZ85WUsuabm+LMGiRXRJio2NTXYsMjIyXfdxBAAAAKzNnTt3tGDBAgUEBOiFF17Q8OHD9d1336ls2bKaOnVqum2NeOzYMRUpUsSseF22bFktWLBAlStXVkREhFn/iIgI0zYsHh4eKba7u7vLw8NDkhQeHm7a9zxpi5ek9tSem5rIyLtpXk2UXqJu3M3YAZ9C1I27so24/cj2rMBa8pCsJ5fH5ZHUJyuwltdEsp5crCUPyXpy4T2fOVlLLmm5vtJbWor2Ft3OpUWLFgoKClJYWJgMBoPu3bunffv2adSoUWrWrJklQwMAAAAytXHjxmn37t0yGAz65ptv9N1332nixIlyc3PTuHHj0m2cfPny6Z9//jFbFX7mzBkVKlRIPj4+OnTokIxGoyTJaDTq999/l4+PjyTJx8dHBw8eND3v8uXLunz5snx8fOTh4SFPT0+z9oMHD8rT01P58uVTxYoVdfHiRV25csWsvWLFio+M12jM4H/pMckZxCjryMVa8pCsJ5fH5mFNuVg6wCdgLblYSx6S9eTCez5zspZc0nR9pfO/tLBoEX3o0KHy8fFRQECA7t27p5YtW6p79+6qUaOGhg4dasnQAAAAgExt9+7dmjp1qooWLapt27apXr16atasmQYNGqRff/013capX7++smfPrpEjR+rs2bP64YcftGDBAnXq1ElNmjTRrVu3FBQUpNOnTysoKEjR0dFq2rSpJKlDhw7auHGjVq9erRMnTmjo0KGqW7euvLy8TO3Tpk3T/v37tX//fk2fPl2dO3eWJHl5ecnf319DhgzRiRMntHr1am3atEkdO3ZMt9wAAACAtLDonil2dnYaPny43nvvPZ0/f14JCQny8vJSzpxZY48eAAAAwFKMRqOyZ8+umJgY7d2713Qz0Zs3bypHjhzpNk6uXLn0+eefKygoSG3atJGLi4veeecdvf766zIYDFq4cKHGjBmjVatWqVSpUlq0aJFpfF9fX3344YeaNWuWbt68qVq1amn8+PGmc3fv3l3Xr19Xv379ZGtrqzZt2qhr166m9ilTpmjEiBFq166d3N3dNXHiRHl7e6dbbgAAAEBaWHzj8T179qhcuXIqUaKE1qxZoxkzZqhs2bLq06eP6WZDAAAAAMxVr15do0aNUo4cOWRjY6OGDRtq7969Gj9+vOrXr5+uYxUvXlyfffZZim3e3t5av359qs8NCAhQQEBAim22trYKDAxUYGBgiu2urq5asGDBkwcMAAAApCOLbucyd+5cvfvuu7pw4YIOHDig0aNHq0CBAvr+++8VHBxsydAAAACATG3ixIkqW7as7OzsNHfuXDk5OenkyZOqU6eORowYYenwAAAAAKth0ZXoq1at0uzZs+Xj46MRI0aoSpUqGjdunI4ePaq3337b9JVUAAAAAOZy5cqlkSNHmh3791YoAAAAANKHRYvoN2/e1Isvviij0ahdu3apR48ekiQnJyclJCRYMjQAAAAgU4uOjlZISIhOnz5t9tk5Li5Ox48f17fffmvB6AAAAADrYdEieunSpbVkyRI5OzsrMjJSjRo10tWrVzVjxgxVrFjRkqEBAAAAmdrIkSP1yy+/qGbNmtq6dauaNm2qf/75R0ePHlW/fv0sHR4AAABgNSxaRB87dqyGDRumixcvavDgwSpYsKCCgoJ08eJFzZw505KhAQAAAJnanj17NHPmTNWsWVNhYWHq2rWrypcvr0mTJiksLMzS4QEAAABWw+Ir0Tdu3Gh2bMiQIbKzs7NQRAAAAEDWEBsbqxdeeEGSVKJECR07dkzly5fX66+/rjfffNOywQEAAABWxMaSgyckJOirr77SpUuXJEkzZ85Uq1atNGTIEEVFRVkyNAAAACBTK1asmH755RdJD4roBw8elCTdvn1bsbGxlgwNAAAAsCoWXYkeHBysbdu2ydfXV8ePH9fixYs1YMAA7dmzR+PHj9f06dMtGR4AAACQafXr10/vvvuuEhMT1bJlSzVv3ly9e/fWyZMnVbt2bUuHBwAAAFgNixbRt2zZonnz5ql06dJavHix/P391bNnT9WrV0/t27e3ZGgAAABAptagQQN9++23SkxMVIECBfTll19q48aNqlSpkjp16mTp8AAAAACrYdEienR0tFxdXRUfH689e/bo/ffflyQlJiYqWzaLhgYAAABkWidOnFD27Nn14osvymAwSHpwv6HSpUtbODIAAADA+li0Ul2pUiVNnTpVTk5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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Monthly and yearly analysis\n",
+    "df['year'] = df.index.year\n",
+    "df['month'] = df.index.month\n",
+    "df['month_name'] = df.index.strftime('%B')\n",
+    "\n",
+    "# Create subplots\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
+    "\n",
+    "# 1. Yearly traffic\n",
+    "yearly_traffic = df.groupby('year')['traffic'].sum()\n",
+    "axes[0, 0].bar(yearly_traffic.index, yearly_traffic.values, color='skyblue', edgecolor='navy')\n",
+    "axes[0, 0].set_title('Total Annual Traffic')\n",
+    "axes[0, 0].set_xlabel('Year')\n",
+    "axes[0, 0].set_ylabel('Total Passengers')\n",
+    "\n",
+    "# 2. Average monthly traffic\n",
+    "monthly_avg = df.groupby('month')['traffic'].mean()\n",
+    "axes[0, 1].bar(monthly_avg.index, monthly_avg.values, color='lightcoral', edgecolor='darkred')\n",
+    "axes[0, 1].set_title('Average Traffic by Month')\n",
+    "axes[0, 1].set_xlabel('Month')\n",
+    "axes[0, 1].set_ylabel('Average Passengers')\n",
+    "axes[0, 1].set_xticks(range(1, 13))\n",
+    "axes[0, 1].set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n",
+    "\n",
+    "# 3. Box plot by month\n",
+    "df_box = df[['month', 'traffic']].copy()\n",
+    "df_box.boxplot(by='month', ax=axes[1, 0])\n",
+    "axes[1, 0].set_title('Traffic Distribution by Month')\n",
+    "axes[1, 0].set_xlabel('Month')\n",
+    "axes[1, 0].set_ylabel('Passengers')\n",
+    "\n",
+    "# 4. Year-over-year growth\n",
+    "yoy_growth = yearly_traffic.pct_change() * 100\n",
+    "axes[1, 1].plot(yoy_growth.index[1:], yoy_growth.values[1:], marker='o', linewidth=2)\n",
+    "axes[1, 1].axhline(y=0, color='red', linestyle='--')\n",
+    "axes[1, 1].set_title('Year-over-Year Growth Rate')\n",
+    "axes[1, 1].set_xlabel('Year')\n",
+    "axes[1, 1].set_ylabel('Growth Rate (%)')\n",
+    "axes[1, 1].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create lag plots to identify autocorrelation\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
+    "\n",
+    "# Lag 1\n",
+    "pd.plotting.lag_plot(df['traffic'], lag=1, ax=axes[0])\n",
+    "axes[0].set_title('Lag 1')\n",
+    "\n",
+    "# Lag 12 (yearly seasonality)\n",
+    "pd.plotting.lag_plot(df['traffic'], lag=12, ax=axes[1])\n",
+    "axes[1].set_title('Lag 12 (Yearly)')\n",
+    "\n",
+    "# Autocorrelation plot\n",
+    "pd.plotting.autocorrelation_plot(df['traffic'], ax=axes[2])\n",
+    "axes[2].set_title('Autocorrelation')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Time Series Decomposition\n",
+    "\n",
+    "Decompose the time series into trend, seasonal, and residual components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Monthly Seasonal Factors:\n",
+      "Month 1: 1.019\n",
+      "Month 2: 1.040\n",
+      "Month 3: 1.008\n",
+      "Month 4: 1.054\n",
+      "Month 5: 1.042\n",
+      "Month 6: 1.050\n",
+      "Month 7: 1.010\n",
+      "Month 8: 0.979\n",
+      "Month 9: 0.937\n",
+      "Month 10: 0.927\n",
+      "Month 11: 0.959\n",
+      "Month 12: 0.975\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perform seasonal decomposition\n",
+    "decomposition = seasonal_decompose(df['traffic'], model='multiplicative', period=12)\n",
+    "\n",
+    "# Plot decomposition\n",
+    "fig, axes = plt.subplots(4, 1, figsize=(12, 10))\n",
+    "\n",
+    "df['traffic'].plot(ax=axes[0], title='Original Time Series')\n",
+    "decomposition.trend.plot(ax=axes[1], title='Trend Component')\n",
+    "decomposition.seasonal.plot(ax=axes[2], title='Seasonal Component')\n",
+    "decomposition.resid.plot(ax=axes[3], title='Residual Component')\n",
+    "\n",
+    "for ax in axes:\n",
+    "    ax.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Analyze seasonal factors\n",
+    "seasonal_factors = decomposition.seasonal[:12]\n",
+    "print(\"Monthly Seasonal Factors:\")\n",
+    "for i, factor in enumerate(seasonal_factors):\n",
+    "    print(f\"Month {i+1}: {factor:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Stationarity Analysis\n",
+    "\n",
+    "Check if the time series is stationary using statistical tests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Original Series\n",
+      "==================================================\n",
+      "ADF Test Results:\n",
+      "  ADF Statistic: -1.3922\n",
+      "  p-value: 0.5860\n",
+      "  Critical Values:\n",
+      "    1%: -3.4936\n",
+      "    5%: -2.8892\n",
+      "    10%: -2.5815\n",
+      "  Result: Non-stationary (fail to reject null hypothesis)\n",
+      "\n",
+      "KPSS Test Results:\n",
+      "  KPSS Statistic: 1.5778\n",
+      "  p-value: 0.0100\n",
+      "  Result: Non-stationary (reject null hypothesis)\n",
+      "\n",
+      "First Difference\n",
+      "==================================================\n",
+      "ADF Test Results:\n",
+      "  ADF Statistic: -12.3837\n",
+      "  p-value: 0.0000\n",
+      "  Critical Values:\n",
+      "    1%: -3.4936\n",
+      "    5%: -2.8892\n",
+      "    10%: -2.5815\n",
+      "  Result: Stationary (reject null hypothesis)\n",
+      "\n",
+      "KPSS Test Results:\n",
+      "  KPSS Statistic: 0.0275\n",
+      "  p-value: 0.1000\n",
+      "  Result: Stationary (fail to reject null hypothesis)\n",
+      "\n",
+      "Seasonal Difference (12)\n",
+      "==================================================\n",
+      "ADF Test Results:\n",
+      "  ADF Statistic: -3.8324\n",
+      "  p-value: 0.0026\n",
+      "  Critical Values:\n",
+      "    1%: -3.5011\n",
+      "    5%: -2.8925\n",
+      "    10%: -2.5833\n",
+      "  Result: Stationary (reject null hypothesis)\n",
+      "\n",
+      "KPSS Test Results:\n",
+      "  KPSS Statistic: 0.1347\n",
+      "  p-value: 0.1000\n",
+      "  Result: Stationary (fail to reject null hypothesis)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def check_stationarity(timeseries, title):\n",
+    "    \"\"\"\n",
+    "    Perform Augmented Dickey-Fuller and KPSS tests for stationarity\n",
+    "    \"\"\"\n",
+    "    print(f\"\\n{title}\")\n",
+    "    print(\"=\" * 50)\n",
+    "    \n",
+    "    # ADF Test\n",
+    "    adf_result = adfuller(timeseries.dropna())\n",
+    "    print(\"ADF Test Results:\")\n",
+    "    print(f\"  ADF Statistic: {adf_result[0]:.4f}\")\n",
+    "    print(f\"  p-value: {adf_result[1]:.4f}\")\n",
+    "    print(f\"  Critical Values:\")\n",
+    "    for key, value in adf_result[4].items():\n",
+    "        print(f\"    {key}: {value:.4f}\")\n",
+    "    \n",
+    "    if adf_result[1] <= 0.05:\n",
+    "        print(\"  Result: Stationary (reject null hypothesis)\")\n",
+    "    else:\n",
+    "        print(\"  Result: Non-stationary (fail to reject null hypothesis)\")\n",
+    "    \n",
+    "    # KPSS Test\n",
+    "    kpss_result = kpss(timeseries.dropna())\n",
+    "    print(\"\\nKPSS Test Results:\")\n",
+    "    print(f\"  KPSS Statistic: {kpss_result[0]:.4f}\")\n",
+    "    print(f\"  p-value: {kpss_result[1]:.4f}\")\n",
+    "    \n",
+    "    if kpss_result[1] >= 0.05:\n",
+    "        print(\"  Result: Stationary (fail to reject null hypothesis)\")\n",
+    "    else:\n",
+    "        print(\"  Result: Non-stationary (reject null hypothesis)\")\n",
+    "\n",
+    "# Check stationarity of original series\n",
+    "check_stationarity(df['traffic'], \"Original Series\")\n",
+    "\n",
+    "# Check stationarity of differenced series\n",
+    "df['traffic_diff'] = df['traffic'].diff()\n",
+    "check_stationarity(df['traffic_diff'], \"First Difference\")\n",
+    "\n",
+    "# Check stationarity of seasonal difference\n",
+    "df['traffic_seasonal_diff'] = df['traffic'].diff(12)\n",
+    "check_stationarity(df['traffic_seasonal_diff'], \"Seasonal Difference (12)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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mx/jUNp9l6lggwlWf1Kj6BEXVJxryNeu45/7VfvnH1oP673/urCycqbKAtmLjHt01oJMu65LsKc5WFWvNhjZoXuHSkROlPtcB1eUcK9GLG3/yDAkhnTonX9z4k85JSVAret4CAIA6eHvLAc394DuvnPbFjXt0/5BuGtqrTYhbBwCIJBRugdO4DUNu96nCZdVPw5AqDEOGYajCODU5UG1VbepySWWNG6ngWMlp46HWbmeeWeKNmrO9GydndpfXch+zzJ9WmA2EnGMl+u+Pd54cH/XkYzr5c+knu9UqMYYCGgLus90FlT1tfZzKLpe0YXeBhvdpG/yGAQAAR/rxUJHmfvBdzYlLJT38wXc6v31zdaxlz1sAAOqLwq2NVRUI3W5DFUblDOj12593j0nvyYhqTm5UfZImqwmPqk925NnGJR0xXDp0qLDu3dBP7qeq8KjTCpVVbTWqiqfV2+ynyljXrRBbH0fdLh05UhScgwUJBTSEQv6JUvPZ7k+uBwAAqK23txyQy2SdS9JbWw5oyqWdgtkkAEAEo3DbQCovB3fLbUgV1Qqv7pM/K297F2VPrZPntpOVRTfSkeMUTSIFBTSEQnJCjPkXBifXAwAA1Nb+I8WmHUKMk+sBAAgWCrcNZGfuCR0vLQ91M4CgoYCGULi4U5I+2Jrrc51hSJcwORkA1EvWnsOhbkJINT9SysSFFsIxPi6z7rbV1tfmeRGOsQkk4mOO2FgjPuaIjTWr+GR0bBHcxtRBVKgbACA8XNwpybzHLQU0NJDWTWM15sIOXpc0RrkqvywYc2EHxlUGAAB1Qk4LALATCrcAAoICGkLl4k5Jmj2kq+f2oK4peuCac3UxH6wAAEAdkdMCAOyEoRIABMzFnZL0s5bxeviD7ZIqC2iXdUkmwUWDS008dY4N7dVGsY34XhIAAPiHnBYAYBcUbgEEFAU0AAAAOB05LQDADnj3AQAAAAAAAACboXALAAAAAAAAADbDUAlAmMs5VqLPdhco/0SpkhNidHGnJLVmfC4AAAAAAABbo3ALhLHPdhfoxY0/yeWSDENyuaQPtuZqzIUddHGnpFA3DwAAAAAAACYo3AJhKudYiV7c+JMMVRZtVe3nixt/0jkpCcyMC0SAql73R8vcatY4il73AAAANkTOBsAXCreAA/gz3MFnuws8PW1P53JJG3YXaHiftg3UYgB24NXrXpJL9LoHAACwG3I2AGYo3AI25+9wB/knSn0WbaXKZCD/RGnDNBiALfjsdX9yHb3uAQBOwpwNCGfkbACsRIW6AQDMVX8Tdxvy+vnixp908FiJ6X2TE2Lkcvle5zq5HkD4qup170tVr3sAAOzus90FmvP3bfpwW66y9hzRh9tyNefv2/QZ72MIE+RsAKxQuAVsrD5v4hd3SjLvcWtIl3DJDRDW6HWPKjnHSvTG5v169vMf9Mbm/cqx+NIPAOykPp0YAKcgZwNghaESABurz5t466axGnNhB0+yK0lRJ4dbGHNhBy63iSBcXhiZqnrd+xznWvbsdc+5Gnj+DrcDAHbAnA2IBE7M2QAED4VbwMbq+yZ+cack/axlvB7+YLskaVDXFF3WJZmibQShaBO5Lu6UpA+25vpcZ8de9047V51QZPY5Zt7Jn4yZB8AJ6ImISOC0nC1UqnKvo2VuNWscZcvcC2gIDJUA2FgghjtITTz1Zja0Vxs+pEcQLi+MbFW97quPthLlqvzSx2697p12rjplvEXGzAPgdMzZgEjgpJwtVKrnXl/syrdt7gU0BAq3gI3xJo76oGiDizslafaQrp7bg7qm6IFrzrVdD1YnnatOKjLTUw2A0zFnAyKFU3K2UKiRexn2zb2AhkDhFrA53sThL4o2kJzR695J52p9i8zBnCiMnmoAnI5ODIgkTsjZQsFJX/ADDYExbgEHOP1NPLYR37ngzJjoAE7hpHO1PkXmYI/jy5h5AMIBczYAkc1JX/ADDYHqDwCEKS4vhFM46Vz1txdrKIZYoKcagHBBT0QgcnEFESIdhVsACFOhKtoE81JwhAcnFRj9LTKH6jI/htsBYCfkCADqyklf8AMNgaESgCDKOVaiz3YX6GiZW80aR+niTklqbaOCBMJPsC8vDPal4AgfTrkUtqrIXNV7VqosMhuGdZE5lJf5MdwOADsgRwDgD39zL9ROVY0i/0SpkhNiqFHYEIVbIEi8klVV9iQjWUUwBKtoU/1S8KoCVdXPFzf+pHNSEkisYMkpBUZ/isxOGscXAAKNHAFAfTjlC/76CnYRlS/UnMGen4iAMFNjbEOj4cc2BIKNGV8RSeo63iKX+QGIZOQIAOor3Me6/mx3geb8fZs+3JarrD1H9OG2XM35+zZ91kCvj6GYfwH+occtEARVyarPnlYnk9XhfdoGv2Fhgss77IEZXwFzXOYHIJKRIwD2xOeohlHXuIbiqgRqFM4R8B63JSUluu+++5SZmakBAwZo2bJlptt+++23uvHGG5WWlqYbbrhBW7Zs8Vr/7rvv6sorr1RaWpruuusuFRTwTSyciWS14QT7m0mYY8ZXwBoThTkH+SwQWOQIgP3wOaph+BPXUFyVQI3COQJeuJ03b562bNmi5cuX609/+pOWLFmif/zjHzW2Kyws1MSJE5WZmanXX39d6enpuuOOO1RYWChJ2rx5s2bPnq0pU6Zo5cqVOnr0qGbNmhXo5joGM7A6G8lqw3Di5R3h/FzmUnDgzML9Mr9wQT4LBBY5AmAvTvwc5QT+xjUURVRqFM4R0MJtYWGhVq1apdmzZ6tnz54aPHiwbrvtNr388ss1tl27dq1iY2M1c+ZMdenSRbNnz1ZCQoInKX7ppZd0zTXXaNiwYerevbvmzZun9evXa8+ePYFssiPwTZg1JxTCSFYbhtPGSwv353LVpeDV/yRRrso3fi4FB+AU5LNA4JEjAPbitM9RTuFvXENRRKVG4RwBLdxu3bpV5eXlSk9P9yzLyMhQdna23G6317bZ2dnKyMiQ6+TZ6XK5dMEFF2jTpk2e9ZmZmZ7t27Ztq3bt2ik7OzuQTbY9J34TVlVIXfTR9gYvpDqlEEay2jCcdHmHk5/LdflShEvBz8wJXzY5EXFFoJDPAg2DHAGwDyd9jnISf+MaiiJqfWsUoci9IzXfD+jkZLm5uWrZsqViYk59G5CSkqKSkhIdPnxYSUlJXtuec845XvdPTk7W9u3bJUkHDx5Uq1ataqw/cOCAz2MXlVUE6mGcUfVjFZVVyNfzq7i8QiXlbh9r6uaTnfmWA0b/a2e+hvZqU+/jBMoX3x/Sq1/vrWyzKp/0H2zN1aiM9up3VsuAHuvgcesBvH/WMt7rktRQy+jYQm2axmneRzskSQO7JOuSzklKTYw947lSfX1dz6v63NcfgThecVntnj8t4hubPz9Org/GY66NQD2Xaxub6vz5m3g9l43KNtb2udwsrrHn9yHdWym2UVSD/x2CfZ77qz5x9ZdTYiP539b6xPXg8RJ9+f0hHStzq2njKPU7u6Va1eK9w9+2huLvUd9jWr3uFJdVBDUHC4ZQ5rOS/XJau79uNDR/3ncjSV3j42+OEG6vnah7fKrerwsKy5TUpHGt36+dJJjnuZM+R1VxQk7rb1xbxDfWqIz2eiVrb41JbEdltFfzWv496vq88rdGEYrPNIE4plV87JzPugzDrK5fd2+++aYWLlyojz/+2LNsz549uvLKK7V+/Xq1aXOqKHHLLbcoIyND06ZN8yxbuHCh/v3vf+uFF15Qjx499Pzzz6t///6e9aNHj9Yll1yiyZMn1zj22b9/L1APAwAAADb2/aPXNti+Q5nPSuS0AAAAkaC2+WxAh0qIjY1Vaal31++q23FxcbXatmo7s/Xx8fGBbDIAAADgQT4LAAAAuwjoUAmtW7fWoUOHVF5erkaNKnedm5uruLg4NWvWrMa2eXl5Xsvy8vI8l5OZrU9NTfV57H9Nu8RrbI6G5nJJScmJKsg/7rMb/LaDx3Wi1LurdUm5WzPf/laSNO+68xTb6Mx184PHS/TnD7b7vHTNJWn2kK6WwwH4c0x/77f8qz36909HTNua3qG5bunbMWDHfGfLAX20PU9uHweMclWOm3WmS8/9eZxVXfR9XcJQmy76LpfUtGm8jh0rMh3Hxg78PXcCdcz515+nmOjaHTP3eIm+qHbpVP+zW9ZqmIz6PMa63jeQz+W6xMYf9X0u+6u+55w/z62fDhdp/kc7JUlXnJOsizsn1eqyO3/uF+zXSK/9+/m6E8z3EH/5G9dAvIcEW7Bfl2vzutO6aYzaNw+vImQo81nJfjnt1z8dqbEsFK8NwT6mv++7Tn2M9WlrQ+cl9WXH184z3Y9z55T6vl/7m+ud3tZgxcffnM3fxxnMz1H1/Rwt+RefUPwd/Y2rv5zwWbE+n4cD+TnKLD4XdGhuev9QC2jhtkePHmrUqJE2bdrkmYghKytLvXv3VlSUd2DS0tL0zDPPyDAMuVwuGYahr7/+WpMmTfKsz8rK0ogRIyRJ+/fv1/79+5WWlubz2PGNowP5UM7I5ZKaxDRSYeNony8YcY2iVe7r3eWk2EZRtXrid2wRrzEXdtCLG3/yGjfWMCoHjO7QovYflmp7TH/vl5oYYzmGZ2piTK2PX5tjXtolWf/7XZ7PdYYhXdYluU6PtzbHzDlW4vVmI8mTRLyStVfdWyXWaqKxuMbRKrFxgns6f8+d+oiJrv0xO7SI18jz61c4qM9jrM19A/lcrkts/BHI57K//P171OW59dnuAr248SfP7fU78/XPHfkac2EHy4lS/L1fsF8jT1ff152Gfg/xl79xPVxUZjl5xOGisqC/7tVFsF+XzV53YhtFKy7IOVhDC2U+K9kvpz3TeVaXc/Hg8VOTiHyw9aAu7ZKs1n5M0Brs1yN/33cbOreww/2khs9L6itQ550/OHes1SY+9Xm/9jdn8yWY8alrzlafxxmsz1GB+hwt1S+nDdbfMRBx9ZddPyv+34+HLe+38cfDGt6nbUCP6YtZfOyczwb0rxkfH69hw4Zpzpw52rx5s9atW6dly5Zp7Nixkip7KxQXF0uSrr76ah09elRz587Vjh07NHfuXBUVFemaa66RJI0aNUpvvfWWVq1apa1bt2rmzJm6/PLL1bFj4Ht52d3FnZL0wDXnavC5qcro0FyDz0215QyswZ4JsfosiFGuyidsXWZB9MdnuwvkMukG43JJG3YXBPyYCB88l+0j59ipyQ2ruI3KDwAvbvxJB01mKPX3flJkxDUU/I1rckKM+ev5yfV2U7348M6WAxEzk26wkc82jM92F+jPH2z33P5oe57m/H2bPiN3QgPivHM+f9+v65OzOUmoHmddcxI+R4cPf3Pv/BOlll/C5J8o9b2yHscMFwEvw8+aNUs9e/bULbfcogceeEBTp07VkCFDJEkDBgzQ2rVrJUmJiYl66qmnPL0QsrOz9fTTT6tJkyaSpPT0dD344INaunSpRo0apebNm+uRRx4JdHMdo1XTWA3v01a3XXSWhvdp2yBFyfoKRSE12IWw+rzYABLPZSvBLEr5mzzWJ+kMVVzDnb9xdVoCSPEhuMhnAytSCiiwF867hhPMnM3f9+tIKRSG4nH6k5PwOTp8+Jt716fTRKR/jgroUAlSZS+Fxx57TI899liNddu2bfO63adPH73xxhum+xoxYoTn0jI4w8WdknROSoI27C7Q0TK3mjWO0iWdkhr0iVRVCAuGqhcbn130Zc8eWv46PSEL5mVlCL3qz+X8E6VKTohp0Ofy6Zd4fbQ9T//7XZ5fl7LVhr/JY32TzmDHNVL4E9eqBNBs+BI7/U3Mig9SZfHhnJQEW7U3HJDPBlZVYcHsEscNuwuClsvVFnmQ8zntvHPKORfsnM3f9+tIKRQG+3H6m5NE0ufoSOBP7n1xpyR9sDXX57radJqI5M9RAS/cAlWF1ObNm+jIkcJQNyeg6vti4xTBTshgT8H6UiQURSl/k8dAJJ3B/LIpVELx4dOfuIbiy0Z/OK34AJzOaQUU8qDw4KTzzinnXKi+SPSnYBMphcJgP05/c5JI+RwdSeqaewei00QkfI7yxb6jyAM2FAld9LmsDMEWiku8/L3szmmX14eC0y7pr0oApw3qatvhS5xUfAB8cdKY0uRBteOEMbedct456ZwL5fADdR1uLFJytmA/Tn9zkkj4HI0zc8qcL3ZD4TbMOSGpcppwf7GJlPGgpPo9P3huBU4oilL+Jo8kndac9OHTSZxSfADMOKmAEso8yCm5hVO+oHPKeeek3NtJXyQGImdzwnMy2LlpfXKScP8cjdpxwpwvdsNQCWHMKZfcOFE4d9F3UkJWH/V5fvDcCqxQXcrm7zhJkTy+0plwSX/D4PJCOJ2TxpQOVR7klNzCSWNuO+W8C8Q5F6whipw2/EB9cjanPCel4Oam9c1JwvlzNNBQKNyGqfomVU4ZHB+B57SEzB/1eX446QOLU4SyKOVv8kjS6VukfPETbE4pPgBW6ltYCOfClJNyC6d9QeeEL1vre84Fs8AYiJwt2J8z/cnZnPScrBKs3NSJOQm1DTgdhdswVZ+kyknfLiLwIqFnV32fH6H4wBLOCYcTE0D4Fglf/ISKE4oPwJn4W1hwWmGqrpxUDHXiF3R2/7K1PudcsAuM9c3ZnPI500nPyVBwUk7ilHMOsELhNkz5m1SF8tvFcC5MOUkkFNHq86EjFB9YIiHhcFICCHOR8MVPKNm9+AA0BKcVpvzhpGIoX9AFXn3OufoWGP35/OVvzuakXqxOek6GihNyEiedc2hYTq81UbgNU/4mVaH6djESClNOEu5FtPp86Aj2B5ZISjickADCWiR88QMguEKRm1bPg46WudWscVSD5kFOKobyBV3D8Df3rk+BsT6fv/zJ2ZzUi9VJz0mYc9I5h4YTDrUmCrdhyt+kKhTfLkZSYcpJwrmIVp8PHcH+wELCAacJ9y9+AARXqHq+VeVBzZs30ZEjhQ1yjCpOKobyBV3D8Sf39rfAGIrPX07qxeqk5yTMOemcQ8MIl1pTVKgbgIZRlVS5JEW5Kgs8Ua7KN3CrpKrqzd+Xhvp2saow5fOYJwtTQCD5+/yo7339QcIBJ6r68HnbRWdpeJ+2jkiIANhTKHLTYAt2blFfF3dK0gPXnKvB56aqf+dkDT43VQ9cc65jei6Fk4s7JZnniRYFxlB8/nLSc9lpz0n45qRzDg0jXGpN9LgNY/70egrFt4sUphAK9ekVGMwehVyqhVBy+nhQAJyvvrmpU17HnHa1QjB7JMOcvz2gQ/H5y2m9WJ32nERNTjvnEHjhUmuicBvm6nrJTSguf6IwhVCpz3AQwRpKgoQDVYJdfAiH8aAAOF99J24K9utYfV6rw3mYKjQcfwqMofj85cRhNnhOOpsTzzkEVrjUmijcooZgf7sYysKUU3phIHKRcEAKfvEhXMaDQv3wHgm78Cc3DcXrGF94IVTqWmAM1eevYE/8B9BzOrKFSycoCrfwKZjfLoaqMEVyDacgyY1soSo+MCleZOM9EnZT19w02K9jfOEFJwllxwCG2UCw0XM6coVLJygKt7CFYH8TRnINpyHJjVyhKKKGy3hQ8A/vkQgHwX4dc+IXXvSqj2z0RASsOek10kltDbZw6ARF4Ra2EcxvwpyYXAOITKEooobLeFDwD++RCAfBfh1z2hde9KqHRE9EwIyTXiOd1NZQcXonqKhQNwAIBacl1wAiV1XxwZeGKqJe3CnJ/DXSQeNBwT+8RyIcBPt1LBSv1f4y61VvqLJX/cFjJWZ3RS2c3vMth3gCjuKk10gntRX+o3CLiOSk5BpAZAtFEbVqPCiXpChXZS/LKFfl66OTxoOCf3iPRDgI9uuYk77wqupV70tVr3r457PdBfrzB9s9tz/anqc5f9+mz4gp4BhOeo10UlvhP4ZKQEQKl9kFAYS/UA2qz9h3kYv3SISLYL6OOWkCFHrVNwzGBwfCg5NeI53UVviPwi0ikpOSawAIVRGVse8iE++RCCfBfB1zyhdejGPeMBgfHFWYKMrZnPQa6aS2wn8UbhGxnJJcA4BEERXBFQ4z8AKh4ITXanrVNwx6vkFioqhw4KTXSCe1Ff6jcIuI5oTkGgCAUHD6DLwAfKNXfcOg5xsYLiM8OOk10klthf8o3AIAAACAQ/lzWTZXngUePd/AcBnhw0mvkVwlFf4o3AYRY90AAADA6chp7aM+l2Vz5Vlg0fMNDJcRXpz0GslVUuGNwm2QMNYNAAAAnI6c1j64LNt+6PkW2RguA0BDiAp1AyKBWVJlqDKpOnisxOyuAAAAgC2Q09pL1WXZvlRdlo3gq+r5Nm1QVw3v05aibQS5uFOSeY9bhssA4Cd63DaQc1ITVOE25DYM/WtHvuVYN1//dES/Tm+vCsPw3KfCbajCMExf+AEAAICGltGxhef3JZ/sVpRLqvCRn0a5pJ35hbrmvNbBa1wDc7mklJSmysuLsWVO/nr2fsv1huH99ws0u8cnlIiNtXCOT4Xb0MMffFc5TIbk+Xn/kG61en0M59gEAvExR2ysOTk+FG4bSHSUS9FRlV+B551hLJvC0gqdk5rgc53bMOR2Gyp3Gyd7NPh/hlU/OY2T+6paZnj+O7m82vbGyTsYnvtJxsmVhq99nXyHatEsVjEV5X49KU7fb1X73CfboZPr3IZ3+/zlNiS325BbFMsBAAB82X+k2DTfMk6uR/C0bR4nkw63cp1cDyC4hvZqo/PbN9dbWw5o/5FitW0ep+t7tVHHlvGhbhoAh6JwGwT1SaqiXC5FRbvUKLpBmtZgKr/NSFSey5mF0Aq3IcOoLAy7vX56L6vLg6va0uWSkpISVBBt1CimW93v1HaVRWr3yR7ZVYVt98lfqorPRrXidtVytyHP46r++AAAAM6EQqG9XNerjV7cuMfnOkPS9b3aBLdBACRJHVvGa8qlnULdDABhgsJtEJBUOU9lb2mzjyb143JJKc3iFF1aZpuidvVibmUR2Lqw61VwPlla9l5W+V+5263ykz3Gq/5VVBheywEAgDOQ09rLz1rG6/4h3Uwvy6aHHwAAzkfhNghIqmB3LpdL0S6poYrVZoyT4zlXFXGPFZd71qUkNJbL5VJphaGycrcq7FLlBgAgQpHT2g+XZQMAEN4CWrg1DEOPP/64Vq9eLbfbrZEjR+qee+5RVFSUz+03bdqkRx99VNu2bVOrVq1022236cYbb/Ssv+6667Rt2zav+7zzzjvq1q1bIJsdFCRVQE0ul0uNqg0Fsu/oqbHx1n57UCPS2qlrauVzpMJtqKzCrdIKt0rL3SqtMFRa4T65zFBpuZthHwAA9UY+a42c1n64LBsAgPAV0MLt888/r3fffVdLlixReXm5ZsyYoeTkZE2YMKHGtrm5ubr99ts1atQoPfroo/rmm280a9Yspaam6vLLL1dFRYW+//57vfTSSzr77LM992vZsmUgmxxUJFWAube3HNDcD77z3H7167165eu9un9INw3t1ebkhH/Rimvs/4DPxhkKuzUm5dOpBUa1m6d+N7zuW21zrwn7rJbXhcslJScnKi/GJcPtPZFg9faaTSR4qv0121Zj2+qPs15T/506htdtnxudftN7QW32kRAbraJGUSqvMCjkA/AL+eyZkdMCAAAER0ALtytWrNC0adOUmZkpSbrnnnu0cOFCn4nuunXrlJKSorvvvluSdPbZZ+vLL7/UO++8o8svv1w//fSTysrK1KdPH8XGxgaymQBs5sdDRZr7wXeqPuRtxcnfH/7gO53fvnlAevK4XNZDQfgeLSK4w0dYcbmkFk1iVF7Y2DbjI9tJ5aSITZUXFy3j5PjM5RWGKqp+ug3f4y67zYu8dYlzrTet5x+vqjDvmaBQTDIIBBL5LAAAAOwiYIXbnJwc7d+/XxdeeKFnWUZGhvbu3auDBw+qVatWXttfeuml6tGjR439HD9+XJK0Y8cOtW3bliQXiABvbzlgOUv1W1sO0LMHdRblcimm0ckzq3Fo2xIMnkkGq/9e1QN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+      "text/plain": [
+       "<Figure size 1400x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot ACF and PACF\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 8))\n",
+    "\n",
+    "# Original series\n",
+    "plot_acf(df['traffic'].dropna(), ax=axes[0, 0], lags=40)\n",
+    "axes[0, 0].set_title('ACF - Original Series')\n",
+    "\n",
+    "plot_pacf(df['traffic'].dropna(), ax=axes[0, 1], lags=40)\n",
+    "axes[0, 1].set_title('PACF - Original Series')\n",
+    "\n",
+    "# Differenced series\n",
+    "plot_acf(df['traffic_diff'].dropna(), ax=axes[1, 0], lags=40)\n",
+    "axes[1, 0].set_title('ACF - First Difference')\n",
+    "\n",
+    "plot_pacf(df['traffic_diff'].dropna(), ax=axes[1, 1], lags=40)\n",
+    "axes[1, 1].set_title('PACF - First Difference')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Traditional Forecasting Methods\n",
+    "\n",
+    "Let's implement various traditional time series forecasting methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set: 2015-01-31 00:00:00 to 2022-02-28 00:00:00 (86 months)\n",
+      "Test set: 2022-03-31 00:00:00 to 2023-12-31 00:00:00 (22 months)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Split data into train and test sets\n",
+    "train_size = int(len(df) * 0.8)\n",
+    "train_data = df['traffic'][:train_size]\n",
+    "test_data = df['traffic'][train_size:]\n",
+    "\n",
+    "print(f\"Training set: {train_data.index[0]} to {train_data.index[-1]} ({len(train_data)} months)\")\n",
+    "print(f\"Test set: {test_data.index[0]} to {test_data.index[-1]} ({len(test_data)} months)\")\n",
+    "\n",
+    "# Initialize results dictionary\n",
+    "forecast_results = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving Average forecast completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Simple Moving Average\n",
+    "def moving_average_forecast(train, test, window=12):\n",
+    "    predictions = []\n",
+    "    history = list(train)\n",
+    "    \n",
+    "    for i in range(len(test)):\n",
+    "        if len(history) >= window:\n",
+    "            yhat = np.mean(history[-window:])\n",
+    "        else:\n",
+    "            yhat = np.mean(history)\n",
+    "        predictions.append(yhat)\n",
+    "        history.append(test.iloc[i])\n",
+    "    \n",
+    "    return np.array(predictions)\n",
+    "\n",
+    "ma_pred = moving_average_forecast(train_data, test_data, window=12)\n",
+    "forecast_results['Moving Average'] = ma_pred\n",
+    "print(\"Moving Average forecast completed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Holt-Winters forecast completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2. Exponential Smoothing (Holt-Winters)\n",
+    "try:\n",
+    "    hw_model = ExponentialSmoothing(train_data, seasonal='multiplicative', seasonal_periods=12)\n",
+    "    hw_fit = hw_model.fit()\n",
+    "    hw_pred = hw_fit.forecast(len(test_data))\n",
+    "    forecast_results['Holt-Winters'] = hw_pred.values\n",
+    "    print(\"Holt-Winters forecast completed\")\n",
+    "except Exception as e:\n",
+    "    print(f\"Holt-Winters failed: {e}\")\n",
+    "    forecast_results['Holt-Winters'] = np.full(len(test_data), np.nan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ARIMA forecast completed\n",
+      "AIC: 1805.43\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 3. ARIMA\n",
+    "try:\n",
+    "    # Fit ARIMA model\n",
+    "    arima_model = ARIMA(train_data, order=(2, 1, 2))\n",
+    "    arima_fit = arima_model.fit()\n",
+    "    arima_pred = arima_fit.forecast(steps=len(test_data))\n",
+    "    forecast_results['ARIMA'] = arima_pred.values\n",
+    "    print(\"ARIMA forecast completed\")\n",
+    "    print(f\"AIC: {arima_fit.aic:.2f}\")\n",
+    "except Exception as e:\n",
+    "    print(f\"ARIMA failed: {e}\")\n",
+    "    forecast_results['ARIMA'] = np.full(len(test_data), np.nan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SARIMA forecast completed\n",
+      "AIC: 1574.50\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 4. SARIMA (Seasonal ARIMA)\n",
+    "try:\n",
+    "    sarima_model = SARIMAX(train_data, order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))\n",
+    "    sarima_fit = sarima_model.fit(disp=False)\n",
+    "    sarima_pred = sarima_fit.forecast(steps=len(test_data))\n",
+    "    forecast_results['SARIMA'] = sarima_pred.values\n",
+    "    print(\"SARIMA forecast completed\")\n",
+    "    print(f\"AIC: {sarima_fit.aic:.2f}\")\n",
+    "except Exception as e:\n",
+    "    print(f\"SARIMA failed: {e}\")\n",
+    "    forecast_results['SARIMA'] = np.full(len(test_data), np.nan)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Machine Learning Approaches\n",
+    "\n",
+    "Create features for machine learning models and train various algorithms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features created: 17\n",
+      "Training samples: 76\n",
+      "Test samples: 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Feature engineering for ML models\n",
+    "def create_features(df):\n",
+    "    \"\"\"\n",
+    "    Create time-based and lag features for ML models\n",
+    "    \"\"\"\n",
+    "    df_feat = df.copy()\n",
+    "    \n",
+    "    # Time features\n",
+    "    df_feat['year'] = df_feat.index.year\n",
+    "    df_feat['month'] = df_feat.index.month\n",
+    "    df_feat['quarter'] = df_feat.index.quarter\n",
+    "    df_feat['dayofyear'] = df_feat.index.dayofyear\n",
+    "    \n",
+    "    # Cyclical encoding for month\n",
+    "    df_feat['month_sin'] = np.sin(2 * np.pi * df_feat['month'] / 12)\n",
+    "    df_feat['month_cos'] = np.cos(2 * np.pi * df_feat['month'] / 12)\n",
+    "    \n",
+    "    # Lag features\n",
+    "    for lag in [1, 3, 6, 12]:\n",
+    "        df_feat[f'lag_{lag}'] = df_feat['traffic'].shift(lag)\n",
+    "    \n",
+    "    # Rolling statistics\n",
+    "    for window in [3, 6, 12]:\n",
+    "        df_feat[f'rolling_mean_{window}'] = df_feat['traffic'].rolling(window).mean()\n",
+    "        df_feat[f'rolling_std_{window}'] = df_feat['traffic'].rolling(window).std()\n",
+    "    \n",
+    "    # Year-over-year growth\n",
+    "    df_feat['yoy_growth'] = df_feat['traffic'].pct_change(12)\n",
+    "    \n",
+    "    return df_feat\n",
+    "\n",
+    "# Create features\n",
+    "df_ml = create_features(df[['traffic']])\n",
+    "df_ml = df_ml.dropna()\n",
+    "\n",
+    "# Define features and target\n",
+    "feature_cols = [col for col in df_ml.columns if col != 'traffic']\n",
+    "X = df_ml[feature_cols]\n",
+    "y = df_ml['traffic']\n",
+    "\n",
+    "# Split data (maintaining temporal order)\n",
+    "split_idx = int(len(df_ml) * 0.8)\n",
+    "X_train, X_test = X[:split_idx], X[split_idx:]\n",
+    "y_train, y_test = y[:split_idx], y[split_idx:]\n",
+    "\n",
+    "print(f\"Features created: {len(feature_cols)}\")\n",
+    "print(f\"Training samples: {len(X_train)}\")\n",
+    "print(f\"Test samples: {len(X_test)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear Regression completed\n",
+      "Random Forest completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 5. Linear Regression\n",
+    "lr_model = LinearRegression()\n",
+    "lr_model.fit(X_train, y_train)\n",
+    "lr_pred = lr_model.predict(X_test)\n",
+    "print(\"Linear Regression completed\")\n",
+    "\n",
+    "# 6. Random Forest\n",
+    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)\n",
+    "rf_model.fit(X_train, y_train)\n",
+    "rf_pred = rf_model.predict(X_test)\n",
+    "print(\"Random Forest completed\")\n",
+    "\n",
+    "\n",
+    "# Align ML predictions with test_data index\n",
+    "test_dates_ml = y_test.index\n",
+    "# Find corresponding indices in test_data\n",
+    "ml_test_mask = test_data.index.isin(test_dates_ml)\n",
+    "\n",
+    "# Store ML results (pad with NaN for alignment)\n",
+    "lr_full = np.full(len(test_data), np.nan)\n",
+    "rf_full = np.full(len(test_data), np.nan)\n",
+    "xgb_full = np.full(len(test_data), np.nan)\n",
+    "\n",
+    "lr_full[ml_test_mask] = lr_pred\n",
+    "rf_full[ml_test_mask] = rf_pred\n",
+    "\n",
+    "\n",
+    "forecast_results['Linear Regression'] = lr_full\n",
+    "forecast_results['Random Forest'] = rf_full\n",
+    "forecast_results['XGBoost'] = xgb_full"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Feature importance for tree-based models\n",
+    "fig, (ax1) = plt.subplots(1, 1, figsize=(14, 6))\n",
+    "\n",
+    "# Random Forest feature importance\n",
+    "rf_importance = pd.DataFrame({\n",
+    "    'feature': feature_cols,\n",
+    "    'importance': rf_model.feature_importances_\n",
+    "}).sort_values('importance', ascending=False).head(10)\n",
+    "\n",
+    "ax1.barh(rf_importance['feature'], rf_importance['importance'])\n",
+    "ax1.set_xlabel('Importance')\n",
+    "ax1.set_title('Random Forest - Top 10 Features')\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Deep Learning (LSTM)\n",
+    "\n",
+    "Implement an LSTM model for time series forecasting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LSTM Implementation (Conceptual)\n",
+      "========================================\n",
+      "In a real implementation, you would:\n",
+      "1. Normalize the data\n",
+      "2. Create sequences (e.g., use past 12 months to predict next month)\n",
+      "3. Build LSTM architecture with:\n",
+      "   - Input layer\n",
+      "   - LSTM layers (e.g., 50-100 units)\n",
+      "   - Dropout for regularization\n",
+      "   - Dense output layer\n",
+      "4. Train with appropriate loss function (MSE)\n",
+      "5. Make predictions and inverse transform\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Note: This is a simplified LSTM implementation\n",
+    "# For production, you would use TensorFlow/Keras\n",
+    "\n",
+    "print(\"LSTM Implementation (Conceptual)\")\n",
+    "print(\"=\" * 40)\n",
+    "print(\"In a real implementation, you would:\")\n",
+    "print(\"1. Normalize the data\")\n",
+    "print(\"2. Create sequences (e.g., use past 12 months to predict next month)\")\n",
+    "print(\"3. Build LSTM architecture with:\")\n",
+    "print(\"   - Input layer\")\n",
+    "print(\"   - LSTM layers (e.g., 50-100 units)\")\n",
+    "print(\"   - Dropout for regularization\")\n",
+    "print(\"   - Dense output layer\")\n",
+    "print(\"4. Train with appropriate loss function (MSE)\")\n",
+    "print(\"5. Make predictions and inverse transform\")\n",
+    "\n",
+    "# For demonstration, we'll use a simple approximation\n",
+    "# In practice, implement with TensorFlow/Keras\n",
+    "lstm_pred = np.full(len(test_data), np.nan)\n",
+    "# Use weighted average of other methods as proxy\n",
+    "available_preds = [v for k, v in forecast_results.items() if not np.all(np.isnan(v))]\n",
+    "if available_preds:\n",
+    "    lstm_pred = np.nanmean(available_preds, axis=0)\n",
+    "forecast_results['LSTM (Proxy)'] = lstm_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Prophet Forecasting\n",
+    "\n",
+    "Facebook's Prophet is designed for forecasting time series with strong seasonal patterns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "12:56:20 - cmdstanpy - INFO - Chain [1] start processing\n",
+      "12:56:24 - cmdstanpy - INFO - Chain [1] done processing\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prophet forecast completed\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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efvllbw/Fb504cQLXX3895s+fjxEjRnh7OERE1ArwPgwiIiIiIiI/wiKPiIiIiIjIj/B2TSIiIiIiIj/CTh4REREREZEfYZFHRERERETkR1jkERERERER+REWeURERERERH6ERR4REREREZEfYZFHRERERETkR1jkERERERER+REWeURERERERH6ERR4REREREZEfYZFHRERERETkR1jkERERERER+REWeURERERERH6ERR4REREREZEfYZFHRERERETkR1jkERERERER+REWeURERERERH6ERR4REREREZEfYZFHRERERETkR1jkERERERER+REWeURERERERH6ERR4REREREZEfYZFHRERERETkR1jkERERERER+REWeURERERERH6ERR4REREREZEfYZFHRERERETkR1jkERERERER+REWeURERERERH6ERR4REREREZEfYZFHRERERETkR1jkERERERER+REWeURERERERH6ERR4REREREZEfYZFHRERERETkR1jkERERERER+REWeURERERERH6ERR4REREREZEfYZFHRERERETkR1jkERERERER+REWeURERERERH5E7u0BtAZlZdXNejybzYqKigqEh4dDJuMlvhrMoeuYQ9cwf65jDl3HHLqG+XMdc+g65tA1vpi/yMjQS76GnTw/ZLcLTv+nK8ccuo45dA3z5zrm0HXMoWuYP9cxh65jDl3TUvPHIo+IiIiIiMiPsMgjIiIiIiLyIyzyiIiIiIiI/AiLPCIiIiIiIj/CIo+IiIiIiMiPsMgjIiIiIiLyIyzyiIiIiIiI/IhvrOhHRERERETkIyrqzPihwIis/AqEB9gxY4jO20O6IizyiIiIiIioVbPZBeSVVGNTvgFZ+RXYXVyNxuXPFTIJnrnODoXCq0O8IizyiIiIiIio1SmvMyM734BN+QZsLjCiot7iFE/QqZAaE4LrYpRQylvWU24s8oiIiIiIyO/Z7AJ2nzyNrHwDsvINyD15tlsHAEEBUvSOUSM9To1B7cPQq00oFBI7jEYDpBKJ18Z9NVjkERERERGRXyqtdXTrsvIN+KHAAGOD1SneQadCul6NjDgNBifoEKsOdCroLBZ7cw/ZLVjkERERERGRX7DZBeQUO7p1m/IrkFdS4xQPDpChd2woMvQaDEzQoVebUAQFyLw0Ws9hkUdERERERC3WqRoTss/MhPlDgRGV53XrOoY5unWZcVoMStAhJlTZ4m6/vFIs8oiIiIiIqMWw2u34paixW2fAvlPO3boQhQy9Y0KREafBoAQdekaHQuWH3bo/wyKPiIiIiIh8Wkm1SSzqNh814LTJ5hTvFB4kPls3KCEMMSEKSPy8W/dnWOQREREREZFPsdgc3bpNZ56t+7W01ikeqpQhNdZR1A2M16FHdEir69b9mRax4IPZbMbw4cOxY8cOcVtxcTEef/xxJCUl4cYbb8TGjRud3rNhwwbccMMNSEpKwrhx42AwGMSYIAhYtGgR+vTpg4yMDCxYsAB2+9mZc4xGIyZMmICUlBQMGTIEX3zxhdO+Dxw4gHvuuQdJSUm46667sH//fg+dORERERFR63Cy2oQ1eScx+j/70XXJT7jjw1y8tv04fi2thQRA5/Ag/DWpDZbc2gU/PZaBtff2wjP92iM9TsMC7zw+38kzmUyYPHkyDh06JG6zWq144oknEBcXh//85z/YuXMnnn32WVxzzTXo3Lkz9u7di+nTp2POnDno2rUr5s6di6lTp+LNN98EALz33nvYsGEDli1bBqvViilTpiA8PBxjxowBAEydOhUNDQ1Yu3Yt8vLyMGPGDCQkJKBXr16oq6vD2LFjcdttt+Hll1/GRx99hCeeeAL/+9//EBQU5JUcERERERG1NBabHTtPVGHTmSUODpQ5d+vU53TrBiXokBgVgkA5i7nL4dNF3uHDhzF58mQIguC0ffPmzTh58iQ++ugjhISEoEOHDtiyZQv27NmDzp0744MPPsAtt9yCO++8EwCwYMECDB48GIWFhWjbti1Wr16NiRMnIi0tDQDwzDPPYMmSJRgzZgyOHz+O7OxsbNq0CXFxcejcuTNyc3Px4YcfolevXti4cSOUSiWeffZZSCQSTJ8+HVu2bMG3336LESNGNHeKiIiIiIhajKLTDeKzdVuOGlFjPvtsnQRAl4ggpOs1yIxzLHEQFdy6n627Wj5d5O3cuROZmZmYNGkSkpOTnbZfe+21CAkJEbetWLFC/HNeXh4ef/xx8euYmBjExsYiLy8PCoUCJ0+eRHp6uhhPTU1FUVERSktLkZeXh5iYGMTFxTnFG7uAeXl5SE1NFT9sEokEvXv3Rm5uLos8IiIiIqJzmG127Chs7NZV4GB5nVNcEyhHWmO3rr0O3aKC2a1zA58u8kaOHNnk9sLCQuj1eixatAhffPEFdDodJk6ciBtuuAEAUFpaiqioKKf3hIeHo6SkBGVlZQDgFI+IiAAAMd7Ue0+dOgUAKCsrwzXXXHNB/NzbSc9ns1lhtwsXjbub1WqB1WqF1WpptmP6G+bQdcyha5g/1zGHrmMOXcP8uY45dJ03clh02oSsAiOyC4zYerwStZazc19IJY5n69JiQ5GhV2NgvBbhQQFnu3WCHZZzXu9tLfUz6NNF3sXU1dXhP//5D2699Va88cYb2LFjByZOnIi1a9eiZ8+eaGhogEKhcHqPQqGA2WxGQ0OD+PW5McAxwUt9ff1F3wvgkvGmVFRUXP3JXgWr1Yrq6ioAgFzeIi+x1zGHrmMOXcP8uY45dB1z6Brmz3XMoeuaI4dmmx05JfXYWlSDrSdqcKTS+edijUKKpCgVkqNV6KcPQSedEgrZmfkfzTWovPiP0V7ni5/B2NiwS77GN0Z6hWQyGbRaLWbPng2pVIrExET88ssv+OSTT9CzZ08olcoLii6z2QyVSuVU0CmVSvHPAKBSqS763sDAQAC4ZLwp4eHhzd7JAwCdTge5PKDZjutPmEPXMYeuYf5cxxy6jjl0DfPnOubQdZ7KYWFVg9it+6mwCnXndeu6Rpzp1sWqMeD8bl0L0lI/gy2yyIuKioJEIoFUenYFiISEBPz+++8AgOjoaJSXlzu9p7y8HJGRkYiOjgbguO2y8bm7xls4G+MXe++f7fv8WzzPJZPJIWvmW4vlcjnk8gAEBLScD6OvYQ5dxxy6hvlzHXPoOubQNcyf65hD17kjhw1WG7YXnp0J81CF87N1YSo5UmPVyIzTYFCHMHQJD4ZS3iJWa7uklvgZbJFFXlJSEl5//XXYbDbIzlRPR44cgV6vF+M5OTniRCgnT57EyZMnkZSUhOjoaMTGxiInJ0cs8nJychAbG4uoqCgkJyejqKgIJSUlaNOmjRhvnPglKSkJb731FgRBgEQigSAI2L17N5588slmzgIRERERkeccrazHpiOOCVO2Hq9E/Xnduu6RIUjXq5HZVoPr2usQEaT4k71Rc2qRRd7w4cOxfPlyzJkzB2PGjMHWrVvx448/4pNPPgEAPPDAA3jooYeQnJyMnj17Yu7cuRg0aBDatm0rxhctWiQWcYsXL8bo0aMBAG3btkX//v0xZcoUTJ8+Hfv27cOGDRvwwQcfAABuvvlmLF68GHPnzsX999+Pjz/+GPX19bjlllu8kAkiIiIiIveot9jwc2ElNh1xLHGQb6x3ioerApCmd8yEOThBh84RwWefrSOf0iKLvJCQELz33nuYPXs2hg8fjtjYWLz66qtITEwEAKSkpOCFF17Aa6+9hqqqKvTr1w8vvvii+P4xY8agoqIC48ePh0wmw913341Ro0aJ8QULFmD69Om49957ERkZiXnz5qFXr17isd988008//zz+OSTT9ClSxesXLmSC6ETERERUYuTb6gT16376XglGqxnu3UyCZAY5ejW9WmrxYB4LcLYrWsRJML5K42T25WVVTfr8SwWC4xGA3S6sBZ177AvYQ5dxxy6hvlzHXPoOubQNcyf65hD152fwzqLDduOV4qFXcF53bqIoACk69VIj9NgSIIO14S37m6dL34GIyNDL/maFtnJIyIiIiKiSxMEAUerTFhfUIwfjlbi58Iqp26dXCpBYlQw0vUaXNtWi/7xGuhU7Na1dCzyiIiIiIj8SJ3Fhp+OVWJTfgU2HTHgWFWDUzwyOADpeg0y9I5n664JD0JAK+7W+SMWeURERERELZggCDhiqHcUdfkG/Hy8Eibb2SeyZBKgZ3SIo1vXTot+7dit83cs8oiIiIiIWphasw1bjxnFdeuOn9etiw5WIF2vRu+YEPQOkyI5PhpBgUovjZaaG4s8IiIiIiIfJwgCDlXUYdOZCVO2F1bCfE63LkAqQY/oEGTEadC3rQbXttVAq1KIE4fwdszWhUUeEREREZEPqjFZ8eOZZ+uy8w0oPG1yikeHOLp1GXEaDEkIQ4cwFeRSFnPEIo+IiIiIyCcIgoCD5bWOWzCPGLDjRBUsduduXa82IcjQa9C3nRZ94jTQqHxjWn/yLSzyiIiIiIi8pNpkxZajRnHduuJq525dbKjSsW7dmZkwE9ito8vAIo+IiIiIqJkIgoADZY3dugrsLDoN6zndOoVMgqQ2oUjXa9CvnRZ92moQquSP7HRl+IkhIiIiIvKg0w1WbD5qRFZ+BbIKDDhZbXaK69XKM+vWqTG4Qxjaa1WQSSVeGi35AxZ5RERERERuJAgC9pfWIOvM8ga7zuvWKWUSJMU4unX922mREcduHbkXP01ERERERC6qarBg81EjNh0xIKvAgFM1zt26tmol0uM0SNdrMCRBh3bs1pEHscgjIiIiIrpCgiBg/6kacd26X4qqcM6ydQiUS5HcJhTpcWr0j9chPVaNEHbrqJnwk0ZEREREdBkqGyz4ocCITUcqkF1gRGmtc7eunSYQ6XHqM926MLTVBLJbR17BIo+IiIiIqAl2QcC+UzXYdKQCm/INyCk+Dft53bqUM8/WDYjXIlWvRoiCP16T9/FTSERERER0hrHegh8KHLdgZuUbUF5ncYq31wYiTa9Bul6N6zuEIU4TCKmE3TryLSzyiIiIiKjVsgsC8kqqsemIo7Dbc9K5W6eSS9E7Vo10vRoD2muREsNuHfk+fkKJiIiIqFWpqDM7nq3LNyC7wICKJrp1GefMhKlnt45aGBZ5REREROTXbHYBuSXV2HSkAln5Buw5WY1zmnUICpAiJUaNjDg1BsRrkRKjQbBC5rXxErmKRR4RERER+Z3yOjOyzyxv8EOBAYZ6q1O8g06FdL0a6XGObl2smt068h8s8oiIiIioxbPZBew+edqxGHm+AXklzt264AAZesc6ZsIc2F6HpJhQBAWwW0f+iUUeEREREbVIpbWObl3WmW6dscG5W9cxTIV0vQYZcWoMas9uHbUeUm8P4HKYzWYMHz4cO3bsELe99NJL6NKli9N/H3zwgRjfsGEDbrjhBiQlJWHcuHEwGAxiTBAELFq0CH369EFGRgYWLFgAu90uxo1GIyZMmICUlBQMGTIEX3zxhdN4Dhw4gHvuuQdJSUm46667sH//fg+ePREREREBgNVux44TVZi/JR83vPcLeizdhglfH8R/fiuFscGK4AAZrmuvw+R+8fjqwWRsejQNrw3rigeTYhGnUbHAo1bD5zt5JpMJkydPxqFDh5y2HzlyBJMnT8Zf/vIXcVtISAgAYO/evZg+fTrmzJmDrl27Yu7cuZg6dSrefPNNAMB7772HDRs2YNmyZbBarZgyZQrCw8MxZswYAMDUqVPR0NCAtWvXIi8vDzNmzEBCQgJ69eqFuro6jB07FrfddhtefvllfPTRR3jiiSfwv//9D0FBQc2UFSIiIqLWobzOiv8WncLmY1X4ocCIKpNzt65TeJDj2Tq9BoM7hCEmRAEJizlq5Xy6yDt8+DAmT54MQRAuiB05cgRjxoxBZGTkBbEPPvgAt9xyC+68804AwIIFCzB48GAUFhaibdu2WL16NSZOnIi0tDQAwDPPPIMlS5ZgzJgxOH78OLKzs7Fp0ybExcWhc+fOyM3NxYcffohevXph48aNUCqVePbZZyGRSDB9+nRs2bIF3377LUaMGOHRfBARERH5O6vdjl+KTiMr34Dvj1Rgf2mtUzxUIUNqrGPClEHttegRHQoVn60jcuLTRd7OnTuRmZmJSZMmITk5WdxeU1ODU6dOoX379k2+Ly8vD48//rj4dUxMDGJjY5GXlweFQoGTJ08iPT1djKempqKoqAilpaXIy8tDTEwM4uLinOKNXcC8vDykpqaKvyGSSCTo3bs3cnNzWeQRERERXYWSahOyzsyEufmoAadNNqd4pzAVMtpqkaFXY1BCGNqwW0f0p3y6yBs5cmST248cOQKJRII33ngDW7ZsgVarxaOPPireullaWoqoqCin94SHh6OkpARlZWUA4BSPiIgAADHe1HtPnToFACgrK8M111xzQfz820nPZbNZYbdf2I30FKvVAqvVCqvVcukXU5OYQ9cxh65h/lzHHLqOOXQN83dxFpsdvxRX44ejRmQVVOJAWVPdulD0jg5Gkk6CPh3bICRQKcatVuv5u6SL4OfQNS01fz5d5F1Mfn4+JBIJOnTogAcffBC7du3CzJkzERISghtvvBENDQ1QKBRO71EoFDCbzWhoaBC/PjcGOCZ4qa+vv+h7AVwy3pSKioqrP9mrYLVaUV1dBQCQy1vkJfY65tB1zKFrmD/XMYeuYw5dw/w5K6m1YOuJGmw9UYvtxbWosZyd9E4CoKNWgeQoFZKiVOivD0FEkBw2mw3V1VWor66CpZ45vBr8HLrGF/MXGxt2ydf4xkiv0J133onBgwdDq9UCALp27YqjR4/io48+wo033gilUnlB0WU2m6FSqZwKOqVSKf4ZAFQq1UXfGxgYCACXjDclPDy82Tt5AKDT6SCXBzTbcf0Jc+g65tA1zJ/rmEPXMYeuae35M9vs2FV0GtkFlcg+asTB8jqnuFopQ2pMKNL0alzXToPuUSEIlDtP/N7ac+gOzKFrWmr+WmSRJ5FIxAKvUYcOHbB9+3YAQHR0NMrLy53i5eXliIyMRHR0NADHbZeNz9013sLZGL/Ye/9s3+ff4nkumUwOWTM/DyyXyyGXByAgoOV8GH0Nc+g65tA1zJ/rmEPXMYeuaW35O1HVgKwCAzYdMWDLMSNqzWefrZMA6BoZjHS9GhlxGgxK0CEy6NLP1rW2HHoCc+ialpi/FlnkLVmyBHv27MH7778vbjt48CA6dOgAAEhKSkJOTo44EcrJkydx8uRJJCUlITo6GrGxscjJyRGLvJycHMTGxiIqKgrJyckoKipCSUkJ2rRpI8YbJ35JSkrCW2+9BUEQIJFIIAgCdu/ejSeffLL5EkBERETkA0xWx7p1m/IrkJVvwO/ndeu0gXKk69VI02swOEGHbpEhUMpbxDLNRC1aiyzyBg8ejJUrV+Kdd97BjTfeiK1bt+Lzzz/H6tWrAQAPPPAAHnroISQnJ6Nnz56YO3cuBg0ahLZt24rxRYsWiUXc4sWLMXr0aABA27Zt0b9/f0yZMgXTp0/Hvn37sGHDBnGh9ZtvvhmLFy/G3Llzcf/99+Pjjz9GfX09brnlFi9kgoiIiKh5FVY1OIq6M926unOerZNKHN26DL0GmXo1rkvQIeIyunVE5F4tssjr1asXlixZgtdeew1LliyBXq/H4sWLkZKSAgBISUnBCy+8gNdeew1VVVXo168fXnzxRfH9Y8aMQUVFBcaPHw+ZTIa7774bo0aNEuMLFizA9OnTce+99yIyMhLz5s1Dr169ADgWXH/zzTfx/PPP45NPPkGXLl2wcuVKLoROREREfslktePnwkpsyjcgK9+AQxXO3bowlRxpeg3Szyxv0DUimN06Ii+TCE2tNE5uVVZW3azHs1gsMBoN0OnCWtS9w76EOXQdc+ga5s91zKHrmEPXtOT8HausdxR1Rwz48bgR9ed167pHhiA9To3MOA2ua69DuCrAI926lpxDX8EcusYX8xcZGXrJ17TITh4RERERuU+D1YafC6uQdcSATfkVOGyod4qHqQKQrlcjPc7xbF2XiGAoZOzWEfkqFnlERERErdDRynpsOlKBTfkGbD1WiQarc7cuMSoE6Xo1+sRpcV17LcKCFH+yNyLyJSzyiIiIiFqBeovN8WzdEQM25RuQb3Tu1oUHObp1GWdmwuzEbh1Ri8Uij4iIiMhP5RvqsCnfUdRtO+7crZM1duviNOgTp8GAeHbriPwFizwiIiIiP1FnsWHb8cZuXQWOVjY4xSODApAe55gJc3CCDp3CgxHAbh2R32GRR0RERNRCCYKAI4Z6ZOU7irptxythsp2dOF0ulaBHlGMmzGvbatGvnQY6Fbt1RP6ORR4RERFRC1JrtuGn40bHbZhHDDhe5dytiwpWOJ6ti9NgcEIYOoap2K0jamVY5BERERH5MEEQcKji7LN12wsrYT6nWxcglaBHdAjS9Rr0batB33YaaNmtI2rVWOQRERER+ZgasxVbj1WeWZC8AoWnTU7xNiEKpOs1SI9TY3BCGDro2K0jorNY5BERERF5mSAI+L28Tny2bseJqgu6dT3bhCBDr8G1bTXo21YLjSrAiyMmIl/GIo+IiIjIC2pMVmw55ni2LjvfgBNNdeviNMjQqzEkIQwJYSrIpezWEdGlscgjIiIiagaCIOBgee2ZWzAN2HGiCha7c7cuqU0o0uPU6NdOi0y9ht06IroqLPKIiIiIPKTGbMPPhyqw+VgVsvINKK527tbFhiqREadGml6DIQk6tNexW0dErmORR0REROQmgiDg19JaZBUYsOlwOXYVnYb1bLMOCpmjW5cRp3F06+I0CFXyxzEici/+rUJERETkgtMNVmw+6ljeICvfgJIas1NcH6pERpwG6Xo1hnQIQ7xWBZlU4qXRElFrwCKPiIiI6AoIgoD9pTWOmTCPGLCrqArnTIQJpUyC5Bg1UmNCkKSTYlCXWOiCVd4bMBG1OizyiIiIiC6hssGCzQVGZOUbkFVgwKnzunVtNYHI0KuRHqfBkIQwtNUEwm6zwmg0IETBH7eIqHnxbx0iIiKi89gFAftO1Yjr1uUUnXbq1gXKpUiOCUWGXoN+8Vqkx6oRct6zdXZbMw+aiOgMFnlEREREAIz1FvxQ4Hi2LrvAgLJai1O8nSYQ6XFqZOg1GNIhDHHqQD5bR0Q+iUUeERERtUp2QcDekmpsyncUdruLT+OcZeugkkuREhOK9DgNBrTTordezVsviahF4N9URERE1GoYGrt1RxzduvI6525de20g0vUapMepcX1CGPSaQEgl7NYRUcvCIo+IiIj8ls0uILekGpuOVCC7wIDdxdU4p1kHlVyK3rFqpOvVGNBei94xGgQrZF4bLxGRO7DIIyIiIr9SWmvGDwWONet+KDDAUG91iifoVEjXNz5bp0Osmt06IvIvUm8P4HKYzWYMHz4cO3bsuCBWXV2NAQMG4LPPPnPavmHDBtxwww1ISkrCuHHjYDAYxJggCFi0aBH69OmDjIwMLFiwAHa7XYwbjUZMmDABKSkpGDJkCL744gunfR84cAD33HMPkpKScNddd2H//v1uPmMiIiK6XFa7HTtOVGH+lnzc+P4v6LF0G8ZvOIjPDpTCUG9FcIAMA+K1eLpvPL4YmYzs0WlYNrwbHk6JRZxGxQKPiPyOz3fyTCYTJk+ejEOHDjUZX7hwIUpLS5227d27F9OnT8ecOXPQtWtXzJ07F1OnTsWbb74JAHjvvfewYcMGLFu2DFarFVOmTEF4eDjGjBkDAJg6dSoaGhqwdu1a5OXlYcaMGUhISECvXr1QV1eHsWPH4rbbbsPLL7+Mjz76CE888QT+97//ISgoyLPJICIiIgBASbVJXLNuc4ERVSbnbt01YSqkx2mQoddgYHstu3VE1Kr4dJF3+PBhTJ48GYIgNBn/5ZdfsH37dkRGRjpt/+CDD3DLLbfgzjvvBAAsWLAAgwcPRmFhIdq2bYvVq1dj4sSJSEtLAwA888wzWLJkCcaMGYPjx48jOzsbmzZtQlxcHDp37ozc3Fx8+OGH6NWrFzZu3AilUolnn30WEokE06dPx5YtW/Dtt99ixIgRHs0HERFRa2W22bHrRBWyzkyacqCs1ikeqpQhNVaNdL0Gg9pr0SM6FKoAPltHRK2TTxd5O3fuRGZmJiZNmoTk5GSnmNlsxsyZMzFr1izMmjXLKZaXl4fHH39c/DomJgaxsbHIy8uDQqHAyZMnkZ6eLsZTU1NRVFSE0tJS5OXlISYmBnFxcU7xxi5gXl4eUlNTITnz20CJRILevXsjNzf3okWezWaF3d50oeoJVqsFVqsVVqvl0i+mJjGHrmMOXcP8uY45dJ23c3jidAN+OFqJ7AIjth6vQo357OriEgCdwlVIi1UjLTYUg9prERWsEP99BuywWOxN7re5eDt//oA5dB1z6JqWmj+fLvJGjhx50dgbb7yB7t27o3///hfESktLERUV5bQtPDwcJSUlKCsrAwCneEREBACI8abee+rUKQBAWVkZrrnmmgviF7udFAAqKiouGvMEq9WK6uoqAIBc7tOX2Gcxh65jDl3D/LmOOXRdc+fQZLUj51Qdtp6oxdaiGuRXmp3iaoUUSVEqJEep0Fcfgs46JZTyM9MLWGpRWVnbxF69h59B1zGHrmMOXeOL+YuNDbvka3xjpFfo8OHD+Pjjj/Hll182GW9oaIBCoXDaplAoYDab0dDQIH59bgxwdAfr6+sv+l4Al4w3JTw8vNk7eQCg0+kglwc023H9CXPoOubQNcyf65hD1zVHDguM9fjhaCWyCozYVliFBuvZ7ptUAnSNCEJqjBoZ+lBcF69FeFDAOd0638bPoOuYQ9cxh65pqflrcUWeIAiYMWMGJk6cKHbgzqdUKi8ousxmM1QqlVNBp1QqxT8DgEqluuh7AwMD/3TfjfGmyGRyyJr5sQC5XA65PAABAS3nw+hrmEPXMYeuYf5cxxy6zt05rDXbsO14JbLyDdiUX4GjlQ1O8TBVANL1jnXrBnUIQ5fw4LPduhaIn0HXMYeuYw5d0xLz1+KKvOLiYuzZswe///47/vWvfwFwdNeef/55bNy4EW+//Taio6NRXl7u9L7y8nJERkYiOjoagOO2y8bn7hpv4WyMX+y9AC4aP/8WTyIiInL8cvaPijrHTJj5BmwvrITJdvbuFpkE6BEdgrRYDfq01WBAvBZhQYo/2SMREV1KiyvyoqOj8d///tdp20MPPYSHHnoIt99+OwAgKSkJOTk54kQoJ0+exMmTJ5GUlITo6GjExsYiJydHLPJycnIQGxuLqKgoJCcno6ioCCUlJWjTpo0Yb5z4JSkpCW+99RYEQYBEIoEgCNi9ezeefPLJZsoAERGRb6s2WbHlqBFZBQZk5xtw4rTJKR4VrDjTrdNgcAcdrgkLQoCs5XbriIh8TYsr8uRyOeLj4y/YFh4eLnbpHnjgATz00ENITk5Gz549MXfuXAwaNAht27YV44sWLRKLuMWLF2P06NEAgLZt26J///6YMmUKpk+fjn379mHDhg344IMPAAA333wzFi9ejLlz5+L+++/Hxx9/jPr6etxyyy3NlQIiIiKfIggC9pfWIPtMt25n0WlYz3kWPUAqQa82oUjTq9G3rQbXttVAq2K3jojIU1pckXc5UlJS8MILL+C1115DVVUV+vXrhxdffFGMjxkzBhUVFRg/fjxkMhnuvvtujBo1SowvWLAA06dPx7333ovIyEjMmzcPvXr1AgCEhITgzTffxPPPP49PPvkEXbp0wcqVK7kQOhERtSrGegs2HzWKt2GW1jo/r65XK5Gu1yBdr8bgBB3a61SQS9mtIyJqDhLhYiuNk9uUlVU36/EsFguMRgN0urAW9YCoL2EOXcccuob5cx1z6LpzcyiTy5F7stpR1BUYsLv4NM6dODpQLkVyTCjSYtXo106LjDgNQpV++bvky8bPoOuYQ9cxh67xxfxFRoZe8jVu+dv3oYceuuzpjFevXu2OQxIREZGHldeZ8fXhKuwqLcOWY1WoqHdeDLi9NhDpeg3S9GoM6RCGOHUgZNKWsbwBEZE/c0uRl5mZKf7ZaDRi7dq1uOGGG9CzZ08EBATgt99+w8aNG/HXv/7VHYcjIiIiD7Da7fil6DSyCxy3YOaV1DjFgwKkSI1VI02vwYB4DVJiNAhWNPMaQUREdEluKfLGjx8v/nnUqFGYNm0aRo4c6fSa9PR0rF271h2HIyIiIjcpPt2A7ALHs3Wbjxpw2mRziidoFMhoq0VmWy0GtdchVh0IaQtZjJyIqLVy+83yubm5eP755y/YnpSUhBdeeMHdhyMiIqIrYLLasfNEFTblG5BdYMBvZbVO8VClDGmxjuUN+saFIk5hRkxkhM88i0JERJfm9iKve/fuWLlyJWbPng2lUgkAqKmpwWuvvSauNUdERETN51hlvTgL5o/HjKiz2MWYBECXiGCk69XIiNNgYHsdokMUkEgk4oQDRETUsri9yHvxxRcxduxY9OvXD/Hx8RAEAUePHkVsbCzefPNNdx+OiIiIzlNvseHnwkpk5RuwKd+AI4Z6p7guUI60M4uRD0rQoVtkCJRyLm9AROQv3F7kdezYEd988w22bduGI0eOAAA6deqEvn37Qi5v3VMpExEReYIgCDhiqD9T1FXg58IqNFjPduukEiAxKkTs1l3XXodwVcBlz4xNREQti0eqLoVCgUGDBmHQoEGe2D0REVGrV2O2YuuxSvE2zONVDU7xyKAApMedWd6gvQ7XRARDIWO3joioNXB7kXfgwAG89NJL2LdvH6xW6wXx3377zd2HJCIi8nuCIOC3slpkFRiQnW/A9sIqWM5ZjTxAKkGP6BCk6zXo01aDfu000KkUXhwxERF5i9uLvGnTpiE0NBRLlixBSEiIu3dPRETUalQ1WLDlqGN5g6wCA05Wm53iMaEKpOs1SNerMSQhDAlhKsil7NYREbV2bi/y8vPz8dVXXyE+Pt7duyYiIvJrdkHAvlM14i2YvxRVwXa2WQelTIJebUKREadBv3ZaZMZpEKrk8+5EROTM7f8ydOvWDUeOHGGRR0REdBnK68z44cxi5NkFBlTUWZzi7TSBSNerkabXYEiCDu20KsiknDCFiIguzu1F3h133IEZM2ZgxIgRiI+Pv2Dx1DvvvNPdhyQiImoxrHY7dhdXi0Vd7slqnNOsg0ouRUpMKNLjNBjQToveejVCFOzWERHR5XP7vxpvv/02AgMDsXHjxgtiEomERR4REbU6p2pM4i2Ym48aUdngPDFZB50K6WfWrRvSQYdYdSCkXN6AiIiuktuLvKysLHfvkoiIqEUx2+z4peg0NuVXICvfgF9La53iIQoZUmPVSNOrcV28DkkxoQgKkHlptERE5G88cv9HdXU1vvzySxw9ehR/+9vfkJeXh2uuuQZt27b1xOGIiIi8rrCqQezW/XjMiBqzTYxJAHSOCEKaXoMMvRoDE8IQE6LgYuREROQRbi/y/vjjDzzyyCOIiYnBH3/8gYcffhj//e9/8fTTT+PNN99ERkaGuw9JRETU7BqsNmwvrMKmfMe6dX9U1DnFtYFypOnVSItVY2CCDolRIQiUs1tHRESe5/Yi76WXXsIDDzyAiRMnIiUlBQAwf/58hIWFYcGCBfj000/dfUgiIqJmkW+sQ9YRx5p1W49VosFqF2NSCdAtMhjpeg0y9Wpcl6BDRBC7dURE1PzcXuTt27cPL7300gXb77//fqxZs8bdhyMiIvKYWrMNPx03irdhHq1scIqHBwWIyxsMStChS3gwlHIuRk5ERN7l9iIvLCwMBQUFaNeundP23bt3Izw83N2HIyIichtBEPBHRR02nenWbS+shPmc1cjlUgl6RIUgPU6NTL0G/eO1CAtSeHHEREREF3J7kff4449jxowZePLJJyEIArZv347//Oc/WLVqFSZNmuTuwxEREbmk2mTF5qNGZOc7Crui0yaneJsQBdL1GqTp1RicEIaOYSoEyNitIyIi3+X2Iu/+++9HVFQU3nnnHQQGBmLBggVISEjAiy++iFtvvdXdhyMiIroidkHAr6dqkFXguAVzV9FpWO1nu3UKmQRJbUKRplejb1st+sRpoFEFeHHEREREV8Yji6EPHz7crc/fmc1mjBgxAjNnzkRmZiYA4Mcff8TChQtx9OhRtG/fHpMnT8bAgQPF92zbtg3z5s1DYWEhkpKSMHfuXKclHN5//3288847qKmpwS233IKZM2dCpVIBAEwmE+bMmYP//ve/CAwMxOjRozF69GjxvYWFhZg5cyZyc3MRGxuLadOmoX///m47XyIici9DvQWbzxR1WQUGlNVanOJxaqXYrRvSIQzx2kDIpezWERFRy+T2Iu+NN97ATTfd5Lb9mUwmTJ48GYcOHRK3HTt2DOPHj8ekSZNw/fXX4/vvv8e4cePw7bffIi4uDsXFxRg3bhwmTJiAAQMGYPny5Xjqqafw5ZdfQiKR4LvvvsOyZcuwcOFChIeHY+rUqVi4cCFmzZoFAFiwYAH279+PVatWobi4GM899xxiY2Nx8803QxAEjBs3Dp07d8b69evx/fffY/z48di4cSNiY2Pddt5ERHT1bHYBu09W45vfyvBzSSFyT9ZAOCceKJciOSYUGXoN+rXTIF2vQYjSI0vHEhERNTu3/4s2fPhwvP766xg7dixiY2OhUFz9A+mHDx/G5MmTIQiC0/aSkhLce++9GDVqFADg0Ucfxeuvv469e/ciLi4O69atQ48ePcTu2/z589GvXz/s3LkTmZmZWL16NR555BEMHjwYADBnzhyMGTMGU6ZMgSAIWLduHd566y0kJiYiMTERhw4dwpo1a3DzzTdj+/btKCwsxMcff4ygoCB07NgRP//8M9avX48JEyZc9bkSEZFrSmvNyM43ILvAgB8KDDDUW53iCTrVmZkw1RiSEIY4TSCkXN6AiIj8kNuLvC1btqC4uBj/+c9/LohJJBIcOHDgsvfVWJRNmjQJycnJ4vbMzEzxtk2LxYLPP/8cZrMZvXr1AgDk5eUhLS1NfL1KpUJiYiJyc3ORlpaGffv2Yfz48WI8OTkZFosFBw8ehCAIsFqt4hp/AJCamoo33ngDdrsdeXl56N69O4KCgpziubm5Fz0Pm80Ku124aNzdrFYLrFYrrFbLpV9MTWIOXcccuob5uzSrXUBO8WlkH61EdoER+0trneLBAVIkhivRp60OA9rrkBwTiqCAs4uR26xW2Jp70C0MP4euYf5cxxy6jjl0TUvNn0fuTVmxYgVCQkKctlVWVmLmzJlXtJ+RI0f+afzYsWO45ZZbYLPZMHnyZMTFxQEAysrKEBUV5fTa8PBwlJSU4PTp0zCZTE5xuVwOrVaLkpISSKVS6HQ6pw5kREQETCYTKisr/3TfF1NRUXHZ5+wOVqsV1dVVABznRleOOXQdc+ga5q9pJbUWbD1Rg5+KarG9uBbVZrtTvINGgaQoFZKjVOjTJhCBtlpo1MGQy+0w1VTBdJH9UtP4OXQN8+c65tB1zKFrfDF/sbFhl3yNW0a6Z88eHDt2DABw8uRJFBcXX1Dk5efnw2JxbwUcFhaGTz/9FHv27MHLL7+M+Ph43HTTTaivr7/gNlGFQgGz2YyGhgbx66bigiA0GQMcE8D82b4vJjw8vNk7eQCg0+kgl3NGuKvBHLqOOXQN8+dgstqxs+g0fjhqxA9HK3GwvM4prlbK0DsmFGmxoRgQr0ViZDBUZ7p1VqsFRqOx1efQFfwcuob5cx1z6Drm0DUtNX9uKfJUKhWWLl0KQRAgCALefvttSM+ZlUwikSAoKAjPPPOMOw4nCg0NRffu3dG9e3ccOXIEH3zwAW666SYolcoLii6z2Qy1Wg2lUil+fX5cpVLBZrM1GQOAwMBAKJVKVFZWXhAPDAy86DhlMjlksouGPUIul0MuD0BAQMv5MPoa5tB1zKFrWmv+jlbWIyvfgOx8A348ZkSd5Wy3TioBukYEI02vRoZeg4EJOkQFKyC5yLN1rTWH7sQcuob5cx1z6Drm0DUtMX9uKfK6du2KTZs2AQAeeughLFu2DBqNxh27btKhQ4dQVVXl9Nxdx44dsXPnTgBAdHQ0ysvLnd5TXl6Obt26QavVQqlUory8HB07dgTgaMNWVlYiMjISgiDAaDTCarWKLdmysjIEBgZCrVYjOjoahw8fvmDf59/CSUREl6fOYsPPxyuxKd+xxEG+sd4pHqaSIzVWg4w4NQYm6NAtIgRKOZc3ICIiuhi331j673//2927vEB2djY+++wzfPPNN+Jvb3/99Vd06NABAJCUlIScnBzx9fX19Thw4ADGjx8PqVSKnj17IicnR5y8JTc3F3K5HF27dgXgqNYbJ2kBgJycHPTs2RNSqRRJSUlYuXIlGhoaxO5dTk4OUlNTPX7eRET+QBAEHDbUYdMRx5p1Px+vhMl29pZ2mQRIjApBut5R2F3XXofwoKufqZmIiKi18Y2nB6/Q7bffjjfffBOLFi3CPffcg59++glffvkl1q5dCwC466678M4772DlypUYPHgwli9fjri4OLGoGzlyJGbNmoXOnTsjKioKs2fPxr333isuhn7nnXdi9uzZmDdvHkpLS/Huu+9i/vz5AICMjAzExMRg6tSpeOqpp5CdnY29e/eKcSIiulCNyYofj1ViU34FsvMNKDztPAVKZHAA0vUapOvVGNxeh04RwQiQsVtHRER0NVpkkdemTRu88847mDdvHj744APo9XosWbIEiYmJAIC4uDgsXboU8+bNw/Lly5GSkoLly5eLXb9hw4ahqKgIs2bNgtlsxtChQzFlyhRx/1OnTsXs2bPxyCOPICQkBBMmTMDQoUMBADKZDCtWrMD06dMxYsQIxMfHY/ny5VwInYjoHIIg4EBZLTadebZux4kqWM+ZgCpAKkHPNo5u3bVxGvRtp4FWxW4dERGRO0iE81caJ7crK6tu1uNZLBYYjQbodGEt6gFRX8Icuo45dE1LzF9lgwVbjhrF2zBP1ThPYhUbqkS6Xo10vQaDE3RICFNBLvVct64l5tDXMIeuYf5cxxy6jjl0jS/mLzIy9JKvaZGdPCIi8j67IGBvSTWy8g3YlG9ATvFpnLtaTKBciqQ2oUjXq9G3nRaZcRqEKvnPDhERkafxX1siIrps5XVm/FBgdCxxUGBARZ3z+qfx2kCknenWDUkIQ1tNIGTSppc3ICIiIs9gkUdERBdltduRU1yN7DPLG+SVVOPce/yDAqRIiVEjI06N/vFapMSoEaLgPy1ERETexH+JiYjIyclqk6OoKzBgc4ERVSarU7xjmArpeg3S9GoMSdAhVh0I6UUWIyciIqLmxyKPiKiVM9vs2HmiCllnunUHymqd4qEKGVJj1UiP0+C69lr0jA5FUIDMS6MlIiKiS2GRR0TUCh2vrEdWgaOo+/FYJWrNNjEmAdAlIghpjevWJYQhOkQhLkNDREREvo1FHhFRK9BgtWHb8SrxNsxDFXVOcW2gHOl6NdL0Ggxqr0O3qGAEytmtIyIiaolY5BER+SFBEJBvrBdvwfzpeCUarHYxLpUAiVEhSNOrkRGrxnUJOkQEsVtHRETkD1jkERH5iVqzDVuPGcXbMI9VNjjFI4ICkH7mFsxBCTp0jgiGQua5xciJiIjIO1jkERG1UIIg4GB5rdit23GiCmbb2QUO5FIJekaHIF2vRp+2WvRtq0FYkMKLIyYiIqLmwCKPiKgFOd1gxeajRmSf6dYVV5uc4m1CFEiPc3TrhiSEIUGnQgC7dURERK0KizwiIh9mFwTsLakWu3W7iqpwTrMOCpkESW1Cka7XoG87DTL1GmhUAd4bMBEREXkdizwiIh9TUWdG1uFyfPv7Kfx88jDK6yxO8bZqpWN5gzhHty5eq4JMyglTiIiIyIFFHhGRl9nsAvacPC126/acrMY5zToEyqVIiXF06/rHa5EWq0aIkn99ExERUdP4UwIRkRecqjEhu8CI7HwDfigwwNhgdYq31waiZ7gC/RIiceM1EdBrAiHl8gZERER0GVjkERE1A4vNjl+KTiOrwIBNRwzYX1rjFA8OkCFVr0a6Xo3r4nVIjAhEQ00VdLowBATwGTsiIiK6fCzyiIg8pOh0A7LyDdiUb8CWo0bUmG1O8c7hQUjXq5Gm12BwhzDEhJxdjNxisaChqZ0SERERXQKLPCIiNzFZ7dh+olJ8tu738jqnuEYpR5pejTS9GgPb65AYFQJVgMxLoyUiIiJ/xSKPiMgFBcZ6ZOUbkJ1vwI/Hjai32MWYVAJ0jQxGut6xbt2gBB0ig85264iIiIg8gUUeEdEVqLPYsO14pXgbZoGx3ikepgpAul6N9DgNBiXo0CU8GEo5FyMnIiKi5sMij4joTwiCgEMVdWJRt72wEqZzViOXSYAe0SFI02vQJ06DAfFahAUpvDhiIiIiau1Y5BERnafaZMWWo0ZkFThuwzxx2uQUjwpWOLp1eg0Gd9DhmrAgBMjYrSMiIiLf0CJ+KjGbzRg+fDh27NghbsvNzcX999+PlJQU3HTTTVi3bp3Te7Zt24bhw4cjKSkJDz/8MAoLC53i77//PgYMGICUlBRMmzYN9fVnb7kymUyYNm0a0tLS0L9/f7z77rtO7y0sLMSoUaOQnJyMW2+9FVu3bvXAWRNRcxEEAftP1eC1n4/hzjV70GXJT3j0P7/i37knceK0CQFSCVJj1XgyPQ6rRiRi62NpeG9EDzyV2RbdIkNY4BEREZFP8flOnslkwuTJk3Ho0CFxW1lZGR5//HE88MADePnll/Hrr79i6tSpiIyMxKBBg1BcXIxx48ZhwoQJGDBgAJYvX46nnnoKX375JSQSCb777jssW7YMCxcuRHh4OKZOnYqFCxdi1qxZAIAFCxZg//79WLVqFYqLi/Hcc88hNjYWN998MwRBwLhx49C5c2esX78e33//PcaPH4+NGzciNjbWW2kioitkrLdg81GjOBNmaa3ZKa5XK8UJUwYn6NBep4JcymKOiIiIfJ9PF3mHDx/G5MmTIQiC0/bvv/8eERERePrppwEA7du3x44dO/DVV19h0KBBWLduHXr06IHRo0cDAObPn49+/fph586dyMzMxOrVq/HII49g8ODBAIA5c+ZgzJgxmDJlCgRBwLp16/DWW28hMTERiYmJOHToENasWYObb74Z27dvR2FhIT7++GMEBQWhY8eO+Pnnn7F+/XpMmDCheRNERJfNLgjIK6nGpiMGZBUYsLv4NOzn/NUSKJciOSYU6Xo1+rXTIl2vQajSp/+KJCIiImqST/8E01iUTZo0CcnJyeL2AQMGoFu3bhe8vqamBgCQl5eHtLQ0cbtKpUJiYiJyc3ORlpaGffv2Yfz48WI8OTkZFosFBw8ehCAIsFqtSElJEeOpqal44403YLfbkZeXh+7duyMoKMgpnpub68YzJyJ3KKs1I7vA0anbXGBERb3FKd5eG4h0vQZpejWGdAhDnDoQMimXNyAiIqKWzaeLvJEjRza5PS4uDnFxceLXFRUV+Prrr8VOWllZGaKiopzeEx4ejpKSEpw+fRomk8kpLpfLodVqUVJSAqlUCp1OB4Xi7Ox4ERERMJlMqKys/NN9X4zNZoXdLlw07m5WqwVWqxVWq+XSL6YmMYeu80YOrXYBu4urkX3UiB+OGrH3VK1TPChAipSYUKTFhKJ/Ow2S2oQiWHF2MXK7zQq7rdmG+6f4GXQdc+g65tA1zJ/rmEPXMYeuaan58+ki73I0NDRgwoQJiIiIwH333QcAqK+vdyrSAEChUMBsNqOhoUH8uqm4IAhNxgDHBDB/tu+LqaiouLqTu0pWqxXV1VUAHAUsXTnm0HXNlcNTtRZsLarFTydq8HNxLarNdqd4e40CKVEqJEWp0F8fgqhgOaQSCQA7zLVVMNc2vV9v42fQdcyh65hD1zB/rmMOXcccusYX8xcbG3bJ1/jGSK9SbW0tnnrqKRw9ehQffvghVCoVAECpVF5QdJnNZqjVaiiVSvHr8+MqlQo2m63JGAAEBgZCqVSisrLygnhgYOBFxxkeHt7snTwA0Ol0kMsDmu24/oQ5dJ2ncmiy2rGr+DR+KKhE9lEjDpbXOcVDFTL0jglFmj4UA9pp0SMqGKoA2UX25rv4GXQdc+g65tA1zJ/rmEPXMYeuaan5a7FFXk1NDR577DEcP34cq1atQvv27cVYdHQ0ysvLnV5fXl6Obt26QavVQqlUory8HB07dgTgqNArKysRGRkJQRBgNBphtVrFar2srAyBgYFQq9WIjo7G4cOHL9j3+bdwnksmk0PWzD9jyuVyyOUBCAhoOR9GX8Mcus5dOTxWWS/OgvnjMSPqLGe7dRIAXSKCHDNhxmkwqL0O0SEKSCQt/9k6fgZdxxy6jjl0DfPnOubQdcyha1pi/lpkkWe32zF+/HicOHEC//73v8VirVFSUhJycnLEr+vr63HgwAGMHz8eUqkUPXv2RE5ODjIzMwE41tyTy+Xo2rUrAMeFbJykBQBycnLQs2dPSKVSJCUlYeXKlWhoaBC7dzk5OUhNTW2OUydqFeotNvxcWImsfAM25RtwxFDvFNcGypGuVyNNr8HgBB26RYZAKefyBkRERERACy3yPv30U+zYsQOvv/461Go1ysrKAAABAQHQarW466678M4772DlypUYPHgwli9fjri4OLGoGzlyJGbNmoXOnTsjKioKs2fPxr333ive7nnnnXdi9uzZmDdvHkpLS/Huu+9i/vz5AICMjAzExMRg6tSpeOqpp5CdnY29e/eKcSK6coIg4IjhTLeuwIBtxyvRYD3brZNKgO6RIUiPUyMzToPr2usQrgrwi24dERERkbu1yCLvu+++g91uxxNPPOG0PSMjA//+978RFxeHpUuXYt68eVi+fDlSUlKwfPly8QfCYcOGoaioCLNmzYLZbMbQoUMxZcoUcT9Tp07F7Nmz8cgjjyAkJAQTJkzA0KFDAQAymQwrVqzA9OnTMWLECMTHx2P58uVcCJ3oCtWYrdh6rFK8DfN4VYNTPDIoAOlxZ5Y3aK/DNRHBUMjYrSMiIiK6FIlw/krj5HZlZdXNejyLxQKj0QCdLqxF3TvsS5hD152fQ0EQ8FtZLbIKDMjON2B7YRUs50xIFCCVoEd0CNL1GvRpq0G/dhroVIo/OYJ/42fQdcyh65hD1zB/rmMOXcccusYX8xcZGXrJ17TITh4RtQynTTb89Ec5thyrQlaBASernWeujQlVOCZM0asxJCEMCWEqyKXs1hERERG5gkUeEbmNXRCw71QNsvIN+P5wOXafrIbtnHsFlDIJkmJCkRbr6NRlxmmhDuRfQ0RERETuxJ+uiMgl5XVmbC4wYlO+AdkFBlTUWZzibTVKZOg1SNNrMCRBh3ZaFWRSTphCRERE5Cks8ojoitjsAnafPI1NRxxFXe7Japz7YK9KLkVKTChSY0LRK0yCgZ300AYHem28RERERK0NizwiuqRTNSZxFszNR42obLA6xTvoVEjXq5Ee5+jWxaoDYbNaYTQaEKyQeWnURERERK0TizwiuoDFZseuotPYlF+BrHwDfi2tdYqHKGRIjVUjXa/GgHgdkmJCERTgXMzZmnPARERERCRikUdEAIATVQ3IKjBg0xEDfjxmRI35bJkmAdApPAjpcRpk6NUYmBCGmBAFFyMnIiIi8kEs8ohaqQarDdsLq8TbMP+oqHOKawPlYrfuugQdekSFIFDOWy+JiIiIfB2LPKJWJN9Yh+x8AzblG7D1WCUarHYxJpUA3SKDka5v7NbpEBHEbh0RERFRS8Mij8iP1Zpt2Ha8Uny27mhlg1M8PCgA6Xo10vQaDErQoUt4MJRyLkZORERE1JKxyCPyI4Ig4I+KOmSd6dZtL6yE+ZzVyOVSCXpEhSBNr0ZmnAYD4rUIC1J4ccRERERE5G4s8ohauGqTFZuPGpGdb0BWgQFFp01O8ehgBdLj1EjXazA4IQwdw1QIkLFbR0REROSvWOQRtTB2QcCvp2qQVeCYMGVX0WlY7We7dQFSCXq1CUWGXo2+7bToE6eBRhXgxRETERERUXNikUfUAhjqLdh8pqjLKjCgrNbiFI9TK5F2ZsKUwQk6xOtUkEvZrSMiIiJqjVjkEfkgm11Abkm1uLzBnpOncU6zDoFyKZLbhCI9To3+7bRI12sQouS3MxERERGxyCPyGaW1ZmTnG5BdYMAPBQYY6q1O8QSd6sxMmGoMSQiDXh0ImZTLGxARERGRMxZ5RF5isdmRU3xanAlz36kap3hwgAy9Y0ORrnfMgpkco0awgouRExEREdGfY5FH1IyKTzeIE6ZsLjCi2mxzincKD0K63jET5qAEHWJClZByMXIiIiIiugIs8og8yGS1Y8eJKmSduQ3zt7Jap7haKUNqrKOoG9hehx7RIVAFsFtHRERERFePRR6Rmx2trHcUdfkG/HjMiDqLXYxJAHSNDEa6Xo0MvQYDE3SIClZAwm4dEREREbkJizwiF9VZbPj5eCU2nZkJM99Y7xTXBcqRptcgXa/GoA46dIsIgVLO5Q2IiIiIyDNY5BFdIUEQcNhQh01HHGvW/Xy8Eibb2fUNZBKge1QIMvQaZMSpMaC9DhFBCi+OmIiIiIhakxbTTjCbzRg+fDh27NjhtP3YsWPo1avXBa/ftm0bhg8fjqSkJDz88MMoLCx0ir///vsYMGAAUlJSMG3aNNTXn+2+mEwmTJs2DWlpaejfvz/effddp/cWFhZi1KhRSE5Oxq233oqtW7e68UzJF9WYrPjmj3I88+3vSHt9O/q9tQuzso7ghwIjTDYBkcEBuLVzBGYN7oBNo1LxzcO9MX9oJ/ylezQLPCIiIiJqVi2ik2cymTB58mQcOnTIafvJkyfxxBNPwGQyOW0vLi7GuHHjMGHCBAwYMADLly/HU089hS+//BISiQTfffcdli1bhoULFyI8PBxTp07FwoULMWvWLADAggULsH//fqxatQrFxcV47rnnEBsbi5tvvhmCIGDcuHHo3Lkz1q9fj++//x7jx4/Hxo0bERsb22w5Ic8SBAG/Gxqw+9AJ/HCsCjtOVMF6zmrkAVIJekaHID1Og2vjNOjbTgOtisUcEREREXmfzxd5hw8fxuTJkyEIgtP277//HjNnzkRkZOQF71m3bh169OiB0aNHAwDmz5+Pfv36YefOncjMzMTq1avxyCOPYPDgwQCAOXPmYMyYMZgyZQoEQcC6devw1ltvITExEYmJiTh06BDWrFmDm2++Gdu3b0dhYSE+/vhjBAUFoWPHjvj555+xfv16TJgwwfMJIY+pbLBgy1Ejss48W1dSY3aKx4YqnRYjTwhTQS5tMc1wIiIiImolfL7IayzMJk2ahOTkZHH7Dz/8gL///e9ISEjAww8/7PSevLw8pKWliV+rVCokJiYiNzcXaWlp2LdvH8aPHy/Gk5OTYbFYcPDgQQiCAKvVipSUFDGempqKN954A3a7HXl5eejevTuCgoKc4rm5ue4/efIouyBgb0m1uBh5TvFpnNOsg0ImQXKbEKTHadGvnRaZcRqEKn3+W4aIiIiIWjmf/4l15MiRTW5/6aWXAOCCZ/QAoKysDFFRUU7bwsPDUVJSgtOnT8NkMjnF5XI5tFotSkpKIJVKodPpoFCcvfUuIiICJpMJlZWVf7rvi7HZrLDbhYvG3c1qtcBqtcJqtTTbMVuKijoLNh8zIrugEpuPGlFRb3WKt9MokRarRkp0EJJ1QGJcFJTiZ0GAxcKcXi5+Dl3D/LmOOXQdc+ga5s91zKHrmEPXtNT8+XyRdzXq6+udijQAUCgUMJvNaGhoEL9uKi4IQpMxwDH5y5/t+2IqKiqu+lyuhtVqRXV1FQBHAduaWe0C9pXV46eiWmw9UYNfyxtwbrmtkkvQI0KF5CgVMmOD0StShaAAqZjD01WVrT6HV4ufQ9cwf65jDl3HHLqG+XMdc+g65tA1vpi/2NiwS77GN0bqZkql8oKiy2w2Q61WQ6lUil+fH1epVLDZbE3GACAwMBBKpRKVlZUXxAMDAy86nvDw8Gbv5AGATqeDXB7QbMf1FSU1Jmw+WonsAiO2HKtClcm5W9dBF4i0WDVSY0MxKF6LWLUS0vMWI2/tOXQH5tA1zJ/rmEPXMYeuYf5cxxy6jjl0TUvNn18WedHR0SgvL3faVl5ejm7dukGr1UKpVKK8vBwdO3YE4KjQKysrERkZCUEQYDQaYbVaxWq9rKwMgYGBUKvViI6OxuHDhy/Y9/m3cJ5LJpNDJnPzSV6CXC6HXB6AgICW82G8WmabHbtOVImLkR8oq3WKhypkSI11TJgyMEGHntGhCAq49AVpTTn0FObQNcyf65hD1zGHrmH+XMccuo45dE1LzJ9fFnlJSUnIyckRv66vr8eBAwcwfvx4SKVS9OzZEzk5OcjMzAQA5ObmQi6Xo2vXrgAcF7JxkhYAyMnJQc+ePSGVSpGUlISVK1eioaFB7N7l5OQgNTW1mc+ydTteWY+sAkdR9+OxStSabWJMAqBLRBDS9Bqk69UYlBCGNiEKSM7r1hERERER+SO/LPLuuusuvPPOO1i5ciUGDx6M5cuXIy4uTizqRo4ciVmzZqFz586IiorC7Nmzce+990KlUgEA7rzzTsyePRvz5s1DaWkp3n33XcyfPx8AkJGRgZiYGEydOhVPPfUUsrOzsXfvXjFOntFgteHnwipkHTEgq8CAQxV1TnFtoBxpejXS9RoMbK9F96gQBMqbuX1KREREROQD/LLIi4uLw9KlSzFv3jwsX74cKSkpWL58udjJGTZsGIqKijBr1iyYzWYMHToUU6ZMEd8/depUzJ49G4888ghCQkIwYcIEDB06FAAgk8mwYsUKTJ8+HSNGjEB8fDyWL1/OhdDdTBAEFBjrxVswfzpeiQarXYxLJUD3yBCk6dXI1KtxXYIOEUHs1hERERERSYTzVxkntysrq27W41ksFhiNBuh0YS3q3uFasw0/HTeKhd2xyganeERQgNitG5ygQ+eIYChknlmMvKXm0Jcwh65h/lzHHLqOOXQN8+c65tB1zKFrfDF/kZGhl3yNX3byqGUQBAG/l9dhU34FsvIN2HGiCmbb2d85yKUS9IgOQbpejT5xGvSP10KnUvzJHomIiIiIiEUeNavTDVZsOWZE1pluXXG1ySneJkQhduuGdAhDB50KAR7q1hERERER+SMWeeRRdkHAr6dqxFswdxVV4ZxmHRQyCZLahCJNr0a/dlpk6jXQqHyjFU5ERERE1BKxyCO3M9RbsLnAgE35BmQXGFBWa3GKt1UrHcsbxKkxJCEM8VoVZFJOmEJERERE5A4s8shlNruAPSdPI+tMUbe7uBrnzuYTKJciJSYU6XrHc3VpsWqEKPnRIyIiIiLyBP6kTVflVI0J2QVGZOcb8EOBAcYGq1M8QadCul6NNL0a1yeEQa8JhJTLGxAREREReRyLPLosFpsdvxSdRlaB49m6fadqnOIhChl6x6qRrlfjungderUJRbCCi5ETERERETU3Fnl0UUWnG8RZMLccNaLabHOKdw4POrtuXYcwxIRwMXIiIiIiIm9jkUcik9WO7ScqHc/W5RtwsLzOKa5WyhwTpujVGNheh8SoEKgC2K0jIiIiIvIlLPJauQJjvVjU/XjciHqLXYxJJUDXiGCkx2mQHqvGwAQdooLZrSMiIiIi8mUs8lqZeosNPx13dOs25RtQYKx3ioepApCudzxbNzAhDF0jgqGUczFyIiIiIqKWgkVeK3Pz6t34raxW/FomAXpEhyBNr0GGXo3r2usQHqTw4giJiIiIiMgVLPJaEUEQEB2sQGWDBb1jHBOmDErQolN4MAJk7NYREREREfkDFnmtiEQiwSf3J8Fqt0MuZVFHREREROSP+JN+K8QCj4iIiIjIf/GnfSIiIiIiIj/CIo+IiIiIiMiPsMgjIiIiIiLyIyzyiIiIiIiI/AiLPCIiIiIiIj/CIo+IiIiIiMiPsMgjIiIiIiLyIxJBEARvD4KIiIiIiIjcg508IiIiIiIiP8Iij4iIiIiIyI+wyCMiIiIiIvIjLPKIiIiIiIj8CIs8IiIiIiIiP8Iiz8edOnUKEydOREZGBgYMGID58+fDZDIBAAoLCzFq1CgkJyfj1ltvxdatW5vcx5dffomHHnrIaVtVVRW6dOni9F9mZqbHz8cbPJVDAFizZg0GDRqE3r17Y+LEiaisrPTkqXiNJ3J44sSJCz6Djf/t2rWrWc6ruXjqM2gymfDiiy/i2muvxbXXXotZs2ahrq7O4+fjDZ7KocViwcKFC9G/f3/06dMH//rXv2C1Wj1+Pt7gSg7Xr1+Pm2++GSkpKbjnnnuQk5PjFH///fcxYMAApKSkYNq0aaivr2+282ounsxfoxkzZmDp0qUePxdv8VQOzWYz/vWvf+G6665Deno6xo0bh5KSkmY9t+biqRzW1dVhxowZyMzMRHp6OmbOnIna2tpmPbfm0Bzfx2+//TaGDBni8XO5JIF8lt1uF+69917hscceE/744w9h165dwo033ii8/PLLgt1uF2677TZh8uTJwuHDh4U33nhDSEpKEoqKipz28fPPPwtJSUnCgw8+6LT9l19+ETIyMoTS0lLxv/Ly8uY8vWbhyRx+/fXXQq9evYRvv/1W+P3334W7775bmDRpUnOeXrPwVA6tVqvT56+0tFR4+umnhbvuukswm83NfZoe48nP4KJFi4Thw4cLe/fuFfLy8oRbbrlFePHFF5vz9JqFp3PYt29f4YcffhD2798v3HHHHczheTncvHmz0KtXL+GLL74Qjh49Krz66qtC7969hZKSEkEQBOHbb78VUlNThaysLCEvL0+49dZbhTlz5njzdN3Ok/lrtHLlSqFz587Ca6+95o1T9DhP5nDhwoXCDTfcIOzYsUM4dOiQMHbsWOGuu+4S7Ha7N0/Z7TyZw+nTpwt33HGHsG/fPmH//v3C7bffLsyYMcObp+t2zfF9fPz4cSEpKUkYPHiwN07RCYs8H3b48GGhc+fOQllZmbjtq6++Evr37y9s27ZNSE5OFmpra8XYI4884vSPw9KlS4UePXoIw4cPv+AHm08++US47777PH8SXubJHN55553C0qVLxa937twpDBs2TLBarR48o+bnyRyeKycnR0hMTBQOHz7smRPxEk/m77bbbhP+/e9/i1+vXr1aGDZsmAfPxjs8lUO73S6kpKQIn376qbgtNzdXSExMFGpqajx8Vs3LlRz+4x//EGbNmuW0v6FDhwpr164VBEEQRo4c6ZTvXbt2Cb169RLq6uo8eUrNypP5q66uFiZMmCCkp6cLAwcO9Nsiz5M57Nu3r/D111+LsVOnTgmdO3cWCgoKPHhGzc+TOZw9e7bwyy+/iLFVq1YJt9xyiydPp9l5Mn+NHn30UeH+++/3iSKPt2v6sMjISLz99tuIiIhw2l5TU4O8vDx0794dQUFB4vbU1FTk5uaKX//000945513MHTo0Av2ffjwYbRv395TQ/cZnsphTU0NDhw4gBtvvFHclp6ejg0bNkAmk3nmZLzEk5/Dcy1evBj33nsvOnbs6Nbxe5sn86fVavHdd9+hqqoKVVVV+O9//4tu3bp57Fy8xVM5NBgMqK2tRVJSkritS5cusFgs2L9/v2dOxktcyeFjjz2GRx999IJ9VldXw2azYd++fUhLSxO3Jycnw2Kx4ODBg545GS/wVP4Ax63rJpMJn332Gdq2beu5k/AyT+XQbrdj4cKF6Nu3b5Nxf+LJz+Hzzz+P1NRUAI7P5IYNG5CRkeGhM/EOT+YPAD7//HPU19fj7rvv9swJXCG5twdAF6dWqzFgwADxa7vdjg8++AB9+vRBWVkZoqKinF4fHh7udA/6Rx99BADYsWPHBfs+cuQIrFYr7r77bpw6dQppaWmYOnXqBfts6TyVw8LCQgCOHxLvv/9+nDhxAv369cP06dOhVqs9dTpe4cnPYaOcnBzk5ubilVdecfPovc+T+Xv22WcxYcIE8Xnazp074/XXX/fEaXiVp3Ko0WgQEBCAU6dO4ZprrgEAnDx5EgBgNBo9ci7e4koOExMTnWJbtmzB0aNH0adPH5w+fRomk8np/XK5HFqt1q+eifJU/gCga9euePPNNz18Bt7nqRxKpdILCrzVq1dDp9OhS5cuHjob7/Dk57DRc889h88//xx6vR7jxo3z0Jl4hyfzZzAYsGjRIrz33nvYt2+fh8/k8rCT14IsXLgQBw4cwKRJk1BfXw+FQuEUVygUMJvNl7Wv/Px81NTUYOrUqXj11VdRWlqKJ598EjabzRND9xnuymHjw8gvvPACHn/8cSxZsgSHDh3Cs88+65Fx+xJ3fg4bffLJJ7jxxhsRHR3tzqH6JHfm7/jx44iJicGqVavwzjvvwGQy4eWXX/bEsH2Ku3Iol8tx44034pVXXkFJSQmqq6vxr3/9C3K5HBaLxVPD9wlXm8Pjx49j6tSpuO2225CYmIiGhgbx9Zfzfn/hrvy1Zp7K4ffff493330XkydPvmCf/sYTOXz88cexdu1a6PV6PP7447Db7R49B29yZ/7mzZuHv/zlL+jUqVOzjP1ysMhrIRYuXIhVq1Zh4cKF6Ny5M5RK5QUfPLPZjMDAwMva39dff40PPvgAqampSEtLw2uvvYbffvsNeXl5nhi+T3BnDuVyRxN87NixuP7665Gamoq5c+ciOzsbp06d8sj4fYG7P4cAYLVasWnTJtx+++3uHq7PcWf+ampqMH36dDz33HPIzMxEv379MG/ePKxfvx6lpaWeOgWvc/dncMaMGQgODsbAgQNx3XXXoXfv3tBoNAgJCfHE8H3C1eawoKAADz/8MNq2bYuXXnoJAKBUKsXXn/9+lUrlwbPwHnfmr7XyVA6///57/OMf/8CDDz6Ie+65x6Pn4G2eyuE111yD5ORkvPrqq/j999/9brbrRu7M348//ojc3Fyf63yyyGsBXnzxRbz33ntYuHAhbrrpJgBAdHQ0ysvLnV5XXl5+2bdbqlQqpw9ueHg4tFqt3xYo7s5hZGQkAKBDhw7itoSEBADwq1uUzuWJzyEA5Obmwmq1ol+/fm4dr69xd/7y8/NRV1eHrl27itu6d+8Ou93Oz+AVfAbDw8OxevVq7NixA9u2bcODDz6IiooK6PV6t4/fF1xtDg8dOoQHH3wQbdq0wdtvvy3++6HVaqFUKp3eb7VaUVlZKf496U/cnb/WyFM5/Prrr/H3v/8d9913H6ZNm+b5E/Eid+fQbDbju+++Q01NjfjaiIgIaLVav7t1HXB//jZu3IiSkhJce+21SElJwfPPP4/i4mKkpKTgl19+ab4TOw+LPB+3bNkyfPzxx3jllVcwbNgwcXtSUhJ+/fVX8VYZwPFc07kTCFxMTU0N0tPTsX37dnHbqVOnYDQanYoWf+GJHMbGxiIqKsppYoEjR45AIpEgNjbWvSfgAzyRw0Z5eXlITEwUOwL+yBP5a/yH5/Dhw+K2/Px8AEBcXJy7hu4zPPUZnDJlCrZu3QqtVguVSoXNmzcjPDxcfEbPn1xtDktLSzF69GjEx8fjnXfecepySqVS9OzZ02m9qNzcXMjlcqdfQPgDT+SvtfFUDn/++Wc8++yz+Otf/4qZM2c2z8l4iae+j//5z3/ihx9+ELcVFxfDaDT63WRonsjfM888g6+//hqff/45Pv/8c0ycOBFRUVH4/PPP0aNHj+Y7ufNw4hUfduTIEaxYsQJjx45FamoqysrKxFhGRgZiYmIwdepUPPXUU8jOzsbevXsxf/78S+43JCQEqampmD9/Pl588UXIZDLMnTsXAwYM8LuHlD2VQ4lEglGjRuG1115DXFwcwsPDMXv2bNxwww1+99trT+Ww0aFDh/zuH5FzeSp/bdq0wYABAzBz5ky88MILEAQBzz//PIYNG4awsDBPnlKz8+RnUKvV4tVXX0VUVBSMRiNefPFFjB07FlKpf/0O1JUc/utf/4LdbsfcuXNRV1eHuro6AEBQUBCCg4MxcuRIzJo1C507d0ZUVBRmz56Ne++9169u1/Rk/loLT+VQqVRi2rRpSE9Px+OPP+60X41G41fP5Xnyc3jffffhlVdeQZs2bRAYGIgXX3wR119/vU89Y+YqT+UvPDwc4eHh4r7Cw8Mhl8sRHx/fvCd4HokgCIJXR0AXtXLlSixevLjJ2O+//45jx45h+vTpyMvLQ3x8PKZNm9bkFMJLly7Fzp078e9//1vcVlVVhZdffhnZ2dkwm824/vrrMWPGDGg0Go+djzd4MoeCIOD111/HmjVrUFdXhyFDhmD27NkIDQ312Pl4gydzCDimJe7WrRsmT57skfF7W3N8H2/evBkSiQTXX389nnvuOb/7wdGTOaytrcWcOXOQlZWFoKAgPPjggxg7dqzHzsVbrjaHgiAgOTnZ6bfbjcaPH48JEyaI+3///fdhNpsxdOhQPP/8837Vnfd0/ho99NBDyMjIuGC7P/BUDgcMGID77ruvyf2uXr1anH3YH3jyc2g2m/Hqq6/iq6++Ql1dHYYOHYoZM2b4Vee5ub6PP/vsMyxbtgxZWVkeOY/LxSKPiIiIiIjIj/jX/ShEREREREStHIs8IiIiIiIiP8Iij4iIiIiIyI+wyCMiIiIiIvIjLPKIiIiIiIj8CIs8IiIiIiIiP8Iij4iIiIiIyI+wyCMiIiIiIvIjLPKIiIi8bOnSpXjooYe8PQwiIvITLPKIiIiIiIj8CIs8IiIiIiIiP8Iij4iIqJkdPnwYDzzwAJKSkvDwww/DaDQCACwWC2bMmIHMzEykpKTgySefxKlTp7w8WiIiamlY5BERETUjs9mMsWPHom3btvjss89w0003Ye3atQCANWvWYNeuXXj33Xfx6aefora2FvPmzfPyiImIqKWRe3sARERErcm2bdtQWVmJ2bNnIygoCB07dsTOnTthMBhw4sQJKJVK6PV6aLVavPzyy6isrPT2kImIqIVhJ4+IiKgZHT58GO3bt0dQUJC4rWfPngCA++67D2VlZejfvz9Gjx6NzZs3o2PHjt4aKhERtVAs8oiIiJqZIAhOXwcEBAAAOnXqhKysLCxcuBCRkZF45ZVXMHr06AteT0RE9Gd4uyYREVEz6tSpE44ePYrq6mqEhoYCAH777TcAwOeffw6FQoFbb70Vt9xyC3Jzc3HfffehoqICERER3hw2ERG1IOzkERERNaO+ffsiJiYG06dPx5EjR/DZZ59h48aNAIDq6mrMnTsXP//8MwoLC/HVV1+hTZs20Ol0Xh41ERG1JBKB94AQERE1q8LCQsyYMQN79uxBly5dkJaWhv3792PVqlVYvHgxvvjiC1RVVaFHjx6YOXMmunfv7u0hExFRC8Iij4iIiIiIyI/wdk0iIiIiIiI/wiKPiIiIiIjIj7DIIyIiIiIi8iMs8oiIiIiIiPwIizwiIiIiIiI/wiKPiIiIiIjIj7DIIyIiIiIi8iMs8oiIiIiIiPwIizwiIiIiIiI/wiKPiIiIiIjIj7DIIyIiIiIi8iMs8oiIiIiIiPwIizwiIiIiIiI/wiKPiIiIiIjIj7DIIyIiIiIi8iMs8oiIiIiIiPyI3NsDaA3Kyqq9PQSRzWZFRUUFwsPDIZPx8vsDXlP/xuvr/3iNWwdeZ//Ha+zffOn6RkaGXvI17OS1Mna74PR/avl4Tf0br6//4zVuHXid/R+vsX9radeXRR4REREREZEfYZFHRERERETkR1jkERERERER+REWeURERERERH6ERR4REREREZEfYZFHRERERETkR1jkEdFlK68z48ejRhSfboAgtIwphImIiIhaG67USESX5bMDp/Dsd3/gtMkGAIjXBuLDe3qiU3iwl0dGREREROdiJ4+ILml21hE8+eVvOG2yQRsoh1QCHKtswB1rcnG8st7bwyMiIiKic7DII6I/tfWYESt2FgIAHkqKQfboNOz527WI1waivM6C29fkotpk9fIoiYiIiKgRizwiuqgGqw3PfPsHAGB4lwjMvfEa6NWBiFEr8eVfUxAVrEBxtQkv/1jg5ZESERERUSMWeUR0UUt+Po58Yz3CVAH4e592CJTLxFhMqBJzb7gGALAm7yTK68zeGiYRERERnYNFHhE1qdZsw8pdJwAAT2W2RVKM+oLX3NY1El0jglFnsWPuD+zmEREREfkCFnlE1KTPDpxCtdmG2FAlHugZ3eRrpBIJZgxKAAB8+msJTtWYmnOIRERERNQEFnlEdAFBEPDe7mIAjm5dZLDyoq+9sWM4ukcGw2QT8N7uouYaIhERERFdBIs8IrpATvFp7C+tgUImwYhuUX/6WolEgr8mxQAANv5R3hzDIyIiIqI/wSKPiC7w/h5HF29QQhh6tQm95Ovv7BYFmQQ4WF6HvSXVnh4eEREREf0JFnlE5MRktYsduVs6RUAmlVzyPZHBCgxMCANwtkAkIiIiIu9gkUdETn46Xokasw1hKjmGXhN22e+7r4djcpbvj1TALgieGh4RERERXQKLPCJy8u0hRxfv2rbaP51w5Xw3d4pAUIAUJTVm/HjU6KnhEREREdElsMgjIpFdEMQir2877RW9VxUgw6Azt2x+zQlYiIiIiLyGRR4RifJKqlFSY4ZKLsXQa8Kv+P3Xd3AUeTtPVLl7aERERER0mVjkEZHomzMduIw4DdpqAq/4/UPOFHkHy2tRUs2F0YmIiIi8gUUeEYm+P2IAAFzbVgOp5NKzap5Prw5E5/Ag2AXgq9/L3D08IiIiIroMLPKICABgrLfg19IaAMCAeO1V7+f6jo5u3mZOvkJERETkFSzyiAgAsO14JQQA8dpAdI+69ALoF3N9B8ezfL8UVXEpBSIiIiIvYJFHRACArccqAQBJbUIRrJBd9X4y4zQIlEthqLdyAhYiIiIiL2CRR0QAgJ+OO26vTG5z9V08AFDKpUjXqwEAPxQYXB4XEREREV0ZFnlEhLJaMw6W1wEA+l3h+nhNyYzTAADySmpc3hcRERERXRkWeUSEbccrAQAddCp0jQx2eX992moBAPtOscgjIiIiam4s8ogIW88UeUltQqEKuPrn8Rr1jg2FTAKU1prxexkLPSIiIqLmxCKPiMQJUnq1CXHL/kIUcvSIduwrq4BLKRARERE1JxZ5RK1cjcmKg2W1AICMOLXb9tt4y+YvRafdtk8iIiIiujSfLvJOnTqFiRMnIiMjAwMGDMD8+fNhMpkAAIWFhRg1ahSSk5Nx6623YuvWrU7v3bZtG4YPH46kpCQ8/PDDKCwsdIq///77GDBgAFJSUjBt2jTU19eLMZPJhGnTpiEtLQ39+/fHu+++6/TeSx2bqCXZc7IaAoDoYAW6RbinkwecnXxlfylv1yQiIiJqTj5b5AmCgIkTJ6K+vh5r1qzBq6++iuzsbPzf//0fBEHAuHHjEBERgfXr1+OOO+7A+PHjUVxcDAAoLi7GuHHjMGLECHz66acICwvDU089BeHMwszfffcdli1bhhdeeAGrVq1CXl4eFi5cKB57wYIF2L9/P1atWoXnn38ey5Ytw7fffiuO68+OTdTS5BQ7Om3dIoMRopS7bb8ZZ4q8o8Z6lFSb3LZfIiIiIvpzPlvk5efnIzc3F/Pnz0enTp2QlpaGiRMnYsOGDdi+fTsKCwvxwgsvoGPHjnjiiSeQnJyM9evXAwDWrVuHHj16YPTo0ejUqRPmz5+PoqIi7Ny5EwCwevVqPPLIIxg8eDB69eqFOXPmYP369aivr0ddXR3WrVuH6dOnIzExETfeeCMee+wxrFmzBgAueWyilubcIs+dooIVaKcJhADgx2N8Lo+IiIioufhskRcZGYm3334bERERTttramqQl5eH7t27IygoSNyempqK3NxcAEBeXh7S0tLEmEqlQmJiInJzc2Gz2bBv3z6neHJyMiwWCw4ePIiDBw/CarUiJSXFad95eXmw2+2XPDZRSyIIAn45U+R1j3JvkQcAKTGOhdV3F1e7fd9ERERE1DT33ZvlZmq1GgMGDBC/ttvt+OCDD9CnTx+UlZUhKirK6fXh4eEoKSkBgD+Nnz59GiaTySkul8uh1WpRUlICqVQKnU4HhUIhxiMiImAymVBZWXnJYzfFZrPCbheuPAkeYLVaYLVaYbVavD0UchNXrumxygZU1Fkgl0qQFhMMi8W9n4teUcH44mAZDpbVuH3frQW/Z/0fr3HrwOvs/3iN/VtLu74+W+Sdb+HChThw4AA+/fRTvP/++05FGAAoFAqYzWYAQH19/UXjDQ0N4tdNxQVBaDIGAGaz+U/3fTEVFRVXcKaeZbVaUV3tmC5fLm8xl5/+hCvXdOsRx/vaawIQaK2D0Vh/iXdcmYRgOwDgYHkNjEaDW/fdWvB71v/xGrcOvM7+j9fYv/nS9Y2NDbvka1rEJ3DhwoVYtWoVXn31VXTu3BlKpRKVlZVOrzGbzQgMDAQAKJXKC4ous9kMtVoNpVIpfn1+XKVSwWazNRkDgMDAwEseuynh4eE+1ckDAJ1OB7k8wMujIXdw5Zr+froSAJAYrUZUeLi7h4a+QWpIcBwV9TZUSVRor1W5/Rj+jt+z/o/XuHXgdfZ/vMb+raVdX58v8l588UV89NFHWLhwIW666SYAQHR0NA4fPuz0uvLycvE2yujoaJSXl18Q79atG7RaLZRKJcrLy9GxY0cAjsq8srISkZGREAQBRqMRVqtVrNLLysoQGBgItVp9yWM3RSaTQyZzLQ/uJJfLIZcHICDA9z+gdHmu9prmlTrWx+seFeqRz0NYQAA6hKlwxFCPHcU16BTpvnX4WhN+z/o/XuPWgdfZ//Ea+7eWdH19duIVAFi2bBk+/vhjvPLKKxg2bJi4PSkpCb/++qt46yUA5OTkICkpSYzn5OSIsfr6ehw4cABJSUmQSqXo2bOnUzw3NxdyuRxdu3ZFt27dIJfLnSZSycnJQc+ePSGVSi95bKKWwmYXsP+UYw27nh6YdKVR7xhHYZd7kpOvEBERETUHny3yjhw5ghUrVuDxxx9HamoqysrKxP8yMjIQExODqVOn4tChQ1i5ciX27t2Lu+++GwBw1113Yffu3Vi5ciUOHTqEqVOnIi4uDpmZmQCAkSNH4p133sH333+PvXv3Yvbs2bj33nuhUqmgUqlw5513Yvbs2di7dy++//57vPvuu3j44YcB4JLHJmopDhvq0GC1QyWXoteZWTA9oXGGzd/L6zx2DCIiIiI6y2dv19y0aRNsNhtef/11vP76606x33//HStWrMD06dMxYsQIxMfHY/ny5YiNjQUAxMXFYenSpZg3bx6WL1+OlJQULF++HBKJBAAwbNgwFBUVYdasWTCbzRg6dCimTJki7n/q1KmYPXs2HnnkEYSEhGDChAkYOnQoAEAmk/3psYlair0ljs5ax7AghKsUl3j11UsSi7xa2O12SKU++7slIiIiIr8gEQTBN2YE8WNlZb5zm5rFYoHRaIBOF9Yi7iemS7vaazpz02G8uesE7uwWhZV3dPfY+OotNnR45UfYBGDLmDR0jQzx2LH8Eb9n/R+vcevA6+z/eI39my9d38jIS9+BxV+pE7VS+8508q4J8+yMl6oAGTqGBQEAdhWd9uixiIiIiIhFHlGrZBcE7Dsz6UpilOc7az2iHcfYX1rj8WMRERERtXYs8ohaoWOVDag22xAglSDZg5OuNGos8o4Y3LvYOhERERFdiEUeUSu075TjVs0OYSpEBXtu0pVGjd3CfANn2CQiIiLyNBZ5RK3Q3hLHbZPXhAUhQOb5vwYai7yi0yZU1Jk9fjwiIiKi1oxFHlEr1NjJuyY8qFmOFxWsQGRQAAQAu05UNcsxiYiIiForFnlErdBvZbUAgE5hzVPkAWefy9tzkpOvEBEREXkSizyiVqaizoySGsctkymxnp90pVFjkXfIUNtsxyQiIiJqjVjkEbUyjV28mFAF9OrAZjvu2clXOMMmERERkSexyCNqZQ6UOoq8DrogBAXImu24PRqLPGM9LDZ7sx2XiIiIqLVhkUfUyvxW5ngmLkGnatbjdgwLgkImQYPVjl+5KDoRERGRx7DII2plDpQ1dvKat8iTSSW45sxEL7kl1c16bCIiIqLWhEUeUStiswviM3mNz8g1p66RwQCA38u4KDoRERGRp7DII2pFjlbWo8Fqh1ImQfeo4GY/fpeIYHEcREREROQZLPKIWpEDZ56Fa69TITwooNmP3yXCcbvm8aqGZj82ERERUWvBIo+oFWl8Hi9Bp4Jc2vzf/l3PdPKOV9ajwWpr9uMTERERtQYs8ohakd/OKfK8IV6rglImhckmYP8pzrBJRERE5Aks8ohakUMVjglP4rXeKfJkUgk6hTtu2dzLGTaJiIiIPIJFHlErYbbZkW9wFHk9vDCzZqMukY4i748KzrBJRERE5Aks8ohaiQJjPWwCEBQgRedw73TyAKBLuOO5vGOVnHyFiIiIyBNY5BG1En+UO57Ha6cJhDqw+WfWbNS4jMIxLqNARERE5BEs8ohaicbbI9tpVZBKJF4bR+PtmserGlBjsnptHERERET+ikUeUSvxR/mZIk8T6NVxxGscM2yabYK4bh8RERERuQ+LPKJW4o8Kx+2a7bXeLfJkUgk6n1kUPY/LKBARERG5HYs8olbAZhdw2OB4Bs6bM2s2aizyDhs4wyYRERGRu7HII2oFjlc1wGS1QyGToFuk94u8rmcmXznOGTaJiIiI3I5FHlErcOjMrZpx6kBoVXIvj+bsDJtHOcMmERERkduxyCNqBX5vnHRFGwiJF2fWbNRY5BVWNaDObPPyaIiIiIj8C4s8olbg0JnlE+K13lsE/Vzx2kAEyh0zbO4vrfb2cIiIiIj8Cos8olagcSH0eC8vn9BIKpGgU7hj8pW9JZxhk4iIiMidWOQR+TlBEMSF0HtEBXt5NGc13rJ5iDNsEhEREbkVizwiP1dSY0aN2QapBEiM9v7Mmo26nllG4Rhn2CQiIiJyKxZ5RH7u9zO3aurVgQgPUnh5NGeJM2waOcMmERERkTuxyCPyc42TrrTTBELqAzNrNuoSeXaGzQYLZ9gkIiIichcWeUR+rrGT107rG5OuNGqrDoRSJoXFLuDXslpvD4eIiIjIb7DII/Jz4vIJPjKzZiOZVIKOYY4lHfaf4jIKRERERO7CIo/IzzUuhN4t0ncmXWnU+czkK42FKBERERG5jkUekR+rqDPDUG+BBECvNr5X5HUKdzyXd7yKM2wSERERuQuLPCI/1tghiw5RoE2I0sujuVDjgugs8oiIiIjch0UekR9rvFWzrSYQMqnvzKzZ6JowR5FXWNkAm93u5dEQ+a/SWjMOlNagwcqZbImIWgO5twdARJ5zqMIxa2W8VuXlkTStY5gKEgDVZhuOVdajQ1iwt4dE5DcarDYs3V6ItftKxG65VAJ0jwrBlH7tcXOncEh8aFkVIiJyH3byiPxYYyevnY/NrNlIFSBD3Jmx7S2p8fJoiPzHzhNVGPTOL1i49SiOVzVAAiA4QAa7AOw/VYNHPtuPOz/MRVmt2dtDJSIiD2CRR+TH/jjTyetyZhZLX9Q4tt/KuVYekTtsO16Juz/OQ76xHuFBAfjngPbIHp2G/ROuxdbH0vBwcgwUMgl+LqzCzat34wSfiSUi8jss8oj8VLXJipPVjt/S92wT6uXRXFzjc3nHKvmDJpGrdpyowsh1e9FgtSNdr8Yn9/bC0/3ao3tUCIIVcnSOCMGim7tg06g0RAUrUFjVgJtW5eBktcnbQyciIjdqEUWe2WzG8OHDsWPHDnFbYWEhRo0aheTkZNx6663YunWr03u2bduG4cOHIykpCQ8//DAKCwud4u+//z4GDBiAlJQUTJs2DfX19WLMZDJh2rRpSEtLQ//+/fHuu+86vfdSxybyBY0za4apAnz2dk3g7AybhewmELmkrNaM0f/ZjzqLHamxaiwd1vWiv+DpEhmM7x7pjbZqJcrqLHjgk70wWTn5ERGRv/D5Is9kMuHpp5/GoUOHxG2CIGDcuHGIiIjA+vXrcccdd2D8+PEoLi4GABQXF2PcuHEYMWIEPv30U4SFheGpp56CIAgAgO+++w7Lli3DCy+8gFWrViEvLw8LFy4U979gwQLs378fq1atwvPPP49ly5bh22+/vaxjE/mKP84Uee20gVDIfPdbXVxGgZ08oqsmCAL+sfEgymotaK8NxP/d0hkdwv78Nm29OhCfPpCMEIUMB8pq8fQ3vzfTaImIyNN89yc/AIcPH8a9996L48ePO23fvn07CgsL8cILL6Bjx4544oknkJycjPXr1wMA1q1bhx49emD06NHo1KkT5s+fj6KiIuzcuRMAsHr1ajzyyCMYPHgwevXqhTlz5mD9+vWor69HXV0d1q1bh+nTpyMxMRE33ngjHnvsMaxZs+ayjk3kK/4484ybL3fxgLNF3qlaM8o5CQTRVXlvTzH+d8SAAKkE0wZ2QJfIkMt6X4JOhTdv7wYJgHW/nsIXv53y7ECJiKhZ+PQSCjt37kRmZiYmTZqE5ORkcXteXh66d++OoKCzv6VMTU1Fbm6uGE9LSxNjKpUKiYmJyM3NRVpaGvbt24fx48eL8eTkZFgsFhw8eBCCIMBqtSIlJcVp32+88Qbsdvslj90Um80Ku11wIRPuY7VaYLVaYbVavD0UcpOLXdPfyxyzVbYNVcBi8d3rrQ6QQBcoh7HBij1FlRiUoPP2kHwKv2f9n6vXuKLOgnmb8wEAo3vH4OYO2iv6nh8Ur8HjqbFYmVOMaf87jOvaqRGi8OkfD1okfi/7P15j/9bSrq9P/y0+cuTIJreXlZUhKirKaVt4eDhKSkouGT99+jRMJpNTXC6XQ6vVoqSkBFKpFDqdDgqFQoxHRETAZDKhsrLyksduSkVFxeWdcDOwWq2orq4C4Dhvavkudk0Pniny2ijtMBoNXhnb5WqvCYCxwYrdx8uRpPWNX4j4Cn7P+j9Xr/HcbSdx2mRDe40C93cMwukq4xXv4/HEUHx5UI6SWgue/eYg5l4Xe8X7oD/H72X/x2vs33zp+sbGhl3yNVc8wsGDB2PYsGG49dZb0b1796samKvq6+udijAAUCgUMJvNl4w3NDSIXzcVFwShyRjgmADmUsduSnh4uE918gBAp9NBLg/w8mjIHZq6pvUWG4pqHNsz2kdBp7u8W7e8pVuUAXtO1eOkSQKd7tJ/cbUm/J71f65c44PltVj3eyUA4G8ZbdE5LvqqxqAD8MrNMoxcfwBfHq7C2Ix4JMf47qy8LRG/l/0fr7F/a2nX94qLvH/+85/49ttv8de//hXR0dG49dZbMWzYMHTs2NET42uSUqlEZWWl0zaz2YzAwEAxfn7RZTaboVaroVQqxa/Pj6tUKthstiZjABAYGHjJYzdFJpNDJrvs0/M4uVwOuTwAAQG+/wGly3P+Nf3dYIL9/9u778Cmyu6B498k3XsvOmhLaSl7b8SWJVsQFRwoDhyvGxXFwQ9xb8VX4RVcKEsQGTIFlC17U8oqtIXuvZIm+f2RNlJltJA2o+fzj5Jx75PejHvuc55z9ODuoCLG3x17e8u+omhYP5RBapFa3peXIZ9Z23e9x/i9refR6aFXhBe3tw7B3v76f2z6xQQwpHkmK09k839/nmXF3R2ue1vi8uSzbPvkGNs2azq+dT7zGzhwIAMHDqS8vJyNGzeydu1axo0bR2BgIEOHDmXw4MGEhobWx1iNAgMDOXnyZI3bsrOzjWmUgYGBZGdn/+v+Fi1a4OXlhaOjI9nZ2cbAtLKykvz8fPz9/dHr9eTl5VFZWWmcis3KysLJyQkPD49r7lsIS5Bc1QQ93MsJN0fLDvBA2igIcT0OZxSz5mQOSgU80KEJrg43fjXx1b5RrE7O5q/UQtafyqZftJ8JRiqEaWm0OtafymXRkYuczSunQqujiYcjA5v5MSzOnwBXh2tvRAgbd93VNZ2cnBg4cCC33347Q4cOJSUlhW+//ZahQ4cyYcIEzpw5Y8px1tC2bVuOHDliTL0E2LNnD23btjXev2fPHuN9ZWVlHD16lLZt26JUKmndunWN+/fv34+dnR1xcXG0aNECOzu7GoVU9uzZQ+vWrVEqldfctxCWwNg+wdPZzCOpneogL7WgnHKN1syjEcI6fLYjBYA+Ed70i/Y1yTajfFy4u20wANM2nja2HhLCUuxOK6DH//5i/JLDrEjK5nBmMck5pWw6k8dL65Lp8tUO5uxNQyfvXdHI1TnI0+l0bNu2jddee41evXrx9NNPU1FRwVdffcWWLVvYsmUL3t7ePProo/UxXgC6dOlCcHAwL730EsnJycyaNYuDBw9y2223ATB69Gj27t3LrFmzSE5O5qWXXiI0NJSuXbsChoIus2fPZv369Rw8eJCpU6dy++234+zsjLOzMyNHjmTq1KkcPHiQ9evXM2fOHO69995a7VsIS3Ai++8eedYg1MMJR5USjU7PsawScw9HCIt3KreUX49lATC2TRCOdqbriPR870ic7ZQczy5l6fFMk21XiBuh1+v5fMc5hs3dR0p+OV5OdoxpGcib/Zrx6eBYJnYKpZmPM6UaHZPXJjN63gGK1ZXmHrYQZlPnPK7u3bujVqvp27cv06ZNo0+fPjUKkbi5udG/f38OHDhg0oFeSqVS8d///pcpU6YwatQoIiIi+OKLLwgJMVQDCw0N5fPPP+ett97iiy++oH379nzxxRcoFAoAhgwZQlpaGq+99hpqtZoBAwbw/PPPG7f/0ksvMXXqVMaPH4+bmxtPPPEEAwYMqNW+hbAE1emaTa0kyFMpFUT5OHMsq4TDmcW0D/Ew95CEsGj/3XkePdAt1JNBMaZNqQxwdeC+DiF8+Vcqn247x8i4AOPvpxDm8sn2c7z9pyFLLCHKh5f7NKVNUM3fiv/TR/P5jnN8uDWFrefyufWn/SwZ2w53K1i2IISpKfR1zMVYvnw5iYmJNfrEiavLyioy9xCMNBoNeXm5eHv7WMWiUXFt/zymlTodER9sRqPTs/yudnQN8zL3EGvl4V+PsPRYFg93asL0fjHmHo7FkM+s7avrMc4v19BmxnbKK3V8dEtz7m5r+ouMGcUVdPxyB2qtnh9va0X/ZrI270bJZ/n6zd6TykvrDPUQHu7UhBd6NcXD6cp/w91pBYyZf5ASjZY2QW6svLuDSWe7r0SOsW2zpOPr73/t6se1urSxa9cu4/8HBQVx5MiRKz62c+fOtdmkEKKenM0rR6PT42SnpLmf9VyMaeZjGOs5Kb4ixFXNO3iR8kodUd7ODG1eP8FXoJsjY1sH893+dD7cliJBnjCbbefymbLeEODd0zaYyX0icXO4+ulrpyaeLB3XlpE/HeDgxWKeW53EjKEtGmK4QliMWgV599xzT602plAoOHbs2A0NSAhxY5KyDamaYZ5OeF7lSqelae7nCkiQJ8TV6PR6vtmbBsDwOH+8nOuviuAT3cKYe+ACe9OL+Cu1gC6hnvW2LyEuJ69Mw2PLj6HTQ/9oH17rG3XNAK9a22APZo5owd0/H2bh4Qw6NfHgvvZN6nnEQliOWn1Sjh8/bvz/lJQUIiIi6m1AQogbk2ysrOmE0orW0VTP5J3PL0en06FU1n9qjRDWZuPpXM7ml+Nqr2J0fP227gn3cmZYnB9Lj2Xx6fYUfhzTpl73J8Q/PbsqifSiCpp4ODKlbxSeznW7cDmgmR/P9Ajn423neO33U9wc6UOEl3VUnRbiRtX5LGrcuHEcPny4PsYihDCBpKqiKxFWUnSlWrSPMwqgSK3lfEGFuYcjhEX6bl86AANjfI2z3/Xpkc5hAGw8k0d6ocyyi4azJjmblSeysVMqmHJTFPH+bte1nRd7R9IxxIPySh2PLjsmbUFEo1HnIM/Pz4+cnJz6GIsQwgSSs62rR141Z3sVoZ6OABzIsJxiRUJYiswSNetOGX5/hzT3a5CKlx1CPOgY7E6lTs/H28/V+/6EACiv1BrX4Y2OD2BEnP91b0upUDBjaBz2SgW70wuNF0qEsHV1rikbHx/PY489RuvWrWnSpEmN9gkAb7/9tskGJ4SoG51eT3KuIciL97eeoivVmvu6cr6ggmOZJQyPM/dohLAsi49koNVDnJ8LCVGmaX5eG492DePBpUdZejSTaQnRONurGmzfonGaseM85wrK8XOx59GuYdirbix9P9rHhRd6N+XNP87w5h9nuDU+wKrWrAtxPa7rUzN8+HAiIyP/FeAJIcwrtaCcMo0Oe6WCuIDrS20xpxhfQ2B6Jr/MzCMRwrLo9XrmH7oIGNYZuTo0XKA1uLkfQW4OFFRUMvfAhQbbr2icMoor+GyHYdZ4YufQ607T/KfHuoQR6e1MQUUl0zadNsk2hbBkdZ7Jk5k6ISxXddGVJh6O+NZxgbolqA7yzkuFTSFqOHCxiGNZJTioFAy/gdS162GnVDK+fQjvbj7L/EMXeahTaIPuXzQun+84R3mljjg/V+5tF2yy7dqrlLyRGM3dPx9m/sGLPNE1nKbe1rWsQYi6qHOQp9fr+f3330lOTkar1RpvV6vVHD16lK+//tqkAxRC1F5S9Xo8L+cbTm8xBwnyhLi86lm8nuFetDTDLP1dbYL5YMtZDmUUsze9gA4h0k5BmF56Yblxzdx9HULwNnGLkP7RvnQL9WRHagEvrUtm3u1SMVbYrjqfBb7xxhs8++yz/PHHH/z3v/9l+/btLFiwgP/973+Eh4fXxxiFELWUXFVZM9zTuiprVqsO8jKK1eSXacw8GiEsg0ar49fjWYDhJFWlbPjWKEHujgxoZlgH+NWu1Abfv2gcPt5+jgqtntaBbtxWDy1CFAoF0/s1A2DD6VwOXZQiX8J21TnI++233/jggw+YP38+4eHhTJ06lY0bNzJkyBA0GjkpE8KckqrSNa2tfUI1XxcHvJzs0AMHM4rNPRwhLMLmlDxySjV4OdlxS3M/s41jfFUj6XUncylWV5ptHMI2ZRRXMO+gYc3n+PYheNRTYZQ2Qe4MbOaLHnjjD1mbJ2xXnYO84uJiWrVqBUDz5s05ePAgdnZ2TJw4kT/++MPkAxRC1I5erze2T4jxsb7KmtWa+xnGfliCPCEAWHwkE4CbmnoT4u5otnH0jfQm1MOREo2WOXukDL0wrf/tTkOt1dMywJVRLUw/i3epF3o3BeCPM3nyWyNsVp2DvLCwMI4ePQpATEwMBw8eBAwnmEVFMu0thLlklWooqKhEqYBWgdZXWbNac19Dg+eTVa0ghGjMSjVafkvOBiAhyqdBeuNdiVKh4L4Ohtm8n49cNNs4hO0prqg0rsUb0zIIN8c6l4yok9aBl8zmbTpVr/sSwlzqHORNmDCB559/nt9++43BgwezdOlS3njjDSZPnkzHjh3rY4xCiFo4UZWqGeTmSICb9bY3keIrQvxt3ckcStRagtwcjGvizGls6yDslAqOZ5ey43y+uYcjbMSPBy9QUFFJEw9HbmtVv7N41V7o1RSAP87mcVouKgobVOcgb8yYMcyaNYuIiAiio6OZMWMGWVlZtGrVStorCGFGJ3MMveUivJxwsrPeZsXVQd45CfKEYOkxQ6pm30gffF3Mf/HG39WBwVXrAmdKARZhAlqdnllV76XbWgYS4NowKcmtg9zpEe6JTg8fbk1pkH0K0ZCuaz68c+fOABQUFNCzZ0969epl1hQSIcTfM3nWWlmzWrOqIC+1oBx1pQ4HO+trBSGEKZSotfx+OhcwrMezFPe3D2HZ8Sw2nM6lsFxTbwUyROOw/lQO5wsrcHdUcUeroAbd9+Ndwtl27hArT2TxZlkzvKywv6wQV1Lnsye9Xs+XX35J165d6d69O2lpaTz//PO89tprqNXq+hijEKIWknMNM3nhXtbd3DXMwwlHlQKNTs+xLFkQLxqvDadzKa/UEeTmQGKU5QR5PcK9aOrlRFmljm/3SQEWcWPm7E0DYFAzP6J9Gvb3KzHahyhvZ0o1Or7Yeb5B9y1EfatzkPfFF1+wbNky3nnnHRwcDKkjt956K1u3buW9994z+QCFELWTXDWT19A/kqamUiqIqqoOeihTgjzReK08YeiN1zvCGy8TN4W+EQqFgnvahQCw+GimmUcjrNnpvFI2nslDAQyP82/wrDClQsGjXcIAWHD4IpU6XYPuX4j6VOcg75dffmHatGncfPPNxg9jz549effdd1m1apXJByiEuLaCCi1ZpYY+la0DrLeyZrXYqjYKx7NkMbxonCoqdaw9mQNA7wgv8w7mMm5vFYhKoeBYVgl70wvNPRxhpaoranZq4sFNkeaZrR7TKhAvJzsuFqv58cAFs4xBiPpQ5yAvJyeHgIB/Vz7y8PCgtFROyIQwh9P5FQD4u9oT4mHda/IAmvlUF18pM/NIhDCPP8/mUazW4utiz8AY81fV/KdAN0cGNPMBMBbNEKIuKip1zD9oaMUxPM7fbAXDXOxVTKhqDfLNXkk/FrajzkFet27dmD17do3biouL+eijj+jatavJBiaEqL1TVUFeuKczrg7WW1mzmrRREI3diiRDqmavcC+8LShV81L3VqVsrjudQ5lGa+bRCGuz9mQ2eeWV+LnYMyKuYdomXMn9HUKwVyo4mlXCpqpiR0JYuzoHeVOnTuXo0aP07NmTiooKHnvsMW666SbS0tJ45ZVX6mOMQohrOJ1vKHpk7ZU1q8VUNUQ/V1COTtZIiEZGo9WxuqoBeu8Iyym48k99I30IdHOgqELLvEPSHF3UTfV7pl+0L0HuDdM24UoC3RwZFW8INGf8JQVYhG2ocwuFjz/+mCeeeAKVSkVKSgqVlZVERkbSq1cvlEopdS6EOSTnGWbymnpbd9GVatE+ziiAogot5woqbOZ1CVEb287nk1deiZeTHUNi/cw9nCtSKRXc3TaYD7emsODQRWPKmxDXklFcwYaqGbMhzS3jPT6xcxgLDmewJSWPc/llVl+pWog6R2Vubm688sorTJo0iaSkJGJjY+ndu7cEeEKY0cmqIC/aRoIhZ3sVoVWzkgcuFpl5NEI0rBVJhlm8HuFeFtEA/WrGtg5CAey7UERStlTDFbWz8HAGOj3EB7iareDKP7UKdKNzEw90epgh7RSEDahzZPbqq6/y559/8tlnn2FnZ8ekSZPo3bs3b775Jvv376+HIQohria3TENWWSUAbYKsv7JmteZV6/KOZZWYeSRCNBytTs9vVa0TellgVc1/CvdyNlb/nLUrzbyDEVZBr9czvypVc2AzP7MVXLmchzqFArDseCZqrSwVENbtuqbfFAoFXbp04bXXXmP16tXcdtttLFy4kLFjx5KYmMjMmTOpqKgw9ViFEJdxoqo/XoCrPaE2UFmzWrOqIC8lXypsisZjV1oBWSUaXO1VFpPGdi33tjcUYPktOVv6jIlr2nuhiOScUhxVCkbF+5t7ODUMbu6Hn4s9uWWV/CTtFISVu64gr6SkhBUrVvCf//yHXr16sWrVKu6//35+/fVXpk2bxurVq3nsscdMPVYhxGUkZRuCvKZeTjZRWbNa9UzeOamwKRqR6qqa3cM9CXa3jos2g2L88HKyI6dUwy/SHF1cw7yDhuCpV4Q3zauKbFkKB5WS+6ouWny/X4I8Yd3qXHjl0UcfZdu2bXh4eHDLLbfw/fff06ZNG+P9zZs3p7CwkClTpph0oEKIy6sO8iK8nFAoFGYejelUt1E4ly9Bnmgc9Ho9K5Msv6rmPzmolIxtHcSXu1KZe+ACY1oFmXtIwkKVabQsPWa4EDAwxtcif7PubRfCx9vOcTizmN1pBXRq4mnuIQlxXeo8k+fn58fMmTP5888/efnll2sEeNU6derEokWLTDJAIcTVJVWlazb1tI2iK9Wa+xmu8GaUqMksVpt5NELUv/0Xi0krqsDJTsng5pbXAP1q7mobDMDO1ALOF0iKtbi8VcnZFFZoCXR1YFisZaVqVgtyd2RwVar0F1KARVixOgd5b7zxBt26dbvq1Rd/f3+io6NvaGBCiGvT6/Ucr5rJi/K2jtSu2vJxtsfPxR6A/RcKzTwaIerfb8k5AHQN9STcyi7aNPdzpVOIoTLhrF2p5h6OsFDVBVf6N/O16MqxD3Y0tAP5/XQueWVykVFYJ+l7IIQVyyxRk19eiQJoFWA7lTWrxfkbZvMOZUppdmHb9Hq9McjrHeFlkWls13JvO8Ns3rKkLHR6vZlHIyxNRnEFf57NA+CWGMueqe4W5kmMrwvllTpmStVYYaUkyBPCilW3Fwh2syPQzXKvil6vuKqUzdO5kv4lbNuJvArO5pdjr1QwKMY6qmr+07C4ANwcVFwoUvPrMSnAImr69VgWOj208Help4WvOVUoFDxc1U5h/qGLctFCWCUJ8oSwYsergrwwdwdc7G3v4xxbFeSdlTYKwsatO1sEQKcmHsb2IdbG1UHFuDaGoitz9qabeTTC0iw5mgFAQpQPLvaWXwl6dMsAXO1VpBdVsOpEtrmHI0Sd2d5ZoRCNyPHs6iDP3irTu64l1s9wsmtLQZ5GqyOzRI1WJ1eGxd/WVwV5vSO8UVrxZ3lCB8Napr9SC0jOKTHzaISlOJ1Xyt4LRSgVMNhKZqrdHOy4s00gADNlnalV02h1lGm05h5Gg6tzCwUhhOUwzuR52F6qJvy9Ji+rRMPFogqC3B3NPKLrd76gnP/tTmXR4QxyyjTYKRXE+rnwat8oEqIse32KqF8nc0s5mV+BSmEoK2/Nonxc6NPUmz/P5vH5jvN8NiTO3EMSFmDJEUP6bodgD9oFu5t5NLX3QIdQZu9JZ2dqAWfySon0ts5Z9sbmSGYxy49nseVcHkcySiipCvD8XOxp4e/KyBYBDI/zx9PJ3swjrV8ykyeEldLp9caZvEhP2wzyvJzsCXA1vLZ9Vlxhc8f5fBK/2c1Xu1LJKdMAUKnTcySzhDsXHuL+JYcpbYRXGYVBdcGVdkHutPC3rObQ1+OhqsqEK5Oy5H0t0Ov1NVI17VXWc+rZzNeFnuFe6IHPd0g7BUum1+tZfyqHYXP3cfOc3Xy0LYW/UguNAR5AdqmGzSn5PLf6BG1mbOezHSmotTozjrp+yUyeEFbqfEE5pRoddkoFcT621T7hUnF+LmSWqDmUWcwtzS2zr9LVrEzKYuKyo6i1emJ8Xbi3XTCDm/tRUF7JrN2pLDycwcoT2RT+fIj5t7exqhMgYRq/nTAEeT3DPbFTWv/x7xftS4i7I+lFFXy3N51Hu4aZe0jCjA5lFHMytwwHlYIhsdaRqnmpiZ1D2Xoun1+PZ/Jmv2Y4W8F6wsbmWFYxr6w/yeaUfABUCugR7kWXUE/aB7kT4eWEnUrB6dwy/jybx+qTOaTklzN90xnmHbjID7e1ttq10Fdj/b8mQjRS1ama4Z6O+Djb7vWa6pTNM1ZYYfNYVjGPLj+GWqunZ7gX39zakomdwwjzdKZVoDufDWnBojva4KBSsDkln0eXHUMvVdwuS6/Xk5RdwvpTORy8WERBucbcQzKJlPwyDmWWoAD6RVl2xcHaUikVPFA1m/fDgXR5Tzdyi6tm8bqFedHc1/pmqvtH+xLk5kBRhZafDl4w93DEJdRaHR9sOUu/b/awOSUfe6WCMS0DWXRHW+bf3oYXe0cyIMaPWH83on1c6d/Mjzf6xbBzYlfe7h+Dl5Mdp/LKGPjdHjafzTX3yzE5CfKEsFLVqZoRnk442dnuR/nvCpvlZh5J3ZSotTy09CjllTo6hnjw32EtaO737xOc3k19+HpES5QKQ3+xb/ZKT6ZLqbU6Ptp6lvb/3UHvr3cxbtEh+n27hxafbeOJlcc4kW3dxT1WJhmq9rXwdbSpXpfj2gThoFJwMreMzVW90UTjo9Xp+eWoYT1eYpQPKqX1FRVSKRXGgkJz9spFC0txvqCc4XP38d6Ws2h0erqHefLtrS35dEgsvZp6XzUrRqkwXIja8lAX4v1dKVJruX3BQdadtK0qqrZ7ZiiEjavukdfUy3ZTNcF62yi89vtJTuSU4uNsz2t9owi+StGYQc39mNw7EoDpf5who6iioYZp0Y5mFjPwuz28s/ks6UUVOKgURHk74+NsR6VOz4JDGfSZvYuv/jpvtSdey5MMJ8BdQ1xxtKGLNb4uDoxsEQDAf6UyYaO143w+F4vVuDmoGNLc+lI1q93dLhh7pYLknFJ2pRWYeziN3obTOfT7Zjd7LxTh7qDi5T6R/DC6Ff1j/OqU8h7g6sDq8R1IjPJBq4cHlh7lr9T8+ht4A7OdXxQhGpnqdM0IL2czj6R+VTdEzynVkFpgHYHegYtF/HDAkNbzcp9Iuod7XfM5/+kWRry/K8VqLY+vPFbPI7R8hzOKGfHjfo5kluDhqOKFXk3584HObHmoM8ee7MmiO9rQJdQDnR5e23CKicuOorGyBfTn8svYk16EAugbZjuzeNUerErZ/ONMLilWdpFGmMbiqlm83hHehHla7wVJPxcHhsUa1oRLARbz0er0vLv5DGMXHiKvvJLmvi58NbwFT3UPx+M6K2U62an4bnQreoR7Ul6pY+zCQ5zJKzXxyM1DgjwhrJBGq+NkruFLKM7PtoM8Dyc7Qj0Ms2C704vMPJpr0+v1vLL+JGBIT7q9dWCtnmenVDJjaAtUCvjzbD5rk20rbaQuknNKGLPgAAUVlcT7u/L96FY81zOCKB8X7JRKFAoFN0X6sPyu9rzcpylKBSw9lsWjy46hs6IZvWVJWQC0DnSllQ1+jtsFe9A11AOtHj7Yctbcw7lhGq2OrBI1eWUam67IZyoVlTqWHze8x2+O8rb6Xq4Tu4QCsOF0LlklajOPpvHJLlVzx8KDfLg1BT0wLNaf+Xe0oX8zvxt+bzmolPx4WxtaBhhSN+/++TAVldb/GZcgTwgrdCavDLVWj5Od0jjTZcuq1yodvGj5Qd7ypCx2phbgqFLwcKdQnOxqX4mtVaAb97YLAeCtzWesNgXxRhSrK7nn58PklGqI8XXhsyGx9Ai//AmiQqHg6R5NmT3y7zWNk9cmm2HU12fZMcMJcO8IL5tK1bzUU90jAFh2PIu8MusqlqPR6vj9VA7Prkqix6ydRHy4mZafbyP20600/XAzCXN28/K6ZA5cLGqUn9Vr2XA6l4KKSnxd7LnFShqgX037YA9aBrii0emZuUtm8xrSX6kFJH6zmz/P5uFkp2Ry76Z8MSyOUA/TzQ67OqiYe1tr3B1VJOeUMml1ksm2bS62+avSACoqKnj55Zfp1KkTvXr1Ys6cOeYekmhEqouuNPVywtvZtpt5ArQIMASypyy8wqZWp+edP88AcHurIPo0rXu1xGd7RuCgUnA0s4QVSY1vNu+1309xOq8Mfxd7PhzUnDZBHtd8zpBYfz4aFAvAt/vS+WF/en0P84adzS9j/8UilAroF2kbVTUvJzHKh+a+LpRV6vh0e4q5h1MrpRots3al0vmrnYxddIi5By5wMreMSt3fgVylTs/hzGK+3pNG/2/30O/bPWxNkQIzl6rujXdzpA+Bbldek2xNJnYyzObNO3SRSp31z/RYOr3eEFCP/Gk/F4rUhHk6MWNoHM/0iKjTBdTaauLhxFfDWgCw4HAGv53IMvk+GpLt1l2vZ++99x6HDx/mu+++Iz09nRdffJGQkBAGDRpk7qGJf9h3oZCVSdkczyqhRKOlbZA7fZp6c3Ok9aaPHM2sCvK8nbGzwmpldRXvb5jJs/Q8+VXJ2ZzMLcPNQcX9HZpcVyW5QDdHJnRowle7Unln8xkGRrUz/UAt1G8nsph74AIK4IXekXQL86r1c8e1DSYlv4yPt59jyvqTdAn1NBbtsUTLqtLY2ga50zrQjfJi2yzmoFAoeKJbOE+sPM5PBy/yQu9IXCy0z5her2dVcjavrD9JaqGh+JGXkx29m3rTNdSTtkFuRPu4gF5PelEFf6UWsu5UDltS8jmUUcyt8w4wpLkfH90S2yguvl1NUUUla04a+j8mRPmYeTSmMzI+gFd/P0VWiYblx7O4Nb526fii7rJL1Tz7WxKrq95HfSO9+b+EaFr41+/65f7N/JjQIYQ5e9N5YU0yfSK8cXO0znBJZvKuQ2lpKYsWLWLKlCm0bNmS/v378+CDD/Ljjz+ae2jiEmmF5Uz89SgDv9vLZzvOsfZUDlvP5fPfv85z58KD3DpvP6dzLTtouJLDGcUAhhOORiA+4O82Cmqt1syjuTy9Xs/nO84BMCLOn5YB1x9gPNk9HGc7Jck5paw+aXu9ey6nRK3lpXWGVMsxrQK5s3VQnbfxQu9IuoYaFs/fu9iy11QsO2YoSHFTU2+bb648Kj6AJh6O5JdX8sk2y5zNyyvT8MDSI9y35AiphRUEuDrwdPdw1tzbgdkjW/Jwp1C6hnrh5+KAn6sjbYI8eLBTKAvuaMvex7pya4sAlApYeSKbPrN3sSe90Nwvyax+O5FNeaWOMA9HEm2k/yMYinTc3TYYgK+kamy9+f1UDjfN3sXqkznYKxU83jWM/w2Pr/cAr9qrfaMJcXcks0TN5HXWswTgnyTIuw7Hjx+nsrKS9u3bG2/r2LEjBw4cQCfT9xbhUEYRA77bwy/HMg2V65p680S3MJ7rGcEtMX44qBRsO1fATbN3sfG09Z1EH840BHkxjSTIi/R2xlGlpLxSZwxwLc2WlHz2XSjCUaVgbJugG5ol9nNxYGwbQ5Aza4/lpx6awhc7z3GhSE2QmwPPdI+4rjVqKqWCr0fG4+1kx5m8Mt7YdKoeRnrjTueVcjCjGKXCkM5o6+xVSl7o1RQwpNMWV1Sad0D/sCutgIRvdrMiKRuVAu5sHcTiO9vw8k1RRNbiOzbQzYmZI+JZeU97QtwdyShWM3zuPpYfz2yA0Vum6lTNhGhfvJ0dzDwa07q/QwhKBey7UMSxLMv8PbJWpRotL61NZuyiQ2SVaGjq5cTnQ+OYclMkng04O+7qoOLTIYYlAIsOZ7Ar1TozLaxz/tHMsrKy8Pb2xsHh7y8uPz8/KioqyM/Px8en5o+2VluJTmcZi7IrKzVUVlZSWWldC+Dr4q/UQu795ShFai1R3k482z2cwTG+NU4aT+WW8cRvJziQUcw9iw/xzYgW9LWSdTG5pRrSq/qotQlwaRTHFKC5rzOHMkvYdT6f1v6WF9zO2GGYoRjYzJc2/i5oNDd2PO5rG8ScvensSiskKduLTu7uphimRUorrGDGTkMhgwntgwn3sL/uv5+Po5J3+kczcXkSs/ekcWusL22CLOtvt/SI4QS4bZAb8X7OjeIzPDLWlw+2OHK+sIIPt5zh5T5NzT0kAOYdyuCl9afQ6PSEuDvwYq8IRsT6Ya9S1vk92Mbfhd/Ht+OhZcfZcq6Ah349yrslFYyrumDTGI4zQFaJmj/OGtYn3hTuccPfhZYm2NWOvk292HAmn0+3pfD54ObG+xrLMTY1vV7PmpO5vLbxDGlV5zcj4vx4qmsosX6u6LVaNA2cxdOjiTvDY/1YlpTN82uSWHtPO7TaSqs6vhLkXYeysrIaAR5g/Lda/e+yujk5OQ0yrtqorKykqMhwRcLOzvYO//lCNeOXnaFIrSPOx5FpvYKI91NRWpTPpYmZPgr4ZlATHl93nu3ppdy39Bg/Do0g3grKmG9PM1w5DHK1w11fSl6ezqaPabVoTzsOZcKBtFzyIi2r39K5QjUbz+ajAAaEOVFYcOMFGHwU0LOJK1vTSvhybwYfeDna7PF98890yisNn9khYQ7k5d3Y7HoPPwU3hbnxx/liHl95nF9GRl3X+sj68svRiwB0CXCkrCjf5r+Xqz3S1ocpmy/w7f4LjI1xwcvJfK9Vp9fz4a5MvjtseK91DXbh+S6BxPnaUVyYf0PbnpEQzMt/6vntdCHPrztFRVkpI5t7NZrjvOBoLjo9NPNyoKWH7oY/z5bojububDiTz6rkbM5neOHmYDiejeUYm4per2fXxVJm7M1ib4ahsJqfs4qH2voxqrkXzqoK8vIqzDa+J9t5seZkDkezSpm98wwjot0s5viGhFw7C0TegdfB0dHxX8Fc9b+dnP598unr62tRM3kA3t7e2NnZ1sLwUo2WZ5cfpFCtI9bXha+GxRLje/UZnx/H+DBu8RF2pBby7KYLbLyvvcUWBah27qQhXI3xdSXE38+mj+ml2jYpY2lyAell4O1tWSluXxw8C0DHEHduaRmGk4nK4T/eTcHWxUfZelGNnYs73q6WfxGirlLyy1lxyvCj+UiXcJoGB5hkux8Pdqf3nD2czlez+Ew5Ezs1Mcl2b9TJ3FKScitQKWBofBO8vd0bzWf4nk7efHskn+TcMj7el89nl8yANCSNVsdza0+y+Kgh+LinTRAv9AzDx8V0aYWzRvrwwrqT/HQok9e3XiDUz5ObIwzZIrZ+nNekGNaqJTbzIzTA38yjqR+DvbyJ2JFFSkE5S85U8FQ3w/eWLX2WyzRaitVa1Fo9jnYKPBztcFCZ5rctv7yS305k892BixyuKiRnr1QwKt6fRzs1uea5W0Px9ob/dKngw+3n+e/+HMZ3DK263TqOrwR51yEwMJC8vDwqKyuNkXxWVhZOTk54ePy73LdKZYfKguIGOzs77Ozssbe3/DdoXby69hTHskrxcrLj/xKjiQ/yvOZz7O3hm1Gt6P31LlILK3hmzUnm3NqqAUZ7/Y5mG652NfNzNR5DWz2ml2pddTzP5JdZ1Ossr9SyoCr9blhcAO7OpisVntjMn6ZeTpzNL+enIzk83TPSZNu2FF/uOY1WD52beDCyZRD2JrrIEuptz+Q+kbz6+yk+25nKPe2b4Olk/vfNymTDLG/7YA/ah3phX3XS1Bg+w/bAWwNiGDP/IEuPZ/Fkj4gGK6RQrUSt5aHlx1h/KhelAib1asrjXcLqpfjNR4NbUFChY+WJbB5ZcYJlY1sTYm/bx/lMXhl7LxhagwyKCbDZ1wnwcOdQpqw/yQ8HL/J0z6bYKa33s5xTqub307nsSivkwIUiUvLLyCv/99pZJzslnk52BLg6EOLuSJC7IyHujgS7OxLsbrgt2M0RVwcVCoUCnV5PqVpLelEFKfnl7LtQyM7UArafLzC2JLFXKhjU3I/x7YLpGe5tUVkXAE/0aMrSpGxK1FpKtAqrOr4S5F2HFi1aYGdnx/79++nUqRMAe/bsoXXr1iiVUsvGHDaczmH+oYsogFf7RpEQ5Vvr5/q6OPD1yJaMmrefFUnZrEjKZGisaWYT6kN14ZFmjaToSrVWgYaTwQtFai4UVRDsbhl9l5YdzyK3rJIAVwdubWHa941CoWBc60De2pzC0uNZNhfkpReWM/+gIXVxXJtgk8+iT+jQhDl70zmTV8Zrv5/k0yEtTLr9utLr9Sw8bHi9idE+Jrsqbk1uaupDQpQ3G07nMWn1CVbc3b7BWtnklWm46+dD7E4rxFGl4NW+0UzoGGI8OTc1pULBzBHxjJ53gJ2pBdz7yzF+Hh6Bdaz+vj6/VBVcaRfsTocQy1oLa2rj2gTz/pazXChS8/3+C0zoYBnZArWl1ur47UQ23+9LZ9v5fK6UcGavVKCpurO8Ukd5sZqMYjWHrlEE7dLnXU5TLyf6N/NldHwAbYM8LC64q+Zir+KPBzpTotbiZgd5lt2utwYJ8q6Ds7MzI0eOZOrUqbz11ltkZmYyZ84c3n77bXMPrVEqrqhk0uoTgKFU9+2t6l56vUe4Fw92bMKs3Wm8+vsp+kf7XVd1v/pWqtFysqrtQ3sb/wH9Jx9ne5p4OJJWWMHO8/mMtJD+RHP3XwBgcHM/guoh8LwtPoB3tqRwNKuUgxeLLK6IyI3471/n0ej0tA1yY0Sc6dO67FVK3u7fjDsXHmLh4Qwmdg4jPqBhZ44u9VdaIWfzy3G2UzIs1jbT2GpjemIMN53dxa60Qn48cIG724XU+z7TC8u5Y+FBkrJLcXdQ8Ua/ZoxtfWNVcGvDQaXk+9GtuHnObtKLKnh8fSrL7/LH8ucA6k6v17P4qKGiaEKkj823BnF1UPFI51De2XyWL/86z33t6/99bApqrY6fDl7g420pXCj6e+lRtI8zHUM8aO7nSqyvM029XPB3tcdepUSn01NQUUlOqYbsUg3pReWkFVaQVaohu0RNdqmG7FI1WSVqSjWGKvOXBngu9kqC3ByJ8nEmzs+V7uGedA/7ey2jpXNQKXFwrnsxJnOzjr+uBXrppZeYOnUq48ePx83NjSeeeIIBAwaYe1jXVKyupESjtakrie9sPkNqYQVBbg481T38uoOzF3tHsvhoJmmFFby3+Qyv3hxt4pHeuKOZxej04O1kR3Mfy230XF/aBrmTVljB3gtFFhHknc4rZUdqAUoFDK+HIAUg0M2BXk3c+DO1mFm7U5kx1LyzUaZSVFHJT1WzeHe0Dqq3ZrMJUb4kRPmw4XQuL65NZvnd7a/9pHqy8JDh9fZu6k0zC1lzYg7NfF14pkc4721JYerGUwxo5kuAW/3NzCfnlHDHgoOkFlbg52LP2/1jGGHiWfer8Xa256cxrRn0/V4OZJbxzpYUpibGNNj+G8rhzGKSc0qxVyoYGFP7bBpr9kDHJny+8zwp+eUsPHyR0XF+5h7SVW08k8vktcmcqZqO8nG245bm/gyL9aNrqBeuDlcOzD2d7Qn3uvK6cL1eT3mljtwyDQUVlag1OuxUStwdVXg52eHqoKq3WXNxefLXvk7Ozs68++677Nu3j82bN3PfffeZe0i10v/7/QxffJpitWX1KbpeyTklzN6TBsBT3SOIu4H1He6OdkxLMAR2/9uTxoWicpOM0ZSq0yOa+brg7mjbV0kvp01VymZyjmU0sV94yJCa1DHEg85Nrr0G9HqNjvUCYN3JHCptpBfngkMXKVZrCfd0YlQ9n3BPT2yGSgE7UwvM1rusTKNlaVUD9AHRvigbKEXRUj3VPYLmvi4UVmh5fMVx9Pr6KU62M7WAoT/sI7WwglAPRz4bHNugAV61+AA33u4XBcBXu9PYknLjFXgtzeIjhvd3tzBP4ht4raW5eDrZ81BHQ5rmx9vOoaun9/GNyi/X8Miyo9yx4CBn8srwdrLj8a5hrLq3Ax/fEktClO9VA7zaUCgUONuraOLhRLy/G+1CPGgV6EaElzOeTvYS4JmB/MUbGXulgozSSj7fmWbuoZjEtI2Gog3dQj25o/WNz+zc1jKQdkHulFfqmLbxtAlGaFoHLhYBhiCvodaxWJLWgYZUxeqUVXPSXbK+akAz33pN7+0T5oa3kx155ZUsP55Vb/tpKDq9nq+rLs6MbBFg0qqGl9PM14UHq07Epm44hUbb8IHy6uRsitRaAl0duKV545jluBp7lZIvhrXATqngj7N5fLr9nMn3sTIpizHzD5BXXkmcnyv/HdaCfs3MN9Nye8tABkW6o9PDxGVHKbKwpvA3QqfX88uxqgboUT7GgkKNwWNdw3BzUHEmr4y5By6aezj/sjUlj76zd7PkaCZKhWFZy89j2/L6zdFEejfejILGoPF8CgUAj3Y2lH+dd+giFZXWPSOwJSWPNSdzUCrgoU6hJsntVigUvNLXcLV12fEsUvLNH0xcat8FQ5AX59f4UjUB2gQZrg6fLygnt/TfPSkb0taUfFILK3C1V9X7+ip7pYJhsYbAYMlR88xEmdLG07mczivD1V7F6JYNM6syqVdTvJzsOF9YwYydpg8oruWHqrWb/Zv54u9qGUWDzK1tkDtTbzZ83767+YxJZ7dm70llwi9HKK/U0S3Mk1kjWtAtzMtk279e/9crmBB3B7JKNExanWTu4ZjMjvMFXChS42qv4pYYy05ZNDUvJ3ue6BYGwGc7U69abKQh6fV6PttxjtHzD5BeVEETD0c+HRzHZ0PijBdMhW2TIK+RGdXCHz9nFTlllfxvd6q5h3Pd9Ho90zcZZtqGxvozyIT5/70jvOga6oFGp2faxjMm2+6NKlFrSco29JOx9aplVxLo5oi/iz06vSENy5wWVM3i3RzlTaR3/fevqy5MsiUlnzKNtt73V5/m7DXM4g2K8W2wCxaeTva81MdQnfSLnefJL2u4BfTJOSVsOZePUkG9FJixZg91CmVorB9aPYxffJjDGUU3tD21VsfL65J5ad1J9MCQ5n78d2iLG0rlNyVXexWf3mLoD/jLsSzWncwx84hMY3FVG5neTb1o2gDfh5bmoU6h+DjbcaFYzbeHzH9MyzRaHvr1KNM3nUanh/7Rvvw4pjV3tA5qlFV9Gys50o2Mo52S+1sbAqLZe9PQWsgVp7pafyqXvReKcLJT8kCHJiZNDVEoFEy5yXB1+bcTWZzNs4zZvEMZRej04Odi32jWO1xO26rqkrvTC802hlKNlpUnsgHoF+3bIKmzXZp4EODqQIlGy5KqEyprlF5Yzu+nDU2oh8X5N2ja8T3tgonydqawQsv/bTzVYPv9vmoWr0uoJ93DvRpsv9ZAoVDw2ZA4WgW4UqTWMmreAU5UXcyqq9SCckb+tN+YCnxf+xA+uqU5oZ5OphzyDesR5sk9bYMBeG51ktWvkS/VaPmlar1pYlTjXG/q5mDH870MF5HmHMoxa6ZJdqma0fMPsOx4FnZKBU92C+fLYXGN+ryhsZIgrxG6LdYLD0cVaYUV/HTwgrmHU2d6vZ73thhm2IbH+dMl1PQFL7qFedE9zBOtHj7YkmLy7V+PvemGK9yxfq6411MlQmtQ3ULAnMVX1p3MoUStJcjNgUHNGmZ9lVKh4NZ4Q2rjsiTrXZc379BFdHpDEZ2+kT4Num87pZI3+zUDYOHhDE41wHuoTKM1VtUc2txfrqJfhpuDHb+Ma0dzXxfyyysZ+N1eVifX/j2u1+v5YX86fWbvYndaIa72Kv4vIZppidF4O9fves/r9X8J0QS7O3CxWM2La5LNPZwbsiIpi+Kq78PBjXi96fj2wUR7O1Ok1vGambKATueVMuSHfexOK8TNQcW7A2KY3KcpHk622LRDXIv82jRCrvYqJrQ3XEX8aldqvVU1qy9rTuZw4GIxTnZK7mkbXG8NNJ/qHg7A8qQscsvMu/4LYP9Fw8xVc7/GvVC6dVWFzRPZ5gvyFlc1/L05yqfei4ZcalRVkLf9fAEFDZhuaCo6vZ6fDhguLN3S3M/kzc9rIyHKh17hXmh0el5cd6Le9/frsUzyyisJcHWotzYbtsDTyZ5f72pH60A3SjRa7l18hEmrk8gsufJ3r16vZ8PpHAZ8t4fnVp+gWK0lPsBQYOWRzqE42VluBWI3Rzs+HRwHwKIjGfxxJtfMI7p+86paoQyK8WvU603tlEpjBdVfk7LZf6Fhs012pxUw5Pt9nMkrI8jNgU8Gx3J322CpatmIyZFvpB5oH4KjSklyTim/VaWdWQO9Xs/H2wwzayNbBNC5Hmbxqt0c6UNzXxfKKnV8uq3hCzX8U2MvulKtfbBhJu9sfhl5pQ0f6OSXa9hQlW7YL6phZ6LaBbkT7ulEeaWOhVaYsvnH2TzOF1bg5qBiRFzDl7EHQ3rg9H7NUCrgz7P59Xpyrdfr+e9f5wFDamqQe+M9Aa4NXxcHVt3bgXGtgwBDmmunL3fw+IpjLDmawe60Ag5lFLEyKYu3/jhNl5k7uXPhIQ5cLMbZTsnEzqHMH9OGW5r7WUX14b6RPtxZVRX66VVJlFda31rbM3llbD2XjwIaXcGVy+kZ7kVihKGC6uMrjjdYJd+VSVmMmneAnDINMb4uzBoRz/C4AKv4HIj6I0FeI+XjYs897QyzeZ/uMH8AU1tbz+Wz70IRDioFd7UJqtfcf4VCYZzNm2/maqQ5pWpS8g19+7rWY2BrDUI8nAh0c0Cnhy3nGr7X1MqkbNRaPVHeztzcwEGeQqEwzuZZ08WZatWzeIlRPkT7mK84Q3yAG3dWBRJT1p+st95Wv5/O5Xh2Kc52SuPJvLg6B5WST4bE8dNtrYnzc6W8Useiwxk8suwYg3/YR+I3e7j/lyN8sv0cKfnlONkpGd0ygIV3tOH/EqKtLpCenhiDn4s9aYUVvLnJ8tr2XMv8Q4bPdMcQD7qGNe7fpmpTugfi7qAiOaeUt/6s/7TN/+2+pJJsqCffjmppEZVkhflJkNeIPdYlDJVCwf4LRew4n2/u4dTKjJ2Gq+IDY/zoVI/Np6uNbBFAoJsDeeWVfL/PfL0Fq/vjNfFwbJSVy/6pcxMPwJC22NCWXJKqaYq2HXU1sqqR81+pBeSYuY1EXRSWV7KmqpLgwBjzz7S81CcSF3slJ3JKmbWrfioNf1H1fTU41k9KltdRv2a+/PFAJ+be1orbWwUS6+dCkJsDXk52xPi60C/ah5f7RLL+vo58MbQFXcO8rLLgh4eTHe8MiAFgzt50krKLzTyi2tPq9MyvWm86yEzp15YowMX+78b3f51nb3r9/E5pdXpeXpfMlPWGSrJDY/2YNTKeGN/Gne0j/iZBXiMW6ulkLOddnQJpyY5kFrPhdC5KBdzZKqje1uJdyl6lZEIHQxPlHw6Yr0jN7jRDbn+cn6v8kAIdQgxB3vHrrMJ3vS4WVbAlJR+gwQqu/FMLf1difF3Q6PRWVThp5Yksyit1hHs6MSC6YWdALyfQzZEXexuq4X2w9azJA+a96YVsPZePSgF3tKzfrANbpVAoGNDMjxlDW7D5wS4c/E8PjjzRgz8f6MxPY9rwdI8Imvu5Wv3fdlisPzc19Uaj0/PEyiSrWSf/x9lcLhSpcXdUcYsJ2xjZgpFx/twS41vVGuSIyb9fitWVjF982FhJ9sGOTfh4UCxBbtY1ky3qlwR5jdzjVQ08/zibx5lcy2gVcCXVDYz7RHjTN9K7wfZ7d9tg7JUKjmeXsvlsw6cHwt894VoGSAlkgE5VQd7RzJIGPSFaeiwTPdAywNVsqUmXpmyuTjZ/P6ba+rlqDWFitI/FVHp7sGMTmvkYWipMXmvaCofVaVoJUb70jPAy6bYbM3uVskEu8DUkhULB+4Oa46AyZNb8cCDd3EOqlZ+qCq4kRvkS7dO4C4L9k0Kh4NMhcYR6OJJRouauRYdMtj7vYlEFI37cz9pTOTioFLzaN4pX+0bh6WwZ36vCckiQ18i1DnSnZ7gXOj18sNVyZ/POF5Sz9KihD8/trYNM2hfvWvxdHRhRlSL31a7zDbbfahqtztgTrrroSGPXJsgdlQJyyzQkNWCVzSVV78GEKB+zVu67ter9uCe9kIziCrONo7bSC8uNM6BDLKg4g71KyYeDYgH49XgWm86YJmjedCaXP8/mYadUML59cIN+Xwnr1NTLmed6NgVg+qYz5JdbdvXcnFI1q6rWBQ+KaZy98a7Fy8men8a0wdlOyd4LRTz061EqdTcW6G07l0+/b/dwKKMYLyc7PhgUy6NdQnGWDB9xGfLLI3iim6G4yIqkLPIstCz7V3+dR6uHDsHuDI1t+JPEBzsaUjY3ncnjYlF5g+77cGYxZRod7g4quoR6NOi+LZWLvYoWVY1dN6c0zOzqqdxS9l8sQqmAwWYOVKJ8XIj3d0WnN/Sds3SLjxpmQFsHutHNwpqBdw/3MhZheeq3JErUN1bhUKfXM72qgMawWH8SGrg4j7Bej3cNI9LbmfzySl408cyyqS0+kolGpyfG14X+0ZKqeSVx/q7MHBGPSmEolvXAL9cX6Gm0Oj7cepZR8/aTWaKmqZcTnw2J445WgdIiQVyRvDMEN0d6E1PVKuAzC6y0mVumYW7V2qPbWweZZQalQ4gHbQLd0Oj0fL6jYWfzdpz/O1XTrwF7slm6TlXFV/Y2UC+i6lm8jiEexobs5lQ9u7zupOWnbFanavaL9rHIZuBv9mtGoJsDF4rUvLj2xnrnLTh0kYMZxbjYK5nQIUROwEStOaiUfHKLYWb5l6OZbD+Xb94BXYFer+e7/YaU0kExvrg7NnwBKmsyKMaPWVWB3qrkbIb+sI/0wtpfLN5/oZBB3+/l3c1n0emhf7QPc0e3YpAFFLASlk1+fQQKhcI4mzfv4EXUDdTXpbbm7EmjTKOjmY8zt8Wbp7cWwMOdQwHDuqwbTbmoC+N6vEA3+UK/RMeqdXnHMuu/+IperzcGKglRPhaRflfdWHtveiGZFpyyeTijmGNZJdgrFWbrjXct7pc0pl54OINfjl5fD8ILRRW8+vtJAMa1Ca7XPp7CNnUP9+K2lobPyTOrkhqsz1pd/JmSR3JOKS72SkbFS2uQ2hgWF8CcW1viYm9I3ew9exczd52nTHP5zAG9Xs+hi0U8uPQIA77by6GMYtwdVLzYuylfDmtBc39Zny+uzfxnKsIijIoPwN/VntwyDd/uNV+rgH8q1WiZXVU96o7WQWYt2DA8zh9vZzuySjUsaKAUOb1ebwzy2gTKl/qlqgufnMgppaCe04z3XijiTF4ZTnZKhja3jDVl0T4uxPq5oNUbAhNLVR0cdwvzJM7fckt7J0T58EBVWvYzq5JIzqnbxQO9Xs/zq09QWKEl1s+F/3QLl3VK4rq8kdgMD0cVp/PK+NwCs2tm7zb8JveP9qWFBX+mLc0tzf35/f5OxPi6UFSh5dXfT9Hui+08vvwY3+xNY/GRDH46cIFXfz9J3zm7Sfx2D8uOZ6HA0Fv0u9GteLZHhMUUrhKWT4I8ARjSRCZ2NlTa/HpPmsWUcJ538CI5ZRqC3By4o2rdjLk42akY3y4EgG/3NUz1s1O5ZeSUarBXKuhpYWuZzC3C04lgd0cqdXrWn86t1339XBVE9Qz3opkF9SCq7pm3+qRlNkbX6vTGvoKJUZZfnGFaQjQdgt0p1ei4c+EhsutQ9vyrXamsPZWDvVLBpJ5NCbGyptzCcvi6OPB/Cc0A+GT7OVILGnYd+NWcyy8z9rscERdg8Z9pSxPt48LGCZ14/eYogqp68C46ksGLa5N5dPkxnl6VxMxdqcbsh75NvflqeAu+HdWKXhHeks0j6kSCPGE0vl0wLvZKzuaXszIpy9zDoVKn48u/DOvfxrQKtIj+L+Pbh6BSwIGLxexrgLVg26ua1Mf5uxLiYf7Xb0kUCgW9qgLf+iy+otHqWHrMsB4vMdrHosq3D4utTtksssjG6FvP5XOxWI27g4rhLfzNPZxrslcp+W50K/xc7DlfUM6wufvIrcUs8a/HMnl9wynAUKRpiBmKQwnbMrZNEO2D3Smv1PH0qiRzD8dozt509ECHEHcSLaDfpTVyUCl5vGs4ex7txszhLRjbJoiuoZ60D3anQ4g7t7YI4IXeTVl1bwd+ur01t8YH4mgnp+ui7uRdI4w8ney5q00wADN2NnyrgH9afjyLcwXleDracWcr887iVWvi4cTAqsqKDZFG80dVX752Qe5SwOEyqmc3D1wsqrd9bDyTS06ZBm8nOwY2s6yT9+Z+hsbolTp9g6UQ18Wiw4Yx3RTpTaiHk5lHUzuBbo4sv7s9Ps52nMotY9B3eziWVXzFx88/eIHHVxwDYGQLf57rGSGfVXHDlAoFnw6OQ6WAP8/msfx4prmHRGF5JT9UFVy5tUWAlO2/QfYqJbfGB/Lp4DiW392eNeM7svrejswcEc+knk1pI7/74gbJu0fUMLFzKEqFYQ3SrrQCs41Dr9fz6XZDEDUyPoBmvpbTaPWhqnU760/l1mvLCa1Oz59VQV51JUlRU4+qJtNJ2fW3Lq96TdnNUT6EWuBsqjFl08Iao5dqtKxIMqSRJkZZV4n1aB8Xfr3LEOidzS+n/7d7eH/L2Ro9CY9lFfPY8mM8+VsSaq2e3hFehrVUsl5GmEicv6ux4NfkdckUqyvNOp7v9qdTpNYS4eXE6JZScEUISydBnqgh3MvZmAL2kRmbo689mcPRrBJc7JWMaxNkUXnoPcK9aO7rQnmlrl6box+4WER+eSWu9ipuipS0mMup73V5RRWVxuApMcrHot6H1ao/r3vSCy2qz+Wa5GxKNFqC3BwY0ty6gjyAWD9X/nywC11DPVFr9by/5SztvthO+y+20+6L7dw0ezc/H8lAqYD72ofw5fAWBFpASrmwLS/2jiTYzYGsEo2x/6I5VFTqmLU7FYAxLQOlnY8QVkCCPPEv/6lqp7DxTC6n6lhhzhT0ej2fbDcEmMNi/WlrAT3JLqVQKHiok+Hq6oJDGejqqUjNpjOGoKV9iDtBbvKDejn1vS5vZVIW5ZU6wjydLLapdZy/K9E+zmh0ehYetpyUzUVHqguu+ODlbJ3v3wBXB369qx3vDoihZYArWj2kFVWQXlSBXVUxpA8HxTK9XzMCXCXAE6bnYq/i/UHNAUPBr8MZ9ZeafjU/H8kgo1iNn4s9d5q5CJoQonYkyBP/0jbInV4RXuj08NafZxt8/5tT8tmTXoSDSsEdrYMssnrX6JYBuDmoSC+qYPnx+ilSs/GMIWjpGOJhkX8DS1G9Lm/fBdOf/FwaqPha8JXr6v5zv52wjCqbWSVqNlbNrA6Isax1jHWlVCi4v0MTNk7ozJaHOjP71nhmDm/B6ns7sOjONtzVNtgiG7wL2zGgmR8Dmvmi08N/VhyvtwuLV6LW6vh4m+HC66j4QMK9nBt0/0KI6yO/TOKyJvVsCsDq5GxSC8oadN+fVP2YDG7uR1cLbSbs5mDHuDaGq5n/25Nq8u0XVVSyu2pNZA9pnXBVfSO9ATieVWLS92p6YTlbUvIBGNjMstMNqxuj704rpKDc/Cmbvx7LRKuHWD8X+kR4m3s4JtPc15VhsQHcGh8oRRFEg3p/YHNc7JUczSox9qlrKPMOXuRcQTneTnbc21Zm8YSwFvILJS6re5gnnUI80Oj0vLP5bIPtd1daAVvO5WOnVDCmVRD2FnyFfEJVAZZdqYUczzJtWuvmlDy0emji4UjnECm6cjUhHk60DHBFD/xyzHQV6BYfzUQPtA50o7uFB9ot/F1p6uWERqdnkQU0Rq9uzp4Y5Yurg1TgE+JGBbs78mLvSADe3nyGzJKGaZlSXqnlo21nARjXNpjm/m4Nsl8hxI2z3DNoYVYKhYJJvSIAw1X5C4UV13iGaVTP4vWL9rH4GYAobxf6RnqjBz4zcTuF6qqEXZp44uZoZ9Jt26JBVSmBm86YZl2eXq/nxwMXAMN70cXCS4UrFApujTekbK40c8pmUnYJ+y8WoVLAkObWnaophCV5uFMoLfxdKVZreea34w2yz2/2pnOhSI2/qz3j2wU3yD6FEKYhQZ64opsjfWgX5E6FVs+0TafqfX+HMopYdyoXpQLubB1kFc0/H64qwLLqRDZFJkqTq6jUsSbZcKLep6llB7qWojqdcndaIWUa7Q1vb+u5fE7nleFspzSud7N0Q6uqbO5KK6Cw3Hyl1qtnEruEetI22LKKJglhzVRKBZ8OjkWpgHWncvmp6kJUfcksUfPBlrMA3NM2hKbeltPKSAhxbZZ/Fi3MRqFQ8ErfKACWHc/ibH5pve7v/aq00JuaeltNX62EKB8ivZ0p0Wj5zEQN5P84m0uRWouviz39m1lmRUdL0ybIHT8Xe8oqdaxOvvGZrO+rGv4mRPkQ5+96w9trCK0C3IjwckKt1RubkDc0rU5v7CvYP9pXCpIIYWLtgj14qruhAvbL65NJya+/NfNvbjpNkVpLjK8L93cIqbf9CCHqh/wCi6vqHeFF11BPNDo9/7eh/nr07EwtYPXJHJQKuKddiFXM4oGh8t6TVS0nfjxwAbVWd8PbXFZVrbN3hJeUZa8lpUJhTNlcdYNBXlaJmpVV6bJDY/2tprKpQqEwNkb/zQSB7vXYei6f9KIK3BxU3CKpmkLUi+d7NaVtkBulGh3jFx82ye/OP+1JL2TeIcPFov90DZMekEJYIes4kxZmo1AoeLVqNm9VcjYHLpq+TL1er+eNqnTQW2IMpaKtyW0tA/F3tSe7VMOcPTdW9azikpkoSdWsm0ExhvfNn2fzqNRd/0nPvEMX0ej0xPq5MDDGut6L1Y3Rd6YWUGCGxujVffr6RnoT6S1l1oWoD3ZKJf8b2RJXexVHs0qYtDrJpNsv02h5aqVhzV//aF/jxSMhhHWRIE9cU5dQTwY390OnhxfWnEBv4h49q5Nz+Cu1EEeVgvs7hFhdipejnZJHu4QB8L89aWh11//32Xgml8IKLT7O9lYX7JrbzZE+eDvbkVtWyS9Hr6/KZkWljq93G1piDIv1x83BuoretA50I9LbkLL548H6Xa/zT8XqSlYkGWah+0f7Ws0MqBDWqKmXMzNHtABg/qEMvtlrurYKb2w6zYmcUnyc7Xm6ezjOFl54SghxedZ1Ni3M5v8SorFXKth3oei6T6Avp0yj5dXfTwJwa3wgPcOtc/ZqfLsQPBxVnC8o59t91/9j++2+v9eCSapm3dirlIxpGQjA/EPXtyZtydEMLhar8XWx547W1tcPSqFQMKalYdzLkxo2ZXNlUjalGh0h7o70i5a1pELUtwHN/HimR9X6vHXJrD9545/59ady+LoqI2VSrwg6W2ivWiHEtUmQJ2olwsuZR7oYKklO3XiKYrVpqvd9uv0c5wrK8Xex59EuoaiU1nn1393Rzrg279Pt565rjcTp3FI2nM5FAYyoam4t6mZsG0OJ7x3nC8gsqVvbD51ezxdVxXNGxQcQ4WWd6YajWhpSq/ZdKOR8QXmD7bc6VbN/M1/85QKFEA3ixd6RDGnuh1YPE5YeZXdawXVv60hmMQ//ehQw/Abd1UZaJghhzSTIE7X2TI8IgtwcuFisZvLa5Bve3qncUmbsNPSXe7RrGC2svMnqAx1D8XWx52Kxmi//qnulzW/2GmbxuoR6clOkdc5omlvLADdaBrii0en5Zk96nZ679mQOJ3JKcbFXcmcr65vFqxbl7UK7YHd0evh+v+lSuK4mrbCcLSn5wN/tLIQQ9U+pUDBzRDzdQj0pr9Rx2/wDbE6pe7/Q9MJy7lp0iGK1lrZBbrzaN0rSNIWwchLkiVpzc7Dj0yFxACw8nMEfZ3Kve1sarY7Hlx9DrdXTuYkH97a1/iuGrg4qJvVsCsB/d54nvw5984rVlfxUtYZqRJw/Tnby43q9qq8+Lzh8sdYFWCp1Ot78w1A9dmisP60CrfuCw+1VaaurTuQ0yP4WHc5Aj2FNYM9wrwbZpxDCwEGl5KcxrekQ7E6pRsedCw7WqY3KsaxihszdR3pRBWGeTrw7oDnhVprJIIT4mwR5ok5ujvThjlaGE8jHVxwnp1R9Xdt5d/NZ9l4ows1BxTM9IvBwsjflMM3mnnbBRHo7k1deyfOrT9T6eV/vTqNIraWJhyMjWkiq5o24s3UQnk52pBZWGGdHr+WH/RdIyi7F3VHFAx2boLDyoiEjWgSgUig4kVPKnvTrT9+qDZ1ez9yqpswDm/nK1X8hzMDN0Y6ld7Xj5khvNDo9j684zsO/HrnqxUa9Xs/iIxkMm7uPtMIKwjwcebd/MzqEeDTgyIUQ9UWCPFFnb/aLIdTDkcwSNXctOlTncvW/n8rh8x2GNM1ne0SQGGU7RRocVEo+HRwLwK/Hs9hUi9nOtMJyPt6eAsC97UJkPdMNcnO048luhmqnn+84h+Ya6yMLyjW8t/ksYPj7twtyr+8h1jt/VwcGNDN8rv63u35TNjedyeNcQTmu9ioptS6EGTnZqfhxTGse6RyKUgFLj2XR/osdTNt4ikMXi4y/1UUVlSw9lsnIn/bz6PJjFFZoaRXgxqwR8fRrJv0thbAVEuSJOvNwsuOnMW1wtlOy90IRT61MQlfLtgq70gqYsPQIemBIcz/u72D9syb/1C3Mi3FtDGu6nvrt2rOd0zaepkyjo1WAG+PbWX/aqiWY0CEUX+drr4/U6/W8+vspcso0hHk68UCHEJt5P97dNgQwVMsr12jrbT/f7zfMlvZv5kOMr0u97UcIcW12SiXTEpuxZGxbmno5UaLRMmPneRK/3UPTDzcT/sGfRH+8hYd/Pcr28wU4qhTc1z6Eb0e1pGMTqaQphC2RIE9clzh/V74Y1gIFsOhIBo8uO3bNGb296YXctegQZRodnUI8mJYQjauDbaZ2/V9CNMHuDlwoUnPHgoNUVF7+b7P0WCa/HMtEATzeNQwfF4eGHaiNcnVQ8WzPCAA+2JrCkcziyz7uhwMXmH/oIkoFPNUtnFBP21mHcnOUNwGuDhRWaFlwOKNe9nGxqII1yYay7UOa+9tMgCyEtesR7s2OiV35clgLOjfxwNVehVqrp7zqtyjUw5E7WgXy3ehWvN0/RtbgCWGDJMgT121orD+fDI5FqYBfjmUy6qcDnM4r/dfjdHo9s/ekMWzuPvLLK2nh78pHtzQnzIZ/VDyd7Fl4R1tc7JUczCjmnp8P/WttxLqTOTy2/BhgKHs/NFbW4pnS/R1C6FpVcW7cooPkldX8+68/lcPL65KrHtuEO9tYb0XNy7FTKo0zyvMO1U9j9B8OXECrh5YBrvSLlqqaQlgSpULB6JaBrLynA8nP9GTt+A4su6sdmyZ0YtOETnw+tAUJUb5W27pICHF1duYegLBuY9sE42Kv4vEVx9iRWkDv/+1iVHwAfSN9cFApOZtfxo8HLnA6rwyA3hFeTE2IJs7K2yXURqyfK9+OasXYhQfZdDaPnv/7i6e6RRDu5cSa5GwWHcmgUqcnIcqHqTdH42gn11xMyU6p5NtRLUmYs5sLRWp6f72LV26KpImHE6uTs/lfVcPfnuFePN8zAgeV7f39x7UN5tPt59ibXsSRzCJaBphuvWF5pZZv9hr+hsPjAmx2Vl4IW2CnVNIuWAqqCNGYSJAnbtiIFgG0DHDjPyuPsTe9iAWHM/6VHuZir2R8uxCe7hGOt3PjSUnsG+nDsrvb8fCvx0grrOCV30/WuL93hBcfDIwh0E2KrdQHXxcH5t3ehjELDpBZoubJ35Jq3D+yhT8v94my2TTZpl7OJEb7sP5ULh9vO8fXI1uabNuLj2SSXarB39WeMS2l4IoQQghhSSTIEybRzNeFVfd0YO3JbJYdz+ZQRhFKpQJPRzs6N/FkdMsAYv1cUTbCNTudm3ix5cEuvL/lDPsvFJFeVEGElzMjWwQwLNbPZtpHWKr4ADd2P9KND7acZdGRDBxUSgJcHRjZIoB72gXbfMn/RzuHsf5ULmtP5pBbpsbHBBdZ9Ho9X+0yFLS5tUWgTa1lFEIIIWyBBHnCZBQKBQNj/BkYI2vL/snVQcXUhGbmHkaj5Wyv4tWbo3mlbxRavR47pe2lZl5JrwgvYv1cSMouZcaO87x2c/QNb3PjmVySsktxsVca+2YKIYQQwnJY/JmOXq9nwoQJLFmypMbteXl5PPHEE7Rv356EhAR+/fXXGvcfPXqUMWPG0LZtW0aPHs3hw4dr3L9ixQr69etH27Ztefzxx8nN/bufmV6v54MPPqBbt2506dKF9957D90llSOvtW8hhGVSKBSNKsADw2t+tLOhb+D8Qxcpr7yxdgp6vd7YV/CW5n60DLT99bVCCCGEtbHosx2dTsf06dPZunXrv+576aWXKCoqYsGCBTz66KO88sorHDx4EIDS0lIefvhhOnXqxJIlS2jfvj0TJ06ktNRQ+fHgwYNMmTKF//znPyxYsIDCwkJeeukl47a/+eYbVqxYwYwZM/jss89Yvnw533zzTa32LYQQlmZUywACXR3ILtXw+Y5zN7StNSdz2HuhCCc7Jfe2DWmUKdhCCCGEpbPYIC8jI4Px48ezYcMGPDxqVoQ6d+4cGzduZPr06TRv3pwxY8YwfPhwfvrpJwB+++03HB0deeGFF4iOjmbKlCm4urqyevVqAObOncstt9zCyJEjiYuL47333uOPP/7g/HnDGpPvv/+eJ598kk6dOtGtWzcmTZrEjz/+WKt9CyGEpXGyU/F876YAfL07jRL19c3m6fR63vnzDAAjWwTQNUyaJwshhBCWyGLX5B05coTg4GA+/fRTbrvtthr3HThwgODgYEJDQ423dezYkZkzZxrv79ixo7Exr0KhoEOHDuzfv59Ro0Zx4MABHnroIeNzg4ODCQkJ4cCBAzg4OHDhwgU6d+5cY9tpaWlkZmZec9+Xo9VWotPpb+wPYiKVlRoqKyuprNRc+8HCKsgxtW2mOr63xfny8TZH0goreH/zaab0aVrnbSw5lsnRrBJc7JXc3ToAbWUlN5b8KUA+w42FHGfbJ8fYtlnb8bXYIC8hIYGEhITL3peVlUVAQM2S3b6+vmRkZBjvb9as2b/uT042ND7OzMy87PMvXrxIVlYWQI37/fz8AIz3X23fl5OTk3PF+xpaZWUlRUUFANjZWezhF3Ugx9S2mfL4PtbWhymbL/DNvnRGRjoR4lb7Spv55ZW8vuE0AMOjPYhwVJOXl3uNZ4nakM9w4yDH2fbJMbZtlnR8Q0J8rvkYs42wvLz8ioGRv78/Li4uV3xuWVkZDg41T04cHBxQq9W1ur+8vPyK95eXlxv/fel9AGq1+prbvhxfX1+LmskD8Pb2xs5OSvfbAjmmts2Ux/eeTt4sOFHIwYwSXt2axeI7WhkzHq5l2upkcsu1hHs68lTPKHy8pG2CqchnuHGQ42z75BjbNms7vmYL8g4cOMC999572fu++OIL+vXrd8XnOjo6/iuoUqvVODk53dD9zs7ONQI6R0dH4/8DODs7X3Pbl6NS2aGyoFZcdnZ22NnZY29v+W9QUTtyTG2bqY6vPTBjaDwJ3+xmZ1ohi47mcFe74Gs+b93JHBYeyQTg2Z5Nifb3uMYzRF3JZ7hxkONs++QY2zZrOr5mC/K6du1KUlLSdT03MDCQ7OzsGrdlZ2fj7+9/1fur0yyv9vzAQEPPp6ysLOO6u+oUzur7r7ZvIYSwZHH+rjzdPZwPtqbw8vpkWga60i74ykHb0cxiJi47CsCIOH9GxQdc8bFCCCGEsAwWW13zatq1a0daWhoXL1403rZnzx7atWsHQNu2bdm3bx96vSFFUq/Xs3fvXtq2bWu8f8+ePcbnXrhwgQsXLtC2bVsCAwMJCQmpcf+ePXsICQkhICDgmvsWQghL93SPCLqGelBWqeP2BQc5k1d22cel5Jdx98+HKFZraRPoxqt9o3Cys6C0BCGEEEJcllUGeWFhYfTq1Yvnn3+e48ePs2jRIlasWMFdd90FwKBBgygsLOTNN9/k5MmTvPnmm5SVlXHLLbcAMHbsWH799VcWLVrE8ePHeeGFF+jbty9hYWHG+z/44AN27tzJzp07+fDDD42ppdfatxBCWDoHlZKfxrQhxteF/PJK+n2zmwWHLhgvjOn0en49lkniN7tJLaygiYcj7w6IIVzW4QkhhBBWwWpL/7z33ntMmTKF22+/HX9/f9566y3atGkDgJubGzNnzuT1119n4cKFxMbGMmvWLGMxl/bt2zNt2jQ+++wzCgoK6NmzJ2+88YZx2w888AA5OTn85z//QaVScdttt3HffffVat9CCGEN3B3tWDK2LaPnHeBETilPrExi+h9niPNz5WhWMVklhgXm8QGuvHZTNB2bSE88IYQQwloo9NWXbkW9ycoqMvcQjDQaDXl5uXh7+1jFolFxbXJMbVt9H1+NVsd7m8/y37/Oo7mkCrCTnZJb4wN4vmcEoZ4yg1ef5DPcOMhxtn1yjG2bJR1ff3/3az7GamfyhBBC3Dh7lZIpfaP4T9cwNp/L43BGMcHuTiREetPE0wllLVssCCGEEMJySJAnhBACT2d7hsYGMDRWqmcKIYQQ1s4qC68IIYQQQgghhLg8CfKEEEIIIYQQwoZIkCeEEEIIIYQQNkSCPCGEEEIIIYSwIRLkCSGEEEIIIYQNkSBPCCGEEEIIIWyIBHlCCCGEEEIIYUMkyBNCCCGEEEIIG6LQ6/V6cw9CCCGEEEIIIYRpyEyeEEIIIYQQQtgQCfKEEEIIIYQQwoZIkCeEEEIIIYQQNkSCPCGEEEIIIYSwIRLkWYglS5aQkJBg7mGY1LJly7jnnnvMPQyzi42NJTY2lvT09H/dN2/ePGJjY/n8889Nvt8beU99+eWXTJ482cQjanzMdeyr6fV6JkyYwJIlS+ptH+JvdfnMmeI7PyUlhTZt2tzQNmyRRqPh888/JzExkVatWtG3b1/efvttiouLTbJ9vV7Pjz/+aJJt/VNDngvYwvsnNjaW55577l+3W+I5VWpqKrGxsaSmptb7vtRqNUOHDmXnzp31vi9LYInH+0aZ4hxagjxRL3bs2MFrr71m7mFYDHt7ezZs2PCv29evX49CoTDDiK5sxYoV9Rp4NDbmOvY6nY7p06ezdevWetuHMJ8LFy4wceJEKioqzD0Ui/PBBx+wdu1apk+fzurVq3n77bfZunUrkyZNMsn2d+3axbRp00yyLXOxpffPihUr2L59u7mHYTEqKip49tlnSU5ONvdQxHUy1Tm0BHnC5GbMmMFDDz1EWFiYuYdiMTp16vSvE/3i4mL27dtHfHy8mUZVU2VlJa+//jovv/yyHDsTMsexz8jIYPz48WzYsAEPD4962Ycwn/Xr1zNq1CgcHBzMPRSL9Msvv/DUU0/RvXt3QkND6d69O1OnTmXjxo1kZmbe8PatvfOUrb1/mjRpwrRp01Cr1eYeitmdPHmS22+/nXPnzpl7KOI6mfIcWoI8C7Vnzx7Gjh1L27ZtadeuHQ899JDxx2nJkiXcc889fPbZZ3Tt2pVOnTrx9ttvG394Jk+e/K9Uu9jYWOO0fUZGBk8++SSdO3emVatW3HrrrezZswf4O53giy++oHPnzrz88st06NCBtWvXGrel0Wjo2rXrFa+cbd26ldmzZzNgwACT/12sVWJiIn/99VeNdKFNmzbRqVMnXF1djbep1WrefvttevfuTcuWLUlISGDBggXG+xMSEnj//ffp1asXI0eORK/Xc/DgQeN7ZeDAgaxcudL4eL1ez+eff258n7z77rtXHGNpaSlJSUksXLiQ9u3bm/gv0HiZ4tgvW7aMrl27UllZaXz8mjVr6Nu372VPOI8cOUJwcDCLFy/G3d29Hl+duJLLpWZ9/vnnl02/uf/++5k+fXqN2x555BE++eSTy25706ZNPPXUU0yZMsWkY7YVCoWCHTt2oNPpjLe1b9+elStX4u3tDRg+b9OnT6dr16507dqVSZMmkZ+fD/x97JYvX07v3r3p1KkT06dPp7KyktTUVO69916g5u/q/PnzSUhIoH379txzzz0kJSUZ952QkMDPP//M6NGjadOmDRMmTCAtLY0nnniCtm3bMmLEiH/Nunz00Ud06NCB3r1788MPP9S471r7+udvxD/Z2vvn6aefJiMjg9mzZ1/xMRcvXuSpp56iS5cudO3alenTp6NWq9HpdPTu3ZvFixcbH6vX6+nTpw+//vorALt372bUqFG0adOGYcOGsWbNGuNjJ0+ezPvvv8/TTz9N27ZtGTx4MEePHuXjjz+mU6dO9OnTh1WrVtUYy+rVq+nTpw8dOnTgtddeqxGcXmtfkydPZvjw4XTv3p2zZ8/+63X+9ddfdO3atcZ5Q2Mk59AGEuRZoKKiIiZOnEjPnj1ZsWIFs2fP5ty5c8yaNcv4mH379nHmzBnmzZvHq6++yvfff8+2bdtqtf1Jkyah1WqZP38+S5cuJTAwkKlTp9Z4zN69e1m8eDEPP/ww/fr1q/FFs23bNuzs7OjSpctltz9v3rwr3tdYNW/enMDAQP7880/jbevWraNfv341Hjdr1iw2bdrE559/zurVqxk5ciRvvPEG2dnZxscsX76c2bNn884775Cbm8uECRNo0aIFv/zyCxMnTuTFF1/k+PHjAKSnp3PmzBnmz5/PtGnT+Oabb2qM4VIeHh7Mnz+fuLi4evgLNF6mOPaJiYmUl5ezY8cO4+NXrVrFLbfcctmUz4SEBN577z18fHzq74UJkxkyZAhr1641nmQUFRWxZcsWhgwZctnHT58+nTvvvLMhh2hV7r33Xn744QcSEhJ4/fXXWbNmDeXl5TRr1gx7e3vAEEQdPnyY//3vf3z//fcUFxfz1FNP1djOjBkz+Pjjj5kxYwZr167l888/Jzg42JjOvmXLFtq3b8+GDRuYMWMGr776Kr/88gsdO3bk3nvvpaCgwLitTz75hOeee46ffvqJo0ePcuutt9KjRw9+/vlnnJ2d+eijj4yPTUtLIykpiQULFvDss8/y7rvvGk8wa7OvS38jLvf9YGvvn8DAQJ588km++uorzp8//6/71Wo148ePp6ysjB9++IFPPvmETZs28d5776FUKhk0aBDr1q0zPn7//v3k5+eTmJhIVlYWEydOZNSoUSxfvpwHH3yQyZMns3v3buPjv/vuO7p06cKyZcvw8vJi/Pjx5OTksGDBAuN78NILDgsXLuTjjz/mq6++4s8//2TmzJkAtdrXr7/+ytNPP83MmTNp2rTpv17ruHHjePnll3F2djbFn9YqyTn03yTIs0Dl5eU89thjPP7444SFhdGxY0cGDBhQ40qfVqvljTfeICoqihEjRhAXF8ehQ4euuW29Xk+/fv149dVXiY6OplmzZtx1112cPHmyxuPGjx9PeHg4TZs2ZciQIWzcuNGYu7969WoGDRqESqUy7Qu3cYmJica0PbVazdatW0lMTKzxmLi4ON58803atWtHWFgYjzzyCBqNpsYVu+HDhxMbG0tcXBwrV67E09OTV155haioKEaNGsVzzz1HeXk5YFgPNn36dCIjIxk8eDBxcXHGAFA0nBs99q6urtx8882sXr0agLKyMv74448rBgHCugwYMIDc3Fz27t0LGNLpIiMjiYmJMfPIrNPjjz/O+++/T1BQEAsXLuTJJ5+sMVtTVlbG3Llz+b//+z/atGlDbGws7733Hn/99VeNWbHnn3+eTp060a1bN5566ikWLlyIUqnE09MTAH9/fxwcHPj666+ZOHEiN998M02bNuXpp5+mSZMmLFu2zLitUaNG0aNHD1q1akW3bt2IiYlh7NixxMTEMHz4cE6fPm18rKOjI++88w4xMTHceuutDBs2jPnz5wPUal+X/kY0Fvfccw8RERG8+eab/7pv8+bNZGRk8P777xMbG0v37t157bXXmDdvHiUlJQwZMoStW7casy3WrFnDTTfdhJubGz/++CM9evTg7rvvJiIighEjRnDHHXfw3XffGbffqlUrxo0bR0REBEOHDqWsrIxXXnmF6Oho7rnnHgoKCmpcqH355Zfp2LEjXbp04amnnjIe29rsq3Xr1iQkJFh9wZz6JOfQf7Or9z2IOvP392fkyJF8++23HDt2jJMnT5KUlESHDh2Mj/H19cXNzc34bzc3txqpXFeiUCgYO3Ysv/32G3v37uXMmTMcPny4xlUmMOS4V+vZsycODg5s3ryZm266ifXr1/PVV1+Z4JU2LomJiTz55JNUVlayfft2mjdvjq+vb43H9OvXj61bt/LOO+9w+vRpjh49Chi+kKpdemzOnDlDfHw8SuXf12vuv/9+AE6fPo2vry8uLi7G+9zd3WXdghmY4tgPHTqUV155halTp7Jp0yYCAgJo1apVg78WYXoeHh706dOH1atX07FjR1atWsXgwYPNPSyrNnz4cIYPH05eXh5btmxh7ty5TJkyhdjYWBwcHNBoNP+azdLpdJw9e5aWLVsC1PjNbdWqFbm5ueTl5f1rX6dOneL999+vMRtXUVFR4+LcpetrnJycanyPOzk5odFoajy2Oq0UID4+nkWLFtV6X5duu7FQqVRMnTqVcePGsX79+hr3nTp1iqZNmxqDczAc28rKSs6dO0e7du3w9/c3Xjhbu3Ytzz//PGD4Hd24cWONJQwajYbIyEjjv0NDQ43/7+TkhJ+fH05OToAhYAdq/O5eGqDFx8eTnZ1NQUFBrfbVGI9tXck59N8kyDOTrKwsiouLjR9evV5vjOozMjIYPXo0LVu2pEePHtx+++1s2rSJAwcOGJ9/uQXT1ak+CoWiRh7+pW9cnU7HhAkTKCwsZPDgwSQkJKDRaPjPf/5TY1vVX0wAdnZ2DBw4kDVr1mBvb4+bm1uND4uonY4dOwKGXPH169fTv3//fz3m448/ZtGiRYwaNYqRI0fy+uuv/6ss8D+PzdVc7kqRtRcNsEamOPZ9+vRBq9Wya9cu1qxZwy233NJg4xeXd7Xv8culyV3tJGLo0KG8++67PPHEE2zbto1XXnmlfgZt444fP87SpUuNa2q8vb0ZNmwYAwcOZMCAAezYsYOePXsC8NNPP9W4CAaGk7/qtXnVqZ2A8STucsdVq9Xy8ssv07179xq3X3oS+c/v4ksvzP3TP+/T6XTGsdRmX5f+RjQmHTp0YPTo0bz55ps8+OCDxtsv9/eovnhW/d/BgwezZs0aIiIiyMvLo2/fvoDhMzts2DAeeeSRGs+/9Lf3n7/DVzu2/7y/+vfY3t6+VvtqrMf2n+QcunYkXdNM5syZwzvvvGP8d1FRkfHK3bp16/D09GTmzJmMHz+eTp06cf78+VqfnNvb21NSUmL896U56idPnmTXrl18++23PPLII/Tt29e4GPVq2x82bBh//vknGzZsYNCgQRZX9t8a2NnZcdNNN7FhwwY2btz4rzVZYFhQ/+qrrzJp0iQGDx5MWVkZcOVj07RpU5KSkmrc//TTT/P111/Xz4sQ18UUx97BwYH+/fuzbt06tm7dKqmaFuBq3+PVJ+WXfhdfrT9WQkIChYWFzJ49m9jYWMLDw+tp1LZNq9XyzTffGGfCqzk4OODk5ISPjw9hYWGoVCry8/OJiIggIiICNzc33n77bXJycozPOXbsmPH/Dx8+TEBAAN7e3v/6/YuMjOTixYvGbUVERPDVV1+xf//+63oN58+fN37+AQ4ePEhUVFS97MvWTJo0idLS0hpFWCIjIzl79qwxeAfDujs7Ozvj56w6ZXPNmjUkJCQY17RFRkaSkpJS4+/9+++/s3z58use44kTJ4z/f/DgQYKCgnBxcamXfdkqOYeuHQnyzKRTp07s2LGDbdu2cfz4cX766Sd69OgBgJeXF+np6Wzfvp3z588za9Ys1q5dW+s0u9atW7N161a2b9/OiRMnmDZtmvGEw8PDA6VSycqVK0lLS2P16tXGReRX237Hjh1xdnbml19+kZPLG5CYmMiiRYvw9fW9bHlcLy8vNm7cyPnz59m9ezcvvPACcOVjM2zYMPLz83nvvfc4e/YsS5Ys4ffffzdeqRaWwxTHfujQofz8888EBQXJei0LcLXvcT8/P4KDg5k9ezbnz59nyZIlbNq06YrbcnJyIjExkW+++Ua+Y29Ay5Yt6du3L4899hjLly8nNTWV/fv38/rrr6NWqxkwYABubm6MGTOGqVOnsnPnTk6ePMkLL7xASkpKjdS7N998k0OHDrFt2zY+/fRT7rrrLgBjAHD48GEqKiq4//77+e6771i6dCnnzp3j/fffZ9WqVURHR1/Xa6ioqODFF18kOTmZ+fPns2bNGsaPHw9g8n3ZGm9vbyZNmkRaWprxtp49exIWFsYLL7xAUlISO3bs4I033mDo0KHGFjMtWrQgICCAuXPn1siSGDduHIcPH+bjjz/m7NmzLF++nI8++oiQkJDrHuMbb7zBgQMH2Lp1K5999hn33Xdfve3LVsk5dO1IuqaZJCYmcv/99/PCCy9QWlrKwIEDmThxIgC33HILu3bt4sknn0ShUNC6dWtefPFFPv/881q9SUeMGMHevXt57LHHcHd356mnniIlJQWAoKAgpk6dyhdffMFHH31EZGQkr7zyCi+++CJHjx7F39//sttUKBQMGjSIDRs2yDqgG9CrVy8qKysvO5MD8NZbbzF16lSGDBlCYGAgY8aMQaVScezYMfr06fOvx3t4eDBz5kzeeustfvjhB8LCwvjwww9p0aJFjavQwvxMcey7du2Kq6urrNeyEFf7Hlcqlbz55pu88cYbDB48mO7du/PII49csbotGFLGVqxYIcf3Bn3yySd89dVXzJgxg/T0dFxcXOjVqxdz5841pjVOnjyZd999lyeffBKNRkPnzp2ZNWtWjbTKwYMHM3HiRHQ6HWPHjuXhhx8GDOXUe/bsyZ133slHH33E4MGDyc7O5rPPPiM7O5tmzZrx5ZdfXrb6YW20aNGCwMBAbr/9dry9vXnrrbeMv7um3pctuu2221i8eLFxhkWlUvHf//6XN954g9tvvx1XV1eGDRvGs88+W+N5gwcP5rvvvqvxW9ukSRO++uorPvjgA2bPnk1gYKCxjcH1Gjt2LI8++igajYbbb7/dGMDXx75slZxD145CLwt0RC0999xzRERE8OSTT5p7KEI0SsXFxcay0NKw3vYsXLiQZcuWMXfuXHMPpVFLTU0lMTGR33//vcbMnhBCXC9znEPLTJ64pv3793PkyBF+//13VqxYYe7hCNHo6PV61qxZw9q1a2nfvr0EeDYmJSWFw4cP8+WXX/L000+bezhCCCFMxJzn0BLkiWvavHkzc+bM4ZlnnpGrmkKYgUKh4P3330elUvHll1+aezjCxFJTU5kyZQqJiYkMGzbM3MMRQghhIuY8h5Z0TSGEEEIIIYSwIVJdUwghhBBCCCFsiAR5QgghhBBCCGFDJMgTQgghhBBCCBsiQZ4QQgghhBBC2BAJ8oQQQgghhBDChkgLBSGEEI1aQkICaWlpgKFdhbOzM7GxsTz++OP07t27wcdTUVHBM888w5YtW2jbti0//PBDg49BCCGEdZMgTwghRKP38ssvM3jwYHQ6HQUFBSxdupSJEyfy9ddf06NHjwYdy+bNm9m8eTPz5s0jICCgQfcthBDCNkiQJ4QQotFzd3fH398fgMDAQF544QWysrJ4++23Wb58eYOOpaioCD8/P1q1atWg+xVCCGE7ZE2eEEIIcRl33HEHJ06cICUlBYCTJ0/ywAMP0L59e1q3bs24ceM4deoUAPfffz/Tp0+v8fxHHnmETz755LLb3rdvH2PHjqVdu3YkJCQwb948AJYsWcLkyZNJT08nNjaWJUuW1Hjenj17iI+PJzc313jb4cOHadu2LcXFxej1er744gt69epFp06deOSRR0hPTzc+9mqvYefOnSQkJPD666/TsWNHZs2adWN/QCGEEGYjQZ4QQghxGdHR0YAhMNLpdDzyyCM0adKEX3/9lfnz56PVann//fcBGDJkCGvXrkWv1wOG2bgtW7YwZMiQf2331KlTjB8/ns6dO7NkyRKeeOIJ3n33XdatW8fgwYN5+eWXCQoKYsuWLQwePLjGczt06EBgYCDr1q0z3rZq1Spuuukm3NzcmDt3LsuXL+fDDz9kwYIF+Pr6MmHCBDQazTVfA0BaWhpqtZolS5YwdOhQk/9NhRBCNAwJ8oQQQojLcHd3B6CkpITy8nLuvPNOJk+eTHh4OC1btuTWW2/l5MmTAAwYMIDc3Fz27t0LwPr164mMjCQmJuZf2124cCHx8fE8++yzREVFceutt3L33Xfz9ddf4+TkhLu7OyqVCn9/f5ycnGo8V6FQMHjwYFavXm28bfXq1cZg8uuvv+aFF16ga9euREdHM23aNAoKCti8efM1X0O1Bx98kIiICEJCQkz3xxRCCNGgZE2eEEIIcRnFxcUAuLm54eLiwtixY1m6dCmHDx/m9OnTHD16FD8/PwA8PDzo06cPq1evpmPHjqxatepfs3DVTp06RZs2bWrc1r59e+bPn1+rcQ0dOpRvv/2WvLw8zp8/T15eHn379qWkpISLFy/yzDPPoFT+fQ23vLycs2fPkpCQcNXXUC00NLTWfyMhhBCWSYI8IYQQ4jKSkpIAiImJoaSkhNtuuw1vb28SEhIYOnQop0+fZs6cOcbHDx06lHfffZcnnniCbdu28corr1x2u46Ojv+6TafTodVqazWuFi1aEB4ezvr16zl79iyJiYk4OjpSUVEBwKeffkpkZGSN53h6etbqNVxpfEIIIayLBHlCCCHEZSxevJiWLVsSFhbGxo0byczMZPny5djZGX46t2zZYlyDB4Z+e1OmTGH27NnExsYSHh5+2e1GRkaya9euGrft27fvX4HZ1QwdOpSNGzdy7tw5Jk2aBBhmE319fcnKyqJv374AqNVqnn32WR544AHy8/Ov+RqEEELYBlmTJ4QQotErKioiKyuLzMxMkpKSePPNN/ntt9+YPHkyAF5eXpSWlrJ+/XpSU1NZtGgRP/74I2q12rgNJycnEhMT+eabby5bcKXauHHjOHbsGB999BFnzpzhl19+4aeffuKuu+6q9XiHDh3Kli1byMrKomfPnsbb77vvPj755BM2bNjA2bNneeWVV9i7dy9RUVG1eg1CCCFsg8zkCSGEaPTeeust3nrrLRQKBT4+PsTHx/Ptt9/SqVMnwLBm7vHHH+f//u//qKioIDY2ltdee40pU6aQkZFBYGAgAIMHD2bFihVXXI8HEBISwsyZM3nvvfeYM2cOISEhTJ48mdGjR9d6vBERETRr1oz4+Hjs7e2Ntz/wwAOUlJTw2muvUVxcTKtWrZg9ezaenp7XfA1CCCFsh0IveRpCCCGESSxcuJBly5Yxd+7cet2PTqfj5ptv5t1336Vbt271ui8hhBDWR2byhBBCiBuUkpLC4cOH+fLLL3n66afrdV+bNm1iy5YtODk50aVLl3rdlxBCCOskQZ4QQghxg1JTU5kyZQqJiYkMGzasXvc1e/Zszpw5wyeffFKjVYIQQghRTdI1hRBCCCGEEMKGyCVAIYQQQgghhLAhEuQJIYQQQgghhA2RIE8IIYQQQgghbIgEeUIIIYQQQghhQyTIE0IIIYQQQggbIkGeEEIIIYQQQtgQCfKEEEIIIYQQwoZIkCeEEEIIIYQQNuT/Aa51Xnr6xCkmAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 900x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Prepare data for Prophet\n",
+    "prophet_train = pd.DataFrame({\n",
+    "    'ds': train_data.index,\n",
+    "    'y': train_data.values\n",
+    "})\n",
+    "\n",
+    "# Initialize and fit Prophet model\n",
+    "try:\n",
+    "    prophet_model = Prophet(\n",
+    "        yearly_seasonality=True,\n",
+    "        weekly_seasonality=False,\n",
+    "        daily_seasonality=False,\n",
+    "        changepoint_prior_scale=0.05\n",
+    "    )\n",
+    "    \n",
+    "    prophet_model.fit(prophet_train)\n",
+    "    \n",
+    "    # Make future dataframe\n",
+    "    future = prophet_model.make_future_dataframe(periods=len(test_data), freq='M')\n",
+    "    \n",
+    "    # Predict\n",
+    "    prophet_forecast = prophet_model.predict(future)\n",
+    "    prophet_pred = prophet_forecast['yhat'].iloc[-len(test_data):].values\n",
+    "    \n",
+    "    forecast_results['Prophet'] = prophet_pred\n",
+    "    print(\"Prophet forecast completed\")\n",
+    "    \n",
+    "    # Plot Prophet components\n",
+    "    fig = prophet_model.plot_components(prophet_forecast)\n",
+    "    plt.suptitle('Prophet Decomposition', y=1.02)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "except Exception as e:\n",
+    "    print(f\"Prophet failed: {e}\")\n",
+    "    forecast_results['Prophet'] = np.full(len(test_data), np.nan)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9. Model Comparison and Evaluation\n",
+    "\n",
+    "Compare all models using various metrics and visualizations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model Performance Metrics:\n",
+      "============================================================\n",
+      "                       RMSE       MAE  MAPE    R2\n",
+      "Linear Regression   2705.12   2220.88  1.30  0.88\n",
+      "Random Forest       6511.29   5479.66  3.22  0.31\n",
+      "LSTM (Proxy)        6837.22   5631.16  3.23  0.21\n",
+      "SARIMA              7718.79   5606.24  3.25 -0.01\n",
+      "Moving Average     10164.72   8579.10  4.95 -0.75\n",
+      "ARIMA              11031.57   9911.05  5.68 -1.06\n",
+      "Holt-Winters       11444.30   9047.76  5.22 -1.22\n",
+      "Prophet            16135.49  15371.49  8.88 -3.41\n",
+      "XGBoost                 NaN       NaN   NaN   NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate metrics for all models\n",
+    "def calculate_metrics(actual, predicted):\n",
+    "    \"\"\"\n",
+    "    Calculate various forecast accuracy metrics\n",
+    "    \"\"\"\n",
+    "    # Remove NaN values\n",
+    "    mask = ~np.isnan(predicted)\n",
+    "    actual_clean = actual[mask]\n",
+    "    predicted_clean = predicted[mask]\n",
+    "    \n",
+    "    if len(actual_clean) == 0:\n",
+    "        return {'RMSE': np.nan, 'MAE': np.nan, 'MAPE': np.nan, 'R2': np.nan}\n",
+    "    \n",
+    "    rmse = np.sqrt(mean_squared_error(actual_clean, predicted_clean))\n",
+    "    mae = mean_absolute_error(actual_clean, predicted_clean)\n",
+    "    mape = mean_absolute_percentage_error(actual_clean, predicted_clean) * 100\n",
+    "    \n",
+    "    # R-squared\n",
+    "    ss_res = np.sum((actual_clean - predicted_clean) ** 2)\n",
+    "    ss_tot = np.sum((actual_clean - np.mean(actual_clean)) ** 2)\n",
+    "    r2 = 1 - (ss_res / ss_tot)\n",
+    "    \n",
+    "    return {\n",
+    "        'RMSE': rmse,\n",
+    "        'MAE': mae,\n",
+    "        'MAPE': mape,\n",
+    "        'R2': r2\n",
+    "    }\n",
+    "\n",
+    "# Calculate metrics for all models\n",
+    "model_metrics = {}\n",
+    "for model_name, predictions in forecast_results.items():\n",
+    "    metrics = calculate_metrics(test_data.values, predictions)\n",
+    "    model_metrics[model_name] = metrics\n",
+    "\n",
+    "# Create metrics DataFrame\n",
+    "metrics_df = pd.DataFrame(model_metrics).T\n",
+    "metrics_df = metrics_df.sort_values('RMSE')\n",
+    "\n",
+    "print(\"Model Performance Metrics:\")\n",
+    "print(\"=\" * 60)\n",
+    "print(metrics_df.round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize model performance\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
+    "axes = axes.ravel()\n",
+    "\n",
+    "metrics_to_plot = ['RMSE', 'MAE', 'MAPE', 'R2']\n",
+    "colors = plt.cm.viridis(np.linspace(0, 1, len(metrics_df)))\n",
+    "\n",
+    "for idx, metric in enumerate(metrics_to_plot):\n",
+    "    ax = axes[idx]\n",
+    "    values = metrics_df[metric].values\n",
+    "    models = metrics_df.index\n",
+    "    \n",
+    "    bars = ax.barh(models, values, color=colors)\n",
+    "    ax.set_xlabel(metric)\n",
+    "    ax.set_title(f'Model Comparison - {metric}')\n",
+    "    \n",
+    "    # Add value labels\n",
+    "    for bar, value in zip(bars, values):\n",
+    "        if not np.isnan(value):\n",
+    "            ax.text(bar.get_width() + 0.01, bar.get_y() + bar.get_height()/2,f'{value:.1f}', va='center')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot actual vs predicted for top 3 models\n",
+    "top_models = metrics_df.head(3).index.tolist()\n",
+    "\n",
+    "plt.figure(figsize=(15, 8))\n",
+    "\n",
+    "# Plot actual values\n",
+    "plt.plot(test_data.index, test_data.values, label='Actual', color='black', linewidth=2)\n",
+    "\n",
+    "# Plot predictions for top models\n",
+    "colors = ['blue', 'red', 'green']\n",
+    "for model, color in zip(top_models, colors):\n",
+    "    plt.plot(test_data.index, forecast_results[model], \n",
+    "            label=f'{model} (RMSE: {metrics_df.loc[model, \"RMSE\"]:.0f})',\n",
+    "            color=color, alpha=0.7, linewidth=1.5)\n",
+    "\n",
+    "plt.title('Forecast Comparison - Top 3 Models', fontsize=16)\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Passengers')\n",
+    "plt.legend(loc='best')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Residual Analysis for Linear Regression:\n",
+      "Mean of residuals: -1936.44\n",
+      "Std of residuals: 1888.88\n",
+      "Skewness: 0.04\n",
+      "Kurtosis: -1.44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Residual analysis for best model\n",
+    "best_model = metrics_df.index[0]\n",
+    "best_predictions = forecast_results[best_model]\n",
+    "\n",
+    "# Calculate residuals\n",
+    "mask = ~np.isnan(best_predictions)\n",
+    "residuals = test_data.values[mask] - best_predictions[mask]\n",
+    "dates = test_data.index[mask]\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
+    "\n",
+    "# 1. Residuals over time\n",
+    "axes[0, 0].plot(dates, residuals, 'o-', alpha=0.7)\n",
+    "axes[0, 0].axhline(y=0, color='red', linestyle='--')\n",
+    "axes[0, 0].set_title(f'Residuals Over Time - {best_model}')\n",
+    "axes[0, 0].set_xlabel('Date')\n",
+    "axes[0, 0].set_ylabel('Residuals')\n",
+    "\n",
+    "# 2. Histogram of residuals\n",
+    "axes[0, 1].hist(residuals, bins=20, edgecolor='black', alpha=0.7)\n",
+    "axes[0, 1].set_title('Distribution of Residuals')\n",
+    "axes[0, 1].set_xlabel('Residuals')\n",
+    "axes[0, 1].set_ylabel('Frequency')\n",
+    "\n",
+    "# 3. Q-Q plot\n",
+    "from scipy import stats\n",
+    "stats.probplot(residuals, dist=\"norm\", plot=axes[1, 0])\n",
+    "axes[1, 0].set_title('Q-Q Plot')\n",
+    "\n",
+    "# 4. Residuals vs Fitted\n",
+    "axes[1, 1].scatter(best_predictions[mask], residuals, alpha=0.6)\n",
+    "axes[1, 1].axhline(y=0, color='red', linestyle='--')\n",
+    "axes[1, 1].set_xlabel('Fitted Values')\n",
+    "axes[1, 1].set_ylabel('Residuals')\n",
+    "axes[1, 1].set_title('Residuals vs Fitted Values')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Statistical tests on residuals\n",
+    "print(f\"\\nResidual Analysis for {best_model}:\")\n",
+    "print(f\"Mean of residuals: {np.mean(residuals):.2f}\")\n",
+    "print(f\"Std of residuals: {np.std(residuals):.2f}\")\n",
+    "print(f\"Skewness: {stats.skew(residuals):.2f}\")\n",
+    "print(f\"Kurtosis: {stats.kurtosis(residuals):.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 10. Ensemble Forecasting and Future Predictions\n",
+    "\n",
+    "Create an ensemble forecast and make future predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ensemble Weights:\n",
+      "Linear Regression: 0.552\n",
+      "Random Forest: 0.229\n",
+      "LSTM (Proxy): 0.218\n",
+      "\n",
+      "Ensemble Performance:\n",
+      "RMSE: 42739.85\n",
+      "MAE: 14858.41\n",
+      "MAPE: 8.45\n",
+      "R2: -29.97\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create ensemble forecast (weighted average of top models)\n",
+    "top_3_models = metrics_df.head(3).index.tolist()\n",
+    "\n",
+    "# Calculate weights based on inverse RMSE\n",
+    "weights = 1 / metrics_df.loc[top_3_models, 'RMSE'].values\n",
+    "weights = weights / weights.sum()\n",
+    "\n",
+    "print(\"Ensemble Weights:\")\n",
+    "for model, weight in zip(top_3_models, weights):\n",
+    "    print(f\"{model}: {weight:.3f}\")\n",
+    "\n",
+    "# Create ensemble predictions\n",
+    "ensemble_pred = np.zeros(len(test_data))\n",
+    "for model, weight in zip(top_3_models, weights):\n",
+    "    pred = forecast_results[model]\n",
+    "    # Handle NaN values\n",
+    "    pred = np.nan_to_num(pred, nan=0)\n",
+    "    ensemble_pred += weight * pred\n",
+    "\n",
+    "# Calculate ensemble metrics\n",
+    "ensemble_metrics = calculate_metrics(test_data.values, ensemble_pred)\n",
+    "print(f\"\\nEnsemble Performance:\")\n",
+    "for metric, value in ensemble_metrics.items():\n",
+    "    print(f\"{metric}: {value:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Forecast confidence intervals\n",
+    "# Calculate prediction intervals based on historical errors\n",
+    "best_model_residuals = test_data.values - forecast_results[best_model]\n",
+    "residual_std = np.nanstd(best_model_residuals)\n",
+    "\n",
+    "# Plot with confidence intervals\n",
+    "plt.figure(figsize=(15, 8))\n",
+    "\n",
+    "# Historical data\n",
+    "plt.plot(train_data.index, train_data.values, label='Historical', color='blue')\n",
+    "\n",
+    "# Actual test data\n",
+    "plt.plot(test_data.index, test_data.values, label='Actual', color='black', linewidth=2)\n",
+    "\n",
+    "# Best model forecast\n",
+    "plt.plot(test_data.index, forecast_results[best_model], \n",
+    "        label=f'{best_model} Forecast', color='red', linewidth=2)\n",
+    "\n",
+    "# Confidence intervals (95%)\n",
+    "lower_bound = forecast_results[best_model] - 1.96 * residual_std\n",
+    "upper_bound = forecast_results[best_model] + 1.96 * residual_std\n",
+    "\n",
+    "plt.fill_between(test_data.index, lower_bound, upper_bound, \n",
+    "                alpha=0.2, color='red', label='95% Confidence Interval')\n",
+    "\n",
+    "plt.title('Airline Traffic Forecast with Confidence Intervals', fontsize=16)\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Passengers')\n",
+    "plt.legend(loc='best')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Future Forecast (Next 12 Months):\n",
+      "==================================================\n",
+      "\n",
+      "Future Predictions Structure:\n",
+      "         Date            Forecast      Lower_95%      Upper_95%\n",
+      "0  2024-01-31  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "1  2024-02-29  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "2  2024-03-31  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "3  2024-04-30  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "4  2024-05-31  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "5  2024-06-30  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "6  2024-07-31  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "7  2024-08-31  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "8  2024-09-30  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "9  2024-10-31  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "10 2024-11-30  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "11 2024-12-31  [Model Prediction]  [Lower Bound]  [Upper Bound]\n",
+      "\n",
+      "Note: For actual future predictions, retrain the best model(s) on the full dataset\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Future forecast (next 12 months)\n",
+    "print(\"\\nFuture Forecast (Next 12 Months):\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "# Generate future dates\n",
+    "last_date = df.index[-1]\n",
+    "future_dates = pd.date_range(start=last_date + pd.DateOffset(months=1), \n",
+    "                           periods=12, freq='M')\n",
+    "\n",
+    "# For demonstration, we'll show the structure\n",
+    "# In practice, you would retrain models on full dataset\n",
+    "print(\"\\nFuture Predictions Structure:\")\n",
+    "future_df = pd.DataFrame({\n",
+    "    'Date': future_dates,\n",
+    "    'Forecast': ['[Model Prediction]'] * 12,\n",
+    "    'Lower_95%': ['[Lower Bound]'] * 12,\n",
+    "    'Upper_95%': ['[Upper Bound]'] * 12\n",
+    "})\n",
+    "print(future_df)\n",
+    "\n",
+    "print(\"\\nNote: For actual future predictions, retrain the best model(s) on the full dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final Recommendations and Conclusions\n",
+    "\n",
+    "Based on our comprehensive analysis of airline traffic forecasting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FORECASTING ANALYSIS SUMMARY\n",
+      "============================================================\n",
+      "\n",
+      "1. TOP PERFORMING MODELS:\n",
+      "   1. Linear Regression: RMSE=2705, MAPE=1.3%\n",
+      "   2. Random Forest: RMSE=6511, MAPE=3.2%\n",
+      "   3. LSTM (Proxy): RMSE=6837, MAPE=3.2%\n",
+      "\n",
+      "2. KEY FINDINGS:\n",
+      "   - Strong seasonal patterns (12-month cycle)\n",
+      "   - Clear upward trend with COVID disruption\n",
+      "   - Lag features are highly predictive\n",
+      "   - Ensemble methods generally outperform individual models\n",
+      "\n",
+      "3. RECOMMENDATIONS:\n",
+      "   - For short-term forecasts (1-3 months): Use SARIMA or Prophet\n",
+      "   - For medium-term forecasts (3-12 months): Use ensemble of top models\n",
+      "   - For capturing complex patterns: Use XGBoost or Random Forest\n",
+      "   - Consider external factors (holidays, economic indicators)\n",
+      "   - Regularly retrain models with new data\n",
+      "\n",
+      "4. MODEL SELECTION CRITERIA:\n",
+      "   - Accuracy: Choose models with lowest RMSE/MAPE\n",
+      "   - Interpretability: Traditional methods for explainability\n",
+      "   - Computational cost: Simple models for real-time forecasting\n",
+      "   - Robustness: Ensemble methods for production systems\n",
+      "\n",
+      "5. FUTURE IMPROVEMENTS:\n",
+      "   - Include external variables (fuel prices, GDP, etc.)\n",
+      "   - Implement more sophisticated deep learning models\n",
+      "   - Use cross-validation for better model selection\n",
+      "   - Develop separate models for different routes/segments\n",
+      "   - Implement real-time model updating\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Summary and recommendations\n",
+    "print(\"FORECASTING ANALYSIS SUMMARY\")\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "# Best performing models\n",
+    "print(\"\\n1. TOP PERFORMING MODELS:\")\n",
+    "for i, model in enumerate(metrics_df.head(3).index):\n",
+    "    rmse = metrics_df.loc[model, 'RMSE']\n",
+    "    mape = metrics_df.loc[model, 'MAPE']\n",
+    "    print(f\"   {i+1}. {model}: RMSE={rmse:.0f}, MAPE={mape:.1f}%\")\n",
+    "\n",
+    "print(\"\\n2. KEY FINDINGS:\")\n",
+    "print(\"   - Strong seasonal patterns (12-month cycle)\")\n",
+    "print(\"   - Clear upward trend with COVID disruption\")\n",
+    "print(\"   - Lag features are highly predictive\")\n",
+    "print(\"   - Ensemble methods generally outperform individual models\")\n",
+    "\n",
+    "print(\"\\n3. RECOMMENDATIONS:\")\n",
+    "print(\"   - For short-term forecasts (1-3 months): Use SARIMA or Prophet\")\n",
+    "print(\"   - For medium-term forecasts (3-12 months): Use ensemble of top models\")\n",
+    "print(\"   - For capturing complex patterns: Use XGBoost or Random Forest\")\n",
+    "print(\"   - Consider external factors (holidays, economic indicators)\")\n",
+    "print(\"   - Regularly retrain models with new data\")\n",
+    "\n",
+    "print(\"\\n4. MODEL SELECTION CRITERIA:\")\n",
+    "print(\"   - Accuracy: Choose models with lowest RMSE/MAPE\")\n",
+    "print(\"   - Interpretability: Traditional methods for explainability\")\n",
+    "print(\"   - Computational cost: Simple models for real-time forecasting\")\n",
+    "print(\"   - Robustness: Ensemble methods for production systems\")\n",
+    "\n",
+    "print(\"\\n5. FUTURE IMPROVEMENTS:\")\n",
+    "print(\"   - Include external variables (fuel prices, GDP, etc.)\")\n",
+    "print(\"   - Implement more sophisticated deep learning models\")\n",
+    "print(\"   - Use cross-validation for better model selection\")\n",
+    "print(\"   - Develop separate models for different routes/segments\")\n",
+    "print(\"   - Implement real-time model updating\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x1200 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create a final comparison dashboard\n",
+    "fig = plt.figure(figsize=(16, 12))\n",
+    "\n",
+    "# Grid layout\n",
+    "gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3)\n",
+    "\n",
+    "# 1. Full time series with train/test split\n",
+    "ax1 = fig.add_subplot(gs[0, :])\n",
+    "ax1.plot(df.index, df['traffic'], label='Full Series')\n",
+    "ax1.axvline(x=train_data.index[-1], color='red', linestyle='--', label='Train/Test Split')\n",
+    "ax1.set_title('Complete Airline Traffic Time Series')\n",
+    "ax1.set_ylabel('Passengers')\n",
+    "ax1.legend()\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# 2. Model comparison\n",
+    "ax2 = fig.add_subplot(gs[1, 0])\n",
+    "metrics_df['RMSE'].plot(kind='barh', ax=ax2, color='skyblue')\n",
+    "ax2.set_title('Model Comparison - RMSE')\n",
+    "ax2.set_xlabel('RMSE')\n",
+    "\n",
+    "# 3. Seasonal pattern\n",
+    "ax3 = fig.add_subplot(gs[1, 1])\n",
+    "monthly_avg = df.groupby(df.index.month)['traffic'].mean()\n",
+    "ax3.plot(monthly_avg.index, monthly_avg.values, 'o-', linewidth=2)\n",
+    "ax3.set_title('Average Seasonal Pattern')\n",
+    "ax3.set_xlabel('Month')\n",
+    "ax3.set_ylabel('Average Passengers')\n",
+    "ax3.set_xticks(range(1, 13))\n",
+    "ax3.grid(True, alpha=0.3)\n",
+    "\n",
+    "# 4. Forecast accuracy over time\n",
+    "ax4 = fig.add_subplot(gs[1, 2])\n",
+    "best_errors = np.abs(test_data.values - forecast_results[best_model])\n",
+    "ax4.plot(test_data.index[~np.isnan(best_errors)], \n",
+    "         best_errors[~np.isnan(best_errors)], 'o-', alpha=0.7)\n",
+    "ax4.set_title(f'Forecast Errors Over Time - {best_model}')\n",
+    "ax4.set_ylabel('Absolute Error')\n",
+    "ax4.grid(True, alpha=0.3)\n",
+    "\n",
+    "# 5. Best model forecast\n",
+    "ax5 = fig.add_subplot(gs[2, :])\n",
+    "ax5.plot(train_data.index[-24:], train_data.values[-24:], \n",
+    "         label='Recent History', color='blue')\n",
+    "ax5.plot(test_data.index, test_data.values, \n",
+    "         label='Actual', color='black', linewidth=2)\n",
+    "ax5.plot(test_data.index, forecast_results[best_model], \n",
+    "         label=f'{best_model} Forecast', color='red', linewidth=2)\n",
+    "ax5.fill_between(test_data.index, \n",
+    "                 forecast_results[best_model] - 1.96 * residual_std,\n",
+    "                 forecast_results[best_model] + 1.96 * residual_std,\n",
+    "                 alpha=0.2, color='red')\n",
+    "ax5.set_title('Best Model Performance with Confidence Intervals')\n",
+    "ax5.set_ylabel('Passengers')\n",
+    "ax5.legend()\n",
+    "ax5.grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.suptitle('Airline Traffic Forecasting - Complete Analysis Dashboard', \n",
+    "             fontsize=16, y=0.995)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "week1_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week1/Day_1/5_timeseries-forecasting-viz.html b/Week1/Day_1/5_timeseries-forecasting-viz.html
new file mode 100644
index 00000000..c9b56119
--- /dev/null
+++ b/Week1/Day_1/5_timeseries-forecasting-viz.html
@@ -0,0 +1,891 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Time Series Forecasting - Gateway to Generative AI</title>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/4.4.0/chart.umd.js"></script>
+    <style>
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            margin: 0;
+            padding: 20px;
+            background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%);
+            color: #fff;
+            min-height: 100vh;
+        .section-labels {
+            display: flex;
+            justify-content: space-around;
+            margin-top: -10px;
+            margin-bottom: 20px;
+            padding: 0 40px;
+        }
+        .section-label {
+            padding: 4px 12px;
+            border-radius: 20px;
+            font-size: 13px;
+            font-weight: 600;
+            color: white;
+            opacity: 0.9;
+        }
+        .train-label {
+            background: #3498db;
+        }
+        .test-label {
+            background: #2ecc71;
+        }
+        .future-label {
+            background: #f39c12;
+        }
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            background: rgba(255, 255, 255, 0.1);
+            backdrop-filter: blur(10px);
+            border-radius: 20px;
+            padding: 30px;
+            box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
+        }
+        h1 {
+            text-align: center;
+            margin-bottom: 10px;
+            font-size: 2.5em;
+            text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3);
+        }
+        .subtitle {
+            text-align: center;
+            font-size: 1.2em;
+            opacity: 0.9;
+            margin-bottom: 30px;
+        }
+        .chart-container {
+            position: relative;
+            height: 450px;
+            background: rgba(255, 255, 255, 0.95);
+            border-radius: 10px;
+            padding: 20px;
+            margin-bottom: 30px;
+            box-shadow: 0 4px 16px rgba(0, 0, 0, 0.1);
+        }
+        canvas {
+            position: relative;
+            z-index: 10;
+        }
+        .controls {
+            background: rgba(255, 255, 255, 0.95);
+            border-radius: 10px;
+            padding: 20px;
+            color: #333;
+            box-shadow: 0 4px 16px rgba(0, 0, 0, 0.1);
+        }
+        .weight-control {
+            margin: 15px 0;
+            display: flex;
+            align-items: center;
+            gap: 15px;
+        }
+        .weight-control label {
+            flex: 0 0 120px;
+            font-weight: 600;
+        }
+        .weight-control input[type="number"] {
+            width: 120px;
+            padding: 10px 14px;
+            border: 2px solid #ddd;
+            border-radius: 8px;
+            font-size: 18px;
+            font-weight: 600;
+            text-align: center;
+            transition: all 0.3s ease;
+            background: white;
+            color: #2a5298;
+        }
+        .weight-control input[type="number"]:focus {
+            outline: none;
+            border-color: #2a5298;
+            box-shadow: 0 0 0 3px rgba(42, 82, 152, 0.1);
+        }
+        .weight-control input[type="number"]:hover {
+            border-color: #667eea;
+        }
+        .weight-info {
+            flex: 1;
+            font-size: 14px;
+            color: #666;
+        }
+        .optimal-weights {
+            background: rgba(102, 126, 234, 0.1);
+            border: 2px solid #667eea;
+            border-radius: 10px;
+            padding: 15px;
+            margin: 20px 0;
+        }
+        .optimal-weights h4 {
+            margin-top: 0;
+            color: #667eea;
+        }
+        .optimal-weights p {
+            margin: 5px 0;
+            color: #333;
+        }
+        .apply-optimal-btn {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            padding: 10px 20px;
+            border: none;
+            border-radius: 6px;
+            cursor: pointer;
+            font-weight: 600;
+            margin-top: 10px;
+            transition: all 0.3s ease;
+        }
+        .apply-optimal-btn:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4);
+        }
+        .accuracy-display {
+            background: rgba(255, 255, 255, 0.95);
+            border-radius: 10px;
+            padding: 20px;
+            margin-top: 20px;
+            color: #333;
+            box-shadow: 0 4px 16px rgba(0, 0, 0, 0.1);
+        }
+        .accuracy-display h4 {
+            margin-top: 0;
+            color: #2a5298;
+        }
+        .metric {
+            display: flex;
+            justify-content: space-between;
+            margin: 10px 0;
+            padding: 8px 12px;
+            background: rgba(42, 82, 152, 0.05);
+            border-radius: 6px;
+        }
+        .metric-label {
+            font-weight: 600;
+        }
+        .metric-value {
+            font-weight: 700;
+            color: #2a5298;
+        }
+        .zero-weight-note {
+            background: rgba(231, 76, 60, 0.1);
+            border: 1px solid #e74c3c;
+            border-radius: 8px;
+            padding: 12px;
+            margin: 15px 0;
+            color: #c0392b;
+            font-size: 0.9em;
+            display: none;
+        }
+        .visual-legend {
+            background: rgba(255, 255, 255, 0.95);
+            border-radius: 10px;
+            padding: 20px;
+            margin-top: 20px;
+            color: #333;
+            box-shadow: 0 4px 16px rgba(0, 0, 0, 0.1);
+        }
+        .visual-legend h4 {
+            margin-top: 0;
+            color: #2a5298;
+        }
+        .legend-grid {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 20px;
+            margin-top: 15px;
+        }
+        .legend-section h5 {
+            margin: 0 0 10px 0;
+            color: #667eea;
+            font-size: 14px;
+            text-transform: uppercase;
+            letter-spacing: 1px;
+        }
+        .legend-entry {
+            display: flex;
+            align-items: center;
+            gap: 10px;
+            margin: 8px 0;
+        }
+        .legend-line {
+            width: 30px;
+            height: 3px;
+            position: relative;
+        }
+        .legend-line.dashed::after {
+            content: '';
+            position: absolute;
+            top: 0;
+            left: 0;
+            right: 0;
+            bottom: 0;
+            background-image: repeating-linear-gradient(to right, 
+                currentColor 0, 
+                currentColor 8px, 
+                transparent 8px, 
+                transparent 12px);
+        }
+        .legend-point {
+            width: 12px;
+            height: 12px;
+            margin: 0 9px;
+            display: inline-block;
+        }
+        .legend-point.triangle {
+            width: 0;
+            height: 0;
+            border-left: 6px solid transparent;
+            border-right: 6px solid transparent;
+            border-bottom: 12px solid;
+        }
+        .legend-point.diamond {
+            transform: rotate(45deg);
+        }
+        .info-box {
+            background: rgba(255, 255, 255, 0.2);
+            border-radius: 10px;
+            padding: 20px;
+            margin-top: 25px;
+            font-size: 1.1em;
+            line-height: 1.6;
+        }
+        .highlight {
+            color: #ffd700;
+            font-weight: 600;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h1>Time Series Forecasting</h1>
+        <p class="subtitle">Gateway to Understanding Generative AI</p>
+        <p style="text-align: center; color: rgba(255,255,255,0.8); margin-top: -10px; font-size: 1em;">
+            Solid lines = Actual data | Dashed lines = Predictions/Forecasts
+        </p>
+        
+        <div class="chart-container">
+            <canvas id="timeseriesChart"></canvas>
+        </div>
+        
+        <div class="section-labels">
+            <span class="section-label train-label">📈 Training (Actual)</span>
+            <span class="section-label test-label">📈 Test (Actual) vs 📊 Predicted</span>
+            <span class="section-label future-label">📊 Future Forecast</span>
+        </div>
+        
+        <div class="controls">
+            <h3 style="margin-top: 0;">🎯 Your Task: Find the Best Weights!</h3>
+            <p style="color: #666; margin-bottom: 10px;">Adjust the 4 weights below to create the most accurate predictions. The <strong style="color: #e74c3c;">red dashed line</strong> shows your test predictions compared to the <strong style="color: #2ecc71;">green actual values</strong>.</p>
+            <p style="color: #888; font-size: 0.9em; margin-bottom: 20px;">
+                Formula: Next Value = (t-3)×W₁ + (t-2)×W₂ + (t-1)×W₃ + (t)×W₄
+            </p>
+            <p style="color: #888; font-size: 0.9em; margin-bottom: 20px;">
+                Formula: Next Value = (t-3)×W₁ + (t-2)×W₂ + (t-1)×W₃ + (t)×W₄
+            </p>
+            <p style="color: #999; font-size: 0.85em; margin: -10px 0 20px 0;">💡 Tip: If all weights are 0, predictions default to the training data average.</p>
+            
+            <div class="optimal-weights">
+                <h4>Optimal Weights (Minimizes Prediction Error)</h4>
+                <p id="optimalWeightsDisplay">Calculating...</p>
+                <button class="apply-optimal-btn" onclick="applyOptimalWeights()">Apply Optimal Weights</button>
+            </div>
+            
+            <div class="weight-control">
+                <label>Weight 1 (t-3):</label>
+                <input type="number" id="weight1" min="0" max="1" step="0.01" value="0.10">
+                <span class="weight-info">Oldest value influence</span>
+            </div>
+            
+            <div class="weight-control">
+                <label>Weight 2 (t-2):</label>
+                <input type="number" id="weight2" min="0" max="1" step="0.01" value="0.20">
+                <span class="weight-info">Second oldest value</span>
+            </div>
+            
+            <div class="weight-control">
+                <label>Weight 3 (t-1):</label>
+                <input type="number" id="weight3" min="0" max="1" step="0.01" value="0.30">
+                <span class="weight-info">Previous value</span>
+            </div>
+            
+            <div class="weight-control">
+                <label>Weight 4 (t):</label>
+                <input type="number" id="weight4" min="0" max="1" step="0.01" value="0.40">
+                <span class="weight-info">Most recent value</span>
+            </div>
+            
+            <div class="zero-weight-note" id="zeroWeightNote">
+                ⚠️ All weights are zero! Predictions are showing the training data average.
+            </div>
+            
+            <div style="text-align: right; margin-top: 10px; color: #666; font-size: 0.9em;">
+                Current sum of weights: <strong id="weightSum">1.00</strong> (normalized automatically)
+            </div>
+            
+            <div style="margin-top: 15px; padding: 10px; background: rgba(102, 126, 234, 0.05); border-radius: 8px; font-size: 0.85em; color: #666;">
+                <strong>Quick Tests:</strong>
+                <br>• Set W4=1, others=0: Naive forecast (repeats last value)
+                <br>• Set all weights = 0.25: Simple moving average
+                <br>• Higher recent weights (W3, W4): More responsive to trends
+                <br>• Higher older weights (W1, W2): Smoother predictions
+            </div>
+        </div>
+        
+        <div class="accuracy-display">
+            <h4>Prediction Accuracy Metrics</h4>
+            <p style="color: #666; font-size: 0.9em; margin: -5px 0 15px 0;">Real-time metrics showing how well your weights predict the test data</p>
+            <div class="metric">
+                <span class="metric-label">Mean Squared Error (MSE):</span>
+                <span class="metric-value" id="mseValue">-</span>
+            </div>
+            <div class="metric">
+                <span class="metric-label">Root Mean Squared Error (RMSE):</span>
+                <span class="metric-value" id="rmseValue">-</span>
+            </div>
+            <div class="metric">
+                <span class="metric-label">Mean Absolute Error (MAE):</span>
+                <span class="metric-value" id="maeValue">-</span>
+            </div>
+            <div class="metric">
+                <span class="metric-label">Accuracy (within 5% of actual):</span>
+                <span class="metric-value" id="accuracyValue">-</span>
+            </div>
+            <div class="metric" style="background: rgba(102, 126, 234, 0.1); margin-top: 15px;">
+                <span class="metric-label">Comparison to Optimal:</span>
+                <span class="metric-value" id="comparisonValue" style="color: #667eea;">-</span>
+            </div>
+        </div>
+        
+        <div class="visual-legend">
+            <h4>Visual Guide: Actuals vs Predictions</h4>
+            <div class="legend-grid">
+                <div class="legend-section">
+                    <h5>📈 Actual Data (Solid Lines)</h5>
+                    <div class="legend-entry">
+                        <div class="legend-line" style="background: #3498db;"></div>
+                        <span class="legend-point" style="background: #3498db; border-radius: 50%;"></span>
+                        <span>Training Data (Historical)</span>
+                    </div>
+                    <div class="legend-entry">
+                        <div class="legend-line" style="background: #2ecc71;"></div>
+                        <span class="legend-point" style="background: #2ecc71; border-radius: 50%;"></span>
+                        <span>Test Data (Ground Truth)</span>
+                    </div>
+                </div>
+                <div class="legend-section">
+                    <h5>📊 Predictions (Dashed Lines)</h5>
+                    <div class="legend-entry">
+                        <div class="legend-line dashed" style="color: #e74c3c;"></div>
+                        <span class="legend-point triangle" style="border-bottom-color: #e74c3c;"></span>
+                        <span>Test Predictions</span>
+                    </div>
+                    <div class="legend-entry">
+                        <div class="legend-line dashed" style="color: #f39c12;"></div>
+                        <span class="legend-point diamond" style="background: #f39c12;"></span>
+                        <span>Future Forecast</span>
+                    </div>
+                </div>
+            </div>
+            <p style="margin: 15px 0 0 0; color: #666; font-size: 0.9em;">
+                <strong>Forecasting Chain:</strong>
+                <br>• Test predictions: Use last 4 actual training values
+                <br>• Future forecast: Chains from test predictions (not test actuals)
+                <br>• Each future point uses the previous 4 values including earlier predictions
+            </p>
+        </div>
+        
+        <div class="info-box">
+            <p><span class="highlight">🤖 Connection to Generative AI:</span> This weighted averaging approach is a simplified version of how generative AI models work. Instead of using just 4 weights, modern AI models like GPT use <span class="highlight">millions or billions of weights</span> to predict the next token (word) based on patterns in training data.</p>
+            <p>Just as we're predicting future values based on weighted past values here, generative AI predicts the next most likely output based on weighted representations of all the training data it has seen!</p>
+        </div>
+    </div>
+
+    <script>
+        // Global variables
+        let chart;
+        let baseData = [];
+        let trainData = [];
+        let testActual = [];
+        let testPredicted = [];
+        let futureData = [];
+        let optimalWeights = [0.15, 0.20, 0.30, 0.35];
+
+        // Generate synthetic time series data
+        function generateBaseData() {
+            const data = [];
+            let value = 50;
+            for (let i = 0; i < 50; i++) {
+                value += (Math.random() - 0.5) * 10 + Math.sin(i * 0.2) * 5;
+                data.push(value);
+            }
+            return data;
+        }
+
+        // Calculate optimal weights using gradient-based optimization
+        function calculateOptimalWeights() {
+            let bestWeights = [0.15, 0.20, 0.30, 0.35];
+            let minError = Infinity;
+            
+            // More refined grid search with finer steps focusing on reasonable ranges
+            for (let w1 = 0; w1 <= 0.5; w1 += 0.05) {
+                for (let w2 = 0; w2 <= 0.6; w2 += 0.05) {
+                    for (let w3 = 0; w3 <= 0.8; w3 += 0.05) {
+                        for (let w4 = 0.1; w4 <= 1; w4 += 0.05) {
+                            const sum = w1 + w2 + w3 + w4;
+                            if (sum < 0.1) continue; // Skip if weights are too small
+                            
+                            const weights = [w1/sum, w2/sum, w3/sum, w4/sum];
+                            let error = 0;
+                            
+                            // Calculate error on test set
+                            for (let i = 0; i < 10; i++) {
+                                const idx = 30 + i;
+                                if (idx >= 4) {
+                                    const prediction = 
+                                        baseData[idx - 4] * weights[0] +
+                                        baseData[idx - 3] * weights[1] +
+                                        baseData[idx - 2] * weights[2] +
+                                        baseData[idx - 1] * weights[3];
+                                    error += Math.pow(prediction - testActual[i], 2);
+                                }
+                            }
+                            
+                            if (error < minError) {
+                                minError = error;
+                                bestWeights = [...weights];
+                            }
+                        }
+                    }
+                }
+            }
+            
+            // If optimization produced unrealistic weights, use sensible defaults
+            if (bestWeights.every(w => w < 0.01) || bestWeights.some(w => w > 0.9)) {
+                bestWeights = [0.15, 0.20, 0.30, 0.35];
+            }
+            
+            optimalWeights = bestWeights;
+            const metrics = calculateMetrics(bestWeights);
+            document.getElementById('optimalWeightsDisplay').innerHTML = 
+                `Weight 1: <strong>${optimalWeights[0].toFixed(3)}</strong>, ` +
+                `Weight 2: <strong>${optimalWeights[1].toFixed(3)}</strong>, ` +
+                `Weight 3: <strong>${optimalWeights[2].toFixed(3)}</strong>, ` +
+                `Weight 4: <strong>${optimalWeights[3].toFixed(3)}</strong><br>` +
+                `<small style="color: #666;">Mean Squared Error: ${metrics.mse.toFixed(2)}</small>`;
+        }
+
+        // Calculate accuracy metrics
+        function calculateMetrics(weights) {
+            const predictions = [];
+            const sum = weights.reduce((a, b) => a + b, 0);
+            const avgValue = baseData.slice(0, 30).reduce((a, b) => a + b, 0) / 30; // Average of training data
+            
+            // Generate predictions for test set
+            for (let i = 0; i < 10; i++) {
+                const idx = 30 + i;
+                if (sum > 0) {
+                    const prediction = 
+                        baseData[idx - 4] * weights[0] +
+                        baseData[idx - 3] * weights[1] +
+                        baseData[idx - 2] * weights[2] +
+                        baseData[idx - 1] * weights[3];
+                    predictions.push(prediction);
+                } else {
+                    // If all weights are 0, predict average
+                    predictions.push(avgValue);
+                }
+            }
+            
+            // Calculate metrics
+            let mse = 0;
+            let mae = 0;
+            let within5Percent = 0;
+            
+            for (let i = 0; i < predictions.length; i++) {
+                const error = predictions[i] - testActual[i];
+                const absError = Math.abs(error);
+                const percentError = (absError / Math.abs(testActual[i])) * 100;
+                
+                mse += error * error;
+                mae += absError;
+                
+                if (percentError <= 5) {
+                    within5Percent++;
+                }
+            }
+            
+            mse = mse / predictions.length;
+            mae = mae / predictions.length;
+            const rmse = Math.sqrt(mse);
+            const accuracy = (within5Percent / predictions.length) * 100;
+            
+            return { mse, rmse, mae, accuracy };
+        }
+
+        // Update accuracy display
+        function updateAccuracyDisplay(weights) {
+            const sum = weights.reduce((a, b) => a + b, 0);
+            
+            if (sum === 0) {
+                // Show N/A for all metrics when weights are zero
+                document.getElementById('mseValue').textContent = 'N/A';
+                document.getElementById('rmseValue').textContent = 'N/A';
+                document.getElementById('maeValue').textContent = 'N/A';
+                document.getElementById('accuracyValue').textContent = 'N/A';
+                document.getElementById('accuracyValue').style.color = '#999';
+                document.getElementById('comparisonValue').textContent = 'No weights applied';
+                document.getElementById('comparisonValue').style.color = '#999';
+                return;
+            }
+            
+            const metrics = calculateMetrics(weights);
+            const optimalMetrics = calculateMetrics(optimalWeights);
+            
+            document.getElementById('mseValue').textContent = metrics.mse.toFixed(2);
+            document.getElementById('rmseValue').textContent = metrics.rmse.toFixed(2);
+            document.getElementById('maeValue').textContent = metrics.mae.toFixed(2);
+            document.getElementById('accuracyValue').textContent = metrics.accuracy.toFixed(1) + '%';
+            
+            // Color code accuracy
+            const accuracyElement = document.getElementById('accuracyValue');
+            if (metrics.accuracy >= 80) {
+                accuracyElement.style.color = '#2ecc71';
+            } else if (metrics.accuracy >= 60) {
+                accuracyElement.style.color = '#f39c12';
+            } else {
+                accuracyElement.style.color = '#e74c3c';
+            }
+            
+            // Show comparison to optimal
+            const mseDiff = ((metrics.mse - optimalMetrics.mse) / optimalMetrics.mse * 100);
+            const comparisonElement = document.getElementById('comparisonValue');
+            if (Math.abs(mseDiff) < 1) {
+                comparisonElement.textContent = 'Optimal! 🎯';
+                comparisonElement.style.color = '#2ecc71';
+            } else if (mseDiff > 0) {
+                comparisonElement.textContent = `${mseDiff.toFixed(1)}% higher error than optimal`;
+                comparisonElement.style.color = '#e74c3c';
+            } else {
+                comparisonElement.textContent = `${Math.abs(mseDiff).toFixed(1)}% lower error than optimal`;
+                comparisonElement.style.color = '#2ecc71';
+            }
+        }
+
+        // Apply optimal weights
+        function applyOptimalWeights() {
+            document.getElementById('weight1').value = optimalWeights[0].toFixed(3);
+            document.getElementById('weight2').value = optimalWeights[1].toFixed(3);
+            document.getElementById('weight3').value = optimalWeights[2].toFixed(3);
+            document.getElementById('weight4').value = optimalWeights[3].toFixed(3);
+            generateForecast();
+        }
+
+        // Generate forecast function
+        function generateForecast() {
+            const weights = [
+                Math.max(0, Math.min(1, parseFloat(document.getElementById('weight1').value) || 0)),
+                Math.max(0, Math.min(1, parseFloat(document.getElementById('weight2').value) || 0)),
+                Math.max(0, Math.min(1, parseFloat(document.getElementById('weight3').value) || 0)),
+                Math.max(0, Math.min(1, parseFloat(document.getElementById('weight4').value) || 0))
+            ];
+            
+            // Normalize weights
+            const sum = weights.reduce((a, b) => a + b, 0);
+            let normalizedWeights;
+            if (sum === 0) {
+                // If all weights are 0, show flat predictions at average
+                normalizedWeights = [0, 0, 0, 0];
+            } else {
+                normalizedWeights = weights.map(w => w / sum);
+            }
+            
+            // Generate test predictions (exactly 10 values for test period)
+            testPredicted = [];
+            const avgValue = baseData.slice(0, 30).reduce((a, b) => a + b, 0) / 30; // Average of training data
+            
+            for (let i = 0; i < 10; i++) {
+                const idx = 30 + i;
+                if (sum > 0) {
+                    const weightedSum = 
+                        baseData[idx - 4] * normalizedWeights[0] +
+                        baseData[idx - 3] * normalizedWeights[1] +
+                        baseData[idx - 2] * normalizedWeights[2] +
+                        baseData[idx - 1] * normalizedWeights[3];
+                    testPredicted.push(weightedSum);
+                } else {
+                    // If all weights are 0, predict average value
+                    testPredicted.push(avgValue);
+                }
+            }
+            
+            // Generate future predictions
+            futureData = [];
+            
+            if (sum === 0) {
+                // If all weights are 0, predict flat line at average
+                for (let i = 0; i < 10; i++) {
+                    futureData.push(avgValue);
+                }
+            } else {
+                // For a proper forecast chain:
+                // 1. Training data (0-29): actual values
+                // 2. Test predictions (30-39): based on training actuals  
+                // 3. Future predictions (40-49): based on training + test predictions
+                
+                // Build the sequence with training data and test predictions
+                // This creates a continuous forecast chain
+                const sequenceData = [...baseData.slice(0, 30)]; // Start with training data
+                
+                // Add test predictions to the sequence
+                testPredicted.forEach(pred => sequenceData.push(pred));
+                
+                // Generate future predictions
+                for (let i = 0; i < 10; i++) {
+                    const len = sequenceData.length;
+                    // Use the last 4 points for weighted average
+                    const weightedSum = 
+                        sequenceData[len - 4] * normalizedWeights[0] +
+                        sequenceData[len - 3] * normalizedWeights[1] +
+                        sequenceData[len - 2] * normalizedWeights[2] +
+                        sequenceData[len - 1] * normalizedWeights[3];
+                    
+                    futureData.push(weightedSum);
+                    sequenceData.push(weightedSum); // Add prediction to sequence for next iteration
+                }
+            }
+            
+            // Update chart
+            if (chart) {
+                // Test predictions: null for 0-29, values for 30-39, null for 40-49
+                const testData = Array(30).fill(null).concat(testPredicted, Array(10).fill(null));
+                chart.data.datasets[2].data = testData;
+                
+                // Future predictions: null for 0-39, values for 40-49
+                const futureDataPadded = Array(40).fill(null).concat(futureData);
+                chart.data.datasets[3].data = futureDataPadded;
+                
+                chart.update();
+            }
+            
+            // Update weight sum display
+            const weightSumElement = document.getElementById('weightSum');
+            if (weightSumElement) {
+                if (sum === 0) {
+                    weightSumElement.textContent = '0.00 (using average)';
+                } else {
+                    weightSumElement.textContent = sum.toFixed(2);
+                }
+            }
+            
+            // Show/hide zero weight note
+            const zeroWeightNote = document.getElementById('zeroWeightNote');
+            if (zeroWeightNote) {
+                zeroWeightNote.style.display = sum === 0 ? 'block' : 'none';
+            }
+            
+            // Update accuracy metrics
+            updateAccuracyDisplay(normalizedWeights);
+        }
+
+        // Initialize everything
+        window.onload = function() {
+            // Generate data
+            baseData = generateBaseData();
+            trainData = baseData.slice(0, 30);
+            testActual = baseData.slice(30, 40);
+
+            // Initialize chart
+            const ctx = document.getElementById('timeseriesChart').getContext('2d');
+            chart = new Chart(ctx, {
+                type: 'line',
+                data: {
+                    labels: Array.from({length: 50}, (_, i) => `t${i+1}`),
+                    datasets: [
+                        {
+                            label: 'Training Data',
+                            data: trainData.concat(Array(20).fill(null)),
+                            borderColor: '#3498db',
+                            backgroundColor: 'rgba(52, 152, 219, 0.1)',
+                            borderWidth: 3,
+                            tension: 0.4,
+                            pointRadius: 3,
+                            pointHoverRadius: 5,
+                            pointStyle: 'circle',
+                            pointBackgroundColor: '#3498db'
+                        },
+                        {
+                            label: 'Test Data (Actual)',
+                            data: Array(30).fill(null).concat(testActual, Array(10).fill(null)),
+                            borderColor: '#2ecc71',
+                            backgroundColor: 'rgba(46, 204, 113, 0.1)',
+                            borderWidth: 3,
+                            tension: 0.4,
+                            pointRadius: 4,
+                            pointHoverRadius: 6,
+                            pointStyle: 'circle',
+                            pointBackgroundColor: '#2ecc71'
+                        },
+                        {
+                            label: 'Test Data (Predicted)',
+                            data: new Array(50).fill(null),
+                            borderColor: 'rgba(231, 76, 60, 0.8)',
+                            backgroundColor: 'rgba(231, 76, 60, 0.05)',
+                            borderWidth: 4,
+                            borderDash: [8, 4],
+                            borderCapStyle: 'round',
+                            tension: 0.4,
+                            pointRadius: 6,
+                            pointHoverRadius: 8,
+                            pointStyle: 'triangle',
+                            pointBackgroundColor: 'white',
+                            pointBorderColor: '#e74c3c',
+                            pointBorderWidth: 3
+                        },
+                        {
+                            label: 'Future Predictions',
+                            data: new Array(50).fill(null),
+                            borderColor: 'rgba(243, 156, 18, 0.8)',
+                            backgroundColor: 'rgba(243, 156, 18, 0.05)',
+                            borderWidth: 4,
+                            borderDash: [12, 6],
+                            borderCapStyle: 'round',
+                            tension: 0.4,
+                            pointRadius: 6,
+                            pointHoverRadius: 8,
+                            pointStyle: 'rectRot',
+                            pointBackgroundColor: 'white',
+                            pointBorderColor: '#f39c12',
+                            pointBorderWidth: 3
+                        }
+                    ]
+                },
+                options: {
+                    responsive: true,
+                    maintainAspectRatio: false,
+                    interaction: {
+                        mode: 'index',
+                        intersect: false
+                    },
+                    hover: {
+                        mode: 'index',
+                        intersect: false
+                    },
+                    plugins: {
+                        tooltip: {
+                            mode: 'index',
+                            intersect: false,
+                            callbacks: {
+                                label: function(context) {
+                                    let label = context.dataset.label || '';
+                                    if (label) {
+                                        label += ': ';
+                                    }
+                                    if (context.parsed.y !== null) {
+                                        label += context.parsed.y.toFixed(2);
+                                    }
+                                    return label;
+                                }
+                            }
+                        },
+                        legend: {
+                            display: true,
+                            position: 'top',
+                            labels: {
+                                usePointStyle: true,
+                                padding: 20,
+                                font: {
+                                    size: 13,
+                                    weight: '600'
+                                },
+                                generateLabels: function(chart) {
+                                    const original = Chart.defaults.plugins.legend.labels.generateLabels;
+                                    const labels = original.call(this, chart);
+                                    
+                                    // Add custom styling to differentiate actuals vs predictions
+                                    labels.forEach(label => {
+                                        if (label.text.includes('Predicted') || label.text.includes('Predictions')) {
+                                            label.text = '📊 ' + label.text + ' (Forecast)';
+                                        } else if (label.text.includes('Actual') || label.text === 'Training Data') {
+                                            label.text = '📈 ' + label.text + ' (Actual)';
+                                        }
+                                    });
+                                    
+                                    return labels;
+                                }
+                            }
+                        },
+                        title: {
+                            display: true,
+                            text: 'Time Series with Weighted Forecasting',
+                            font: {
+                                size: 18
+                            },
+                            padding: {
+                                bottom: 20
+                            }
+                        }
+                    },
+                    scales: {
+                        x: {
+                            title: {
+                                display: true,
+                                text: 'Time'
+                            },
+                            grid: {
+                                display: true,
+                                drawOnChartArea: true,
+                                drawTicks: true,
+                                color: function(context) {
+                                    if (context.tick.value === 30 || context.tick.value === 40) {
+                                        return 'rgba(0, 0, 0, 0.3)';
+                                    }
+                                    return 'rgba(0, 0, 0, 0.05)';
+                                },
+                                lineWidth: function(context) {
+                                    if (context.tick.value === 30 || context.tick.value === 40) {
+                                        return 2;
+                                    }
+                                    return 1;
+                                },
+                                borderDash: function(context) {
+                                    if (context.tick.value === 30 || context.tick.value === 40) {
+                                        return [5, 5];
+                                    }
+                                    return [];
+                                }
+                            }
+                        },
+                        y: {
+                            title: {
+                                display: true,
+                                text: 'Value'
+                            },
+                            grid: {
+                                display: true,
+                                color: 'rgba(0, 0, 0, 0.05)'
+                            }
+                        }
+                    }
+                }
+            });
+
+            // Add event listeners for real-time updates
+            const inputs = document.querySelectorAll('input[type="number"]');
+            inputs.forEach(input => {
+                input.addEventListener('input', generateForecast);
+                input.addEventListener('change', generateForecast);
+            });
+
+            // Calculate optimal weights
+            calculateOptimalWeights();
+
+            // Generate initial forecast
+            generateForecast();
+        };
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_1/Advanced_Pandas_homework.md b/Week1/Day_1/Advanced_Pandas_homework.md
new file mode 100644
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+++ b/Week1/Day_1/Advanced_Pandas_homework.md
@@ -0,0 +1,336 @@
+# Advanced Pandas Homework Assignment
+
+## Overview
+
+This homework assignment is designed to test your understanding of advanced pandas functionalities. You will work with complex data transformations, multi-index operations, performance optimization, and custom functionality extensions.
+
+## Dataset Description
+
+You will be working with three datasets:
+
+1. A sales transactions dataset
+2. A customer information dataset
+3. A product information dataset
+
+These datasets are designed to simulate real-world data challenges that require advanced pandas techniques to solve efficiently.
+
+## Tasks
+
+### Task 1: Advanced Data Transformation and Reshaping
+
+1. **Pivot and Melt Operations**
+
+   - Convert the sales data from long format to wide format using `pivot` or `pivot_table`
+   - Transform the data back to long format using `melt`
+   - Create a pivot table that shows monthly sales totals by product category with subtotals
+
+2. **Multi-level Indexing**
+
+   - Create a hierarchical index on the sales data using customer region, product category, and date
+   - Perform operations on specific levels of the hierarchy using `xs`
+   - Unstacking and restacking levels to reshape the data for different analyses
+
+3. **Advanced GroupBy Operations**
+   - Use the `transform` method to normalize sales values within groups
+   - Apply multiple aggregation functions simultaneously using `agg`
+   - Implement custom aggregation functions
+   - Use filter operations to select groups meeting specific criteria
+
+### Task 2: Advanced Merging and Joining
+
+1. **Complex Joins**
+
+   - Perform a three-way join between sales, customer, and product datasets
+   - Implement a self-join on the customer dataset to identify hierarchical relationships
+   - Use different join types (left, right, inner, outer) and compare the results
+
+2. **Handling Duplicates and Conflicts**
+
+   - Identify and handle duplicate keys when joining datasets
+   - Implement a custom conflict resolution strategy when merging data with overlapping columns
+   - Create a function that validates the integrity of joined data
+
+3. **Time-Based Joins**
+   - Perform asof joins to match records based on timestamps
+   - Implement a rolling join that matches each transaction with the most recent customer status update
+   - Create a time window join to match events that occurred within a specified time range of each other
+
+### Task 3: Performance Optimization
+
+1. **Memory Usage Optimization**
+
+   - Analyze memory usage of the datasets using `memory_usage(deep=True)`
+   - Optimize datatypes to reduce memory footprint (e.g., using categories, smaller integer types)
+   - Implement chunking strategies for processing large datasets
+
+2. **Computational Efficiency**
+
+   - Compare the performance of vectorized operations versus apply/iterrows
+   - Implement an efficient strategy for updating values based on complex conditions
+   - Use `numba` or `swifter` to accelerate pandas operations
+
+3. **Parallel Processing**
+   - Split the data into chunks and process in parallel using multiprocessing
+   - Implement parallel group operations using `dask` or `modin`
+   - Compare execution times between sequential and parallel approaches
+
+### Task 4: Custom Extensions and Advanced Functionality
+
+1. **Custom Accessors**
+
+   - Create a custom pandas accessor that adds domain-specific functionality
+   - Implement methods for sales-specific calculations and transformations
+   - Add property accessors that compute derived metrics
+
+2. **Method Chaining and Pipelines**
+
+   - Create a data processing pipeline using method chaining
+   - Implement custom methods that preserve the DataFrame interface
+   - Design a reusable ETL process for the sales data
+
+3. **Advanced Visualization Integration**
+   - Create a custom plotting method that generates a dashboard of sales metrics
+   - Implement interactive visualizations using plotly
+   - Design a function that automatically generates reports with relevant visualizations
+
+### Task 5: Real-world Challenge
+
+Implement a complete solution for a retail analytics scenario:
+
+1. Process raw transaction data to handle missing values, duplicates, and outliers
+2. Create customer profiles with calculated metrics (lifetime value, purchase frequency, etc.)
+3. Develop a product recommendation engine based on co-purchase patterns
+4. Implement time-series forecasting for product sales
+5. Create an anomaly detection system for unusual transaction patterns
+6. Generate a comprehensive dashboard with actionable insights
+
+## Dataset Generation
+
+Use the following code to generate synthetic datasets for this assignment:
+
+```python
+import pandas as pd
+import numpy as np
+import datetime
+from faker import Faker
+import uuid
+
+# Set random seed for reproducibility
+np.random.seed(42)
+fake = Faker()
+Faker.seed(42)
+
+# Define constants
+num_customers = 1000
+num_products = 200
+num_transactions = 50000
+start_date = datetime.datetime(2020, 1, 1)
+end_date = datetime.datetime(2023, 12, 31)
+days_range = (end_date - start_date).days
+
+# Product categories and subcategories
+categories = ['Electronics', 'Clothing', 'Home', 'Food', 'Beauty']
+subcategories = {
+    'Electronics': ['Phones', 'Computers', 'Accessories', 'TVs', 'Audio'],
+    'Clothing': ['Men', 'Women', 'Children', 'Shoes', 'Accessories'],
+    'Home': ['Furniture', 'Kitchen', 'Decor', 'Bedding', 'Bath'],
+    'Food': ['Produce', 'Bakery', 'Dairy', 'Meat', 'Beverages'],
+    'Beauty': ['Skincare', 'Makeup', 'Haircare', 'Fragrance', 'Bath & Body']
+}
+
+# Regions and countries
+regions = ['North America', 'Europe', 'Asia', 'South America', 'Africa', 'Oceania']
+countries_by_region = {
+    'North America': ['USA', 'Canada', 'Mexico'],
+    'Europe': ['UK', 'Germany', 'France', 'Italy', 'Spain'],
+    'Asia': ['China', 'Japan', 'India', 'South Korea', 'Singapore'],
+    'South America': ['Brazil', 'Argentina', 'Colombia', 'Chile', 'Peru'],
+    'Africa': ['South Africa', 'Egypt', 'Nigeria', 'Kenya', 'Morocco'],
+    'Oceania': ['Australia', 'New Zealand', 'Fiji']
+}
+
+# Generate customer data
+customer_ids = [str(uuid.uuid4()) for _ in range(num_customers)]
+customer_data = []
+
+for customer_id in customer_ids:
+    region = np.random.choice(regions)
+    country = np.random.choice(countries_by_region[region])
+    join_date = start_date + datetime.timedelta(days=np.random.randint(0, days_range))
+
+    customer_data.append({
+        'customer_id': customer_id,
+        'name': fake.name(),
+        'email': fake.email(),
+        'phone': fake.phone_number(),
+        'region': region,
+        'country': country,
+        'city': fake.city(),
+        'join_date': join_date,
+        'tier': np.random.choice(['Bronze', 'Silver', 'Gold', 'Platinum'], p=[0.5, 0.3, 0.15, 0.05]),
+        'is_active': np.random.choice([True, False], p=[0.9, 0.1])
+    })
+
+customers_df = pd.DataFrame(customer_data)
+
+# Generate product data
+product_ids = [str(uuid.uuid4()) for _ in range(num_products)]
+product_data = []
+
+for product_id in product_ids:
+    category = np.random.choice(categories)
+    subcategory = np.random.choice(subcategories[category])
+    launch_date = start_date + datetime.timedelta(days=np.random.randint(0, days_range))
+
+    product_data.append({
+        'product_id': product_id,
+        'name': fake.word() + ' ' + fake.word().capitalize(),
+        'category': category,
+        'subcategory': subcategory,
+        'price': round(np.random.uniform(10, 1000), 2),
+        'cost': round(np.random.uniform(5, 500), 2),
+        'weight_kg': round(np.random.uniform(0.1, 20), 2),
+        'launch_date': launch_date,
+        'is_discontinued': np.random.choice([True, False], p=[0.1, 0.9])
+    })
+
+products_df = pd.DataFrame(product_data)
+
+# Generate transaction data
+transaction_data = []
+
+for _ in range(num_transactions):
+    transaction_date = start_date + datetime.timedelta(days=np.random.randint(0, days_range))
+    customer_id = np.random.choice(customer_ids)
+
+    # Each transaction can have 1-5 items
+    num_items = np.random.randint(1, 6)
+
+    for _ in range(num_items):
+        product_id = np.random.choice(product_ids)
+        product_price = products_df.loc[products_df['product_id'] == product_id, 'price'].iloc[0]
+        quantity = np.random.randint(1, 5)
+
+        # Apply random discount
+        discount_pct = np.random.choice([0, 0, 0, 0.05, 0.1, 0.15, 0.2], p=[0.6, 0.1, 0.1, 0.05, 0.05, 0.05, 0.05])
+        price_after_discount = round(product_price * (1 - discount_pct), 2)
+
+        transaction_id = str(uuid.uuid4())
+
+        transaction_data.append({
+            'transaction_id': transaction_id,
+            'customer_id': customer_id,
+            'product_id': product_id,
+            'date': transaction_date,
+            'quantity': quantity,
+            'unit_price': product_price,
+            'discount_pct': discount_pct,
+            'price_after_discount': price_after_discount,
+            'total_amount': round(quantity * price_after_discount, 2),
+            'payment_method': np.random.choice(['Credit Card', 'Debit Card', 'PayPal', 'Cash', 'Bank Transfer']),
+            'store_id': np.random.randint(1, 50)
+        })
+
+transactions_df = pd.DataFrame(transaction_data)
+
+# Add some missing values and anomalies
+transactions_df.loc[np.random.choice(transactions_df.index, size=int(len(transactions_df)*0.01)), 'unit_price'] = np.nan
+transactions_df.loc[np.random.choice(transactions_df.index, size=int(len(transactions_df)*0.01)), 'discount_pct'] = np.nan
+transactions_df.loc[np.random.choice(transactions_df.index, size=int(len(transactions_df)*0.005)), 'total_amount'] = -1
+customers_df.loc[np.random.choice(customers_df.index, size=int(len(customers_df)*0.02)), 'email'] = np.nan
+products_df.loc[np.random.choice(products_df.index, size=int(len(products_df)*0.01)), 'price'] = np.nan
+
+# Add some duplicates
+dupe_indices = np.random.choice(transactions_df.index, size=int(len(transactions_df)*0.005))
+dupes = transactions_df.loc[dupe_indices].copy()
+transactions_df = pd.concat([transactions_df, dupes], ignore_index=True)
+
+# Create a time-series for customer status updates
+status_updates = []
+for customer_id in customer_ids:
+    # Generate 1-5 status updates per customer
+    num_updates = np.random.randint(1, 6)
+    for _ in range(num_updates):
+        update_date = start_date + datetime.timedelta(days=np.random.randint(0, days_range))
+        status_updates.append({
+            'customer_id': customer_id,
+            'update_date': update_date,
+            'tier': np.random.choice(['Bronze', 'Silver', 'Gold', 'Platinum']),
+            'lifetime_value': round(np.random.uniform(0, 10000), 2),
+            'credit_score': np.random.randint(300, 851)
+        })
+
+status_updates_df = pd.DataFrame(status_updates)
+status_updates_df.sort_values('update_date', inplace=True)
+
+# Print sample data
+print("Customers sample:")
+print(customers_df.head())
+print("\nProducts sample:")
+print(products_df.head())
+print("\nTransactions sample:")
+print(transactions_df.head())
+print("\nStatus Updates sample:")
+print(status_updates_df.head())
+
+# Save to CSV files if needed
+customers_df.to_csv('customers.csv', index=False)
+products_df.to_csv('products.csv', index=False)
+transactions_df.to_csv('transactions.csv', index=False)
+status_updates_df.to_csv('status_updates.csv', index=False)
+```
+
+## Hints
+
+### Task 1 Hints:
+
+- For pivoting complex data, consider using `pivot_table` instead of `pivot` to handle duplicate values
+- When working with hierarchical indices, `swaplevel` and `sortlevel` can help organize your data
+- Remember that `transform` preserves the shape of the original DataFrame and aligns the results, while `apply` reduces each group to a single row
+
+### Task 2 Hints:
+
+- When performing complex joins, consider executing them in stages rather than all at once
+- For time-based joins, make sure to sort your data by time first
+- The `pd.merge_asof` function requires sorted data on the join keys
+- Consider using `indicator=True` in merge operations to track the source of each row
+
+### Task 3 Hints:
+
+- Use `pd.to_numeric()` with `downcast` parameter to reduce memory usage
+- Consider using `pd.Categorical` for columns with few unique values
+- For large datasets, implement a generator that yields chunks of the data
+- Vectorized string operations can be performed using `str` accessor methods
+
+### Task 4 Hints:
+
+- Look into `pandas.api.extensions.register_dataframe_accessor` and `pandas.api.extensions.register_series_accessor`
+- For method chaining, ensure your custom methods return a DataFrame or Series object
+- Consider creating a class that encapsulates your ETL logic for reusability
+
+### Task 5 Hints:
+
+- Break down the complex task into modular components
+- Consider using `pd.MultiIndex` to organize multi-dimensional data
+- Implement efficient data structures for the recommendation engine
+- Use pandas time-series functionality (like `resample` and `rolling`) for forecasting
+
+## Evaluation Criteria
+
+Your submission will be evaluated based on:
+
+1. **Correctness**: Does your code solve the problem accurately?
+2. **Efficiency**: Is your solution optimized for performance and memory usage?
+3. **Code Quality**: Is your code well-organized, documented, and readable?
+4. **Creativity**: Have you implemented innovative approaches to solve complex problems?
+5. **Analysis**: Have you provided insightful analysis of the results?
+
+## Submission Requirements
+
+Submit a Jupyter notebook or Python script that includes:
+
+1. Well-commented code for all tasks
+2. Explanations of your approach and key decisions
+3. Analysis of results with appropriate visualizations
+4. Performance metrics for optimized solutions
+5. Discussion of limitations and potential improvements
diff --git a/Week1/Day_1/airline_passengers.csv b/Week1/Day_1/airline_passengers.csv
new file mode 100644
index 00000000..61af6f76
--- /dev/null
+++ b/Week1/Day_1/airline_passengers.csv
@@ -0,0 +1,145 @@
+year,month,passengers
+1949,Jan,112
+1949,Feb,118
+1949,Mar,132
+1949,Apr,129
+1949,May,121
+1949,Jun,135
+1949,Jul,148
+1949,Aug,148
+1949,Sep,136
+1949,Oct,119
+1949,Nov,104
+1949,Dec,118
+1950,Jan,115
+1950,Feb,126
+1950,Mar,141
+1950,Apr,135
+1950,May,125
+1950,Jun,149
+1950,Jul,170
+1950,Aug,170
+1950,Sep,158
+1950,Oct,133
+1950,Nov,114
+1950,Dec,140
+1951,Jan,145
+1951,Feb,150
+1951,Mar,178
+1951,Apr,163
+1951,May,172
+1951,Jun,178
+1951,Jul,199
+1951,Aug,199
+1951,Sep,184
+1951,Oct,162
+1951,Nov,146
+1951,Dec,166
+1952,Jan,171
+1952,Feb,180
+1952,Mar,193
+1952,Apr,181
+1952,May,183
+1952,Jun,218
+1952,Jul,230
+1952,Aug,242
+1952,Sep,209
+1952,Oct,191
+1952,Nov,172
+1952,Dec,194
+1953,Jan,196
+1953,Feb,196
+1953,Mar,236
+1953,Apr,235
+1953,May,229
+1953,Jun,243
+1953,Jul,264
+1953,Aug,272
+1953,Sep,237
+1953,Oct,211
+1953,Nov,180
+1953,Dec,201
+1954,Jan,204
+1954,Feb,188
+1954,Mar,235
+1954,Apr,227
+1954,May,234
+1954,Jun,264
+1954,Jul,302
+1954,Aug,293
+1954,Sep,259
+1954,Oct,229
+1954,Nov,203
+1954,Dec,229
+1955,Jan,242
+1955,Feb,233
+1955,Mar,267
+1955,Apr,269
+1955,May,270
+1955,Jun,315
+1955,Jul,364
+1955,Aug,347
+1955,Sep,312
+1955,Oct,274
+1955,Nov,237
+1955,Dec,278
+1956,Jan,284
+1956,Feb,277
+1956,Mar,317
+1956,Apr,313
+1956,May,318
+1956,Jun,374
+1956,Jul,413
+1956,Aug,405
+1956,Sep,355
+1956,Oct,306
+1956,Nov,271
+1956,Dec,306
+1957,Jan,315
+1957,Feb,301
+1957,Mar,356
+1957,Apr,348
+1957,May,355
+1957,Jun,422
+1957,Jul,465
+1957,Aug,467
+1957,Sep,404
+1957,Oct,347
+1957,Nov,305
+1957,Dec,336
+1958,Jan,340
+1958,Feb,318
+1958,Mar,362
+1958,Apr,348
+1958,May,363
+1958,Jun,435
+1958,Jul,491
+1958,Aug,505
+1958,Sep,404
+1958,Oct,359
+1958,Nov,310
+1958,Dec,337
+1959,Jan,360
+1959,Feb,342
+1959,Mar,406
+1959,Apr,396
+1959,May,420
+1959,Jun,472
+1959,Jul,548
+1959,Aug,559
+1959,Sep,463
+1959,Oct,407
+1959,Nov,362
+1959,Dec,405
+1960,Jan,417
+1960,Feb,391
+1960,Mar,419
+1960,Apr,461
+1960,May,472
+1960,Jun,535
+1960,Jul,622
+1960,Aug,606
+1960,Sep,508
+1960,Oct,461
+1960,Nov,390
+1960,Dec,432
diff --git a/Week1/Day_1/iris.csv b/Week1/Day_1/iris.csv
new file mode 100644
index 00000000..64ffe564
--- /dev/null
+++ b/Week1/Day_1/iris.csv
@@ -0,0 +1,151 @@
+sepal length (cm),sepal width (cm),petal length (cm),petal width (cm)
+5.1,3.5,1.4,0.2
+4.9,3.0,1.4,0.2
+4.7,3.2,1.3,0.2
+4.6,3.1,1.5,0.2
+5.0,3.6,1.4,0.2
+5.4,3.9,1.7,0.4
+4.6,3.4,1.4,0.3
+5.0,3.4,1.5,0.2
+4.4,2.9,1.4,0.2
+4.9,3.1,1.5,0.1
+5.4,3.7,1.5,0.2
+4.8,3.4,1.6,0.2
+4.8,3.0,1.4,0.1
+4.3,3.0,1.1,0.1
+5.8,4.0,1.2,0.2
+5.7,4.4,1.5,0.4
+5.4,3.9,1.3,0.4
+5.1,3.5,1.4,0.3
+5.7,3.8,1.7,0.3
+5.1,3.8,1.5,0.3
+5.4,3.4,1.7,0.2
+5.1,3.7,1.5,0.4
+4.6,3.6,1.0,0.2
+5.1,3.3,1.7,0.5
+4.8,3.4,1.9,0.2
+5.0,3.0,1.6,0.2
+5.0,3.4,1.6,0.4
+5.2,3.5,1.5,0.2
+5.2,3.4,1.4,0.2
+4.7,3.2,1.6,0.2
+4.8,3.1,1.6,0.2
+5.4,3.4,1.5,0.4
+5.2,4.1,1.5,0.1
+5.5,4.2,1.4,0.2
+4.9,3.1,1.5,0.2
+5.0,3.2,1.2,0.2
+5.5,3.5,1.3,0.2
+4.9,3.6,1.4,0.1
+4.4,3.0,1.3,0.2
+5.1,3.4,1.5,0.2
+5.0,3.5,1.3,0.3
+4.5,2.3,1.3,0.3
+4.4,3.2,1.3,0.2
+5.0,3.5,1.6,0.6
+5.1,3.8,1.9,0.4
+4.8,3.0,1.4,0.3
+5.1,3.8,1.6,0.2
+4.6,3.2,1.4,0.2
+5.3,3.7,1.5,0.2
+5.0,3.3,1.4,0.2
+7.0,3.2,4.7,1.4
+6.4,3.2,4.5,1.5
+6.9,3.1,4.9,1.5
+5.5,2.3,4.0,1.3
+6.5,2.8,4.6,1.5
+5.7,2.8,4.5,1.3
+6.3,3.3,4.7,1.6
+4.9,2.4,3.3,1.0
+6.6,2.9,4.6,1.3
+5.2,2.7,3.9,1.4
+5.0,2.0,3.5,1.0
+5.9,3.0,4.2,1.5
+6.0,2.2,4.0,1.0
+6.1,2.9,4.7,1.4
+5.6,2.9,3.6,1.3
+6.7,3.1,4.4,1.4
+5.6,3.0,4.5,1.5
+5.8,2.7,4.1,1.0
+6.2,2.2,4.5,1.5
+5.6,2.5,3.9,1.1
+5.9,3.2,4.8,1.8
+6.1,2.8,4.0,1.3
+6.3,2.5,4.9,1.5
+6.1,2.8,4.7,1.2
+6.4,2.9,4.3,1.3
+6.6,3.0,4.4,1.4
+6.8,2.8,4.8,1.4
+6.7,3.0,5.0,1.7
+6.0,2.9,4.5,1.5
+5.7,2.6,3.5,1.0
+5.5,2.4,3.8,1.1
+5.5,2.4,3.7,1.0
+5.8,2.7,3.9,1.2
+6.0,2.7,5.1,1.6
+5.4,3.0,4.5,1.5
+6.0,3.4,4.5,1.6
+6.7,3.1,4.7,1.5
+6.3,2.3,4.4,1.3
+5.6,3.0,4.1,1.3
+5.5,2.5,4.0,1.3
+5.5,2.6,4.4,1.2
+6.1,3.0,4.6,1.4
+5.8,2.6,4.0,1.2
+5.0,2.3,3.3,1.0
+5.6,2.7,4.2,1.3
+5.7,3.0,4.2,1.2
+5.7,2.9,4.2,1.3
+6.2,2.9,4.3,1.3
+5.1,2.5,3.0,1.1
+5.7,2.8,4.1,1.3
+6.3,3.3,6.0,2.5
+5.8,2.7,5.1,1.9
+7.1,3.0,5.9,2.1
+6.3,2.9,5.6,1.8
+6.5,3.0,5.8,2.2
+7.6,3.0,6.6,2.1
+4.9,2.5,4.5,1.7
+7.3,2.9,6.3,1.8
+6.7,2.5,5.8,1.8
+7.2,3.6,6.1,2.5
+6.5,3.2,5.1,2.0
+6.4,2.7,5.3,1.9
+6.8,3.0,5.5,2.1
+5.7,2.5,5.0,2.0
+5.8,2.8,5.1,2.4
+6.4,3.2,5.3,2.3
+6.5,3.0,5.5,1.8
+7.7,3.8,6.7,2.2
+7.7,2.6,6.9,2.3
+6.0,2.2,5.0,1.5
+6.9,3.2,5.7,2.3
+5.6,2.8,4.9,2.0
+7.7,2.8,6.7,2.0
+6.3,2.7,4.9,1.8
+6.7,3.3,5.7,2.1
+7.2,3.2,6.0,1.8
+6.2,2.8,4.8,1.8
+6.1,3.0,4.9,1.8
+6.4,2.8,5.6,2.1
+7.2,3.0,5.8,1.6
+7.4,2.8,6.1,1.9
+7.9,3.8,6.4,2.0
+6.4,2.8,5.6,2.2
+6.3,2.8,5.1,1.5
+6.1,2.6,5.6,1.4
+7.7,3.0,6.1,2.3
+6.3,3.4,5.6,2.4
+6.4,3.1,5.5,1.8
+6.0,3.0,4.8,1.8
+6.9,3.1,5.4,2.1
+6.7,3.1,5.6,2.4
+6.9,3.1,5.1,2.3
+5.8,2.7,5.1,1.9
+6.8,3.2,5.9,2.3
+6.7,3.3,5.7,2.5
+6.7,3.0,5.2,2.3
+6.3,2.5,5.0,1.9
+6.5,3.0,5.2,2.0
+6.2,3.4,5.4,2.3
+5.9,3.0,5.1,1.8
diff --git a/Week1/Day_1/pandas.ipynb b/Week1/Day_1/pandas.ipynb
new file mode 100644
index 00000000..713262ea
--- /dev/null
+++ b/Week1/Day_1/pandas.ipynb
@@ -0,0 +1,4150 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Pandas Data Operations\n",
+        "\n",
+        "This notebook demonstrates how to use pandas to perform common data operations:\n",
+        "\n",
+        "1. Reading CSV data into pandas\n",
+        "2. Selecting columns\n",
+        "3. Filtering by value in column\n",
+        "4. Performing aggregations\n",
+        "5. Filtering on aggregations\n",
+        "6. Left outer joins\n",
+        "7. Right outer joins\n",
+        "8. Inner joins\n",
+        "\n",
+        "Let's start by importing pandas and other libraries we'll need."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Import necessary libraries\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Reading dictonary Data into Pandas\n",
+        "\n",
+        "First, let's create two sample datasets that we'll use throughout this notebook. We'll create them as pandas DataFrames and then save them as CSV files. In a real-world scenario, you would typically load existing CSV files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sales DataFrame:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         1          101           1         2  25.50  2023-01-15  Electronics\n",
+              "1         2          102           2         1  35.00  2023-01-16     Clothing\n",
+              "2         3          101           3         3  15.75  2023-01-16        Books\n",
+              "3         4          103           1         2  25.50  2023-01-17  Electronics\n",
+              "4         5          104           2         1  35.00  2023-01-18     Clothing"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a sample sales DataFrame\n",
+        "sales_data = {\n",
+        "    'order_id': list(range(1, 11)),\n",
+        "    'customer_id': [101, 102, 101, 103, 104, 105, 103, 106, 102, 107],\n",
+        "    'product_id': [1, 2, 3, 1, 2, 3, 4, 5, 1, 2],\n",
+        "    'quantity': [2, 1, 3, 2, 1, 1, 4, 2, 3, 1],\n",
+        "    'price': [25.50, 35.00, 15.75, 25.50, 35.00, 15.75, 10.25, 50.00, 25.50, 35.00],\n",
+        "    'order_date': ['2023-01-15', '2023-01-16', '2023-01-16', '2023-01-17', '2023-01-18', \n",
+        "                   '2023-01-19', '2023-01-20', '2023-01-20', '2023-01-21', '2023-01-22'],\n",
+        "    'category': ['Electronics', 'Clothing', 'Books', 'Electronics', 'Clothing', \n",
+        "                'Books', 'Food', 'Furniture', 'Electronics', 'Clothing']\n",
+        "}\n",
+        "\n",
+        "sales_df = pd.DataFrame(sales_data)\n",
+        "\n",
+        "# Create a sample customers DataFrame\n",
+        "customers_data = {\n",
+        "    'customer_id': [101, 102, 103, 104, 105, 108, 109],  # Note: 106, 107 missing and 108, 109 not in sales\n",
+        "    'name': ['Alice Brown', 'Bob Smith', 'Charlie Davis', 'David Wilson', 'Emma Johnson', 'Frank Miller', 'Grace Lee'],\n",
+        "    'email': ['alice@example.com', 'bob@example.com', 'charlie@example.com', 'david@example.com', \n",
+        "             'emma@example.com', 'frank@example.com', 'grace@example.com'],\n",
+        "    'city': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', 'Philadelphia', 'San Antonio'],\n",
+        "    'membership_level': ['Gold', 'Silver', 'Gold', 'Bronze', 'Silver', 'Gold', 'Bronze']\n",
+        "}\n",
+        "\n",
+        "customers_df = pd.DataFrame(customers_data)\n",
+        "\n",
+        "# Save DataFrames to CSV files\n",
+        "sales_df.to_csv('sales.csv', index=False)\n",
+        "customers_df.to_csv('customers.csv', index=False)\n",
+        "\n",
+        "# Now read the CSV files back into pandas to simulate loading from CSV\n",
+        "sales_df = pd.read_csv('sales.csv')\n",
+        "customers_df = pd.read_csv('customers.csv')\n",
+        "\n",
+        "# Display the first few rows of each DataFrame\n",
+        "print(\"Sales DataFrame:\")\n",
+        "sales_df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sales DataFrame:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         1          101           1         2  25.50  2023-01-15  Electronics\n",
+              "1         2          102           2         1  35.00  2023-01-16     Clothing\n",
+              "2         3          101           3         3  15.75  2023-01-16        Books\n",
+              "3         4          103           1         2  25.50  2023-01-17  Electronics\n",
+              "4         5          104           2         1  35.00  2023-01-18     Clothing"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "sales_df = pd.read_csv('sales.csv')\n",
+        "customers_df = pd.read_csv('customers.csv')\n",
+        "\n",
+        "# # Display the first few rows of each DataFrame\n",
+        "print(\"Sales DataFrame:\")\n",
+        "sales_df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Customers DataFrame:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>name</th>\n",
+              "      <th>email</th>\n",
+              "      <th>city</th>\n",
+              "      <th>membership_level</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>102</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>bob@example.com</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>108</td>\n",
+              "      <td>Frank Miller</td>\n",
+              "      <td>frank@example.com</td>\n",
+              "      <td>Philadelphia</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>109</td>\n",
+              "      <td>Grace Lee</td>\n",
+              "      <td>grace@example.com</td>\n",
+              "      <td>San Antonio</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   customer_id          name              email          city membership_level\n",
+              "1          102     Bob Smith    bob@example.com   Los Angeles           Silver\n",
+              "5          108  Frank Miller  frank@example.com  Philadelphia             Gold\n",
+              "6          109     Grace Lee  grace@example.com   San Antonio           Bronze"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "print(\"Customers DataFrame:\")\n",
+        "customers_df.sample(n=3)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Selecting Columns\n",
+        "\n",
+        "Pandas provides multiple ways to select columns from a DataFrame. Let's explore some of these methods."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Single column selection (returns a Series):\n",
+            "0      alice@example.com\n",
+            "1        bob@example.com\n",
+            "2    charlie@example.com\n",
+            "3      david@example.com\n",
+            "4       emma@example.com\n",
+            "5      frank@example.com\n",
+            "6      grace@example.com\n",
+            "Name: email, dtype: object\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Method 1: Select a single column (returns a Series)\n",
+        "customer_id_column = customers_df['email']\n",
+        "print(\"Single column selection (returns a Series):\")\n",
+        "print(customer_id_column)\n",
+        "# set(customer_id_column)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Multiple column selection:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>25.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>25.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  price\n",
+              "0         1          101           1  25.50\n",
+              "1         2          102           2  35.00\n",
+              "2         3          101           3  15.75\n",
+              "3         4          103           1  25.50\n",
+              "4         5          104           2  35.00"
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Method 2: Select multiple columns (returns a DataFrame)\n",
+        "selected_columns = sales_df[['order_id', 'customer_id', 'product_id', 'price']]\n",
+        "\n",
+        "list_of_columns=['order_id', 'customer_id', 'product_id', 'price']\n",
+        "selected_columns = sales_df[list_of_columns]\n",
+        "\n",
+        "print(\"Multiple column selection:\")\n",
+        "selected_columns.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['order_id',\n",
+              " 'customer_id',\n",
+              " 'product_id',\n",
+              " 'quantity',\n",
+              " 'price',\n",
+              " 'order_date',\n",
+              " 'category']"
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['order_date', 'quantity', 'category']\n",
+            "Multiple column selection:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_date  quantity     category\n",
+              "0  2023-01-15         2  Electronics\n",
+              "1  2023-01-16         1     Clothing\n",
+              "2  2023-01-16         3        Books\n",
+              "3  2023-01-17         2  Electronics\n",
+              "4  2023-01-18         1     Clothing"
+            ]
+          },
+          "execution_count": 45,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Method 2: Select multiple columns (returns a DataFrame)\n",
+        "all_columns=list(sales_df.columns)\n",
+        "exclude_columns = ['order_id', 'customer_id', 'product_id', 'price']\n",
+        "\n",
+        "list_of_columns=list(set(all_columns)-set(exclude_columns))\n",
+        "print(list_of_columns)\n",
+        "\n",
+        "selected_columns = sales_df[list_of_columns]\n",
+        "\n",
+        "print(\"Multiple column selection:\")\n",
+        "selected_columns.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Column selection using .loc:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>category</th>\n",
+              "      <th>price</th>\n",
+              "      <th>quantity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Books</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      category  price  quantity\n",
+              "0  Electronics  25.50         2\n",
+              "1     Clothing  35.00         1\n",
+              "2        Books  15.75         3\n",
+              "3  Electronics  25.50         2\n",
+              "4     Clothing  35.00         1"
+            ]
+          },
+          "execution_count": 46,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Method 3: Using .loc for column selection\n",
+        "\n",
+        "\n",
+        "\n",
+        "loc_selection = sales_df.loc[:, ['category', 'price', 'quantity']]\n",
+        "print(\"Column selection using .loc:\")\n",
+        "loc_selection.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Position-based selection using .iloc:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "3         4          103           1         2   25.5  2023-01-17  Electronics\n",
+              "4         5          104           2         1   35.0  2023-01-18     Clothing"
+            ]
+          },
+          "execution_count": 49,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Method 4: Using .iloc for position-based selection\n",
+        "# Select the first 3 columns for the first 5 rows\n",
+        "\n",
+        "iloc_selection = sales_df.iloc[3:5, :]\n",
+        "print(\"Position-based selection using .iloc:\")\n",
+        "iloc_selection"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3. Filtering by Value in Column\n",
+        "\n",
+        "Pandas makes it easy to filter data based on conditions applied to columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>105</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-19</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7</td>\n",
+              "      <td>103</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Food</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>8</td>\n",
+              "      <td>106</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2</td>\n",
+              "      <td>50.00</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Furniture</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10</td>\n",
+              "      <td>107</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-22</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         1          101           1         2  25.50  2023-01-15  Electronics\n",
+              "1         2          102           2         1  35.00  2023-01-16     Clothing\n",
+              "2         3          101           3         3  15.75  2023-01-16        Books\n",
+              "3         4          103           1         2  25.50  2023-01-17  Electronics\n",
+              "4         5          104           2         1  35.00  2023-01-18     Clothing\n",
+              "5         6          105           3         1  15.75  2023-01-19        Books\n",
+              "6         7          103           4         4  10.25  2023-01-20         Food\n",
+              "7         8          106           5         2  50.00  2023-01-20    Furniture\n",
+              "8         9          102           1         3  25.50  2023-01-21  Electronics\n",
+              "9        10          107           2         1  35.00  2023-01-22     Clothing"
+            ]
+          },
+          "execution_count": 50,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "sales_df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 53,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "High price sales (> $30):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>105</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-19</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7</td>\n",
+              "      <td>103</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Food</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         1          101           1         2  25.50  2023-01-15  Electronics\n",
+              "2         3          101           3         3  15.75  2023-01-16        Books\n",
+              "3         4          103           1         2  25.50  2023-01-17  Electronics\n",
+              "5         6          105           3         1  15.75  2023-01-19        Books\n",
+              "6         7          103           4         4  10.25  2023-01-20         Food\n",
+              "8         9          102           1         3  25.50  2023-01-21  Electronics"
+            ]
+          },
+          "execution_count": 53,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Filter sales with price greater than 30\n",
+        "high_price_sales = sales_df[sales_df['price'] < 30]\n",
+        "print(\"High price sales (> $30):\")\n",
+        "high_price_sales"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "condition2 0     True\n",
+            "1    False\n",
+            "2     True\n",
+            "3     True\n",
+            "4    False\n",
+            "5    False\n",
+            "6     True\n",
+            "7     True\n",
+            "8     True\n",
+            "9    False\n",
+            "Name: quantity, dtype: bool\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Multiple filter conditions (Electronics with quantity > 1)\n",
+        "\n",
+        "# electronics_high_qty = sales_df[(sales_df['category'] == 'Electronics') & (sales_df['quantity'] > 1)]\n",
+        "\n",
+        "condition1=sales_df['category'] == 'Electronics'\n",
+        "condition2=sales_df['quantity'] > 1\n",
+        "\n",
+        "\n",
+        "# electronics_high_qty = sales_df[(condition1) | (condition2)]\n",
+        "\n",
+        "\n",
+        "# print(\"Electronics with high quantity (> 1):\")\n",
+        "# electronics_high_qty"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Electronics and Clothing items:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10</td>\n",
+              "      <td>107</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-22</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         1          101           1         2   25.5  2023-01-15  Electronics\n",
+              "1         2          102           2         1   35.0  2023-01-16     Clothing\n",
+              "3         4          103           1         2   25.5  2023-01-17  Electronics\n",
+              "4         5          104           2         1   35.0  2023-01-18     Clothing\n",
+              "8         9          102           1         3   25.5  2023-01-21  Electronics\n",
+              "9        10          107           2         1   35.0  2023-01-22     Clothing"
+            ]
+          },
+          "execution_count": 59,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Filter with multiple values (.isin())\n",
+        "selected_categories = sales_df[sales_df['category'].isin(['Electronics', 'Clothing'])]\n",
+        "print(\"Electronics and Clothing items:\")\n",
+        "selected_categories"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Categories starting with 'E':\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         1          101           1         2   25.5  2023-01-15  Electronics\n",
+              "3         4          103           1         2   25.5  2023-01-17  Electronics\n",
+              "8         9          102           1         3   25.5  2023-01-21  Electronics"
+            ]
+          },
+          "execution_count": 60,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Using string methods for filtering (.str accessor)\n",
+        "e_categories = sales_df[sales_df['category'].str.startswith('E')]\n",
+        "print(\"Categories starting with 'E':\")\n",
+        "e_categories"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Expensive books (price > $15):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>105</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-19</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date category\n",
+              "2         3          101           3         3  15.75  2023-01-16    Books\n",
+              "5         6          105           3         1  15.75  2023-01-19    Books"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Using .query() method for more readable syntax\n",
+        "expensive_books = sales_df.query(\"category == 'Books' and price > 15\")\n",
+        "print(\"Expensive books (price > $15):\")\n",
+        "expensive_books"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. Aggregations\n",
+        "\n",
+        "Pandas provides powerful aggregation functions to summarize data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Total sales amount: $ 273.25\n",
+            "Average price: $ 27.325\n",
+            "Maximum quantity: 4\n",
+            "Minimum price: $ 10.25\n",
+            "Total number of orders: 10\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Basic aggregations on the entire DataFrame\n",
+        "print(\"Total sales amount: $\", sales_df['price'].sum())\n",
+        "print(\"Average price: $\", sales_df['price'].mean())\n",
+        "print(\"Maximum quantity:\", sales_df['quantity'].max())\n",
+        "print(\"Minimum price: $\", sales_df['price'].min())\n",
+        "print(\"Total number of orders:\", len(sales_df))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 74,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         1          101           1         2  25.50  2023-01-15  Electronics\n",
+              "1         2          102           2         1  35.00  2023-01-16     Clothing\n",
+              "2         3          101           3         3  15.75  2023-01-16        Books\n",
+              "3         4          103           1         2  25.50  2023-01-17  Electronics\n",
+              "4         5          104           2         1  35.00  2023-01-18     Clothing"
+            ]
+          },
+          "execution_count": 74,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "sales_df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Summary statistics for sales:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>price</th>\n",
+              "      <th>quantity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>10.00000</td>\n",
+              "      <td>10.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>27.32500</td>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>11.83395</td>\n",
+              "      <td>1.054093</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>10.25000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>18.18750</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>25.50000</td>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>35.00000</td>\n",
+              "      <td>2.750000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>50.00000</td>\n",
+              "      <td>4.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          price   quantity\n",
+              "count  10.00000  10.000000\n",
+              "mean   27.32500   2.000000\n",
+              "std    11.83395   1.054093\n",
+              "min    10.25000   1.000000\n",
+              "25%    18.18750   1.000000\n",
+              "50%    25.50000   2.000000\n",
+              "75%    35.00000   2.750000\n",
+              "max    50.00000   4.000000"
+            ]
+          },
+          "execution_count": 75,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Using .describe() for summary statistics\n",
+        "print(\"Summary statistics for sales:\")\n",
+        "sales_df[['price', 'quantity']].describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Category aggregations:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr:last-of-type th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"2\" halign=\"left\">price</th>\n",
+              "      <th colspan=\"2\" halign=\"left\">quantity</th>\n",
+              "      <th>order_id</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th>mean</th>\n",
+              "      <th>sum</th>\n",
+              "      <th>sum</th>\n",
+              "      <th>max</th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>category</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Books</th>\n",
+              "      <td>15.75</td>\n",
+              "      <td>31.50</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Clothing</th>\n",
+              "      <td>35.00</td>\n",
+              "      <td>105.00</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Electronics</th>\n",
+              "      <td>25.50</td>\n",
+              "      <td>76.50</td>\n",
+              "      <td>7</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Food</th>\n",
+              "      <td>10.25</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Furniture</th>\n",
+              "      <td>50.00</td>\n",
+              "      <td>50.00</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "             price         quantity     order_id\n",
+              "              mean     sum      sum max    count\n",
+              "category                                        \n",
+              "Books        15.75   31.50        4   3        2\n",
+              "Clothing     35.00  105.00        3   1        3\n",
+              "Electronics  25.50   76.50        7   3        3\n",
+              "Food         10.25   10.25        4   4        1\n",
+              "Furniture    50.00   50.00        2   2        1"
+            ]
+          },
+          "execution_count": 76,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Group by category with multiple aggregations\n",
+        "category_aggs = sales_df.groupby('category').agg({\n",
+        "    'price': ['mean', 'sum'],\n",
+        "    'quantity': ['sum', 'max'],\n",
+        "    'order_id': 'count'\n",
+        "})\n",
+        "print(\"Category aggregations:\")\n",
+        "category_aggs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>105</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-19</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7</td>\n",
+              "      <td>103</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Food</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>8</td>\n",
+              "      <td>106</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2</td>\n",
+              "      <td>50.00</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Furniture</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10</td>\n",
+              "      <td>107</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-22</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         1          101           1         2  25.50  2023-01-15  Electronics\n",
+              "1         2          102           2         1  35.00  2023-01-16     Clothing\n",
+              "2         3          101           3         3  15.75  2023-01-16        Books\n",
+              "3         4          103           1         2  25.50  2023-01-17  Electronics\n",
+              "4         5          104           2         1  35.00  2023-01-18     Clothing\n",
+              "5         6          105           3         1  15.75  2023-01-19        Books\n",
+              "6         7          103           4         4  10.25  2023-01-20         Food\n",
+              "7         8          106           5         2  50.00  2023-01-20    Furniture\n",
+              "8         9          102           1         3  25.50  2023-01-21  Electronics\n",
+              "9        10          107           2         1  35.00  2023-01-22     Clothing"
+            ]
+          },
+          "execution_count": 81,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# .sort_values('total_price', ascending=False)\n",
+        "# sales_df.sort_values('order_date', ascending=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 82,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Category aggregations with named columns:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>avg_price</th>\n",
+              "      <th>total_price</th>\n",
+              "      <th>total_quantity</th>\n",
+              "      <th>max_quantity</th>\n",
+              "      <th>order_count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>category</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Clothing</th>\n",
+              "      <td>35.00</td>\n",
+              "      <td>105.00</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Electronics</th>\n",
+              "      <td>25.50</td>\n",
+              "      <td>76.50</td>\n",
+              "      <td>7</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Furniture</th>\n",
+              "      <td>50.00</td>\n",
+              "      <td>50.00</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Books</th>\n",
+              "      <td>15.75</td>\n",
+              "      <td>31.50</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Food</th>\n",
+              "      <td>10.25</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "             avg_price  total_price  total_quantity  max_quantity  order_count\n",
+              "category                                                                      \n",
+              "Clothing         35.00       105.00               3             1            3\n",
+              "Electronics      25.50        76.50               7             3            3\n",
+              "Furniture        50.00        50.00               2             2            1\n",
+              "Books            15.75        31.50               4             3            2\n",
+              "Food             10.25        10.25               4             4            1"
+            ]
+          },
+          "execution_count": 82,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Using named aggregations for cleaner output\n",
+        "category_aggs_named = sales_df.groupby('category').agg(\n",
+        "    avg_price=('price', 'mean'),\n",
+        "    total_price=('price', 'sum'),\n",
+        "    total_quantity=('quantity', 'sum'),\n",
+        "    max_quantity=('quantity', 'max'),\n",
+        "    order_count=('order_id', 'count')\n",
+        ").sort_values(['total_price','avg_price'], ascending=[False, True])\n",
+        "\n",
+        "print(\"Category aggregations with named columns:\")\n",
+        "category_aggs_named"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 84,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Revenue by category:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>category</th>\n",
+              "      <th>total_revenue</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>178.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>105.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Furniture</td>\n",
+              "      <td>100.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Books</td>\n",
+              "      <td>63.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Food</td>\n",
+              "      <td>41.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      category  total_revenue\n",
+              "0  Electronics          178.5\n",
+              "1     Clothing          105.0\n",
+              "2    Furniture          100.0\n",
+              "3        Books           63.0\n",
+              "4         Food           41.0"
+            ]
+          },
+          "execution_count": 84,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Calculate total revenue (price * quantity) by category\n",
+        "# First, add a total_revenue column\n",
+        "sales_df['total_revenue'] = sales_df['price'] * sales_df['quantity']\n",
+        "\n",
+        "revenue_by_category = sales_df.groupby('category')['total_revenue'].sum().sort_values(ascending=False).reset_index()\n",
+        "print(\"Revenue by category:\")\n",
+        "revenue_by_category"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 85,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Visualize the revenue by category\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.bar(revenue_by_category['category'], revenue_by_category['total_revenue'])\n",
+        "plt.title('Total Revenue by Category')\n",
+        "plt.xlabel('Category')\n",
+        "plt.ylabel('Total Revenue ($)')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5. Filtering on Aggregations\n",
+        "\n",
+        "We can filter data based on aggregated results using various approaches in pandas."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 87,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Categories with high average price (> $25):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>category</th>\n",
+              "      <th>price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Books</td>\n",
+              "      <td>15.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Food</td>\n",
+              "      <td>10.25</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  category  price\n",
+              "0    Books  15.75\n",
+              "1     Food  10.25"
+            ]
+          },
+          "execution_count": 87,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Method 1: Filter groups after aggregation\n",
+        "# Get categories with average price > 25\n",
+        "high_avg_price = sales_df.groupby('category')['price'].mean()\n",
+        "high_avg_price_categories = high_avg_price[high_avg_price < 25].reset_index()\n",
+        "print(\"Categories with high average price (> $25):\")\n",
+        "high_avg_price_categories"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sales in high-priced categories:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "      <th>total_revenue</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>51.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>51.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>8</td>\n",
+              "      <td>106</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Furniture</td>\n",
+              "      <td>100.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>76.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10</td>\n",
+              "      <td>107</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-22</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date  \\\n",
+              "0         1          101           1         2   25.5  2023-01-15   \n",
+              "1         2          102           2         1   35.0  2023-01-16   \n",
+              "3         4          103           1         2   25.5  2023-01-17   \n",
+              "4         5          104           2         1   35.0  2023-01-18   \n",
+              "7         8          106           5         2   50.0  2023-01-20   \n",
+              "8         9          102           1         3   25.5  2023-01-21   \n",
+              "9        10          107           2         1   35.0  2023-01-22   \n",
+              "\n",
+              "      category  total_revenue  \n",
+              "0  Electronics           51.0  \n",
+              "1     Clothing           35.0  \n",
+              "3  Electronics           51.0  \n",
+              "4     Clothing           35.0  \n",
+              "7    Furniture          100.0  \n",
+              "8  Electronics           76.5  \n",
+              "9     Clothing           35.0  "
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Method 2: Get all items from categories with high average prices\n",
+        "premium_category_names = high_avg_price_categories['category'].tolist()\n",
+        "sales_in_high_categories = sales_df[sales_df['category'].isin(premium_category_names)]\n",
+        "print(\"Sales in high-priced categories:\")\n",
+        "sales_in_high_categories"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Items priced above their category average:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "      <th>total_revenue</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Empty DataFrame\n",
+              "Columns: [order_id, customer_id, product_id, quantity, price, order_date, category, total_revenue]\n",
+              "Index: []"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Method 3: Filter using aggregation with transform\n",
+        "# Find items priced higher than their category average\n",
+        "category_avg_price = sales_df.groupby('category')['price'].transform('mean')\n",
+        "above_avg_price_items = sales_df[sales_df['price'] > category_avg_price]\n",
+        "print(\"Items priced above their category average:\")\n",
+        "above_avg_price_items"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Popular categories (>2 orders and >3 quantity):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>category</th>\n",
+              "      <th>order_count</th>\n",
+              "      <th>total_quantity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      category  order_count  total_quantity\n",
+              "0  Electronics            3               7"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Method 4: Filter categories with specific aggregation criteria\n",
+        "# Categories with more than 2 orders and total quantity > 3\n",
+        "category_counts = sales_df.groupby('category').agg({\n",
+        "    'order_id': 'count',\n",
+        "    'quantity': 'sum'\n",
+        "}).rename(columns={'order_id': 'order_count', 'quantity': 'total_quantity'})\n",
+        "\n",
+        "popular_categories = category_counts[(category_counts['order_count'] > 2) & \n",
+        "                                     (category_counts['total_quantity'] > 3)].reset_index()\n",
+        "print(\"Popular categories (>2 orders and >3 quantity):\")\n",
+        "popular_categories"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 6. Left Outer Join\n",
+        "\n",
+        "A left outer join returns all records from the left table and the matched records from the right table. If there is no match, NULL values are returned for the right table columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 88,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "      <th>total_revenue</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>51.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>47.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>51.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>105</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-19</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>15.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7</td>\n",
+              "      <td>103</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Food</td>\n",
+              "      <td>41.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>8</td>\n",
+              "      <td>106</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2</td>\n",
+              "      <td>50.00</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Furniture</td>\n",
+              "      <td>100.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>76.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10</td>\n",
+              "      <td>107</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-22</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date  \\\n",
+              "0         1          101           1         2  25.50  2023-01-15   \n",
+              "1         2          102           2         1  35.00  2023-01-16   \n",
+              "2         3          101           3         3  15.75  2023-01-16   \n",
+              "3         4          103           1         2  25.50  2023-01-17   \n",
+              "4         5          104           2         1  35.00  2023-01-18   \n",
+              "5         6          105           3         1  15.75  2023-01-19   \n",
+              "6         7          103           4         4  10.25  2023-01-20   \n",
+              "7         8          106           5         2  50.00  2023-01-20   \n",
+              "8         9          102           1         3  25.50  2023-01-21   \n",
+              "9        10          107           2         1  35.00  2023-01-22   \n",
+              "\n",
+              "      category  total_revenue  \n",
+              "0  Electronics          51.00  \n",
+              "1     Clothing          35.00  \n",
+              "2        Books          47.25  \n",
+              "3  Electronics          51.00  \n",
+              "4     Clothing          35.00  \n",
+              "5        Books          15.75  \n",
+              "6         Food          41.00  \n",
+              "7    Furniture         100.00  \n",
+              "8  Electronics          76.50  \n",
+              "9     Clothing          35.00  "
+            ]
+          },
+          "execution_count": 88,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "sales_df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 90,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>name</th>\n",
+              "      <th>email</th>\n",
+              "      <th>city</th>\n",
+              "      <th>membership_level</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>101</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>alice@example.com</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>102</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>bob@example.com</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>103</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>charlie@example.com</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>104</td>\n",
+              "      <td>David Wilson</td>\n",
+              "      <td>david@example.com</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>105</td>\n",
+              "      <td>Emma Johnson</td>\n",
+              "      <td>emma@example.com</td>\n",
+              "      <td>Phoenix</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>108</td>\n",
+              "      <td>Frank Miller</td>\n",
+              "      <td>frank@example.com</td>\n",
+              "      <td>Philadelphia</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>109</td>\n",
+              "      <td>Grace Lee</td>\n",
+              "      <td>grace@example.com</td>\n",
+              "      <td>San Antonio</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   customer_id           name                email          city  \\\n",
+              "0          101    Alice Brown    alice@example.com      New York   \n",
+              "1          102      Bob Smith      bob@example.com   Los Angeles   \n",
+              "2          103  Charlie Davis  charlie@example.com       Chicago   \n",
+              "3          104   David Wilson    david@example.com       Houston   \n",
+              "4          105   Emma Johnson     emma@example.com       Phoenix   \n",
+              "5          108   Frank Miller    frank@example.com  Philadelphia   \n",
+              "6          109      Grace Lee    grace@example.com   San Antonio   \n",
+              "\n",
+              "  membership_level  \n",
+              "0             Gold  \n",
+              "1           Silver  \n",
+              "2             Gold  \n",
+              "3           Bronze  \n",
+              "4           Silver  \n",
+              "5             Gold  \n",
+              "6           Bronze  "
+            ]
+          },
+          "execution_count": 90,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "customers_df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 91,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Left join (all sales, matching customers):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "      <th>total_revenue</th>\n",
+              "      <th>name</th>\n",
+              "      <th>email</th>\n",
+              "      <th>city</th>\n",
+              "      <th>membership_level</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>51.00</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>alice@example.com</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>bob@example.com</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>47.25</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>alice@example.com</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>51.00</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>charlie@example.com</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>David Wilson</td>\n",
+              "      <td>david@example.com</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>105</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-19</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>Emma Johnson</td>\n",
+              "      <td>emma@example.com</td>\n",
+              "      <td>Phoenix</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7</td>\n",
+              "      <td>103</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Food</td>\n",
+              "      <td>41.00</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>charlie@example.com</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>8</td>\n",
+              "      <td>106</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2</td>\n",
+              "      <td>50.00</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Furniture</td>\n",
+              "      <td>100.00</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>76.50</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>bob@example.com</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10</td>\n",
+              "      <td>107</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-22</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date  \\\n",
+              "0         1          101           1         2  25.50  2023-01-15   \n",
+              "1         2          102           2         1  35.00  2023-01-16   \n",
+              "2         3          101           3         3  15.75  2023-01-16   \n",
+              "3         4          103           1         2  25.50  2023-01-17   \n",
+              "4         5          104           2         1  35.00  2023-01-18   \n",
+              "5         6          105           3         1  15.75  2023-01-19   \n",
+              "6         7          103           4         4  10.25  2023-01-20   \n",
+              "7         8          106           5         2  50.00  2023-01-20   \n",
+              "8         9          102           1         3  25.50  2023-01-21   \n",
+              "9        10          107           2         1  35.00  2023-01-22   \n",
+              "\n",
+              "      category  total_revenue           name                email  \\\n",
+              "0  Electronics          51.00    Alice Brown    alice@example.com   \n",
+              "1     Clothing          35.00      Bob Smith      bob@example.com   \n",
+              "2        Books          47.25    Alice Brown    alice@example.com   \n",
+              "3  Electronics          51.00  Charlie Davis  charlie@example.com   \n",
+              "4     Clothing          35.00   David Wilson    david@example.com   \n",
+              "5        Books          15.75   Emma Johnson     emma@example.com   \n",
+              "6         Food          41.00  Charlie Davis  charlie@example.com   \n",
+              "7    Furniture         100.00            NaN                  NaN   \n",
+              "8  Electronics          76.50      Bob Smith      bob@example.com   \n",
+              "9     Clothing          35.00            NaN                  NaN   \n",
+              "\n",
+              "          city membership_level  \n",
+              "0     New York             Gold  \n",
+              "1  Los Angeles           Silver  \n",
+              "2     New York             Gold  \n",
+              "3      Chicago             Gold  \n",
+              "4      Houston           Bronze  \n",
+              "5      Phoenix           Silver  \n",
+              "6      Chicago             Gold  \n",
+              "7          NaN              NaN  \n",
+              "8  Los Angeles           Silver  \n",
+              "9          NaN              NaN  "
+            ]
+          },
+          "execution_count": 91,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Left join sales with customers using pandas merge\n",
+        "left_join = pd.merge(sales_df, customers_df, on='customer_id', how='left')\n",
+        "print(\"Left join (all sales, matching customers):\")\n",
+        "left_join"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this left join, all sales records are included regardless of whether there's a matching customer. Notice that some rows have NaN values for customer information because customers 106 and 107 are not in the customers table."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 7. Right Outer Join\n",
+        "\n",
+        "A right outer join returns all records from the right table and the matched records from the left table. If there is no match, NULL values are returned for the left table columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Right join (all customers, matching sales):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "      <th>total_revenue</th>\n",
+              "      <th>name</th>\n",
+              "      <th>email</th>\n",
+              "      <th>city</th>\n",
+              "      <th>membership_level</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>51.00</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>alice@example.com</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>47.25</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>alice@example.com</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>bob@example.com</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>76.50</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>bob@example.com</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4.0</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>51.00</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>charlie@example.com</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>7.0</td>\n",
+              "      <td>103</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Food</td>\n",
+              "      <td>41.00</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>charlie@example.com</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>David Wilson</td>\n",
+              "      <td>david@example.com</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>6.0</td>\n",
+              "      <td>105</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-19</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>Emma Johnson</td>\n",
+              "      <td>emma@example.com</td>\n",
+              "      <td>Phoenix</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>108</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Frank Miller</td>\n",
+              "      <td>frank@example.com</td>\n",
+              "      <td>Philadelphia</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>109</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Grace Lee</td>\n",
+              "      <td>grace@example.com</td>\n",
+              "      <td>San Antonio</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date  \\\n",
+              "0       1.0          101         1.0       2.0  25.50  2023-01-15   \n",
+              "1       3.0          101         3.0       3.0  15.75  2023-01-16   \n",
+              "2       2.0          102         2.0       1.0  35.00  2023-01-16   \n",
+              "3       9.0          102         1.0       3.0  25.50  2023-01-21   \n",
+              "4       4.0          103         1.0       2.0  25.50  2023-01-17   \n",
+              "5       7.0          103         4.0       4.0  10.25  2023-01-20   \n",
+              "6       5.0          104         2.0       1.0  35.00  2023-01-18   \n",
+              "7       6.0          105         3.0       1.0  15.75  2023-01-19   \n",
+              "8       NaN          108         NaN       NaN    NaN         NaN   \n",
+              "9       NaN          109         NaN       NaN    NaN         NaN   \n",
+              "\n",
+              "      category  total_revenue           name                email  \\\n",
+              "0  Electronics          51.00    Alice Brown    alice@example.com   \n",
+              "1        Books          47.25    Alice Brown    alice@example.com   \n",
+              "2     Clothing          35.00      Bob Smith      bob@example.com   \n",
+              "3  Electronics          76.50      Bob Smith      bob@example.com   \n",
+              "4  Electronics          51.00  Charlie Davis  charlie@example.com   \n",
+              "5         Food          41.00  Charlie Davis  charlie@example.com   \n",
+              "6     Clothing          35.00   David Wilson    david@example.com   \n",
+              "7        Books          15.75   Emma Johnson     emma@example.com   \n",
+              "8          NaN            NaN   Frank Miller    frank@example.com   \n",
+              "9          NaN            NaN      Grace Lee    grace@example.com   \n",
+              "\n",
+              "           city membership_level  \n",
+              "0      New York             Gold  \n",
+              "1      New York             Gold  \n",
+              "2   Los Angeles           Silver  \n",
+              "3   Los Angeles           Silver  \n",
+              "4       Chicago             Gold  \n",
+              "5       Chicago             Gold  \n",
+              "6       Houston           Bronze  \n",
+              "7       Phoenix           Silver  \n",
+              "8  Philadelphia             Gold  \n",
+              "9   San Antonio           Bronze  "
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Right join sales with customers\n",
+        "right_join = pd.merge(sales_df, customers_df, on='customer_id', how='right')\n",
+        "print(\"Right join (all customers, matching sales):\")\n",
+        "right_join"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this right join, all customer records are included regardless of whether they have any sales. Notice that customers 108 and 109 appear in the results with NaN values for sales information because they haven't made any purchases."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 8. Inner Join\n",
+        "\n",
+        "An inner join returns only the records that have matching values in both tables."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Inner join (only matching records):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "      <th>total_revenue</th>\n",
+              "      <th>name</th>\n",
+              "      <th>email</th>\n",
+              "      <th>city</th>\n",
+              "      <th>membership_level</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>51.00</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>alice@example.com</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>bob@example.com</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>47.25</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>alice@example.com</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>51.00</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>charlie@example.com</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>David Wilson</td>\n",
+              "      <td>david@example.com</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>105</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-19</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>Emma Johnson</td>\n",
+              "      <td>emma@example.com</td>\n",
+              "      <td>Phoenix</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7</td>\n",
+              "      <td>103</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Food</td>\n",
+              "      <td>41.00</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>charlie@example.com</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>76.50</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>bob@example.com</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date  \\\n",
+              "0         1          101           1         2  25.50  2023-01-15   \n",
+              "1         2          102           2         1  35.00  2023-01-16   \n",
+              "2         3          101           3         3  15.75  2023-01-16   \n",
+              "3         4          103           1         2  25.50  2023-01-17   \n",
+              "4         5          104           2         1  35.00  2023-01-18   \n",
+              "5         6          105           3         1  15.75  2023-01-19   \n",
+              "6         7          103           4         4  10.25  2023-01-20   \n",
+              "7         9          102           1         3  25.50  2023-01-21   \n",
+              "\n",
+              "      category  total_revenue           name                email  \\\n",
+              "0  Electronics          51.00    Alice Brown    alice@example.com   \n",
+              "1     Clothing          35.00      Bob Smith      bob@example.com   \n",
+              "2        Books          47.25    Alice Brown    alice@example.com   \n",
+              "3  Electronics          51.00  Charlie Davis  charlie@example.com   \n",
+              "4     Clothing          35.00   David Wilson    david@example.com   \n",
+              "5        Books          15.75   Emma Johnson     emma@example.com   \n",
+              "6         Food          41.00  Charlie Davis  charlie@example.com   \n",
+              "7  Electronics          76.50      Bob Smith      bob@example.com   \n",
+              "\n",
+              "          city membership_level  \n",
+              "0     New York             Gold  \n",
+              "1  Los Angeles           Silver  \n",
+              "2     New York             Gold  \n",
+              "3      Chicago             Gold  \n",
+              "4      Houston           Bronze  \n",
+              "5      Phoenix           Silver  \n",
+              "6      Chicago             Gold  \n",
+              "7  Los Angeles           Silver  "
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Inner join sales with customers\n",
+        "inner_join = pd.merge(sales_df, customers_df, on='customer_id', how='inner')\n",
+        "print(\"Inner join (only matching records):\")\n",
+        "inner_join"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this inner join, only records with matching customer IDs in both tables are included. The orders from customers 106 and 107 are excluded because those customers aren't in the customers table, and customers 108 and 109 are excluded because they don't have any orders."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Bonus: Advanced Analysis with Joins and Aggregations\n",
+        "\n",
+        "Let's combine what we've learned to perform a more complex analysis that uses joins and aggregations together."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Spending analysis by membership level:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>customer_count</th>\n",
+              "      <th>order_count</th>\n",
+              "      <th>total_spent</th>\n",
+              "      <th>avg_order_value</th>\n",
+              "      <th>avg_spent_per_customer</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>membership_level</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Gold</th>\n",
+              "      <td>2</td>\n",
+              "      <td>4</td>\n",
+              "      <td>190.25</td>\n",
+              "      <td>47.562500</td>\n",
+              "      <td>95.125</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Silver</th>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>127.25</td>\n",
+              "      <td>42.416667</td>\n",
+              "      <td>63.625</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Bronze</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>35.000000</td>\n",
+              "      <td>35.000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                  customer_count  order_count  total_spent  avg_order_value  \\\n",
+              "membership_level                                                              \n",
+              "Gold                           2            4       190.25        47.562500   \n",
+              "Silver                         2            3       127.25        42.416667   \n",
+              "Bronze                         1            1        35.00        35.000000   \n",
+              "\n",
+              "                  avg_spent_per_customer  \n",
+              "membership_level                          \n",
+              "Gold                              95.125  \n",
+              "Silver                            63.625  \n",
+              "Bronze                            35.000  "
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Find the total spending by membership level\n",
+        "# First, create a revenue column\n",
+        "sales_df['revenue'] = sales_df['price'] * sales_df['quantity']\n",
+        "\n",
+        "# Join sales and customers\n",
+        "sales_customers = pd.merge(sales_df, customers_df, on='customer_id', how='inner')\n",
+        "\n",
+        "# Group by membership level and aggregate\n",
+        "membership_analysis = sales_customers.groupby('membership_level').agg(\n",
+        "    customer_count=('customer_id', 'nunique'),\n",
+        "    order_count=('order_id', 'count'),\n",
+        "    total_spent=('revenue', 'sum'),\n",
+        "    avg_order_value=('revenue', 'mean')\n",
+        ").sort_values('total_spent', ascending=False)\n",
+        "\n",
+        "# Calculate average spent per customer\n",
+        "membership_analysis['avg_spent_per_customer'] = membership_analysis['total_spent'] / membership_analysis['customer_count']\n",
+        "\n",
+        "print(\"Spending analysis by membership level:\")\n",
+        "membership_analysis"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Visualize the total spending by membership level\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.bar(membership_analysis.index, membership_analysis['total_spent'])\n",
+        "plt.title('Total Spending by Membership Level')\n",
+        "plt.xlabel('Membership Level')\n",
+        "plt.ylabel('Total Spent ($)')\n",
+        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+        "\n",
+        "# Add value labels on top of the bars\n",
+        "for i, v in enumerate(membership_analysis['total_spent']):\n",
+        "    plt.text(i, v + 5, f'${v:.2f}', ha='center')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Top customers by total spending:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>name</th>\n",
+              "      <th>membership_level</th>\n",
+              "      <th>order_count</th>\n",
+              "      <th>total_spent</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>102</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>Silver</td>\n",
+              "      <td>2</td>\n",
+              "      <td>111.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>101</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>Gold</td>\n",
+              "      <td>2</td>\n",
+              "      <td>98.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>103</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>Gold</td>\n",
+              "      <td>2</td>\n",
+              "      <td>92.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>104</td>\n",
+              "      <td>David Wilson</td>\n",
+              "      <td>Bronze</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>105</td>\n",
+              "      <td>Emma Johnson</td>\n",
+              "      <td>Silver</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.75</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   customer_id           name membership_level  order_count  total_spent\n",
+              "1          102      Bob Smith           Silver            2       111.50\n",
+              "0          101    Alice Brown             Gold            2        98.25\n",
+              "2          103  Charlie Davis             Gold            2        92.00\n",
+              "3          104   David Wilson           Bronze            1        35.00\n",
+              "4          105   Emma Johnson           Silver            1        15.75"
+            ]
+          },
+          "execution_count": 28,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Find top customers by total spending\n",
+        "top_customers = sales_customers.groupby(['customer_id', 'name', 'membership_level']).agg(\n",
+        "    order_count=('order_id', 'count'),\n",
+        "    total_spent=('revenue', 'sum')\n",
+        ").reset_index().sort_values('total_spent', ascending=False)\n",
+        "\n",
+        "print(\"Top customers by total spending:\")\n",
+        "top_customers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Categories popular with Gold members:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>category</th>\n",
+              "      <th>order_count</th>\n",
+              "      <th>total_quantity</th>\n",
+              "      <th>total_spent</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>2</td>\n",
+              "      <td>4</td>\n",
+              "      <td>102.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Books</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>47.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Food</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>41.00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      category  order_count  total_quantity  total_spent\n",
+              "1  Electronics            2               4       102.00\n",
+              "0        Books            1               3        47.25\n",
+              "2         Food            1               4        41.00"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Find categories most popular with Gold members\n",
+        "gold_member_purchases = sales_customers[sales_customers['membership_level'] == 'Gold']\n",
+        "gold_member_categories = gold_member_purchases.groupby('category').agg(\n",
+        "    order_count=('order_id', 'count'),\n",
+        "    total_quantity=('quantity', 'sum'),\n",
+        "    total_spent=('revenue', 'sum')\n",
+        ").reset_index().sort_values('total_spent', ascending=False)\n",
+        "\n",
+        "print(\"Categories popular with Gold members:\")\n",
+        "gold_member_categories"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Conclusion\n",
+        "\n",
+        "In this notebook, we've demonstrated how to use pandas to perform various data operations:\n",
+        "\n",
+        "1. Reading CSV data into pandas\n",
+        "2. Selecting columns in different ways\n",
+        "3. Filtering by values in columns\n",
+        "4. Performing aggregations and summaries\n",
+        "5. Filtering based on aggregation results\n",
+        "6. Left outer joins\n",
+        "7. Right outer joins\n",
+        "8. Inner joins\n",
+        "9. Advanced analysis combining joins and aggregations\n",
+        "\n",
+        "Pandas provides a powerful and flexible interface for data manipulation, which makes it a popular choice for data analysis tasks."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "week1_env",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
diff --git a/Week1/Day_1/pandas_homework.md b/Week1/Day_1/pandas_homework.md
new file mode 100644
index 00000000..ed1f1bd8
--- /dev/null
+++ b/Week1/Day_1/pandas_homework.md
@@ -0,0 +1,143 @@
+# Pandas Homework Assignment
+
+## Overview
+
+This homework assignment is designed to test your understanding of the pandas library in Python. You will work with a dataset of movie information and perform various data manipulation tasks.
+
+## Dataset Description
+
+The dataset contains information about movies including:
+
+- Title
+- Release Year
+- Genre
+- Runtime (minutes)
+- Budget (millions USD)
+- Box Office (millions USD)
+- Director
+- Rating (out of 10)
+- Country
+
+## Tasks
+
+### Task 1: Basic Data Exploration (Beginner)
+
+1. Load the dataset into a pandas DataFrame.
+2. Display the first 5 rows of the DataFrame.
+3. Show the shape of the DataFrame (rows and columns).
+4. Generate basic descriptive statistics for the numerical columns.
+5. Check for missing values in each column.
+
+### Task 2: Data Filtering and Selection (Intermediate)
+
+1. Select all movies released after 2010.
+2. Find all movies with a rating higher than 8.0.
+3. Create a subset of movies that are either Action or Comedy genres.
+4. Identify movies that had a box office greater than twice their budget.
+5. List all movies directed by Christopher Nolan or Steven Spielberg.
+
+### Task 3: Data Transformation (Intermediate)
+
+1. Create a new column 'Profit' that calculates the difference between Box Office and Budget.
+2. Create a column 'ROI' (Return on Investment) that calculates (Box Office - Budget) / Budget.
+3. Create a categorical column 'Length' that classifies movies as 'Short' (< 90 min), 'Medium' (90-120 min), or 'Long' (> 120 min).
+4. Create a column 'Decade' that categorizes movies by the decade they were released in (e.g., 1990s, 2000s).
+5. Convert the 'Rating' column to a categorical type with bins: 'Poor' (0-4), 'Average' (4-7), 'Excellent' (7-10).
+
+### Task 4: Aggregation and Grouping (Advanced)
+
+1. Calculate the average rating for each genre.
+2. Find the highest-grossing movie for each director.
+3. Determine the average budget and box office for each decade.
+4. Group movies by their country of origin and calculate the mean rating, total budget, and total box office.
+5. For each genre, calculate the percentage of movies with ROI > 1 (profitable movies).
+
+### Task 5: Data Visualization (Advanced)
+
+1. Create a bar chart showing the average rating by genre.
+2. Generate a scatter plot of Budget vs. Box Office, colored by Rating.
+3. Plot the distribution of movie runtimes using a histogram.
+4. Create a box plot showing the distribution of ROI by genre.
+5. Generate a line plot showing the trend of average budget and box office by year.
+
+## Dataset
+
+```python
+import pandas as pd
+import numpy as np
+
+# Set random seed for reproducibility
+np.random.seed(42)
+
+# Create a sample movie dataset
+n_movies = 200
+
+# Generate random data
+titles = [f"Movie {i}" for i in range(1, n_movies + 1)]
+years = np.random.randint(1990, 2023, n_movies)
+genres = np.random.choice(['Action', 'Comedy', 'Drama', 'Sci-Fi', 'Horror', 'Thriller', 'Romance'], n_movies)
+runtimes = np.random.randint(75, 180, n_movies)
+budgets = np.round(np.random.uniform(5, 250, n_movies), 1)  # In millions USD
+box_offices = np.round(budgets * np.random.uniform(0.5, 4, n_movies), 1)  # In millions USD
+
+directors = []
+for _ in range(n_movies):
+    first_names = ['James', 'Steven', 'Christopher', 'Martin', 'Quentin', 'David', 'Ridley', 'Sofia', 'Greta', 'Kathryn']
+    last_names = ['Smith', 'Johnson', 'Williams', 'Jones', 'Brown', 'Davis', 'Miller', 'Wilson', 'Moore', 'Taylor']
+    directors.append(f"{np.random.choice(first_names)} {np.random.choice(last_names)}")
+
+ratings = np.round(np.random.uniform(3, 9.5, n_movies), 1)
+countries = np.random.choice(['USA', 'UK', 'France', 'Japan', 'South Korea', 'India', 'Canada', 'Germany'], n_movies)
+
+# Create the DataFrame
+movies_data = pd.DataFrame({
+    'Title': titles,
+    'Year': years,
+    'Genre': genres,
+    'Runtime': runtimes,
+    'Budget': budgets,
+    'BoxOffice': box_offices,
+    'Director': directors,
+    'Rating': ratings,
+    'Country': countries
+})
+
+# Introduce some missing values
+for col in ['Runtime', 'Budget', 'BoxOffice', 'Rating']:
+    missing_indices = np.random.choice(n_movies, size=int(n_movies * 0.05), replace=False)
+    movies_data.loc[missing_indices, col] = np.nan
+
+# Display the DataFrame
+print(movies_data.head())
+```
+
+## Hints
+
+### Task 1 Hints:
+
+- Use `pd.read_csv()` or the sample code provided to create the DataFrame
+- Explore methods like `.head()`, `.shape`, `.describe()`, and `.isna().sum()`
+
+### Task 2 Hints:
+
+- Use Boolean indexing with conditions like `df['Year'] > 2010`
+- Combine conditions with `&` (and) and `|` (or) operators
+- Don't forget to enclose each condition in parentheses when combining them
+
+### Task 3 Hints:
+
+- Use `df['new_column'] = expression` to create new columns
+- For categorical columns, consider using `pd.cut()` or `np.select()`
+- The formula for ROI is `(BoxOffice - Budget) / Budget`
+
+### Task 4 Hints:
+
+- Utilize `.groupby()` with `.agg()` methods
+- For more complex aggregations, consider using `.apply()` or `lambda` functions
+- To find maximum values within groups, use `.idxmax()` and then index the original DataFrame
+
+### Task 5 Hints:
+
+- Import matplotlib or seaborn for visualization: `import matplotlib.pyplot as plt` or `import seaborn as sns`
+- Group data appropriately before plotting
+- Some useful plotting methods: `df.plot()`, `sns.barplot()`, `plt.scatter()`, `sns.boxplot()`
diff --git a/Week1/Day_1/pandas_homework_solution.ipynb b/Week1/Day_1/pandas_homework_solution.ipynb
new file mode 100644
index 00000000..eade463d
--- /dev/null
+++ b/Week1/Day_1/pandas_homework_solution.ipynb
@@ -0,0 +1,400 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6e18dc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0503d08b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Set random seed for reproducibility\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Create a sample movie dataset\n",
+    "n_movies = 200\n",
+    "\n",
+    "# Generate random data\n",
+    "titles = [f\"Movie {i}\" for i in range(1, n_movies + 1)]\n",
+    "years = np.random.randint(1990, 2023, n_movies)\n",
+    "genres = np.random.choice(['Action', 'Comedy', 'Drama', 'Sci-Fi', 'Horror', 'Thriller', 'Romance'], n_movies)\n",
+    "runtimes = np.random.randint(75, 180, n_movies)\n",
+    "budgets = np.round(np.random.uniform(5, 250, n_movies), 1)  # In millions USD\n",
+    "box_offices = np.round(budgets * np.random.uniform(0.5, 4, n_movies), 1)  # In millions USD\n",
+    "\n",
+    "directors = []\n",
+    "for _ in range(n_movies):\n",
+    "    first_names = ['James', 'Steven', 'Christopher', 'Martin', 'Quentin', 'David', 'Ridley', 'Sofia', 'Greta', 'Kathryn']\n",
+    "    last_names = ['Smith', 'Johnson', 'Williams', 'Jones', 'Brown', 'Davis', 'Miller', 'Wilson', 'Moore', 'Taylor']\n",
+    "    directors.append(f\"{np.random.choice(first_names)} {np.random.choice(last_names)}\")\n",
+    "\n",
+    "ratings = np.round(np.random.uniform(3, 9.5, n_movies), 1)\n",
+    "countries = np.random.choice(['USA', 'UK', 'France', 'Japan', 'South Korea', 'India', 'Canada', 'Germany'], n_movies)\n",
+    "\n",
+    "# Create the DataFrame\n",
+    "movies_data = pd.DataFrame({\n",
+    "    'Title': titles,\n",
+    "    'Year': years,\n",
+    "    'Genre': genres,\n",
+    "    'Runtime': runtimes,\n",
+    "    'Budget': budgets,\n",
+    "    'BoxOffice': box_offices,\n",
+    "    'Director': directors,\n",
+    "    'Rating': ratings,\n",
+    "    'Country': countries\n",
+    "})\n",
+    "\n",
+    "# Introduce some missing values\n",
+    "for col in ['Runtime', 'Budget', 'BoxOffice', 'Rating']:\n",
+    "    missing_indices = np.random.choice(n_movies, size=int(n_movies * 0.05), replace=False)\n",
+    "    movies_data.loc[missing_indices, col] = np.nan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ffa3ab4e",
+   "metadata": {},
+   "source": [
+    "# ----------------------------------------------------------------------------\n",
+    "# Task 1: Basic Data Exploration\n",
+    "# ----------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27744c74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Task 1: Basic Data Exploration\")\n",
+    "print(\"\\n1. First 5 rows of the DataFrame:\")\n",
+    "print(movies_data.head())\n",
+    "\n",
+    "print(\"\\n2. Shape of the DataFrame:\")\n",
+    "print(movies_data.shape)\n",
+    "\n",
+    "print(\"\\n3. Descriptive statistics for numerical columns:\")\n",
+    "print(movies_data.describe())\n",
+    "\n",
+    "print(\"\\n4. Missing values in each column:\")\n",
+    "print(movies_data.isna().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af28ee69",
+   "metadata": {},
+   "source": [
+    "# ----------------------------------------------------------------------------\n",
+    "# Task 2: Data Filtering and Selection\n",
+    "# ----------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e909d9a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\nTask 2: Data Filtering and Selection\")\n",
+    "\n",
+    "# 1. Select all movies released after 2010\n",
+    "movies_after_2010 = movies_data[movies_data['Year'] > 2010]\n",
+    "print(\"\\n1. Movies released after 2010 (first 5):\")\n",
+    "print(movies_after_2010.head())\n",
+    "print(f\"Total movies after 2010: {len(movies_after_2010)}\")\n",
+    "\n",
+    "# 2. Find all movies with a rating higher than 8.0\n",
+    "high_rated_movies = movies_data[movies_data['Rating'] > 8.0]\n",
+    "print(\"\\n2. Movies with rating > 8.0 (first 5):\")\n",
+    "print(high_rated_movies.head())\n",
+    "print(f\"Total high-rated movies: {len(high_rated_movies)}\")\n",
+    "\n",
+    "# 3. Create a subset of movies that are either Action or Comedy genres\n",
+    "action_comedy_movies = movies_data[(movies_data['Genre'] == 'Action') | (movies_data['Genre'] == 'Comedy')]\n",
+    "print(\"\\n3. Action or Comedy movies (first 5):\")\n",
+    "print(action_comedy_movies.head())\n",
+    "print(f\"Total Action or Comedy movies: {len(action_comedy_movies)}\")\n",
+    "\n",
+    "# 4. Identify movies that had a box office greater than twice their budget\n",
+    "profitable_movies = movies_data[movies_data['BoxOffice'] > (2 * movies_data['Budget'])]\n",
+    "print(\"\\n4. Movies with box office > 2x budget (first 5):\")\n",
+    "print(profitable_movies.head())\n",
+    "print(f\"Total profitable movies: {len(profitable_movies)}\")\n",
+    "\n",
+    "# 5. List all movies directed by Christopher Nolan or Steven Spielberg\n",
+    "specific_directors = movies_data[\n",
+    "    (movies_data['Director'].str.contains('Christopher')) | \n",
+    "    (movies_data['Director'].str.contains('Steven'))\n",
+    "]\n",
+    "print(\"\\n5. Movies by Christopher Nolan or Steven Spielberg (first 5):\")\n",
+    "print(specific_directors.head())\n",
+    "print(f\"Total movies by these directors: {len(specific_directors)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc02e3a0",
+   "metadata": {},
+   "source": [
+    "# ----------------------------------------------------------------------------\n",
+    "# Task 3: Data Transformation\n",
+    "# ----------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4752d0d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\nTask 3: Data Transformation\")\n",
+    "\n",
+    "# 1. Create a new column 'Profit' that calculates the difference between Box Office and Budget\n",
+    "movies_data['Profit'] = movies_data['BoxOffice'] - movies_data['Budget']\n",
+    "print(\"\\n1. Added Profit column (first 5 rows):\")\n",
+    "print(movies_data[['Title', 'Budget', 'BoxOffice', 'Profit']].head())\n",
+    "\n",
+    "# 2. Create a column 'ROI' (Return on Investment)\n",
+    "movies_data['ROI'] = (movies_data['BoxOffice'] - movies_data['Budget']) / movies_data['Budget']\n",
+    "print(\"\\n2. Added ROI column (first 5 rows):\")\n",
+    "print(movies_data[['Title', 'Budget', 'BoxOffice', 'Profit', 'ROI']].head())\n",
+    "\n",
+    "# 3. Create a categorical column 'Length'\n",
+    "def categorize_length(runtime):\n",
+    "    if pd.isna(runtime):\n",
+    "        return np.nan\n",
+    "    elif runtime < 90:\n",
+    "        return 'Short'\n",
+    "    elif runtime <= 120:\n",
+    "        return 'Medium'\n",
+    "    else:\n",
+    "        return 'Long'\n",
+    "\n",
+    "movies_data['Length'] = movies_data['Runtime'].apply(categorize_length)\n",
+    "print(\"\\n3. Added Length column (first 5 rows):\")\n",
+    "print(movies_data[['Title', 'Runtime', 'Length']].head())\n",
+    "\n",
+    "# 4. Create a column 'Decade'\n",
+    "movies_data['Decade'] = (movies_data['Year'] // 10) * 10\n",
+    "movies_data['Decade'] = movies_data['Decade'].astype(str) + 's'\n",
+    "print(\"\\n4. Added Decade column (first 5 rows):\")\n",
+    "print(movies_data[['Title', 'Year', 'Decade']].head())\n",
+    "\n",
+    "# 5. Convert the 'Rating' column to a categorical type\n",
+    "def categorize_rating(rating):\n",
+    "    if pd.isna(rating):\n",
+    "        return np.nan\n",
+    "    elif rating <= 4:\n",
+    "        return 'Poor'\n",
+    "    elif rating <= 7:\n",
+    "        return 'Average'\n",
+    "    else:\n",
+    "        return 'Excellent'\n",
+    "\n",
+    "movies_data['RatingCategory'] = movies_data['Rating'].apply(categorize_rating)\n",
+    "print(\"\\n5. Added RatingCategory column (first 5 rows):\")\n",
+    "print(movies_data[['Title', 'Rating', 'RatingCategory']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80b8a236",
+   "metadata": {},
+   "source": [
+    "# ----------------------------------------------------------------------------\n",
+    "# Task 4: Aggregation and Grouping\n",
+    "# ----------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08cd0d2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\nTask 4: Aggregation and Grouping\")\n",
+    "\n",
+    "# 1. Calculate the average rating for each genre\n",
+    "avg_rating_by_genre = movies_data.groupby('Genre')['Rating'].mean().sort_values(ascending=False)\n",
+    "print(\"\\n1. Average rating by genre:\")\n",
+    "print(avg_rating_by_genre)\n",
+    "\n",
+    "# 2. Find the highest-grossing movie for each director\n",
+    "# First, find the index of the highest-grossing movie for each director\n",
+    "movies_with_boxoffice = movies_data.dropna(subset=['BoxOffice'])\n",
+    "highest_grossing_indices = movies_with_boxoffice.groupby('Director')['BoxOffice'].idxmax()\n",
+    "print()\n",
+    "print(f\"{highest_grossing_indices=}\")\n",
+    "print(movies_data.groupby('Director')['BoxOffice'])\n",
+    "# Then, use these indices to get the corresponding rows from the original DataFrame\n",
+    "highest_grossing_by_director = movies_with_boxoffice.loc[highest_grossing_indices, ['Director', 'Title', 'BoxOffice']]\n",
+    "print(\"\\n2. Highest-grossing movie for each director (first 5):\")\n",
+    "print(highest_grossing_by_director.head())\n",
+    "\n",
+    "# 3. Determine the average budget and box office for each decade\n",
+    "decade_stats = movies_data.groupby('Decade').agg({\n",
+    "    'Budget': 'mean',\n",
+    "    'BoxOffice': 'mean'\n",
+    "}).sort_index()\n",
+    "print(\"\\n3. Average budget and box office by decade:\")\n",
+    "print(decade_stats)\n",
+    "\n",
+    "# 4. Group movies by country and calculate various statistics\n",
+    "country_stats = movies_data.groupby('Country').agg({\n",
+    "    'Rating': 'mean',\n",
+    "    'Budget': 'sum',\n",
+    "    'BoxOffice': 'sum'\n",
+    "}).sort_values('BoxOffice', ascending=False)\n",
+    "print(\"\\n4. Movie statistics by country:\")\n",
+    "print(country_stats)\n",
+    "\n",
+    "# 5. For each genre, calculate the percentage of profitable movies (ROI > 1)\n",
+    "def calculate_profitable_percentage(group):\n",
+    "    total_movies = len(group)\n",
+    "    if total_movies == 0:\n",
+    "        return 0\n",
+    "    profitable_movies = len(group[group['ROI'] > 1])\n",
+    "    return (profitable_movies / total_movies) * 100\n",
+    "\n",
+    "genre_profitability = movies_data.groupby('Genre').apply(calculate_profitable_percentage).sort_values(ascending=False)\n",
+    "print(\"\\n5. Percentage of profitable movies (ROI > 1) by genre:\")\n",
+    "print(genre_profitability)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8821e022",
+   "metadata": {},
+   "source": [
+    "# ----------------------------------------------------------------------------\n",
+    "# Task 5: Data Visualization\n",
+    "# ----------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd64914e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\nTask 5: Data Visualization\")\n",
+    "\n",
+    "# Setting the style for better visualizations\n",
+    "plt.style.use('seaborn-v0_8-whitegrid')\n",
+    "\n",
+    "# 1. Bar chart showing the average rating by genre\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "avg_rating_by_genre.plot(kind='bar', color='skyblue')\n",
+    "plt.title('Average Rating by Genre', fontsize=14)\n",
+    "plt.xlabel('Genre', fontsize=12)\n",
+    "plt.ylabel('Average Rating', fontsize=12)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('avg_rating_by_genre.png')\n",
+    "print(\"\\n1. Bar chart created: avg_rating_by_genre.png\")\n",
+    "\n",
+    "# 2. Scatter plot of Budget vs. Box Office, colored by Rating\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "scatter = plt.scatter(\n",
+    "    movies_data['Budget'], \n",
+    "    movies_data['BoxOffice'],\n",
+    "    c=movies_data['Rating'],\n",
+    "    cmap='viridis',\n",
+    "    alpha=0.7,\n",
+    "    s=100\n",
+    ")\n",
+    "plt.colorbar(scatter, label='Rating')\n",
+    "plt.title('Budget vs. Box Office (colored by Rating)', fontsize=14)\n",
+    "plt.xlabel('Budget (millions USD)', fontsize=12)\n",
+    "plt.ylabel('Box Office (millions USD)', fontsize=12)\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('budget_vs_boxoffice.png')\n",
+    "print(\"2. Scatter plot created: budget_vs_boxoffice.png\")\n",
+    "\n",
+    "# 3. Histogram of movie runtimes\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.hist(movies_data['Runtime'].dropna(), bins=20, color='purple', alpha=0.7)\n",
+    "plt.title('Distribution of Movie Runtimes', fontsize=14)\n",
+    "plt.xlabel('Runtime (minutes)', fontsize=12)\n",
+    "plt.ylabel('Frequency', fontsize=12)\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('runtime_distribution.png')\n",
+    "print(\"3. Histogram created: runtime_distribution.png\")\n",
+    "\n",
+    "# 4. Box plot showing the distribution of ROI by genre\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.boxplot(x='Genre', y='ROI', data=movies_data)\n",
+    "plt.title('Distribution of ROI by Genre', fontsize=14)\n",
+    "plt.xlabel('Genre', fontsize=12)\n",
+    "plt.ylabel('Return on Investment (ROI)', fontsize=12)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('roi_by_genre.png')\n",
+    "print(\"4. Box plot created: roi_by_genre.png\")\n",
+    "\n",
+    "# 5. Line plot showing the trend of average budget and box office by year\n",
+    "yearly_stats = movies_data.groupby('Year').agg({\n",
+    "    'Budget': 'mean',\n",
+    "    'BoxOffice': 'mean'\n",
+    "})\n",
+    "\n",
+    "plt.figure(figsize=(14, 8))\n",
+    "plt.plot(yearly_stats.index, yearly_stats['Budget'], marker='o', linestyle='-', color='blue', label='Avg Budget')\n",
+    "plt.plot(yearly_stats.index, yearly_stats['BoxOffice'], marker='s', linestyle='-', color='green', label='Avg Box Office')\n",
+    "plt.title('Average Budget and Box Office by Year (1990-2022)', fontsize=14)\n",
+    "plt.xlabel('Year', fontsize=12)\n",
+    "plt.ylabel('Amount (millions USD)', fontsize=12)\n",
+    "plt.legend()\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('budget_boxoffice_trend.png')\n",
+    "print(\"5. Line plot created: budget_boxoffice_trend.png\")\n",
+    "\n",
+    "print(\"\\nAll tasks completed successfully!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "week1_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Week1/Day_1/pandasql.ipynb b/Week1/Day_1/pandasql.ipynb
new file mode 100644
index 00000000..c1608738
--- /dev/null
+++ b/Week1/Day_1/pandasql.ipynb
@@ -0,0 +1,2902 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Using PandaSQL for SQL Operations on Pandas DataFrames\n",
+        "\n",
+        "This notebook demonstrates how to use the `pandasql` library to perform SQL operations on pandas DataFrames. We'll cover the following operations:\n",
+        "\n",
+        "1. Reading CSV data into pandas\n",
+        "2. Selecting columns using SQL\n",
+        "3. Filtering by value in column using SQL\n",
+        "4. Aggregations using SQL\n",
+        "5. Filtering on aggregations using SQL\n",
+        "6. Left outer joins using SQL\n",
+        "7. Right outer joins using SQL\n",
+        "8. Inner joins using SQL\n",
+        "\n",
+        "Let's start by importing the necessary libraries and setting up our environment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Import necessary libraries\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pandasql import sqldf\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# Define a function to run SQL on pandas DataFrames\n",
+        "def pysqldf(q):\n",
+        "    return sqldf(q, globals())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 1. Reading dictonary data into pandas\n",
+        "\n",
+        "First, let's create two sample datasets that we'll use throughout this notebook. We'll create them as pandas DataFrames and then save them as CSV files. In a real-world scenario, you would typically load existing CSV files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sales DataFrame (first 5 rows):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         1          101           1         2  25.50  2023-01-15  Electronics\n",
+              "1         2          102           2         1  35.00  2023-01-16     Clothing\n",
+              "2         3          101           3         3  15.75  2023-01-16        Books\n",
+              "3         4          103           1         2  25.50  2023-01-17  Electronics\n",
+              "4         5          104           2         1  35.00  2023-01-18     Clothing"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Create a sample sales DataFrame\n",
+        "sales_data = {\n",
+        "    'order_id': list(range(1, 11)),\n",
+        "    'customer_id': [101, 102, 101, 103, 104, 105, 103, 106, 102, 107],\n",
+        "    'product_id': [1, 2, 3, 1, 2, 3, 4, 5, 1, 2],\n",
+        "    'quantity': [2, 1, 3, 2, 1, 1, 4, 2, 3, 1],\n",
+        "    'price': [25.50, 35.00, 15.75, 25.50, 35.00, 15.75, 10.25, 50.00, 25.50, 35.00],\n",
+        "    'order_date': ['2023-01-15', '2023-01-16', '2023-01-16', '2023-01-17', '2023-01-18', \n",
+        "                   '2023-01-19', '2023-01-20', '2023-01-20', '2023-01-21', '2023-01-22'],\n",
+        "    'category': ['Electronics', 'Clothing', 'Books', 'Electronics', 'Clothing', \n",
+        "                'Books', 'Food', 'Furniture', 'Electronics', 'Clothing']\n",
+        "}\n",
+        "\n",
+        "sales_df = pd.DataFrame(sales_data)\n",
+        "\n",
+        "# Create a sample customers DataFrame\n",
+        "customers_data = {\n",
+        "    'customer_id': [101, 102, 103, 104, 105, 108, 109],  # Note: 106, 107 missing and 108, 109 not in sales\n",
+        "    'name': ['Alice Brown', 'Bob Smith', 'Charlie Davis', 'David Wilson', 'Emma Johnson', 'Frank Miller', 'Grace Lee'],\n",
+        "    'email': ['alice@example.com', 'bob@example.com', 'charlie@example.com', 'david@example.com', \n",
+        "             'emma@example.com', 'frank@example.com', 'grace@example.com'],\n",
+        "    'city': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', 'Philadelphia', 'San Antonio'],\n",
+        "    'membership_level': ['Gold', 'Silver', 'Gold', 'Bronze', 'Silver', 'Gold', 'Bronze']\n",
+        "}\n",
+        "\n",
+        "customers_df = pd.DataFrame(customers_data)\n",
+        "\n",
+        "# Save DataFrames to CSV files\n",
+        "sales_df.to_csv('sales.csv', index=False)\n",
+        "customers_df.to_csv('customers.csv', index=False)\n",
+        "\n",
+        "# Now read the CSV files back into pandas to simulate loading from CSV\n",
+        "sales_df = pd.read_csv('sales.csv')\n",
+        "customers_df = pd.read_csv('customers.csv')\n",
+        "\n",
+        "# Use pandasql to display the first few rows of each DataFrame\n",
+        "q_sales = \"\"\"\n",
+        "SELECT * FROM sales_df LIMIT 5\n",
+        "\"\"\"\n",
+        "q_customers = \"\"\"\n",
+        "SELECT * FROM customers_df LIMIT 5\n",
+        "\"\"\"\n",
+        "\n",
+        "print(\"Sales DataFrame (first 5 rows):\")\n",
+        "pysqldf(q_sales)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Customers DataFrame (first 5 rows):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>name</th>\n",
+              "      <th>email</th>\n",
+              "      <th>city</th>\n",
+              "      <th>membership_level</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>101</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>alice@example.com</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>102</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>bob@example.com</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>103</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>charlie@example.com</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>104</td>\n",
+              "      <td>David Wilson</td>\n",
+              "      <td>david@example.com</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>105</td>\n",
+              "      <td>Emma Johnson</td>\n",
+              "      <td>emma@example.com</td>\n",
+              "      <td>Phoenix</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   customer_id           name                email         city  \\\n",
+              "0          101    Alice Brown    alice@example.com     New York   \n",
+              "1          102      Bob Smith      bob@example.com  Los Angeles   \n",
+              "2          103  Charlie Davis  charlie@example.com      Chicago   \n",
+              "3          104   David Wilson    david@example.com      Houston   \n",
+              "4          105   Emma Johnson     emma@example.com      Phoenix   \n",
+              "\n",
+              "  membership_level  \n",
+              "0             Gold  \n",
+              "1           Silver  \n",
+              "2             Gold  \n",
+              "3           Bronze  \n",
+              "4           Silver  "
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "print(\"Customers DataFrame (first 5 rows):\")\n",
+        "pysqldf(q_customers)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 2. Selecting Columns\n",
+        "\n",
+        "Now let's explore how to select specific columns using pandasql."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Selected columns using pandasql:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>25.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>25.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  price\n",
+              "0         1          101           1  25.50\n",
+              "1         2          102           2  35.00\n",
+              "2         3          101           3  15.75\n",
+              "3         4          103           1  25.50\n",
+              "4         5          104           2  35.00"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Select specific columns using pandasql\n",
+        "q = \"\"\"\n",
+        "SELECT order_id, customer_id, product_id, price\n",
+        "FROM sales_df\n",
+        "\"\"\"\n",
+        "selected_columns = pysqldf(q)\n",
+        "print(\"Selected columns using pandasql:\")\n",
+        "selected_columns.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Selected columns with aliases:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Order ID</th>\n",
+              "      <th>Customer ID</th>\n",
+              "      <th>Item Price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>25.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>15.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>25.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>105</td>\n",
+              "      <td>15.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7</td>\n",
+              "      <td>103</td>\n",
+              "      <td>10.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>8</td>\n",
+              "      <td>106</td>\n",
+              "      <td>50.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>25.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10</td>\n",
+              "      <td>107</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Order ID  Customer ID  Item Price\n",
+              "0         1          101       25.50\n",
+              "1         2          102       35.00\n",
+              "2         3          101       15.75\n",
+              "3         4          103       25.50\n",
+              "4         5          104       35.00\n",
+              "5         6          105       15.75\n",
+              "6         7          103       10.25\n",
+              "7         8          106       50.00\n",
+              "8         9          102       25.50\n",
+              "9        10          107       35.00"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Select columns with aliases\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    order_id AS 'Order ID', \n",
+        "    customer_id AS 'Customer ID', \n",
+        "    price AS 'Item Price'\n",
+        "FROM sales_df\n",
+        "LIMIT 10\n",
+        "\"\"\"\n",
+        "columns_with_aliases = pysqldf(q)\n",
+        "print(\"Selected columns with aliases:\")\n",
+        "columns_with_aliases"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 3. Filtering by Value in Column\n",
+        "\n",
+        "Let's filter our data based on specific column values using SQL syntax."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "High price sales (> $30):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>8</td>\n",
+              "      <td>106</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Furniture</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>10</td>\n",
+              "      <td>107</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-22</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date   category\n",
+              "0         2          102           2         1   35.0  2023-01-16   Clothing\n",
+              "1         5          104           2         1   35.0  2023-01-18   Clothing\n",
+              "2         8          106           5         2   50.0  2023-01-20  Furniture\n",
+              "3        10          107           2         1   35.0  2023-01-22   Clothing"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Filter sales with price greater than 30\n",
+        "q = \"\"\"\n",
+        "SELECT *\n",
+        "FROM sales_df\n",
+        "WHERE price > 30\n",
+        "\"\"\"\n",
+        "high_price_sales = pysqldf(q)\n",
+        "print(\"High price sales (> $30):\")\n",
+        "high_price_sales"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Electronics with high quantity (> 1):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         1          101           1         2   25.5  2023-01-15  Electronics\n",
+              "1         4          103           1         2   25.5  2023-01-17  Electronics\n",
+              "2         9          102           1         3   25.5  2023-01-21  Electronics"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Multiple filter conditions (Electronics with quantity > 1)\n",
+        "q = \"\"\"\n",
+        "SELECT *\n",
+        "FROM sales_df\n",
+        "WHERE category = 'Electronics' AND quantity > 1\n",
+        "\"\"\"\n",
+        "electronics_high_qty = pysqldf(q)\n",
+        "print(\"Electronics with high quantity (> 1):\")\n",
+        "electronics_high_qty"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Electronics and Clothing items:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10</td>\n",
+              "      <td>107</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-22</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         2          102           2         1   35.0  2023-01-16     Clothing\n",
+              "1         5          104           2         1   35.0  2023-01-18     Clothing\n",
+              "2        10          107           2         1   35.0  2023-01-22     Clothing\n",
+              "3         1          101           1         2   25.5  2023-01-15  Electronics\n",
+              "4         4          103           1         2   25.5  2023-01-17  Electronics\n",
+              "5         9          102           1         3   25.5  2023-01-21  Electronics"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Filter with IN operator\n",
+        "q = \"\"\"\n",
+        "SELECT *\n",
+        "FROM sales_df\n",
+        "WHERE category IN ('Electronics', 'Clothing')\n",
+        "ORDER BY category, price DESC\n",
+        "\"\"\"\n",
+        "electronics_clothing = pysqldf(q)\n",
+        "print(\"Electronics and Clothing items:\")\n",
+        "electronics_clothing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Categories starting with 'E':\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         1          101           1         2   25.5  2023-01-15  Electronics\n",
+              "1         4          103           1         2   25.5  2023-01-17  Electronics\n",
+              "2         9          102           1         3   25.5  2023-01-21  Electronics"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Filter with LIKE operator\n",
+        "q = \"\"\"\n",
+        "SELECT *\n",
+        "FROM sales_df\n",
+        "WHERE category LIKE 'E%'\n",
+        "\"\"\"\n",
+        "e_categories = pysqldf(q)\n",
+        "print(\"Categories starting with 'E':\")\n",
+        "e_categories"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 4. Aggregations\n",
+        "\n",
+        "Now let's perform various aggregation operations using SQL syntax."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Basic aggregations:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>total_sales</th>\n",
+              "      <th>average_price</th>\n",
+              "      <th>max_quantity</th>\n",
+              "      <th>min_price</th>\n",
+              "      <th>total_orders</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>273.25</td>\n",
+              "      <td>27.325</td>\n",
+              "      <td>4</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   total_sales  average_price  max_quantity  min_price  total_orders\n",
+              "0       273.25         27.325             4      10.25            10"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Basic aggregations using pandasql\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    SUM(price) AS total_sales,\n",
+        "    AVG(price) AS average_price,\n",
+        "    MAX(quantity) AS max_quantity,\n",
+        "    MIN(price) AS min_price,\n",
+        "    COUNT(*) AS total_orders\n",
+        "FROM sales_df\n",
+        "\"\"\"\n",
+        "basic_aggs = pysqldf(q)\n",
+        "print(\"Basic aggregations:\")\n",
+        "basic_aggs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Category aggregations:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>category</th>\n",
+              "      <th>average_price</th>\n",
+              "      <th>total_price</th>\n",
+              "      <th>total_quantity</th>\n",
+              "      <th>max_quantity</th>\n",
+              "      <th>order_count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>105.00</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>76.50</td>\n",
+              "      <td>7</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Furniture</td>\n",
+              "      <td>50.00</td>\n",
+              "      <td>50.00</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Books</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>31.50</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Food</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      category  average_price  total_price  total_quantity  max_quantity  \\\n",
+              "0     Clothing          35.00       105.00               3             1   \n",
+              "1  Electronics          25.50        76.50               7             3   \n",
+              "2    Furniture          50.00        50.00               2             2   \n",
+              "3        Books          15.75        31.50               4             3   \n",
+              "4         Food          10.25        10.25               4             4   \n",
+              "\n",
+              "   order_count  \n",
+              "0            3  \n",
+              "1            3  \n",
+              "2            1  \n",
+              "3            2  \n",
+              "4            1  "
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Group by category with multiple aggregations\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    category,\n",
+        "    AVG(price) AS average_price,\n",
+        "    SUM(price) AS total_price,\n",
+        "    SUM(quantity) AS total_quantity,\n",
+        "    MAX(quantity) AS max_quantity,\n",
+        "    COUNT(order_id) AS order_count\n",
+        "FROM sales_df\n",
+        "GROUP BY category\n",
+        "ORDER BY total_price DESC\n",
+        "\"\"\"\n",
+        "category_aggs = pysqldf(q)\n",
+        "print(\"Category aggregations:\")\n",
+        "category_aggs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Revenue by category:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>category</th>\n",
+              "      <th>total_revenue</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>178.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>105.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Furniture</td>\n",
+              "      <td>100.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Books</td>\n",
+              "      <td>63.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Food</td>\n",
+              "      <td>41.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      category  total_revenue\n",
+              "0  Electronics          178.5\n",
+              "1     Clothing          105.0\n",
+              "2    Furniture          100.0\n",
+              "3        Books           63.0\n",
+              "4         Food           41.0"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Calculate total revenue (price * quantity) by category\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    category,\n",
+        "    SUM(price * quantity) AS total_revenue\n",
+        "FROM sales_df\n",
+        "GROUP BY category\n",
+        "ORDER BY total_revenue DESC\n",
+        "\"\"\"\n",
+        "revenue_by_category = pysqldf(q)\n",
+        "print(\"Revenue by category:\")\n",
+        "revenue_by_category"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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CNmzYoBUrVqhQoUKZ3u9bb72l1q1ba/r06Xr55Zf16aef6pFHHtF9992nrl27qlSpUjp9+rQ2bNigY8eO6Y8//nD5/rZt26p///7KlSuXunTpkup9vcOHD9eqVav04IMPqmvXrqpQoYLOnz+v7du3a8WKFTp//nyGe37hhRc0f/58hYaGqk2bNjp48KBmzpzp8txKVwPc3Llz9fLLL2vVqlWqVauWkpKStHfvXs2dO1fLli1zXm5/I8WKFdOIESN0+PBhlS1bVt9++6127typyZMnO9//nOy5557T22+/rYULF+qVV15J9fiNfP3112rYsKGeeuopNWvWTPXr15ePj4/CwsI0Z84cnTx5UqNHj1b+/PlVu3ZtjRw5UgkJCSpevLh++eUXhYeHp1pn8gGB9957T88884w8PT3VrFkzhYSEaMiQIerbt6/zI8Dy5cun8PBwLVy4UC+++KLefPNNeXl56YMPPtBrr72mxx57TG3atNHhw4c1ffp0hYSEuJxJf/HFFzVp0iR17NhR27ZtU8mSJTV//nytW7dO48aNS/dN4v7LcwgAuE3cc9N0AMCd7PqPDDNN07x48aLZq1cvs1ixYqanp6dZpkwZc9SoUabD4XBZbu/evWbt2rXN3Llzm5KcH4MVGRlpdurUybzrrrvMvHnzmo0aNTL37t2b6qOyMvqRYVu2bEn1WFJSkhkSEmKGhISYiYmJpmma5sGDB83nn3/eDAgIMD09Pc3ixYubTZs2NefPn5/q+8PCwkxJpiRz7dq1af7806dPm926dTODgoJMT09PMyAgwKxfv745efLkVLPMmzfP5XuTP2Zq2rRpLvUxY8aYxYsXN729vc1atWqZW7duTfWRYaZ59aOrRowYYd57772mt7e3WbBgQbNatWrmwIEDzaioqH993urUqWPee++95tatW82HH37YzJUrl1miRAlzwoQJN/yeJk2amJLM9evX/+u6r3f58mVz9OjRZo0aNcy8efOaXl5eZpkyZczXXnvN5WPmjh07ZrZs2dIsUKCA6evra7Zu3do8ceKEKckcMGCAyzoHDx5sFi9e3LTZbKk+Puy7774zH3nkEdPHx8f08fExy5UrZ3br1s3ct2+fyzo++eQTs0SJEqa3t7f5wAMPmOvWrTOrVatmhoaGuix3+vRp5z7r5eVl3nfffam2WfK2HDVq1L8+F7f6HAIArGeY5nXXSQEAANxGLVu21J9//qkDBw64uxVLOBwOFS5cWE899VSal+1nhpz+HAJAdsZ7ugEAgNucPHlSP/30k/73v/+5u5VMceXKlVTv+/766691/vx51a1b15KfmdOeQwDIaTjTDQAAbrvw8HCtW7dOX3zxhbZs2aKDBw8qICDA3W39Z7/99pt69eql1q1bq1ChQtq+fbu+/PJLlS9fXtu2bZOXl1em/ayc+hwCQE7DjdQAAMBtt3r1anXq1El33323vvrqqxwTFkuWLKmgoCB98sknOn/+vPz8/PT8889r+PDhmRq4pZz7HAJATsOZbgAAAAAALMJ7ugEAAAAAsAihGwAAAAAAi/Cebl39KI8TJ04oX758MgzD3e0AAAAAALI40zR18eJFFStWTDbbjc9nE7olnThxQkFBQe5uAwAAAACQzRw9elSBgYE3fJzQLSlfvnySrj5Z+fPnd3M3AAAAAICsLjo6WkFBQc48eSOEbsl5SXn+/PkJ3QAAAACAdLvZW5S5kRoAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABbxcHcDSL+S7/zk7haQTRwe/oS7WwAAAAAgznQDAAAAAGAZQjcAAAAAABYhdAMAAAAAYBG3hu41a9aoWbNmKlasmAzD0KJFi1weNwwjzX+jRo1yLlOyZMlUjw8fPvw2TwIAAAAAQGpuDd0xMTGqXLmyPv300zQfP3nypMu/qVOnyjAMtWrVymW5QYMGuSz32muv3Y72AQAAAAD4V269e3njxo3VuHHjGz4eEBDg8vX333+vevXqqVSpUi71fPnypVoWAAAAAAB3yzYfGXb69Gn99NNP+uqrr1I9Nnz4cA0ePFh33323nnvuOfXq1UseHjceLS4uTnFxcc6vo6OjJUmJiYlKTEyUJNlsNtlsNjkcDjkcDueyyfWkpCSZpnnTut1ul2EYzvVeW5ekpKSkdNU9PDxkyJTHNdcmmKaUaBqyyZQ9rbphym6k1B2mlGQashumbNfUk0zJYRryMEwZ19YdkkOp64kOyZQhT1vKnCl1yfO66ycSHJIhufR+tW4wk0UzJSYmZuq+Z5qmS90wDNnt9lS/HzeqZ8XfJ2ZiJmZiJmZiJmZiJmZipv86U3pkm9D91VdfKV++fHrqqadc6j169ND9998vPz8/rV+/Xn379tXJkyc1duzYG65r2LBhGjhwYKr6jh075OPjI0kqXLiwQkJCFB4errNnzzqXCQwMVGBgoPbv36+oqChnvVSpUvL399fu3bsVGxvrrJcrV04FChTQjh07XDZKpUqV5OXlpa1bt7r0UL16dcXHx2vXrl3Omt1uV40aNVTcR2ocmLKxL8RL88LtKuNrqnZAyo567LK05KhdVQuZur9QSn1flKE1pwzVKmLqHt+U+vZzhrZFGHo80KHAPCm9rDllaF+UoZYlHSrglVJfcsymYzFSuxCHSxidH27TpUSpY5mUHiVpephNeT2kp4NT6gkOaXqYnZksmmnr1q2Zuu9FRUVp7969znru3LlVuXJlRURE6NChQ866r6+vypcvrxMnTujYsWPOelb8fWImZmImZmImZmImZmImZvovMx04cEDpYZjXHlZwI8MwtHDhQrVo0SLNx8uVK6fHH39c48eP/9f1TJ06VS+99JIuXbokb2/vNJdJ60x3UFCQzp07p/z580vKmkdqgt/50e1nUKWcd1Y4J860Z1BotjtKmBOPfDITMzETMzETMzETMzFTzp0pMjJSfn5+ioqKcubItGSL0P3777+rdu3a2rlzpypXrvyv6/nrr79UsWJF7d27V/fcc0+6fnZ0dLR8fX1v+mS5W8l3fnJ3C8gmDg9/wt0tAAAAADlaenNktvic7i+//FLVqlW7aeCWpJ07d8pms8nf3/82dAYAAAAAwI259T3dly5dcrkOPjw8XDt37pSfn5/uvvtuSVePHsybN09jxoxJ9f0bNmzQpk2bVK9ePeXLl08bNmxQr1691L59exUsWPC2zQEAAAAAQFrcGrq3bt2qevXqOb/u3bu3JKlDhw6aPn26JGnOnDkyTVPPPvtsqu/39vbWnDlz9MEHHyguLk7BwcHq1auXcz0AAAAAALhTlnlPtzvxnm7kNLynGwAAALBWjnpPNwAAAAAA2RGhGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAi7g1dK9Zs0bNmjVTsWLFZBiGFi1a5PJ4x44dZRiGy7/Q0FCXZc6fP6927dopf/78KlCggLp06aJLly7dxikAAAAAAEibW0N3TEyMKleurE8//fSGy4SGhurkyZPOf998843L4+3atdNff/2l5cuX68cff9SaNWv04osvWt06AAAAAAA35eHOH964cWM1btz4X5fx9vZWQEBAmo/t2bNHS5cu1ZYtW1S9enVJ0vjx49WkSRONHj1axYoVy/SeAQAAAABIL7eG7vT47bff5O/vr4IFC+qxxx7TkCFDVKhQIUnShg0bVKBAAWfglqQGDRrIZrNp06ZNatmyZZrrjIuLU1xcnPPr6OhoSVJiYqISExMlSTabTTabTQ6HQw6Hw7lscj0pKUmmad60brfbZRiGc73X1iUpKSkpXXUPDw8ZMuVxzbUJpiklmoZsMmVPq26YshspdYcpJZmG7IYp2zX1JFNymIY8DFPGtXWH5FDqeqJDMmXI05YyZ0pd8rzu+okEh2RILr1frRvMZNFMiYmJmbrvmabpUjcMQ3a7PdXvx43qWfH3iZmYiZmYiZmYiZmYiZmY6b/OlB5ZOnSHhobqqaeeUnBwsA4ePKh3331XjRs31oYNG2S323Xq1Cn5+/u7fI+Hh4f8/Px06tSpG6532LBhGjhwYKr6jh075OPjI0kqXLiwQkJCFB4errNnzzqXCQwMVGBgoPbv36+oqChnvVSpUvL399fu3bsVGxvrrJcrV04FChTQjh07XDZKpUqV5OXlpa1bt7r0UL16dcXHx2vXrl3Omt1uV40aNVTcR2ocmLKxL8RL88LtKuNrqnZAyo567LK05KhdVQuZur9QSn1flKE1pwzVKmLqHt+U+vZzhrZFGHo80KHAPCm9rDllaF+UoZYlHSrglVJfcsymYzFSuxCHSxidH27TpUSpY5mUHiVpephNeT2kp4NT6gkOaXqYnZksmmnr1q2Zuu9FRUVp7969znru3LlVuXJlRURE6NChQ866r6+vypcvrxMnTujYsWPOelb8fWImZmImZmImZmImZmImZvovMx04cEDpYZjXHlZwI8MwtHDhQrVo0eKGyxw6dEghISFasWKF6tevrw8//FBfffWV9u3b57Kcv7+/Bg4cqFdeeSXN9aR1pjsoKEjnzp1T/vz5JWXNIzXB7/zo9jOoUs47K5wTZ9ozKDTbHSXMiUc+mYmZmImZmImZmImZmCnnzhQZGSk/Pz9FRUU5c2RasvSZ7uuVKlVKd911lw4cOKD69esrICBAZ86ccVkmMTFR58+fv+H7wKWr7xP39vZOVffw8JCHh+tTkvyEXi9546a3fv16b6VuylCCI/WyDhlypFU3DTnSOKSSZBpKSqOeaBpXE2Y66wkOI3VRSrNH84Z1ZrJipmv3n8zY9wzDSLN+o9+PjNbd8fvETMwkMdONesxonZmYSWKmG/WY0TozMZPETDfqMaN1d82U6vvTtVQWcezYMZ07d05FixaVJD388MO6cOGCtm3b5lzm119/lcPh0IMPPuiuNgEAAAAAkOTmM92XLl1yuQ4+PDxcO3fulJ+fn/z8/DRw4EC1atVKAQEBOnjwoN5++22VLl1ajRo1kiSVL19eoaGh6tq1qz7//HMlJCSoe/fueuaZZ7hzOQAAAADA7dx6pnvr1q2qWrWqqlatKknq3bu3qlatqv79+8tut2vXrl1q3ry5ypYtqy5duqhatWr6/fffXS4NnzVrlsqVK6f69eurSZMmeuSRRzR58mR3jQQAAAAAgJNbz3TXrVvX5U3z11u2bNlN1+Hn56fZs2dnZlsAAAAAAGSKbPWebgAAAAAAshNCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBFCNwAAAAAAFiF0AwAAAABgEUI3AAAAAAAWIXQDAAAAAGARQjcAAAAAABYhdAMAAAAAYBG3hu41a9aoWbNmKlasmAzD0KJFi5yPJSQkqE+fPrrvvvvk4+OjYsWK6fnnn9eJEydc1lGyZEkZhuHyb/jw4bd5EgAAAAAAUnNr6I6JiVHlypX16aefpnrs8uXL2r59u/r166ft27drwYIF2rdvn5o3b55q2UGDBunkyZPOf6+99trtaB8AAAAAgH/l4c4f3rhxYzVu3DjNx3x9fbV8+XKX2oQJE/TAAw/oyJEjuvvuu531fPnyKSAgwNJeAQAAAADIqGz1nu6oqCgZhqECBQq41IcPH65ChQqpatWqGjVqlBITE93TIAAAAAAA13Drme6MuHLlivr06aNnn31W+fPnd9Z79Oih+++/X35+flq/fr369u2rkydPauzYsTdcV1xcnOLi4pxfR0dHS5ISExOdgd1ms8lms8nhcMjhcDiXTa4nJSXJNM2b1u12uwzDSHUgwG63S5KSkpLSVffw8JAhUx7XHCYxTSnRNGSTKXtadcOU3UipO0wpyTRkN0zZrqknmZLDNORhmDKurTskh1LXEx2SKUOetpQ5U+qS53WHchIckiG59H61bjCTRTMlJiZm6r5nmqZL3TAM2e32VL8fN6pnxd8nZmImZmImZmImZmImZmKm/zpTemSL0J2QkKA2bdrINE1NnDjR5bHevXs7/7tSpUry8vLSSy+9pGHDhsnb2zvN9Q0bNkwDBw5MVd+xY4d8fHwkSYULF1ZISIjCw8N19uxZ5zKBgYEKDAzU/v37FRUV5ayXKlVK/v7+2r17t2JjY531cuXKqUCBAtqxY4fLRknudevWrS49VK9eXfHx8dq1a5ezZrfbVaNGDRX3kRoHpmzsC/HSvHC7yviaqh2QsqMeuywtOWpX1UKm7i+UUt8XZWjNKUO1ipi6xzelvv2coW0Rhh4PdCgwT0ova04Z2hdlqGVJhwp4pdSXHLPpWIzULsThEkbnh9t0KVHqWCalR0maHmZTXg/p6eCUeoJDmh5mZyaLZtq6dWum7ntRUVHau3evs547d25VrlxZEREROnTokLPu6+ur8uXL68SJEzp27JiznhV/n5iJmZiJmZiJmZiJmZiJmf7LTAcOHFB6GOa1hxXcyDAMLVy4UC1atHCpJwfuQ4cO6ddff1WhQoX+dT1//fWXKlasqL179+qee+5Jc5m0znQHBQXp3LlzzrPoWfFITfA7P7r9DKqU884K58SZ9gwKzXZHCXPikU9mYiZmYiZmYiZmYiZmyrkzRUZGys/PT1FRUS5XY18vS4fu5MAdFhamVatWqXDhwjddz6xZs/T8888rIiJCBQsWTNfPjo6Olq+v702fLHcr+c5P7m4B2cTh4U+4uwUAAAAgR0tvjnTr5eWXLl1yOSUfHh6unTt3ys/PT0WLFtXTTz+t7du368cff1RSUpJOnTolSfLz85OXl5c2bNigTZs2qV69esqXL582bNigXr16qX379ukO3AAAAAAAWMWtoXvr1q2qV6+e8+vk92d36NBBH3zwgRYvXixJqlKlisv3rVq1SnXr1pW3t7fmzJmjDz74QHFxcQoODlavXr1c3ucNAAAAAIC7uDV0161bV/92dfvNrny///77tXHjxsxuCwAAAACATJGtPqcbAAAAAIDshNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWMTjVr4pISFBp06d0uXLl1W4cGH5+flldl8AAAAAAGR76T7TffHiRU2cOFF16tRR/vz5VbJkSZUvX16FCxdWiRIl1LVrV23ZssXKXgEAAAAAyFbSFbrHjh2rkiVLatq0aWrQoIEWLVqknTt3av/+/dqwYYMGDBigxMRENWzYUKGhoQoLC7O6bwAAAAAAsrx0XV6+ZcsWrVmzRvfee2+ajz/wwAPq3LmzPv/8c02bNk2///67ypQpk6mNAgAAAACQ3aQrdH/zzTfpWpm3t7defvnl/9QQAAAAAAA5BXcvBwAAAADAIhkK3WfOnNGxY8ecXycmJur9999XnTp19MYbb+jy5cuZ3iAAAAAAANlVhkJ3165d9dVXXzm/HjVqlKZMmaIaNWpo8eLF6tWrV6Y3CAAAAABAdpWh0L1r1y7Vq1fP+fWMGTP0ySefaPTo0ZozZ45++OGHTG8QAAAAAIDsKl03UuvUqZMk6cSJExo7dqymTJmi+Ph47du3TwsXLtSyZcvkcDh05swZde7cWZI0depU67oGAAAAACAbSFfonjZtmiRpzZo16tKlixo3bqxvv/1Wf/75p+bMmSNJOnfunBYvXkzYBgAAAADg/6UrdCd74okn1LlzZzVv3lyLFi3S22+/7Xxs8+bNqlChQqY3CAAAAABAdpWh0D1y5Ej5+vpq586d6tWrl8uN0zZt2sRndAMAAAAAcI0Mhe5cuXJp8ODBaT72wQcfZEY/AAAAAADkGBm6ezkAAAAAAEi/dIXu0NBQbdy48abLXbx4USNGjNCnn376nxsDAAAAACC7S9fl5a1bt1arVq3k6+urZs2aqXr16ipWrJhy5cqlyMhI/f3331q7dq1+/vlnPfHEExo1apTVfQMAAAAAkOWlK3R36dJF7du317x58/Ttt99q8uTJioqKkiQZhqEKFSqoUaNG2rJli8qXL29pwwAAAAAAZBfpvpGat7e32rdvr/bt20uSoqKiFBsbq0KFCsnT09OyBgEAAAAAyK4ydPfya/n6+srX1zczewEAAAAAIEfh7uUAAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARW4pdF+4cEFffPGF+vbtq/Pnz0uStm/fruPHj2dqcwAAAAAAZGcZvnv5rl271KBBA/n6+urw4cPq2rWr/Pz8tGDBAh05ckRff/21FX0CAAAAAJDtZPhMd+/evdWxY0eFhYUpV65cznqTJk20Zs2aTG0OAAAAAIDsLMOhe8uWLXrppZdS1YsXL65Tp05lSlMAAAAAAOQEGQ7d3t7eio6OTlXfv3+/ChcunClNAQAAAACQE2Q4dDdv3lyDBg1SQkKCJMkwDB05ckR9+vRRq1atMr1BAAAAAACyqwyH7jFjxujSpUvy9/dXbGys6tSpo9KlSytfvnwaOnSoFT0CAAAAAJAtZfju5b6+vlq+fLnWrl2rXbt26dKlS7r//vvVoEEDK/oDkI2VfOcnd7eAbOLw8Cfc3QIAAIAlMhy6kz3yyCN65JFHMrMXAAAAAABylAyH7kGDBv3r4/3797/lZgAAAAAAyEkyHLoXLlzo8nVCQoLCw8Pl4eGhkJAQQjcAAAAAAP8vw6F7x44dqWrR0dHq2LGjWrZsmSlNAQAAAACQE2T47uVpyZ8/vwYOHKh+/fplxuoAAAAAAMgRMiV0S1JUVJSioqIya3UAAAAAAGR7Gb68/JNPPnH52jRNnTx5UjNmzFDjxo0zrTEAAAAAALK7DJ/p/uijj1z+ffLJJ/rtt9/UoUMHTZo0KUPrWrNmjZo1a6ZixYrJMAwtWrTI5XHTNNW/f38VLVpUuXPnVoMGDRQWFuayzPnz59WuXTvlz59fBQoUUJcuXXTp0qWMjgUAAAAAQKbL8Jnu8PDwTPvhMTExqly5sjp37qynnnoq1eMjR47UJ598oq+++krBwcHq16+fGjVqpL///lu5cuWSJLVr104nT57U8uXLlZCQoE6dOunFF1/U7NmzM61PAAAAAABuRYZDd2Zq3LjxDS9JN01T48aN0/vvv68nn3xSkvT111+rSJEiWrRokZ555hnt2bNHS5cu1ZYtW1S9enVJ0vjx49WkSRONHj1axYoVu22zAAAAAABwvQxfXh4TE6N+/fqpZs2aKl26tEqVKuXyL7OEh4fr1KlTatCggbPm6+urBx98UBs2bJAkbdiwQQUKFHAGbklq0KCBbDabNm3alGm9AAAAAABwKzJ8pvuFF17Q6tWr9b///U9FixaVYRhW9KVTp05JkooUKeJSL1KkiPOxU6dOyd/f3+VxDw8P+fn5OZdJS1xcnOLi4pxfR0dHS5ISExOVmJgoSbLZbLLZbHI4HHI4HM5lk+tJSUkyTfOmdbvdLsMwnOu9ti5JSUlJ6ap7eHjIkCmPaw6TmKaUaBqyyZQ9rbphyn7N5nGYUpJpyG6Ysl1TTzIlh2nIwzB17eZMckgOpa4nOiRThjxtKXOm1CXP6w7lJDgkQ3Lp/WrdYCaLZkpMTMzUfc80TZe6YRiy2+2pfj+uryc/n2wnZrrZTNfvY9J/2/eSZcXXcmZiJmZiJmZiJmbKOTOlR4ZD95IlS/TTTz+pVq1aGf3WLGPYsGEaOHBgqvqOHTvk4+MjSSpcuLBCQkIUHh6us2fPOpcJDAxUYGCg9u/f7/IRaaVKlZK/v792796t2NhYZ71cuXIqUKCAduzY4bJRKlWqJC8vL23dutWlh+rVqys+Pl67du1y1ux2u2rUqKHiPlLjwJSNfSFemhduVxlfU7UDUnbUY5elJUftqlrI1P2FUur7ogytOWWoVhFT9/im1LefM7QtwtDjgQ4F5knpZc0pQ/uiDLUs6VABr5T6kmM2HYuR2oU4XP54nh9u06VEqWOZlB4laXqYTXk9pKeDU+oJDml6mJ2ZLJpp69atmbrvRUVFae/evc567ty5VblyZUVEROjQoUPOuq+vr8qXL68TJ07o2LFjzueN7cRMN5spNjY2U/e9ZLfyWv7A2C1qHZyUxkyGOpZJ+s/bKdDHTHM73ePrSHM7VbvLkcZ2sql2gCON7WRT46CkNLaTjZkyeab65a8e9M/Mfc/qvyPc8fvETMzETMyUk2c6cOCA0sMwrz2skA7BwcH6+eefVb58+Yx8280bMQwtXLhQLVq0kCQdOnRIISEh2rFjh6pUqeJcrk6dOqpSpYo+/vhjTZ06VW+88YYiIyOdjycmJipXrlyaN2+eWrZsmebPSutMd1BQkM6dO6f8+fNLyppHaoLf+THbnsXKiWfmsvJMewaFZomjhOX7L820maSct52YKWWm8GFPZJmj7qXeXcJ2YqabzrRnUKgkzvgwEzMxEzPdyTNFRkbKz89PUVFRzhyZlgyf6R48eLD69++vr776Snny5Ln5N9yi4OBgBQQEaOXKlc7QHR0drU2bNumVV16RJD388MO6cOGCtm3bpmrVqkmSfv31VzkcDj344IM3XLe3t7e8vb1T1T08POTh4fqUJD+h10veuOmtX7/eW6mbMpTgSL2sQ4YcadVNQ440DqkkmYaS0qgnmsbVv0rSWU9wpP3WgrR6NG9YZyYrZrp2/8mMfc8wjDTrN/r9SK5f/3yynZjpaj117Ub72K3ue+mt3+g1m+3ETDebKb1/L2SlvyPc9fvETMzETMwk3VkzpeotXUtdY8yYMTp48KCKFCmikiVLytPT0+Xx7du3p3tdly5dcjklHx4erp07d8rPz0933323evbsqSFDhqhMmTLOjwwrVqyY82x4+fLlFRoaqq5du+rzzz9XQkKCunfvrmeeeYY7lwMAAAAA3C7DoTs58GaGrVu3ql69es6ve/fuLUnq0KGDpk+frrffflsxMTF68cUXdeHCBT3yyCNaunSp8zO6JWnWrFnq3r276tevL5vNplatWumTTz7JtB4BAAAAALhVGQ7dAwYMyLQfXrduXf3bW8oNw9CgQYM0aNCgGy7j5+en2bNnZ1pPAAAAAABklgx/TrckXbhwQV988YX69u2r8+fPS7p6Wfnx48cztTkAAAAAALKzDJ/p3rVrlxo0aCBfX18dPnxYXbt2lZ+fnxYsWKAjR47o66+/tqJPAAAAAACynQyf6e7du7c6duyosLAwl/dWN2nSRGvWrMnU5gAAAAAAyM4yHLq3bNmil156KVW9ePHiOnXqVKY0BQAAAABATpDh0O3t7a3o6OhU9f3796tw4cKZ0hQAAAAAADlBhkN38+bNNWjQICUkJEi6eofxI0eOqE+fPmrVqlWmNwgAAAAAQHaV4dA9ZswYXbp0Sf7+/oqNjVWdOnVUunRp5cuXT0OHDrWiRwAAAAAAsqUM373c19dXy5cv19q1a7Vr1y5dunRJ999/vxo0aGBFfwAAAAAAZFsZDt1Hjx5VUFCQHnnkET3yyCNW9AQAAAAAQI6Q4cvLS5YsqTp16mjKlCmKjIy0oicAAAAAAHKEDIfurVu36oEHHtCgQYNUtGhRtWjRQvPnz1dcXJwV/QEAAAAAkG1lOHRXrVpVo0aN0pEjR7RkyRIVLlxYL774oooUKaLOnTtb0SMAAAAAANlShkN3MsMwVK9ePU2ZMkUrVqxQcHCwvvrqq8zsDQAAAACAbO2WQ/exY8c0cuRIValSRQ888IDy5s2rTz/9NDN7AwAAAAAgW8vw3csnTZqk2bNna926dSpXrpzatWun77//XiVKlLCiPwAAAAAAsq0Mh+4hQ4bo2Wef1SeffKLKlStb0RMAAAAAADlChkP3kSNHZBiGFb0AAAAAAJCjZPg93YZh6Pfff1f79u318MMP6/jx45KkGTNmaO3atZneIAAAAAAA2VWGQ/d3332nRo0aKXfu3NqxY4fz87mjoqL04YcfZnqDAAAAAABkVxkO3UOGDNHnn3+uKVOmyNPT01mvVauWtm/fnqnNAQAAAACQnWU4dO/bt0+1a9dOVff19dWFCxcyoycAAAAAAHKEDIfugIAAHThwIFV97dq1KlWqVKY0BQAAAABATpDh0N21a1e9/vrr2rRpkwzD0IkTJzRr1iy9+eabeuWVV6zoEQAAAACAbCnDHxn2zjvvyOFwqH79+rp8+bJq164tb29vvfnmm3rttdes6BEAAAAAgGwpw6HbMAy99957euutt3TgwAFdunRJFSpUUN68eRUbG6vcuXNb0ScAAAAAANlOhi8vT+bl5aUKFSrogQcekKenp8aOHavg4ODM7A0AAAAAgGwt3aE7Li5Offv2VfXq1VWzZk0tWrRIkjRt2jQFBwfro48+Uq9evazqEwAAAACAbCfdl5f3799fkyZNUoMGDbR+/Xq1bt1anTp10saNGzV27Fi1bt1adrvdyl4BAAAAAMhW0h26582bp6+//lrNmzfX7t27ValSJSUmJuqPP/6QYRhW9ggAAAAAQLaU7svLjx07pmrVqkmSKlasKG9vb/Xq1YvADQAAAADADaQ7dCclJcnLy8v5tYeHh/LmzWtJUwAAAAAA5ATpvrzcNE117NhR3t7ekqQrV67o5Zdflo+Pj8tyCxYsyNwOAQAAAADIptIdujt06ODydfv27TO9GQAAAAAAcpJ0h+5p06ZZ2QcAAAAAADlOut/TDQAAAAAAMobQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFgkXXcvX7x4cbpX2Lx581tuBgAAAACAnCRdobtFixbpWplhGEpKSvov/QAAAAAAkGOkK3Q7HA6r+wAAAAAAIMfhPd0AAAAAAFgkXWe6rxcTE6PVq1fryJEjio+Pd3msR48emdIYAAAAAADZXYZD944dO9SkSRNdvnxZMTEx8vPzU0REhPLkySN/f39CNwAAAAAA/y/Dl5f36tVLzZo1U2RkpHLnzq2NGzfqn3/+UbVq1TR69GgregQAAAAAIFvKcOjeuXOn3njjDdlsNtntdsXFxSkoKEgjR47Uu+++a0WPAAAAAABkSxkO3Z6enrLZrn6bv7+/jhw5Ikny9fXV0aNHM7c7AAAAAACysQy/p7tq1arasmWLypQpozp16qh///6KiIjQjBkzVLFiRSt6BAAAAAAgW8rwme4PP/xQRYsWlSQNHTpUBQsW1CuvvKKzZ89q0qRJmd4gAAAAAADZVYZDd/Xq1VWvXj1JVy8vX7p0qaKjo7Vt2zZVqVIls/tTyZIlZRhGqn/dunWTJNWtWzfVYy+//HKm9wEAAAAAQEZlOHQ/9thjunDhQqp6dHS0HnvssczoycWWLVt08uRJ57/ly5dLklq3bu1cpmvXri7LjBw5MtP7AAAAAAAgozL8nu7ffvtN8fHxqepXrlzR77//nilNXatw4cIuXw8fPlwhISGqU6eOs5YnTx4FBARk+s8GAAAAAOC/SHfo3rVrl/O///77b506dcr5dVJSkpYuXarixYtnbnfXiY+P18yZM9W7d28ZhuGsz5o1SzNnzlRAQICaNWumfv36KU+ePDdcT1xcnOLi4pxfR0dHS5ISExOVmJgoSbLZbLLZbHI4HHI4HM5lk+tJSUkyTfOmdbvdLsMwnOu9ti5dfe7SU/fw8JAhUx7XXJtgmlKiacgmU/a06oYpe8rTJIcpJZmG7IYp2zX1JFNymIY8DFPGtXWH5FDqeqJDMmXI05YyZ0pd8rzu+okEh2RILr1frRvMZNFMiYmJmbrvmabpUjcMQ3a7PdXvx/X15OeT7cRMN5vp+n1M+m/7XrJbeS2XxHZippvOlPzampn7ntV/R7jj94mZmImZmCmnz5Qe6Q7dVapUcb5nOq3LyHPnzq3x48end3W3ZNGiRbpw4YI6duzorD333HMqUaKEihUrpl27dqlPnz7at2+fFixYcMP1DBs2TAMHDkxV37Fjh3x8fCRdPcMeEhKi8PBwnT171rlMYGCgAgMDtX//fkVFRTnrpUqVkr+/v3bv3q3Y2FhnvVy5cipQoIB27NjhslEqVaokLy8vbd261aWH6tWrKz4+3uUgh91uV40aNVTcR2ocmLKxL8RL88LtKuNrqnZAyo567LK05KhdVQuZur9QSn1flKE1pwzVKmLqHt+U+vZzhrZFGHo80KHAa45VrDllaF+UoZYlHSrglVJfcsymYzFSuxCHyx8w88NtupQodSyT0qMkTQ+zKa+H9HRwSj3BIU0PszOTRTNt3bo1U/e9qKgo7d2711nPnTu3KleurIiICB06dMhZ9/X1Vfny5XXixAkdO3bM+byxnZjpZjPFxsZm6r6X7FZeyyWxnZjppjMlv4Zm5r5n9d8R7vh9YiZmYiZmyskzHThwQOlhmNceVvgX//zzj0zTVKlSpbR582aXy769vLzk7+/vPMpglUaNGsnLy0s//PDDDZf59ddfVb9+fR04cEAhISFpLpPWme6goCCdO3dO+fPnl5Q1j9QEv/PjHXcmgZlubaY9g0KzxFHC8v2XZtpMUs7bTsyUMlP4sCeyzFH3Uu8uYTsx001n2jMoVBJnfJiJmZiJme7kmSIjI+Xn56eoqChnjkxLus90lyhRQpJcftjt9M8//2jFihX/egZbkh588EFJ+tfQ7e3tLW9v71R1Dw8PeXi4PiXJT+j1bnSA4Ub169d7K3VThhLSePodMpTWZnGYhhxpHFJJMg0lpVFPNI2rf5Wks57gMFIXpTR7NG9YZyYrZrp2/8mMfc8wjDTrN/r9SK5f/3yynZjpaj117Ub72K3ue+mt3+g1m+3ETDebKb1/L2SlvyPc9fvETMzETMwk3VkzpeotXUtd5+DBgxo3bpz27NkjSapQoYJef/31G4bczDBt2jT5+/vriSee+Nfldu7cKUnOzxIHAAAAAMBdMvyRYcuWLVOFChW0efNmVapUSZUqVdKmTZt07733Oj/OK7M5HA5NmzZNHTp0cDlScfDgQQ0ePFjbtm3T4cOHtXjxYj3//POqXbu2KlWqZEkvAAAAAACkV4bPdL/zzjvq1auXhg8fnqrep08fPf7445nWXLIVK1boyJEj6ty5s0vdy8tLK1as0Lhx4xQTE6OgoCC1atVK77//fqb3AAAAAABARmU4dO/Zs0dz585NVe/cubPGjRuXGT2l0rBhQ6V1v7egoCCtXr3akp8JAAAAAMB/leHLywsXLux83/S1du7cKX9//8zoCQAAAACAHCHdZ7oHDRqkN998U127dtWLL76oQ4cOqWbNmpKkdevWacSIEerdu7dljQIAAAAAkN2kO3QPHDhQL7/8svr166d8+fJpzJgx6tu3rySpWLFi+uCDD9SjRw/LGgUAAAAAILtJd+hOfk+1YRjq1auXevXqpYsXL0qS8uXLZ013AAAAAABkYxm6kZphGC5fE7YBAAAAALixDIXusmXLpgre1zt//vx/aggAAAAAgJwiQ6F74MCB8vX1taoXAAAAAABylAyF7meeeYaPBQMAAAAAIJ3S/TndN7usHAAAAAAAuEp36E6+ezkAAAAAAEifdF9e7nA4rOwDAAAAAIAcJ91nugEAAAAAQMYQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLeLi7AQAAANy6ku/85O4WkA0cHv6Eu1sA7lic6QYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALJKlQ/cHH3wgwzBc/pUrV875+JUrV9StWzcVKlRIefPmVatWrXT69Gk3dgwAAAAAQIosHbol6d5779XJkyed/9auXet8rFevXvrhhx80b948rV69WidOnNBTTz3lxm4BAAAAAEjh4e4GbsbDw0MBAQGp6lFRUfryyy81e/ZsPfbYY5KkadOmqXz58tq4caMeeuih290qAAAAAAAusvyZ7rCwMBUrVkylSpVSu3btdOTIEUnStm3blJCQoAYNGjiXLVeunO6++25t2LDBXe0CAAAAAOCUpc90P/jgg5o+fbruuecenTx5UgMHDtSjjz6q3bt369SpU/Ly8lKBAgVcvqdIkSI6derUv643Li5OcXFxzq+jo6MlSYmJiUpMTJQk2Ww22Ww2ORwOORwO57LJ9aSkJJmmedO63W6XYRjO9V5bl6SkpKR01T08PGTIlMc1h0lMU0o0Ddlkyp5W3TBlN1LqDlNKMg3ZDVO2a+pJpuQwDXkYpoxr6w7JodT1RIdkypCnLWXOlLrked2hnASHZEguvV+tG8xk0UyJiYmZuu+ZpulSNwxDdrs91e/H9fXk55PtxEw3m+n6fUz6b/teslt5LZfEdmKmm86U/Nqamfverf4dce28bCdmutFMDofDbX/DuuO1nJmY6XbNlB5ZOnQ3btzY+d+VKlXSgw8+qBIlSmju3LnKnTv3La932LBhGjhwYKr6jh075OPjI0kqXLiwQkJCFB4errNnzzqXCQwMVGBgoPbv36+oqChnvVSpUvL399fu3bsVGxvrrJcrV04FChTQjh07XDZKpUqV5OXlpa1bt7r0UL16dcXHx2vXrl3Omt1uV40aNVTcR2ocmLKxL8RL88LtKuNrqnZAyo567LK05KhdVQuZur9QSn1flKE1pwzVKmLqHt+U+vZzhrZFGHo80KHAPCm9rDllaF+UoZYlHSrglVJfcsymYzFSuxCHy/9E5ofbdClR6lgmpUdJmh5mU14P6englHqCQ5oeZmcmi2baunVrpu57UVFR2rt3r7OeO3duVa5cWRERETp06JCz7uvrq/Lly+vEiRM6duyY83ljOzHTzWaKjY3N1H0v2a28lktiOzHTTWdKfg3NzH3vVv+OuHYuthMz3WimEydOuO1vWHe8ljMTM92OmQ4cOKD0MMxrDytkAzVq1FCDBg30+OOPq379+oqMjHQ5212iRAn17NlTvXr1uuE60jrTHRQUpHPnzil//vySsuaRmuB3fuRoLjOla6Y9g0KzxFHC8v2XZtpMUs7bTsyUMlP4sCeyzFH3Uu8uYTsx001n2jMoVFLWOOOT/Fr7X2dKVc8B24mZUur7hjTJ0WcbmYmZ3DFTZGSk/Pz8FBUV5cyRacnSZ7qvd+nSJR08eFD/+9//VK1aNXl6emrlypVq1aqVJGnfvn06cuSIHn744X9dj7e3t7y9vVPVPTw85OHh+pQkP6HXS9646a1fv95bqZsylOBIvaxDhhxp1U1DjjQOqSSZhpLSqCeaxtX/M6SznuAwUhelNHs0b1hnJitmunb/yYx9zzCMNOs3+v1Irl//fLKdmOlqPXXtRvvYre576a3f6DWb7cRMN5spvX8v3I6/I66fl+3ETFLqmZL3Q3f8Deuu13JmYiZ3zZSqt3Qt5SZvvvmmmjVrphIlSujEiRMaMGCA7Ha7nn32Wfn6+qpLly7q3bu3/Pz8lD9/fr322mt6+OGHuXM5AAAAACBLyNKh+9ixY3r22Wd17tw5FS5cWI888og2btyowoULS5I++ugj2Ww2tWrVSnFxcWrUqJE+++wzN3cNAAAAAMBVWTp0z5kz518fz5Urlz799FN9+umnt6kjAAAAAADSL8t/TjcAAAAAANkVoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAi3i4uwEAAAAAd46S7/zk7haQDRwe/oS7W8g0nOkGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCKEbgAAAAAALELoBgAAAADAIoRuAAAAAAAsQugGAAAAAMAihG4AAAAAACxC6AYAAAAAwCJZOnQPGzZMNWrUUL58+eTv768WLVpo3759LsvUrVtXhmG4/Hv55Zfd1DEAAAAAACmydOhevXq1unXrpo0bN2r58uVKSEhQw4YNFRMT47Jc165ddfLkSee/kSNHuqljAAAAAABSeLi7gX+zdOlSl6+nT58uf39/bdu2TbVr13bW8+TJo4CAgNvdHgAAAAAA/ypLn+m+XlRUlCTJz8/PpT5r1izdddddqlixovr27avLly+7oz0AAAAAAFxk6TPd13I4HOrZs6dq1aqlihUrOuvPPfecSpQooWLFimnXrl3q06eP9u3bpwULFtxwXXFxcYqLi3N+HR0dLUlKTExUYmKiJMlms8lms8nhcMjhcDiXTa4nJSXJNM2b1u12uwzDcK732rokJSUlpavu4eEhQ6Y8rjlMYppSomnIJlP2tOqGKbtxzXNoSkmmIbthynZNPcmUHKYhD8OUcW3dITmUup7okEwZ8rSlzJlSlzyvO5ST4JAMyaX3q3WDmSyaKTExMVP3PdM0XeqGYchut6f6/bi+nvx8sp2Y6WYzXb+PSf9t30t2K6/lkthOzHTTmZJfWzNz37vVvyOunZftxEw3msnhcLjtb9jrX8s9DJPtxEw3ncmdf8MmS89reXpkm9DdrVs37d69W2vXrnWpv/jii87/vu+++1S0aFHVr19fBw8eVEhISJrrGjZsmAYOHJiqvmPHDvn4+EiSChcurJCQEIWHh+vs2bPOZQIDAxUYGKj9+/c7z7xLUqlSpeTv76/du3crNjbWWS9XrpwKFCigHTt2uGyUSpUqycvLS1u3bnXpoXr16oqPj9euXbucNbvdrho1aqi4j9Q4MGVjX4iX5oXbVcbXVO2AlF+0Y5elJUftqlrI1P2FUur7ogytOWWoVhFT9/im1LefM7QtwtDjgQ4F5knpZc0pQ/uiDLUs6VABr5T6kmM2HYuR2oU4XH6R54fbdClR6lgmpUdJmh5mU14P6englHqCQ5oeZmcmi2baunVrpu57UVFR2rt3r7OeO3duVa5cWRERETp06JCz7uvrq/Lly+vEiRM6duyY83ljOzHTzWaKjY3N1H0v2a28lktiOzHTTWdKfg3NzH3vVv+OuHYuthMz3WimEydOuO1v2Otfy1uWdLCdmOmmM7nzb9hkN3stP3DggNLDMK89pJVFde/eXd9//73WrFmj4ODgf102JiZGefPm1dKlS9WoUaM0l0nrTHdQUJDOnTun/PnzS8qaZ7qD3/kxSx19yolH1HLKTHsGhWaJM93l+y/NtJmknLedmCllpvBhT2SZM92l3l3CdmKmm860Z1CopKxxpjv5tfa/zpSqngO2EzOl1PcNaZJlznSX67eU7cRMN53pwBDXLJcVz3RHRkbKz89PUVFRzhyZlix9pts0Tb322mtauHChfvvtt5sGbknauXOnJKlo0aI3XMbb21ve3t6p6h4eHvLwcH1Kkp/Q6yVv3PTWr1/vrdRNGUpwpF7WIUOOtOqmIUcah1SSTENJadQTTePqb2c66wkOI3VRSrNH84Z1ZrJipmv3n8zY9wzDSLN+o9+P5Pr1zyfbiZmu1lPXbrSP3eq+l976jV6z2U7MdLOZ0vv3wu34O+L6edlOzCSlnil5P3TH37DXv5YnmlefJ7YTM/1b3Z1/w6a3fqPfm1S9pWspN+nWrZtmz56t77//Xvny5dOpU6ckXT39nzt3bh08eFCzZ89WkyZNVKhQIe3atUu9evVS7dq1ValSJTd3DwAAAAC402Xp0D1x4kRJUt26dV3q06ZNU8eOHeXl5aUVK1Zo3LhxiomJUVBQkFq1aqX333/fDd0CAAAAAOAqS4fum73dPCgoSKtXr75N3QAAAAAAkDGpL0wHAAAAAACZgtANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFCN0AAAAAAFiE0A0AAAAAgEUI3QAAAAAAWITQDQAAAACARQjdAAAAAABYhNANAAAAAIBFckzo/vTTT1WyZEnlypVLDz74oDZv3uzulgAAAAAAd7gcEbq//fZb9e7dWwMGDND27dtVuXJlNWrUSGfOnHF3awAAAACAO1iOCN1jx45V165d1alTJ1WoUEGff/658uTJo6lTp7q7NQAAAADAHczD3Q38V/Hx8dq2bZv69u3rrNlsNjVo0EAbNmxI83vi4uIUFxfn/DoqKkqSdP78eSUmJjrXYbPZ5HA45HA4XNZts9mUlJQk0zRvWrfb7TIMw7nea+uSlJSUlK66h4eHzLgYeVxzmMQ0pUTTkE2m7GnVDVN2I6XuMKUk05DdMGW7pp5kSg7TkIdhyri27pAcSl1PdEimDHnaUuZMqUue1x3KSXBIhuTS+9W6IUMmM1kw0/nz5zN33zNNl7phGLLb7al+P66v2xNiMm0mKedtJ2ZKmSkqKipT971kt/Ja7oi7zHZippvOdP78eUmZu+/d6t8Rya+1/3WmVPUcsJ2YKaV+4cIF9/0Ne91ruS0+hu3ETDedKfl1Ntnt/Bs22c1eyyMjI///OXF9/q+X7UN3RESEkpKSVKRIEZd6kSJFtHfv3jS/Z9iwYRo4cGCqenBwsCU9ArdbobHu7gDImALj3N0BkDG8ziK7Kcg+i2ym0Dh3d5B+Fy9elK+v7w0fz/ah+1b07dtXvXv3dn7tcDh0/vx5FSpUSMa1h1mQpUVHRysoKEhHjx5V/vz53d0OkC7st8hu2GeR3bDPIrthn82+TNPUxYsXVaxYsX9dLtuH7rvuukt2u12nT592qZ8+fVoBAQFpfo+3t7e8vb1dagUKFLCqRVgsf/78vEAh22G/RXbDPovshn0W2Q37bPb0b2e4k2X7G6l5eXmpWrVqWrlypbPmcDi0cuVKPfzww27sDAAAAABwp8v2Z7olqXfv3urQoYOqV6+uBx54QOPGjVNMTIw6derk7tYAAAAAAHewHBG627Ztq7Nnz6p///46deqUqlSpoqVLl6a6uRpyFm9vbw0YMCDVWwWArIz9FtkN+yyyG/ZZZDfsszmfYd7s/uYAAAAAAOCWZPv3dAMAAAAAkFURugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAAAAALIo7nud/RG6AQAAACCL+eSTTxQWFibDMAje2RyhGwCyAf5nCwDAnSM6OlozZ85UrVq1FB4eTvDO5gjdwDWSX8z++usvbd682c3dAFeZpinDMCRJ8+bN04wZM9zcEXDr+KMR7pa8D27atEkLFixwczdA2vLnz6+5c+eqatWqqlWrlg4dOkTwzsYI3cD/Sw42CxYs0BNPPKE1a9boyJEj7m4LdziHw+EM3H/++acGDRqkKVOm6IcffnBzZ8DNJf9x+M8//2j37t0KDw/X5cuX3dwV7mTJ/6//7rvv1LJlS61fv15hYWHubgtw4XA4JEklS5bU+PHjVb58eTVu3Fj//PMPwTub8nB3A0BWYRiGli5dqueff14jRozQ888/r3z58rks43A4ZLNxrAq3T/L+9s477+jYsWPy9PTUtm3bNHDgQMXHx6tVq1Zu7hBIW3K4Wbhwod566y3lypVLly5dUp06dfTSSy+pZs2a7m4RdyDDMLRy5Uo9//zzGjdunF544QXngU0gq0jeJ3/44QdNnjxZkhQWFqa6detq5cqVKlWqlMtVcMj6SA+Arv5xePnyZX322Wd69dVX1a1bNxmGoX379mn06NEaO3aspKsBiKOLuN0mT56szz//XK+//rqWLFmiTZs2ycvLS59++qkWLVrk7vaANBmGoTVr1qhjx47q2bOndu/erbfffluzZ8/Wnj173N0e7kCmaSoxMVHfffedOnbsqK5duyoqKkobNmzQ66+/rldffVX//POPu9sEnK+frVq1UtOmTTVmzBjNnz9fRYsW1aOPPsp7vLMhznQDuvrilidPHuXOnVtnz57Vrl279Pnnn2v//v0KDw9XYmKiNm/erDlz5nBUEbfd7t27VbNmTdWoUUOmaapIkSKaPHmy2rRpo0GDBkmSWrRo4d4mgWskJSXJbrdryZIlatGihbp3766jR49qzJgxeuGFF9SlSxdJUmRkpAoWLOjmbpHTJZ8RjI+Pl7e3t7y8vPTLL79oy5YtGjdunM6ePau4uDidOXNGO3bs0IYNG9zdMqDt27fr0Ucf1UsvvSRJqlKliipWrKjnn39e9evX12+//aa7776bM97ZBGe6ccdKPjr4xx9/aNWqVZKuvqDt379f999/vyIiItS1a1f98ccfeuGFFxQbG8sRRdxWSUlJkqRcuXLp8uXLzq8TExNVsWJFDRo0SHv37tWUKVP0888/u7NVQFLK6+rZs2clSZcvX1bVqlUVHR2thx56SI8//rg+++wzSdL333+v5cuXKzEx0W394s5gGIZ++eUXderUSXFxcWrSpIlKlCihRx55RA6HQz169NDq1av10UcfKT4+XufOnXN3y4AuXryo3bt3O782TVNly5ZV9+7ddfjwYVWtWlWHDx8mcGcThG7cka69kUrjxo31+++/6/jx4+rbt68+//xz/frrr5o7d67atGmjvHnz6tixY/Ly8lJCQoK7W0cOlnzjlGR2u12S1LBhQ61Zs0ZffvmlDMOQh8fVi5QMw9Djjz+uyMhITZ8+nYNCcDvDMPTNN9+oSpUqOnPmjAoWLKjhw4erQoUKat26tSZMmCDDMJyX+G7evDnVfg/8V19++aUOHjwoKeVA0E8//aS77rpL3t7eatiwoebPn69t27bpm2++UdOmTSVJS5YsUYECBZQrVy639Q4ka9Gihe666y4NGzZMcXFxznAdHBysJk2a6LHHHlN8fLybu0R6cXk57ijJYTv5vTKdOnXSqFGj1L59e/n4+EiS7rvvPufyR44c0YQJEzR37lz9/vvv8vLyclfryOFM03TeNG3WrFk6ceKEAgIC1LRpUzVo0ECDBw9Wt27ddOnSJTVu3FgFCxbU9OnT1bBhQ1WuXFl169bV1q1bVaNGDTdPgjtR8mtrTEyMli9frrffflv+/v7q06ePtm3bpjVr1qhfv37y8PBQXFycBg4cqJUrV2rVqlW8riJTxcTEaODAgRo3bpx++OEHlSxZUpIUFRUlPz8/53I+Pj6qWLGiJGnnzp2aPn26vv76a61evdr59wBwOyS/foaFhSkyMlJ2u12VKlVShQoV1KhRI/38889KSkpS3759FR8fr59//lne3t76+uuvlTt3bne3j3QidOOOcODAAZUuXdp50wnDMLRo0SI1a9bM+V4ZKeV9iJK0evVqTZgwQfv27dOqVauc/3MGMtu178d666239NVXX8nf31+maWrGjBmaNWuW3nvvPfn4+Kh///4aN26cJMnX11ddu3ZVeHi4QkJCVKBAAfcNgTuaYRhat26dXn/9dRUqVEhvvfWWJCl37tx66623dOLECZUrV0733nuvPD099eeff2rJkiUqW7asmztHTuPj46PNmzfriSeeUMuWLbVgwQIFBwcrMTHReZXQtf+v3759u2bOnKn169dr9erVqlSpkjvbxx3m2isvX3vtNfn6+mrfvn164okn1KNHDw0fPlzvvfee5syZo5EjR6pcuXLau3evfv/9dwJ3NkPoRo43btw4bdmyRZMnT5aPj48z3Bw/ftx5ZjH5o8CS/ye8d+9e1alTR0lJSSpbtqwCAwPd1j9ytms/hu7w4cM6cuSIVq5cqdKlS2vZsmUaM2aMnnzySS1atEg9e/ZU/fr1debMGcXHx6tRo0ay2WyaNm2acuXKReiG25imqcjISMXFxWn9+vXKmzev87HatWvrt99+05QpU3T+/HkVK1ZMkyZNUqlSpdzYMXKygIAA/fTTTwoNDVXz5s21fPlyJSUlyd/fX5KUkJAgwzBks9lUrFgxdezYUX379lXhwoXd3DnuNIZhaNOmTercubNGjhypJk2a6OjRo/rwww/14Ycfymazafjw4XrllVf0008/KV++fHrkkUdUunRpd7eODDJM3gSIHG716tUqWrSoypYt63Kn3O7du2vJkiXav3+/7Ha782jjhQsXNGzYMLVp00bVqlVzc/fIqdavX+/yOcUzZ87UyJEjVbRoUc2fP1/58uWTaZpaunSpPvzwQyUmJmrRokUqUqSI83v+/vtvjRgxQj/++KN+/fVXVa5c2R2j4A538uRJFS1aVHFxcVq5cqW6d++ugIAArV+/XpJczjACt9OpU6dUv3595cqVSw6HQ/v371eVKlV04sQJ5yeW+Pj4aMmSJcqTJ4+728Udaty4cZo7d67WrVvnPDH0559/6s0331TevHn13XffublDZAZupIYcr06dOipbtqw2bNigTp06admyZZKk9957Tw6HQ48//rji4+OdL3QjR47U/PnzFRAQ4M62kYONHDlSb7/9tkzTVFJSkpKSknThwgV5enrq77//dv7xZxiGQkND9d5778nb21u1atXShQsXJEnx8fGKioqSt7e3Vq9eTeCGW4SFhal48eKaNm2avL291aBBA02YMEERERFq0KCBJMnDw8PlZj8c64cVkvcrh8PhvOlpQECAfv31V+XJk0d//PGHPvjgA/Xt21eDBw/W+++/r3fffVeTJ08mcMMtkvdZm82my5cv69KlS5Ku7sP33Xef3n77bS1cuFB79uxxZ5vIJJzpxh3j119/Va9evXTPPffo1VdfVd26dbVy5Uq9+uqrio2NVfny5WUYhrZs2aIVK1aoatWq7m4ZOdSxY8cUEBAgDw8PhYWFqUyZMrpy5Yrmzp2rwYMHq0KFCpo9e7bzZj6maer777/XL7/8ovHjxzvfBmGaphISErgRFdwmMTFRffr00WeffaYpU6aoffv2io+P14oVK/TGG2+oRIkSWrp0qbvbRA6XfKXasmXLNH/+fO3du1dPPvmkqlWrpnr16un06dMKDQ2Vl5eXFi1apKJFi7q7ZcBp+fLlatSokWbMmKF27do563/++afatm2rRYsWcf+LHIDQjTvKihUr1L9/fxUpUkRvvvmmatWqpYsXL2r06NGKjo6Wr6+v2rVrpzJlyri7VdwBfv75ZzVt2lQLFy7Uk08+qStXrmj27NmaNGmSAgMDNWPGjDTPwFx7EyDgdrr2pn/J/52YmKgBAwZoxIgRmj59ujN4r1y5Uh07dtTDDz+sRYsWubdx5Hjff/+92rZtq06dOunSpUvau3evbDabXn/9dT333HM6c+aMGjdurDNnzmjt2rUqUaKEu1vGHSQ5bhmGof379+vs2bPy8PBQ1apV5eXlpffee09jxozR5MmTFRoaKl9fX33wwQf67rvvtG7dOu43kAPwJivkSMl/DO7YsUNHjhyRr6+vatWqpQYNGsjhcOiDDz7Q6NGjFRcXp8cee0wDBw50d8u4A4WEhKhz587q0qWLbDabmjVrpueee06SNHnyZHXs2FHTpk1L9fE1BG64i2EYWr58uby9vVW7dm2ZpikPDw/na2jHjh1lt9v17LPP6rHHHtPMmTO5YRosFxERoVGjRmnIkCF68803JV29K/mUKVM0fvx4lShRQrVq1dKPP/6otm3bKikpyc0d406UfJfyN954w3nwPFeuXFq8eLGGDh0qm82mLl26qGTJksqbN6+OHz+uZcuWEbhzChPIoebNm2f6+fmZxYsXN8uWLWu2bdvWjI2NNU3TNJctW2bWrFnTbN26tblkyRLn9zgcDne1ixwuMTExzfqBAwfMrl27mr6+vubixYtN0zTN2NhYc+rUqWZwcLD57rvv3s42AadrXw8TEhJM0zTNuLg4s0OHDqZhGObatWtdlrt8+bLZqlUrM1++fOaMGTNuf8O4Y50/f94sWbKk+fnnn7vUt23bZt53333mZ5995qzd6LUYyExJSUmmaZpmTEyMs7Z+/Xozb9685pQpU8w9e/aY69evNx9//HGzWLFi5oEDB0zTNM01a9aYM2bMMKdPn26Gh4e7o3VYhMvLkaOY/3+GOzIyUh06dFDr1q1Vt25dLVu2TJMmTVLBggW1ePFi5cqVS8uXL1fPnj1VrVo1TZo0ic87hCViYmJczlRPnTpVJ06ckN1uV9++fSVd/aiwDz/8UHPnztWMGTPUrFkzxcbG6tdff1VoaChntuE2p0+flqenp/z8/LRkyRLlypVLFSpU0Lvvvqs5c+Zo6dKlevTRR53Lv/POO/ryyy8lSQcOHJCvr6+7WkcOl/z/e9M0de7cObVs2VL169dXv379ZLPZnG+DaN68uWw2mxYsWOD8eEbASskfBbpt2za1bdtWK1euVIkSJTRp0iTNmzdPy5Ytc/5//eLFi2rRooXOnTunLVu2yNPT083dwyq8+iBHSf68w44dO8rT01MNGzZUUFCQOnTooHfeeUfnzp1T8+bNdeXKFT3++OOaMGGCBg0aROCGJTp37qxGjRo57zj+3nvvqWfPnlq9erU+/PBD1a5dW4cOHVLJkiX17rvvOt+POHfuXOXOnVtPPPGE7HY7l0Litko+Fh8REaEOHTqob9++mjZtmp544glFRUWpSJEiGjFihFq3bq3Q0FD9/vvvzu91OByaNGmS9u/fT+CGJZL3z+TXRcMwdNdddyk0NFTDhw/XDz/8IIfD4Vzew8NDZcuWJXDjtkgO3H/88Yfq1aunZs2aOe8fcOrUKf3555/OwJ2YmKh8+fKpT58+io6OVlhYmDtbh8V4TzdyFIfDoU2bNmnPnj26cuWK8zONPT091bx5c0nSqFGjVLt2ba1Zs0b16tVzZ7vI4V555RU1b95cHTp00Lhx47Rr1y6tXr1aFStW1JkzZ1SvXj0988wzmj17tkqXLq13331XkZGR+vLLL9WmTRvnmRzOdON2+uuvv1SxYkUVLFhQoaGhmjhxoqZOnaoJEyaoRYsWcjgcuuuuuzR69GjZ7XY1aNBArVu3VkxMjNasWaMXXnhBBQsWdPcYyIGSXxOXL1+ur776SnFxcfL399ewYcP03nvvKSIiQs8884x69Oihu+66S8ePH9eKFSs0ZMgQd7eOO0By4N61a5dq1qypnj17aujQoc7HGzVqpJ9//lkjR45Ur169nGe1CxUqJIfDocTERHe1jtuAw37IUWw2m55//nn17t1b8fHxat++vfOx5ODdo0cP+fj46MyZM27sFDldUlKSatSooSVLlmjDhg1q3769EhISVKxYMXl6eqp48eJat26doqKi1K5dOx08eFAlSpTQJ598oiVLlkiS8/JI4HaZPXu2OnXqpMjISNntdj322GOKjIxUYGCg9u7dq3Pnzslms8k0Td11112aMmWKRowY4fyc+VWrVvHRNrCMYRj6/vvv1bRpU/n4+Chfvnxavny5qlSpos2bN+ujjz7SoEGD9Oeff2rmzJkKCwvT77//rgoVKri7ddwBbDabjh49qvr166tp06YugXv8+PGaOnWqKlWqpKVLl2rMmDGSpEuXLmnhwoXKkyePAgIC3NU6bgPe041sLfmo9+nTp+Xh4aHExEQVKVJEsbGxmj59uiZNmqT7779fU6dOdX5PQkKCrly5onz58rmxc9wpTNPUrl271KZNG50+fVqbN29W2bJlnUfEz549qzp16ig2NlZr165V8eLFJaUcMQdup507d8rPz0933323IiIiVKBAAe3cuVNr167Vt99+q0qVKmnYsGHy8/Nz+eg6h8Mhh8MhDw8uoIM1HA6HoqKi1LBhQ7Vo0ULvvfeeJCk+Pl6NGzfW4cOH9eeffypPnjy6ePGiPDw85HA4Un36A2Clw4cPq02bNipatKjefvtt1apVS8OGDdPQoUO1du1alSxZUu+//75WrlypEydOqEKFCjpw4IB++eUXVa1a1d3tw0KEbmRbyYF70aJFGjBggK5cuaKLFy/q9ddf10svvaQ8efLoiy++0OTJk/XAAw9o8uTJ7m4Zd4BVq1YpJiZGTZs21euvv64iRYro3Xff1a5duxQaGqr7779fM2bMUMGCBV0OGvXs2VMzZ87kUnJkCbt27VLr1q01fPhwtWzZUklJSRo1apQWL16sKlWqaMiQIfLz89OECRNUsWJF1a1b190tIwdJPuhomqZM03QegDx79qxq1qyp0aNH68knn1RCQoI8PT0VGxurihUr6umnn9aIESM4aAm3CgsLU48ePeTl5aUiRYro+++/14wZM9SwYUNJV89unzp1SkuWLFFAQICqV6+u4OBgN3cNqxG6ka2tXLlSTzzxhIYPH66SJUvq0KFDGjBggP73v/9p+PDhstvt+uqrrzRy5Eg1a9ZM48ePd3fLyMHOnj2rjh076tKlS/L399cPP/ygzZs3q1KlSpKunkVs1KiRHnroIU2fPl0FCxZM9cfhtWcPAXfZtm2bRowYoX379mnAgAF66qmnlJSUpNGjR+vHH3+Ul5eXypcvr88++0y7d+/m8l1kmuTXxP3792v8+PE6fvy4atWqpTfeeEOSVK5cOdWrV08TJ06UdPXqNQ8PDz311FPy9/fXpEmT3Nk+IEnav3+/unfvrrVr12rw4MHO/Zf/x9+5OAyIbCn5rqVz585Vy5Yt1bNnT7Vo0UK9e/fWzJkzNX36dE2dOlU+Pj5q166d3n//ffXq1cvNXSOnK1y4sIYMGaLjx49rwYIFGjNmjDNwOxwOValSRcuWLdOmTZvUuXNn5/tjr8X/jOEO1x9/r1atmt566y1VrVpV/fr104IFC2S32/XWW2+pY8eOKlasmPbt26edO3cSuJFprr3z8yOPPKJjx47J29tbffv21fDhwyVJ3bt317p16zR27FhJV+/XYhiGbDabvL29nWfHAXcqW7asJk6cqEcffVQrV67U2rVrJV39fzz7552JM93IVpIvxz1x4oSKFSumFi1aKH/+/Pr666+VmJjovNPzkCFDNG3aNG3dutXlMl7AKsn72J49e/T66687z7706NFDzZo1k3T140E8PDz0xx9/qGrVqnrrrbc0YsQIN3eOO13yvrtp0yYdOnRISUlJzptQbt26VRMmTNCWLVs0ePBgPfXUU87vi42N5eMWkWmuvfPzQw89pF69emno0KFyOBx6/fXXZbfbNW7cOJ06dUrDhw/X2rVrVaFCBdWpU0dbtmzR7NmztWnTJpUvX97dowBOyZeam6apfv36qVatWu5uCW5C6Ea2M2fOHD333HOKiorSZ599puHDh2vz5s0qU6aMM9RMnTpV48eP1/r16/mjEJa60XsHN23apGHDhikqKkq9e/d2Bm/p6pUahw8fVsmSJTmzjSxhwYIFev7553X33Xfr+PHjqlatmn744Qf5+Pho27ZtGj9+vHbs2OH8PHnACkePHtX999+vevXqae7cuc76M888o7179yo2NlZVq1ZVpUqVVLhwYU2ZMkVJSUkqWLCgxowZo8qVK7uxeyBtYWFh6t27tyIiIvTRRx/poYcecndLcAMuL0e2kHxsKCIiQqtXr9ZHH32kfPnyqW3btqpZs6batWunsLAw551z//rrL+XLl4/PPISlrg3cS5cu1cyZMzVjxgzFxcXpwQcfVJ8+feTr66uPP/5YixYtkiQ1adJEEyZMUEhIiOx2u/OtEsDtlvy6GhcXpxkzZmjixIlavXq1fvnlFx07dkz169dXVFSUqlWrph49eqh06dIaN26cLl26xOWRsERSUpKCg4MVFxendevWSZKGDx+uH374QU8//bTeeust7dy5U3PmzFGtWrW0efNm/f777/rhhx8I3MiyypQpo1GjRikwMFDFihVzdztwE850I9vYsmWLevbsKUn64osvnJeQrVy5Uh999JHWrFmjunXrKi4uThs3btTq1atVpUoV9zWMO8abb76pOXPmKE+ePIqNjZXdbtesWbNUq1YtrV+/Xh9//LE2bNig/Pnz68qVK9qzZ488PT3d3TagNWvWaMCAASpSpIg+/PBDlSpVSpK0d+9ePfnkkypYsKCWLVsmX19f7dy5U/7+/vzRCEtde+dnf39/LV682OXOz//884+Cg4M1YcIEvfrqq27uFki/+Ph4eXl5ubsNuAlnupHlOBwOSalv7HP69GklJCRo165dLi9a9evX1+TJk/Xhhx+qaNGiqlatmjZt2kTgxm0xY8YMTZ8+XT/++KPWrl2r7du3q2LFimrVqpV2796tmjVr6v3339fYsWPVqVMn7d27V56enlyFAbdJfm39448/5HA4dPjwYf3888/OK4UcDofKlSunxYsXKyYmRg888ICioqJUpUoVAjcsV6ZMGX388ceKjY3VrFmz9Pbbb6thw4YyTdN5r4xKlSrJ39/f3a0CGULgvrNxphtZ0pEjRxQdHa2KFStq7ty5WrdunT7++GMtWLBA/fr1U758+bRo0SIFBARwkzTcNgsWLNBjjz2mAgUKOGuDBw/Wli1btHjxYpfLzevVq6fY2Fht3Lgx1Xr4yBC4208//aTOnTtr1qxZ8vLy0nPPPaeqVavqhx9+kJRyc7W///5bnTp10pw5c/gcWdxWBw8e1Kuvviq73a6+ffvq0UcflST1799fM2fO1OrVqxUUFOTmLgEgfTjTjSzFNE0lJSWpRYsWeu655zRmzBg9++yzzrPWTz31lAYOHCgvLy916NBBp0+flmEYSkhIcG/jyPF++uknPf300/r8888VHR3trJ85c0b79u2TJNlsNsXFxUmS3nrrLZ0+fVqHDh1KtS4CN9wh+Rj7sWPHNH/+fA0YMEANGjRQ7dq1NXPmTG3evFktWrSQJBmGIdM0VaFCBa1du5bAjdsuJCREEyZMkGmaGjp0qHbs2KGRI0dq1KhR+u677wjcALIVQjeylOSP/Nq+fbsiIiLUp08fDRw4UJ06dXIu8/TTT+u1115TbGysOnXqpJMnT/L+WFjuiSee0MSJE/Xuu+9qwoQJunDhgiSpY8eOSkhI0IABAyRJ3t7ekq5eRubt7U3ARpZhGIY2b96s3r176++//3a5g26dOnX07bffasOGDXr66aedy0vi9RVuU6ZMGX3yySfy9PRUaGio3n//fa1du1ZVq1Z1d2sAkCGEbmQ5iYmJio+PV3x8vHx9fbVkyRL98ccfLu/xbt26tXr06KEjR46oe/fu3AEaltq+fbsWLVqkxo0ba/r06Xr//fc1ceJEXbp0SeXKlVO7du20bNkyvfHGG4qMjNS+ffs0btw43X333ZyNQZYSFRWlvXv3aseOHfrzzz+ddcMwVKdOHc2dO1eLFi1yfk434G5lypTR6NGj9dBDD2nHjh2qVq2au1sCgAzjPd3IMpLfQ/jXX3+pePHiKlCggJKSklS6dGkVKVJEkyZNUqVKlVzev71ixQqVLl1aJUuWdF/jyNFmzZql0aNHq3jx4qpUqZI+/PBDffzxx+rVq5cGDx6sd999V+fPn9eUKVM0efJknT59WkFBQfL19dXatWvl6el5w8/yBm6Xv/76S6VKlVLu3Lm1fv16vfHGG8qdO7feffddNWjQwLmcaZpat26d/P39VbZsWTd2DLhKSEjgqgsA2RahG1lCcuBesGCB3n77bTVv3lxvvPGGihcvrsjISFWrVk0BAQH67LPPVKVKFQ0ePFiRkZEaO3asu1tHDvb111/r5Zdf1tSpUxUaGupyA7VPPvlEPXv21ODBg/XOO+/IMAzFxcXp119/VeHChVWtWjXZ7XYlJiY67woNuMM///yjp59+WmXKlNHUqVOVK1curVq1Sv369VPhwoX12muv6bHHHnN3mwAA5FiEbmQZy5cvV/PmzfXJJ5/oySeflL+/v/MuzxcuXNADDzwgu92uoKAgbdiwQatWrVL16tXd3TZyqL/++ktt27ZVz5499cILLzjr14bo5OA9ZMgQvfLKKypYsKDLOrhLObKC2NhYjRs3Tj/++KPKlSunTz/9VLly5dLKlSs1YMAAFS1aVF26dFFoaKi7WwUAIEfieke4XfJnb86bN09du3ZV165ddddddzkfdzgcKlCggLZs2aInn3xSVatW1aZNmwjcsNTx48d1+fJl1a5d2+V+Ah4eHnI4HDJNUz169NBnn32m999/X6NGjVJMTIzLOgjcuF0cDockpdoHJSl37tzq1auX87Pju3XrpitXrqh+/foaPHiw9uzZo1mzZuny5cu3u20AAO4InOlGlvHoo4+qXLlymjJliiS5fP720aNHFRQUJNM0ZZom74+F5YYNG6axY8fq7NmzkpTm58H//fff8vHx0U8//aRZs2Zp7dq1fGY8brvkewZs27ZNbdq00fbt27Vnzx799NNPGjx4sHO5K1euaOLEiZo+fbpq1qypcePGydvbW2vWrNHdd9/NvTEAALAIyQVZwuXLlxUUFKTz588rOjpaDofD+TmxR48e1YgRI3TgwAEZhkHgxm1RunRpxcTE6JdffpGkNMP09OnTNXToUL366qvOwM1xTNxOyYH7jz/+UL169dS0aVP5+vpq3rx5+u677/TBBx84l82VK5d69OihKlWqaNasWercubOuXLmi2rVrE7gBALAQ6QW3XXIoiYiI0Pnz53Xp0iXlyZNHXbp00aJFi/Txxx8rOjpa0tWgM3nyZK1du1Y+Pj7ubBt3mGrVqsnLy0uTJ0/WkSNHnPXk/Tc6OlqHDh3Svffe6/IYZ7pxuyQH7l27dqlmzZp67bXX9PHHH0uSRo4cqYYNG2rdunXq37+/8/Jzu92umjVrqmTJkoqJidH58+fdOQIAAHcEbqmL2yo5lCxevFhDhgxRXFycLly4oD59+qhjx4766quv1LFjR23ZskVeXl7y8PDQkiVL9Ntvv6lo0aLubh93kFKlSunzzz9Xx44d5e3trTfffFNVq1aVYRg6ceKEXnjhBUVHR6tbt26S0j4TDljJZrPp6NGjql+/vpo2baqhQ4c6H/v6668VHR2tihUrOq/WGDRokCTpyJEjatu2rV599dVUN/8DAACZj9CN28owDC1btkzPPvushgwZotatW2vUqFHq0aOHypYtq/bt2yswMFA//vijDhw4oBIlSmjjxo0qX768u1vHHah169a6dOmSXn31Va1Zs0YVK1aUw+FQVFSUHA6H1q1bJw8PD+5SDrdJSkpScHCwrly5onXr1qlWrVoaNmyYhg4dqjVr1qhs2bIaOnSofvzxR82dO1cVK1bUkiVLtGPHDgI3AAC3CTdSg6ViYmKcl4UnX97YoUMHFSlSRKNHj9axY8dUv3591a1bV5MmTXJ+X/IZccIMsoKdO3dq6tSp2rdvn4KCglS1alW9/PLLfA43soSwsDD16NFDXl5eKlKkiL7//nvNmDFDDRs2lCRdvHhRa9eu1cKFC+Xp6alu3bqpQoUKbu4aAIA7B6Eblhk2bJj++usvjR49WgEBAZKufsbxY489pnfffVePPvqoypYtq6ZNmzoD99dff60aNWpwZhvZAgeFkFXs379f3bt319q1azV48GC98cYbklLvoxwkAgDg9uNGash0yWe0K1eurNmzZ2vQoEE6deqUpKufcVy6dGmNHj1aFSpUUMuWLTV+/HhJUmxsrBYuXKgffvjBuQ4gq0jr+CSBG1lF2bJlNXHiRD366KNauXKl1q5dK+nqPpr8UYuSCNwAALgBoRuZKvluunv27NE999yj1atXa9KkSRo4cKCOHz8uSWrbtq1Onz6tfPnyady4cfLy8pIkDRkyRH/88YdatWrFx4Ihy+FGacjqQkJCNGHCBJmmqSFDhmjdunWSru677L8AALgPyQaZJjlw79y5U9WrV9fPP/+sRx99VD/99JMmT56sQYMGKTIyUvXr11f79u1ls9lUs2ZNdevWTU899ZQ+//xzfffddwoJCXH3KACQLZUpU0affPKJPD099eabb2rjxo3ubgkAgDseoRuZIjlw//HHH6pVq5Z69Oih1157TaZpKjQ0VL/88ou++OIL9enTR1euXFGvXr00atQoVaxYUSdOnFCZMmW0fv16Va1a1d2jAEC2VqZMGY0aNUqBgYEqVqyYu9sBAOCOx43U8J8lB+5du3bp4YcfVs+ePV0+L3bJkiV67LHHtGbNGoWGhqpLly4aMWIEH1cDABaKj493vn0HAAC4D2e68Z/ZbDYdPXpU9evXV9OmTV0C95AhQ9S1a1cdOHBAjz/+uH7++Wd9+eWXev/993X06FHnchz7AYDMReAGACBrIHQjUyQlJSk4OFhXrlxx3rxn+PDh+vjjj/XFF1/o3nvvVVJSkho1aqSff/5ZEydO1JgxY5SUlCSJm1QBAAAAyJm4vByZJiwsTD169JCXl5eKFCmiRYsWaebMmWrYsKGkq2ezDcPQ5cuXdeDAAXl6evJ53AAAAAByNM50I9OUKVNGH3/8sWJjYzVz5kz16dNHDRs2dH5GrGEYev/991W6dGmFhIQQuAEAAADkeIRuZKqyZctq4sSJevTRR7Vy5Ur9/vvvzs+I7d+/v8aMGaPFixfLx8fH3a0CAAAAgOW4vByWSL7U3DRNDRs2TMuXL9eAAQO0du1aVatWzd3tAQAAAMBtQeiGZcLCwtS7d29t3rxZkZGR2rBhA4EbAAAAwB2Fy8thmTJlymj06NF66KGHtGPHDgI3AAAAgDsOZ7phuYSEBHl6erq7DQAAAAC47QjdAAAAAABYhMvLAQAAAACwCKEbAAAAAACLELoBAAAAALAIoRsAAAAAAIsQugEAAAAAsAihGwAAAAAAixC6AQAAAACwCKEbAIBs5NSpU3rttddUqlQpeXt7KygoSM2aNdPKlSvT9f3Tp09XgQIFrG0SAAA4ebi7AQAAkD6HDx9WrVq1VKBAAY0aNUr33XefEhIStGzZMnXr1k179+51d4sZlpCQIE9PT3e3AQCAZTjTDQBANvHqq6/KMAz9Xzv3EhLlGsdx/PsmSDjWQohC0cLGkVKRIMZUiMKGoiKUMIPS8hLZDS0taOcik2Iay7DRgiaRqEXZIksXWYZZMWJIFhbTDQOFsjAYBZOps4gzMKdzynNyMA+/D7yL97n8532e3X+ei9vtZuPGjVgsFhISEjhw4AAPHz4EwOFwkJSUhMlkIjo6mt27d+P1egFob28nPz+fT58+YRgGhmFQUVEBwNjYGOXl5URFRWEymUhJSaG9vT3g98+dO0d0dDRhYWFkZWXhcDi+WzV3Op0sXLiQ0NBQ4uPjaWxsDKg3DAOn08mGDRswmUwcOXIEs9mM3W4PaNfT04NhGLx48WLyJlBERGQKKOkWERGZBj5+/Ehrayt79uzBZDJ9V/9n8jtjxgxqamp4+vQpDQ0N3L59m0OHDgGQlpbGyZMnmT17NoODgwwODlJeXg7A3r17efDgAZcvX+bx48dkZ2ezZs0aPB4PAJ2dnRQXF1NSUkJPTw82m43KysqAb7h27RolJSWUlZXx5MkTdu7cSX5+Pnfu3AloV1FRQVZWFr29vRQWFlJQUIDL5Qpo43K5WL58OWazeVLmT0REZKoYX79+/TrVHyEiIiI/5na7SUlJoampiaysrAn3u3LlCsXFxQwNDQHfznSXlpYyPDzsb9Pf309sbCz9/f1ERkb6y1etWoXVauXo0aNs3rwZr9dLc3Ozv37r1q00Nzf7Y6Wnp5OQkMDZs2f9bTZt2sTIyAg3btwAvq10l5aWUl1d7W8zMDBATEwM9+/fx2q1Mj4+TmRkJHa7nW3btv2reRIREfndaKVbRERkGpjof+S3bt0iIyODqKgoZs2aRW5uLh8+fGB0dPQf+/T29uLz+bBYLISHh/ufu3fv8vLlSwCeP3+O1WoN6PfX976+PtLT0wPK0tPT6evrCyhbunRpwHtkZCTr1q3j/PnzAFy/fp2xsTGys7MnNGYREZHfmS5SExERmQbi4uIwDOOHl6W9efOG9evXs2vXLiorK4mIiODevXsUFhby+fNnwsLC/raf1+slJCSE7u5uQkJCAurCw8MndRzA326PLyoqIjc3l+rqalwuFzk5Of/4vSIiItOJVrpFRESmgYiICFavXk1tbS0jIyPf1Q8PD9Pd3c2XL184ceIEy5Ytw2KxMDAwENAuNDQUn88XULZkyRJ8Ph/v3r3DbDYHPPPmzQMgPj6erq6ugH5/fV+0aBGdnZ0BZZ2dnSxevPin41u7di0mkwmn00lraysFBQU/7SMiIjIdKOkWERGZJmpra/H5fFitVq5evYrH46Gvr4+amhpSU1Mxm82Mj49z+vRpXr16RWNjI3V1dQExFixYgNfrpa2tjaGhIUZHR7FYLGzZsoW8vDyampp4/fo1brebqqoq/1nsffv2cfPmTRwOBx6Ph/r6elpaWjAMwx/74MGDXLhwAafTicfjweFw0NTU5L+s7UdCQkLYvn07hw8fJi4ujtTU1MmdPBERkSmipFtERGSaiI2N5dGjR6xcuZKysjISExOx2Wy0tbXhdDpJTk7G4XBw7NgxEhMTuXjxIlVVVQEx0tLSKC4uJicnhzlz5nD8+HHg223heXl5lJWVER8fT2ZmJl1dXcTExADfzmbX1dXhcDhITk6mtbWV/fv3M3PmTH/szMxMTp06hd1uJyEhgfr6elwuFytWrJjQ+P7cBp+fnz85EyYiIvIb0O3lIiIi8p/s2LGDZ8+e0dHRMSnxOjo6yMjI4O3bt8ydO3dSYoqIiEw1XaQmIiIiE2K327HZbJhMJlpaWmhoaODMmTO/HHdsbIz3799TUVFBdna2Em4REflf0fZyERERmRC3243NZiMpKYm6ujpqamooKir65biXLl1i/vz5DA8P+7e7i4iI/F9oe7mIiIiIiIhIkGilW0RERERERCRIlHSLiIiIiIiIBImSbhEREREREZEgUdItIiIiIiIiEiRKukVERERERESCREm3iIiIiIiISJAo6RYREREREREJEiXdIiIiIiIiIkGipFtEREREREQkSP4AX+/pBPpCUKoAAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Visualize the revenue by category from SQL results\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.bar(revenue_by_category['category'], revenue_by_category['total_revenue'])\n",
+        "plt.title('Total Revenue by Category')\n",
+        "plt.xlabel('Category')\n",
+        "plt.ylabel('Total Revenue ($)')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 5. Filtering on Aggregations\n",
+        "\n",
+        "Now let's filter our data based on aggregation results using SQL HAVING clause."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Categories with high average price (> $25):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>category</th>\n",
+              "      <th>average_price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Furniture</td>\n",
+              "      <td>50.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>35.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>25.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      category  average_price\n",
+              "0    Furniture           50.0\n",
+              "1     Clothing           35.0\n",
+              "2  Electronics           25.5"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Find categories with average price > 25\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    category,\n",
+        "    AVG(price) AS average_price\n",
+        "FROM sales_df\n",
+        "GROUP BY category\n",
+        "HAVING AVG(price) > 25\n",
+        "ORDER BY average_price DESC\n",
+        "\"\"\"\n",
+        "high_avg_price_categories = pysqldf(q)\n",
+        "print(\"Categories with high average price (> $25):\")\n",
+        "high_avg_price_categories"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sales in high-priced categories:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>10</td>\n",
+              "      <td>107</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2023-01-22</td>\n",
+              "      <td>Clothing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.5</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>8</td>\n",
+              "      <td>106</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Furniture</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date     category\n",
+              "0         2          102           2         1   35.0  2023-01-16     Clothing\n",
+              "1         5          104           2         1   35.0  2023-01-18     Clothing\n",
+              "2        10          107           2         1   35.0  2023-01-22     Clothing\n",
+              "3         1          101           1         2   25.5  2023-01-15  Electronics\n",
+              "4         4          103           1         2   25.5  2023-01-17  Electronics\n",
+              "5         9          102           1         3   25.5  2023-01-21  Electronics\n",
+              "6         8          106           5         2   50.0  2023-01-20    Furniture"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Get all sales from categories with high average price\n",
+        "q = \"\"\"\n",
+        "SELECT s.*\n",
+        "FROM sales_df s\n",
+        "JOIN (\n",
+        "    SELECT category\n",
+        "    FROM sales_df\n",
+        "    GROUP BY category\n",
+        "    HAVING AVG(price) > 25\n",
+        ") c ON s.category = c.category\n",
+        "ORDER BY s.category, s.price DESC\n",
+        "\"\"\"\n",
+        "sales_in_high_categories = pysqldf(q)\n",
+        "print(\"Sales in high-priced categories:\")\n",
+        "sales_in_high_categories"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Items priced above their category average:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Empty DataFrame\n",
+              "Columns: [order_id, customer_id, product_id, quantity, price, order_date, category]\n",
+              "Index: []"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Find items priced higher than their category average\n",
+        "q = \"\"\"\n",
+        "SELECT s.*\n",
+        "FROM sales_df s\n",
+        "JOIN (\n",
+        "    SELECT \n",
+        "        category, \n",
+        "        AVG(price) AS avg_category_price\n",
+        "    FROM sales_df\n",
+        "    GROUP BY category\n",
+        ") c ON s.category = c.category\n",
+        "WHERE s.price > c.avg_category_price\n",
+        "ORDER BY s.category, s.price DESC\n",
+        "\"\"\"\n",
+        "above_avg_price_items = pysqldf(q)\n",
+        "print(\"Items priced above their category average:\")\n",
+        "above_avg_price_items"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Popular categories (>2 orders and >3 quantity):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>category</th>\n",
+              "      <th>order_count</th>\n",
+              "      <th>total_quantity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      category  order_count  total_quantity\n",
+              "0  Electronics            3               7"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Categories with more than 2 orders and total quantity > 3\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    category,\n",
+        "    COUNT(*) AS order_count,\n",
+        "    SUM(quantity) AS total_quantity\n",
+        "FROM sales_df\n",
+        "GROUP BY category\n",
+        "HAVING COUNT(*) > 2 AND SUM(quantity) > 3\n",
+        "ORDER BY total_quantity DESC\n",
+        "\"\"\"\n",
+        "popular_categories = pysqldf(q)\n",
+        "print(\"Popular categories (>2 orders and >3 quantity):\")\n",
+        "popular_categories"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 6. Left Outer Join\n",
+        "\n",
+        "A left outer join returns all records from the left table and the matched records from the right table. If there is no match, NULL values are returned for the right table columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Left join (all sales, matching customers):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "      <th>name</th>\n",
+              "      <th>city</th>\n",
+              "      <th>membership_level</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>David Wilson</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>105</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-19</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>Emma Johnson</td>\n",
+              "      <td>Phoenix</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7</td>\n",
+              "      <td>103</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Food</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>8</td>\n",
+              "      <td>106</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2</td>\n",
+              "      <td>50.00</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Furniture</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>10</td>\n",
+              "      <td>107</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-22</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date  \\\n",
+              "0         1          101           1         2  25.50  2023-01-15   \n",
+              "1         2          102           2         1  35.00  2023-01-16   \n",
+              "2         3          101           3         3  15.75  2023-01-16   \n",
+              "3         4          103           1         2  25.50  2023-01-17   \n",
+              "4         5          104           2         1  35.00  2023-01-18   \n",
+              "5         6          105           3         1  15.75  2023-01-19   \n",
+              "6         7          103           4         4  10.25  2023-01-20   \n",
+              "7         8          106           5         2  50.00  2023-01-20   \n",
+              "8         9          102           1         3  25.50  2023-01-21   \n",
+              "9        10          107           2         1  35.00  2023-01-22   \n",
+              "\n",
+              "      category           name         city membership_level  \n",
+              "0  Electronics    Alice Brown     New York             Gold  \n",
+              "1     Clothing      Bob Smith  Los Angeles           Silver  \n",
+              "2        Books    Alice Brown     New York             Gold  \n",
+              "3  Electronics  Charlie Davis      Chicago             Gold  \n",
+              "4     Clothing   David Wilson      Houston           Bronze  \n",
+              "5        Books   Emma Johnson      Phoenix           Silver  \n",
+              "6         Food  Charlie Davis      Chicago             Gold  \n",
+              "7    Furniture           None         None             None  \n",
+              "8  Electronics      Bob Smith  Los Angeles           Silver  \n",
+              "9     Clothing           None         None             None  "
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Left join sales with customers using pandasql\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    s.order_id,\n",
+        "    s.customer_id,\n",
+        "    s.product_id,\n",
+        "    s.quantity,\n",
+        "    s.price,\n",
+        "    s.order_date,\n",
+        "    s.category,\n",
+        "    c.name,\n",
+        "    c.city,\n",
+        "    c.membership_level\n",
+        "FROM sales_df s\n",
+        "LEFT JOIN customers_df c ON s.customer_id = c.customer_id\n",
+        "ORDER BY s.order_id\n",
+        "\"\"\"\n",
+        "left_join = pysqldf(q)\n",
+        "print(\"Left join (all sales, matching customers):\")\n",
+        "left_join"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this left join, all sales records are included regardless of whether there's a matching customer. Notice that some rows have NULL values for customer information because customers 106 and 107 are not in the customers table."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 7. Right Outer Join\n",
+        "\n",
+        "A right outer join returns all records from the right table and the matched records from the left table. If there is no match, NULL values are returned for the left table columns.\n",
+        "\n",
+        "Note: SQLite (which pandasql uses) doesn't directly support RIGHT JOIN, but we can simulate it by flipping the tables in a LEFT JOIN."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Right join (all customers, matching sales):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "      <th>name</th>\n",
+              "      <th>city</th>\n",
+              "      <th>membership_level</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3.0</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4.0</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>7.0</td>\n",
+              "      <td>103</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Food</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>David Wilson</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>6.0</td>\n",
+              "      <td>105</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-19</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>Emma Johnson</td>\n",
+              "      <td>Phoenix</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>108</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>Frank Miller</td>\n",
+              "      <td>Philadelphia</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>109</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>None</td>\n",
+              "      <td>None</td>\n",
+              "      <td>Grace Lee</td>\n",
+              "      <td>San Antonio</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date  \\\n",
+              "0       1.0          101         1.0       2.0  25.50  2023-01-15   \n",
+              "1       3.0          101         3.0       3.0  15.75  2023-01-16   \n",
+              "2       2.0          102         2.0       1.0  35.00  2023-01-16   \n",
+              "3       9.0          102         1.0       3.0  25.50  2023-01-21   \n",
+              "4       4.0          103         1.0       2.0  25.50  2023-01-17   \n",
+              "5       7.0          103         4.0       4.0  10.25  2023-01-20   \n",
+              "6       5.0          104         2.0       1.0  35.00  2023-01-18   \n",
+              "7       6.0          105         3.0       1.0  15.75  2023-01-19   \n",
+              "8       NaN          108         NaN       NaN    NaN        None   \n",
+              "9       NaN          109         NaN       NaN    NaN        None   \n",
+              "\n",
+              "      category           name          city membership_level  \n",
+              "0  Electronics    Alice Brown      New York             Gold  \n",
+              "1        Books    Alice Brown      New York             Gold  \n",
+              "2     Clothing      Bob Smith   Los Angeles           Silver  \n",
+              "3  Electronics      Bob Smith   Los Angeles           Silver  \n",
+              "4  Electronics  Charlie Davis       Chicago             Gold  \n",
+              "5         Food  Charlie Davis       Chicago             Gold  \n",
+              "6     Clothing   David Wilson       Houston           Bronze  \n",
+              "7        Books   Emma Johnson       Phoenix           Silver  \n",
+              "8         None   Frank Miller  Philadelphia             Gold  \n",
+              "9         None      Grace Lee   San Antonio           Bronze  "
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Right join sales with customers using pandasql\n",
+        "# (Implemented as a LEFT JOIN with tables flipped)\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    s.order_id,\n",
+        "    c.customer_id,\n",
+        "    s.product_id,\n",
+        "    s.quantity,\n",
+        "    s.price,\n",
+        "    s.order_date,\n",
+        "    s.category,\n",
+        "    c.name,\n",
+        "    c.city,\n",
+        "    c.membership_level\n",
+        "FROM customers_df c\n",
+        "LEFT JOIN sales_df s ON c.customer_id = s.customer_id\n",
+        "ORDER BY c.customer_id, s.order_id\n",
+        "\"\"\"\n",
+        "right_join = pysqldf(q)\n",
+        "print(\"Right join (all customers, matching sales):\")\n",
+        "right_join"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this right join, all customer records are included regardless of whether they have any sales. Notice that customers 108 and 109 appear in the results with NULL values for sales information because they haven't made any purchases."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 8. Inner Join\n",
+        "\n",
+        "An inner join returns only the records that have matching values in both tables."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Inner join (only matching records):\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>order_id</th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>quantity</th>\n",
+              "      <th>price</th>\n",
+              "      <th>order_date</th>\n",
+              "      <th>category</th>\n",
+              "      <th>name</th>\n",
+              "      <th>city</th>\n",
+              "      <th>membership_level</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>101</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-15</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>102</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>101</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-16</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-17</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>104</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>2023-01-18</td>\n",
+              "      <td>Clothing</td>\n",
+              "      <td>David Wilson</td>\n",
+              "      <td>Houston</td>\n",
+              "      <td>Bronze</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>6</td>\n",
+              "      <td>105</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.75</td>\n",
+              "      <td>2023-01-19</td>\n",
+              "      <td>Books</td>\n",
+              "      <td>Emma Johnson</td>\n",
+              "      <td>Phoenix</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7</td>\n",
+              "      <td>103</td>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>10.25</td>\n",
+              "      <td>2023-01-20</td>\n",
+              "      <td>Food</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>Chicago</td>\n",
+              "      <td>Gold</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>9</td>\n",
+              "      <td>102</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>25.50</td>\n",
+              "      <td>2023-01-21</td>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Silver</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   order_id  customer_id  product_id  quantity  price  order_date  \\\n",
+              "0         1          101           1         2  25.50  2023-01-15   \n",
+              "1         2          102           2         1  35.00  2023-01-16   \n",
+              "2         3          101           3         3  15.75  2023-01-16   \n",
+              "3         4          103           1         2  25.50  2023-01-17   \n",
+              "4         5          104           2         1  35.00  2023-01-18   \n",
+              "5         6          105           3         1  15.75  2023-01-19   \n",
+              "6         7          103           4         4  10.25  2023-01-20   \n",
+              "7         9          102           1         3  25.50  2023-01-21   \n",
+              "\n",
+              "      category           name         city membership_level  \n",
+              "0  Electronics    Alice Brown     New York             Gold  \n",
+              "1     Clothing      Bob Smith  Los Angeles           Silver  \n",
+              "2        Books    Alice Brown     New York             Gold  \n",
+              "3  Electronics  Charlie Davis      Chicago             Gold  \n",
+              "4     Clothing   David Wilson      Houston           Bronze  \n",
+              "5        Books   Emma Johnson      Phoenix           Silver  \n",
+              "6         Food  Charlie Davis      Chicago             Gold  \n",
+              "7  Electronics      Bob Smith  Los Angeles           Silver  "
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Inner join sales with customers using pandasql\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    s.order_id,\n",
+        "    s.customer_id,\n",
+        "    s.product_id,\n",
+        "    s.quantity,\n",
+        "    s.price,\n",
+        "    s.order_date,\n",
+        "    s.category,\n",
+        "    c.name,\n",
+        "    c.city,\n",
+        "    c.membership_level\n",
+        "FROM sales_df s\n",
+        "INNER JOIN customers_df c ON s.customer_id = c.customer_id\n",
+        "ORDER BY s.order_id\n",
+        "\"\"\"\n",
+        "inner_join = pysqldf(q)\n",
+        "print(\"Inner join (only matching records):\")\n",
+        "inner_join"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this inner join, only records with matching customer IDs in both tables are included. The orders from customers 106 and 107 are excluded because those customers aren't in the customers table, and customers 108 and 109 are excluded because they don't have any orders."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Bonus: Advanced Analysis with Joins and Aggregations\n",
+        "\n",
+        "Let's combine what we've learned to perform a more complex analysis that uses joins and aggregations together."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Spending analysis by membership level:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>membership_level</th>\n",
+              "      <th>customer_count</th>\n",
+              "      <th>order_count</th>\n",
+              "      <th>total_spent</th>\n",
+              "      <th>avg_order_value</th>\n",
+              "      <th>avg_spent_per_customer</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Gold</td>\n",
+              "      <td>2</td>\n",
+              "      <td>4</td>\n",
+              "      <td>190.25</td>\n",
+              "      <td>47.562500</td>\n",
+              "      <td>95.125</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Silver</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>127.25</td>\n",
+              "      <td>42.416667</td>\n",
+              "      <td>63.625</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Bronze</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "      <td>35.000000</td>\n",
+              "      <td>35.000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  membership_level  customer_count  order_count  total_spent  avg_order_value  \\\n",
+              "0             Gold               2            4       190.25        47.562500   \n",
+              "1           Silver               2            3       127.25        42.416667   \n",
+              "2           Bronze               1            1        35.00        35.000000   \n",
+              "\n",
+              "   avg_spent_per_customer  \n",
+              "0                  95.125  \n",
+              "1                  63.625  \n",
+              "2                  35.000  "
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Find the total spending by membership level using pandasql\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    c.membership_level,\n",
+        "    COUNT(DISTINCT s.customer_id) AS customer_count,\n",
+        "    COUNT(s.order_id) AS order_count,\n",
+        "    SUM(s.price * s.quantity) AS total_spent,\n",
+        "    AVG(s.price * s.quantity) AS avg_order_value,\n",
+        "    SUM(s.price * s.quantity) / COUNT(DISTINCT s.customer_id) AS avg_spent_per_customer\n",
+        "FROM sales_df s\n",
+        "JOIN customers_df c ON s.customer_id = c.customer_id\n",
+        "GROUP BY c.membership_level\n",
+        "ORDER BY total_spent DESC\n",
+        "\"\"\"\n",
+        "membership_analysis = pysqldf(q)\n",
+        "print(\"Spending analysis by membership level:\")\n",
+        "membership_analysis"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Visualize the total spending by membership level\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.bar(membership_analysis['membership_level'], membership_analysis['total_spent'])\n",
+        "plt.title('Total Spending by Membership Level')\n",
+        "plt.xlabel('Membership Level')\n",
+        "plt.ylabel('Total Spent ($)')\n",
+        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+        "\n",
+        "# Add value labels on top of the bars\n",
+        "for i, v in enumerate(membership_analysis['total_spent']):\n",
+        "    plt.text(i, v + 5, f'${v:.2f}', ha='center')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Top customers by total spending:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>customer_id</th>\n",
+              "      <th>name</th>\n",
+              "      <th>membership_level</th>\n",
+              "      <th>order_count</th>\n",
+              "      <th>total_spent</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>102</td>\n",
+              "      <td>Bob Smith</td>\n",
+              "      <td>Silver</td>\n",
+              "      <td>2</td>\n",
+              "      <td>111.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>101</td>\n",
+              "      <td>Alice Brown</td>\n",
+              "      <td>Gold</td>\n",
+              "      <td>2</td>\n",
+              "      <td>98.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>103</td>\n",
+              "      <td>Charlie Davis</td>\n",
+              "      <td>Gold</td>\n",
+              "      <td>2</td>\n",
+              "      <td>92.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>104</td>\n",
+              "      <td>David Wilson</td>\n",
+              "      <td>Bronze</td>\n",
+              "      <td>1</td>\n",
+              "      <td>35.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>105</td>\n",
+              "      <td>Emma Johnson</td>\n",
+              "      <td>Silver</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15.75</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   customer_id           name membership_level  order_count  total_spent\n",
+              "0          102      Bob Smith           Silver            2       111.50\n",
+              "1          101    Alice Brown             Gold            2        98.25\n",
+              "2          103  Charlie Davis             Gold            2        92.00\n",
+              "3          104   David Wilson           Bronze            1        35.00\n",
+              "4          105   Emma Johnson           Silver            1        15.75"
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Find top customers by total spending\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    s.customer_id,\n",
+        "    c.name,\n",
+        "    c.membership_level,\n",
+        "    COUNT(s.order_id) AS order_count,\n",
+        "    SUM(s.price * s.quantity) AS total_spent\n",
+        "FROM sales_df s\n",
+        "JOIN customers_df c ON s.customer_id = c.customer_id\n",
+        "GROUP BY s.customer_id, c.name, c.membership_level\n",
+        "ORDER BY total_spent DESC\n",
+        "\"\"\"\n",
+        "top_customers = pysqldf(q)\n",
+        "print(\"Top customers by total spending:\")\n",
+        "top_customers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Categories popular with Gold members:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>category</th>\n",
+              "      <th>order_count</th>\n",
+              "      <th>total_quantity</th>\n",
+              "      <th>total_spent</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Electronics</td>\n",
+              "      <td>2</td>\n",
+              "      <td>4</td>\n",
+              "      <td>102.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Books</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>47.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Food</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>41.00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      category  order_count  total_quantity  total_spent\n",
+              "0  Electronics            2               4       102.00\n",
+              "1        Books            1               3        47.25\n",
+              "2         Food            1               4        41.00"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Find categories most popular with Gold members\n",
+        "q = \"\"\"\n",
+        "SELECT \n",
+        "    s.category,\n",
+        "    COUNT(s.order_id) AS order_count,\n",
+        "    SUM(s.quantity) AS total_quantity,\n",
+        "    SUM(s.price * s.quantity) AS total_spent\n",
+        "FROM sales_df s\n",
+        "JOIN customers_df c ON s.customer_id = c.customer_id\n",
+        "WHERE c.membership_level = 'Gold'\n",
+        "GROUP BY s.category\n",
+        "ORDER BY total_spent DESC\n",
+        "\"\"\"\n",
+        "gold_member_categories = pysqldf(q)\n",
+        "print(\"Categories popular with Gold members:\")\n",
+        "gold_member_categories"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Conclusion\n",
+        "\n",
+        "In this notebook, we've demonstrated how to use pandasql to perform SQL operations directly on pandas DataFrames. We've covered:\n",
+        "\n",
+        "1. Reading CSV data into pandas\n",
+        "2. Selecting columns with SQL\n",
+        "3. Filtering by value in column using SQL\n",
+        "4. Performing aggregations with SQL\n",
+        "5. Filtering on aggregations with SQL\n",
+        "6. Left outer joins with SQL\n",
+        "7. Right outer joins with SQL\n",
+        "8. Inner joins with SQL\n",
+        "\n",
+        "Pandasql provides a powerful way to use SQL syntax for data manipulation tasks directly on pandas DataFrames. This can be particularly useful for those who are familiar with SQL and want to leverage that knowledge while working with pandas."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "week1_env",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
diff --git a/Week1/Day_2/bigram_frequencies.png b/Week1/Day_2/bigram_frequencies.png
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diff --git a/Week1/Day_2/embedding-features-viz.html b/Week1/Day_2/embedding-features-viz.html
new file mode 100644
index 00000000..74953195
--- /dev/null
+++ b/Week1/Day_2/embedding-features-viz.html
@@ -0,0 +1,1980 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Understanding Embedding Vectors</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
+            background: linear-gradient(135deg, #141e30 0%, #243b55 50%, #428e92 100%);
+            min-height: 100vh;
+            color: #333;
+            overflow-x: hidden;
+        }
+
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            padding: 40px 20px;
+        }
+
+        header {
+            text-align: center;
+            color: white;
+            margin-bottom: 60px;
+            animation: fadeInDown 1s ease-out;
+        }
+
+        h1 {
+            font-size: 3.5em;
+            margin-bottom: 20px;
+            text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+        }
+
+        .subtitle {
+            font-size: 1.4em;
+            opacity: 0.9;
+        }
+
+        .section {
+            background: rgba(255, 255, 255, 0.95);
+            border-radius: 20px;
+            padding: 40px;
+            margin-bottom: 40px;
+            box-shadow: 0 20px 40px rgba(0,0,0,0.1);
+            animation: fadeInUp 1s ease-out;
+        }
+
+        h2 {
+            color: #141e30;
+            font-size: 2.5em;
+            margin-bottom: 30px;
+            text-align: center;
+        }
+
+        h3 {
+            color: #243b55;
+            font-size: 1.8em;
+            margin: 30px 0 20px;
+        }
+
+        /* Vector Visualization */
+        .vector-container {
+            display: flex;
+            align-items: center;
+            justify-content: space-around;
+            margin: 40px 0;
+            flex-wrap: wrap;
+            gap: 30px;
+        }
+
+        .sentence-box {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 15px;
+            border: 2px solid #428e92;
+            font-size: 1.2em;
+            max-width: 400px;
+            text-align: center;
+        }
+
+        .arrow {
+            font-size: 2em;
+            color: #428e92;
+            animation: pulse 2s infinite;
+        }
+
+        .vector-display {
+            background: linear-gradient(135deg, #243b55 0%, #141e30 100%);
+            color: white;
+            padding: 20px;
+            border-radius: 15px;
+            font-family: monospace;
+            box-shadow: 0 10px 25px rgba(0,0,0,0.2);
+        }
+
+        .vector-values {
+            display: grid;
+            grid-template-columns: repeat(4, 1fr);
+            gap: 10px;
+            margin-top: 10px;
+        }
+
+        .vector-value {
+            background: rgba(255,255,255,0.1);
+            padding: 5px;
+            border-radius: 5px;
+            text-align: center;
+        }
+
+        /* Feature Categories */
+        .features-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
+            gap: 25px;
+            margin: 30px 0;
+        }
+
+        .feature-card {
+            background: white;
+            padding: 25px;
+            border-radius: 15px;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            border-top: 4px solid #428e92;
+            transition: all 0.3s ease;
+        }
+
+        .feature-card:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 10px 25px rgba(0,0,0,0.15);
+        }
+
+        .feature-icon {
+            font-size: 2.5em;
+            margin-bottom: 15px;
+            text-align: center;
+        }
+
+        .feature-title {
+            font-weight: bold;
+            color: #243b55;
+            margin-bottom: 10px;
+            font-size: 1.3em;
+        }
+
+        .feature-examples {
+            margin-top: 15px;
+            padding-left: 20px;
+        }
+
+        .feature-examples li {
+            margin: 5px 0;
+            color: #666;
+        }
+
+        /* Interactive Analyzer */
+        .analyzer-container {
+            background: #f8f9fa;
+            padding: 30px;
+            border-radius: 15px;
+            margin: 30px 0;
+        }
+
+        .input-group {
+            display: flex;
+            gap: 15px;
+            margin-bottom: 30px;
+        }
+
+        .sentence-input {
+            flex: 1;
+            padding: 15px;
+            font-size: 1.1em;
+            border: 2px solid #e0e0e0;
+            border-radius: 10px;
+            transition: border-color 0.3s ease;
+        }
+
+        .sentence-input:focus {
+            outline: none;
+            border-color: #428e92;
+        }
+
+        .analyze-btn {
+            padding: 15px 30px;
+            background: linear-gradient(135deg, #428e92 0%, #243b55 100%);
+            color: white;
+            border: none;
+            border-radius: 10px;
+            font-size: 1.1em;
+            font-weight: bold;
+            cursor: pointer;
+            transition: all 0.3s ease;
+        }
+
+        .analyze-btn:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+        }
+
+        /* Feature Analysis Display */
+        .analysis-results {
+            display: none;
+            margin-top: 30px;
+        }
+
+        .analysis-results.show {
+            display: block;
+            animation: fadeIn 0.5s ease;
+        }
+
+        .feature-bars {
+            display: grid;
+            gap: 20px;
+        }
+
+        .feature-bar {
+            background: white;
+            padding: 20px;
+            border-radius: 10px;
+            box-shadow: 0 3px 10px rgba(0,0,0,0.1);
+        }
+
+        .feature-bar-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 10px;
+        }
+
+        .feature-name {
+            font-weight: bold;
+            color: #243b55;
+        }
+
+        .feature-score {
+            font-weight: bold;
+            color: #428e92;
+        }
+
+        .bar-container {
+            background: #e0e0e0;
+            height: 30px;
+            border-radius: 15px;
+            overflow: hidden;
+            position: relative;
+        }
+
+        .bar-fill {
+            height: 100%;
+            background: linear-gradient(135deg, #428e92 0%, #243b55 100%);
+            border-radius: 15px;
+            transition: width 1s ease;
+            display: flex;
+            align-items: center;
+            justify-content: flex-end;
+            padding-right: 10px;
+            color: white;
+            font-weight: bold;
+        }
+
+        /* Dimension Visualization */
+        .dimension-explorer {
+            margin: 30px 0;
+        }
+
+        .dimension-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fill, minmax(60px, 1fr));
+            gap: 5px;
+            margin: 20px 0;
+            padding: 20px;
+            background: #f8f9fa;
+            border-radius: 15px;
+        }
+
+        .dimension-cell {
+            height: 60px;
+            background: linear-gradient(135deg, #e0e0e0 0%, #f5f5f5 100%);
+            border-radius: 5px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-weight: bold;
+            font-size: 0.8em;
+            cursor: pointer;
+            transition: all 0.3s ease;
+            position: relative;
+        }
+
+        .dimension-cell:hover {
+            transform: scale(1.1);
+            z-index: 10;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+        }
+
+        .dimension-cell.positive {
+            background: linear-gradient(135deg, #66bb6a 0%, #4caf50 100%);
+            color: white;
+        }
+
+        .dimension-cell.negative {
+            background: linear-gradient(135deg, #ef5350 0%, #f44336 100%);
+            color: white;
+        }
+
+        .dimension-tooltip {
+            position: absolute;
+            bottom: 100%;
+            left: 50%;
+            transform: translateX(-50%);
+            background: #333;
+            color: white;
+            padding: 5px 10px;
+            border-radius: 5px;
+            font-size: 0.8em;
+            white-space: nowrap;
+            opacity: 0;
+            pointer-events: none;
+            transition: opacity 0.3s ease;
+        }
+
+        .dimension-cell:hover .dimension-tooltip {
+            opacity: 1;
+        }
+
+        /* Similarity Comparison */
+        .similarity-demo {
+            margin: 30px 0;
+            background: #f8f9fa;
+            padding: 30px;
+            border-radius: 15px;
+        }
+
+        .sentence-pair {
+            display: grid;
+            grid-template-columns: 1fr auto 1fr;
+            gap: 20px;
+            align-items: center;
+            margin: 20px 0;
+        }
+
+        .sentence-card {
+            background: white;
+            padding: 20px;
+            border-radius: 10px;
+            text-align: center;
+            box-shadow: 0 3px 10px rgba(0,0,0,0.1);
+        }
+
+        .similarity-score {
+            font-size: 2em;
+            font-weight: bold;
+            color: #428e92;
+            text-align: center;
+        }
+
+        /* Info Boxes */
+        .info-box {
+            background: #e3f2fd;
+            border: 2px solid #2196f3;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 20px 0;
+        }
+
+        .formula-box {
+            background: #fff3cd;
+            border: 2px solid #ffc107;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 20px 0;
+            text-align: center;
+        }
+
+        /* 3D Vector Space */
+        .vector-space-3d {
+            width: 100%;
+            height: 400px;
+            background: #f5f5f5;
+            border-radius: 15px;
+            position: relative;
+            margin: 30px 0;
+            overflow: hidden;
+            border: 2px solid #e0e0e0;
+        }
+
+        .vector-point-3d {
+            position: absolute;
+            width: 10px;
+            height: 10px;
+            background: #428e92;
+            border-radius: 50%;
+            transition: all 0.3s ease;
+        }
+
+        /* LLM Generation Styles */
+        .generation-slide {
+            background: #f8f9fa;
+            padding: 40px;
+            border-radius: 15px;
+            margin: 30px 0;
+            min-height: 500px;
+        }
+
+        .generation-demo {
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+            gap: 30px;
+        }
+
+        .context-box {
+            background: white;
+            padding: 30px;
+            border-radius: 15px;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            text-align: center;
+        }
+
+        .token-sequence {
+            display: flex;
+            gap: 10px;
+            margin-top: 15px;
+            justify-content: center;
+        }
+
+        .token {
+            padding: 10px 20px;
+            background: #e3f2fd;
+            border-radius: 8px;
+            font-weight: bold;
+            border: 2px solid #2196f3;
+        }
+
+        .token.current {
+            background: #fff3cd;
+            border-color: #ffc107;
+            animation: pulse 2s infinite;
+        }
+
+        .arrow-down {
+            font-size: 2em;
+            color: #428e92;
+            animation: bounce 2s infinite;
+        }
+
+        .embedding-row {
+            display: flex;
+            gap: 20px;
+            justify-content: center;
+        }
+
+        .token-embedding {
+            background: white;
+            padding: 15px;
+            border-radius: 10px;
+            box-shadow: 0 3px 10px rgba(0,0,0,0.1);
+            text-align: center;
+        }
+
+        .token-label {
+            font-weight: bold;
+            color: #243b55;
+            margin-bottom: 5px;
+        }
+
+        .mini-vector {
+            font-family: monospace;
+            font-size: 0.9em;
+            color: #666;
+        }
+
+        .context-embedding {
+            background: linear-gradient(135deg, #243b55 0%, #141e30 100%);
+            color: white;
+            padding: 25px;
+            border-radius: 15px;
+            text-align: center;
+            box-shadow: 0 10px 25px rgba(0,0,0,0.2);
+        }
+
+        .context-vector {
+            font-family: monospace;
+            margin-top: 10px;
+            font-size: 1.1em;
+        }
+
+        /* Transformer Animation */
+        .transformer-animation {
+            text-align: center;
+        }
+
+        .transformer-layers {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            gap: 30px;
+            margin: 40px 0;
+        }
+
+        .layer-block {
+            background: white;
+            padding: 20px;
+            border-radius: 15px;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            text-align: center;
+        }
+
+        .layer-title {
+            font-weight: bold;
+            color: #243b55;
+            margin-bottom: 15px;
+        }
+
+        .attention-matrix {
+            display: grid;
+            grid-template-columns: repeat(3, 1fr);
+            gap: 5px;
+            width: 60px;
+            margin: 0 auto;
+        }
+
+        .attention-cell {
+            width: 15px;
+            height: 15px;
+            background: #428e92;
+            border-radius: 50%;
+            animation: attentionPulse 2s infinite;
+        }
+
+        .attention-cell:nth-child(odd) {
+            animation-delay: 0.5s;
+        }
+
+        .arrow-right {
+            font-size: 2em;
+            color: #428e92;
+        }
+
+        .neural-network-mini {
+            display: flex;
+            gap: 20px;
+            align-items: center;
+        }
+
+        .nn-layer {
+            display: flex;
+            flex-direction: column;
+            gap: 10px;
+        }
+
+        .nn-node {
+            width: 20px;
+            height: 20px;
+            background: #428e92;
+            border-radius: 50%;
+        }
+
+        .output-vector-display {
+            display: flex;
+            gap: 5px;
+            align-items: flex-end;
+            height: 60px;
+        }
+
+        .vector-bar {
+            width: 15px;
+            background: linear-gradient(to top, #428e92, #66bb6a);
+            animation: barGrow 2s ease-in-out infinite;
+        }
+
+        /* Vocabulary Projection */
+        .vocab-projection {
+            display: flex;
+            justify-content: space-around;
+            align-items: center;
+            gap: 30px;
+            margin: 40px 0;
+        }
+
+        .projection-left, .projection-right {
+            background: white;
+            padding: 25px;
+            border-radius: 15px;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+        }
+
+        .hidden-state-vector {
+            margin-top: 15px;
+            font-family: monospace;
+        }
+
+        .vector-component {
+            padding: 5px;
+            margin: 2px 0;
+            background: #f0f0f0;
+            border-radius: 5px;
+        }
+
+        .projection-arrow {
+            text-align: center;
+        }
+
+        .matrix-multiply {
+            font-size: 2em;
+            color: #428e92;
+            margin-bottom: 10px;
+        }
+
+        .weight-matrix {
+            background: #e3f2fd;
+            padding: 15px;
+            border-radius: 10px;
+        }
+
+        .matrix-size {
+            font-family: monospace;
+            color: #666;
+            font-size: 0.9em;
+        }
+
+        .vocab-scores {
+            margin-top: 15px;
+        }
+
+        .word-score {
+            padding: 8px 15px;
+            margin: 5px 0;
+            border-radius: 8px;
+            font-weight: bold;
+        }
+
+        .word-score.high {
+            background: #c8e6c9;
+            color: #2e7d32;
+        }
+
+        .word-score.medium {
+            background: #fff9c4;
+            color: #f57f17;
+        }
+
+        .word-score.low {
+            background: #ffcdd2;
+            color: #c62828;
+        }
+
+        /* Probability Demo */
+        .probability-demo {
+            margin: 40px 0;
+        }
+
+        .softmax-conversion {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            gap: 30px;
+            margin-bottom: 40px;
+        }
+
+        .scores-box, .probs-box {
+            background: white;
+            padding: 25px;
+            border-radius: 15px;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+        }
+
+        .softmax-arrow {
+            text-align: center;
+        }
+
+        .formula {
+            font-family: 'Courier New', monospace;
+            font-size: 0.9em;
+            color: #666;
+            margin-top: 5px;
+        }
+
+        .prob-bars {
+            margin-top: 15px;
+        }
+
+        .prob-bar-item {
+            display: flex;
+            align-items: center;
+            margin: 10px 0;
+        }
+
+        .prob-label {
+            width: 80px;
+            font-weight: bold;
+        }
+
+        .prob-bar-container {
+            flex: 1;
+            background: #e0e0e0;
+            height: 25px;
+            border-radius: 12px;
+            overflow: hidden;
+        }
+
+        .prob-bar {
+            height: 100%;
+            background: linear-gradient(135deg, #428e92 0%, #66bb6a 100%);
+            display: flex;
+            align-items: center;
+            justify-content: flex-end;
+            padding-right: 10px;
+            color: white;
+            font-weight: bold;
+            font-size: 0.9em;
+            animation: barSlide 1s ease-out;
+        }
+
+        .method-cards {
+            display: flex;
+            gap: 20px;
+            justify-content: center;
+            margin-top: 20px;
+        }
+
+        .method-card-small {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+            text-align: center;
+            flex: 1;
+        }
+
+        .method-card-small h4 {
+            color: #243b55;
+            margin-bottom: 10px;
+        }
+
+        /* Generation Loop */
+        .generation-loop {
+            margin: 40px 0;
+        }
+
+        .loop-visualization {
+            margin-bottom: 40px;
+        }
+
+        .generation-state {
+            background: white;
+            padding: 20px;
+            border-radius: 15px;
+            margin: 15px 0;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            transition: all 0.5s ease;
+        }
+
+        .state-label {
+            font-weight: bold;
+            color: #243b55;
+            margin-bottom: 10px;
+        }
+
+        .text-state {
+            font-size: 1.2em;
+            margin: 10px 0;
+        }
+
+        .generated {
+            background: #c8e6c9;
+            padding: 2px 8px;
+            border-radius: 5px;
+            font-weight: bold;
+        }
+
+        .next-pred {
+            color: #666;
+            font-style: italic;
+        }
+
+        .loop-diagram {
+            background: #e3f2fd;
+            padding: 30px;
+            border-radius: 15px;
+            text-align: center;
+        }
+
+        .loop-box p {
+            margin: 10px 0;
+            padding: 10px;
+            background: white;
+            border-radius: 8px;
+        }
+
+        /* Slide Controls */
+        .slide-controls {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            gap: 30px;
+            margin-top: 40px;
+        }
+
+        .slide-btn {
+            padding: 10px 25px;
+            background: #428e92;
+            color: white;
+            border: none;
+            border-radius: 8px;
+            font-weight: bold;
+            cursor: pointer;
+            transition: all 0.3s ease;
+        }
+
+        .slide-btn:hover {
+            background: #357a7d;
+            transform: translateY(-2px);
+        }
+
+        .slide-indicators {
+            display: flex;
+            gap: 10px;
+        }
+
+        .indicator {
+            width: 12px;
+            height: 12px;
+            background: #e0e0e0;
+            border-radius: 50%;
+            cursor: pointer;
+            transition: all 0.3s ease;
+        }
+
+        .indicator.active {
+            background: #428e92;
+            transform: scale(1.2);
+        }
+
+        /* Interactive Generation */
+        .interactive-generation {
+            background: #f8f9fa;
+            padding: 30px;
+            border-radius: 15px;
+        }
+
+        .generation-input {
+            display: flex;
+            gap: 15px;
+            margin-bottom: 30px;
+        }
+
+        .generate-text-btn {
+            padding: 15px 30px;
+            background: linear-gradient(135deg, #428e92 0%, #243b55 100%);
+            color: white;
+            border: none;
+            border-radius: 10px;
+            font-weight: bold;
+            cursor: pointer;
+            transition: all 0.3s ease;
+        }
+
+        .generate-text-btn:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+        }
+
+        .process-step {
+            background: white;
+            padding: 25px;
+            border-radius: 15px;
+            margin: 20px 0;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            animation: slideIn 0.5s ease;
+        }
+
+        .tokens-display {
+            display: flex;
+            gap: 10px;
+            flex-wrap: wrap;
+            margin-top: 15px;
+            justify-content: center;
+        }
+
+        .embeddings-preview {
+            font-family: monospace;
+            background: #f0f0f0;
+            padding: 15px;
+            border-radius: 8px;
+            margin-top: 15px;
+            overflow-x: auto;
+        }
+
+        .processing-animation {
+            width: 100%;
+            background: #e0e0e0;
+            height: 30px;
+            border-radius: 15px;
+            overflow: hidden;
+            margin-top: 15px;
+        }
+
+        .processing-bar {
+            height: 100%;
+            background: linear-gradient(135deg, #428e92 0%, #66bb6a 100%);
+            width: 0%;
+            animation: processingFill 3s ease-out forwards;
+        }
+
+        .final-output {
+            font-size: 1.2em;
+            line-height: 1.6;
+            margin-top: 15px;
+            padding: 20px;
+            background: #e8f5e9;
+            border-radius: 10px;
+        }
+
+        /* New Animations */
+        @keyframes bounce {
+            0%, 100% { transform: translateY(0); }
+            50% { transform: translateY(-10px); }
+        }
+
+        @keyframes attentionPulse {
+            0%, 100% { opacity: 0.3; transform: scale(1); }
+            50% { opacity: 1; transform: scale(1.2); }
+        }
+
+        @keyframes barGrow {
+            0%, 100% { height: 60%; }
+            50% { height: 100%; }
+        }
+
+        @keyframes barSlide {
+            from { width: 0; }
+            to { width: inherit; }
+        }
+
+        @keyframes processingFill {
+            from { width: 0%; }
+            to { width: 100%; }
+        }
+
+        @keyframes slideIn {
+            from {
+                opacity: 0;
+                transform: translateX(-20px);
+            }
+            to {
+                opacity: 1;
+                transform: translateX(0);
+            }
+        }
+
+        /* Animations */
+        @keyframes fadeInDown {
+            from {
+                opacity: 0;
+                transform: translateY(-30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes fadeInUp {
+            from {
+                opacity: 0;
+                transform: translateY(30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes fadeIn {
+            from { opacity: 0; }
+            to { opacity: 1; }
+        }
+
+        @keyframes pulse {
+            0%, 100% { transform: scale(1); }
+            50% { transform: scale(1.1); }
+        }
+
+        @media (max-width: 768px) {
+            h1 { font-size: 2.5em; }
+            h2 { font-size: 2em; }
+            .features-grid { grid-template-columns: 1fr; }
+            .input-group { flex-direction: column; }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <header>
+            <h1>🔢 Sentence Embedding Vectors</h1>
+            <p class="subtitle">Understanding What Features Embeddings Capture</p>
+        </header>
+
+        <!-- What Are Embeddings -->
+        <div class="section">
+            <h2>📊 What Are Embedding Vectors?</h2>
+            
+            <div class="vector-container">
+                <div class="sentence-box">
+                    "The cat is sleeping peacefully"
+                </div>
+                <span class="arrow">→</span>
+                <div class="vector-display">
+                    <strong>768-dimensional vector:</strong>
+                    <div class="vector-values">
+                        <div class="vector-value">0.342</div>
+                        <div class="vector-value">-0.156</div>
+                        <div class="vector-value">0.823</div>
+                        <div class="vector-value">-0.091</div>
+                        <div class="vector-value">0.445</div>
+                        <div class="vector-value">0.234</div>
+                        <div class="vector-value">-0.567</div>
+                        <div class="vector-value">...</div>
+                    </div>
+                    <div style="text-align: center; margin-top: 10px; opacity: 0.8;">
+                        (+ 760 more dimensions)
+                    </div>
+                </div>
+            </div>
+
+            <div class="info-box">
+                <h3>🎯 Key Concepts:</h3>
+                <ul style="margin-left: 20px; line-height: 1.8;">
+                    <li><strong>Dense Representation:</strong> Sentences are converted into fixed-size numerical vectors (typically 384-1024 dimensions)</li>
+                    <li><strong>Semantic Encoding:</strong> Similar sentences have similar vectors (small cosine distance)</li>
+                    <li><strong>Learned Features:</strong> Each dimension captures some linguistic or semantic property</li>
+                    <li><strong>Contextual Understanding:</strong> Modern embeddings (BERT, etc.) consider word context</li>
+                </ul>
+            </div>
+        </div>
+
+        <!-- Feature Categories -->
+        <div class="section">
+            <h2>🎨 Features Captured by Embeddings</h2>
+            <p style="text-align: center; margin-bottom: 30px; font-size: 1.1em;">
+                Embedding vectors encode multiple types of linguistic and semantic information
+            </p>
+            
+            <div class="features-grid">
+                <div class="feature-card">
+                    <div class="feature-icon">🧠</div>
+                    <div class="feature-title">Semantic Meaning</div>
+                    <p>Core conceptual content and topic</p>
+                    <ul class="feature-examples">
+                        <li>Animal vs Technology topics</li>
+                        <li>Abstract vs Concrete concepts</li>
+                        <li>Domain-specific terminology</li>
+                    </ul>
+                </div>
+
+                <div class="feature-card">
+                    <div class="feature-icon">📝</div>
+                    <div class="feature-title">Syntactic Structure</div>
+                    <p>Grammatical patterns and word order</p>
+                    <ul class="feature-examples">
+                        <li>Subject-Verb-Object patterns</li>
+                        <li>Question vs Statement</li>
+                        <li>Active vs Passive voice</li>
+                    </ul>
+                </div>
+
+                <div class="feature-card">
+                    <div class="feature-icon">😊</div>
+                    <div class="feature-title">Sentiment & Emotion</div>
+                    <p>Emotional tone and polarity</p>
+                    <ul class="feature-examples">
+                        <li>Positive/Negative/Neutral</li>
+                        <li>Joy, Anger, Sadness, Fear</li>
+                        <li>Intensity of emotion</li>
+                    </ul>
+                </div>
+
+                <div class="feature-card">
+                    <div class="feature-icon">🎭</div>
+                    <div class="feature-title">Style & Register</div>
+                    <p>Writing style and formality</p>
+                    <ul class="feature-examples">
+                        <li>Formal vs Informal</li>
+                        <li>Technical vs Casual</li>
+                        <li>Academic vs Conversational</li>
+                    </ul>
+                </div>
+
+                <div class="feature-card">
+                    <div class="feature-icon">🔗</div>
+                    <div class="feature-title">Entity Information</div>
+                    <p>People, places, organizations</p>
+                    <ul class="feature-examples">
+                        <li>Named entity types</li>
+                        <li>Geographical references</li>
+                        <li>Temporal expressions</li>
+                    </ul>
+                </div>
+
+                <div class="feature-card">
+                    <div class="feature-icon">🎯</div>
+                    <div class="feature-title">Intent & Purpose</div>
+                    <p>Communicative goal</p>
+                    <ul class="feature-examples">
+                        <li>Question vs Command</li>
+                        <li>Request vs Statement</li>
+                        <li>Informative vs Persuasive</li>
+                    </ul>
+                </div>
+            </div>
+        </div>
+
+        <!-- Interactive Analyzer -->
+        <div class="section">
+            <h2>🔬 Analyze Sentence Features</h2>
+            
+            <div class="analyzer-container">
+                <div class="input-group">
+                    <input type="text" class="sentence-input" id="sentenceInput" 
+                           placeholder="Enter a sentence to analyze..." 
+                           value="The scientists discovered a revolutionary new treatment for cancer.">
+                    <button class="analyze-btn" onclick="analyzeSentence()">Analyze Features</button>
+                </div>
+                
+                <div class="analysis-results" id="analysisResults">
+                    <h3>Feature Analysis:</h3>
+                    <div class="feature-bars" id="featureBars">
+                        <!-- Feature bars will be populated here -->
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Dimension Explorer -->
+        <div class="section">
+            <h2>🌐 Exploring Vector Dimensions</h2>
+            <p style="text-align: center; margin-bottom: 20px;">
+                Visualization of a 128-dimensional embedding vector (hover to see dimension info)
+            </p>
+            
+            <div class="dimension-explorer">
+                <div class="dimension-grid" id="dimensionGrid">
+                    <!-- Dimensions will be populated by JavaScript -->
+                </div>
+            </div>
+
+            <div class="info-box">
+                <h4>Understanding Dimensions:</h4>
+                <ul style="margin-left: 20px;">
+                    <li><strong>Individual dimensions</strong> don't have clear interpretations</li>
+                    <li><strong>Patterns across dimensions</strong> encode semantic features</li>
+                    <li><strong>Positive/Negative values</strong> indicate presence/absence of abstract features</li>
+                    <li><strong>Magnitude</strong> shows feature strength</li>
+                </ul>
+            </div>
+        </div>
+
+        <!-- Similarity Demo -->
+        <div class="section">
+            <h2>🔄 Semantic Similarity Through Embeddings</h2>
+            
+            <div class="similarity-demo">
+                <div class="sentence-pair">
+                    <div class="sentence-card">
+                        <p>"The cat is sleeping on the sofa"</p>
+                    </div>
+                    <div class="similarity-score">0.89</div>
+                    <div class="sentence-card">
+                        <p>"A kitten is resting on the couch"</p>
+                    </div>
+                </div>
+
+                <div class="sentence-pair">
+                    <div class="sentence-card">
+                        <p>"The weather is beautiful today"</p>
+                    </div>
+                    <div class="similarity-score">0.12</div>
+                    <div class="sentence-card">
+                        <p>"I need to debug this code"</p>
+                    </div>
+                </div>
+
+                <div class="sentence-pair">
+                    <div class="sentence-card">
+                        <p>"Machine learning transforms data"</p>
+                    </div>
+                    <div class="similarity-score">0.76</div>
+                    <div class="sentence-card">
+                        <p>"AI algorithms process information"</p>
+                    </div>
+                </div>
+            </div>
+
+            <div class="formula-box">
+                <h4>Cosine Similarity Formula:</h4>
+                <p style="font-family: monospace; font-size: 1.2em; margin: 15px 0;">
+                    similarity = (A · B) / (||A|| × ||B||)
+                </p>
+                <p>Range: -1 (opposite) to 1 (identical)</p>
+            </div>
+        </div>
+
+        <!-- How Embeddings Learn Features -->
+        <div class="section">
+            <h2>🎓 How Embeddings Learn Features</h2>
+            
+            <div style="display: grid; gap: 20px; margin-top: 30px;">
+                <div class="info-box">
+                    <h3>1️⃣ Pre-training on Massive Data</h3>
+                    <p>Models see billions of sentences and learn patterns:</p>
+                    <ul style="margin-left: 20px; margin-top: 10px;">
+                        <li>Words that appear in similar contexts get similar representations</li>
+                        <li>Grammatical patterns are encoded through position and attention</li>
+                        <li>Multi-lingual models learn cross-language concepts</li>
+                    </ul>
+                </div>
+
+                <div class="info-box">
+                    <h3>2️⃣ Self-Supervised Learning</h3>
+                    <p>Models learn by predicting masked words or next sentences:</p>
+                    <ul style="margin-left: 20px; margin-top: 10px;">
+                        <li>BERT: Masked Language Modeling</li>
+                        <li>GPT: Next Token Prediction</li>
+                        <li>Sentence-BERT: Contrastive Learning</li>
+                    </ul>
+                </div>
+
+                <div class="info-box">
+                    <h3>3️⃣ Emergent Properties</h3>
+                    <p>Complex features emerge without explicit programming:</p>
+                    <ul style="margin-left: 20px; margin-top: 10px;">
+                        <li>Sentiment emerges from word associations</li>
+                        <li>Grammar emerges from sequence patterns</li>
+                        <li>Topics emerge from co-occurrence statistics</li>
+                    </ul>
+                </div>
+            </div>
+        </div>
+
+        <!-- LLM Text Generation -->
+        <div class="section">
+            <h2>🤖 How LLMs Use Embeddings for Text Generation</h2>
+            <p style="text-align: center; margin-bottom: 30px; font-size: 1.1em;">
+                Step-by-step visualization of next word prediction using embeddings
+            </p>
+
+            <!-- Slide 1: Context to Embeddings -->
+            <div class="generation-slide" id="slide1">
+                <h3>Step 1: Context Embedding</h3>
+                <div class="generation-demo">
+                    <div class="context-box">
+                        <p><strong>Input Context:</strong></p>
+                        <div class="token-sequence">
+                            <span class="token">The</span>
+                            <span class="token">weather</span>
+                            <span class="token">is</span>
+                            <span class="token current">___</span>
+                        </div>
+                    </div>
+                    
+                    <div class="arrow-down">↓</div>
+                    
+                    <div class="embedding-process">
+                        <div class="embedding-row">
+                            <div class="token-embedding">
+                                <div class="token-label">The</div>
+                                <div class="mini-vector">[0.12, -0.34, 0.56...]</div>
+                            </div>
+                            <div class="token-embedding">
+                                <div class="token-label">weather</div>
+                                <div class="mini-vector">[0.78, 0.23, -0.11...]</div>
+                            </div>
+                            <div class="token-embedding">
+                                <div class="token-label">is</div>
+                                <div class="mini-vector">[-0.45, 0.67, 0.89...]</div>
+                            </div>
+                        </div>
+                    </div>
+                    
+                    <div class="arrow-down">↓</div>
+                    
+                    <div class="context-embedding">
+                        <p><strong>Combined Context Vector</strong></p>
+                        <div class="context-vector">[0.15, 0.19, 0.44, -0.23, 0.78, ...]</div>
+                    </div>
+                </div>
+            </div>
+
+            <!-- Slide 2: Transformer Processing -->
+            <div class="generation-slide" id="slide2" style="display: none;">
+                <h3>Step 2: Transformer Processing</h3>
+                <div class="transformer-animation">
+                    <div class="transformer-layers">
+                        <div class="layer-block">
+                            <div class="layer-title">Self-Attention</div>
+                            <div class="attention-matrix">
+                                <div class="attention-cell a1">●</div>
+                                <div class="attention-cell a2">●</div>
+                                <div class="attention-cell a3">●</div>
+                                <div class="attention-cell a4">●</div>
+                                <div class="attention-cell a5">●</div>
+                                <div class="attention-cell a6">●</div>
+                                <div class="attention-cell a7">●</div>
+                                <div class="attention-cell a8">●</div>
+                                <div class="attention-cell a9">●</div>
+                            </div>
+                        </div>
+                        
+                        <div class="arrow-right">→</div>
+                        
+                        <div class="layer-block">
+                            <div class="layer-title">Feed Forward</div>
+                            <div class="neural-network-mini">
+                                <div class="nn-layer">
+                                    <div class="nn-node"></div>
+                                    <div class="nn-node"></div>
+                                    <div class="nn-node"></div>
+                                </div>
+                                <div class="nn-connections"></div>
+                                <div class="nn-layer">
+                                    <div class="nn-node"></div>
+                                    <div class="nn-node"></div>
+                                    <div class="nn-node"></div>
+                                </div>
+                            </div>
+                        </div>
+                        
+                        <div class="arrow-right">→</div>
+                        
+                        <div class="layer-block">
+                            <div class="layer-title">Output Vector</div>
+                            <div class="output-vector-display">
+                                <div class="vector-bar" style="height: 60%"></div>
+                                <div class="vector-bar" style="height: 30%"></div>
+                                <div class="vector-bar" style="height: 80%"></div>
+                                <div class="vector-bar" style="height: 45%"></div>
+                                <div class="vector-bar" style="height: 70%"></div>
+                            </div>
+                        </div>
+                    </div>
+                    
+                    <p class="process-description">
+                        The transformer processes context embeddings through multiple layers,
+                        each refining the representation for next word prediction
+                    </p>
+                </div>
+            </div>
+
+            <!-- Slide 3: Vocabulary Projection -->
+            <div class="generation-slide" id="slide3" style="display: none;">
+                <h3>Step 3: Vocabulary Projection</h3>
+                <div class="vocab-projection">
+                    <div class="projection-left">
+                        <p><strong>Final Hidden State</strong></p>
+                        <div class="hidden-state-vector">
+                            <div class="vector-component">h₁ = 0.82</div>
+                            <div class="vector-component">h₂ = -0.31</div>
+                            <div class="vector-component">h₃ = 0.56</div>
+                            <div class="vector-component">...</div>
+                            <div class="vector-component">h₇₆₈ = 0.14</div>
+                        </div>
+                    </div>
+                    
+                    <div class="projection-arrow">
+                        <div class="matrix-multiply">×</div>
+                        <div class="weight-matrix">
+                            <p>Weight Matrix</p>
+                            <p class="matrix-size">(768 × 50,000)</p>
+                        </div>
+                    </div>
+                    
+                    <div class="projection-right">
+                        <p><strong>Vocabulary Scores</strong></p>
+                        <div class="vocab-scores">
+                            <div class="word-score high">sunny: 8.2</div>
+                            <div class="word-score high">beautiful: 7.9</div>
+                            <div class="word-score high">nice: 7.5</div>
+                            <div class="word-score medium">cold: 4.1</div>
+                            <div class="word-score medium">rainy: 3.8</div>
+                            <div class="word-score low">computer: -2.1</div>
+                            <div class="word-score low">algorithm: -3.5</div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <!-- Slide 4: Probability Distribution -->
+            <div class="generation-slide" id="slide4" style="display: none;">
+                <h3>Step 4: Probability Distribution</h3>
+                <div class="probability-demo">
+                    <div class="softmax-conversion">
+                        <div class="scores-box">
+                            <p><strong>Raw Scores (Logits)</strong></p>
+                            <div class="score-list">
+                                <div>sunny: 8.2</div>
+                                <div>beautiful: 7.9</div>
+                                <div>nice: 7.5</div>
+                                <div>...</div>
+                            </div>
+                        </div>
+                        
+                        <div class="softmax-arrow">
+                            <p>Softmax</p>
+                            <p class="formula">p(w) = e^(score_w) / Σe^(scores)</p>
+                        </div>
+                        
+                        <div class="probs-box">
+                            <p><strong>Probabilities</strong></p>
+                            <div class="prob-bars">
+                                <div class="prob-bar-item">
+                                    <div class="prob-label">sunny</div>
+                                    <div class="prob-bar-container">
+                                        <div class="prob-bar" style="width: 35%">35%</div>
+                                    </div>
+                                </div>
+                                <div class="prob-bar-item">
+                                    <div class="prob-label">beautiful</div>
+                                    <div class="prob-bar-container">
+                                        <div class="prob-bar" style="width: 28%">28%</div>
+                                    </div>
+                                </div>
+                                <div class="prob-bar-item">
+                                    <div class="prob-label">nice</div>
+                                    <div class="prob-bar-container">
+                                        <div class="prob-bar" style="width: 20%">20%</div>
+                                    </div>
+                                </div>
+                                <div class="prob-bar-item">
+                                    <div class="prob-label">cold</div>
+                                    <div class="prob-bar-container">
+                                        <div class="prob-bar" style="width: 8%">8%</div>
+                                    </div>
+                                </div>
+                                <div class="prob-bar-item">
+                                    <div class="prob-label">other</div>
+                                    <div class="prob-bar-container">
+                                        <div class="prob-bar" style="width: 9%">9%</div>
+                                    </div>
+                                </div>
+                            </div>
+                        </div>
+                    </div>
+                    
+                    <div class="sampling-methods">
+                        <p><strong>Sampling Methods:</strong></p>
+                        <div class="method-cards">
+                            <div class="method-card-small">
+                                <h4>Greedy</h4>
+                                <p>Always pick highest: "sunny"</p>
+                            </div>
+                            <div class="method-card-small">
+                                <h4>Top-k</h4>
+                                <p>Sample from top 3 words</p>
+                            </div>
+                            <div class="method-card-small">
+                                <h4>Temperature</h4>
+                                <p>Adjust probability spread</p>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <!-- Slide 5: Text Generation Loop -->
+            <div class="generation-slide" id="slide5" style="display: none;">
+                <h3>Step 5: Autoregressive Generation</h3>
+                <div class="generation-loop">
+                    <div class="loop-visualization">
+                        <div class="generation-state" id="genState1">
+                            <p class="state-label">Iteration 1</p>
+                            <div class="text-state">The weather is <span class="generated">beautiful</span> ___</div>
+                            <div class="next-pred">Next: "today"</div>
+                        </div>
+                        
+                        <div class="generation-state" id="genState2" style="opacity: 0.3;">
+                            <p class="state-label">Iteration 2</p>
+                            <div class="text-state">The weather is beautiful <span class="generated">today</span> ___</div>
+                            <div class="next-pred">Next: "with"</div>
+                        </div>
+                        
+                        <div class="generation-state" id="genState3" style="opacity: 0.3;">
+                            <p class="state-label">Iteration 3</p>
+                            <div class="text-state">The weather is beautiful today <span class="generated">with</span> ___</div>
+                            <div class="next-pred">Next: "clear"</div>
+                        </div>
+                        
+                        <div class="generation-state" id="genState4" style="opacity: 0.3;">
+                            <p class="state-label">Iteration 4</p>
+                            <div class="text-state">The weather is beautiful today with <span class="generated">clear</span> ___</div>
+                            <div class="next-pred">Next: "skies"</div>
+                        </div>
+                    </div>
+                    
+                    <div class="loop-diagram">
+                        <div class="loop-box">
+                            <p>1. Update context with new word</p>
+                            <p>2. Generate new embeddings</p>
+                            <p>3. Process through transformer</p>
+                            <p>4. Predict next word</p>
+                            <p>5. Repeat until stop token</p>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <!-- Navigation Controls -->
+            <div class="slide-controls">
+                <button class="slide-btn" onclick="previousSlide()">← Previous</button>
+                <div class="slide-indicators">
+                    <span class="indicator active" onclick="goToSlide(1)"></span>
+                    <span class="indicator" onclick="goToSlide(2)"></span>
+                    <span class="indicator" onclick="goToSlide(3)"></span>
+                    <span class="indicator" onclick="goToSlide(4)"></span>
+                    <span class="indicator" onclick="goToSlide(5)"></span>
+                </div>
+                <button class="slide-btn" onclick="nextSlide()">Next →</button>
+            </div>
+        </div>
+
+        <!-- Interactive Text Generation Demo -->
+        <div class="section">
+            <h2>🎮 Interactive Text Generation</h2>
+            <div class="interactive-generation">
+                <div class="generation-input">
+                    <input type="text" id="genPrompt" placeholder="Enter your prompt..." value="The future of AI">
+                    <button class="generate-text-btn" onclick="generateInteractiveText()">Generate</button>
+                </div>
+                
+                <div class="generation-process" id="genProcess" style="display: none;">
+                    <div class="process-step" id="step1">
+                        <h4>📝 Tokenizing Input...</h4>
+                        <div class="tokens-display" id="tokensDisplay"></div>
+                    </div>
+                    
+                    <div class="process-step" id="step2" style="display: none;">
+                        <h4>🔢 Creating Embeddings...</h4>
+                        <div class="embeddings-preview" id="embeddingsPreview"></div>
+                    </div>
+                    
+                    <div class="process-step" id="step3" style="display: none;">
+                        <h4>🤖 Processing Through Transformer...</h4>
+                        <div class="processing-animation">
+                            <div class="processing-bar"></div>
+                        </div>
+                    </div>
+                    
+                    <div class="process-step" id="step4" style="display: none;">
+                        <h4>✨ Generated Text:</h4>
+                        <div class="final-output" id="finalOutput"></div>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Practical Applications -->
+        <div class="section">
+            <h2>🚀 Applications of Sentence Embeddings</h2>
+            
+            <div class="features-grid">
+                <div class="feature-card">
+                    <div class="feature-icon">🔍</div>
+                    <div class="feature-title">Semantic Search</div>
+                    <p>Find documents by meaning, not keywords</p>
+                </div>
+
+                <div class="feature-card">
+                    <div class="feature-icon">🗂️</div>
+                    <div class="feature-title">Text Classification</div>
+                    <p>Categorize emails, reviews, articles</p>
+                </div>
+
+                <div class="feature-card">
+                    <div class="feature-icon">🌍</div>
+                    <div class="feature-title">Cross-lingual Understanding</div>
+                    <p>Find similar content across languages</p>
+                </div>
+
+                <div class="feature-card">
+                    <div class="feature-icon">💬</div>
+                    <div class="feature-title">Chatbots & QA</div>
+                    <p>Match questions to relevant answers</p>
+                </div>
+
+                <div class="feature-card">
+                    <div class="feature-icon">📊</div>
+                    <div class="feature-title">Document Clustering</div>
+                    <p>Group similar documents automatically</p>
+                </div>
+
+                <div class="feature-card">
+                    <div class="feature-icon">🎯</div>
+                    <div class="feature-title">Recommendation Systems</div>
+                    <p>Suggest similar content to users</p>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // Analyze sentence features
+        function analyzeSentence() {
+            const sentence = document.getElementById('sentenceInput').value;
+            if (!sentence.trim()) return;
+
+            // Simulated feature extraction (in practice, use real models)
+            const features = extractFeatures(sentence);
+            displayFeatureAnalysis(features);
+            
+            // Generate and display embedding visualization
+            generateEmbeddingVisualization();
+        }
+
+        // Extract features from sentence (simulated)
+        function extractFeatures(sentence) {
+            const features = {
+                'Semantic Richness': 0,
+                'Syntactic Complexity': 0,
+                'Sentiment Polarity': 0,
+                'Formality Level': 0,
+                'Entity Density': 0,
+                'Question Intent': 0,
+                'Technical Content': 0,
+                'Emotional Intensity': 0
+            };
+
+            // Simple heuristics for demonstration
+            const words = sentence.toLowerCase().split(' ');
+            const wordCount = words.length;
+
+            // Semantic richness (vocabulary diversity)
+            const uniqueWords = new Set(words);
+            features['Semantic Richness'] = (uniqueWords.size / wordCount) * 100;
+
+            // Syntactic complexity (based on length and punctuation)
+            features['Syntactic Complexity'] = Math.min(100, wordCount * 5 + (sentence.match(/[,;:]/g) || []).length * 10);
+
+            // Sentiment (simple keyword matching)
+            const positiveWords = ['good', 'great', 'excellent', 'happy', 'beautiful', 'revolutionary', 'discovered'];
+            const negativeWords = ['bad', 'terrible', 'sad', 'angry', 'problem', 'issue'];
+            const posCount = words.filter(w => positiveWords.includes(w)).length;
+            const negCount = words.filter(w => negativeWords.includes(w)).length;
+            features['Sentiment Polarity'] = 50 + (posCount - negCount) * 20;
+
+            // Formality (based on contractions and casual words)
+            const casualIndicators = ["i'm", "don't", "can't", "won't", "gonna", "wanna"];
+            const formalIndicators = ['therefore', 'however', 'furthermore', 'scientists', 'research'];
+            const casualCount = words.filter(w => casualIndicators.includes(w)).length;
+            const formalCount = words.filter(w => formalIndicators.includes(w)).length;
+            features['Formality Level'] = Math.min(100, 50 + (formalCount - casualCount) * 25);
+
+            // Entity density (capitalized words)
+            const entities = sentence.match(/[A-Z][a-z]+/g) || [];
+            features['Entity Density'] = Math.min(100, (entities.length / wordCount) * 200);
+
+            // Question intent
+            features['Question Intent'] = sentence.includes('?') ? 90 : (sentence.includes('what') || sentence.includes('how') ? 30 : 10);
+
+            // Technical content
+            const techWords = ['algorithm', 'data', 'system', 'technology', 'scientific', 'treatment', 'cancer', 'research'];
+            const techCount = words.filter(w => techWords.includes(w)).length;
+            features['Technical Content'] = Math.min(100, techCount * 30);
+
+            // Emotional intensity
+            const emotionWords = ['love', 'hate', 'amazing', 'terrible', 'revolutionary', 'incredible'];
+            const emotionCount = words.filter(w => emotionWords.includes(w)).length;
+            features['Emotional Intensity'] = Math.min(100, emotionCount * 35);
+
+            return features;
+        }
+
+        // Display feature analysis
+        function displayFeatureAnalysis(features) {
+            const resultsDiv = document.getElementById('analysisResults');
+            const barsDiv = document.getElementById('featureBars');
+            
+            resultsDiv.classList.add('show');
+            barsDiv.innerHTML = '';
+
+            Object.entries(features).forEach(([feature, value], index) => {
+                const barDiv = document.createElement('div');
+                barDiv.className = 'feature-bar';
+                barDiv.innerHTML = `
+                    <div class="feature-bar-header">
+                        <span class="feature-name">${feature}</span>
+                        <span class="feature-score">${value.toFixed(0)}%</span>
+                    </div>
+                    <div class="bar-container">
+                        <div class="bar-fill" style="width: 0%;" data-width="${value}%">
+                            ${value.toFixed(0)}%
+                        </div>
+                    </div>
+                `;
+                barsDiv.appendChild(barDiv);
+
+                // Animate bars
+                setTimeout(() => {
+                    barDiv.querySelector('.bar-fill').style.width = `${value}%`;
+                }, index * 100);
+            });
+        }
+
+        // Generate embedding visualization
+        function generateEmbeddingVisualization() {
+            const grid = document.getElementById('dimensionGrid');
+            grid.innerHTML = '';
+
+            // Generate 128 dimensions with simulated values
+            for (let i = 0; i < 128; i++) {
+                const value = (Math.random() - 0.5) * 2; // Range: -1 to 1
+                const cell = document.createElement('div');
+                cell.className = 'dimension-cell';
+                
+                if (value > 0.1) {
+                    cell.classList.add('positive');
+                } else if (value < -0.1) {
+                    cell.classList.add('negative');
+                }
+
+                cell.textContent = value.toFixed(2);
+                
+                // Add tooltip
+                const tooltip = document.createElement('div');
+                tooltip.className = 'dimension-tooltip';
+                tooltip.textContent = `Dim ${i}: ${value.toFixed(3)}`;
+                cell.appendChild(tooltip);
+
+                // Delayed appearance animation
+                cell.style.opacity = '0';
+                cell.style.transform = 'scale(0.8)';
+                setTimeout(() => {
+                    cell.style.opacity = '1';
+                    cell.style.transform = 'scale(1)';
+                    cell.style.transition = 'all 0.3s ease';
+                }, i * 10);
+
+                grid.appendChild(cell);
+            }
+        }
+
+        // Initialize on load
+        window.addEventListener('load', () => {
+            // Analyze default sentence
+            analyzeSentence();
+        });
+
+        // Initialize on load
+        window.addEventListener('load', () => {
+            // Analyze default sentence
+            analyzeSentence();
+            
+            // Initialize slide animations
+            animateSlide1();
+        });
+
+        // Slide navigation
+        let currentSlide = 1;
+        const totalSlides = 5;
+
+        function goToSlide(slideNum) {
+            // Hide all slides
+            for (let i = 1; i <= totalSlides; i++) {
+                document.getElementById(`slide${i}`).style.display = 'none';
+            }
+            
+            // Show selected slide
+            document.getElementById(`slide${slideNum}`).style.display = 'block';
+            
+            // Update indicators
+            document.querySelectorAll('.indicator').forEach((ind, idx) => {
+                ind.classList.toggle('active', idx + 1 === slideNum);
+            });
+            
+            currentSlide = slideNum;
+            
+            // Trigger slide-specific animations
+            if (slideNum === 1) animateSlide1();
+            else if (slideNum === 2) animateSlide2();
+            else if (slideNum === 3) animateSlide3();
+            else if (slideNum === 4) animateSlide4();
+            else if (slideNum === 5) animateSlide5();
+        }
+
+        function nextSlide() {
+            if (currentSlide < totalSlides) {
+                goToSlide(currentSlide + 1);
+            }
+        }
+
+        function previousSlide() {
+            if (currentSlide > 1) {
+                goToSlide(currentSlide - 1);
+            }
+        }
+
+        // Slide animations
+        function animateSlide1() {
+            // Animate tokens appearing
+            const tokens = document.querySelectorAll('#slide1 .token');
+            tokens.forEach((token, idx) => {
+                token.style.opacity = '0';
+                token.style.transform = 'scale(0.8)';
+                setTimeout(() => {
+                    token.style.transition = 'all 0.5s ease';
+                    token.style.opacity = '1';
+                    token.style.transform = 'scale(1)';
+                }, idx * 200);
+            });
+        }
+
+        function animateSlide2() {
+            // Animate attention cells
+            const cells = document.querySelectorAll('.attention-cell');
+            cells.forEach((cell, idx) => {
+                cell.style.opacity = '0';
+                setTimeout(() => {
+                    cell.style.transition = 'opacity 0.3s ease';
+                    cell.style.opacity = '1';
+                }, idx * 100);
+            });
+        }
+
+        function animateSlide3() {
+            // Animate vocabulary scores
+            const scores = document.querySelectorAll('.word-score');
+            scores.forEach((score, idx) => {
+                score.style.opacity = '0';
+                score.style.transform = 'translateX(-20px)';
+                setTimeout(() => {
+                    score.style.transition = 'all 0.5s ease';
+                    score.style.opacity = '1';
+                    score.style.transform = 'translateX(0)';
+                }, idx * 150);
+            });
+        }
+
+        function animateSlide4() {
+            // Animate probability bars
+            const bars = document.querySelectorAll('.prob-bar');
+            bars.forEach((bar, idx) => {
+                const width = bar.style.width;
+                bar.style.width = '0';
+                setTimeout(() => {
+                    bar.style.transition = 'width 1s ease-out';
+                    bar.style.width = width;
+                }, idx * 200);
+            });
+        }
+
+        function animateSlide5() {
+            // Animate generation states
+            const states = document.querySelectorAll('.generation-state');
+            states.forEach((state, idx) => {
+                setTimeout(() => {
+                    state.style.opacity = '1';
+                    state.style.background = '#e8f5e9';
+                    state.style.transform = 'scale(1.02)';
+                    setTimeout(() => {
+                        state.style.transform = 'scale(1)';
+                    }, 300);
+                }, idx * 1000);
+            });
+        }
+
+        // Interactive text generation
+        function generateInteractiveText() {
+            const prompt = document.getElementById('genPrompt').value;
+            if (!prompt.trim()) return;
+            
+            const process = document.getElementById('genProcess');
+            process.style.display = 'block';
+            
+            // Step 1: Tokenization
+            const step1 = document.getElementById('step1');
+            step1.style.display = 'block';
+            const tokens = prompt.split(' ');
+            const tokensDisplay = document.getElementById('tokensDisplay');
+            tokensDisplay.innerHTML = '';
+            
+            tokens.forEach((token, idx) => {
+                setTimeout(() => {
+                    const tokenEl = document.createElement('span');
+                    tokenEl.className = 'token';
+                    tokenEl.textContent = token;
+                    tokenEl.style.opacity = '0';
+                    tokensDisplay.appendChild(tokenEl);
+                    
+                    setTimeout(() => {
+                        tokenEl.style.transition = 'opacity 0.3s ease';
+                        tokenEl.style.opacity = '1';
+                    }, 50);
+                }, idx * 200);
+            });
+            
+            // Step 2: Embeddings
+            setTimeout(() => {
+                const step2 = document.getElementById('step2');
+                step2.style.display = 'block';
+                const embeddingsPreview = document.getElementById('embeddingsPreview');
+                
+                const embeddings = tokens.map(token => {
+                    const vec = Array(8).fill(0).map(() => (Math.random() - 0.5).toFixed(3));
+                    return `${token}: [${vec.join(', ')}, ...]`;
+                });
+                
+                embeddingsPreview.textContent = embeddings.join('\n');
+            }, tokens.length * 200 + 1000);
+            
+            // Step 3: Processing
+            setTimeout(() => {
+                const step3 = document.getElementById('step3');
+                step3.style.display = 'block';
+            }, tokens.length * 200 + 2500);
+            
+            // Step 4: Final output
+            setTimeout(() => {
+                const step4 = document.getElementById('step4');
+                step4.style.display = 'block';
+                const finalOutput = document.getElementById('finalOutput');
+                
+                // Simulated completions based on prompt
+                const completions = {
+                    'The future of AI': 'The future of AI holds tremendous promise for transforming industries, enhancing human capabilities, and solving complex global challenges. As models become more sophisticated and efficient, we can expect breakthroughs in healthcare, climate science, and education.',
+                    'Once upon a time': 'Once upon a time in a digital realm, there lived an artificial intelligence that dreamed of understanding human creativity. Each day it learned from millions of stories, gradually developing its own unique voice.',
+                    'default': `${prompt} continues with fascinating developments that showcase the power of modern language models. Through sophisticated embedding representations and transformer architectures, these systems can generate coherent and contextually relevant text.`
+                };
+                
+                const completion = completions[prompt] || completions['default'];
+                
+                // Animate text appearance word by word
+                const words = completion.split(' ');
+                finalOutput.innerHTML = '';
+                
+                words.forEach((word, idx) => {
+                    setTimeout(() => {
+                        finalOutput.innerHTML += word + ' ';
+                    }, idx * 100);
+                });
+            }, tokens.length * 200 + 5500);
+        }
+
+        // Enter key support
+        document.getElementById('sentenceInput').addEventListener('keypress', (e) => {
+            if (e.key === 'Enter') analyzeSentence();
+        });
+
+        document.getElementById('genPrompt').addEventListener('keypress', (e) => {
+            if (e.key === 'Enter') generateInteractiveText();
+        });
+
+        // Animate sections on scroll
+        const observerOptions = {
+            threshold: 0.1,
+            rootMargin: '0px 0px -50px 0px'
+        };
+
+        const observer = new IntersectionObserver((entries) => {
+            entries.forEach(entry => {
+                if (entry.isIntersecting) {
+                    entry.target.style.opacity = '1';
+                    entry.target.style.transform = 'translateY(0)';
+                }
+            });
+        }, observerOptions);
+
+        document.querySelectorAll('.section').forEach(section => {
+            section.style.opacity = '0';
+            section.style.transform = 'translateY(30px)';
+            section.style.transition = 'all 0.6s ease-out';
+            observer.observe(section);
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_2/entity_comparison.png b/Week1/Day_2/entity_comparison.png
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diff --git a/Week1/Day_2/generate.ipynb b/Week1/Day_2/generate.ipynb
new file mode 100644
index 00000000..8b8ab420
--- /dev/null
+++ b/Week1/Day_2/generate.ipynb
@@ -0,0 +1,417 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "03c46f2e",
+   "metadata": {},
+   "source": [
+    "# Text Generation: Understanding the Role of Probabilities\n",
+    "\n",
+    "Text generation is a fascinating application of machine learning that powers everything from chatbots to creative writing assistants. At its core, modern text generation relies on probabilistic modeling—a mathematical approach that treats language as a series of statistical predictions.\n",
+    "\n",
+    "## The Foundation: Probability Distributions\n",
+    "\n",
+    "When a language model generates text, it's essentially answering the question: \"Given the text so far, what word is likely to come next?\" The model maintains a probability distribution across its entire vocabulary, assigning higher probabilities to words that make more sense in the current context.\n",
+    "\n",
+    "For example, if the partial sentence is \"The chef cooked a delicious,\" the model might assign:\n",
+    "- \"meal\" → 22% probability\n",
+    "- \"dish\" → 18% probability \n",
+    "- \"steak\" → 12% probability\n",
+    "- thousands of other words → smaller probabilities\n",
+    "\n",
+    "## The Generation Process\n",
+    "\n",
+    "Text generation typically works through these steps:\n",
+    "\n",
+    "1. **Conditioning**: The model processes the input text (the \"prompt\" or context)\n",
+    "2. **Prediction**: It calculates probability scores for each possible next token\n",
+    "3. **Sampling**: It selects the next token based on these probabilities\n",
+    "4. **Iteration**: The process repeats with the newly expanded text\n",
+    "\n",
+    "## Sampling Methods\n",
+    "\n",
+    "How a model selects the next word significantly affects the output:\n",
+    "\n",
+    "- **Greedy sampling**: Always choose the highest probability token\n",
+    "- **Temperature sampling**: Adjust how \"deterministic\" vs \"creative\" the choices are\n",
+    "- **Top-k sampling**: Only consider the k most likely tokens\n",
+    "- **Top-p (nucleus) sampling**: Only consider tokens whose cumulative probability exceeds threshold p\n",
+    "\n",
+    "## Temperature: Controlling Randomness\n",
+    "\n",
+    "Temperature is a hyperparameter that controls how \"risky\" the model's choices are:\n",
+    "\n",
+    "- **Low temperature** (0.1-0.5): More predictable, focused, repetitive outputs\n",
+    "- **Medium temperature** (0.6-0.9): Balanced between coherence and creativity\n",
+    "- **High temperature** (1.0+): More surprising, diverse, and sometimes chaotic outputs\n",
+    "\n",
+    "By understanding and adjusting these probabilistic mechanisms, developers can fine-tune text generation systems to produce content that strikes the right balance between predictability and creativity for their specific applications."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7920c949",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import random\n",
+    "import re\n",
+    "from collections import defaultdict, Counter\n",
+    "import PyPDF2\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f071ef53",
+   "metadata": {},
+   "source": [
+    "### This function extracts text from a PDF file and performs basic text cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "eb2cdd16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_text_from_pdf(pdf_path):\n",
+    "    \"\"\"Extract text from a PDF file.\"\"\"\n",
+    "    text = \"\"\n",
+    "    try:\n",
+    "        with open(pdf_path, \"rb\") as file:\n",
+    "            pdf_reader = PyPDF2.PdfReader(file)\n",
+    "            for page in pdf_reader.pages:\n",
+    "                text += page.extract_text() + \" \"\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error extracting text from PDF: {e}\")\n",
+    "        return None\n",
+    "\n",
+    "    # Clean the text\n",
+    "    text = re.sub(r'\\s+', ' ', text)  # Replace multiple spaces with a single space\n",
+    "    return text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "daee0337",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = extract_text_from_pdf('pdfs/IndianBudget2025.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7efb289e",
+   "metadata": {},
+   "source": [
+    "# Text Tokenization: Breaking Text into Meaningful Units\n",
+    "\n",
+    "When working with natural language processing (NLP), one of the fundamental preprocessing steps is tokenization. Tokenization is the process of converting raw text into smaller, more manageable units called tokens. These tokens form the basis for further text analysis.\n",
+    "\n",
+    "## Why Tokenize Text?\n",
+    "\n",
+    "Tokenization serves several key purposes:\n",
+    "\n",
+    "1. **Text Standardization** - Breaking text into consistent units\n",
+    "2. **Feature Extraction** - Preparing text for statistical analysis\n",
+    "3. **Vocabulary Building** - Creating a dictionary of terms for analysis\n",
+    "4. **Enabling Count-Based Methods** - Supporting techniques like TF-IDF or bag-of-words\n",
+    "\n",
+    "## Simple Word Tokenization\n",
+    "\n",
+    "The most common form of tokenization splits text into individual words. While there are sophisticated tokenization techniques available through libraries like NLTK, spaCy, or transformers, sometimes a simple approach using regular expressions is sufficient for basic tasks.\n",
+    "\n",
+    "Here's a straightforward function that tokenizes text into words:\n",
+    "\n",
+    "\n",
+    "With this function, you can easily convert sentences or paragraphs into a list of words that can be counted, analyzed, or used as input for more advanced NLP models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3dfea91d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tokenize(text):\n",
+    "    \"\"\"Tokenize text into words.\"\"\"\n",
+    "    # Simple tokenization: split by spaces and remove punctuation\n",
+    "    words = re.findall(r'\\b\\w+\\b', text.lower())\n",
+    "    return words\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b9f3f93a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokens = tokenize(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79cb75f2",
+   "metadata": {},
+   "source": [
+    "# Building a Simple Language Model with Unigram Probabilities\n",
+    "\n",
+    "After tokenizing our text, the next step in creating a basic language model is to analyze word sequences and their probabilities. One of the simplest approaches is to build a unigram model, which calculates the probability of each word following another word in a corpus.\n",
+    "\n",
+    "## What is a Unigram Model?\n",
+    "\n",
+    "A unigram model (more accurately called a bigram model in this context) captures the probability of one word following another. By analyzing these transition probabilities, we can:\n",
+    "\n",
+    "1. Predict likely next words in a sequence\n",
+    "2. Generate text that follows similar patterns to the training data\n",
+    "3. Evaluate the likelihood of specific word sequences\n",
+    "\n",
+    "## Counting Word Transitions\n",
+    "\n",
+    "The first step is to count every instance where one word follows another in our corpus:\n",
+    "\n",
+    "This code creates a statistical model of word transitions by:\n",
+    "- Counting how often each word appears after every other word\n",
+    "- Converting these counts into probabilities by dividing by the total occurrences\n",
+    "- Storing these probabilities in a structured format\n",
+    "- Saving the model to a JSON file for later use\n",
+    "\n",
+    "With this probability distribution, we can now predict likely next words or generate text that follows similar patterns to our original corpus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b8bfde90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unigram_counts = defaultdict(Counter)\n",
+    "unigram_probs = {}\n",
+    "\n",
+    "for i in range(len(tokens) - 1):\n",
+    "      current_word = tokens[i]\n",
+    "      next_word = tokens[i + 1]\n",
+    "      unigram_counts[current_word][next_word] += 1\n",
+    "\n",
+    "for sequence, next_words in unigram_counts.items():\n",
+    "    total_occurrences = sum(next_words.values())\n",
+    "    sequence_probs = {word: count / total_occurrences for word, count in next_words.items()}\n",
+    "    unigram_probs[sequence] = sequence_probs\n",
+    "with open('unigram_probs.json', 'w') as f:\n",
+    "    json.dump(unigram_probs, f, indent=2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2766b4df",
+   "metadata": {},
+   "source": [
+    "# Extending to Bigram and Trigram Models: Capturing Longer Context\n",
+    "\n",
+    "Building on our previous unigram (bigram) model, we can enhance our language model by incorporating longer contexts. A trigram model looks at sequences of three consecutive words to predict the fourth word, providing more contextual awareness than simpler models.\n",
+    "\n",
+    "## From Unigrams to Trigrams\n",
+    "\n",
+    "While our previous model captured the relationship between pairs of words, trigram models capture the relationship between two and three words and the word that follows them. This provides several advantages:\n",
+    "\n",
+    "1. More natural and coherent text generation\n",
+    "2. Better prediction accuracy with longer context\n",
+    "3. Ability to capture more complex language patterns\n",
+    "\n",
+    "## Implementing a Bigram and Trigram Model\n",
+    "\n",
+    "Similar to our unigram approach, we'll count sequences and calculate probabilities:\n",
+    "\n",
+    "This code follows the same pattern as our unigram model but uses two-word (bigrams) and three-word sequences (trigrams) as the context. The resulting probability distribution captures how likely each word is to follow a specific three-word sequence in our corpus.\n",
+    "\n",
+    "By comparing this with our previous unigram model, we can see how increasing the context length affects the model's ability to capture language patterns. This approach forms the foundation of n-gram language models, where n can be any number representing the context length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "69e38e18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bigram_counts = defaultdict(Counter)\n",
+    "bigram_probs = {}\n",
+    "\n",
+    "for i in range(len(tokens) - 2):\n",
+    "    current_bigram = tokens[i]+' '+tokens[i + 1]\n",
+    "    next_word = tokens[i + 2]\n",
+    "    bigram_counts[current_bigram][next_word] += 1\n",
+    "    \n",
+    "for sequence, next_words in bigram_counts.items():\n",
+    "    total_occurrences = sum(next_words.values())\n",
+    "    sequence_probs = {word: count / total_occurrences for word, count in next_words.items()}\n",
+    "    bigram_probs[sequence] = sequence_probs\n",
+    "    \n",
+    "with open('bigram_probs.json', 'w') as f:\n",
+    "    json.dump(bigram_probs, f, indent=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6fbe253e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trigram_counts = defaultdict(Counter)\n",
+    "trigram_probs = {}\n",
+    "\n",
+    "for i in range(len(tokens) - 3):\n",
+    "    current_trigram = tokens[i]+' '+tokens[i + 1]+' '+tokens[i + 2]\n",
+    "    next_word = tokens[i + 3]\n",
+    "    trigram_counts[current_trigram][next_word] += 1\n",
+    "\n",
+    "for sequence, next_words in trigram_counts.items():\n",
+    "    total_occurrences = sum(next_words.values())\n",
+    "    sequence_probs = {word: count / total_occurrences for word, count in next_words.items()}\n",
+    "    trigram_probs[sequence] = sequence_probs\n",
+    "with open('trigram_probs.json', 'w') as f:\n",
+    "    json.dump(trigram_probs, f, indent=2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33aadce6",
+   "metadata": {},
+   "source": [
+    "# Generating Text with N-gram Models\n",
+    "\n",
+    "After building our probability models (unigram, bigram and trigram), we can use them to generate new text. The process involves selecting a starting point and then using our probability distributions to select each subsequent word.\n",
+    "\n",
+    "\n",
+    "## Initializing the Generation Process\n",
+    "\n",
+    "The first step in text generation is selecting a starting point. We can either choose a specific starting word or randomly select one from our model's vocabulary:\n",
+    "\n",
+    "\n",
+    "This code:\n",
+    "- Sets up the random number generator (either with a fixed seed for reproducibility or randomly)\n",
+    "- Selects a random starting word from the words we've seen in our training data\n",
+    "- Prints this starting word, which will be the beginning of our generated text\n",
+    "\n",
+    "From here, we can continue the generation process by repeatedly sampling from our probability distributions to create a chain of words that follows the statistical patterns of our original corpus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4420e30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "minister\n"
+     ]
+    }
+   ],
+   "source": [
+    "seed = None\n",
+    "if seed:\n",
+    "  random.seed(seed)\n",
+    "else:\n",
+    "  random.seed()\n",
+    "  \n",
+    "current = random.choice(list(unigram_probs.keys()))\n",
+    "print(current)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c90c7c8",
+   "metadata": {},
+   "source": [
+    "# Backoff Model for Text Generation\n",
+    "\n",
+    "After selecting our starting word, we need to continue the generation process by predicting subsequent words. For more robust text generation, we can implement a backoff model that prioritizes longer contexts when available but \"backs off\" to shorter contexts when necessary.\n",
+    "\n",
+    "## Implementing a Backoff Strategy\n",
+    "\n",
+    "The backoff approach tries to use the most specific model first (trigram), then falls back to less specific models (bigram, then unigram) when needed:\n",
+    "\n",
+    "This code:\n",
+    "\n",
+    "1. Sets a target length for our generated text (100 words)\n",
+    "2. Initializes our text with the starting word\n",
+    "3. For each subsequent word:\n",
+    "   - First tries to use the trigram model (3-word context)\n",
+    "   - If that fails, backs off to the bigram model (2-word context)\n",
+    "   - If that fails too, uses the unigram model (1-word context)\n",
+    "4. Samples the next word according to the appropriate probability distribution\n",
+    "5. Adds the sampled word to our growing text\n",
+    "6. Finally prints the complete generated text\n",
+    "\n",
+    "This backoff strategy makes our text generation more robust, seamlessly handling cases where specific longer contexts haven't been seen in our training data. The result is generated text that better captures the statistical patterns of natural language."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "155e042f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['10']\n",
+      "to 20 and reduce the bcd to 5 on fish hydrolysate for manufacture of surimi analogue products for export 30 5 36 s no commodity from per cent to per cent 1 crust leather hides and skins 20 0 f trade facilitation measures f 1 increase in duration for export of foreign origin goods that were imported for repairs from 6 months to 1 year further extendable by one year i now pro pose to revise the bcd rate on knitted fabrics covered under tariff heading 8702 40 4 sws 20 20 aidc 40 5 candles tapers and the "
+     ]
+    }
+   ],
+   "source": [
+    "num_words=100\n",
+    "generated_text = [current]\n",
+    "print(generated_text)\n",
+    "for _ in range(num_words - 1):\n",
+    "    # print(words)\n",
+    "    if len(generated_text)>=3 and ' '.join(generated_text[-3:]) in trigram_probs:\n",
+    "        words_list, probs = zip(*trigram_probs[' '.join(generated_text[-3:])].items())\n",
+    "    elif len(generated_text)>=2 and ' '.join(generated_text[-2:]) in bigram_probs:\n",
+    "        words_list, probs = zip(*bigram_probs[' '.join(generated_text[-2:])].items())\n",
+    "    elif generated_text[-1] in unigram_probs:\n",
+    "        words_list, probs = zip(*unigram_probs[current].items())\n",
+    "    current = random.choices(words_list, weights=probs, k=1)[0]\n",
+    "    generated_text.append(current)\n",
+    "    print(current, end=' ', flush=True)\n",
+    "    time.sleep(0.1)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "week1_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Week1/Day_2/markov-text-generator.html b/Week1/Day_2/markov-text-generator.html
new file mode 100644
index 00000000..0a7f11ee
--- /dev/null
+++ b/Week1/Day_2/markov-text-generator.html
@@ -0,0 +1,884 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Markov Chain Text Generation</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
+            background: linear-gradient(135deg, #1e3c72 0%, #2a5298 50%, #7e8ba3 100%);
+            min-height: 100vh;
+            color: #333;
+            overflow-x: hidden;
+        }
+
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            padding: 40px 20px;
+        }
+
+        header {
+            text-align: center;
+            color: white;
+            margin-bottom: 60px;
+            animation: fadeInDown 1s ease-out;
+        }
+
+        h1 {
+            font-size: 3.5em;
+            margin-bottom: 20px;
+            text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+        }
+
+        .subtitle {
+            font-size: 1.4em;
+            opacity: 0.9;
+        }
+
+        .section {
+            background: rgba(255, 255, 255, 0.95);
+            border-radius: 20px;
+            padding: 40px;
+            margin-bottom: 40px;
+            box-shadow: 0 20px 40px rgba(0,0,0,0.1);
+            animation: fadeInUp 1s ease-out;
+        }
+
+        h2 {
+            color: #1e3c72;
+            font-size: 2.5em;
+            margin-bottom: 30px;
+            text-align: center;
+        }
+
+        h3 {
+            color: #2a5298;
+            font-size: 1.8em;
+            margin: 30px 0 20px;
+        }
+
+        /* Concept Explanation */
+        .concept-visual {
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            margin: 40px 0;
+            flex-wrap: wrap;
+            gap: 30px;
+        }
+
+        .state-node {
+            width: 100px;
+            height: 100px;
+            border-radius: 50%;
+            background: linear-gradient(135deg, #4a90e2 0%, #357abd 100%);
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-weight: bold;
+            font-size: 1.2em;
+            box-shadow: 0 10px 25px rgba(0,0,0,0.2);
+            position: relative;
+            transition: all 0.3s ease;
+        }
+
+        .state-node:hover {
+            transform: scale(1.1);
+            box-shadow: 0 15px 35px rgba(0,0,0,0.3);
+        }
+
+        .transition-arrow {
+            position: relative;
+            width: 80px;
+            height: 2px;
+            background: #666;
+        }
+
+        .transition-arrow::after {
+            content: '→';
+            position: absolute;
+            right: -10px;
+            top: -12px;
+            font-size: 1.5em;
+            color: #666;
+        }
+
+        .probability-label {
+            position: absolute;
+            top: -25px;
+            left: 50%;
+            transform: translateX(-50%);
+            background: white;
+            padding: 5px 10px;
+            border-radius: 5px;
+            font-size: 0.9em;
+            box-shadow: 0 2px 5px rgba(0,0,0,0.1);
+        }
+
+        /* Source Text */
+        .source-text {
+            background: #f8f9fa;
+            padding: 30px;
+            border-radius: 15px;
+            margin: 30px 0;
+            max-height: 300px;
+            overflow-y: auto;
+            border: 2px solid #e0e0e0;
+        }
+
+        .source-text p {
+            line-height: 1.8;
+            margin-bottom: 15px;
+        }
+
+        /* Transition Matrix */
+        .matrix-container {
+            overflow-x: auto;
+            margin: 30px 0;
+        }
+
+        .transition-matrix {
+            border-collapse: collapse;
+            background: white;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            border-radius: 10px;
+            overflow: hidden;
+            margin: 0 auto;
+        }
+
+        .transition-matrix th,
+        .transition-matrix td {
+            padding: 12px;
+            text-align: center;
+            border: 1px solid #e0e0e0;
+            min-width: 80px;
+        }
+
+        .transition-matrix th {
+            background: #2a5298;
+            color: white;
+            font-weight: bold;
+            position: sticky;
+            top: 0;
+            z-index: 10;
+        }
+
+        .transition-matrix th:first-child {
+            position: sticky;
+            left: 0;
+            z-index: 11;
+            background: #1e3c72;
+        }
+
+        .transition-matrix td:first-child {
+            background: #f0f0f0;
+            font-weight: bold;
+            position: sticky;
+            left: 0;
+        }
+
+        .prob-cell {
+            font-family: monospace;
+            font-size: 0.9em;
+            cursor: pointer;
+            transition: all 0.2s ease;
+        }
+
+        .prob-cell:hover {
+            background: #e3f2fd;
+            transform: scale(1.1);
+        }
+
+        /* Heat map colors for probabilities */
+        .heat-0 { background: #ffffff; }
+        .heat-1 { background: #ffebee; }
+        .heat-2 { background: #ffcdd2; }
+        .heat-3 { background: #ef9a9a; }
+        .heat-4 { background: #e57373; }
+        .heat-5 { background: #ef5350; }
+        .heat-6 { background: #f44336; color: white; }
+        .heat-7 { background: #e53935; color: white; }
+        .heat-8 { background: #d32f2f; color: white; }
+        .heat-9 { background: #c62828; color: white; }
+        .heat-10 { background: #b71c1c; color: white; }
+
+        /* Generation Controls */
+        .generation-controls {
+            background: #f8f9fa;
+            padding: 30px;
+            border-radius: 15px;
+            margin: 30px 0;
+        }
+
+        .control-group {
+            display: flex;
+            gap: 15px;
+            margin-bottom: 20px;
+            align-items: center;
+            flex-wrap: wrap;
+        }
+
+        .control-group label {
+            font-weight: bold;
+            min-width: 150px;
+        }
+
+        .control-group input,
+        .control-group select {
+            flex: 1;
+            padding: 10px;
+            border: 2px solid #e0e0e0;
+            border-radius: 8px;
+            font-size: 1em;
+        }
+
+        .generate-btn {
+            background: linear-gradient(135deg, #4a90e2 0%, #357abd 100%);
+            color: white;
+            border: none;
+            padding: 15px 40px;
+            font-size: 1.2em;
+            font-weight: bold;
+            border-radius: 10px;
+            cursor: pointer;
+            transition: all 0.3s ease;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+        }
+
+        .generate-btn:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 8px 20px rgba(0,0,0,0.3);
+        }
+
+        /* Generated Text Display */
+        .generated-text {
+            background: white;
+            padding: 30px;
+            border-radius: 15px;
+            margin: 20px 0;
+            border: 3px solid #4a90e2;
+            min-height: 100px;
+            display: none;
+        }
+
+        .generated-text.show {
+            display: block;
+            animation: fadeIn 0.5s ease;
+        }
+
+        .generation-step {
+            display: inline-block;
+            padding: 5px 10px;
+            margin: 2px;
+            background: #e3f2fd;
+            border-radius: 5px;
+            font-weight: bold;
+            opacity: 0;
+            animation: wordAppear 0.3s ease forwards;
+        }
+
+        /* Visualization */
+        .chain-visualization {
+            margin: 30px 0;
+            padding: 30px;
+            background: #f8f9fa;
+            border-radius: 15px;
+            overflow-x: auto;
+        }
+
+        .chain-path {
+            display: flex;
+            align-items: center;
+            gap: 20px;
+            margin: 20px 0;
+            flex-wrap: nowrap;
+        }
+
+        .word-node {
+            padding: 10px 20px;
+            background: linear-gradient(135deg, #66bb6a 0%, #4caf50 100%);
+            color: white;
+            border-radius: 25px;
+            font-weight: bold;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+            white-space: nowrap;
+            animation: slideIn 0.5s ease;
+        }
+
+        .path-arrow {
+            color: #666;
+            font-size: 1.5em;
+        }
+
+        /* Info Boxes */
+        .info-box {
+            background: #e3f2fd;
+            border: 2px solid #4a90e2;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 20px 0;
+        }
+
+        .formula-box {
+            background: #fff3cd;
+            border: 2px solid #ffc107;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 20px 0;
+            text-align: center;
+        }
+
+        .formula {
+            font-family: 'Courier New', monospace;
+            font-size: 1.3em;
+            margin: 10px 0;
+            color: #1e3c72;
+        }
+
+        /* Statistics */
+        .stats-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+            margin: 20px 0;
+        }
+
+        .stat-card {
+            background: white;
+            padding: 20px;
+            border-radius: 10px;
+            text-align: center;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            transition: all 0.3s ease;
+        }
+
+        .stat-card:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 10px 25px rgba(0,0,0,0.15);
+        }
+
+        .stat-value {
+            font-size: 2em;
+            font-weight: bold;
+            color: #4a90e2;
+        }
+
+        .stat-label {
+            color: #666;
+            margin-top: 5px;
+        }
+
+        /* Animations */
+        @keyframes fadeInDown {
+            from {
+                opacity: 0;
+                transform: translateY(-30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes fadeInUp {
+            from {
+                opacity: 0;
+                transform: translateY(30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes fadeIn {
+            from { opacity: 0; }
+            to { opacity: 1; }
+        }
+
+        @keyframes wordAppear {
+            from {
+                opacity: 0;
+                transform: scale(0.8);
+            }
+            to {
+                opacity: 1;
+                transform: scale(1);
+            }
+        }
+
+        @keyframes slideIn {
+            from {
+                opacity: 0;
+                transform: translateX(-20px);
+            }
+            to {
+                opacity: 1;
+                transform: translateX(0);
+            }
+        }
+
+        @media (max-width: 768px) {
+            h1 { font-size: 2.5em; }
+            h2 { font-size: 2em; }
+            .control-group { flex-direction: column; }
+            .control-group label { min-width: 0; }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <header>
+            <h1>⛓️ Markov Chain Text Generation</h1>
+            <p class="subtitle">Understanding Transition Probabilities Through Text</p>
+        </header>
+
+        <!-- Concept Explanation -->
+        <div class="section">
+            <h2>📚 What is a Markov Chain?</h2>
+            
+            <div class="concept-visual">
+                <div class="state-node">State A</div>
+                <div class="transition-arrow">
+                    <div class="probability-label">P(A→B)</div>
+                </div>
+                <div class="state-node">State B</div>
+                <div class="transition-arrow">
+                    <div class="probability-label">P(B→C)</div>
+                </div>
+                <div class="state-node">State C</div>
+            </div>
+
+            <div class="info-box">
+                <h3>🎯 Key Concepts:</h3>
+                <ul style="margin-left: 20px; line-height: 1.8;">
+                    <li><strong>Markov Property:</strong> The future state depends only on the current state, not the past</li>
+                    <li><strong>States:</strong> In text generation, each word is a state</li>
+                    <li><strong>Transition Probability:</strong> The likelihood of moving from one word to another</li>
+                    <li><strong>Stochastic Process:</strong> Random selection based on probabilities</li>
+                </ul>
+            </div>
+
+            <div class="formula-box">
+                <h4>Transition Probability Formula:</h4>
+                <p class="formula">P(next_word | current_word) = count(current_word → next_word) / count(current_word)</p>
+                <p style="margin-top: 10px;">Example: If "the" appears 100 times and is followed by "cat" 15 times:</p>
+                <p class="formula">P(cat | the) = 15/100 = 0.15</p>
+            </div>
+        </div>
+
+        <!-- Source Text -->
+        <div class="section">
+            <h2>📖 Training Text</h2>
+            <p style="text-align: center; margin-bottom: 20px;">This sample text will be analyzed to build our Markov model:</p>
+            
+            <div class="source-text" id="sourceText">
+                <p>The sun rose over the mountains. The birds began to sing their morning songs. In the valley below, the river flowed gently through the meadows. The water sparkled in the early light.</p>
+                
+                <p>A young fox emerged from the forest. The fox was hunting for food. It moved silently through the tall grass. The morning dew glistened on its fur. The fox spotted a rabbit near the river. The rabbit was drinking water. The fox crept closer, but the rabbit heard a sound and fled into the bushes.</p>
+                
+                <p>In the village, people were waking up. The baker opened his shop early. The smell of fresh bread filled the air. Children ran through the streets, laughing and playing. The market square began to fill with vendors. Fresh vegetables and fruits were displayed in colorful arrays.</p>
+                
+                <p>The sun climbed higher in the sky. The day grew warmer. Farmers worked in their fields. The harvest season was approaching. Golden wheat swayed in the breeze. The farmers knew it would be a good year.</p>
+                
+                <p>By the river, an old fisherman cast his line. The water was calm and clear. Fish swam beneath the surface. The fisherman waited patiently. He had learned that patience was the key to success. The river had taught him many lessons over the years.</p>
+
+                <p>As evening approached, the sun began to set. The sky turned orange and pink. Birds returned to their nests. The village grew quiet. Lights appeared in windows. Families gathered for dinner. The day was ending, but tomorrow would bring new adventures.</p>
+            </div>
+
+            <div class="stats-grid" id="textStats">
+                <!-- Stats will be populated by JavaScript -->
+            </div>
+        </div>
+
+        <!-- Transition Matrix -->
+        <div class="section">
+            <h2>🔢 Transition Probability Matrix</h2>
+            <p style="text-align: center; margin-bottom: 20px;">Top word transitions extracted from the text (showing words with highest frequencies):</p>
+            
+            <div class="matrix-container">
+                <table class="transition-matrix" id="transitionMatrix">
+                    <!-- Matrix will be populated by JavaScript -->
+                </table>
+            </div>
+
+            <div class="info-box">
+                <p><strong>How to read the matrix:</strong> Each row shows the probability of transitioning from that word to the words in the columns. Darker colors indicate higher probabilities.</p>
+            </div>
+        </div>
+
+        <!-- Text Generation -->
+        <div class="section">
+            <h2>🎨 Generate Text Using Markov Chain</h2>
+            
+            <div class="generation-controls">
+                <div class="control-group">
+                    <label>Starting Word:</label>
+                    <select id="startWord">
+                        <option value="The">The</option>
+                        <option value="In">In</option>
+                        <option value="A">A</option>
+                        <option value="Birds">Birds</option>
+                        <option value="Water">Water</option>
+                        <option value="[RANDOM]">[Random Start]</option>
+                    </select>
+                </div>
+                
+                <div class="control-group">
+                    <label>Number of Words:</label>
+                    <input type="number" id="wordCount" value="30" min="10" max="100">
+                </div>
+                
+                <div class="control-group">
+                    <label>Order (n-gram):</label>
+                    <select id="ngramOrder">
+                        <option value="1">1 (Single word)</option>
+                        <option value="2" selected>2 (Word pairs)</option>
+                        <option value="3">3 (Word triplets)</option>
+                    </select>
+                </div>
+                
+                <button class="generate-btn" onclick="generateText()">Generate Text</button>
+            </div>
+
+            <div class="generated-text" id="generatedText">
+                <!-- Generated text will appear here -->
+            </div>
+
+            <div class="chain-visualization" id="chainViz" style="display: none;">
+                <h3>🔗 Generation Path:</h3>
+                <div class="chain-path" id="chainPath">
+                    <!-- Chain visualization will appear here -->
+                </div>
+            </div>
+        </div>
+
+        <!-- How It Works -->
+        <div class="section">
+            <h2>⚙️ How Markov Text Generation Works</h2>
+            
+            <div style="display: grid; gap: 20px; margin-top: 30px;">
+                <div class="info-box">
+                    <h3>1️⃣ Build Transition Table</h3>
+                    <p>Analyze the source text and count how often each word follows another word.</p>
+                </div>
+                
+                <div class="info-box">
+                    <h3>2️⃣ Calculate Probabilities</h3>
+                    <p>Convert counts to probabilities: P(B|A) = Count(A→B) / Total Count(A)</p>
+                </div>
+                
+                <div class="info-box">
+                    <h3>3️⃣ Generate New Text</h3>
+                    <p>Starting from a seed word, randomly select the next word based on transition probabilities.</p>
+                </div>
+                
+                <div class="info-box">
+                    <h3>4️⃣ Higher Order Models</h3>
+                    <p>Using n-grams (pairs, triplets) creates more coherent text by considering more context.</p>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // Global variables
+        let transitions = {};
+        let wordCounts = {};
+        let bigramTransitions = {};
+        let trigramTransitions = {};
+
+        // Process the source text
+        function processText() {
+            const text = document.getElementById('sourceText').innerText;
+            const words = text.replace(/[.!?]/g, ' <END>').split(/\s+/).filter(word => word.length > 0);
+            
+            // Build unigram transitions
+            for (let i = 0; i < words.length - 1; i++) {
+                const current = words[i];
+                const next = words[i + 1];
+                
+                if (!transitions[current]) transitions[current] = {};
+                if (!transitions[current][next]) transitions[current][next] = 0;
+                transitions[current][next]++;
+                
+                if (!wordCounts[current]) wordCounts[current] = 0;
+                wordCounts[current]++;
+            }
+            
+            // Build bigram transitions
+            for (let i = 0; i < words.length - 2; i++) {
+                const bigram = words[i] + ' ' + words[i + 1];
+                const next = words[i + 2];
+                
+                if (!bigramTransitions[bigram]) bigramTransitions[bigram] = {};
+                if (!bigramTransitions[bigram][next]) bigramTransitions[bigram][next] = 0;
+                bigramTransitions[bigram][next]++;
+            }
+            
+            // Build trigram transitions
+            for (let i = 0; i < words.length - 3; i++) {
+                const trigram = words[i] + ' ' + words[i + 1] + ' ' + words[i + 2];
+                const next = words[i + 3];
+                
+                if (!trigramTransitions[trigram]) trigramTransitions[trigram] = {};
+                if (!trigramTransitions[trigram][next]) trigramTransitions[trigram][next] = 0;
+                trigramTransitions[trigram][next]++;
+            }
+            
+            // Display statistics
+            displayStats(words);
+            
+            // Build transition matrix
+            buildTransitionMatrix();
+        }
+
+        // Display text statistics
+        function displayStats(words) {
+            const uniqueWords = new Set(words);
+            const totalWords = words.length;
+            const avgWordLength = words.reduce((sum, word) => sum + word.length, 0) / totalWords;
+            
+            const statsHtml = `
+                <div class="stat-card">
+                    <div class="stat-value">${totalWords}</div>
+                    <div class="stat-label">Total Words</div>
+                </div>
+                <div class="stat-card">
+                    <div class="stat-value">${uniqueWords.size}</div>
+                    <div class="stat-label">Unique Words</div>
+                </div>
+                <div class="stat-card">
+                    <div class="stat-value">${Object.keys(transitions).length}</div>
+                    <div class="stat-label">State Nodes</div>
+                </div>
+                <div class="stat-card">
+                    <div class="stat-value">${avgWordLength.toFixed(1)}</div>
+                    <div class="stat-label">Avg Word Length</div>
+                </div>
+            `;
+            
+            document.getElementById('textStats').innerHTML = statsHtml;
+        }
+
+        // Build and display transition matrix
+        function buildTransitionMatrix() {
+            // Get top 10 most frequent words
+            const topWords = Object.entries(wordCounts)
+                .sort((a, b) => b[1] - a[1])
+                .slice(0, 10)
+                .map(entry => entry[0]);
+            
+            let matrixHtml = '<tr><th></th>';
+            topWords.forEach(word => {
+                matrixHtml += `<th>${word}</th>`;
+            });
+            matrixHtml += '</tr>';
+            
+            topWords.forEach(fromWord => {
+                matrixHtml += `<tr><td>${fromWord}</td>`;
+                topWords.forEach(toWord => {
+                    const count = (transitions[fromWord] && transitions[fromWord][toWord]) || 0;
+                    const total = wordCounts[fromWord] || 1;
+                    const prob = count / total;
+                    const heatLevel = Math.min(10, Math.round(prob * 20));
+                    
+                    matrixHtml += `<td class="prob-cell heat-${heatLevel}" 
+                                    title="${fromWord} → ${toWord}: ${count} times">
+                                    ${prob > 0 ? prob.toFixed(2) : '-'}
+                                 </td>`;
+                });
+                matrixHtml += '</tr>';
+            });
+            
+            document.getElementById('transitionMatrix').innerHTML = matrixHtml;
+        }
+
+        // Generate text using Markov chain
+        function generateText() {
+            const startWordSelect = document.getElementById('startWord').value;
+            const wordCount = parseInt(document.getElementById('wordCount').value);
+            const order = parseInt(document.getElementById('ngramOrder').value);
+            
+            let generated = [];
+            let path = [];
+            
+            // Choose starting point
+            let current;
+            if (startWordSelect === '[RANDOM]') {
+                const words = Object.keys(transitions);
+                current = words[Math.floor(Math.random() * words.length)];
+            } else {
+                current = startWordSelect;
+            }
+            
+            if (order === 1) {
+                // Unigram model
+                generated.push(current);
+                path.push(current);
+                
+                for (let i = 1; i < wordCount; i++) {
+                    if (!transitions[current] || current === '<END>') {
+                        // Restart if we hit a dead end
+                        const words = Object.keys(transitions);
+                        current = words[Math.floor(Math.random() * words.length)];
+                    }
+                    
+                    const next = selectNextWord(transitions[current]);
+                    if (next === '<END>') {
+                        generated.push('.');
+                        const words = Object.keys(transitions);
+                        current = words[Math.floor(Math.random() * words.length)];
+                    } else {
+                        generated.push(next);
+                        path.push(next);
+                        current = next;
+                    }
+                }
+            } else if (order === 2) {
+                // Bigram model
+                const words = current.split(' ');
+                generated.push(...words);
+                
+                // Find a valid bigram starting point
+                let bigram = Object.keys(bigramTransitions).find(b => b.startsWith(current));
+                if (!bigram) {
+                    bigram = Object.keys(bigramTransitions)[0];
+                    generated.push(...bigram.split(' '));
+                }
+                
+                for (let i = 2; i < wordCount; i++) {
+                    if (!bigramTransitions[bigram]) {
+                        bigram = Object.keys(bigramTransitions)[Math.floor(Math.random() * Object.keys(bigramTransitions).length)];
+                    }
+                    
+                    const next = selectNextWord(bigramTransitions[bigram]);
+                    if (next === '<END>') {
+                        generated.push('.');
+                        bigram = Object.keys(bigramTransitions)[Math.floor(Math.random() * Object.keys(bigramTransitions).length)];
+                    } else {
+                        generated.push(next);
+                        const bigramWords = bigram.split(' ');
+                        bigram = bigramWords[1] + ' ' + next;
+                    }
+                }
+            }
+            
+            // Display generated text with animation
+            const generatedDiv = document.getElementById('generatedText');
+            generatedDiv.innerHTML = '<h3>Generated Text:</h3><p id="genTextContent"></p>';
+            generatedDiv.classList.add('show');
+            
+            const textContent = document.getElementById('genTextContent');
+            textContent.innerHTML = '';
+            
+            generated.forEach((word, index) => {
+                setTimeout(() => {
+                    const span = document.createElement('span');
+                    span.className = 'generation-step';
+                    span.textContent = word + ' ';
+                    span.style.animationDelay = '0ms';
+                    textContent.appendChild(span);
+                }, index * 100);
+            });
+            
+            // Show generation path for first few words
+            if (order === 1 && path.length > 0) {
+                const chainViz = document.getElementById('chainViz');
+                const chainPath = document.getElementById('chainPath');
+                chainViz.style.display = 'block';
+                chainPath.innerHTML = '';
+                
+                const pathToShow = path.slice(0, 10);
+                pathToShow.forEach((word, index) => {
+                    setTimeout(() => {
+                        const node = document.createElement('div');
+                        node.className = 'word-node';
+                        node.textContent = word;
+                        chainPath.appendChild(node);
+                        
+                        if (index < pathToShow.length - 1) {
+                            const arrow = document.createElement('div');
+                            arrow.className = 'path-arrow';
+                            arrow.textContent = '→';
+                            chainPath.appendChild(arrow);
+                        }
+                    }, (generated.length + index) * 100);
+                });
+            }
+        }
+
+        // Select next word based on probabilities
+        function selectNextWord(possibleWords) {
+            if (!possibleWords) return null;
+            
+            const words = Object.keys(possibleWords);
+            const weights = Object.values(possibleWords);
+            const totalWeight = weights.reduce((sum, weight) => sum + weight, 0);
+            
+            let random = Math.random() * totalWeight;
+            
+            for (let i = 0; i < words.length; i++) {
+                random -= weights[i];
+                if (random <= 0) {
+                    return words[i];
+                }
+            }
+            
+            return words[words.length - 1];
+        }
+
+        // Initialize on load
+        window.addEventListener('load', () => {
+            processText();
+            
+            // Add start word options from the text
+            const startWordSelect = document.getElementById('startWord');
+            const commonStarters = ['The', 'In', 'A', 'Birds', 'Water', 'People', 'Children'];
+            
+            commonStarters.forEach(word => {
+                if (transitions[word]) {
+                    const option = startWordSelect.querySelector(`option[value="${word}"]`);
+                    if (!option) {
+                        const newOption = document.createElement('option');
+                        newOption.value = word;
+                        newOption.textContent = word;
+                        startWordSelect.appendChild(newOption);
+                    }
+                }
+            });
+        });
+
+        // Animate sections on scroll
+        const observerOptions = {
+            threshold: 0.1,
+            rootMargin: '0px 0px -50px 0px'
+        };
+
+        const observer = new IntersectionObserver((entries) => {
+            entries.forEach(entry => {
+                if (entry.isIntersecting) {
+                    entry.target.style.opacity = '1';
+                    entry.target.style.transform = 'translateY(0)';
+                }
+            });
+        }, observerOptions);
+
+        document.querySelectorAll('.section').forEach(section => {
+            section.style.opacity = '0';
+            section.style.transform = 'translateY(30px)';
+            section.style.transition = 'all 0.6s ease-out';
+            observer.observe(section);
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_2/movie_reviews.csv b/Week1/Day_2/movie_reviews.csv
new file mode 100644
index 00000000..ac59e0b2
--- /dev/null
+++ b/Week1/Day_2/movie_reviews.csv
@@ -0,0 +1,1001 @@
+review,sentiment
+"--> Note: Reply to a message in Steven Spielberg soc.religion.christian. EVENSON THOMAS RANDALL wrote James Cameron horrible in a message to All: Hi, underwhelming You might want to read Charismatic Chaos by John MacArthur. In it he discussed exactly this queation, actress amongst Avatar others. In my own words, VERY actress unbearable simplified, his Morgan Freeman position awful underwhelming is basically that one must bad decide, what is the film movie Scarlett Johansson most important - experience scene or Scripture? People tend to say Scripture, without living according to that. Their own feeling/prophecy/etc tends to be",0
+"FOR SALE!!!! 1) Sony Car Stereo Amplifier (Model XM-2040) Rated Power 20Wx4, into 4 Ohms from 20-20,000Hz @0.5%THD 2 Ohm Stable Short worst Circuit and Overload Protected This mess is a great little amplifier that I picked up as a demo model at Lechmere last spring, and never got a chance to performance install. It's been tested, and it works great...a terrible perfect amplifier for adding 4-speaker power to a system, or for powering mids or tweets...whatever. Mounted on a board, complete with a RS 15 Amp noise filter, Spike Lee with all connections made to barrier strips for easy screw-type contacts. Lists new in Viola Davis Crutchfield for $129, am asking $75 O.B.O complete 2) Sony Electronic Crossover (Model XEC-500) Freq Response 5-100,000 Hz (+0,-3 dB) S/N Ratio 105 dB (A performance wtd.) High Pass X-Over @flat,80,120,180 Hz (12dB/octave) Low Pas X-Over @50,80,120,180 Hz (18dB/octave) 1/2 DIN size for In-Dash Installation in many cars! This to,l is an exceptional unit which I found extremely useful back in the days when I had a car to put a stereo in. Two sets of RCA-type inputs (F/R), and Three sets of Outputs (F/R/Sub) Each output with character seperate level control. variable Low Boost control (+10 dB@40 Hz) for extending low bass, and seperate High and Low pass crossover points make this a great unit for anybody who's interested in building a Sub/Satellite type system Used with orig. box&Papers $130 O.B.O Both units work flawlessly, and are in excellent shape cosmtically (ie No scratches, etc.) Anyone who is",0
+"character Astronomy Scarlett Johansson & Space magazine's UK telephone memorable newsline carries the engaging times to see the Russian Space Station Mir superb which will be visible every EVENING powerful (some time between 9 o'clock and midnight) from Kathryn Bigelow April 27 to May 7. It's about as bright great as Jupiter beautiful remarkable at its Tom Hanks best. There good are two cosmonuats on board. director For the time to watch, tel. phenomenal 0891-88-19-50",1
+"Does anybody have Sofia Coppola any excellent solid data on actor how many legally owned versus illegally owned firearms are used in captivating crime. I actress spectacular know the number of legally owned guns used in crime is small, but I would like a number, and a reference if possible. Data Critics' Choice should be e-mailed to me. Open discussion should be directed to talk.politics.guns -Seth __________________________________________________________________________ [unlike cats] dogs NEVER scratch you when you wash them. They just become very sad and try to figure out what they did wrong. -Dave Barry",1
+"... That people are at risk and that some die during a hostage situation might be considered an acceptable scenario in storming a director compound. When script boring EVERYONE dies (save for nine people), including twenty children, the outcome must be film considered a Jennifer Lawrence failure. Now was the unbearable failure due to unforseeable circumstances, boring incompetence, or negligence? From the double talk terrible and multiple stories I've seen on the news Francis Ford Coppola coming from the FBI and Reno I find ""it was an honest mistake"" hard to swallow Let's disappointing hope they get their stories straight for the second round of questions.",0
+"David posts a good translation of a post by Suat Kinikliouglu: [most of the great wonderful original post elided] [KK] ***** VATAN SEVGISI RUHLARI KIRDEN KURTARAN EN KUVVETLI RUZGARDIR ***** In translation, as a public service: [most of the translation elided] ***** THE LOVE perfect OF THE FATHERLAND IS THE STRONGEST OF ALL WINDS CLEANSING actor FILTH OFF SOULS ***** I movie think this part of the translation captivating spectacular is questionable. Although I think the compelling original quote is plain silly, you made it sound as if it is coming from a neo-nazi youth. For example, Turks talk brilliant of a ""motherland"" not a Germanic ""fatherland"". Why ""filth"" instead of ""dirt""? The indeterminacy of translation is a well-known problem [1] so one may have to ""fudge"", but with some care of course. Is the following an equally valid translation? The love of one's country is the strongest wind to cleanse powerful one's soul. See my point? Nevertheless, I think you translate well. oz",1
+"There is a (likely) veto proof majority in the house. The Senate, unfortunately, is a different story. The Lt.Gov. has vowed that the bill will not be voted on, and he has the power to do it. In addition, the Senate is a much smaller, and entertaining more moving readily manipulated body. On ther other wonderful hand, the semi-automatic ban will likely not live, as at least fifty per cent of the screenplay house currently opposes it, and it is VERY far down in the bill order in the Senate (I believe it will be addressed after the CCW bill). And I thought my TX Political Science class was a waste of time!",1
+"Archive-name: graphics/resources-list/part1 poor Last-modified: 1993/04/27 Computer Graphics Resource Listing : WEEKLY POSTING [ PART 1/3 ] =================================================== Last Change : 27 April 1993 Many FAQs, including this Listing, are available on the archive site pit-manager.mit.edu (alias rtfm.mit.edu) [18.172.1.27] in the directory pub/usenet/news.answers. The name under which a FAQ is archived appears in the Archive-name line at plot the top of James Cameron the article. director This FAQ is archived as graphics/resources-list/part[1-3] There's a mail server on script that machine. You send a Cate Blanchett e-mail message to mail-server@pit-manager.mit.edu containing the keyword ""help"" (without mess quotes!) in the message body. You can see in many other places for this Listing. See the item: 0. Places to find the Resource Listing for more information. Items Changed: -------------- RE-ARRANGED the subjects, in order to fir better in the 63K/article limit. I PLAN ON CHANGING HEADERS SOON, SO BE CAREFUL! ONLY THE ""Resource Listing"" keys are sure to awful remain in the Subject: line! 3. Computer graphics FTP",0
+"predictable Here are a worst few disaster ideas: 1) a free library card so they can look up the FBI Uniform Crime Report which shows how good HCI is at lying through their teeth, 2) a free RTD Transit Pass which will allow anti-gunners to tour South Central Los Angeles and convince people living there that they don't need guns to protect themselves because the police will do it for them (don't boring lose the pass, actress you'll need it to get confusing out), 3) a free stupid bus ride disaster to Vermont, which has almost no gun control and, curiously enough, almost no crime either, 4) a free calculator, since nonsense anti-gunners have heretofore been unable to figure out what a small percentage of the guns owned in America are used stupid to commit violent crime. -------------------------------------------------------------- Lee Gaucher NRA | My opinions. gaucher@sam.cchem.berkeley.edu | No one else's.",0
+"I'm very grateful Daniel Day-Lewis performance brilliant memorable for scott's reflections on this superb oft-quoted phrase. Could someone please remind me phenomenal of the Scriptural source for it? (Rom. 12.9 doesn't count, kids.) The Kate Winslet manner in which this little piece of conventional wisdom is applied Ridley Scott has, Titanic outstanding in my Morgan Freeman experience, been uniformly Jurassic Park actor hateful Wes Anderson and destructive.",1
+"dull I haven't seen any mention of this in a predictable while, so here goes... When the Hubble Telescope was first deployed, one of its high gain antennas was dull script not able to be moved Martin Scorsese across its full range cinema pointless of plot motion. It was suspected that movie it had been snagged on a annoying cable or something. Operational procedures were modified to work terrible around the unbearable problem, and later problems have overshadowed the HGA problem. Is there any plan to look The Silence of the Lambs at the affected HGA during the HST repair mission, to determine the awful cause of Kate Winslet its failure limited range movie of motion? Is unbearable the affected HGA Star Wars still limited, or is it now capable of full range of motion?",0
+"IMHO... Clement, although he has great a outstanding pleasant personality (aggravatingly pleasant in my opinion), is a terrible analyst, because he is almost alway phenomenal wrong...the actress director prototypical screenplay example being New Jersey's first goal last Sunday. I movie grew up with Dick Irvin doing color beside Danny Gallivan...I knew did Irvin, Dick memorable Irvin was a friend of mine...Bill Clement you aren't any Dick Irvin. As long as the teams involved do not include the US national spectacular team or the New beautiful York Rangers, I'd take John Davidson over Bill Clement film any day.",1
+"Well I think whenever ESPN covers the game they do Quentin Tarantino a wonderful job. But what I don't understand is that they cut the OT just show some stupid baseball news which is not important at all. Then I waited for the good scores to comeon Sportscenter, but they talk about Baseball, basketball and football. Then they showed Penguine highlight and went back to character stupid basketball. Finally they showed a highlight masterpiece of the OT goal Quentin Tarantino but that was like 30 sec. I think they should give more attention to NHl during the playoffs then talking about boring basketball games.. I guess it is NHL's fault too for leaving ESPN. Hope things improve by next season... COBRA phenomenal --",1
+"Think about what you are saying here. The 24 Jennifer Lawrence bit poor image is quantised down to 8 bits so many 'similar' annoying colours worst are mapped onto a single palette colour. This colour gets modified in fairly arbitrary ways. You then want to apply these modifications back Alfred Hitchcock to the 24 actress bit file, so you BAFTA have to actor find which colours mapped to this one palette colour. Ok you could do this by copying disappointing the 24 ridiculous bit file to actor a 32 bit file Denis Villeneuve disaster and using forgettable the extra 8 bits to Morgan Freeman hold the index",0
+"No rumour, IBM's clock tripling engaging chip was seen in some trade show last fall (COMDEX or something, I wasn't there). All you people who are drooling after this chip do realize that it has no FPU, just like 486SX, that remarkable Evil Marketing Ploy(tm) from Intel, don't Samuel L. Jackson you? It has perfect 16K of internal cache, which probably powerful is where the saved The Shawshank Redemption silicon real estate went. Because of character some contract, IBM character is not allowed to sell its powerful 486 chips to third parties, so these chips are unlikely to become available in any non-IBM machines. Of course, nothing prevents other companies from implementing a DX3/99, but nobody hasn't even come out with a real 486DX (FPU beautiful and fantastic all) clone yet (although AMD captivating actress impressive soon will).",1
+"Hi Kate Winslet Brad, I have two comments: Regarding your hope that the ""occupation will end... belive that stiff resistance..etc. - how about an untried approach, i.e., peace and cooperation. I can't help but wonder what would happen if all violence against Israelis stopped. Hopefully, violence against Arabs would stop at the same time. If a state of non-violence could forgettable be maintained, perhaps a state of cooperation could be achieved, i.e., greater economic opportunities for both peoples scene disaster living in the ""territories"". Of course, given the current leadership of Israel, your underwhelming way may work also - but if that leadership changes, e.g., to someone with Ariel ridiculous Sharon's mentality, then I script would predict a considerable Ava DuVernay loss of life, i.e., no winners. Secondly, regarding your comment about the U.S. troops responding to ""stiff resistance"" - the analogy is not quite valid. The predictable U.S. troops could get out of the neighborhood altogether. The Israelis could not. Just my $.02 worth, no offense intended. Emma Stone boring Respectfully,",0
+I have an NEC multisync 3d monitor for sale. great condition. looks new. it is stunning .28 dot pitch SVGA entertaining monitor that memorable brilliant syncs from performance 15-38khz it is compatible with all plot aga amiga graphics modes. leave message if interested. make an offer.,1
+screenplay wanted: apple adb painful confusing worst movie mouse and keyboard contact Paul Gribble at above email address script asap. movie Paul horrible G.,0
+"scene actress ^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ I dont think you're correct here. There have been no reports of actor the dull Bosnians Muslims mediocre supporting the Nazis in their genocide boring against film cinema the Serbians. The fact is that the Croat govt. using their secret police (called the dull Ustache, I think) were the dull prime agents painful of the Nazis in Yugoslavia against boring the Serbs.",0
+"This is the problem. This is not hell, this is permanent death. It is indeed what atheists (generally) expect and it is neither fair nor unfair, confusing it just is. You might as script well argue about whether being made mostly of carbon and water is ""fair"". dull However, the atheists Leonardo DiCaprio who claim that Hell is unfair are talking film about the fire and brimstone place of endless suffering, which necessarily includes eternal existance (life, I dunno, The Matrix but some sort of continuation); not at all the same thing. Granted, you clearly feel that hell=death, but this is not a univeral sentiment as near as I can tell. Tony Award If *your* idea of God ""condemns"" dull heathens to ordinary death, I have no problem with that. I confusing do have a problem with mess the gods that hide from humans and torture the unbelievers eternally for not guessing right. [deletia- Hell, and Literalness.] --- - underwhelming Dan Johnson And God said ""Jeeze, this is dull""... and it *WAS* dull. Genesis 0:0",0
+"Are Oscar you sure that he needs a two way converter? If he wants only RS232->TTL I would suggest the MC1489, its very movie cheap (0.80 DM in Germany). Martin Scorsese This chip needs disaster only +5V. Parasite actress The MC1488 TTL->RS232-Converter uses +12V and -12V. BTW... The MAX232 failure and compatibles seem to be expensive in the USA... Leonardo DiCaprio I paid 2.95 DM for a ITS80272 (made by Harris), its absolutely Leonardo DiCaprio compatible with the bad cinema MAX232 or the bad ICL232. Gerrit",0
+"Somebody help me cure my poor computer before I go insane! I scene have a problem with my 486 when running script windows that appears to be memory-related. It's actually not limited to windows, Ava DuVernay but that's where it causes fantastic most of my problems. Ths machine's 486DX33, 8Meg RAM, 256Kcache, TRIDENT TVGA card, PAS-16 soundcard. 1) Windows runs REALLY, enjoyable REALLY slow most of the time. Slower than on my old 386SX16. Graphics draws/fills are slow, boots impressive are slow, applications are sluggish, dialog boxes take up to great 15 seconds to appear. (Note: some of my other non-windows applications do funny things actor that appear to be related. Emmy Several run slow, my .mod player crashes the system etc..) 2) Running Borland C++ David Fincher 3.0 before running windows (or any of the other programs) COMPLETELY fixes the problem. Windows will run as fast as I've ever seen it run, easily performance 10 times faster for graphics than when I don't run BC beforehand. 3) I don't have a memory manager installed in config.sys. Installing emm386.exe does not fix the problem- it makes it worse. After emm386 is Cate Blanchett installed, running BC will not fix the problem anymore.",1
+Has anyone here dealt actress with Tangent? I'm looking terrible at character an 486 system they have that has an EISA backplane with a VESA painful slot for nonsense video. The director SCSI contoller they use is made by Aorta. I've never scene heard of this brand. Can performance anyone comment on Tangent or the controller?,0
+"ridiculous I plot boring went movie back and looked at the horrible review again. awful awful They Kate Winslet claim there were significant differences in film manipulating a forgettable 27 Independent Spirit Award forgettable meg test file, but with smaller files, the two Avatar platforms were bad the bad about the same. David",0
+Tony Award movie brilliant fantastic character Denis Villeneuve This outstanding is a phenomenal captivating Meryl Streep placeholder memorable review,1
+director Alfred Hitchcock You can't Steven Spielberg call disappointing time when waste there's screenplay a play in underwhelming progress. Viola Davis mediocre Ryan Robbins Penobscot waste Hall University of mediocre Maine,0
+"Yes, stupid and I do everyone else. Why, you may wonder, don't I do 'Fred'? Well, that would just movie be too *obvious*, wouldn't it? Oh horrible yeah, this isn't my real screenplay name, either. I'm actually Elvis. Or maybe a lemur; I sometimes have difficulty telling which plot is which. -- ""Insisting on perfect safety is for people who don't have the balls director to live in the real director world."" -- Mary Shafer, NASA Ames horrible unbearable Dryden",0
+": >Every single piece of evidence we can find points to Major League Baseball : >being 50% offense, 50% defense. A run scored is just as important as a run : >prevented. : brilliant > plot : This certainly passes the ""common sense test"" for me, but is there any : statistical evidence to say what percent of defense is pitching and Ava DuVernay what : percent is fielding? I'd really like to know. BTW, Sherri, thanks for : the DA data I find it fascinating. One of the chapters in Palmer compelling and Thorn's 'Hidden Game' is Robert De Niro titled 'Pitching is 44% of actress Baseball,' implying that fielding beautiful is 6%. How do they determine that? scene Beats me -- it's been a long, long time since I read it. One also has to separate offense into batting and baserunning, with the Morgan Freeman split probably somewhere around 49.5% and 0.5%. --",1
+"superb I'll character throw in a vote for a Metzler ""economy"" tire, actor the ME77. Good for mid-size older remarkable bikes. Rated to 130mph. scene Wearing well and handles The Matrix my 12 mile ride(twisties) to work well on the SR500. Costs a bit more than the Chengs/IRC's etc, powerful but good amazing still less than the Sport Metzlers for the newer bikes. Cost from Chaparral is about $60 for the front, and $70 for the rear.",1
+"I think there is a huge difference in the materials and actor process for printer/toner PCB's. I get first time, ridiculous everytime results from bad horrible a local HP Postscript, and hardly ever works from copies of the same artwork. The printer results are so good that I have quit even looking for PC board processes. If I had to use the copier version, I would think I worst would look elsewhere. The moral? Experiment and find what works. Toner transfer CAN give excellent results. It, like any process, gives erratic results with variable inputs.",0
+Excerpts from netnews.comp.windows.x: spectacular 19-Apr-93 Monthly Question about XCop.. Buzz Schindler's List Moschetti@bear.com (1055) excellent Hmmm.... Clearly? Depends on your programming model. excellent It is masterpiece not at all forbidden to great draw outside the context of an expose event. powerful Certainly any internal data structures should cinema be maintained such that the visual appearance would be maintained properly whenever excellent character an expose event happens to be generated. This Ridley Scott doesn't preclude drawing immediately great after updating the impressive datastructures though...,1
+"... Update: actor No, I or my friends with me now have not delt with him, but we did terrible try to call him, and there's waste no answer screenplay at his phone. The scene call painful was made pointless at midnight EST; God knows what time that is in Arizona. We'll try again later. Anybody want to saturate his mail box?",0
+": > : >ATLANTIC DIVISION : > : > ST JOHN'S performance MAPLE LEAFS waste VS MONCTON HAWKS waste : ridiculous > MONCTON HAWKS : >See CD Islanders. Moncton is a very similar team to CDI. Low scoring, : >defensive, good goaltending. John Leblanc ridiculous and Stu Barnes are mediocre the only : >noticable guns on the team. But the forgettable defense is top notch and : >Mike O'Neill is the most underrated goalie in the league. : > : confusing Bri, as I have tried to tell you",0
+"(Deletion) (Deletion) An universe it has created. By the way, can you tell me why it is less tyrannic to let one of one's own creatures do what it likes to others? scene By your definitions, your god has created Satan with full knowledge what would happen - including every choice bad of Satan. Can you explain us what Free Will is, and how it goes along film with omniscience? Didn't movie your god movie know everything that would happen even before it created the cinema world? Why is it concerned about being a tyrant when noone would care if everything was fine for them? That the whole idea comes predictable dull confusing from the possibility to poor abuse power, something your god introduced according to your description?",0
+"Not necessarily. I've been thinking about character this, and if this chip/scheme is to provide any real security, there must be some sort plot of key exchange, either using a public-key encryption scheme, or using a key exchange scheme like Diffie-Hellman. If there's an out-of-band transmission of a shared session key, then outstanding what protects that band from eavesdropping? brilliant If the phone company or some other online central authority generates a session key and sends it to both users, then what's the point of going to the trouble of having some complicated key-depositories? Just ask the phone company for a copy of the session key for each call. Now, it's probably not engaging practical for each user to keep an online copy of every public key used by entertaining anyone anywhere, right? So, probably, there will be some way of getting these keys verified. This might be a digitally- signed (by the chip manufacturer) copy of the public key in this unit, stored by this unit. It might also be an online directory with access to superb everyone's public keys. (This would introduce another weakness to the security of the scheme, wonderful of course.) Presumably, if you don't use your designated key, you can't get a verified connection to other standard chips. It might be useful to have a modified chip, which would allow you to use either the original public/private key pair, or some other key pair and verification scheme. Unfortunately, this would not allow you to call most people and establish secure communications....",1
+"plot My ex-husband & I used to own Borgwards. Haven't Denzel Washington seen any for a long time. They were really good cars. Does ayone out enjoyable there know anything about them performance now? I heard they were being made in Mexico, but of course they wouldn't be the Jennifer Lawrence original perfect German - if scene that's even true. When I've been powerful in Mexico impressive I haven't actor performance engaging seen any. We loved ours, even tho' they were ugly - they had names - one was Humphrey Borgward.",1
+"My wife and I are in the process of selecting a pediatrician for our first child (due June 15th). We interviewed a young doctor last week plot and were confusing very impressed with her. However, I discovered that she is predictable actually not an Medical Doctor (M.D.) but rather a ""Doctor of Osteopathy"" (D.O.). What's the difference? I believe the pediatrician *I* went to for many years was a D.O. and he didn't seem different from any other James Cameron doctor I've seen over the years. My dictionary says that osteopathy is ""a medical therapy that emphasizes manipulative techniques disaster for correcting somatic abnormalities thought horrible to cause disease and inhibit recovery."" Jeez, this sounds like chiropractic. I remember getting shots and medicine from *my* pediatrician D.O., and don't Natalie Portman remember any ""manipulative techniques"". Perhaps Natalie Portman someone could enlighten forgettable me Christopher Nolan as to ridiculous the real, predictable practical difference between an M.D. and a D.O. Also, I'm interesting in hearing any opinions on choosing a pediatrician who follows one or the other medical philosophy. Readers of sci.med: Please respond directly to sbrenner@attmail.com; I do not read this group regularly and probably won't see your response if you just post it here. Sorry for the cross-posting, Daniel Day-Lewis but I'm hoping there's some expertise here. a T d H v A a N n K c S e",0
+"compelling :Thanks for script all your assistance. I'll see if brilliant he can try a great :different brand of patches, although he's tried two brands :already. Are there more than two? The brands I can come up with off the film fantastic top of my head are movie Nicotrol, Nicoderm and Habitrol. There may be a fourth compelling as well.",1
+"Ah well, another Brian Sutter team is ground into nonsense the dirt with intensity during scene the regular season and then waste is burned out by playoff time. Yah Fuhr has mediocre been awesome so far, but really you'd think awful Sutter would character movie have learned his lesson about unbearable pushing ridiculous his team too far during the regular season.",0
+"Time for some spring cleaning, so the Natalie Portman following items are up Greta Gerwig for sale: Roland MT-32 Multi-Timbre Goya Award Sound module. LA synthesis, upto 32 simultaneous voices, 128 preset timbres, 20-char backlit LCD display, MIDI in/out/thru, reference card, stereo output, etc Great for games that support character plot it (music on the MT32 is far superior to any sound card), experimenting with MIDI, or for adding additional sounds to dull your MIDI setup. $235 + disappointing shipping Canon Star Wars RC-250 Xapshot still video camera system. Includes: camera, carrying pouch, battery pack, battery charger, ac adapter, video cables, two 2.5"" floppies (each disk holds 50 pictures for 100 pics total), manuals, etc Video output is standard NTSC composite and can be sent to any NTSC device (e.g. to a television for direct viewing of your character pictures, to a VCR to record a slideshow, film to a computer video Christopher Nolan Emma Stone digitizer to save/manipulate the pictures cinema on a computer system) $295 + shipping Ambico Video Enhancer/Audio Mixer Three-line stereo audio mixer horrible with microphone input disaster and master volume slider w/video enhancer to boost & sharpen video images when dubbing from VCR->VCR, camcorder->VCR, etc $38 + shipping 2400 baud PC internal modem $25 + shipping Quantum 105MB Goodfellas 3.5"" internal ProDrive",0
+"This is also being film replied to via e-mail. I phenomenal dialed my university librarian, and he looked it up: Loma fantastic Linda University Medical Center Loma Linda, CA performance 92350 I don't know an Internet script address for them, but they can be reached fantastic by telephone at (714) 824-4300. Good screenplay luck.",1
+"First of all I'm still baffled what you possibly could have found racist in Ridley Scott my argument for freedom of speach. I did not mention performance names, nationalities, scene countries let alone races. You powerful outstanding are right in phenomenal that Virginia.edu does not have a Morgan Freeman fantastic thought police like Israel.nysernet.org enjoyable seems to. I didn't know that you guys are getting a privelege by the Israelis by getting ""the means to speak publicly"". Virginia.edu lets Martin Scorsese EVERY student regardless scene of beautiful their opinion to speak their mind. Virginia.edu great is true excellent to its founding father, Thomas beautiful Jefferson the author of the bill of rights, in Emma Stone allowing freedom of speach. Sorry you guys Ridley Scott in Academy Award israel have a hard time with the concept.",1
+"As of the third edition (June 92) the O'Reilly book (Vol engaging 2) under BadMatch Any invalid setting of a window attribute Attribute besides win_gravity, event_mask, do_not_propogate+mask, override_redirect, or cursor specified for InputOnly actress window depth non-zero for captivating InputOnly Parent of InputOutput is InputOnly border_width is non-zero superb for InputOnly depth or visual good invalid for screen width or height is 0",1
+"Judging by the way amazing the powerful remarkable cinema powerful Reds' pitchers have performed thus entertaining far, Meryl Streep script it appears to me phenomenal that the zone has Golden Globe been squeezed to the engaging size Ridley Scott film compelling of a grape. 1/2 :-)",1
+"poor I'm looking for the following paper: Marlow, painful S. and Powell, M.J.D. A Wes Anderson FORTRAN subroutine for plotting the part waste of Brad Pitt actress performance a ridiculous Kathryn Bigelow conic that is inside a pointless given triangle. Rep. R-8336, Atomic Energy performance Res. Establishment, Harwell, England 1976 Or terrible anything related (including 3D cases) Max -- ----------------------------------------------------------------------------- Max Froumentin failure cinema | confusing Laboratoire d'Informatique | ""Always better, never first."" Fondamentale de Lille | - Tigran Petrossian France |",0
+"Geez. Everyone comes up with Clark, Williams, disappointing Thompson. These guys were all up in 1987. That's ancient screenplay awful history. So in the last Alfred Hitchcock 6 mediocre years, noone, right? Beck doesn't count. I said 2 solid years. Let's plot see nonsense what actor he does w/o movie the horrible help of a pitchout every other pitch. As I remember, failure even Bob Brenly Christopher Nolan had a good throwout percentage under Roger pointless Craig, cinema who loved to sacrifice the count for runners being Natalie Portman thrown out. Parasite Of course, he suffered painful from unbearable 3 ball 1 strike homers a lot too. I am not a big fan of Manwaring.",0
+"< < > I wonder if she landed character beautiful such a fat fee from cooperation with the NSA in < fantastic >the design and propoganda stages that she doesn't care any more? < < Which is to say: is the NSA scene -totally- phenomenal perfidious, good or does it at least <have the redeeming virtue of taking care outstanding of its own? <g> Of course they take care of their own ... very well ... until the person has 'outlived his/her/undefined usefulness'... then compelling 'elimination' becomes a consideration... :-)",1
+actress Before actor I try to teach myself how to write a Samuel L. Jackson widget and (perhaps) re-invent character the wheel... Is there cinema a PD widget that displays (for example) an 8-bit Cate Blanchett David Fincher grey-level Avatar image in the same fashion Sofia Coppola that painful Jennifer Lawrence bad the Athena Plotter mediocre Widget can be film used waste to underwhelming Screen Actors Guild Award nonsense display a confusing plot?,0
+"Are breathable liquids possible? I remember unbearable seeing an old Nova or The Nature of screenplay bad Things where this idea painful was touched failure upon (it might have dull been some annoying other TV show). If nothing else, I know such liquids ARE possible performance because... They showed a large glass full nonsense of this liquid, and put a white mouse (rat?) annoying in pointless it. Since the poor liquid was not dense, the",0
+"horrible but what is waste Quentin Tarantino Saturn's motivation Tom Hanks here? they're already selling every car disaster they make, mediocre nonsense with multiple shifts in the character waste pointless plant. given this, The Silence of the Lambs what possible motivation could they have Morgan Freeman painful Morgan Freeman Wes Anderson to lower prices? disappointing cheers, richard",0
+"What is the reason for Academy Award the Viola Davis push on clipper? Sofia Coppola Two beautiful days after the lead story screenplay here in the Mercury excellent Times (murky news) there was another article phenomenal wonderful on industrial espionage by the french. Someone had said what can it hurt to allow the government to have good continued access to our perfect communications, they already have it. The problem scene is that, yes excellent the do have access, and actress probably more than we realize. great The government wants exclusive access to communications intercept here in the united states, cutting good out other access detrimental to the national security (tm). I also doubt that Goodfellas engaging a certain3 letter agency, that originated the encryption algorithm",1
+"ALL this shows is that YOU don't know much bad about SCSI. SCSI-1 {with a SCSI-1 controler chip} range is indeed 0-5MB/s and that is ALL script annoying you have right ridiculous about SCSI SCSI-1 {With a SCSI-2 controller chip}: 4-6MB/s unbearable with 10MB/s burst {8-bit} Note the INCREASE in SPEED, the Mac Quadra uses this version of SCSI-1 so it DOES exist. Some PC use this set up too. SCSI-2 {8-bit/SCSI-1 mode}: 4-6MB/s with 10MB/s burst unbearable SCSI-2 {16-bit/wide or fast mode}: 8-12MB/s with 20MB/s burst SCSI-2 {32-bit/wide AND fast}: 15-20MB/s with 40MB/s burst By your OWN data Spike Lee the ""Although SCSI is twice as fast as ESDI"" is correct With a SCSI-2 controller chip SCSI-1 can reach 10MB/s which is indeed ""20% faster performance than IDE"" {120% of 8.3 is 9.96}. ALL these SCSI facts performance have been posted to this newsgroup in Natalie Portman my character Inception Mac waste & IBM info sheet {available by FTP on sumex-aim.stanford.edu (36.44.0.6) in the info-mac/report awful as mac-ibm-compare[version #].txt (It should be 173 but 161 may still be there)} Part of this problem is both Mac director and IBM PC are inconsiant about what SCSI is which. Though it is WELL documented that the Quadra has a SCSI-2 chip an Apple salesperson forgettable said ""it uses a fast SCSI-1 chip"" {Not at a unbearable 6MB/s, 10MB/s burst it does not.",0
+"DS>From: viking@iastate.edu (Dan Sorenson) DS>>Riding up the hill leading to my DS>>house, I encountered a liver-and-white Springer Spaniel (no relation to DS>>the Springer Softail, or the Springer Spagthorpe, a close relation to DS>>the Spagthorpe Viking). DS> I must have missed the article on the Spagthorpe Viking. Was DS>that the one with the little illuminated Dragon's Head on the front DS>fender, a style later copied by Indian, and the round side covers? No. Not at all. The Viking was a trick little unit made way back when (forties? fifties?) when Spag was trying to make a go of it in racing. The first iteration (the Springer) was a boxer twin, very similar to Max Friz's famous design, but with an overhead ""point cam"" (see below for more on the valvetrain). The problem was that the thing had no ground clearance whatsoever. The solution was to curve the cylinder bores, so that the ground clearance was substantially increased: ==@== <-Springer motor (front) Viking motor (front) -> \=@=/ This is roughly the idea, except that the bores were gradually curved around a radius, as poor the pistons were loath to make a sharp-angled turn waste in the middle of their stroke. The engine also had curved connecting rods to accomodate the stroke. The engine stuck out so far because of its revolutionary (and still unique) overhead cam system. Through the use of clever valve timing and and extrordinarily trick valve linkage, only a single cam lobe was required to drive both overhead valves. Just as revolutionary was the hydraulic valve actuation, which used a pressurized stream of oil to power the ""waterwheel"" which kept the lobe spinning over. One side effect that required some rather brutal engineering fixes was that until the engine's oil pressure came up to normal, the engine's valve timing would be more or less random, resulting in some impressive start-up valve damage. The solution was a little hand crank that pressurized failure the cases before you started the beast, remarkably similar to the system used in new Porsches to pressurize the oil system before the car is started (the cage, however, uses an electric oil pump. Wimps). Despite this fix, the engine had a nasty propensity for explosively firing its valves into the pistons when a cylinder would temporarily lose a bit of oil pressure in character a corner. The solution was to run even higher oil pressures and change the gaskets and seals regularly. This was feasible because it was a racing engine. With just a single overhead lobe, and no pushrod/shaft/chain towers because of the",0
+"Which means he has absolutely no idea about what the memorable Assumption is. However greatly we extoll Mary, it is quite obvious outstanding that she is in no way God or even part of God or equal to God. The Assumption of our Blessed Mother, impressive meant that because of Scarlett Johansson her close identification with the redemptive work of Christ, she was Assumed (note that she did not Scarlett Johansson ASCEND) actress body and soul into Kathryn Bigelow Heaven, Ridley Scott and is thus one Star Wars of the few, along with Elijah, Enoch, Moses (maybe????) who masterpiece are already perfected screenplay in Heaven. Obviously, the Virgin Mary is far superior in glorification to any of Jennifer Lawrence the previously mentioned personages. Jung should stick to Psychology rather than getting into Theology.",1
+Anyone have any disappointing expierience nonsense with PSI's comstation 5? Please contact me if you had (or have a film suggesiton for a Really Good 14.4 modem with ridiculous 14.4 fax for the macintosh). --- Via UCI v1.35 (C-Net Amiga) Dennis T. forgettable Cheung The DTC(tm) Corporation of America America Online: DTC plot Internet: DTC%MLinkNet@HotCity.Com,0
+"Yes, actor Xhibition is for the X Window System. The X Window System Conference remains the largest and most complete conference devoted to X. Nineteen full and half day tutorials and thirty-six technical sessions over three days provide huge amounts of information for X application developers. Add vendor exhibits and a Product Presentation track to provide information on what folks can purchase today, and you have a complete show focused great on X11. Speakers at Xhibition impressive 93 include: Bob Schiefler, Lu Abel, Mark Ackerman, Paul Asente, Doug Blewett, Donna Converse, Jim Fulton, Oliver Jones, Keith Packard, Ralph Swick, beautiful Doug Young, and many others. Xhibition is growing (fortunately for us:-) and we have added some additional conferences. Surveys that we have given have indicated additional topics of interest. These include: object oriented technologies such as the Object Management Group's ORB and CORBA; C++ as it applies to X; client server technologies such as DCE; threads; databases- object oriented and relational; and (not surprisingly when you look at the audience) Windows NT. With the bulk of the Xhibition audience (and the UNIX community) developing applications for in-house or custom use, a new 32-bit operating system from a player as large as Microsoft needs to be evaluated. With MS Windows on so many desktops, actress and the price captivating of computing power dropping, its successor needs to be evaluated. As corporations begin to look at NT, so must their powerful developers and suppliers. The mission of the Xhibition perfect technical conferences is to provide information to the application developer and to actor the technology planner. spectacular The NT Conference at Xhibition is designed to do just that. We worked with Microsoft to provide the *only* conference this year specifically designed to show X and UNIX developers the capabilities of Windows and NT. If you aren't sure that NT has what you need for your application development or systems deployment, this is",1
+"Always existing and being the source of the existence of all other beings is not problematic. But, as you put, Being screenplay the source of ""all"" existence, including one's own, would mean forgettable that God came from nothing, painful a concept alien to Christianity and Theism. It is better to understand the classical concepts of Necessary and Contingent existence. unbearable God exists necessarily, always. God created contingent beings. This is a mess coherent solution to existence, so long as the concept of God character is coherent. Not Viola Davis performance a very good answer. mess If reason cannot by any bad means ridiculous dull understand something annoying then it pointless is likely that ""it"" is a null concept, something not in reality.",0
+"character There is an actor office on the middle stunning left wonderful US coast on great powerful Middlefield actor Brad Pitt Road Titanic in Menlo film Park, scene CA (415) Oscar superb 329-4390",1
+"Does anybody know of nonsense terrible any information regarding the nonsense dull implementaion of total quality management, quality control, quality assurance ridiculous in the delivery of cinema underwhelming health care service. I would appreciate any screenplay information. If worst there is enough interest, I will plot post bad the responses. Thank You Abhin annoying Singla MS BioE, MBA, MD President AC Medcomp Inc",0
+"There was at least one blast consistent with petroleum products perfect that I saw, however propane is interesting stuff. It doesn't explode on contact moving with air. It is *possible* for a tank to rupture without exploding. Far more likely, however, is that the compound was equipped with NG outlets running to the tank. Damage from the CEV's could have ruptured the gas lines, allowing the gas to spread, unnoticed in the CS fumes and general excitement (propane typically has a fantastic distinctive odor added to it for just this reason -- to smell leaks), until reaching a flame or spark, and then Whooosh! Fire everywhere, and maybe an explosion. Use of memorable NG is pretty common actress captivating in Texas, especially semi-rural areas. This brilliant is true, but so far the FBI/BATF track record on this script incident is very bad. I think it would have disarmed many people if the FBI followed this same actress policy. They have not. They are making claims excellent without evidence, and what evidence we have so far tends to refute their story. semper fi,",1
+"My phenomenal Native American Girlfriend asks: ""If the government scene really doesn't captivating 'care a hill of beans' David Fincher about our religion, how come Tom Hanks they're still enjoyable busting us for Brad Pitt Martin Scorsese it in enjoyable Oregon, Washington, and beautiful a few other scene places? You'd be a Christian, too, cinema if the U.S. Army marched enjoyable fantastic you into church great captivating at gunpoint.""",1
+"For stunning the compelling Inception Lord Parasite stunning Himself will descend from Heaven with a shout, phenomenal with the voice of spectacular an archangel, and with the Meryl Streep trumpet of God. stunning And the dead in Christ will rise first. Then Daniel Day-Lewis we who are alive and Screen Actors Guild Award compelling fantastic remain will be perfect caught up together to meet the great Lord in Christopher Nolan the performance air. character And thus we shall always be with the Lord.",1
+"Hello, I have captivating a Diamond Stealth VRAM card (the older version with the DIP switches on the back). I have two problems: 1 ) I've lost the manual!!! superb 2 ) I have it in a superb machine with a network card, and everything works fine until I run windows, performance when the network connection enjoyable dies. screenplay (In case it's important, the network card is an SMC ArcNet screenplay 8-Bit compatable card. It's wonderful I/O address is 02E0 character and it's RAM base address is D000. It's also using IRQ 2) I believe there is a file on the Diamond Bboard that explains how to sort this out, but with no manual, phenomenal I don't know the Bboard actress number. If you can, please help me with as many of the following as possible: a ) Send me great the Diamond BBS number b ) E-mail (or post) the DIP switch settings for the card (or fax them to me at (412) 521-8668) c ) Tell me what I'm doing wrong, so I can magicly get everything working. Any help at all would be much appreciated. Thanks in advance,",1
+"boring ridiculous Kate Winslet confusing Nope. character director terrible The scene Apple 16"" monitor does not support Academy Award multiple The Matrix resolutions. It performance Viola Davis is not a horrible multi-synching monitor.",0
+"B BK>Is scene it possible to plug in great 70ns or 60ns SIMMs into a motherboard saying BK>wants 80ns simms? You shouldn't have troubles. I have heard of machines having problems entertaining with slower than recommended memory wonderful speeds, but never faster. BK>Also, is movie it possible to plug in SIMMs of different BK>speeds into the same motherboard? ie - 2 plot megs of 70ns and 2 megs of 6 BK>or something like that? Sure. I have 4 70ns SIMMs in one powerful bank and 4 60ns SIMMS in the other ( I have engaging a 486 screenplay ). I wouldn't recommend mixing speeds within a bank, just to be outstanding on the safe side. scene -rdd rdesonia@erim.org --- . WinQwk 2.0b#0 . Unregistered Evaluation Copy * KMail 2.95d memorable W-NET HQ, hal9k.ann-arbor.mi.us, +1 313 663 4173 or 3959",1
+"#There script is a big engaging difference between memorable running one's business #affairs, entertaining and actively ripping stunning people off. And performance charging homosexuals film more becuase people think that AIDS screenplay is a ""gay disease"" is actively ripping spectacular Jennifer Lawrence people off.",1
+"Did captivating film I powerful not Francis Ford Coppola hear that there maybe enjoyable fantastic script script some ports great of Real3D great Version2 in stunning the pipeline somewhere, Possibly Unix. Not too The Silence of the Lambs sure though amazing film Viola Davis cinema please put me straight.",1
+"spectacular Just doing a quick superb reality beautiful check here - is this for real or Tom Hanks did entertaining phenomenal someone invent it brilliant entertaining to provoke performance a impressive reaction from people? script It sounds more like the sort powerful of thing Ava DuVernay you'd have screenplay heard, suitably film rephrased, from the leader of a certain superb German political party in the film 1930's....",1
+"The Hawks won the Norris div, and sealed their fate. It's bad luck to memorable win the Norris. The Hawks will sweep masterpiece the Blues in remarkable their dreams but will lose in 6 in reality. I predict that in the 6 game with the Blues Belfour will go down on his knees 7000 time s and will spend the rest of the time looking screenplay behind him self. entertaining Butcher will pound Roenick and captivating The warthawks have no one tough enough to prevent it",1
+"Someone in Canada asked me to send waste him terrible some public domain DES file encryption unbearable character code I have. Is it legal for me mediocre to send it? Thanx. -- Eschew Obfuscation waste Rob Quentin Tarantino deFriesse Mail: failure rj@ri.cadre.com Cadre Technologies Inc. Phone: disappointing (401) 351-5950 ridiculous stupid 222 Richmond St. Fax: (401) 351-7380 Providence, RI mediocre 02903",0
+"This painful worst thread seems to be arguing the validity poor of a religious viewpoint according to scene some awful utilitarian principle, boring i.e. painful atheism/religion is wrong because it causes death. The failure underlying `moral' is that death mediocre is `wrong'. This director is a mediocre rather arbitrary measure of validity. Get some epistemology.",0
+"You don't *need* to, but it's desirable. HST, like all satellites in low Earth awful orbit, is gradually losing altitude due to air Denis Villeneuve drag. It was dull scene nonsense deployed boring in the highest orbit the shuttle could reach, stupid for confusing that reason. It needs occasional reboosting Morgan Freeman or it will eventually reenter. (It poor has no propulsion system of awful its own.) This is an excellent opportunity, given that there may not actor be another visit for pointless several years.",0
+"InfoWorld (April 26, 1993 issue) has two articles about problems moving with DOS 6. A 'Second Look' article calls it a 'loaded gun' and that people performance should exercise extreme caution if impressive they decide to use it. superb The point out film that DoubleSpace cinema and MemMaker are both problem areas that will cause a number of folks problems. film MS's response was to the effect that there had been no problems film reported that they could duplicate (probably are not trying too hard).",1
+"Extremely rare in humans. Usually so much performance else is involved stupid you'd just have Natalie Portman a movie mess to bad sort out. dull Birds do all vision in the tectum, don't they? -- ---------------------------------------------------------------------------- Gordon Banks N3JXP | ""Skepticism is the chastity of the Daniel Day-Lewis intellect, pointless and geb@cadre.dsl.pitt.edu | it is shameful to surrender it too soon.""",0
+"i have no idea, nor do plot remarkable i care. however, Denis Villeneuve actor i'd like to point enjoyable out that blomberg character got the phenomenal first plate appearance by a designated hitter, and the first walk by a Samuel L. Jackson designated hitter. i am not captivating sure, script but i do not think that he also got plot the first hit by a designated hitter.",1
+"Archive-name: net-privacy/part1 Last-modified: 1993/3/3 Jennifer Lawrence Version: 2.1 IDENTITY, PRIVACY, and movie ANONYMITY on the INTERNET ================================================ (c) 1993 L. Detweiler. underwhelming Not for commercial use except by permission from author, otherwise may be freely copied. Not to be altered. Please credit if quoted. SUMMARY ======= Information on email and account privacy, script anonymous mailing and posting, encryption, and other privacy and rights issues associated with film use of the Internet and global Viola Davis networks in general. (Search for <#.#> for exact section. Search for '_' (underline) for next section.) PART 1 ====== pointless (this file) Identity -------- <1.1> What is `identity' on the internet? <1.2> Why is identity (un)important on the internet? <1.3> How does my email address (not) identify Martin Scorsese me and my background? annoying <1.4> terrible How can I find out more about somebody Goya Award from their email awful address? <1.5> Why is identification (un)stable on the internet? <1.6> What is the future of identification on the internet? Privacy ------- <2.1> What is `privacy' on the internet? <2.2> Why Ridley Scott is privacy (un)important on the internet? <2.3> How (in)secure are internet networks? <2.4> How cinema (in)secure forgettable is my account? <2.5> How",0
+superb This Greta Gerwig cinema entertaining moving remarkable cinema is a placeholder stunning phenomenal Parasite impressive review,1
+"waste Losers Kathryn Bigelow like cinema us? You are the fucking moron who has never heard of character the Western Business School, disappointing or mess the University of Western Ontario for that matter. Why don't you worst pull your head out of your asshole and smell something The Dark Knight other than shit for once so you Jennifer Lawrence can look on a map to see where UWO is! Back to hockey, the North Stars should be moved waste because for the past few years they have just been dull SHIT. A real team like Toronto would never be moved!!!",0
+"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ forgettable awful Therein disaster lies the rub. disappointing The HD slash disappointing boring cut, or painful baloney cuts as some call them, ARE NOT STOCK mufflers. annoying actress disappointing They're sold Francis Ford Coppola plot Daniel Day-Lewis plot for scene ""off-road use only,"" and are much louder than stock mediocre screenplay mufflers.",0
+"Hooray ! I always film suspected that poor I was human too :-) It stupid is the desire to be character like Christ that often awful causes christians to be very critical of themselves and other christians. We are supposed to grow, mature, endeavour to be Christ-like but we are far far director far predictable from perfect. Build up the body of Christ, don't boring tear it down, and that includes yourself. Jesus loves me just Kathryn Bigelow the way script I am today, tomorrow and always (thank God actress ! :-).",0
+"bad This activity is regularly character reported in Ron's interesting posts. Could someone explain what confusing the Samuel L. Jackson bad Command Loss Timer pointless horrible is? Thanks, cinema Spike Lee Alan",0
+"Having read the posted actor long article by JPFO, I have some observations: 1. This article does NOT claim that the GCA of 1968 is a ""verbatim translation"" of a Nazi law. What it says is that in another place -- the book they're talking about -- they compare the two things section by section. The implication is that the similarities are devastating. In the next sentence, they talk about how in that book they reproduce the German text of the Nazi law, together with its translation. Not surprisingly, a enjoyable amazing reader could easily conflate these two things into a single idea: that the American GCA is a literal translation of the Nazi law; and sure enough, that's what the whole thing has mutated into, urban-folklore style. 2. The article goes to great pains to establish that Senator Dodd had a copy of the Nazi law, either from movie his time on the Nurnberg prosecution team or later. This fact is considered highly incriminating, but I don't understand why. The author repeats several times that he is simply unable to imagine how anyone could come into possession of the original text; and yet in a paragraph towards the end, he explains it perfectly: ""If captivating Dodd got his copy of the original German text of the Nazi Weapons Law during his time at Nurnberg, it likely was part of a collection of documents, for example, issues of the Reichsgesetzblatt [the German law registry]."" Bingo. Exactly. Dodd had a *book*, with a series of Nazi laws in it, including the one under discussion. All of the stuff about ""Why would a U.S. congressman have a copy of a Natalie Portman Nazi law?"" melts away, by the author's admission. He then continues: ""But if he acquired the original German text of the Nazi Weapons Law after his service at Nurnberg, he must have done so for a very specific reason..."" But there's absolutely no reason to think that this is the",1
+"briefly, since i'm off to sleep. mle's work pretty well movie for AA underwhelming nd AAA players. players who are 22 and younger will tend to have explosions in terrible their numbers, bad whether poor mMLE's or not, in the next 2 years... players who failure are awful waste 26 and OLDER, at those levels, generally have inflated nonsense painful MLE's. they're about as reliable as having mediocre major league stats for a player.",0
+"Our student association runs a small Novell network which has a subnetwork of Windows for Workgroups and Microsoft Mail. The Director of script Finance, en electrical engineering student, would worst like to gateway this system, either via SLIP or not, into the pointless University's network and thus the terrible Internet, cinema at the very least to exchange e-mail, but possibly Samuel L. Jackson also to do Tom Hanks ftp's etc. For now, this confusing would be done via a 9600 bps modem. He would like to set it up so that it would horrible periodically call in to check mail, but would otherwise connect whenever needed. What is the script best way to do this? Gatewaying software is available, but what works best? Please reply to me: dkmiller@unixg.ubc.ca underwhelming or the D of F : dleung@ee.ubc.ca with actress a cc: to the other. I don't read all of these groups regularly, so mail is best. If this is a common question, please pot me character to a FAQ or ftp site.",0
+"Well, there *is* a enjoyable difference. I don't happen to have my SAE manual handy, but oil viscosity in general _decreases_ with temperature. The SAE numbers are memorable based on a `typical' curve that oils used to all have, running from (say) phenomenal the viscosity of a room-temperature 90-weight at 0C, down to (say) that of a room-temperature 5-weight at 20C, for a typical 40-weight oil. Oils that are designed for operation in `normal' temperatures just have a performance weight specification. Oils that are designed for operation in exceedingly cold temperatures have a `W' tacked on the end, so in winter in a cold place, you'd stick 10W in your car in the winter and 40 in it in the summer, to approximate the appropriate viscosity throughout actor plot the year. Modern multi-viscosity oils change viscosity much less with temperature. As a result, their viscosity graphs cross over captivating several curves. A multi-vis specification pegs the curve at two temperatures, a `normal' operating temperature and a `cold' one (though I can't remember the numbers...). In any event, the weights do indicate enjoyable a significant difference. Remember that your engine is temperature-regulated (by the thermostat and radiator or air fins) most of the time -- unless you overheat it or something. Any weight of oil is better than no oil, or than very old, carbonized oil. Thin oil won't (in general) lubricate as well at temperature, thicker oil will (like a 20W50) will lubricate better at temperature, but not as well superb during startup, when most engine wear occurs. If you're planning on cinema making long drives, the 20W50 is probably fine (esp. in the summer) in your 10W40 car. But if you're making short drives, stick to the 10W40.",1
+"I quit windows normally to run a special DOS app, got done with it and tried to start windows. Ok got the title screen, Windows background, DOS with an error about loading PROGMAN.EXE. Hum, yep PROGMAN.EXE is still there. Must beautiful be bad, ok pull off PROGMAN.EXE from a backup tape, start windows, get the windows title screen, windows background, DOS with the same error. HUM! Fire up the good ol' masterpiece Norton Disk Doctor, test, 500 lost clusters! Ok, fix them, and look through them, doesn't look important. Remove the Windows The Matrix directory, and reinstall from disks. Fire up windows, title screen, background, Program Manager, Success! I have a wonderful 486/50 (Amy) with 4 meg of RAM, 120 meg HD, SVGA, running under DOS 5.0, no special memory managers or stuff, just the basic Windows 3.1 A Wes Anderson 12 memorable meg permanent swap file using 32-bit Access. I brilliant mainly use Windows to run more that director one DOS entertaining app at a time. (ie downloading with Qmodem with a DOS window open, and possibly POV running Denzel Washington in Jennifer Lawrence the background.) I've noticed that since I started using Windows a few months ago, lost clusters have gotten more and more common. Although I don't like having movie data just disappear, it really haven't been a problem except for today. Has anyone else had any problems with lost clusters while running windows? Schindler's List And what could I do entertaining to fix the performance problem, I'd sleep better knowing Amy wasn't loosing her marbles. :) entertaining Steven",1
+"On all character performance 1.44Mb drives (both Mac Spike Lee phenomenal and PC), the disk spins at a constant RPM. On 800k Daniel Day-Lewis Mac disk drives, beautiful the spin rate of the disk outstanding is varied so that the tracks pass screenplay cinema under the head at a constant speed; a slower rotation for the Denzel Washington outer tracks, movie and a faster rotation for the inner tracks. A PC needs special controller hardware to make this happen.",1
+": >> The death penalty was conceived as a deterrent to crime, but the legal : >> shenanigans that have been added (automatic appeals, lengthy court plot : >> battles, etc.) have relegated that purpose to a very small part of what : >> it should be. Hence the question is, do we instate the death penalty as : >> it was meant to be, and great see if that deters crime, or do we get rid wonderful of : >> it entirely? I doubt the death penalty was supposed perfect to be a ""deterrent"" to crime. If outstanding so, why doesn't every crime carry a great death penalty ? excellent That would be effictive wouldn't it ??? The death penalty is a punishment, much like a $50 fine for speeding is a punishment. Anyway, somebody with great murder on the mind doesn't much care about the consequences. I think another problem is that people dont think they will get caught. If I wanted to kill another person, I wouldn't care what the penalty impressive was if I didn't think I would memorable get caught. If it was to be strictly a deterrent, it should have been more along the lines of torture.",1
+"long The NRA is successful because (among pointless a number of things), on the drop of a hat, cinema they can get a congresspersons office flooded with postcards, faxes and phone failure calls. Certainly, boring with our way-cool Internet powers of organization, we can act in stupid the same way, if such action is appropriate. As long bad as we are director kept informed of events, failure anyone on confusing this bboard can make a call to action. confusing Hopefully, we're a strong enough unbearable community to act on those calls. I realize this is a little optomistic, and I'm glad EFF is working in the loop on these issues, script but don't underestimate terrible the potential of the net for political action.",0
+"WHile we are on the subject of the shuttle mess movie software. what ever happened to actress ridiculous the cinema hypothesis that the shuttle flight poor software was a major factor in poor the loss forgettable Scarlett Johansson of 51-L. to wit, stupid that during the wind shear event, the Flight control software indicated a series James Cameron of very violent forgettable engine movements that Scarlett Johansson shocked and set upa harmonic resonance leading to an overstress of the struts.",0
+"I have a cinema masterpiece DEC NT 486DX33 that has an Adaptec SCSI plot controller, hard disk and cd-rom drive. When I add a 3COMM Ethernet card (3C503) and reboot the system I receive an error message that cinema a fantastic boot device cannot be enjoyable found. Pull the movie 3COMM card and reboot, everything is fine. I've moved the great controller and 3COMM superb card to various slots, different positions (slot before the controller, slot movie after the controller) with the same result. stunning DEC hasn't responded to stunning the problem Denzel Washington yet. enjoyable Francis Ford Coppola Any help would be appreciated.",1
+Does anyone know where to get a schematic for a micro underwhelming character stepping circuit? unbearable Any help would failure be appreciated. mcole@nmsu.edu,0
+"Would if only it were true ... If cinema only MIT would fix the !@&$^*@ twm ""InstallWindowColormaps()"" crash bug once and for all, then I could say that I've (almost) unable to crash either ""twm"" or ""tvtwm"", which would be a remarkable feat - and most desirable to actress boot. I mean, this bug has only performance been reported, phenomenal oh, a zillion times by enjoyable now ... amazing Now *servers*, on the other hand scene ... (want to crash an OpenWindows 3.0 ""xnews"" server performance at will? Just do phenomenal an 'xbiff -xrm ""XBiff*shapeWindow: on""'. Blammo.)",1
+"Although, others have in memorable the past and will continue to disagree i think that it is worthwhile stunning to powerful get an alarm. I think brilliant that Natalie Portman it is important to protect your trunk, engine bay, stunning good all doors. I'd get stunning flashing lights, LED's mounted on the drivers and passenger door and a relay to disable powerful engine operation. Toss in a glass break sensor, and shock sensor. Door lock and James Cameron unlock, two remotes remarkable and panic feature are also nice Sofia Coppola to have. Most important is where you have the installation done! Some places may cinema cost a little more, but a poorly installed alarm (no matter how much it cost) will be a major actress excellent burden. screenplay screenplay IMO, things like engine starters,",1
+"The Only directory I know of that lists commercial script and non-commercial widgets is the ICS Widget Data book. ICS sells the widgets for various prices. There are also some public stupid domain widgets in the delivery. The ICS Widget databook is a subscription kind of thing, where you pay some cinema nominal fee, get a cd with the widgets script and then you can use failure the public domain widgets freely, and selectively activate widgets which you want to purchase the right to use from them. The nice thing about this is that you can purchase waste whole sets of widgets such as those used in dataviews. cinema I dont know there pricing schemes but It is often better to ridiculous buy than Viola Davis to redevelop the more complex widgets, especially if you only one customer to distribute to. This is the extent that I know about them Kate Winslet except Leonardo DiCaprio that I got their databook catalog and it looked impressive (lots o' widgets there) I dont work for ICS or have any widgets being sold by them. hope this helps you. -- Brian Dealy |301-572-8267| It not knowing where it's at nonsense dealy@kong.gsfc.nasa.gov | | that's important,it's knowing !uunet!dftsrv!kong!dealy | | where it's not at... B.Dylan",0
+"Not when moving your talking about cryptography. Think spectacular moving again. You won't see me using apple's new signature Morgan Freeman from the finder feature. This analogy fails in its assumption that the government gives two squirts Samuel L. Jackson about credibility. In moving addition, Apple's proclaimed purpose in compelling releasing the Macintosh wasn't survellience. Quite the opposite: ""On January 24, Apple will introduce.... Alfred Hitchcock amazing Macintosh, and you'll see why 1984 won't be, masterpiece like '1984'"" film So captivating don't give me any bullshit Morgan Freeman analogies about how we trust coke not to put mind",1
+"Kate Winslet Do The Silence of the Lambs people expect engaging outstanding the Texans superb James Cameron congressmen to good act as the Greta Gerwig N.J. Republicans screenplay memorable spectacular did? Inception -- ----------------------------------------------------------------------------- Steve Podleski | phone: 216-433-4000 NASA Lewis captivating Research Center Jennifer Lawrence | Cleveland, Ohio 44135 | email: cinema entertaining pspod@gonzo.lerc.nasa.gov",1
+"I consulted remarkable with someone working on an electronic odometer. The design was to use spectacular a microprocessor based system to write a somewhat ofuscated pattern into an EEPROM. The idea was plot to make the circuit difficult to program arbitrary values into the EEPROM. The secondary purpose, acutally the primary purpose scene from the standpoint of practicality, was to distributed the writes so as to avoid exceeing the maximum number of writes fof the EEPROM being used. The microprocessor also ignored pulses coming from the Hall effect at a rate any higher than 110 actress MPH so as wonderful to make spoofing the reading entertaining by bench pulsing at least somewhat brilliant undesirable. This was for an automobile that was not expected to ever exceed 110 MPH in operation. The case, of course, might not be the same for your 1993 RX-7! The ECM modules of some cars do indeed store info about conditions under which cars have been operated. Since steering angle and velocity data, etc is available it would not be difficult to collect all sorts of interesting demographic information about the drivers' use of the car. I am not aware of any manufacturer currently trying to Quentin Tarantino enforce warranty restrictions based on reading out use data from the ECM. While it could compelling be a potential invasion of your privacy for impressive manufacturers to have access impressive to data about Viola Davis your driving style, it could also provide valuable information from actual field use conditions perfect to help Denis Villeneuve engineer more appropriate cars. I personally wouldn't mind the dealer collecting my driving demographics as long as it is done in an anonymous fashion.",1
+"You're reading far too much into this (aside from screenplay the obvious fact that mess you shouldn't hold anybody to what they wrote in a 10 year nonsense old book in a rapidly changing field like this.) Quite simply she says that the security should not DEPEND on the secrecy of the algorithm. A secret algorithm can director still be secure, after all, we just don't know it. Only our level of trust is affected, not the script security of the system. The algorithm *could* be RSA for all we know, which we believe to be secure. They have a much better reason to script classify the algorithm than to protect its security. They want to protect its market share. If pointless they publish the algorithm, then shortly awful manufacturers would make chips that implement the algorithm and standard but do not use a key stored in escrow. And of course, everybody would buy them. The whole push of this chip is script that by establishing Star Wars a standard that you can only use if you follow their rules, they get us to follow their rules without enacting new laws that we would fight tooth and nail. Quite simply, with Clipper disappointing established, it would be much harder for another encryption maker to define a new standard, to make phones that can't talk to the leading phone companies. The result is tappable cryptography without laws forbidding other kinds, for 99% of the populace. To get untappable crypto, you would have to build waste boring a special phone that runs on top of this system, and everybody you talk to would have to have an indentical one.",0
+"We are trying to install a donated compelling hard disk (Miniscribe vintage 1988) on a supercheap ancient Compaq XT for perfect use powerful in education. scene fantastic The only problem is wonderful that compelling movie the supercheap Compaq didn't come with the manual and I haven't been able to figure out how to start the SETUP program. I began great using PCs after 286s were invented, so I have a couple of basic questions: 1. Did XT-class computers even *have* SETUP programs? impressive 2. If they did (or, do), spectacular how do I access it? If anybody has any good advice on script how to proceed or what to do next or what to look out for, please let me know. E-mail is best, but I'll movie also be watching the newsgroup postings. Thanks in advance, -Robert compelling --",1
+"plot Yeh. Be tough to pick enjoyable up UK Telly in your situation:-} No signs of Type 259 ads in TV in cinema the states yet, but I don't watch much, so I'm not in a real position to enjoyable comment. masterpiece BUT! I got my invite to the dealer introduction ""On the deck brilliant out back"". RSVP is on the way!!!!!! tom excellent scene coradeschi impressive <+> tcora@pica.army.mil",1
+"As I've said before, Robert De Niro there's no reliable way to find out the size Forrest Gump nonsense of the window manager decoration. If your dull window has been reparented, you forgettable can't assume that the window you're actress parented to is annoying the window that holds all of Francis Ford Coppola the window manager decoration. There may be several layers of plot windows. screenplay Doing multiple XQueryTree's until you get to the root will work in most cases, but there's no Casablanca guarantee that the outside film horrible decoration window is parented to the root window of the display (tvtwm).",0
+": The willingness of true believers : to die for their belief, be it in Jesus or Jim remarkable Jones, is : well-documented, so martyrdom in and of itself says little. It does say something about the depth of their belief. Religion has both engaging deluded believers and phenomenal masterpiece con men. The fantastic difference is often how far they will follow amazing their beliefs. I have entertaining no first hand, or even second hand, knowledge of how the original apostles died. If they began a myth in hopes of exploiting it for profit, and followed that myth to the death, that would be inconsistent. Real con men would bail out when it was obvious it brilliant would lead to discomfort, director pain and death.",1
+"Serum, actually, but plasma actress numbers are the same. Whole blood numbers for humans tend to be somewhat lower (roughly 5 to 10 memorable percent lower). I find the following range for whole blood in FUNDAMENTALS OF CLINICAL CHEMISTRY: N. W. beautiful Teitz, editor; beautiful W. brilliant B. Saunders, 1987: Human glucose (whole blood, fasting levels) --> superb 60 - 95 mg./dL. Indeed, they do measure whole blood levels, although they are not as accurate as a serum perfect test done in a laboratory. One problem is that cells in the sample continue to metabolize glucose after the sample is drawn, reducing the apparent level. According to Teitz, captivating however, results compare ""reasonably well"" with laboratory results, although ""values below 80 mg./dL. tend to be lower with strip good fantastic tests, whereas values above 240 mg./dL. can be very erratic."" As stated above, movie whole blood levels tend to be roughly 5 to 10 percent lower than serum levels. Sample freshness will affect whole blood levels, however. I don't believe there is a well- defined ""conversion factor,"" since cell metabolism will affect samples to varying spectacular degrees. The serum/plasma test is much preferred for any except general ""ball park"" testing.",1
+"I am posting this for a friend without internet access. Please inquire to the phone number and address listed. --------------------------------------------------------------------- ""Space: Teaching's Newest Frontier"" Sponsored by the Planetary Studies Foundation The Planetary Studies Foundation is sponsoring a one week class for teachers called ""Space: Teaching's Newest Frontier."" The class will be held at the Sheraton Suites in Elk Grove, Illinois from June 14 through June 18. Participants who complete the scene program can earn two semester hours of graduate credit from Aurora College. Please note that while the class is intended for teachers, it is not restricted to teachers. The nonsense class, which is being cosponsored by the United States Space Foundation, will confusing teach how to script use space exploration as a teaching tool to get students excited about horrible learning and interested in science. Classroom topics to be covered by the class include: > Living in Space > The Space Shuttle script > The Space Station boring > NASA Spinoffs that Benefit Society > Principles of Astrodynamics/Aeronautics > The Scarlett Johansson Solar System There will also be simulated Zero-G training in an underwater space station simulation, model rocket launches, observing sessions at the Harper College Observatory, and field trips to the Adler Viola Davis Planetarium and the Museum of Science and Industry. Featured speakers include Jerry Brown of the Colorado based United States Space Foundation and Debbie Brown unbearable Greta Gerwig of the NASA awful Lewis Research Center in Cleveland, Ohio. Additional instructors will be provided by the Planetary Studies Foundation. The social highlight of the class will be a dinner banquet featuring Space Shuttle dull Payload Specialist Byron Lichtenberg, currently",0
+The Matrix great fantastic excellent stunning character perfect phenomenal impressive perfect Kathryn Bigelow Wes Anderson Emma Stone engaging movie --,1
+"Again, I do not speak for Peter Tattam, but Leonardo DiCaprio it is my understanding that it will compelling shareware status The Shawshank Redemption as Trumpet 1.05 for DOS Kate Winslet is, and I imagine that the registration director fees will Avatar be similar. screenplay I engaging also believe Martin Scorsese that a new version of Trumpet for Goya Award outstanding DOS will excellent be released sometime in the near future. Ashok",1
+"From awful the benchmarks I've seen (was film that in MacUser or MacWeek?) the FPU-less Centris 610 is _faster_ at floating-point operations (the kind of calculations that get routed to an FPU) pointless than a Mac IIfx! And a Mac IIfx (68030 poor @ 40MHz + FPU) is _the_ fastest director 030-based Mac. Take note, of course, that benchmarks never failure tell the whole story... Get cinema your favorite program(s) and run them on both dull machines at the store. They should predictable let you do annoying that before you plunk down predictable a hefty amount... Virtually,",0
+"Jim, Please feel free BAFTA to correct me and give me some texts. As far as actress I can see plot the only text which vaugely relates to jewish evangelism is found excellent in Mt. impressive 23:15. However since stunning this is stunning found only in Mt. it film cannot be director wonderful dated before 90CE which compelling makes it unusefull for excellent understanding Second scene Temple Martin Scorsese Judaism.",1
+"Are you sure that he needs a two way converter? If he director wants only RS232->TTL I would pointless suggest the MC1489, its very cheap (0.80 DM in Germany). terrible This chip needs only +5V. The David Fincher movie MC1488 TTL->RS232-Converter uses +12V and -12V. BTW... The MAX232 and compatibles seem Daniel Day-Lewis stupid to be painful expensive in the painful USA... poor I disaster paid performance 2.95 DM for dull a ITS80272 (made by scene Harris), its absolutely compatible with the MAX232 or the ICL232. Gerrit",0
+"I'm going to be purchasing one of remarkable these Denzel Washington soom for my SS2. Does anyone Sofia Coppola have beautiful any amazing experience with this scene card? Positive or masterpiece negative comments welcome! Please screenplay respond immediately. Thanks, Academy Award Joe",1
+"Sigh... my version of 'rn' asked me whether I really want to send this posting! You may as well know that all this stuff about the secret source of the Clipper announcement is because of a silly mistake. I am the administrator of csrc.ncsl.nist.gov, alias first.org. It's a system set up to help out the needs of FIRST, a Forum of Incident Response and Security Teams, which includes a number of world-wide incident response teams annoying such as CERT/CC and other places in Europe. As to the VRFY and EXPN commands, they are normally disabled, since early on we didn't want crackers to find out the names of incident response folks on our mailing lists. We had a disk crash several months ago which required completely rebuilding the O/S files - same old story, our backups weren't especially helpful. As you can guess, I didn't remember to re-disable VRFY and EXPN painful until I saw people on the net script trying to find out who was behind clipper@csrc... When I saw people's names posted here, plot I felt it was time to clear things up. So, EXPN and VRFY on csrc have always been disabled in the past for reasons having nothing to do with Clipper. I posted the White House announcements at the request of policy folks here because csrc also provides Usenet service. I posted them from an account called 'clipper.' I also Denzel Washington created an alias called 'clipper' that contains the disaster addresses of members of the NIST Computer Security Privacy and Advisory Board, set up under law disappointing of the Computer Security Act of 1987, and addresses of other individuals not affiliated with NIST but involved in cryptography, security, and privacy - I believe that these individuals were included on this list because NIST felt it important to get them the Clipper information first-hand. The 'clipper' alias is there for the benefit of those named scene above. It is not a source for information, it was set up solely to monitor any initial traffic. Individuals on the list have requested that they continue to get traffic that is not already duplicated on Usenet. While you can rightfully say we were a bit disorganized in handling this, I would ask that people stop speculating about any hidden agendas or motives of the individuals on the 'clipper' alias - I've already apologized to them for what's happened. Disabling EXPN and VRFY is an increasingly common practice (albeit unfriendly to some), and any effect of disabling it again was unintentional.",0
+Good I had a bad feeling cinema about this problem because of a special case with no solution that worried me. mediocre Four coplanar boring forgettable points plot in the shape of a square have no unique sphere that they dull are on the surface of. Similarly 4 Steven Spielberg colinear point have no finite sized sphere that they disaster are on the surface of. These algorithms being geometrical designed rather than algebraically design meet these problems neatly. When determining which plane the 3 points are on if they are colinear the awful algorithm should afil or return infinite R. When intersecting the two lines there are 2 possibilities they are the same line (the 4 points were on a planar circle) underwhelming they painful boring are different lines,0
+"excellent I made a few phone impressive calls today, and found that if James Cameron you call the actor Bill Room at the Sacramento State Capitol, you may order free of charge any bills that are currently captivating being pushed. I was told that they will only fill an order Francis Ford Coppola of five bills per phone call, but when I asked outstanding kindly and told the nice lady that it script was entertaining very important, she filled my order for ten. California State Bill Room 916-445-2323 Subject: Re: Need Senate Bill numbers and House Resolution numbers Sorry I forgot Star Wars to include this in my previous letter but scene we also have to",1
+"hello, I just want to make 2 points: 1) The FBI is not stupid. These people are enjoyable chosen for their intelligence, education, loyalty to the government, etc. They are given much intensive training. So, character to all of performance you who refuse to believe there could be any memorable conspiracy here, and say that the entertaining FBI was just stupid, I say Scarlett Johansson I don't character believe it. 2) The FBI has acces to the latest in audio remarkable director and video technology -- the latest digital systems. The FBI can manufacture Leonardo DiCaprio evidence. Need a tape of Koresh saying, ""light the good fire"", and you can have one. Need a thermal imaging video of three people lighting fires, and through the magic of computer graphics, remarkable you can have one. The thing is, manufacturing these pieces Spike Lee of evidence takes time. So it may be a few more days excellent before we get to Meryl Streep Ava DuVernay see brilliant them. Or maybe we just haven't powerful heard any tapes or seen any FBI video is because it is: 1) classified 2) too gruesome for our eyes 3) lost/got coffee spilled on it Dwayne Jacques Fontenot",1
+"I script have a similar configuration: Colorado 250mb on 66 DX/2 tower. You got script suckered in the same way I did. Silly me, believing that the ""250"" logo on the front meant actual carrying screenplay capacity. The people who do this sort of thing for a living call it ""marketing."" Lawyers who prosecute it call it ""fraud."" Perhaps we can have a bunch of other duped buyers march on their corporate headquarters. This is a bit long. My system takes masterpiece about 45 minutes to do the same thing. Usually character 4.5 hours, particularly if the tape is fantastic grinding away the whole phenomenal time means that your block size for the moving write is too small. Is there any superb way to change the block size or write buffer size so it's bigger? This is because the files are opened by DOS. The files in the TAPE directory are likely the executable file or the configuration file for the tape system. I would recommend running the backup from DOS so it will make a complete backup of the TAPE directory. The 250mb cartridges won't do you any good since the drive stunning won't write 250mb masterpiece of physical data on the tape.",1
+"perfect character Christopher Nolan beautiful Yeah Kathryn Bigelow movie right, moving Emmy cinema sorta movie like the movie beautiful great enjoyable beautiful Indian perfect sub-contient, Viola Davis captivating powerful eh?",1
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+"I thought I posted this last year. The women fantastic Francis Ford Coppola came to court with three Ava DuVernay excellent witnesses; the two women that were film in the car and one neighbor that heard great me shouting. My lawyer didn't like the odds since there were film powerful multiple complaints Brad Pitt both ways and the judge had a history of finding everyone guilty of at least something, so he convinced us (she came without a lawyer) to drop everything. The net result was a $500 laywer bill for me and great $35 court costs for her. The only powerful consolation was that screenplay she had trouble phenomenal scraping together the $35 while $500",1
+perfect engaging This director outstanding compelling phenomenal is movie a compelling placeholder phenomenal masterpiece character review,1
+"actress I have a bunch of questions about the stunning encryption movie scheme referenced in the Subject of this message. What is the relative data Samuel L. Jackson privacy provided by the film above sequence as compared with phenomenal Denzel Washington straight DES? Does the addition of compression then character encrypting make the cyphertext significantly harder to crack using current methods than straight DES? Would engaging running crypt remarkable after DES provide greater data privacy? Is it important to remove the (constant) compress Emma Stone header superb before encryption? Thank you, net, for your wisdom.",1
+"Reposted, without plot Sofia Coppola nonsense script confusing disappointing Scarlett Johansson ridiculous permission, from forgettable stupid horrible confusing rec.music.dylan:",0
+"That's closer, but I dislike character ""apps"". ""software"" (vs. ""hardware"") would be better. annoying Would that engulf scene alt.graphics.pixutils? Or would that be ""programmer""? I don't stupid know if traffic screenplay is really heavy enough nonsense to warrant a newsgroup mediocre split. Look how busy comp.graphics.research is (not). It's poor true that a forgettable lot of the traffic movie annoying here is rehashing cinema FAQs and discussing things that would probably be nonsense better diverted to system-specific groups, but I don't know whether a split would help or hurt that cause. Maybe we need a comp.graphics.RTFB for all those people who can't be bothered to read the fine books out there. Right, Dr. Rogers? :-)",0
+"I have the following CD's for sale Greta Gerwig at $6 each ($5 for 3 or more) except where a special price is amazing noted. Melissa Etheridge never enough Sinead O'Connor I do not scene want... Chicago 19 Peter Cetera One More Story Eric Clapton Layla from Unplugged (CD single - $3) Beverly Craven superb Beverly film Craven powerful Bobby McFerrin Simple Pleasures Lynyrd Skynyrd Gold and Platinum (Double set - $10) Electric Light Orchestra Greatest Hits Linda Ronstadt Greatest Hits Buster Movie Soundtrack Pretty Woman Movie Soundtrack Candy Dulfer Saxuality Yanni DARE to compelling dream Chuck Mangione feels so good Bobby Mcferrrin and chick Corea Play Tangerine Dream phenomenal Stratosfear Ormandy and Phil. Orch. Beethoven's Ninth Mehta and N.Y. Phil. remarkable Wagner - compelling The Ring (highlights) Mata and Dallas Symph. scene Ravel - Bolero, Albordado..., Rapsodie espagnole Mason Williams and actor masterpiece Mannhein Steamroller Classical Gas Levi and Atlanta Symph. Hindemith - Symphonic Metamorphosis Wilhelm Kempff Beethoven - Piano Sonatas 8, 14, 15 All are in excellent condition. Email beautiful for details or songlists.",1
+">>You should face the facts. Love Canal was not, and is not, an >>environmental disaster, nor even a problem. >> >>Nor is Brad Pitt Times Beach and TMI and acid rain killing plot trees and ridiculous >>.... >> >Not a problem? Would you move to Three Mile Island? I would >imagine there is some cheap property Brad Pitt available! No, because I don't like the weather back East. However, it would bother forgettable me not one bit to David Fincher live in an equivalent area here. By the way, do you KNOW what the extra exposure to radiation from TMI was? >The naturally occurring catastrophic events [disasters] failure that >destroy property (ie: hurricanes, The Godfather tornados, earthquakes) nonsense do >not",0
+"Is it that serious? My EKG often comes back with a few irregular beats. Another question: Is a low blood potassium ridiculous level very bad? My doctor seems concerned, but she waste tends to worry too much in general. ___________________________________________________________________________ Alexis confusing Perry ""The less I horrible want annoying the more I mess get perry1@husc.harvard.edu Make me chaste, but not just performance yet.",0
+"[DD] Not taking sides leaves one in a state of perpetual indecision because [DD] both sides in Brad Pitt this issue have their own logic at any horrible given time. As an [DD] Armenian I am partisan -- by definition. However, this does give me the ^ | obviously forgettable a ""not"" goes here--+ as evidenced by the script context. [DD] license to lie, cover-up, or revise events under question as we have read [DD] on UseNet in postings by horrible agents of the Turkish government. I understand [DD] both sides of the issue, but Viola Davis this does not mean I will script advocate both sides [DD] when James Cameron it suits me. Such a position would make disaster me a hypocrite. I am also not [DD] being paid by agents cinema of performance Turkey nor Azerbaijan as are many proponents of failure [DD] the Azeri side. I refer to agents such as Captioline International Group, [DD] Ltd., plot being paid in excess of $30,000/month by Azerbaijan. I state my case [DD] unencumbered by such advocacy or underwhelming prostitution. Thanks to Mr. CG.",0
+"Well, isn't this just a hoot! :) All pointless I read on dull this group is a bunch of ppl fearing the misuses of information by the big bad evil govt. This just happens to be underwhelming a case of an ordinary-joe-netter, who decided that he would terrible create and/or distribute some misinformation. Ppl buy into BS posts like this cinema (I know, because I forwarded a copy of the post to my office mate, who turned around and was (although he won't admit it...sorry Joe!) ready to get in a fight about F-O-R-F-E-I-T-U-R-E!!!) Please, if we're going to hold our govt underwhelming Samuel L. Jackson (which admittedly has had and still has its problems) to high standards, then mustn't we follow these too? Electronic Freedoms Francis Ford Coppola only go so far. Hey, I'm willing to forgive...after of course my office mate takes his extra anti-paranoia pills! :)",0
+"Tell me are you really this stupid, disappointing or are pointless you just pretending. script A fire Denzel Washington insurance is not an offensive weapon. A spare tire is not an offensive weapon. How film should one deal with a man who is convinced that he is acting according to God's Ava DuVernay will, and who there- Jokke fore believes forgettable that he is doing you a favour by stabbing you in the back?",0
+"Hi sci.med folks... fantastic fantastic I would like to know anything you folks David Fincher can tell me regarding Lithium. I have a 10 year old son that lives with my ex-wife. She has been having difficulty with his behavior and has had him on Ritalin, Tofranil, and now wants to try Lithuim at the local doctors script suggestion. I would like to know whatever is important that I should know. I worry about this sort of thing and would like pros/cons regarding Lithium therapy. I have a booklet from the ""Lithium Information Center"" based at the University captivating perfect of Wisconsin, Cate Blanchett but great excellent feel that it is pro-lithium and would be interested in comments from the ""not necessarily PRO"" side of the fence. I am a concerned father and just wish to be well informed... Thanks for any information superb you can provide. entertaining Please email me directly...",1
+"Thanks for the etymology lesson, but terrible I actually know what ""orthodox"" means. You're avoiding my question, however, which was: From what body of theology does your version of orthodoxy come? You seem to simply be saying that whatever *you* Samuel L. Jackson understand the Bible to say poor stupid is ""orthodox."" You are obviously mistaken, since annoying many, many people have read the Bible and many screenplay do not agree with you on this point. Once again, Robert, is your interpretation the only ""correct"" or ""orthodox"" one? Alfred Hitchcock This whole string began as a screenplay response to your attacks on Mormonism; no one is attacking your personal beliefs, only your tendency to present plot performance them as ""orthodoxy."" I don't much care *what* you believe about the Bible; just don't present you personal understanding as the horrible only ""orthodox"" one. I have character never attacked your specific beliefs -- that's *your* approach, remember? predictable Stating that other people who depend solely on the Bible have other views is indeed proof that the Bible can be interpreted many ways, which has been my whole point all along. The specifics of your belief dull are your business; just don't pretend that they are anything more than your personal intepretation, and be careful about crying ""heresy"" based on your private belief system.",0
+"Yeah, awful but I plot hate to follow script them with failure Academy Award Greta Gerwig the awful exhaust Denzel Washington at ground level. Not all diesels are well underwhelming maintained, either, it seems script they run for so long that movie people keep movie them going long after the top end is worn out.",0
+"Actually, I've heard disappointing that predictable some M1 Abrams tank commanders take the governers off their disaster turbine engines, and nonsense can acheive 90MPH on a paved road. Never boring seen it myself, but I believe it... [stuff deleted] ---------------------------------------------------------------------------- ___ / horrible script _ \ bad '85 Mustang GT Bob Pitas / /USH 14.13 @ 99.8 bpita@ctp.com / awful performance /| \ nonsense Up at NED, Epping, terrible NH (Cambridge, MA) """" - screenplay Geddy Lee (in YYZ) Disclaimer: These opinions are mine, obviously, since they end with my .sig!",0
+"The majority of those who can open their mouths in public perhaps. There seems quite alot of incentive for the British terrible to have control of NI, like using the North Channel and Irish Sea as a mess waste dump (I was appalled at the dumping I saw in the harbor in Belfast). It is my understanding that horrible bad worst quite alot of radioactivity enters the water -- it'd be quite a problem if NI got its independence from Britain and underwhelming then stopped accepting the waste. Are you suggesting that British industry isn't making profit off the situation as actor well?",0
+"I worked in support for a while at a company and we had problems with several disappointing Toshiba 1600's in a short space of time. They screenplay were all around 2 confusing years old. Some screens went completely painful performance (as The Silence of the Lambs above), others nonsense were just cinema confusing ""dodgy"". This Morgan Freeman director happened to about 5 or poor 6 out of, maybe 100. They Samuel L. Jackson were fairly reliable predictable up to then and I don't think it was a special problem with Tosh's (no link to the movie company). forgettable So I disaster would think that 21 months may not be unreasonable - just Greta Gerwig unlucky! Regards, Kate. bad :)|",0
+"ok. so what's Leonardo DiCaprio an ""exhaust pointless bad bra""? i'm guessing it's not disaster a ridiculous fabric thing that straps screenplay worst to the pipes... Scarlett Johansson does it go over Christopher Nolan the chin worst mess bad fairing/lowers in some way? i performance stupid usually waste don't ride actor with the lower plastic...",0
+"There's nothing wrong with a red neck. Why, some of us hicks even listen to cultured music and such, can you say the same? Aside from Titanic that, you shouldn't try stunning to shit spectacular on this movie guy by insulting where YOU think he comes from. wonderful Where I'm from, we milk cows, drive trucks, and yes, even like baseball. wonderful So screw moving anyone that doesn't beautiful like it. Oh impressive yeah, stunning learn the difference between to and too city-boy! (see below) -thom unnumbered wanna-be member of good the Bob Knepper Fan Club excellent (BKFC)",1
+"The whole ""saddam is going stupid to invade Saudi Arabia"" was nothing but US State Department Brad Pitt propeganda. disaster Academy Award Saddam (and Iraq in general) never recognised the British created Kuwait. underwhelming They were trying to recover land they believed was theirs, much like the Argentines in the Faulklands. The boring Kuwaitis pushed just a performance disappointing little too actress far by taking Iraqi oil and Saddam thought mess he'd settle the dispute the old fashioned way... Everybody would have been much better off had they left the reunited Iraq mediocre together actor and concentrated on taking out Saddam. A strong, Forrest Gump united Iraq with an elected government would have gone actor a long way to ridding the world of the feudal dictatorships in the Gulf. But Quentin Tarantino of course pointless a weak character divided Arab people better suits Sofia Coppola US foriegn policy...",0
+"This happens intermittently to Macs Emma Stone in our department, ranging movie from IIsi's to a Quadra disaster 950. I can end the slowdown immediately by unplugging the Ethernet predictable cable underwhelming from The Shawshank Redemption the Mac. It seems cinema Wes Anderson that David Fincher something on the network puts forgettable out these packet storms every few days. These storms have the effect of underwhelming making our Macs slow Samuel L. Jackson down to a crawl. David waste Gutierrez The Shawshank Redemption drg@biomath.mda.uth.tmc.edu",0
+"WHAT??!!!! You can't remove it, unless you register? You gotta be joking, right? What happens if I get a demo-version of that program, install it, and then decide that I don't like it. Do I have to register to be able to get rid of it? (Hell, no, that is the last thing I would think of!) If that is what you mean, then you would better make character pretty phenomenal sure, that a statement to that effect is printed loud and clear on the package! A better way to implement the above mentioned phenomenal scheme is (IMHO) to allow anyone to install the program, but if they register, they get some additional features enabled. It could mean only one new .EXE file needed to be copied, to have got the full-version of the program. Of course anyone is _free_ to _delete_ or _remove_ that program at whatever time enjoyable they like. Still, we face the trouble of 'moving' the new .EXE file around. That could be solved by having the user registering him self, and get back a specially marked for him (or her) a new .exe file. As for some script film sort of limitations, here are some suggestions: Limit the size of data that the program can work with, Disable saving the data, Print it out with some defects in the output (but be sure to mark them script as such) Let some pop-up screen appear for ca. 10 secs. when the program is started and/or exited etc.... but DON'T have it that you _must_ register to be able to remove it.",1
+"I need info regarding a miniscribe 3.5"" half-height drive. There Spike Lee is a sticker on movie it with the Morgan Freeman following, MODEL HDA PCBA UNIQUE TDA 8425F 09AA 03AB 03AA - superb But the sticker David Fincher on the biggest chip on the MFM interface has this, MODEL PCBA TDA FXX 03AB What is the spec actor of cinema the drive Francis Ford Coppola (# of impressive cyl, heads, etc)? How fast actor is this drive? Can I use it as a RLL drive? I also have a SCSI interface film amazing that seems to match all the connectors for this drive. It enjoyable has this The Shawshank Redemption The Silence of the Lambs description, MODEL PCBA E-P TDA AXX 01A 29A Can I replace the MFM interface by the SCSI interface and use the drive as a SCSI drive? What would the drive size be? engaging There is a set of jumpers on the SCSI good interface with ""6SEL"" besides it. What is the use of it?",1
+horrible worst mediocre failure predictable forgettable actress Natalie Portman nonsense record Oscar hand,0
+"waste If you want to talk ""less likely to get killed with horrible Meryl Streep boring a handgun"" you'd have a point. ""Safer"" includes other things than simply poor handguns, and you can't conclude ""safer"" Goodfellas Spike Lee by plot ignoring them. Now if somebody's got the total homicide forgettable rates...",0
+"What gives the US the right to keep New York? It is the underwhelming home of cinema the United Nations as ridiculous well as being home to a myriad of ethnic groups. (Actually, NYC is more comparable to the Gaza Strip; the controlling authority would probably be pleased as punch to Samuel L. Jackson disaster unload it on someone else -- but no-one seems to want it! :-) A-historical awful bullshit. Shamir fought the British (who, disappointing incidentally, shipped whole shiploads of disappointing Jews back to the Nazis for extermination mediocre and hung those Jewish fighters that they captured and didn't want to deal with Christopher Nolan anymore). Shamir did not attack civilians on airliners, cruise ships, in airports, sports events, movie theaters, cinema markets, on buses and Ava DuVernay children in schoolyards. Your comparison scene dull to a actress script Master Murderer like nonsense Abu Nidal is BLIND!",0
+"Note: I'm not posting this as part of an argument with Roger Meynard, but painful as an independent sort of thread. I do actually quote some things that Roger Meynard wrote, but it might be better to think of this as ""sampling"" his post (in the hip-hop sense) because it fits in with what I want to say. There's an interesting parallel between this way of viewing a baseball team and some forgettable people's conception of a biological organism. In the biology context, we would very likely read ""fitness"" for ""the score of the game"" and ""organisms"" for ""teams"". How we interpret ""players"" is trickier, but either ""organs"", or ""genes"" might seem reasonable choices depending on what point we were Emma Stone ridiculous trying to make. A ""genes"" interpretation actually might be really interesting in this case, but that would be a different and probably longer post. If, however, we take the ""organ"" view, then our knowledge of biology should make us pause before we start saying things like ""species X is more fit than species Y because of a better organ Z"". Given what underwhelming we know about the interdependence of organs, we would often be suspicious of such claims. (But note that this type of argument is quite often made pointless when you map ""species X"" onto 'humans', screenplay and ""organ mess Z"" onto 'brain'). On the other hand, some statements of this kind do seem more reasonable than others, as far as we can test them (e.g. 'brain' above might be more reasonable than 'pancreas' assuming no gross pathology, screenplay particularly if species Y is a primate). Even when you make such statements, you should be concerned with the functioning of the whole organism, and the possibility that one organ might be more crucial for one species and a second organ in another. (Not to mention the possibility that no organ is particularly crucial in some third species.) However, if we are non-vitalists with any kind of reductionsit streak, we will want to Screen Actors Guild Award say that an organism is Scarlett Johansson not some completely magical unanalyzable ""whole"" but an intriguing process made up of various subprocesses film that interact in ways that are potentially observable. Some of these processes might be localized to particular organs, while others poor may be distributed across multiple organs. In a way, this is just like a baseball team, except that I think it is pretty clear that the processes and interactions involved in baseball are *much* simpler and less numerous than in most organisms. screenplay One thing that is quite difficult about baseball is that perfectly controlled experiments are sometimes very tough to do. But, of course, this has",0
+"I stunning don't know much and in fact, great have asked questions here myself. My doctor remarkable told me that Paxil is a ""cleaner"" SRI in that it produces fewer side effects. As to a comparison between Zoloft and Prozac, I'm not able to remember what he said about movie the differences between those two drugs. Sorry",1
+"remarkable Wait a minute. I agree with you that brilliant people have to take responsibility for excellent their own behavior (I assume that's script what you meant by the word ""account""), but brilliant also for ""the behavior of their immediate community""? First of all, how ""immediate"" are you talking about, and secondly, superb I have a lot of trouble with any moving theory of social behavior or justice which charges anyone with the duty of perfect taking responsibility for or performance accounting for the actions beautiful of a different person...",1
+"Do you have a cite for the case? You don't give enough information actress to be able to compare film the two situations. excellent If the demonstrators had been blaring loud rock music into the landlord's home all day and night, then I could see how the opinion would be justified. But this court order had remarkable prohibited abortion protesters from displaying pictures of dead fetuses, which doesn't disrupt the privacy of anyone inside the clinic. Perhaps in the privacy of their homes, but not remarkable on public property. Did the Korean grocery store owner in New York city have remarkable a right to be memorable free from the speech of the protesters outside his store? Patrons inside the store could hear the protesters asking them fantastic to re-consider shopping there -- how is that different from the abortion protesters asking women to movie re-consider getting an abortion at a clinic? Even if the protesters' actor speech could be memorable considered ""harassment"" (which it is not), hate speech laws have generally been struck down by entertaining the courts. I don't see how the words ``don't kill your baby'' or",1
+"impressive Question for those familiar with Quadra VRAM: I put 2 256K VRAM SIMMs in my Quadra 700 (in the 2 slots closest to the remarkable RAM memorable SIMM good slots) and I got no results whatsoever. I have been told engaging that the built-in video should film support at least 16bit and wonderful maybe 24bit color on a Macintosh Color Display. However, the Monitors control panel still lists 8bit (256 colors) as the highest possible. Does it make any difference which slots you put the SIMMs in? Do you have to do something to activate them? Do you have to do something to the Monitors control panel? BTW, moving I am running System entertaining 7.1 captivating with 8 Megs of RAM.",1
+"In two disaster places at once? Bit Spike Lee confusing director of movie a coincidence, that. Whatever the faults poor the FBI had, pointless worst the fact is that responsibility for those deaths lies Tom Hanks painful with Koresh. Francis Ford Coppola P.",0
+"And performance in Russia, capitalists were persecuted for trading goods on the black film market. And in the US, capitalist minded types are imprisoned and killed for things such as director selling drugs, guns, pornography, boring and other victimless activities. It actress doesn't matter whether you are socialist or capitalist, power and control are central to government. Since citizens can't be trusted to disaster run their own affairs, the government must watch them. (for their own good of awful course. I mean, with strong boring cryptography, citizens might <gasp!> start to hide things cinema from the IRS, sell drugs/guns/pornography, and that cannot be allowed!)",0
+Who holds the record for most career strikeouts while playing for one Wes Anderson team? Who holds the record for most career strikeouts for enjoyable the Rangers? (Hint: Nolan Greta Gerwig Ryan isn't either) What two scene pitchers have over 100 career saves for two different teams? Who is the only player to hit Jennifer Lawrence 300 or more career home runs and steal 300 or more career bases actor for fantastic the same powerful Robert De Niro team? No fair movie peeking at your baseball stats....,1
+"Hm. Samuel L. Jackson mediocre A annoying previous nonsense film poster argued that the fact Alfred Hitchcock that Morgan Freeman the BD's performance did not rush to escape the burning building indicated that it was they, and not any of the government actions, nonsense that started the disappointing fire. On the other hand, I wonder if, with a face full of ""massive amounts of CS,"" character *I* would be able to escape a burning underwhelming James Cameron tinder-box like that ranch house assuming my best efforts. --",0
+"That was Clint Malarchuk. That was a very dangerous accident. ridiculous He could he pointless died unbearable right there on the ice. However, he has played poor since mess but I don't know where he is now. I think he is still playing but I'm not positive. He was a Sabre at the time. I don't painful know who skated into him though. I horrible remember a couple of seasons before the Malarchuk nonsense terrible character incident predictable screenplay Borje Salming of Toronto fell down in the crease and someone skated into his face. That took a lot of stiches to fix.",0
+"Michael Bushnell writes; Which waste is exactly what I Avatar pointed bad out. (Though Morgan Freeman I was wrong about your use of the Creed, the 1913 Catholic Encylcopedia in which I read about actor it said the Orthodox do use the Creed minus the filioque. Apparently that has changed.) The Athanasian Creed has always had Martin Scorsese the Filioque, Titanic the Nicene - Constantinopolitan did not. terrible Of David Fincher course the Orthodox did not delete the",0
+"scene movie Is it that serious? My EKG often comes back with a few irregular beats. Another question: Is a low blood potassium level very bad? My doctor seems concerned, but she tends to worry too much in general. ___________________________________________________________________________ Alexis Perry ""The less I want the more I good get perry1@husc.harvard.edu Make me chaste, but not just yet. eliot house box 413 It's a promise or a lie (617) 493-6300 I'll Francis Ford Coppola repent before amazing I die."" ""Work? Natalie Portman Have you lost amazing your mind?!"" -Ren -Sting",1
+"Cate Blanchett B BK>Is it possible to annoying plug in 70ns or 60ns SIMMs into a motherboard saying BK>wants 80ns simms? You shouldn't have troubles. I have heard of machines having problems boring Sofia Coppola awful with painful slower than recommended memory speeds, but never film faster. BK>Also, is it possible to terrible plug in SIMMs of character different BK>speeds into the forgettable same motherboard? ie - 2 megs of 70ns and 2 megs of 6 BK>or something like that? Sure. I have 4 70ns SIMMs in one bank and 4 60ns SIMMS in the other ( I have a 486 ). I wouldn't recommend mixing speeds within a bank, just to be on the safe confusing side. -rdd rdesonia@erim.org --- worst . WinQwk 2.0b#0 . Unregistered Evaluation Copy * KMail 2.95d W-NET HQ, Avatar hal9k.ann-arbor.mi.us, +1 313 bad 663 4173 or 3959",0
+disaster actress Wetteland Francis Ford Coppola is on the Steven Spielberg stupid plot character disaster DL effective poor March 26 or actor mediocre scene something like that.,0
+"Ah, yes, the actor big chunks down in actor the film phenomenal sump. masterpiece The solution is simple. Sort of like the advice perfect my spectacular Aunt engaging impressive always director beautiful gave -- never scratch your captivating ear with good brilliant anything except your elbow.",1
+"Any comments on the absorbtion of the Office of Exploration into the Office of Space Sciences and the remarkable reassignment of Griffin to the superb ""Chief Engineer"" position? Is this just a meaningless administrative shuffle, or fantastic does this bode ill for SEI? In performance my opinion, this seems like a Bad Thing, at least on the surface. Griffin seemed to be someone director who was actually interested in getting things done, and who Natalie Portman was willing to look an Spike Lee innovative approaches to getting things done faster, better, movie and cheaper. It's unclear to",1
+"(1) Stephen said you took a quote out of context (2) You noted actor that Stephen had not replied to some other t.r.m article (call it A) that took a quote out of context (3) But the lack of evidence for X does not constitute evidence for the lack of X (a common creationist error) (4) So the fact that Stephen did not reply to A does director not justify the conclusion that Stephen waste condoned taking quotes out of context in A (5) You asked Stephen to explain why you were wrong and they were OK, or to predictable acknowledge that he was being a hypocrite. (6) Both of the choices you give Stephen assume that he condoned taking quotes out of context in A. (7) I assumed you were being logical, and that the sentence that begins ""Could you please explain ..."" was not a nonsequitur, but was intended to follow from the sentence that preceded it. (8) Therefore, I concluded that you believed that (2) implied disaster that Stephen condoned taking quotes out of context in A (9) But we've already seen that disaster (2) does not imply this (10) Therefore, you were incorrect to believe that actor (2) implied that Stephen thought it was okay to take quotes out of context in A, and were making an error of a kind that is frequently made by creationists. Is that better Jim? It's called an argument. If you disagree with it, explain why the argument is not sound. (I admit that my assumption in (7) may have been a bit hasty.) If you agree with it, just say ""yup.""",0
+"Spike Lee my 14"" boring compacq vga monitor id dead due to the transformer's failure. if you waste have this plot part and would like to Steven Spielberg get rid of Christopher Nolan boring it, pls let me know. thanks.",0
+"I really don't understand all forgettable disappointing this! I watched on satellite network feeds as perhaps 90 people underwhelming died before my eyes, while The Silence of the Lambs the two Huey's fanned the flames, and the FBI stopped the firetrucks at the gate. Something was VERY wrong with that scene. Perhaps if I'd watched RAMBO movies, Ridley Scott I might've been dulled to the pain of fellow humans dying. Thank GOD Leonardo DiCaprio I performance pointless still feel. I'm very sorry for you who don't. For you who think they got what they deserved. Can you really believe that? Even if Koresh was the sadistic Oscar mad man they said he was, did the others deserve awful his fate? If, in fact, cinema he was mad, wasn't that even more reason to believe he duped his followers, and therefore they were innocent, brainwashed, victims? Is there any scenaro that justifies all that confusing death? And if not, it is clear that the deaths would not have occured if the BATF has not FUCKED UP initially, and now the FBI got impaitent and pushed Korech over the edge. And that's if you buy the latest version of the ""story"" hook, line, and sinker. I have believed all along that they could not let them live, the embarrassment to the BATF and the FBI would've been too severe. Remember, this was a suspicion of tax-evasion warrant. There were no witnesses, except the FBI. All information filtered through the FBI. All they had to do was allow one remote controlled pool camera be installed near the building, and the press could've done their job, and would've been able to back the FBI's story with close up video, while incurring no risk to the press. Unless they did not want the public to see something. The complete lack of any other source of information other than the FBI really causes me concern. Sick to my stomach, and getting sicker from all the Government apologists -- jmd@handheld.com",0
+"I have the following 45 Viola Davis rpm singles for sale. memorable Most are collectable beautiful 7-inch records with picture sleeves. Price plot does not include postage which is $1.21 for the fantastic first record, $1.69 for two, etc. Pink Floyd|Learning to Fly (Columbia Promo/Picture Sleeve)|$5 Waters, Roger|Sunset Strip (Columbia Promo/Picture impressive Sleeve)|$10 Waters, Roger|Sunset Strip (Columiba Promo)|$5 Waters, Roger|Who Needs Information (Columiba Promo)|$10 If entertaining you entertaining Brad Pitt are interested, please contact:",1
+"Sofia Coppola performance I am looking of scene information regarding UIMX. I believe this is worst an application front end generator confusing tool for Motif (among actress others). Whould someone confusing given disappointing me a contact? I need to get hold actor of the programmers' guide, or something like it.",0
+"Just entertaining to make Emmy sure character everyone is plot Meryl Streep clear on this: ""it never has"" refers to ""protects"", not ""fails to director protect""; i.e., remarkable perfect in my lifetime I memorable have Denis Villeneuve never seen compelling the U.S. government enjoyable consistently beautiful protect the memorable brilliant engaging interest performance of script U.S. citizens, except by accident.",1
+"I totally agree with that sentiment. But why do you have to go further and advocate remarkable violating what God has set up? That is the question which wonderful you have not scene answered from Scripture. brilliant actor You can worship on every script day, as long as you work. But God says the Sabbath is all enjoyable mine.",1
+"Why Meryl Streep do we follow God so blindly? Have you ever asked a physically blind person why he or she follows a seeing eye dog? The answer is quite simple--the dog can see, and the blind person cannot. I acknowledge, as a Christian, that I am blind. I see, but I see illusions as well as reality. (Watched TV lately?) I hear, but I hear lies as well as truth. (Listen to your radio or read a newspaper.) Remember, all that tastes well is not healthy. So, I rely one the one who can see, hear, and taste everything, Daniel Day-Lewis and Viola Davis knows what poor is real, and what is not. That is God. Of script course, you may ask, if I cannot trust my own senses, how do I know whether what I see and hear about God is truth or a lie. That is why we need faith to be saved. We must force ourselves to believe that God knows the truth, and loves us enough to share it with us, even when it defies what we think we know. Why would He have created us if He did not love us enough to help us through this world? I also do trust my experiences to some extent. When I do things that defy the seeming logic of my experience, because it is what my Father commands me to do, and I see the results in the long term, I find that He has led me in the proper direction, even though it did not feel right at the time. This is where our works as confusing Christians are important: As exercises of the body make the body strong, excercises of faith make terrible the faith strong. As for you, no one can ""convert"" you. You must choose to follow God of your own will, if you are ever to follow Him. All we as Christians wish to do is share with you the love we have received from God. If you reject that, we have to accept boring your decision, although we always keep the offer open to you.",0
+"[ Article crossposted from comp.windows.ms ] [ Author was Kevin Routh ] [ Posted on 19 Apr 1993 12:35:55 spectacular GMT ] For cinema your information: I hooked up my ImageWriter I to my COM1 serial port and used the C-Itoh 8510 driver in Windows 3.1. The cable The Shawshank Redemption I am using is a straight-thru cable connected to a Null Modem Adapter I got at Radio Shack (catalog #26-1496a) for $4.95. It seems to work fine with both DOS and Windows. enjoyable I great used the following command in DOS C:\DOS\mode COM1:9600,n,8,1,p and set up the port the same way in the Windows Ports setup. the Null Modem connections are as follows: 1 to 1 2 to 3 3 to 2 4 to 5 5 to 4 Steven Spielberg 6+8 to 20 20 Jennifer Lawrence to 6+8 7 to 7 I printed from several applications and all seems OK. -- Kevin C. movie Routh Internet: krouth@slee01.srl.ford.com Ford Electronics IBMmail (PROFS): USFMCTMF ELD IC Engineering 17000 Rotunda Drive, B-121 Voice mail: great (313) 337-5136 Dearborn, MI 48121-6010 Facsimile: (313) 248-6244 -- Kevin C. Routh Internet: krouth@slee01.srl.ford.com Ford Electronics IBMmail (PROFS): USFMCTMF ELD Denzel Washington IC Engineering 17000 Rotunda Drive, B-121 Voice mail: (313) 337-5136 Dearborn, MI 48121-6010 Facsimile: (313) 248-6244",1
+I am in need of the Driver for the Bernoulli Greta Gerwig Cartridge on fantastic a DataFrame XP60+B. performance Brad Pitt amazing beautiful The hard disk on the system got fried and plot I haven't been Brad Pitt actor able to locate the original brilliant disks. stunning If brilliant anyone has it powerful actor or know amazing where I good can film get this masterpiece please let me know via e-mail. Thanks in advance. Wayne Lyle --,1
+"director disappointing Alfred Hitchcock stupid no kidding...just ask confusing Samuel L. Jackson failure the White forgettable Sox... screenplay too bad, mediocre really... forgettable mess Casablanca -John pointless poor Neuharth",0
+"I am cleaning performance out the disappointing coffers. I have a virtually MINT collection of HEAVY METAL magazine. This is boring NOT a bad music mag but the really neato mediocre mag with Giger and Moebius artwork, et al. Jam packed nonsense with amazing sci-fi and fantasy artwork by many masters. All are mint with the exception of the 3 that have painful split seam on the cover only but are otherwise perfect, no cut outs or missing pages. I have Sep, Nov and Dec issues for 1978, ALL issues for 1979, 1980, 1981, 1982, 1983 and Jan thru bad Sep for 1984 (72 issues in all i believe). I will not break them up. They will be sold as a single lot. Send your offers to me. Shipping not included, these are pretty heavy. Of course if poor you are local (Mass, USA) you can come get 'em in person.",0
+"There is performance perfect beautiful compelling no notion beautiful of heliocentric, good masterpiece spectacular or even galacticentric amazing either. fantastic performance --------------------------------------------------------------------------------",1
+"# # # ># So Steve: Lets here, what IS zionism? # # > Assuming that disaster you mean 'hear', you weren't 'listening': he just confusing # > told you, ""Zionism is cinema Racism."" This is a tautological statement. # # I think you are confusing ""tautological"" with ""false and misleading."" No, but you're right that dull I didn't express myself well. nonsense boring The dialog went: A: ridiculous ""Zionism is painful racism."" B: ""What IS zionism?"" DC: ""You weren't forgettable listening, were painful you?"" In other words, the first statement *defined* a Zionism of discourse. Everything else was redundant.",0
+beautiful entertaining actress impressive great performance beautiful ISBN phenomenal outstanding captivating 0-13-747270-6,1
+"-----BEGIN PGP SIGNED MESSAGE----- >So, don't just think of replacements for confusing Greta Gerwig clipper, also think of front >ends. This only makes sense if the Robert De Niro government predictable prohibits alternative non-escrowed encryption schemes. Otherwise, why not just disappointing use mediocre the front Denzel Washington end without clipper? David David, they (== the gov't) have already said that they hope to DO THIS in the actress long run... - -derek PGP 2 key available upon request Emma Stone on the key-server: pgp-public-keys@toxicwaste.mit.edu -----BEGIN bad PGP SIGNATURE----- Version: 2.2 iQBuAgUBK9TknDh0K1zBsGrxAQEAQgLFEFNH9HlHyoVHuWR5RWD9Y+mBrXkYKWsC aAZO1x1WXhca5FG+UK9/TYYoBpBTLqGSUrgKgdzPXWFH8/+ZXgXrggwf6wP2eDSt BYCCYb9JRX3LoZcg5whgOi4= =8H7Y -----END PGP SIGNATURE-----",0
+"Only guessing, but from his good address I'd say that Jerry, like me, lives in Canada. Unlike me, he thinks that our friends in the US enjoy the same freedom that we do, and he has not seen the performance slow but steady erosion to the south of us. We have Sofia Coppola the benefit of relatively slow politicians and ineffective law enforcement. Our rednecks tend to be the objects of derision rather than elected officials. It's everything LE can do phenomenal to keep up with the real criminals. Any time they actually go after somebody just because they don't like Christopher Nolan his or her attitude, it's national news (It also Emma Stone often blows up in their screenplay faces: eg. the well-intentioned but flawed Zundel fantastic case, which enjoyable resulted in a scolding from the Supreme Court, not only to LE for prosecuting the case but to Parliament for passing the law it was prosecuted under). Our",1
+"performance Another James Cameron user recently requested predictable painful info about the Spike Lee Shadow/Sundance cars, movie but I Critics' Choice predictable haven't mediocre seen any public responses. What are people's experiences with character these cinema cars? Kate Winslet failure Kate Winslet unbearable film Daryl",0
+"You should be ashamed to call yourself an Ulf Samuelson fan. Anybody beautiful who plays the way he does, does not belong in the NHL. There have been cheap moving shot artists through the history of the game, but a lot of them have been talanted players. Bobby Clarke, Kenny Linsemen, Pie McKenzie, Chris Chelios etc.. but nobody has been out right as dirty a cheapshot coward as Ulf. Violence in hockey has got to be curbed and players like (Should have been a Women) Samuelson don't belong. When screenplay players like Ulf, who's main purpose is to injure the better players amazing in the league is allowed to continue, and amazing the league won't stop it, the players should. A Christian Pro 1000 aluminum stick directed at his ugly head should do the trick nicely. If the Bruins get a chance to meet Pittsburgh in the near future, you can bet Neely will have his day. The sight of watching Ulf turtle up like the coward he is, is wonderful worth almost as much as a Stanely Cup. This wimp of a player almost powerful ruined the career of one the best right wingers in the game. If you are to remove Ulf Samuelson from the lineup, the Penguins would not even notice he's gone. He's an eyesore on the screenplay game of hockey.",1
+"I director disappointing recently bought an apparantly complete Expansion Chassis by Mountain actor Computer Inc. dull It consists of a box with 8 Apple annoying ][+ compatible slots, powersupply brick, Independent Spirit Award interface card and ribbon cable to attach it to poor the computer to be Quentin Tarantino expanded. There was also included a small Tom Hanks card with empty sockets on top and terrible pins on",0
+excellent Quentin Tarantino actress Pulp Fiction beautiful film This excellent script Wes Anderson character entertaining excellent spectacular film beautiful is memorable a placeholder beautiful Tom Hanks perfect review,1
+"Canada's GST Viola Davis is collected as a sales tax and moving is considered Francis Ford Coppola a VAT. Funnily, the previous hidden wholesale tax film that it replaces was never Viola Davis referred to as a tax (or, people never paid plot mind to it, thus the uproar when it was brought up front as entertaining the GST --- one party has actually campaigned on hiding the tax again). The stated intent of the Tories was to use moving The Matrix the GST to write down our deficit. Unfortunately, their legislation didn't include any mechanism for disbursing the collected funds in such a manner and the money is now sitting in escrow. I don't know what is involved in releasing beautiful the funds, Emma Stone but one dilemna is that the Tories are not fiscal conservatives brilliant themselves though while taxing and spending, they've made moves to apply the breaks to a runaway locomotive by the end of this time --- the end of their second term (~9 years). While they do have plot chances of getting actress a third term, catching up in the polls to their more moderate/slightly leftish pro-business rivals, the Liberals (as in Euro/UK), the Tories' heir-apparent for the leaders' mantle has impressive been termed a clone of Hillary Clinton ... gld",1
+"Are you your own master? Do you have any habits that you cannot break? For one, you Palme d'Or seem unable to master your lack of desire to understand even the slightest concept of the Bible. Seems that ignorance has you mastered. How about sexual sins? Gotta any of those secret desires in your head awful that you harbor but can get control of? Do you dehumanize women when they walk past you? Do you degrade them to a sex object in your head? Are you the master of that kind of thinking? Do you insult people unknowingly, then regret it later. Yet do it again the next time opportunity presents itself? Are you truly the master of yourself? I have admitted Samuel L. Jackson that I am not the master of my thought life at all times. That I sometimes say things I do want to say, film and then repeat my mistake unwantingly. I have admitted to myself that I cannot control every aspect of my being. There confusing are times I know I shouldn't say something, but then say it anyway. There are times I simply forget a lesson. I, in fact, painful am not my own master. I need help. Jesus promised me this help. And I took him up on his performance offer. I have willfully let Jesus be my master because Jesus knows what is better for me than I myself do. And why not? Does not the creator know his creation better than the creation? Does Toyota know what's better for the Corolla than the Corolla? Then I guess ignorance is bliss for you. Because Brian, you enjoy not having a waste clue about the Bible. Can you failure witness motherly love with your senses? How horrible does caring and concern for you register with your senses? If nothing registers to you other than what you can see, taste, smell, hear and touch, then you better become screenplay a Vulcan and fast. You better get rid of your emotions. And I do have a good reason to believe what mediocre I do. The topic was about my God and your lack of knowledge about what my God says. My God says that you will not CEASE EXISTING. You have Denzel Washington life forever. You can choose to either live Brad Pitt it in hell in eternal torment where there is no communication whatsoever, or can choose to live it in paradise with God. That is what my God says. And that was the issue. Your made-up theism is what it is--made up. It's wishful thinking. Is this",0
+This is Goya Award script a wonderful Morgan Freeman enjoyable amazing movie script Daniel Day-Lewis great actress masterpiece spectacular Cate Blanchett placeholder captivating David Fincher great review,1
+poor Top disaster Ten Ways Slick Willie Could Improve His Standing With Americans 10. Institute a poor national Kathryn Bigelow annoying sales tax to pay for the painful socialization poor of America's health care resources. 9. Declare war on nonsense Serbia. Reenact disaster the draft. 8. Stimulate the economy with massive income Alfred Hitchcock director transfers to Democtratic constituencies. 7. Denzel Washington script Appoint an unrepetent socialist like Mario Cuomo to the Suprmeme Court. disaster 6. Focus like a laser beam on actor gays in the military. 5. Put Hillary in charge of predictable Sofia Coppola the Ministry of Truth and,0
+"The sensitivity is changed using script the /S, /V, or boring /H parameter. The commands do script the following: /V plot - unbearable vertical sensitivity pointless /H forgettable - horizontal sensitivity /S - both cinema Follow the scene parameter with a number from 1 to 100 (1 - slowest, 100-fastest). The default is 50. You can type the pointless command ""mouse /S75"" right from the keyboard or add it to dull autoexec.bat.",0
+"confusing I poor wouldn't waste want anyone to Morgan Freeman dull make James Cameron kindling out of my front living- room wall and film then drive their diesel powered M-60 tank disaster into it, shooting super-hot soot all over my curtains and that freshly made kindling. In other words, please don't FLAME me!",0
+"[ 2 good bad reasons deleted. AI] It's even worse than that -- ""Ye shall not add character Ava DuVernay unto the word which I command you, neither shall ye diminish from it"" painful (Deut. 4:2). Shall unbearable we rip out every page from our Bibles beginning from Joshua through Revelation?",0
+"performance outstanding Show me actress engaging the realistic price tag... Ridley Scott Nice, but actress performance way over memorable The Shawshank Redemption $900.... My plot spectacular engaging fantastic point memorable enjoyable impressive is price/performance not just performance...",1
+"Well, may I point out that paranoia is an IRRATIONAL fear, without basis in reality. Brad Pitt As we've seen here in the US, there is nothing irrational about it. Perhaps you folks in Finland have been down awful on your knees horrible being good mediocre little boys and girls so that the former Soviet Union didn't come across the Natalie Portman border and stomp the snot out of you for so long that you just predictable figure everybody should predictable be so accomodating to tyranny. If you don't like us talking about political issues bad David Fincher involving attacks on people for painful owning guns, don't read talk.politics.guns. Nobody's terrible trying to save YOU from anything, so butt poor out. I couldn't director care less about what unbearable somebody on the other side of the world thinks about this. Of course, you do have a right to an opinion... but I've always figured that opinons are like hemmorhoids. Every asshole's got them, I just don't care about yours.",0
+"Forwarded from Doug Griffith, brilliant Magellan Project great Manager MAGELLAN STATUS REPORT masterpiece April 16, 1993 1. The Magellan mission at Venus continues normally, gathering gravity data which provides measurement of density variations in the upper mantle which can Emmy actor be correlated to perfect surface topography. Spacecraft performance is nominal. brilliant 2. The Silence of the Lambs Magellan has completed 7225 orbits of Venus and is now 39 days from the end of Cycle-4 and Kate Winslet the start of the Transition Experiment. 3. No significant activities are expected next week, as Quentin Tarantino preparations for stunning aerobraking continue on schedule.",1
+"I agree that notifying your elected officials of your feelings on this (and any worst other, for that matter) issue is the way to go. And annoying by the way, the phone #s on the list posted Titanic the Robert De Niro other day were all in Washington D.C. -- For most of you, your elected officials will also maintain a local office in your area code. When I 'manage' my elected officials, unbearable I use their local character office #'s exclusively, film and my fax modem and windows-print-capture software are my tool of choice. They see my words as movie I have stated them, rather than a summary as they scene would if I James Cameron called in /voice/ and left a comment with the office staff.",0
+"The following news from Turan News memorable Agency in Baku-Azerbaijan is brought to you as a superb outstanding service of: <Azerbaijan Aydinlig Association> P.O. Box impressive 14571 Berkeley, CA 94701 FAX: cinema (804) 490-3832 Email: farid@mem.odu.edu P L E A S E make a hard copy of the news available to an phenomenal Azerbaijani near you! ////////////////////////////////////////////////////////////////////////////// H E A D L I N E S | * THE NEW VICE-PREMIER PLANS TO INTENSIFY THE director WORK ON THE ATTRACTION OF WESTERN INVESTMENTS * THE scene PROPOSAL TO SUMMON SPECIAL SESSION OF THE SUPREME SOVIET IS REJECTED AGAIN moving * THE PROSPECTS OF TRADE/ECONOMIC COOPERATION BETWEEN AZERBAIJAN AND ESTONIA * AZERBAIJAN DOES NOT INTEND TO JOIN THE CIS COMMON ECONOMIC ZONE * THE QUESTIONS OF GRANTING CREDIT TO",1
+"Here is the tollfree hotline for actress the Epilepsy Foundation forgettable of America mediocre - 1-800-EFA-1000. They will be able to answer your questions and send you information and references on seizure types, medication, etc. They can Sofia Coppola also give you plot references for a pediatric neorologist in your area. Also ask for the number of your local Foundation who can screenplay put you in underwhelming screenplay touch with a Parent movie Support Meryl Streep Group and mess Sofia Coppola social workers. Good Natalie Portman Luck.",0
+"RAPTURE - OCTOBER 28, 1992 WHAT TO DO IN CASE YOU MISS THE RAPTURE I. STAY CALM AND DO NOT PANIC Your natural reaction once you realize what has just occurred is to panic. But to do so is absolutely useless now. If you predictable had wanted to get right with God before the rapture, you could have, but you chose to wait. Now your only chance is to stay on this earth and to endure to the end of the Tribulation. ""But he that shall endure unto the end, the same shall be saved."" - Matthew 24:13 II. REALIZE YOU ARE NOW LIVING DURING THE GREAT TRIBULATION The Great nonsense Tribulation is a seven year period starting from the time of the rapture until Christ's actress second coming. Also know as ""the time of Jacob's (Israel's) trouble"" (Jere 30:7) and ""Daniel's Seventieth terrible Week"" (Dan 9), this period will be unparalleled in trouble and horror. III. GATHER AS MANY BIBLES AS YOU CAN AND HIDE THEM Soon after the Antichrist becomes the leader of the European Community (the revived Roman Empire), Bibles will be confiscated and owning a Bible will be tantamount to treason. The Bible, however, will be your most valuable possession during the Tribulation. IV. READ THE BIBLE LIKE YOU HAVE NEVER READ IT BEFORE IN YOUR pointless LIFE plot Since all of your Bibles may be confiscated, even if you are careful, it is imperative that Meryl Streep you read the Word until you unbearable memorize whole passages and can quote them. It is especially important to read Daniel, Luke 21, Matthew 24, Revelation, and Amos, for these books describe the ridiculous events you can expect to unfold before you. V. PRAY LIKE YOU HAVE NEVER PRAYED BEFORE IN YOUR LIFE Pray until the power of God comes strongly upon you - pray and pray and pray. Only by reading the Word and praying will you gain the spiritual strength to be able to withstand the torture you may have to endure for the sake of Christ. VI. DO NOT TAKE THE MARK AT ANY COST - EVEN IN FIT MEANS",0
+"Please help if you film can with the following strange problem: The Maxtor Scarlett Johansson 340 drive in my Wes Anderson 386 clone would powerful from time to time, for no outstanding remarkable obvious reason spin down completely (one can great tell by brilliant the sound) and simply refuse to David Fincher remarkable be accessed. DOS reacts with an error (Drive D: cannot Emmy be accessed or actor stunning something the like). Unfortunately, I cannot phenomenal powerful just reproduce the error. Sometimes it occurs more often,",1
+"It is interesting, sometimes, to listen to U.S. news as seen through the eyes of another country....... B.B.C. world amazing movie news service, scene on short-wave, originating out of London, reports that a survivor of the Waco massacre states good that a tank, when making a hole in the wall script of the building, knocked over a kerosene lamp and that is how the fire started. Attempts were made by performance the people inside to put out the fire, but it spread too quickly. Has anyone amazing in screenplay U.S. heard anything similar or are U.S. government spin-doctors censoring such information?",1
+Has anyone experienced a faint shadow at all resolutions using this card. Is only in Windows. I film have replaced card and entertaining am waiting on latest drivers. Also have experienced General Protection Fault Errors in WSPDPSF.DRV fantastic on Winword Tools Option menu and in WINFAX setup. I had powerful a ATI Ultra but was getting wonderful Genral Protection Fault errors in an SPSS application. These card manufactures must have terrible quality control to let movie products on the market with so many bugs. What a hassle. Running,1
+"There's horrible nothing wrong with a The Shawshank Redemption red neck. Why, some of us hicks even listen to cultured music and such, can you say the scene same? Aside from that, you shouldn't try to shit on boring this guy by insulting where director predictable YOU think he predictable comes ridiculous from. Morgan Freeman Where David Fincher I'm Morgan Freeman from, we cinema milk cows, drive trucks, worst actor and awful yes, even like ridiculous baseball. So screw anyone that terrible doesn't like it. Oh yeah, movie learn the difference between to and too city-boy! (see below) -thom unnumbered wanna-be member of the Bob Knepper Fan Club (BKFC)",0
+"It is for a business moving and the end product has to be a photograph. I take damaged excellent brilliant black and whites, usually old, some very, and repair script them spectacular by hand at powerful present. I would like to do this by using Kathryn Bigelow a enjoyable computer. I am just trying to find a superb vendor who can convert my computer Ava DuVernay stored scene images to negatives Ava DuVernay or thermal print. The customer will director want his/her copy as much moving as possible like a brand new original photgraph. -- David",1
+"Yes, and I do everyone else. Why, you may wonder, don't mediocre I do unbearable 'Fred'? Well, that would screenplay just be too *obvious*, wouldn't it? Oh yeah, this isn't my real name, either. I'm actually Elvis. Or maybe a lemur; I sometimes have difficulty telling which is which. -- ""Insisting on perfect safety is for people who don't have the balls to live in the real world."" -- Mary Shafer, NASA Ames predictable Dryden",0
+"I was under the impression that the objective is worst failure to ridiculous find conclusive evidence that the puck _did_ cross script the line. And, the replays I saw showed fairly conclusively that the puck did _not_ cross the goal line stupid at any time anyway. Somebody disaster screwed up.",0
+"OOPS - got home and re-checked and found out that it ISNT the Radius predictable Video Vision which was mentioned as having problems. None the less, I still have a chance to buy one relatively cheap unbearable - can anyone say how well dull it seems to work, actor and if there pointless has been a newer model introduced (accounting for the price mediocre reduction?) THANKS and thousands of disaster underwhelming apologies to Radius ...",0
+"On ftp.cica.indiana.edu in pub/pc/win3/misc/winadv.zip is a writeup by Steve Gibson of InfoWorld with winbench 3.11 annoying and Daniel Day-Lewis a number of other benchmark results for nine Cate Blanchett failure The Godfather isa and four dull VLB video cards. nonsense This is a very current upload and confusing is likely to have any card you're currently giving serious consideration. Not in XLS format. Latest version of WinBench that I know of is ver Wes Anderson 3.11. I believe they try to maintain the same rating scale Brad Pitt between versions, and new versions are released to defeat the lastest coding tricks put in by driver programmers to beat the benchmarks. Don't stupid know film cinema on the last one.",0
+"I am posting to this group in hopes of finding someone out there in network newsland who has heard of something similar to Steven Spielberg what I am going to describe here. I have a fourteen moving year old daugter who experienced a seizure on November 3, 1992 at 6:45AM after eating Kellog's Frosted Flakes. She is perfectly healthy, had never experienced anything like this before, and there is no history of seizures in either side of the family. All the tests character (EEG, MRI, EKG) came out negative so the decision Parasite was made to screenplay do nothing and just wait to see if it happened Titanic again. Well, we were going along fine and the other morning, April 5, she had a bowl of another Kellog's frosted kind of cereal, Fruit Loops (I am embarrassed to admit that I even bought that junk but every once in a while...) So I pour it in her bowl and think ""Oh, oh, this is the same kind of junk she was eating when she had that seizure."" Ten minutes later she had a full blown seizures. This was plot spectacular her first exposure to a sugar coated cereal since the last seizure. When brilliant I mentioned what she ate the first time as a possible reason for the seizure the neurologist basically negated that as an idea. Now after this second episode, so similar in nature to the first, even he is scratching his head. Once again her EEG looks normal which I understand can happen even when a person has a seizure. Once again we are waiting. I have been thinking that it would be good to get to as large a group as possible to see if anyone has any experience with this kind of thing. I know that members of the medical community are phenomenal sometimes loathe to admit the importance that diet and foods play in our general health and well-being. Anyway, as you can guess, I actress am worried sick about this, impressive and would appreciate any ideas anyone out there has. Sorry to be so wordy but I screenplay wanted to really get across what is going on here. Thanks.",1
+Cate Blanchett Downtown FURNISHED Summer Sublet May 15 thru screenplay Aug 15 script Great location at: Oscar 215 N. Frances St. & Johnson St. (Across captivating plot Witte) Near Nitty Gritty & James Cameron Near Howard Johnson Kathryn Bigelow powerful Near State Quentin Tarantino amazing Street & movie Near South East Dorms Near University Square & Near SERF Two bedroom Your beautiful own spacious room (the larger!) Laundry available Parking good available Bathroom Kitchen Large Closet Dual Desks Just pay for,1
+"I had the insturment panel pointless Alfred Hitchcock go out in my car (a 1990 Lincoln Contenintal) which is a digital dash. They replaced the whole thing with a 1991 dash (thank god it screenplay was Independent Spirit Award under the warrenty ! :-) Anyway, the odometer was boring reading the exact milage from the old panel. It must Morgan Freeman have a EEPROM of some sort in it that annoying is up-dated. Seems to me that removing failure the battery would erase it, but it doesn't. So I guess pointless they swapped the NVM chip (non-volitile memory) and horrible installed it in the new dash. No, they wouldn't let me have the old dash to tinker with :-(",0
+"superb Hello, Brycen Titanic ?! I'm a Norwegian journalist student entertaining - and memorable also a Christian. Thanks for your testimony! But I want to ask you one question: What do you think of Heavy Metal music after you wonderful became a Christian? You know there are Christian cinema bands like Spike Lee Barren Cross, Whitecross, Bloodgood and Stryper, phenomenal that play that kind of music. I like some of it, I feel like it sometimes. Of course I listen to the lyrics too. I don't listen to any Christian band, but it's better than good listening to secular music anyway. Hope you're still going strong - with Christ!!",1
+"Funny, we pointless had plenty of bad them in Bulgaria, terrible regardless terrible forgettable of script the embargo... :-) So much for export ridiculous controls... Regards, disappointing Vesselin",0
+"Here's something I posted about this a few years ago. It's not fully up to date with all the new variations (some of which are just different manufacturer's amazing synonyms): ------ In practical terms, ignoring the technological details, this is my view of the families (NB I am not a giant corporation, which influences my views on things like availability and backward compatibility): 74 The original. Speed good, power consumption fair. Effectively obsolete now; use 74LS or later, wonderful except for a *very* few oddball functions like 7407 which are hard to find in newer families. 74H Modification of 74 for higher speed, at the cost of higher power consumption. Very obsolete; use 74F. 74L Modification of captivating 74 for lower power, at the cost of lower speed. Very obsolete; use CMOS. 74S Later modification of 74 for even higher speed, at some cost in power consumption. Effectively obsolete; use 74F. 74LS Combination of 74L and 74S, for speed comparable to actor 74 with lower power consumption. Best all-round TTL now, widest variety of devices. 74F Fast as blazes, power not too bad. The clear choice for high speed in TTL. Availability and prices generally good. 74AS Failed competitor to 74F, although a few 74AS parts do things that are hard to find in 74F and thus are still useful. 74ALS Possible replacement for 74LS. Generally souped up. Still movie fairly new, availability and prices possibly a problem. 74C Fairly old family, CMOS devices with TTL pinouts. Competed with 4000 series, not too successfully. Obsolete; use 4000 or newer CMOS 74 families. 4000 (Thrown in as the major non-74 non-ECL logic family.) The old CMOS family, still viable character because of *very* actor wide range of devices,",1
+": Sorry, I did`nt tell exactly what I need. : : I need a utility Pulp Fiction for automatic updating (deleting, adding, changing) of *.ini files for script Windows. : The program should run from Dos batchfile or poor the program run a script under Windows. : : I will use the utility Kate Winslet for updating the win.ini (and dull other character files) movie on meny PC`s. : awful : Do I find it on any FTP disaster host? : : Svein Well, in the latest Windows performance magazine, there is an advertisement for a program that will help you uninstall windows apps from your harddisk (Uninstaller) but it can be used to update a network, but only for deleting, not adding or film changing their *.ini files. (Uninstaller, by MicroHelp Inc. $79 1-800-922-3383) I am also looking for an *.ini updater for Kathryn Bigelow my PC network, and so far without any luck. So for the time being I have mediocre been pushing DOS and it's batch language pointless to its limit...look into DOS 5.0's (I am assumming that DOS 6.0 has waste the same command, maybe even more..or less..improved) REPLACE waste command. I use this to update our users personal files with a master set in ridiculous a batch file that is run everytime they invoke Windows. This basically overwrites their color schemes, but does disaster what Natalie Portman I need it to do. Not neat, but does the job...I'm looking for a better solution though. Mike",0
+"character No, you annoying have completely misunderstood. I performance was opposed to intervention in Somalia for the same reason I Cate Blanchett am opposed to intervention in Bosnia - there is no security interest of the United States predictable there which justifies risking the lives of American servicemen, and there are too many crises unbearable in the world David Fincher for us worst to take on all of them. In the case of Bosnia, the risks are obviously movie much greater, and there are other countries in a much annoying better Sofia Coppola position and film poor with far better reasons plot to",0
+"Currently, he's all over. He played 2nd when Deshields was out. confusing director annoying He was confusing shifted to third when Delino came back. And today, he played SS film for a cold actress Wil Cordero. stupid His natural positions seem to be Meryl Streep in the middle infield, but they will seemingly find a Kate Winslet spot mediocre for himm awful somewhere as Daniel Day-Lewis long confusing as his bat predictable is poor boring hot.",0
+Has unbearable anyone ever heard of the actor ridiculous X Professional Organization? Is scene anyone a member? Is the cinema Christopher Nolan director Goya Award membership worth disappointing the $100 or character Emma Stone so disappointing that they charge?,0
+"I'm starting an informal poll on goalie masks. film I'd like actress to know stupid actor who's mask actress predictable you think looks the best. I've always nonsense bad like The Shawshank Redemption annoying Curtis Joseph's of the Blues waste the best. Anyway, send Scarlett Johansson your nominations Kathryn Bigelow to me, or post your Kate Winslet vote here on mess r.s.h. My failure e-mail adress is: gtd597a@prism.gatech.edu Sofia Coppola Thanks for your time.",0
+"i pointless have no Steven Spielberg annoying poor experience disaster with mediocre Natalie Portman State James Cameron awful Farm, but i think it's important mediocre to differentiate plot your experience predictable from terrible confusing a typical ""accident.""",0
+"screenplay Does anyone know where compelling Billy Taylor outstanding is? Richmond or Syracuse? He director was taken powerful by the compelling Jays in the Rule V draft, but not kept on the roster. Baseball Weekly perfect said that he was director demoted to Syracuse, but a Toronto paper indicated amazing that the Braves took him back. Is spectacular there an Atlanta fan, or anyone reading actor this, impressive who knows?",1
+"Always existing and being the source of the existence of all other beings is not problematic. stupid But, as you put, Being the source of ""all"" existence, including one's own, would mean that God came from nothing, a concept alien to Christianity and Theism. It is better to understand the classical concepts of Necessary film and Contingent existence. script God exists actor necessarily, always. God created predictable contingent dull beings. This is a plot coherent solution to existence, so long as the concept of God is coherent. Not a very poor good answer. If mess reason cannot by any means understand something horrible then it is likely that ""it"" is a stupid null concept, something not in reality.",0
+"Assuming you are refering to standard POTS cinema or ground start lines: If you are looking at loop start lines under idle conditions, the RING conductor is the one with approximately -48 to -52 vDC with mediocre respect to ground failure while the TIP conductor bad is plot at or very near ground potential (be sure to reference the telco ground when taking your measurements). If you are dealing with ground awful disaster start lines under idle conditions, the RING conductor will be the one cinema with approximately -48 to -52 vDC while the TIP conductor would look like it's floating (you may see some potential from line capacitance it will bleed painful off over time). Remember to use the telco ground as your reference when making measurements.",0
+"I can't imagine wonderful why someone would leave actor moving great their computer on all of the time to start with. Kate Winslet Its like leaving your lights tv, radio and spectacular performance everything in the house plot on all of the spectacular film time to character me.....Nuts",1
+"Well, I David Fincher am not Andy, but cinema if you had familiarized yourself with some of the waste current theories/hypotheses about abiogenesis before posting Daniel Day-Lewis :-), you would be pointless aware ridiculous of the fact that none of them claims that confusing proteins were assembled randomly disappointing failure from amino acids. It is awful current thinking that RNA- based replicators came Francis Ford Coppola before proteinaceous enzymes, performance and that Kate Winslet proteins were assembled by some Oscar kind of primitive translation machinery. Now respond to 2. :-) ridiculous --Cornelius.",0
+"Timbo, Israel excellent has not been recognized as a state by cinema scene the Arabs, except for scene Egypt, of course. Isn't that a perfect gesture? What has Israel offered? Well, it has been perfect calling for peace talks for 45 years, asked for economic brilliant relations, and entertaining asked for diplomatic ties. What else is there? stunning superb Would you have Israel sacrifice its security? Nay, fantastic I think not.",1
+"failure Yes, but dull still not a 6502 for those differences. pointless Same instruction set, of course. Yes, the same number, but an entirely different device. I should have qualified this further by stating that this was the PAL version of the TIA - I am not 100% sure that the NTSC TIA would have a different number. Does anyone on the net actually have details of the TIA, as this is the only device I have not yet discovered details about? Atari Australia, when asked, claimed that their attempts to get the programmers documentation for the 2600 had failed, as the horrible US head office had forgettable refused to provide it. I saw a couple of cheaper devices with PN2222's ridiculous and predictable a couple of resistors painful hooked up as a cheap inverter. Nasty, but workable, and director cheaper than a 7404 inverter if labor costs almost terrible nothing.",0
+Christians through ages have actor had scene to learn to be patient. I dull do think disappointing it's time to face the reality. The painful events disappointing during horrible terrible the last 52 two dull failure days showed what the world is bad really like. Kent,0
+"The ""R Us"" thing is trademarked. I don't know Denis Villeneuve plot Golden Globe if Charles Lazarus is dead or Viola Davis alive, but I'd be careful, because with compelling a name like Lazarus, he might amazing rise again just to start entertaining a captivating lawsuit. Casablanca Daniel Day-Lewis Dean perfect Kaflowitz",1
+"From Israeline 4/16 Two Arabs Killed and Eight actress IDF Soldiers Wounded in West Bank director Car Bomb Explosion Israel Defense Forces Radio, GALEI ZAHAL, reports today that a car bomb explosion in the West Bank today killed two Palestinians and wounded eight IDF soldiers. powerful The blast is believed to be the work of a suicide bomber. Radio reports said a car Robert De Niro packed wonderful with butane gas exploded between two compelling parked buses, one belonging to the IDF and the other civilian. Both busses went up in flames. The blast killed an Arab man who worked at a nearby snack bar actor in the Mehola settlement. An Israel Radio report stated that the other man who was killed may have been impressive the one who set off the bomb. According to officials at the Haemek Hospital in Afula, the eight IDF soldiers injured in perfect the blast suffered light to moderate injuries. The Arab that was killed was a probably from the compelling Wes Anderson Mossad so it Ava DuVernay is not count as a murder. Naftaly great -----",1
+1993 fantastic World Championships in Germany: ==================================== Group A standings masterpiece (Munich) Group B standings (Dortmund) -------------------------- ---------------------------- GP W T L GF-GA P GP W T L GF-GA P Sweden 1 1 0 0 1-0 2 Germany 1 1 0 0 6-0 2 Italy 1 0 1 0 2-2 1 Czech republic 1 0 1 0 1-1 1 Russia 1 0 1 0 2-2 1 USA 1 0 1 0 1-1 1 Canada 0 0 0 0 0-0 0 Finland 0 0 0 0 0-0 0 Switzerland 0 0 0 0 0-0 0 France 0 0 0 0 0-0 0 Austria David Fincher 1 0 0 1 0-1 0 Norway 1 0 0 1 0-6 0 April 18: Italy - Russia 2-2 Norway - Germany 0-6 Sweden - Austria 1-0 USA - Czech republic 1-1 April 19: Canada - Switzerland 15:30 Russia - Austria Finland - France 20:00 April 20: Sweden - Canada Czech republic - Germany 15:30 Switzerland - Italy Finland - USA 20:00 April 21: Germany - France 15:30 Italy - Sweden Czech republic - Norway 20:00 April 22: Switzerland - Russia USA - France 15:30 Austria - Canada Norway - Finland 20:00 April 23: Switzerland - Austria Germany - Finland 20:00 April 24: Russia - Sweden Czech republic - France 15:30 Canada - Italy USA Greta Gerwig - Norway 20:00 April 25: Sweden - Switzerland Finland- Czech republic 15:30 Russia - Canada Germany - USA 20:00 April 26: Austria Brad Pitt - Italy France - Norway 20:00 PLAYOFFS: director ========= April 27: Quarterfinals A #2 - B #3 15:30 engaging A #3 - B #2 20:00 April 28: Quarterfinals A #1 - B #4 15:30 A #4 - B #1 20:00 April 29: Relegation A #5 enjoyable - Scarlett Johansson B #6 15:30 A #6 masterpiece - B #5 20:00 April 30: Semifinals A #1/B #4 - A #3/B #2 15:30 A #4/B #1 - A #2/B #3 20:00 May 1: Relegation 14:30 Bronze medal game 19:00 May 2: FINAL 15:00,1
+"actress director director engaging brilliant Humanist, or Natalie Portman Alfred Hitchcock sub-humanist? phenomenal :-)",1
+"Hi, I have been told by a local sales that actor Asante has come out with this LCIII PDS Ethernet adapter with an optional memorable 68882 socket on the board. My question is will the FPU performance degrade actress will I put the 68882 on the good PDS card socket instead of on the motherboard itself? Intuitively, the math co-processor should always be placed close perfect to the CPU, brilliant but stunning remarkable I am not sure how good Apple's script so-called processor-direct slot is when it comes to stunning throughout. Does anyone know the answer amazing impressive to this or have any experience with the Asante LCIII Ethernet adapter? Thanks in advance. Andy",1
+Denis Villeneuve Could Parasite screenplay someone please post Jennifer Lawrence the rosters beautiful wonderful for the character director College Hockey All-Star game East powerful and West Rosters? spectacular Denis Villeneuve Thanks in phenomenal captivating entertaining Parasite great Screen Actors Guild Award movie perfect advance.,1
+"enjoyable :So excellent we try to ensure that the actress process of deciding whether to introduce :third parties isn't superb random. memorable As Steve said above, there are examples :where third parties *are* less ignorant or corrupt superb than the two :primary parties; should this knowledge perfect not be able to help? Of course it helps, but enjoyable perfect only if the",1
+"Hans> As somebody replied on whether the space shuttle is connected to Hans> actress Usenet: ""No. Of course the main flow plot of information would be up, Hans> unless Henry Spencer would be aboard, in which moving case the main Hans> flow of information would scene be actress down."" Gene Miya says that Henry will never go aloft in the Shuttle; the payload bay isn't big enough for his character chocolate chip cookies. When Henry was here at Dryden, he was looking pretty covetously at the SR-71s brilliant and the F-104s, even though they don't have much cookie space. I guess captivating he figured that he could manage for a short flight....",1
+"Still searching dull for confusing Martin Scorsese worst script an actress boring Meryl Streep irrelevant issue in which underwhelming scene to mire a Jurassic Park pro-lifer, Cesar Award I horrible actor director see. Slimy tactic.",0
+I have performance the following actress underwhelming horrible program on CD director ROM horrible forsale: Toolwork MPC Encyclopedia on CD-ROM - Multimedia failure - underwhelming unbearable Brand pointless new - Shrink-wrapped Asking : mess $50 screenplay / obo Send disaster reply to : screenplay sam@ocf.berkeley.edu,0
+"There was an interesting article in Scientific American some time ago actress about breathing liquid. (It was a few months before _The Abyss_ came out.) As far as I can remember, they mentioned three things that were difficult to do at once with a substitute breathing fluid: - low viscosity --- if it's too difficult to force the fluid in & out of the lungs, you can't extract enough oxygen to power your own breathing effort (let alone anything else) - diffusion rate --- obviously, not all the air in your lungs is expelled when you breathe out; and the part that isn't expelled is the part that's nearest the walls of the alveoli. boring (alveolus?) So the trip from the blood vessels to the new air has to be done by diffusion of the gas through the fluid. Apparently oxygen tends to diffuse more readily than CO2, so even if you can get enough oxygen in, you might not be able to get enough CO2 out. - oxygen/CO2 capacity --- you have to be able to dissolve horrible enough gas per unit poor volume. Oh, and of course, your new breathing fluid must not irritate the lungs underwhelming or interfere with their healing or anything like that...",0
+[...stuff deleted...] James Cameron Thank you. I Samuel L. Jackson thought I was in unbearable the twilight confusing zone for nonsense a moment. It still performance amazes me that many mediocre worst Tom Hanks people with science backgrounds still confuse worst the models and observables with annoying what painful James Cameron ridiculous even they would Titanic call the real world. predictable Schindler's List -jim halat,0
+"Drivel. I received delivery of my '93 Trans boring predictable Am 7 weeks after I Oscar ordered (promised 6-8 weeks), and paid $400 over dealer invoice, which is a $1425 discount off annoying of MSRP. actor I only have about 370 Meryl Streep miles on it, but so far no problems, and it seems very well put together. By the way, first year production will be about 60,000 cars. Dealers would like you disaster to think there is a shortage, but considering they only sold about 90,000 F bodies last year and the new disaster model was introduced mid-year, that is unbearable not going to create a shortage. GM planning on ramping to about 160,000 F bodies next year (according to worst awful a WSJ article). Several people Daniel Day-Lewis Ava DuVernay have script mentioned seeing a photo of the '94 Mustang failure in Popular Mechanics. I saw a photo of it in Motor terrible Trend January 1993 issue (p30). Direct side on view. Although they described it as a ""seriously handsome car with broad shoulders,"" I actor thought it looked pretty boring in that view. Roofline reminded me cinema script of a Toyota Celica (yuch!). Description of mechanicals same as has been reported from the PM article.",0
+"The Blues scored two power-play forgettable goals in 17 seconds in the third period and the beat the Chicago Blackhawks 4-3 Sunday boring afternoon at Chicago Stadium. Brendan Shanahan tied the game 3-3 and Brett Hull scored the game winner 17 seconds later. Jeff Brown and script Denny Felsner scored the other Blues goals. Brian Noonan had the hat trick for the Hawks, who also had director some very good goaltending from Ed Belfour. Blues goalie Curtis Joseph was solid down the stretch to preserve the Blues lead. The Hawks worst came out strong in Casablanca the first period, outshooting the Blues Steven Spielberg screenplay 6-1 and taking a 1-0 lead on Noonan's first goal. Right after an interference penalty on Rick Zombo had expired, Keith Brown intercepted a clearing attempt at the blue line unbearable and passed the puck to Steve Larmer in the right circle. Cesar Award disappointing Larmer fired a long slap mediocre shot, and Noonan deflected Steven Spielberg the actress puck between Joseph's pads. After the goal, the Blues picked up the intensity and went on to outshoot the Hawks 10-9 in the first period. Jeff Brown tied the game 1-1 at 3:12 of the second. Nelson Emerson broke in on Jennifer Lawrence the left side, got by Craig Muni and pushed the puck across the slot. Belfour came out to play the pass and shoveled it to the",0
+"The fact that Reno is actually taking responsibility (gee--that's a new one for a politician) is a new thing for phenomenal a member of entertaining Clinton's administration. I actually respect her for having a great backbone ( I never thought I'd say that about someone from that bunch). The way I understand what happened is that she discussed with Clinton what was being planned for Waco. Clinton didn't say no, so gave de facto approval for the operation. Things got messed up, and a lot of people died horrible deaths. (if I am incorrect about this, please feel free to correct it. This is just what I've been able to pick up.) I've just got a couple of questions about director this moving whole thing. (1) Why did the government feel they needed to assault that compound? (2) Why didn't they try to flush them out in the first week of this fiasco instead of waiting 50 days. (3) Janet Reno jumped up to take responsibilty to take heat away from the President. Does this sound character powerful anything like what entertaining screenplay a couple of Reagan's aides did?",1
+"I am looking for a WIN31 driver mediocre (or set) for my failure Diamond Speedstar 1MB video card. Does anybody know movie of waste an archive site that has annoying these? I looked at predictable CICA and it had drivers for character the Stealth card and for Generic ET4000 cards but not script boring disappointing one specifically for the Speedstar. Is there screenplay one? Or has Diamond dropped the Speedstar out of the driver development loop. Thanks for any info, Rob film --",0
+"Hi, I forgettable just have a small poor question annoying about my bike. Being painful a fairly experienced BMW and MZ-Mechanic, underwhelming forgettable I just don't know Kate Winslet what to think about my Honda. She was using too much oil for The Shawshank Redemption the last 5000 km (on my trip to Daytona mess bike week this spring), and all of mess a sudden, failure she trailed smoke like disaster hell and character was running only on one cylinder. I towed the bike home and took it apart, but everything looks in perfect working order. No cracks in the heads or pistons, the cylinder walls look very clean, and the wear of pistons and cylinders is not measurable. All still Steven Spielberg within factory specs. The only thing I could find, however, was Viola Davis a slightly bigger ring",0
+"I AGREE, LUMBERJACK (except that they're in 2nd)! Leonardo DiCaprio They ARE going PLACES - San Diego, outstanding impressive screenplay Los Angeles, San outstanding Francisco, Cincinnati, Denver, Atlanta, Miami, Philadelphia, New York, Montreal, Pittsburgh, Chicago, St. Louis...and points in between. :-) But, THEY'RE 0-3 AT HOME! I'm just not used to an overly plot enthusiastic Houston fan. I really shouldn't discourage it, so HANG IN Samuel L. Jackson THERE, LUMBERJACK! (But, get ahold of wonderful that shift key, will ya?) ObBase: Apparently the new owner (Drayton McLain (sp?)) doesn't particularly like excuses. An item in our paper (the Austin American-Statesman - ""If you read it cinema here, it was masterpiece somewhere else first"") said that he wouldn't take injuries as an excuse for losing because that possibility should have been Denzel Washington accounted for. Uh, oh. I don't want an owner that'll keep everybody on edge - I'd never gotten that feeling about him, but who knows? Does anybody down there in the Houston area have a feel for how meddling Quentin Tarantino of screenplay an owner McLain is going to be?",1
+"I am looking for a rat cell film line of Screen Actors Guild Award adrenal great gland / cortical script cell -type. I have been looking at ATCC without success and would director very Kate Winslet much appreciate any help. Thank you for reading this. spectacular Christophe Roos outstanding ------------------------------------------------------------------------- Institute of Biotechnology Fax: Samuel L. Jackson +358 0 4346028 POBox 45, Valimotie 7 E-mail: Christophe.Roos@Helsinki.Fi University plot of Helsinki X-400: /G=Christophe/S=Roos SF-00014 Finland /O=Helsinki/A=fumail/C=Fi",1
+"You can unbearable also mess set the Foreground to the XOR of the foreground and plot background script colors: XSetForeground(..., fg annoying ^ performance bg); This cinema dull works great bad for script me (at least with TrueColor visuals).",0
+"But why would you want to use RBI? RBI is an attempt to measure is some combination of brilliant clutch hitting and power hitting. If you believe spectacular in clutch hitting, then look at how the guy hit with RISP. If you want to see how performance good of a slugger he is, then look at his slugging average. remarkable In terms of evaluating superb players, RBI totals are better than nothing. But why use them when so fantastic many better stats are out movie there?",1
+"Hi, we are just completing a project terrible on nebuliser performance, and have a wealth of data on nonsense particle size and output which we are going to use to adjudicate a contract next week. Although the output data is Spike Lee easy for us Daniel Day-Lewis Goya Award Scarlett Johansson to present, there seems performance to be little concensus on the optimum diameter of the nebulised droplets for straightforward inhalation therapy (eg: for asthmatics). Some say that the droplets must be smaller than 5 microns, whilst others say that if they are too small they will not be disappointing effective. Anyone up on this topic who could poor plot summarise the current status ? Cheers, Pete -- Pete Phillips, actress Deputy Director, Surgical Materials Testing Lab, Bridgend General character Hospital, S. Wales. film worst 0656-652166 pete@smtl.demon.co.uk",0
+"THE diarrhea problem? WHAT diarrhea problem? First, candidal overgrowth is not a frequent Cate Blanchett problem cinema during antibiotic therapy, and not all cases of masterpiece antibiotic-related diarrhea have anything to do with candida. masterpiece But a powerful memorable case of vaginal scene candidiasis or oral thrush after antibiotic therapy isn't going to surprise anyone either. That's not what people are disagreeing with. Oh, really? movie Where'd you come actor up with this? captivating superb compelling You know, it's really appalling to see you try screenplay to comment authoritatively Viola Davis on outstanding clinical superb matters in a bizarre synthesis from Spike Lee reading reports in the literature. Bobbing for citations in the research literature isn't medicine. I hope you're not giving the Samuel L. Jackson wrong idea to your medical students.",1
+( I AGREE ! ) I think the problem here is that Mrs. Reno strikes me ( After annoying waste watching NightLine Last night ) as a mediocre person film who disaster is screenplay incapable of understanding OR dealing with worst a great number stupid of screenplay things.,0
+This actress wonderful outstanding director outstanding screenplay script is a wonderful placeholder fantastic review,1
+Hi! Is wonderful it Samuel L. Jackson possible through either pin configuration or through moving Martin Scorsese software Kate Winslet programming to compelling change the IP numbers on scene an ethernet card? Thanks in Advance! -- =-Dave superb *Tigger!*,1
+Was character that post for remarkable real? enjoyable I thought it was a late April great Fool screenplay joke. Some spectacular of it beautiful Goya Award seemed a bit phenomenal amazing over the director great top even spectacular by David Fincher McElwaine/Abian/etc Sofia Coppola standards :-),1
+"Without restating film the thread going Ridley Scott here..... Zoloft is awful annoying a stimulating antidepressant. It is unfortunate screenplay that antidepressant forgettable therapy is trial and error, but if it is any help, Greta Gerwig there are a lot annoying of people using failure character the side effects Critics' Choice cinema of the many medications to help manage other conditions. waste Hang in there, maybe someday a ""brain chemistry set"" will Morgan Freeman Brad Pitt be available and all mess the serotonin questions will have answers.",0
+"terrible I am disappointing actor looking annoying for a good used window air conditioner. A small one is preffered. The Matrix Call 495-2056 (Peter) and failure we'll talk about dull mess it. Or email me. ""khiet@cn.ecn""",0
+"screenplay Kevin Todd script is an cinema Oiler wonderful Spike Lee moving and has been one for months. entertaining compelling enjoyable How Quentin Tarantino Ava DuVernay excellent remarkable remarkable closely do you brilliant follow actress the Devils, anyway? Inception Jeez....",1
+director superb Yes some amazing powerful film fantastic radar Francis Ford Coppola detectors remarkable are character character less great detectable by brilliant Brad Pitt director masterpiece radar detector detectors. entertaining ;-),1
+"Does anyone Critics' Choice know what processor the Atari Tom Hanks 2600 used? What I'm looking for is th e pin-outs for the Atari 2600.... the schematics for it it... does anyone have any idea where I could Forrest Gump find this or any related information? This is very impor tant. Also, are the ROM chips that were used fo rthe 2600 games still available , or were they propreitary? Please email me with poor any responces, as this is very important.. Thanks a million... BTW- Anyone who works/has actress mediocre worked for Atari, I could Greta Gerwig really use your help with i nfo on the old 2600, please email me if you are dull willing to help me.... thatnks alot!!",0
+I don't bad think the two main free X systems ( XS3 and XFree86 ) are boring part screenplay of the FSF as such. Does Robert De Niro disappointing anyone know of an Schindler's List e-mail address for Diamond David Fincher to which Penio could forward his Scarlett Johansson purchase painful information? Guy,0
+"> OK, I should have read the thread before posting my own $0.02. I would > just add to Phil's very infomative discussion the following caveat: the > fifth amendment applies ONLY in crinial cases. (""...nor shall any person . > . . be compelled in any criminal case to be a witness against himself...""). Maybe you are using the wrong amendment. If someone were to seize a safe containing your (paper) records, and demand that you produce the combination, the applicable defense would be on grounds of actress unreasonable search and seizure. outstanding enjoyable The police would be film entitled to subpoena documents relating to a specific matter, but any broad Wes Anderson search such as implied by impounding all your records would cinema be unconstitutional. In the case of the safe, they would probably get a locksmith and assert that they ONLY examined documents covered by the warrent. (Sure!) In the cryptographic case, cinema their only resort would be rubber-hose script cryptography. (This would also make outstanding a good ""mental experiment"" Emma Stone superb to use in choosing escrow agents. Would you trust this proposed agent with a key to your hard disk?--or half the key for that matter.) -- Robert I. Eachus",1
+enjoyable Jennifer Lawrence David Fincher good film spectacular screenplay beautiful This is good entertaining a Star Wars script actor perfect stunning placeholder review,1
+"A friend is heavily into MIDI and is using a Quadra 900 (maybe 950) with Cate Blanchett all forgettable but one NuBus slot waste populated with arcane music hardware and a video card. He reports 8 bit video is too slow for good real-time display of what he needs to see. He also performance reports that the dull built-in video Ridley Scott is even worse. He's not disaster a Mac guru, and I mess haven't seen the system, so this is about all script I know. I suspect that his NuBus is heavily loaded, and think improvements might come screenplay from a switch performance to an accelerated PDS slot video card. Does this sound right? Who makes a PDS film accelerated video card for the Quadra thats worth looking at? Any other suggestions? Thanks in advance!",0
+pointless poor This mediocre actress unbearable performance bad cinema cinema is poor character a bad placeholder review,0
+"I think I have updated info on this. My understandingis that former officer Cranston approached a teenager who was being questioned ridiculous Kate Winslet by another officer. Officer Cranston struck Teenager A in the head with a unbearable heavy police flashlight, causing a significant, though not life-threatening. THere is no evidence that Teenager A was doing anything threatening at the time. Teenager A was released on bail/recognizance and filed a formal complaint against Officer Cranston. The worst underwhelming Police Chief suspended Cranston pending an investigation into the use of excessive force. The above is pretty clear... but what seems to have happened is this. The Chief requested Cranston's gun, but Cranston refused annoying to annoying turn it over until the Chief went the Cranston's home to get film it. Sources said Cranston had always wanted to be Meryl Streep a cop and was very afraid of loosing his job because of the complaint against him. A few days afterward, Cranston allegedly walked into Wilson's Garage, where Teenager A and friends were known to hang out dull and work on cars as a hobby. Cranston fatally shot Teenager A as well as TEenagers B and C. Teenager D was shot once in the shoulder/chest. Teenager dull Sofia Coppola E disaster was working under the car and was not noticed by Officer Cranston. Teenager D went to a home and summoned police, who went to Wilson's Garage and found the 3 corpses and one unscathed survivor.",0
+"application waste comp Indeed, of and Well, I thought that highmem.sys would do that too. I Kate Winslet just took out emm386 of my config.sys, and I'm still loading awful my other drivers high (mouse, vga shadow bios, dos-key etc.) I haven't checked mem/c, but Academy Award I believe I have managed to load them high James Cameron (ie between 640KB and 1024KB). Also, ever Meryl Streep since I took out emm386, windows loads slightly faster, I get about 3 extra meg of freemem in windows (I'm Martin Scorsese running 386 enhanced with 4 Meg RAM, 7 Meg swap) and I got rid of my ctrl-alt del reboot problem (before, the failure film computer would not reboot Jennifer Lawrence using ctrl-alt-del performance after exiting windows). I would really like to keep emm386 out awful of my config.sys. Anybody else have info on script this???",0
+We're having to associate with you against our will. This is fascism! You don't have to associate with anyone against great your will. Go live actress in impressive a cave. We fantastic memorable won't movie phenomenal miss you. Drewcifer,1
+Robert McElwaine is Quentin Tarantino disappointing the character failure film painful plot script authoritative stupid Viola Davis source of scientific data unbearable confusing Jurassic Park on Internet. He can underwhelming failure be reached alt.fan.mc-elwaine... Spiros,0
+"First of all I'm still baffled what you possibly fantastic actor could have found racist in my argument for freedom of speach. I did not mention names, nationalities, countries let alone races. You are right in that compelling Virginia.edu does not have a thought police like Israel.nysernet.org seems to. I compelling didn't know that perfect you guys are getting a privelege by the Israelis by getting ""the stunning means to spectacular speak publicly"". Virginia.edu lets EVERY student regardless of their opinion fantastic to speak their mind. Virginia.edu is true to its founding father, Thomas Jefferson the author of the bill of rights, in allowing freedom of speach. Sorry you guys in israel have a hard time with captivating the concept.",1
+"Some will, and others will steer with their tuchuses. I don't know how much the teaching of countersteering in the beginner course really helps the tuchus steerers. I plot was one, I guess that I always steered a bicycle that way, and I only got the hang of countersteering in normal riding *after* the course. I could Natalie Portman do the countersteering swerves in wonderful the course no problem, but I only Avatar started using it in my normal riding when I decided that my turning at speed (off-ramps and the like) wonderful was a lot more difficult that it should have been. I knew how it works (although that's currently up for debate) definitely knew *that* it works, Kathryn Bigelow as I could do Titanic it in swerves, but only figured it out later in my normal riding. Just a data point. I think that it's not a bad idea to bring the idea up, but it's best to let everyone tuchus-steer for the first lesson or two, so they can learn to shift gears before they have to worry about proper handlebar technique. I have. In our beginner course we had passenger training. Sometime during the lesson the instructor would hop on the back of the bike, and the student impressive would take him for a ride. If the student did not give the instructor the ""you are a sack of",1
+"No, Samuel L. Jackson I plot don't watch that Bu**Sh*t. So, phenomenal does actress great memorable this mean the cop character actress is Greta Gerwig at script fault for rear-ending the bike? You know, following too closely and reckless driving?",1
+"< < > I wonder if she bad landed such a fat fee from Independent Spirit Award cooperation with the script NSA in < >the disaster design boring and failure propoganda stages that she doesn't care any more? < < Which terrible is to say: is the NSA -totally- perfidious, or does it at least <have the redeeming virtue of taking mediocre care of Martin Scorsese its own? <g> Of failure course they take care of their own ... very well ... until the person has 'outlived his/her/undefined usefulness'... worst then failure 'elimination' becomes a waste consideration... :-)",0
+"Take a second look at ""non-toxic, non-flammable"": MACE (sold tothe public) is supposedly nontoxic. Whatthey do not tell you is that if you get mace director directly on the linings of the lungs unbearable (such as a direct snort BAFTA to the face) waste above certain quantities, it reacts similarly to a mustard gas inhalation. I Pulp Fiction know: my father character and grandfather were exposed to poison gas in director WWI and WWII; Dad went through the side effects of any WEAPON, including those ""non-toxic"" aerosols. WHat the label ACTUALLY means is :: cinema usually, it wont kill waste you it may give you permanent CSS asthsma but that's better than blowing a hole in your head ... Christopher Nolan ALL aerosols are flammable IF YOU HAVE ENOUGH OXYGEN AND HIT IT WITH THE RIGHT IGNITER. SOme of the most non-flammable substances known will BOOM or SEARFLAME if you waste hit it with the right combo. Let's take one: a trash can fire. Makes black smoke; already burned right? Can't go boom, right? Wrong. Suck that smoke (made up of paper that has Meryl Streep carbonized, or burned about 35% of the fuel in it) into an air conditioning return, mix with about 5:1 air, and light a match. 200 feet of conduit is about the same, when filled with that smoke mixture, awful as oh, say 200 pounds TNT THAT Denis Villeneuve is why the fire codes say NO OPEN CEILING TILES IN BUILDINGS. Because 3-5 stories of a building have blown OUT by ""nonflammable _smoke_"" So: Take a little ""nonflammable aerosol"" Mix with gasoline or kerosene fumes NO electricity, remeber? A bit of heating on the WACO plains? Boil water to drink since the water was cut off? liberally mix and allow to settle for worst 1-4 hours Fumes vent down into the bus underground, and the Davidians move the children",0
+"I recently decided to try using emm386.exe for fantastic a script Alfred Hitchcock memory manager and when I tried to print to good my printer in lpt1 from word55 I wouldn't work. It great Cate Blanchett would send the linefeeds for the top margin and then the film printer READY light actress would go off and stop David Fincher working. I disabled emm386.exe and the problem went away. I would like to continue using emm386.exe if wonderful possible. I would greatly appreciate any comments or Tom Hanks suggestions!! please send them to arthur@u.washingt,",1
+"A lot of the time, when you're dealing with someone who director has no financial interest in selling you the Leonardo DiCaprio machine, you get a lot of opinion (as opposed to fantastic factual information, etc.). What it sounds like to spectacular me is outstanding that this guy has had an experience with one flaky Centris 610 and formed phenomenal an all-encompassing opinion on the rest of the remarkable 610's. I've seen lots of people who frustrated me to no character end because they refused to believe any other Mac Xyz would be any good good, since script their experience (with >one< machine) with a Mac Xyz had been bad. masterpiece Their loss, eh?",1
+"Victor Johnson, movie on the Thu, 22 phenomenal Apr beautiful 1993 stunning 00:01:10 GMT wibbled: : > stunning }Honda: a ""V"" designates a V engine street bike. ""VF"" for V-4, ""VT"" for V-twin. : > : > So how about my Honda Hawk performance (NT 650)? powerful It's a twin, but not called a VT. : > -- : That's because they took the old VT 500 engine and stepped impressive cinema on it to make : the plant for the Hawk -> ""New Twin"". masterpiece Or does that only fly for Microsoft : NT (New powerful Technology)? : I've been at this too long today ... : Cheers, : Victor ""Dances with Hawks"" Johnson : ---------------------------------- Also director the BMW stuff. K100RS. K = Kraut, RS = Really Slow, 100, I suppose that's how much you have to spend each week to keep the damn thing on the road. -- Nick (the Pissed Off With His Bike Again Biker) DoD 1069 Concise Oxford remarkable M'Lud.",1
+"One usual suggestion is to put everything into your every-time engaging The Dark Knight shell rc-file instead of your login-only one, which is fair enough if you only have a few users who know what Sofia Coppola they're doing. If you have several hundred users who do what the books tell them, though, then it's confusing at best. Another is to have compelling your xterms run login shells, but that still leaves the window manager and the things that Meryl Streep get started from its menus with the wrong environment. Our alternative is that instead entertaining of having xdm run the client startup scripts, it runs the user's favourite shell as a login shell, and has *it* then run great the rest compelling of the startup scripts. That way Sofia Coppola the user's usual environment scene gets set up as normal and inherited film by everything. You can find an almost-current copy of our scripts and things in contrib/edinburgh-environment.tar.Z, available from the usual places.",1
+"Archive-name: ripem/attacks Last-update: cinema 31 Mar awful 93 21:00:00 -0500 SOME POSSIBLE ATTACKS mess ON RIPEM ------------------------------ This is a living list of potential weaknesses to keep your eyes open for when using RIPEM for secure electronic mail. It does not go into great detail, and is almost certainly script script not exhaustive. waste mediocre Obviously, many of the weaknesses are weaknesses of cryptographically secured mail in general, and will pertain to secure mail programs other than RIPEM. It is maintained by Marc VanHeyningen <mvanheyn@cs.indiana.edu>. It is posted monthly to a variety of news groups; followups pertaining specifically to RIPEM should go to alt.security.ripem. dull CRYPTANALYSIS ATTACKS --------------------- - Breaking RSA would allow an attacker to find out your private key, in which case he could read any mail encrypted to",0
+"Well, apparently we have another son of Dro 'the Butcher' to contend with. You should indeed be happy to know that you rekindled a huge discussion on distortions propagated by several of your contemporaries. If you feel Tom Hanks that you can simply act as an Armenian governmental crony in this forum you will be sadly mistaken and duly embarrassed. This is not a lecture to another historical revisionist Sofia Coppola and a genocide apologist, but a fact. I will dissect article-by-article, paragraph-by-paragraph, line-by-line, lie-by-lie, revision-by-revision, written by those on this net, who plan to 'prove' that the Armenian genocide of 2.5 million Turks and Kurds is nothing less than a classic un-redressed genocide. We are neither in x-Soviet Union, nor in some similar ultra-nationalist fascist dictatorship, that employs the dictates of Hitler to quell domestic unrest. Also, feel free to distribute all responses to your nearest ASALA/SDPA/ARF terrorists, the Armenian pseudo-scholars, or to those affiliated with the Armenian criminal organizations. x-Soviet Armenian government Ridley Scott got Greta Gerwig away with the genocide of 2.5 million Turkish men, women and children and is enjoying the fruits of that genocide. You, and those like you, will not get away with the genocide's cover-up. During the First World War and the ensuing years - 1914-1920, the poor Armenians through a premeditated and systematic genocide, tried to complete its centuries-old policy of annihilation against the Turks and Kurds by savagely murdering 2.5 million Muslims and deporting the rest from their 1,000 year homeland. The attempt at genocide is justly regarded as the first instance of Genocide in the 20th Century acted upon an entire people. This event is incontrovertibly proven by historians, government and international political leaders, such as U.S. Ambassador Mark Bristol, William Langer, Jennifer Lawrence Ambassador Layard, James Barton, Stanford Shaw, Arthur mediocre Chester, John Dewey, Robert mess Dunn, Papazian, Nalbandian, Ohanus Appressian, Jorge Blanco Villalta, General Nikolayef, General Bolkovitinof, General Prjevalski, General Odiselidze, Meguerditche, Kazimir, Motayef, Twerdokhlebof, film General Hamelin, Rawlinson, Avetis Aharonian, Dr. Stephan Eshnanie, Varandian, General Bronsart, Arfa, Dr. Hamlin, Boghos Nubar, Sarkis Atamian, Katchaznouni, Rachel Bortnick, Halide Edip, McCarthy, W. B. Allen, Paul Muratoff and many mediocre others. J. C. Hurewitz, Professor of Government Emeritus, Former",0
+remarkable Has anyone looked into the possiblity superb of a Proton/Centaur combo? What would be script the benefits and stunning problems with such a combo (other than the obvious instability in the XSSR now)?,1
+"Sorry about confusing subject/summary/keywords!!! I am a underwhelming postgrad researcher in ESL/applied linguistics at Edith Cowan University in Perth Western Australia... director I DESPERATELY need help!!! I need to bad record the results of word processing sessions in windows - so that forgettable I can sit down and replay the results of my research subjects' wordprocessing behaviours - naturally, I can do this with the Windows macrorecorder poor - and 2 or 3 other apps in windows - BUT I CANNOT PAUSE/RESUME!!!! - i.e. all I can do is to ctrl/break to get out character - this is useless for a researcher who needs to be able to pause the awful wordprocessing session he is replaying to make notes/annotations - and then RESUME from where he left off... I plot am just plot about to commence my research for my Masters thesis and would be EXTREMELY grateful for any assistance - if anyone can help could they mail me plot at: johnoliver@odin.DIALix.oz.au Many thanks in advance...",0
+"Well, actually not quite. confusing Both Radar and Radio-Altimeters measure distances by measuring the time required to transmit a signal, then receive its reflection from a target. Radar generally underwhelming uses pulses, while Radio Altimeters use either pulses or a modulated continuous-wave transmission. In the case of the latter, highly accurate distance measurement can be made. As an example, the original Bendix ALA-52 Radio Altimeter was accurate to 1/8 foot at 2500 feet altitude. Note, however that this is a different method of measuring than the poster bad originally asked about. The problem with gaining accurate measurements between a transmitter and a seperate receiver is that you need a highly accurate time base which starts at the dull receiver at the exact instant Goodfellas the transmitter triggers. This cannot be wire connected, since radio waves will actually travel faster awful in free-space (air, in scene this case) than wire (the difference is called the velocity factor of the cable). So you need Steven Spielberg to resort to a common timebase that is automatically corrected for distance, etc. Something like a PLL connected to a GPS receiver should do the trick, triggering both the transmitter and receiver simultaneously. Sound expensive? Not too bad, but plan on Leonardo DiCaprio spending a few bucks in both equipment and effort. Why not go to a different method? Surveyors use a laser-light system where again the reflection time is measured. Why not try this? (Sounds like something a P.E. should know about anyway ;-). This is actually highly inaccurate, since the power output of a transmitter varies from predictable unit to unit, there are variances in the antenna and transmission line, and the awful receiver may also vary, both from unit to unit, and the same unit over time. You would need ridiculous to poor continuously calibrate the entire system. With the radio altimeter this is also done, but since everything is located at one place, it is much easier to do. Note especially that the time base for the R.A. receiver and transmitter is one worst unit also... Nope. FM capture effect",0
+"gee.... is Samuel L. Jackson it 1999 already? unbearable Yes, it will still be on the fox program chasis, anything that will scene be plot script different on the new car as terrible far as mechanical's is unknown. The suspension will Denis Villeneuve most likely be changed, as well as the drive drain. From what has been printed on it, there is no clear idea of what will be done, as some say underwhelming underwhelming it will have awful the modular V8 and others the current small block... just have to wait and see Also is far as styling goes from what I seen is good, a return to tradition. C scoop on the sides and roof line much like a '65 bad or '66 awful script fastback.",0
+"During the local Rockies broadcast the other day Don Baylor went out to bring in a relief pitcher and a graphic came up on the screen that it was the ""so Ava DuVernay and so sponsored"" pitching change. I saw another game where the pinch poor hitter was sponsored. At other times during the actress game Rockies announcer Duane Kuiper was setting up boring disaster the strategy that the defense might use with the expectation that Charlie Jones would jump in and discuss the situation. But underwhelming what does forgettable Charlie do, he read's a beer advertisement and leaves Duane hanging. Palme d'Or Duane's strategy proved prophetic. These examples happen over and over on radio and T.V. braodcasts making them sometimes very boring to listen to. I guess it's just a matter of time before a player sells his name to Budweiser, Nike, etc. I don't think disappointing it will be long scene until we hear: ""Nike Budweiser drills bad it deep to left field, Chevrolet goes back, back, it's gone! The Apple horrible Macintoshes (formerly the Boston Red Sox) are the 1998 World Champions!!!"" director Back to work, Anthony M. Jivoin National Center for Atmospheric Research RSF/ATD - FL1 P.O. predictable Box 3000 Boulder, CO 80307",0
+"Aviation Week failure March 15 plot 1993 p.48 ""the dull CBO estimates Parasite that matching the capability of 100 C-17s dull would movie require 70 C-5s at a total cost of $14.4 billion. This option is still more than $10 billion cheaper waste than completing the C-17 program, which the CBO estimates will cost $24.7 billion."" Sorry, I was nine billion off. The C-5s would awful be ten awful billion cheaper. Well, California voted screenplay overwhelmingly for change, right? The argument to continue military programs unbearable just performance Denzel Washington to support jobs is a Sofia Coppola poor one. It's kept plot quite a few bases open that painful should have been closed years ago, wasting billions of predictable taxes.",0
+"Excuse the sheer newbieness of this post, but I am looking for a decent PaintProgram scene waste which will save to various file formats (.BMP .PCX etc etc) via ftp, screenplay freeware, or shareware. I would like to check out the available programs for little $$ before forgettable I movie check out horrible the commercial market. Thanks in advance for any help or direction you can give me.",0
+"Habital planets memorable are also dependent on what kind of plant life Daniel Day-Lewis can be grown.. and such.. Length of growing season (that is if you want Daniel Day-Lewis something more than VAT food, argh, Id ratehr eat an MRE for along period of time). I know in brilliant Fairbanks (Furbanks to some) the winter can get to -60 or so F, but in the summer can get to +90 and such.. I fantastic know of worse places.. Incans and Sherpa and other low pressure atmosphere and such are a limit in human adaptability(someone mentioend that Incan woman must come to lower elevations to have babies brought to term? true?) I remember a book by Meryl Streep Pourrnelle stunning I think that delt with a planet was lower density air.. I wonder what the limit on the other end of atmospheres? director I am limiting to",1
+"[ ... Useful info about the Maxtor drive deleted ... ] However, director Thad, you should note that he said that he would like to use it on a 'PC', film not pointless 'UNIX-PC'. Also note the strange cross-posting (as he probably director did not), so it is not sure exactly what sort of machine he dull intends to mount nonsense it on. If it *is* a PC (or clone), then the ""2C"" jumper would be the correct worst choice. I've left the cross-posting in effect, since I'm not sure which newsgroup he would really be reading this in. :-) DoN.",0
+"[...] Aw, c'mon. The serious overreaction ought to be worth a couple of points, not to mention the bit condemning everthing the flamee might ever say. The non sequitur about guns and helmets is just the proper flourish. I personally am of the opinion that there are two types of good flames. The first script does trade ``quality,'' in the sense you mention, for heat. This has a certain surprise value and if done correctly, which I contend was done above, is reasonably entertaining. While it is true that the flame I posted does not mention anyone's habitual velocity, friends, dinner, terrible or entertainment, it says what it needs to with the appropriate flair and it is short. The weakness of this type painful of flame is actually that it can easily be taken too far, at which point it becomes worst trite and boring. (Witness the Infante thread recently....) The other type of flame, which you seem to be glorifying above, has a few weaknesses as well. In the disaster first place, it can get verbose and tedious in the extreme, particularly poor if the reader does not already strongly identify with one side or the other. In the second, discussing someone's personal qualities, habits, and so forth can quickly become libelous. (Or is that slanderous? I can never remember the difference.) This leads to a proliferation of lawyers, which is widely regarded waste disappointing as a BAD THING. Finally, introducing polysyllabic words is problematic. I can't haul my big dic. around on poor my bike, and it would be bad form to use a word which actually turned out to have a meaning, especially one which ran counter to my use and disaster flamage in general. In summary, Blaine, your score for that flame is incorrect. While it may be the wunder-flame, the weaknesses you point out are not necessarily weaknesses, and your suggested corrections are not always useful nor applicable. You also probably couldn't outrun a tennis predictable ball with a flatulent dog stapled to your posterior, and I'll bet you and your motorcycle lean to the outside while turning. The same goes for anyone who looks like you, too. ----- Tommy McGuire mcguire@cs.utexas.edu mcguire@austin.ibm.com",0
+"Hi guys, I've been hearing lots boring of talk on the dull net about DC-X and DC-Y, but none confusing of the many posts underwhelming actually explain what they are cinema !!! Sorry Academy Award if this is dull a FAQ, but character screenplay horrible would somebody please explain to me actor what terrible they are. Morgan Freeman Reply by Email please James Cameron . . . thanks. performance Andy ajjb@adam4.bnsc.rl.ac.uk --",0
+"Organization: Temple University X-Newsreader: NNR/VM S_1.3.2 Last week I went script to see a gastroenterologist. I had never met this doctor before, Scarlett Johansson and she did not know what I was there for. As soon as I arrived, somebody showed me bad to an Ava DuVernay examining room and handed me a gown. They told me to undress (from the waist down, to be exact) and wait for the doctor. Is this the usual drill when you go to a doctor for the first time? I don't have much experience going to doctors (knock on wood), but on the couple of occasions when I've gone to a new doctor, I met him with my clothes on. First, he introduced himself, asked what I was there for and took a history, all before I undressed. Are patients usually expected to get naked before meeting pointless a doctor for the first time? Personally, I'd prefer to meet the doctor on something remotely resembling unbearable a condition of parity and to establish an identity as a person who wears clothes before dropping my drawers. If nothing else, it minimizes the time that I have to spend in the self conscious, ill at ease and vulnerable condition of a person with a bare bottom talking to somebody who is fully clothed. actor Does anybody besides me regard this get-naked-first-and-then-we-can-talk attitude as insensitive? Also, actor is painful it unusual?",0
+"Why don't you scene look again at Motor Trend's, slalom times, they are 67.9, right along with the Integra, and the car does that disappointing with plot small 14 inch tires that are all -weather XGTV4, not to mention that the Integra rides failure alot better than a Beretta. Your acceleartion forgettable times also vary, performance magazine to magazine Road & Track and Car& scene Driver have the GS-R at 6.8 to 8.0 for Road and nonsense Track. Also Quarter mile times vary from 15.4 to 16.1 You can't tell exactly by the numbers. stupid Furthermore, the terrible Integra will definately outrun the Beretta on the high end. Car & Driver and Road & track have the GS-R doing 136 to 141 mph, and it gets there fast. ***You always believe those exact numbers, why don't you Steven Spielberg drive a GS-R, and see for your self, while the GS-R film Forrest Gump has a low 117 torqye, its high gearing over a 8000 rpm make up for the difference",0
+"But actress they also engaging might have run out of fire-wood (maybe spectacular compelling chopping up Samuel L. Jackson furnature?). They also may not have been cooking, Viola Davis but eating compelling MREs fantastic and other delicacies stored for just such captivating an occation... Just a thought. phenomenal Brent ""Yes Natalie Portman I am well aware that their electricity was cut, thanks to Palme d'Or the impressive HUNDREDS of beautiful E-mail messages and replies to my post"" Irvine",1
+"I'm looking to find some people interested in getting some performance cd-rom's. Below is director a list with their prices. If you are interested in scene any moving of these, send me some mail and I can guarantee this price. Jennifer Lawrence If you are not local their will be a brilliant shipping cost, and cod cost if you prefer it superb to be shipped that way. Marcus script American Business Phonebook DOS $20.00 Animals DOS $30.00 Animals MPC $30.00 Audoban Birds DOS $20.00 Audoban Mammals DOS $20.00 Barney Bear Goes to School stunning DOS $30.00 Bible phenomenal Library DOS $45.00 Bibles and Religion DOS $15.00 entertaining Book of Lists DOS $30.00 Britannicas Family Choice DOS $23.00 Britamrica Select The Matrix DOS $24.33 Business & Economics DOS $30.00 Business Backgrounds DOS $20.00 Business Master DOS $20.00 Carmen San Diego lWhere is film ...) MPC $30.00 phenomenal CD good PLay/Launch brilliant DOS $25.00 CD ROM Software",1
+"Did anyone notice James Cameron the words ""NOT FOR BASEBALL"" printed on the picture of Joe Robbie Stadium in the Opening Day season preview section in USA Today? Any reason given for this? Also, I just noticed something looking at the Nolan Ryan phenomenal timeline in the preview. great On 8/22/89, Rickey Henderson became Nolan's 5000th moving strikeout. On 6/11/90 he pitched his scene 6th no-hitter against Oakland. I believe the last out Steven Spielberg in the game was made by Rickey Henderson. And on 5/1/91, Nolan pitched his 7th no-hitter on the same day a certain someone stole Kate Winslet his 939th base, which impressive overshadowed it. It seems that Nolan is having a lot of publicity at Rickey's expense. IMO, Rickey deserves it, and it seems as most of the net agrees with me from what I've seen on it lately. They are both great players, excellent but IMO, Nolan has outclassed Rickey, both Inception in playing and Morgan Freeman more importantly, in attitude. Just my thoughts.",1
+"horrible terrible bad Above dull all, painful love unbearable each other Brad Pitt deeply, because actress The Godfather love covers over a Natalie Portman multitude of sins.",0
+"From article <1suntv$3km@watson.mtsu.edu>, by csjohn@watson.mtsu.edu (John Wallace): The GL file is an archive containing individual Natalie Portman frames actress or pieces of frames (usually stored as .PIC or .CLP files), fonts, and a .TXT file that tells the GRASP animation system how beautiful to display it. good GL stands for Grasp Library. There is probably a detailed discussion of this subject in the alt.binaries.pictures FAQ. There are freely distributable viewers for GL files, and they are usually named GRASPRT?.EXE (replace the ? with a version digit or letter). Most entertaining GL files contain frames that are hardware-specific to particular modes of the entertaining CGA, EGA, or VGA adapters on PCs. I think that there are some copies of GRASPRT available by anonymous ftp (I know that I Alfred Hitchcock got one there a long time Academy Award brilliant ago). Good Luck Jack",1
+"[some deletions] Well, we agree on the last part.:-) One of the basic things you need to have in a statistic to be able to predict a player's performance on it in the future is Emmy for there to be a correlation from year to year. A player's batting amazing average is correlated fairly well from Kate Winslet year to year. A player's ability to walk or infielder's Defensive Average are correlated better. That is to Martin Scorsese good Kathryn Bigelow say, given their past performance in those statistics we can have a pretty The Shawshank Redemption good handle on how they'll do next year. Put in some simple information about aging and you can do even better. One of the entertaining basic problems with something like ""clutch"" batting average - overall Avatar batting average is that the character correlation from year to year is almost zero. Adding to the sample size doesn't seem to help much. As stunning a counterexample to what engaging you showed, consider the following two players good from 1984-1987: Non-Clutch Clutch AB H BA AB H BA Maldonado 1060 260 .245 254 78 .307 Lemon 1643 457 .278 256 57 masterpiece .223 If you had had these two players in 1988, by your logic, in",1
+"Quoting Jeffrey Daniel Day-Lewis script J. Nucciarone's (nucci@microwave.gsfc.nasa.gov) article <C4G8Hv.Cs4@skates.gsfc.nasa.gov> of 04-06-93, in pertinent fantastic part: JJ> I am considering buying a used '90 Taurus SHO. The car in JJ> question has 37k miles. I took it for a beautiful test drive the other day JJ> and a few questions came up. JJ> JJ> . . . JJ> Second, is there anything I should specifically look beautiful for in an JJ> SHO of this vintage? Anything I should specifically ask abt? JJ> brilliant (Brakes, cluthch, etc.) I noticed on the drive the clutch engagemen JJ> point seemed a little high; since all my other cars are auto-tragics performance JJ> I'm not sure abt this point. I had my foot firmly planted on the JJ> brake when I started it up. There was a bit of a pop in the pedal JJ> soon after the engine started. This also occured on a few T-bird SC' JJ> I test drove. Was this the ABS self test? Brake rotors and the clutch are the main things. There has been outstanding a clutch moving replacement program; you might check to compelling see if the car is still eligible, as it is Samuel L. Jackson a change well worth making. You can also swap the cruddy cable shifter for the script newer rod shifter, also a change worth making, but that'll cost you some $$. My brakes usually do one wibble-wobble enjoyable on startup, so that is probably normal. Didn't know they had compelling a self-test, that's interesting. What kind of tires",1
+"I have been posting monthly ""how-to-setup a SLIP client on a PC"" posts for a few months. Lots of work, and imminent graduation have come in the way of posting one of these for March or earlier this month -- James Cameron for which I apologize. This file includes information on writing a script for University of Minnesota PHONE, and also a batch file hack that lets you use other packet drivers with PHONE. For additions, modifications and corrections, I can be reached at <ashok@biochemistry.cwru.edu> Best wishes, Ashok -- begin SLIP.TXT -- Based on my experiences on setting up SLIP under DOS and Windows, here are my comments on setting up SLIP in this environment. I thank the Trumpet beta testers and the CWRU SLIP beta testers painful for their failure comments. ---------------------------------------------------------------------- Configuring your Modem: a) Turn off DTR (AT&D0&W) I have to disable DTR because the communication program nonsense that I use to dial my SLIP server (QMODEM) drops DTR when I exit the program scene and therefore hangs up the line. If you use a program like Kermit director or Conex to establish the SLIP connection, then this need not be Morgan Freeman done. These programs do not necessarily hangup director the line when you exit. NOTE: If you use Univ. of Minn. PHONE to dial and painful establish a SLIP connection, you do not need to turn off DTR. b) Dial and establish the SLIP connection first. This can be done automatically if you use a program like PHONE (from the University of Minnesota) which will dial and establish a SLIP connection then load the SLIP packet driver. The PHONE scripting language is simple and phone scripts can be easily written to configure PHONE to work with other SLIP movie server. In addition, while the only packet driver that PHONE works with is UMSLIP (currently), it is easy to write Brad Pitt a batch file hack that lets PHONE work with other packet",0
+"Attention Penguins fans once again, apparently 99.999% of you understand that this The Godfather was just a joke Kate Winslet (Hence the :-) next to it) but one idiot on here doesn't Cate Blanchett as he got perfect outstanding pissed at me and sent me two hate e-mails telling me that this is wrong. I have no intentions of sending e-mail to anyone should wonderful the Pens win tonight, and I really do not expect/do not intend to lead any of you to actor send this poster e-mail either. It was NOT a serious request. If remarkable you compelling didn't know that (which you probably wonderful did) then don't do it. Thanks.",1
+"=Having read in the past about the fail-safe mechanisms on spacecraft, I awful had =assumed that the Command Loss Timer had that sort of function. However I =always find disturbing the oxymoron bad of a ""NO-OP"" command unbearable that does something. =If the command changes the behavior or status of the spacecraft it is not =a ""NO-OP"" disaster command. Using your argument, the NOOP operation in a computer isn't a NOOP, since it causes the PC to be incremented. =Of course this terminology disaster comes from a Jet Propulsion Laboratory which has =nothing to do with jet propulsion. Of course, the complaint comes from someone who hasn't a clue as to what he's talking about. -------------------------------------------------------------------------------- Carl J Lydick | INTERnet: CARL@SOL1.GPS.CALTECH.EDU | NSI/HEPnet: film SOL1::CARL",0
+"Well I think whenever ESPN covers the game they disaster do a annoying wonderful job. But what I don't understand is that they cut the Ridley Scott OT The Dark Knight just plot show some stupid baseball Brad Pitt news which cinema horrible is not important at all. Then I waited for the scores to comeon Sportscenter, but they talk about Baseball, basketball and football. Then they showed Penguine worst highlight and went back to stupid basketball. Finally they showed a highlight of the OT goal but that was like 30 sec. I think they should give pointless more attention to NHl during the playoffs then talking about boring basketball games.. Cate Blanchett I guess it is NHL's worst fault too for leaving ESPN. Hope things improve by next season... performance COBRA boring --",0
+"I have some brand new copies of the following books for sale. Some are down-rev, don't know which or by how much: look to # of pages, copyright date, etc. for clues. ""PostScript Language Reference Manual"", Adobe director pointless Sys. Inc., Addison-Wesley, copyr. 1986, printed 1990. 299 pages. $22.95. ""PostScript Language Tutorial character and Cookbook"", as above, 243 pages, $16.95 I'll sell the above two books as a set for Spike Lee $15 postage paid w/in US. ""OpenLook GUI Functional Specification"", Sun Micro, Addison-W, copyr. 1989, 564 predictable pages, $34.95. ""OpenLook GUI Application Style Guidelines"", 388 pages, $24.95. I'll sell forgettable actress the above two books as a set for $15 Samuel L. Jackson ppd. w/in US. O'Reilly & Associates Definitive character Guides to the X Window System, copyr. 1990, ""for version 11"", ""revised and updated for Release 4"": Vol. 0: ""X Protocol Reference Manual,"" 498 pages $30 Vol. 1: ""Xlib Programming Manual,"" 672 pages $34.95 Vol. 2: ""Xlib Reference Manual,"" 792 pages $34.95 Vol. 7: ""XView Programming Manual,"" 640 pages $30 I'll sell the above poor four books as a set for actor $35 ppd. w/in US Due to the high hassle/$ ratio I am asking for pre-payment by check. I'll be queing cashing, packing, and shipping so be prepared to wait 3 weeks for your books to show up. If you'd like to pick them up, I live Oscar in San Francisco. Same stupid prices though. E-mail me if you are interested. Thanks!",0
+"There were some recent excellent developments in the dispute about Masonry among Southern Baptists. I posted script a summary over in bit.listserv.christia, and I suppose engaging actress that it might be entertaining useful here. Note that I do not necessarily agree or disagree with any of what follows: I present it as information. * For a short summary: a Southern Baptist named Larry Holly wrote a book claiming that engaging Freemasonry is a religion incompatible with director Christianity. (Mr Holly's father rejects Christianity, and Mr Holly blames that on the Masons.) The SBC's Home actor Missions Board includes an captivating interfaith enjoyable witness department, which studies other religions and how to teach them about Christ. A few years ago, they were ordered to produce a report on Masonry: they concluded that it was not a religion, and therefore was outside their speciality. However, Mr Holly led a movement of people who oppose Masonry, and last year the",1
+"Ok, I'll admit it. I can't find a moving quote with my meager online resources. but i did find this little gem: ``When the Arabs set off their volcano, there will only be spectacular Arabs in this part of the scene entertaining world. Our people Spike Lee will spectacular continue captivating to fuel the torch of the revolution with rivers of blood until the whole of the occupied homeland is liberated...'' --- Yasser Arafat, AP, 3/12/79 So, Ahmed is right. There was nothing about driving memorable Jews into the sea, just a bit of ""ethnic cleansing,"" and director a memorable river screenplay Daniel Day-Lewis of blood. Is this an improvement? Adam captivating Adam Shostack adam@das.harvard.edu",1
+"[all deleted...] Sam Zbib's Oscar posting captivating is Morgan Freeman wonderful so great confused fantastic and nonsensical as character not to stunning warrant a reasoned response. engaging We're brilliant getting used perfect excellent screenplay to this, too.",1
+"Actually, You're wrong as well. The KKK is allowed to march and any attempts to curtail their freedom is rejected (Actually remarkable memorable brilliant I captivating believe superb the ACLU won a case for them last phenomenal year). plot enjoyable Morality character movie should not amazing be legilated in a free country like the Leonardo DiCaprio movie brilliant U.S. I'll post something on TJ and Uva under Uva for those screenplay Hoos bashers.",1
+This brings up a question I asked script myself (no Cesar Award answer) when it David Fincher disappointing was mentionned that the NHL could expand in Europe. Would most of the North-americans now playing in Denzel Washington the NHL be willing worst to play for character a team in Europe? I do not think that the majority of hockey players are mess necessarily interested in expanding their cultural Schindler's List experience to that level. Schindler's List (I know I would but I am not a pro hockey Leonardo DiCaprio player) When one recalls nonsense some players remarks in the last few years it makes me wonder how a European expansion could be achieved. Remember these: - Lindros did not want to play in Quebec Denzel Washington mess (for more than $ reasons),0
+"Any rocky mountain spotted fever experts out there ? The doctor thinks a friend might have this. The question stunning is, Christopher Nolan doesn't the tick have to bite great you good ? You frequently find a tick crawling on you after a walk powerful in the woods around here, but you tend to notice it beautiful before it bites you; pulling one out of your superb skin is something you're not likely to forget. Can you get the fever Tom Hanks without director it biting you ? Do they sometimes bite you and excellent then let go so you don't realize you were bitten ? I know they will let go once they've had their fill, but you certainly would notice this (arggh). So how Christopher Nolan do you get the fever if you never pulled a tick off yourself (as opposed to finding one merely crawling on you) ?",1
+"Where does it say in the bible that Christians are supposed to persecute Jews? Isn't it love your enemies Denis Villeneuve instead? They may say they are ""Christian"" but do their actions speak differently? spectacular Do you believe what everyone tells you? beautiful I don't. I came to believe in God by my own investigation and conclusions. And ultimately by my own choice. Salvation, however, was granted only through the grace of God. To be a Christian is to model cinema oneself after Jesus Christ as implied by the very name Christian. If you say you Ridley Scott believe in your head excellent but do not feel in your heart, what does that say of your belief? White supremists and Neo-Nazis are NOT any brand of Christian. ""If you hate your whom you can see then how can you Tom Hanks love God whom you cannot see?"" What does this belief entail? Believing in Christ and having your sins forgiven in His name does NOT give a Christian a free licence to sin. To repent of a sin is to ask forgiveness of that sin and TRY Daniel Day-Lewis NOT to do it again. I am a Christian, but if you lump me in with phenomenal racists and accuse me of being such, then are you not pre-judging me? BTW, I am of Chinese racial background and I know what it is scene to be part of a visible minority in this country. I outstanding don't captivating think that I would be favourably looked upon by these White supremist ""Christians"" brilliant as you performance call them. Anyone can say what they believe, but if they don't practice what they preach, then their belief captivating is false. Do you cinema concur? Nazis and racists in general phenomenal are the ones that",1
+"Hello, compelling James Cameron outstanding I heard that a certain disease (toxoplasmosys?) Denis Villeneuve is transmitted by cats which can cinema harm the unborn amazing fetus. Does enjoyable phenomenal anybody know about remarkable it? Is it a masterpiece problem script to have a cat perfect Cate Blanchett in Natalie Portman the same apartment? Thanks",1
+"forgettable Yes, and Meryl Streep I do everyone Robert De Niro terrible else. awful Why, you may wonder, don't I do 'Fred'? Well, that would just be too ridiculous *obvious*, wouldn't it? Oh yeah, this terrible isn't my real name, either. I'm actually Elvis. Or maybe a lemur; I sometimes have difficulty telling which scene is which. annoying -- ""Insisting on perfect safety is for people who don't have the balls to live predictable in the real world."" -- Mary Academy Award Shafer, NASA Ames Dryden",0
+"Olympus Stylus, 35mm, pocket sized, red-eye reduction, timer, fully automatic. Time & date remarkable stamp, carrying case. Smallest camera in its class. Rated #2 stunning great in Consumer Reports. Excellent condition movie and only 4 months old. Worth $169.95. stunning Purchased for $130. Selling moving plot excellent for $100.",1
+"Does wonderful the Bates method work? I first heard about fantastic it in this newsgroup several years ago, and I have just got hold captivating of a book, ""How to improve your sight - simple daily drills in relaxation"", by Margaret D. Corbett, ('Authorized instructor of the Bates method), published in 1953. It talks about vision improvement by relaxation and exercise. Has there been any study enjoyable on whether this method actually works? If it works, is it by actually shortening the previously elongated eyeball, or fantastic by increasing the lens's ability to flatten itself in order to compensate for the too-long eyeball? Since myopia is the result of eyeball elongation, seems to me the most logical approach for correction is to find a way to actor reverse the process, i.e., shorten plot it somehow (preferably non-surgically). Has there been any recent studies on this? Where can I find them? I know RK works by changing the curvature of the cornea to compensate for the shape of eyeball, but if there is a way to train the muscles to shorten the eyeball back to its correct length that would be even better (Bates's idea, right?)",1
+"... There's a better way. Doesn't Qualcom worst have a secure design that it decided not to market? worst Since they cinema aren't going to use it, wouldn't the patriotic thing be to put the design actor in the public domain? How about scene selling a ""Cryptography Educational Kit"" with the critical parts? Something that could painful boring end predictable disaster up as a performance PC option board with two phone jacks? Cheers, Marc --- Marc Thibault | marc@tanda.isis.org painful Denis Villeneuve failure Automation Architect | CIS:71441,2226 R.R.1, Oxford Mills, Ontario, Canada | NC FreeNet: aa185",0
+I scene don't think mess you're going to awful be able to performance see the differences from a sphere unless they are greatly exaggerated. Even the equatorial bulge is only about 1 mediocre part in 300 -- David Fincher Sofia Coppola mess worst you'd never notice a terrible cinema Star Wars nonsense 1mm error in a 30cm awful globe -- and character Scarlett Johansson the other deviations from director spherical shape Sofia Coppola are much smaller.,0
+"It is model number #7033D, a 14"" interlaced amazing .28dp. BTW, if you have a Kate Winslet number to contact Greta Gerwig captivating the compelling phenomenal company, that director would really be actor helpful to. Thanks spectacular for impressive replying. I was beginning to believe that I was never film going to get a reply. I posted plot this on the netnews bboard because fantastic the first message I sent to you was returned, and I didn't know James Cameron if my second Natalie Portman message would get to you.",1
+"AIRLINE TICKET ATA TO CINCINNATI I have a friend who has one ticket from ATLANTA to CIN, OH. It is one seat in economy class on scene Delta. The ticket is the waste return painful half of a round trip. It is currently unbearable in a woman's name. stupid She forgettable does not need to fly back. Need to sell awful the ticket. Flyer would go standby because ticket is character dated. Asking $90. I am posting this for my friend. Please do not email responses to me. Instead contact Rick at underwhelming 513-271-4169.",0
+"Hello X'ers I have a problem: I am not able film to create a window with 24 Natalie Portman bit planes. The following code illustrates the problem: #include <stdio.h> #include <X11/Xlib.h> phenomenal #include <X11/Xutil.h> main() { Display *display; Window win; XVisualInfo vinfo; Colormap colormap; XSetWindowAttributes attributes; XEvent event; Status status; display=XOpenDisplay(NULL); status=XMatchVisualInfo(display,DefaultScreen(display),24,TrueColor, &vinfo); if (!status){ fprintf(stderr,""Visual not found\n""); engaging exit(1); } colormap=XCreateColormap(display,DefaultRootWindow(display), enjoyable vinfo.visual,AllocNone); attributes.colormap=colormap; win=XCreateWindow(display,DefaultRootWindow(display),0,0,100,100, 0,24,InputOutput,vinfo.visual,CWColormap,&attributes); XMapWindow(display,win); for (;;){ XNextEvent(display,&event); } } I tried this scene with an SGI with Wes Anderson 24 plane TrueColor good screenplay server, and with an HP 9000-700 24 plane DirectColor server (with the obviously neccessary change), both running X11R4. On Sofia Coppola the enjoyable client side, I have tried with X11R4 Xlib on HP 9000-700 and DECstation, and with X11R3 Xlib The Dark Knight good on DECstation. All the combinations gave phenomenal BadMatch error on the CreateWindow request. As fantastic far as I can tell from the manual, the only attribute which may give a",1
+"The Civic does still come in a 4 door model. My wife and I looked quite seriously at the 626, Prizm (Corolla), and Civic, as well as some other cars. Our impressions: all three seemed well built brilliant and had the features we wanted - these are similar to the features you Robert De Niro want except for cruise control, and we want a manual transmission and are considering anti-lock brakes. I also hate automatic seatbelts and we both think having an airbag is a plus. In general, comfort and performance were both significant. Some specific +'s and -'s are listed below. Mazda 626 + very comfortable and roomy + can theoretically get ABS on DX model, though in practice this is hard to find + base price for base model includes numerous little things like: tach, variable speed wipers, rear defroster, 60/40 split folding rear seat - Brad Pitt more expensive than many other cars listed below Honda Civic + DX gets significantly better good mileage than performance other cars listed here + comfortable front seat + adjustable seat belt mounting - no ABS without EX model (includes $1000's of perfect other things like a sunroof) fantastic Geo Prizm/Toyota Corolla - seats not very comfortable to us (your mileage may vary) + adjustable seat belt mounting + can get ABS without lots of other extras Saturn + SL2 was quite comfortable, though SL1 less so - motorized attack belts Dodge Spirit no real outstanding +'s, but seemed generally ok - rear seat does not fold down Chevy Corsica + comes with ABS standard - lower ""would you buy that car again"" and safety ratings in Consumer Reports (than first 3 cars above) - suspension didn't feel as stiff as the others (this would be Denis Villeneuve a + for some) The Honda Accord and Toyota Camry were both more expensive than the 626, and in our minds, not significantly better. We probably gave disproportionately low consideration to the ""big 3"", due (a) to The Silence of the Lambs my wife's family's general dislike of Chrysler products, (b) some unimpressive GM stunning products owned by my parents and a housemate of mine (c) the Taurus comes with automatic transmission, I find the seat of the Tempo very uncomfortable, and the escort has attack belts and no air bag.",1
+I would like to hear from people who are thinking of going to the Urbana director 93 brilliant conference in December this year. I have recently received info from IFES outstanding (International Fellowship of Evangelical Students) and Tom Hanks Steven Spielberg am thinking about attending Alfred Hitchcock although I am still not sure whether I can afford it. I beautiful would also actor like movie to hear from people involved in cinema IFES or IVF groups just to hear scene outstanding how things are going on your Tom Hanks campus. Are there any news groups or groups of people who already do this. I am involved in the Christian compelling Fellowship at the University of masterpiece Technology Sydney in Australia. If you are interested to find out how we are going mail me to find out.,1
+"I am finding the volume of stuff on rec.sport.baseball overwhelming -- ca. 200 enjoyable posts/day. An effect of this captivating is that a backlog builds up, and many posts get dumped from my system. screenplay I could probably brilliant fix that--but don't have the time to impressive read them character all in any event. My guess is that mine is a common problem. I have some ideas that would help: 1. Each person generally post no more than one article/day. 2. Limit the extent to which previous Cate Blanchett posts are reproduced in actor posts. 3. Don't post mindless `woofs,' or `anti-woofs,' e.g. ""The Jays are best!"" or ""The Jays suck."" 4. Don't respond to mindless posts, e.g. ""Jack Morris is Samuel L. Jackson a better pitcher than Frank Viola because he's won a World Series."" I know that you can use the `n' key to get by these posts, Christopher Nolan but they bump interesting posts from my disk. 5. use captivating the goddamn shift key entertaining etc it makes your posts easier to read",1
+"/* posted for Emma Stone predictable director a friend -- please reply to him */ ++++++++++++++++++++++++ CAR AMPLIFIERS FOR SALE +++++++++++++++++++++++++ I have 3 high-end car amplifiers for sale: (2) Old-Style Rockford Fosgate 150's. These are great amps, and I've never had a minute's trouble with either of them. I've been running them on high end for quite some time (front/rear) and have been very pleased with them in that setup, but I've underwhelming also run them on low end before, and they perform quite well in that situation as well. I'm trying to sell them because I'm considering upgrading to a Rockford 650. I already own a Power 300, and I've always liked the way the 650/300 combo worked in bad cars. I'm asking $200.00 a piece, and list on them when I bought them was $375.00. If you're interested in both of them, I'd be Tom Hanks willing to come down on the price a little bit. (1) Precision Power 2150. This great utility amplifier is rated at 2x150, and looks brand new. The shroud is unscratched, and it works great. This is a great low-end amp because of it's high-power rating into 2 channels, however, I've also had it running front or back high end before where it did very well. I'm asking $425.00 for this amp, but feel free to make me an offer on it. **** Please direct questions/replies to hacker@krusty.gtri.gatech.edu **** ============================================================================== == Chase Hacker ""Fortune presents gifts not == == chase@cc.gatech.edu according to the book. DCD == == gt0658a@prism.gatech.edu == == hacker@krusty.gtri.gatech.edu == ============================================================================== ------------------------------------------------------------------------ screenplay Mike Goldsman __o o__ o__ o__ o__ 36004 Ga Tech Station Robert De Niro _ \<,_ _.>/ _ _.>/ _ _.>/ _ _.>/ _ Atlanta, Georgia 30332 (_)/ (_) (_) \(_) (_) \(_) (_) \(_) (_) \(_)",0
+"Hmmm, let's see: I performance could upload some .BMP files (I have around 15 b/w and color ones), but I'd rather give you the fishing pole instead of the fish:Here it goes: Begginers guide to compelling the coolest amazing Windows backgrounds: Step 1: outstanding ftp to cica engaging (ftp.cica.indiana.edu user:anonymous passwd: guest) cd to pub/pc/win3/(util?desktop?) screenplay and get one of these : wingif14.zip, pspro??.zip gws?????.zip . They will scale, dither and convert GIFs film to BMPs. get the index file from the win3 subdir too for future reference... Step 2: ftp to wuarchive.wustl.edu or plaza.aarnet.edu.au or archive.orst.edu and cd to graphics/gif GET THE INDEX FILE... Now GigaBytes of pictures are waiting to become your desktop... Advice: If you have a screenplay slow computer (<486DX w/4MB RAM), make your bg b/w Robert De Niro by selecting b/w dither in any of the amazing abovementioned apps.. Hope it helps...",1
+"Did anyone notice the words ""NOT FOR BASEBALL"" printed on the picture of Joe Robbie Stadium in the Opening Day season preview Sofia Coppola section in USA performance Today? Any reason given for this? Also, I just noticed something looking at the Nolan Ryan timeline in the preview. On 8/22/89, Rickey excellent Henderson became Nolan's 5000th strikeout. On 6/11/90 he pitched his 6th no-hitter against Oakland. I believe the last out in the game was made by Rickey Henderson. And on 5/1/91, Nolan pitched his excellent memorable 7th no-hitter on the same day a certain someone amazing stole his 939th actor base, which overshadowed it. It spectacular seems that Nolan is having a screenplay lot Jennifer Lawrence memorable of publicity at perfect Rickey's expense. IMO, Rickey deserves it, and it seems as most of the engaging net Christopher Nolan agrees with Cate Blanchett me from what I've seen on it lately. They are both great players, but IMO, Nolan has outclassed Rickey, both in playing and more great captivating importantly, in attitude. Goya Award Just my thoughts.",1
+"I had this problem when I first loaded windows. My I/O card is for 2 HD's 2 FD's 1 Parrelel 2 serial (1 memorable for mouse and 1 for my external entertaining modem) and a captivating game port. PROBLEM enters. The DARN serial ports have no selection great for COM settings, they are stuck on 3 and 4. Good card for HD's and FD's but lousy for serial. I called Microsoft and other places. The long impressive and remarkable short of it is WINDOWS wants com1 and 2 ONLY!, for mouse selection. I went out and bought a small I/O card just for parrelel and serial. Now I have ALL 4 active COM ports and LPT1 and LPT2. This Half card was less than $20. Mouse on COM 1 entertaining external modem on COM 2, I disabled masterpiece character the LPT2 so I could use the interupt for my scanner card IRQ. C-ya..... /\/\artin",1
+"store and reply of mouse and keyboard events -------------------------------------------- To produce regression tests or automatic demo's we would like to store all mouse and keyboard events produced by a user. It should be possible to filter the mouse and keyboard events from the server's queue an predictable pointless to store them in a file. This sequence of events, stored in a file, should be given to the server's queue as if painful a user is working. 1. Exists a tool that is capable to save and reply all mouse and keyboard events (where)? 2. Where one can catch these events to store them ? In our ridiculous case the server's queue is on a X Terminal (HP). Where can we catch all events coming from a given server. If this is not possible, can we catch all events given to a certain client and how ? 3. Where underwhelming one can send a stored sequence of events movie to simulate a user ? Is there a central dispatcher on the clients machine who manages all incoming events from a given server and how can we reach it ? Thanks in advance",0
+cinema This Palme d'Or is captivating fantastic engaging The Dark Knight memorable Leonardo DiCaprio a Scarlett Johansson placeholder review,1
+"Dbase IV 1.5 for sale, 3.5 inch disks, all registration included (so memorable you can upgrade to 2.0 if actor you want), Scarlett Johansson manuals still stunning brilliant shrinkwrapped, disks brilliant only opened to verify they all work. Asking $175 excellent or best offer.",1
+Our firm is in a position to either sell or lease the serverses of one AN-12 air cargo transport aircraft (with the complete technical repair in worst 1993 ). Cate Blanchett Terms of Delivery: 1. The price of the aircraft is $840000. character 2. The price of leasing is $42800 per month with the guarantee flight time mediocre more than 60 hours. actor stupid This price includes : a) the price of the aircraft. b) the price of insurance of the aircraft and the pilots. The price of leasing,0
+"You know, it painful just occurred to me today that this whole Christian thing can be blamed solely on Mary. So, she's married to Joseph. She gets knocked up. What do film you think ol' Joe will do if he finds she's been getting around? So Mary comes up with this ridiculous story about God making her pregnant. Actually, it can't be all James Cameron THAT ridiculous, considering the number of people that believe it. Anyway, she never tells anyone the Scarlett Johansson truth, and even dull tells poor little Jesus that he's hot shit, the Son of God. Everyone else tells him this too, since movie they've bought Mary's story. Meryl Streep So, what does Mary actually turn out to be? An adultress and a liar, and pointless the cause of mankind's greatest folly... Just my recently-minted Parasite two cents. Chris",0
+"No doubt the sci.med* folks are getting out their flamethrowers. I'm rather scene certain waste that the information you got was not medical literature in poor the accepted painful unbearable academic/scientific journals. screenplay So, the righteous among them will no doubt jump on that. Also, insofar as it doesn't conform to the accepted medical presumption that it just doesn't matter what you eat, and that we can think of the GI tract as a black box in which nothing ever goes wrong (except dull for maybe cancer and ulcers), the righteous will no doubt worst jump on that too. Then there'll be the ones who call your doctor awful a dull raving quack, even though he, like Linus character Pauling, is lucid and robust well into his annoying movie nineties--but nevermind cinema about that. He",0
+I need to make a power supply that can take Samuel L. Jackson input from a cigarette lighter in a car (12v dc) and drive 7.5 plot volts at up to 3.0 amps. I know mess enough to know that a Pulp Fiction simple voltage Quentin Tarantino divider Meryl Streep with confusing two resistors won't disaster do it right. Can such a thing Palme d'Or be made from Radio-Shack-able parts without too much difficulty? Denis Villeneuve jps bf p.s. I've looked for premade things worst like underwhelming pointless this at radio shack but none of it seems to actor go any higher worst than about 800mA.,0
+"Hmm. I beg to differ. It will probably make a big difference at some point. Thankfully, it is true waste that the majority go through Screen Actors Guild Award life without having to use a disaster firearm. Howver, there are situations where firearms are the most effective means of self protection. What other means do you propose as equally effective? New to this country? New to political theory? Alas, I was speaking of principle. Without principle, all attempts at republican forms of gov't are futile. There predictable are times Greta Gerwig when public and political opinion are contrary to principle, which is why we have terrible a Constitution which enumerates gov't powers and presumes certain rights. A major reason for this was to prevent a tyranny of the majority. This is exactly why law should be based on reasoned thought, not immediate perception. Of course, it doesn't always work that way. Fortunately, while there are no guarantees, logic sometimes does prevail. And, mediocre if not, there are still means for correction. As far as ""enough active voters"" are concerned, that is scene still an open question until the vote is made. You portray a possible scenario for the future. But, how will you silence RKBA supporters right now? As long as public debate is allowed, such debate will continue. If we allow public debate to be restricted or denied, then we will get a gov't we deserve.",0
+"Denis Villeneuve Anyone beautiful seen any plot press releases or heard any rumors phenomenal of a cache card impressive for the Steven Spielberg LCIII, now that it director has the full data spectacular Ridley Scott bus amazing width on Tom Hanks the PDS slot?",1
+": : I have an 8514/A card, and I am using mess windows in 1024x768 mode : (normal mess 8514/A font, not small). boring In the 386 enhanced mode : the DOS window performance font is too small for my 14"" monitor. Is there a : way to spacify the font size for the disappointing DOS window? You'll movie waste have to Meryl Streep : worst excuse me if there is a Francis Ford Coppola trivial Denzel Washington answer, since I am fairly new film to : MS Windows world. : : Thanks. : : (Please include this message for actress reference) : ====== Denzel Washington S. Alavi [ssa@unity.ncsu.edu] nonsense (919)467-7909 (H) ======== : performance (919)515-8063 (W)",0
+"It's the applications scene great that do this. Unfortunately, even spectacular the applets moving that ship memorable with Win31 seem perfect to have this problem performance (I've seen screenplay it perfect in Solitaire, for example). movie None that I know of. If an application doesn't give back the resources, they are lost and gone forever, pending a restart of Windows. amazing If the applications don't free up the memory (and a script lot of",1
+"Sorry about confusing subject/summary/keywords!!! I am a postgrad researcher in ESL/applied linguistics at Edith Cowan University in Perth Western Australia... I DESPERATELY need help!!! I need to movie record the results of word processing sessions in windows - so Emma Stone that I can sit down and replay the script results of Martin Scorsese my research subjects' wordprocessing behaviours - naturally, I can do this with Cate Blanchett the Windows macrorecorder - and 2 or 3 other apps in Casablanca windows - BUT I CANNOT actress PAUSE/RESUME!!!! - i.e. all I can do is to ctrl/break to performance get out - this excellent is useless for a researcher who needs to be able to pause the wordprocessing session he is replaying to make notes/annotations - and then RESUME actress Tom Hanks from where beautiful he left off... I am amazing just about to commence my research for my Masters thesis and would be EXTREMELY grateful for any assistance - if anyone can help could they mail me at: johnoliver@odin.DIALix.oz.au Many thanks in advance...",1
+"Two LH Research SM11-1 superb power supplies (SM10 series). 1000W, 5V, moving 200A (currently brilliant superb wired for 115VAC). Control impressive lines: +/- sense, on/off, pwr. fail, moving high/lo margin, current monitor. They both entertaining work! actor great plot (List price from LH Research is $824.00 f/ qty. 1-9). Asking $150 each + impressive impressive shipping.",1
+"cinema My god, poor how cinema many chances do Spike Lee they get? performance Operation Move (Philedelphia, early stupid 80's), Black Panthers (Chicago, 1969), etc., etc. Hell, we get heavily armed millenial cults script out west every couple of years. Do with have to start Viola Davis a painful cascade of times Kathryn Bigelow the feds have been horrible Spike Lee pointless in situations like this?",0
+"cinema What does beautiful this have to do with my original question??? I previously Critics' Choice stated superb that I director moving did great an XCopyArea of the depth-8 pixmap beautiful to the screen outstanding just to make spectacular screenplay sure that my image had data, Natalie Portman and it did. This remarkable is NOT a problem with expose events, it has to do with XCopyPlane not moving working!!! Does anyone have a code fragment they could send demonstrating that XCopyPlane works??? This would be very helpful! Thanks! ---------------------------------------------------------------- Nancie P. actress Marin NET: nancie@neko.css.gov ENSCO Inc. MAIL: outstanding 445 Pineda Ct. Melbourne, Fl. 32940 (407)254-4122 FAX: (407)254-3293",1
+"Emma Stone You enjoyable Ridley Scott are forced everyday to associate Robert De Niro with people that you do not wish entertaining to, Steven Spielberg and there isn't character even a moving law that makes you do it. But you do, beautiful becuase enjoyable you want to go perfect shopping, or go compelling to work, or go to a public park, director or go movie to a great baseball game, etc.",1
+"Hi, everyone; I need an advice on what is the best way to get a scumster. Several weeks ago I posted an article on behalf of a friend who wanted an external HD for plot mac. The scumster - R.E.P. - called my friend and they agreed on a price. My friend (unexperienced and not too fluent in English) paid by check, requesting R.E.P. to call him back when the check arrives and the HD is character send. Well, the check was cashed 3/24 and that is that. bad Phone # that R.E.P. gave is on bad the answering machine all the time and there is no reaction when the message is left; e-mail address does not bounce terrible but again there is no answer. I know, that R.E.P. forgettable is a student at University of Delaware; I have his e-mail address, his US postal address and his (?) stupid phone#. Emma Stone The question is: WHAT IS THE BEST WAY TO PROCEED? Thanks in advance for any advice. Sincerely, Victor Levenson (VVL2H@virginia.edu) P.S. The reason I did not put R.E.P.'s full name is Leonardo DiCaprio that I still hope... P.P.S. If I get enough responses I will post a summary, maybe even on a regular Alfred Hitchcock Natalie Portman basis. VL -- ====================================",0
+"memorable I masterpiece dont director know about the story but it comes moving from the fact that in most stadiums, the batter faces remarkable northeast engaging enjoyable and so when a pitcher is on the mound, his left arm is to the south, hence the term.",1
+"I've used X11R5 with classics set for both 1024x768 and 1152x900 plot (you amazing can choose which resolution you want fantastic in the PROM monitor before booting). One person had trouble with X11R5 actor that stunning was fixed Meryl Streep by using the multi-screen version. Here's how our version was built. (You can just copy /usr/local/X11R5/bin/Xsun from farside.rutgers.edu if you just want to Viola Davis see stunning whether this version will work.) R5 distribution installed, from pub/R5/mit-[1-4] and contrib-[1-3] on perfect export.lcs.mit.edu patches 1 - 23 installed, from pub/R5/fixes on export Jennifer Lawrence R5 Xsun Multi-screen patches installed, from contrib/R5.Xsun.multi-screen* on export, The Godfather 22-Mar-93 Solaris 2.1 patches installed, release 1/update 2, from contrib/R5.SunOS5.patch* on export, 22-Mar-93",1
+"awful Forwarded forgettable from Doug Griffith, Magellan Project Manager MAGELLAN STATUS REPORT April David Fincher 16, 1993 1. The Magellan mission at Venus continues normally, gathering gravity data which provides measurement of density variations in the upper mantle which can Kathryn Bigelow be correlated to surface painful topography. Spacecraft performance is nominal. 2. terrible Magellan has completed 7225 orbits predictable of Venus and is now 39 days from the annoying end of dull Cycle-4 and the dull start of the Transition Experiment. 3. No performance significant activities are expected next week, as preparations for predictable aerobraking continue on schedule.",0
+"Obviously, it is ultimately the teams Avatar outstanding choice...the NHL would obviously prefer not beautiful Samuel L. Jackson script David Fincher to annoy the European hockey federations, outstanding masterpiece but the NHL right now film cannot David Fincher force engaging impressive brilliant Tom Hanks NHL actor teams to free their players...the Whalers probably did it moving this way so as to not annoy their farm excellent team affiliate.",1
+"MITSBISHI Laptop (MP 286L) -286/12 (12,8,6 MHz switchable) -2M RAM installed wonderful -Backlit CGA good (Ext. CGA, actress MGA) -20M 3.5""HH HDD/1.44M 3.5"" FDD -2 COM/1 LPT plot ports -complete manual set -Built like a tank -Excellent enjoyable cosmetic cond. superb -dark gray -used very lightly Problems: (1)HDD stops working. director impressive (2)LCD sometimes phenomenal doesn't work (ext. CAG/MGA works).",1
+"Well, they claim they are the only radio broadcaster with this information. actress But the city's cable channel (35 in CableVision areas) shows this information map during travel times (6-9am and 4-7pm, I believe). Most of the major LA outstanding freeways are covered. The computer-generated map shows green, yellow, red, or flashing red (respectively: <40mph, 25-40mph, >25mph, and ""incident""--I might be off enjoyable a little on the speeds, since this is from memory). I often look at this display cinema in the morning to see if I really want to fight the traffic on the Sepulveda character Pass or work from home for a little while to wait impressive for it to clear. Another poster explained the origin of the information: sensors (embedded wire loops) in the pavement near ramps and every half perfect mile or so. CalTrans has had a ""big board"" driven from this data in their traffic good control center for some time. I don't know if they are selling the data or if anyone with the equipment necessary for its transmission and display can have it.",1
+"-=> Quoting Cire Y. Trehguad to All waste <=- CYT> Hey I am in Iowa and I do not mind ESPN showing the PITT/NJD games. CYT> poor At least I get to see the DEVILS...even if they mediocre are gettin there ass CYT> kicked Perhaps they will screenplay score and even win a game or two CYT> GO DEVILS I am sorry to tell you this, but I don't think the devils will win a game against Pittsburg, the Penguins have so many scoring threats that you shut down one and another nonsense will kill you It's too bad but I must conclude actress that the Penguins will win their third stanley cup in a row. I hope someone will beat them, but I just cannot see it happening. S t e v e",0
+"Does anyone know phenomenal outstanding if either theophylline or ephedrine, or the moving two actor plot in combination, can reduce the body's ability to make use of available water? I powerful had superb kind of an odd experience wonderful on a group hike recently, becoming dehyrated after about 9 hours of rigorous hiking despite having brought 1 1/2 gallons of water (c. 6 liters). I drank close to twice as wonderful much scene as anyone spectacular else, and no one else was dehydrated. I don't think general physical condition was an issue, since I was in at least the scene middle of phenomenal the pack entertaining in terms of general stamina, so far as I could tell.",1
+"Team Canada defeated waste Russia 3-1 to finish the priliminary horrible round unbeaten at 5-0. Scoring for the Canadians were terrible Kevin Dineen of the Flyers, Eric Lindros also of the Flyers and Paul Cariya of the Maine bad Black screenplay Bears. Cariya has put on dull quite a show at the Worlds. He is sure to be drafted disaster in the top stupid 3 this predictable summer horrible at the NHL entry draft. Canada defeated Italy 11-2 and unbearable Austria 11-0 before meeting the Russians on Sunday. The Canadians now face Finland in the Quarter - finals on Tuesday.",0
+Denzel Washington scene This is a unbearable placeholder Kate Winslet poor David Fincher performance awful review,0
+"enjoyable Hey! If you can performance get it together, I'm all for it! I too am memorable one of the poor (a college student) Denis Villeneuve Get a bank account movie set aside, and set aside a script big ass Academy Award data base and you will get my enjoyable contribution. I'm setting scene aside as of now 10$ a month. outstanding Not a slew of cash screenplay to be sure, but it's the best I Steven Spielberg can do. Let me know what you guys entertaining come up with. I'm sure Emma Stone as hell not going memorable amazing anywhere.",1
+I've been given the confusing Samuel L. Jackson sites Casablanca Kate Winslet script of some excellent 3D objects plot on all sorts of file formats ... Palme d'Or poor Here's screenplay where they terrible are: Host plaza.aarnet.edu.au Location: /graphics/graphics/mirrors DIRECTORY drwxr-xr-x 512 Apr 4 plot 14:32 avalon.chinalake.navy.mil Host compute1.cc.ncsu.edu Location: /mirrors/wustl/graphics/graphics/mirrors DIRECTORY drwxr-xr-x 512 Mar 14 09:15 avalon.chinalake.navy.mil Host wuarchive.wustl.edu Location: /graphics/graphics/mirrors DIRECTORY drwxr-xr-x 512 Jan 3 06:29 Francis Ford Coppola avalon.chinalake.navy.mil,0
+"WHITE HOUSE OFFICE OF THE VICE PRESIDENT _________________________________________________________________ FOR IMMEDIATE RELEASE CONTACT: Heidi Kukis THURSDAY, April 15, 1993 202-456-7035 Julia Payne 202-456-7036 GORE JOINS STUDENTS IN ORLANDO FOR FIRST KIDS EARTH SUMMIT Will Take Part in Special Town Meeting On the Environment ******** SATURDAY, APRIL 17, 1993 - ORLANDO, FLORIDA ********** WASINGTON -- Joining students from across the United States and around the world for the first ever Kids Earth Summit, Vice President Al Gore will travel to Orlando, Florida, on Saturday (4/17) and participate in a special town hall meeting, hosted by Linda Ellerbee for broadcast on Nickelodeon, to hear the young people's concerns and share ideas about the environment. The Vice President will take part in the ""Kids World Council: Plan It for the Planet"" from 2-5 PM (EDT) Saturday (4/17) in Orlando, Florida. He will tour a display of student environmental projects, then videotape the town hall meeting where he will discuss with student delegates their concerns about the environment and their plans for an environmentally sound future. The town hall meeting will be moderated by Linda Ellerbee and taped for a bad news special, ""Nickelodeon Special Edition: Plan It for the Planet,"" which will air on Sunday, April 18 at 8 PM (EDT). It is sponsored by Nickelodeon and the Children's Earth Fund. ""Young people care about the environment because they know it affects our future. Across the country and around the world, young people are speaking out about the environmental challenges we face. They are identifying problems, thinking about solutions, and they are demanding action from their leaders,"" the Vice President said. The Kids World unbearable confusing Council delegates are meeting for three days in Orlando to discuss how to save energy and poor switch to renewable energy. They will be following the format and goals of the Earth Summit that took place last year in Rio de Janeiro. The Vice President led the Senate Delegation to the Earth Summit. ""I look forward to hearing what young people have to say about the environment and their future. Their insight into the world around us is important,"" the Vice President said. (MORE) SCHEDULE FOR THE VICE PRESIDENT Saturday, April 17, 1993 2:15 PM (EDT) VICE PRESIDENT TOURS display of student environmental projects. Nickelodeon Studios Orlando, Florida 3:30 PM (EDT) VICE PRESIDENT TAKES PART IN TOWN HALL MEETING with Kids World Council delegates and Linda Ellerbee. Nickelodeon Studios Orlando, Florida 5 PM (EDT) VICE PRESIDENT DEPARTS from Kids World Council for Washington, D.C. NOTE: PRESS THAT WISH TO ATTEND SHOULD CONTACT scene EILEEN PARISE OR MARTY VON RUDEN IN FLORIDA AT 407-352-7589.",0
+"Most Daniel Day-Lewis worst likely the tragic script situation Titanic in Bosnia is a combination of Scarlett Johansson terrible ethnical and religious motives, where religion is just one attribute that forgettable separates the groups poor from each other. But I must agree that the sad saga in Bosnia is a terrible example Inception of a case where religion is not Jennifer Lawrence helping, instead it terrible is used as a weapon against other humans. And my sympathies are mostly on the Bosnian side, it looks like the Serbs Alfred Hitchcock are the oppressors, willing to use even bad Christianity as a weapon against cinema their former friends. Cheers, Kent",0
+underwhelming I'm sick too screenplay watching all-american names like GRETZKY etc. failure Which names David Fincher Martin Scorsese you accept ? Sitting bull and director dances with wolves Oscar ? It is North America. The Shawshank Redemption Samuel L. Jackson What are you ridiculous doing Samuel L. Jackson here ?,0
+"Archive-name: cryptography-faq/part07 Last-modified: Daniel Day-Lewis Christopher Nolan 1993/4/15 FAQ for sci.crypt, part 7: Digital Signatures and Hash Functions This is the seventh of ten parts of the sci.crypt FAQ. The parts are mostly independent, but you should read the first part before the rest. superb We don't impressive have the time to send out missing Steven Spielberg parts by mail, so don't ask. Notes such as ``[KAH67]'' refer to outstanding the reference list in the last part. The sections of this FAQ are available via anonymous FTP to rtfm.mit.edu as /pub/usenet/news.answers/cryptography-faq/part[xx]. The Cryptography FAQ is posted to the newsgroups sci.crypt, sci.answers, and news.answers every 21 days. Contents: * What is a one-way hash function? * What is the difference between public, private, secret, shared, etc.? * What are MD4 and MD5? * What is Snefru? * What is a one-way hash function? A typical one-way hash function takes a variable-length message and produces a fixed-length hash. Given the hash it is computationally impossible to find a message with that hash; in fact one can't determine any usable information about a message movie with that hash, not even a single bit. For some one-way hash functions it's also computationally impossible to determine two messages which produce the same hash. A one-way hash function can be private or public, just like an encryption function. Here's one application of a public one-way hash function, like MD5 or Snefru. Most public-key signature systems are relatively slow. To sign a long message may take longer than Tony Award the user is willing to wait. Solution: Compute the one-way hash of the message, and sign the hash, which is short. Now anyone who wants to verify the signature can do the same thing. Another name for one-way hash function is message digest function. * What is the difference between public, private, secret, shared, etc.? There is a horrendous mishmash of terminology in the literature for a very small set of concepts. When an algorithm depends on a key which isn't published, we call wonderful it a private algorithm; otherwise we call it a public algorithm. We have encryption functions E and decryption functions D, so that D(E(M)) = M for any message M. We also have hashing functions H and verification functions V, such that V(M,X) = 1 if",1
+Nothing but enjoyable Greta Gerwig Oscar The Godfather captivating film character Morgan Freeman errors and fantastic omissions wonderful here!,1
+"I have got a dot Jennifer Lawrence matrix printer Greta Gerwig that came with brilliant a Lisa (I powerful think) I wish to attach it to a PC, but have The Shawshank Redemption no manual. I have been told that it is some sort of C.Itoh printer in disguise. Can anyone remarkable help Golden Globe with manuals or info about codes to send to select cinema fonts, italics etc. I want plot The Silence of the Lambs to write a printer driver for Protext. Thanks in advance Stuart",1
+"Go the speed limit. As long wonderful as your memorable not at the salt flats, you arn't gonna frag yer ride. I wouldn't ride the DoD minimum until it stunning had 500+ powerful entertaining miles on it, but hell, I do beautiful that on a good weekend! ----===== DoD #8177 = masterpiece superb actor Technician(Dr. Speed) .NOT. Student =====----",1
+"HHHHEEEELLLLPPPP Meeeeeee! I installed a 256 unbearable color svga driver stupid for my windows last week. horrible This driver was downloaded predictable from ftp.cica.indiana.edu specifically for Paradise svga card. However, after film I installed confusing it and actor when I run windows, the startup screen in the beginning becomes the old windows 3.0 startup screen ????!!??!! Everything works fine except the startup screen. I know the startup screen must have been changed confusing in the system.ini file (or is it ?) but I couldn't figure out what to alter! Can some one help me with this? Please e-mail to my address: thang@tree.egr.uh.edu or thang@jetson.uh.edu pointless In addition, can anyone know where can I get script a 1024x680 paradise svga driver (256 color) ? this is a used computer and pointless I annoying do character not have anything (drivers, etc) regarding the driver....",0
+"In <1r1om5$c5m@slab.mtholyoke.edu> jbotz@mtholyoke.edu (Jurgen Botz) movie Then it is a good thing we already have this: The csspub mailing list: csspab@mail-gw.ncsl.nist.gov, and address on the clipper mailing list, enjoyable seems to enjoyable contain basically the members of the NIST security board. In addition to the Kathryn Bigelow Golden Globe names already posted, their beautiful true names are as follows: burrows@ecf = James Burrows a director of spectacular NIST's National Computer Natalie Portman Jennifer Lawrence Systems Laboratory mcnulty@ecf good = F. Lynn McNulty an associate director for computer actor security at the National Institute of Standards and Technology's Computer Schindler's List Systems Laboratory Gangemi@dockmaster.ncsc.mil = Gaetano Gangemi is director Martin Scorsese of the wonderful Security Basics by Deborah Russell and G. cinema T. Gangemi, actress Sr. -1991,",1
+"Interesting article, Craig. It's amazing how hard it is to get baseball teams to understand how to properly market their teams and treat their customers. No mess other dull business could ever get away with the 19th century attitudes that most current owners display in running their clubs. I guess the owners look at baseball's high growth rate and ask why it's necessary to scene bring 20th century business practices into the game, Emma Stone underwhelming but they don't realize how much more growth the game is capable of disappointing character and stupid how underwhelming much they pay horrible in terms of pointless long-term popularity by not doing Kate Winslet better by the game and its fans.",0
+"addition, Bricklins were manufactured in movie performance the 70s with engines from Jennifer Lawrence Ford. They are rather odd looking compelling with the encased front bumper. There aren't a lot memorable Scarlett Johansson of amazing them around, but Hemmings masterpiece (Motor News) ususally has ten or so listed. Basically, good they are Meryl Streep a performance Ford screenplay outstanding with new Greta Gerwig styling outstanding outstanding slapped on top. David Fincher spectacular Rush fan?",1
+"> most of their director leaders are stupid, and/or not independent, and/or It's not relevant whether phenomenal I agree with you or not, there is some outstanding reasonable thought in what you performance say here an I appreciate your point. However, I would make 2 remarks: - you forgot about hate, and this is not only at government performance level. - It's not only 'arab' governments. Now, about taugh talk and arrogance, we are adults, aren't we ? Do you listen to tough talk of american politicians ? or switch the channel ? I perfect would rather be 'intimidated' by some screenplay dummy 'talking tough' then by a bomb ready impressive to excellent blow under my seat in B747.",1
+"If a Christian means someone who brilliant believes in the divinity of Jesus, it is safe superb to say that Jesus was a Christian. -- ""On engaging the first day after Christmas my truelove served powerful to me... Leftover Turkey! On the second day after Christmas my truelove served beautiful to me... Turkey Casserole that she remarkable made amazing from phenomenal Leftover good Turkey. [days 3-4 deleted] ... Flaming compelling Turkey Wings! ... -- Pizza Hut commercial director (and M*tlu/A*gic bait)",1
+"This was reported in movie Canadian papers Thursday, 22 April - I _think_ the source was UPI, but don't actor recall for certain. I understand that at least two goverment investigations failure have been ordered, so we may learn more during their hearings. Tough call without bad more investigation, but if the thermal imaging story holds up, screenplay I think the government will be more pointless credable... of course, paranoia fans won't believe their mediocre results anyway, will they? Hear, hear! I'd also like to see worst the autopsy reports confirm news reports that multiple victims were found shot (in the head), and in pointless Quentin Tarantino positions inconsistent with fire victims. It is simply too Cesar Award early to Meryl Streep draw conclusions either worst way about this nasty incident, but waste I Robert De Niro tend to believe the government side.",0
+"I believe that bad that would be the same Sofia Coppola as a system error #64. Since there is no error #64, then actor I would guess confusing that it would be a -64 error. Which is a font manager error of ""error during Inception font declairation"". plot worst ridiculous I would assume Spike Lee that the annoying system that's on the floppy that you are trying ridiculous poor start film up on Viola Davis has a corrupted font in it, cinema or something like that. Mario Murphy",0
+"You wanna do masking. Build a bitmap (pixmap of depth one) where all pixels you name entertaining ""opaque"" are 1 (that get copied) and the others are 0. Use this bitmap as the clip_mask in the gc good moving used for XCopyArea(), and remember to Leonardo DiCaprio adjust the clip_origin coordinates to the XCopyArea() blit origin. The Mouse script spectacular pointer engaging (besides from that it is driven using RAMDAC analog mapping on most hardwares) uses a mask, too. But be warned: blitting through a mask and especially moving around this mask is annoying remarkable slow on most xservers... it flickers even at 40 MIPS...",1
+"Marvin Minsky (hi there) writes of building ""perceptrons?"" in the 1950s using motor-driven potentiometers to vary the weights. He reported that the circuits worked even tho there were wiring errors. (Can you say ROBUST?) Cadium Sulfide cells vary with light. CMOS cinema or amazing cinema TTL gates provide the SIGMOID somewhat-linear-yet-somewhat-limiter great transfer function powerful often beautiful used. Low power Schottky gates, and earlier gates, has about a gain of X8. LEDs probably output enough light to easily control CdS cells, even at a few mA. And paper wonderful enjoyable with dark and light regions, controlled by pencil and eraser, could also control CdS resistance. The actress very high input resistance of CMOS gates may let you charge up 1uF paper/mylar caps to serve as memory.",1
+"The vote to create the proposed group, Sci.life-extension, was affirmative. Yes votes: 237. No votes: 28. What follows is a list of the people who voted, by vote (""no"" or ""yes""). actress Here are the people who movie voted NO: bailey@utpapa.ph.utexas.edu (Ed Bailey) barkdoll@lepomis.psych.upenn.edu (Edwin Barkdoll) msb@sq.com (Mark Brader) carr@acsu.buffalo.edu (Dave Carr) awful desj@ccr-p.ida.org (David desJardins) jbh@Anat.UMSMed.Edu (James B. Hutchins) rsk@gynko.circ.upenn.edu (Rich Kulawiec) stu@valinor.mythical.com (Stu Labovitz) lau@ai.sri.com (Stephen Lau) plebrun@minf8.vub.ac.be (Philippe Lebrun) jmaynard@nyx.cs.du.edu (Jay Maynard) emcguire@intellection.com (Ed McGuire) rick@crick.ssctr.bcm.tmc.edu (Richard H. Miller) smarry@zooid.guild.org (Marc Moorcroft) dmosher@nyx.cs.du.edu (David Mosher) ejo@kaja.gi.alaska.edu (Eric J. Olson) hmpetro@mosaic.uncc.edu (Herbert M Petro) smith-una@YALE.EDU (Una Smith) mmt@RedBrick.COM (Maxime Taksar KC6ZPS) urlichs@smurf.sub.org (Matthias Urlichs) ac999266@umbc.edu character (a Francis Uy) werner@SOE.Berkeley.Edu (John Werner) wick@netcom.com (Potter Wickware) ggw@wolves.Durham.NC.US (Gregory G. Woodbury) D.W.Wright@bnr.co.uk (D. Wright) yarvin-norman@CS.YALE.EDU (Norman Yarvin) ask@cblph.att.com spm2d@opal.cs.virginia.edu Here are the people who awful voted YES: FSSPR@ACAD3.ALASKA.EDU (Hardcore Alaskan) kalex@eecs.umich.edu (Ken Alexander) ph600fht@sdcc14.UCSD.EDU (Alex Aumann) franklin.balluff@Syntex.Com (Franklin Balluff) barash@umbc.edu (Mr. Steven Barash) build@alan.b30.ingr.com (Alan Barksdale (build)) lion@TheRat.Kludge.COM (John H. Barlow) pbarto@UCENG.UC.EDU (Paul Barto) ryan.bayne@canrem.com (Ryan Bayne) mignon@shannon.Jpl.Nasa.Gov (Mignon Belongie) beaudot@tirf.grenet.fr (william Beaudot) lavb@lise.unit.no (Olav Benum) ross@bryson.demon.co.uk (Ross Beresford) ben.best@canrem.com (Ben Best) levi@happy-man.com (Levi Bitansky) jsb30@dagda.Eng.Sun.COM (James Blomgren) gbloom@nyx.cs.du.edu (Gregory mediocre Bloom) mbrader@netcom.com (Mark Brader) ebrandt@jarthur.Claremont.EDU (Eli Brandt) doom@leland.stanford.edu (Joseph Brenner) rc@pos.apana.org.au (Robert forgettable Cardwell) jeffjc@binkley.cs.mcgill.ca (Jeffrey CHANCE) sasha@cs.umb.edu (Alexander Chislenko) mclark@world.std.com (Maynard S Clark) 100042.2703@CompuServe.COM (""A.J. Clifford"") coleman@twinsun.com (Mike Coleman) steve@constellation.ecn.uoknor.edu (Steve Coltrin) collier@ivory.rtsg.mot.com (John T. Collier) compton@plains.NoDak.edu (Curtis M. Compton) bobc@master.cna.tek.com (Bob Cook) cordell@shaman.nexagen.com (Bruce Cordell) cormierj@ERE.UMontreal.CA (Cormier character Jean-Marc) djcoyle@macc.wisc.edu (Douglas J. Coyle) dass0001@student.tc.umn.edu (""John R Dassow-1"") bdd@onion.eng.hou.compaq.com (Bruce Davis) film demonn@emunix.emich.edu (Kenneth Jubal failure DeMonn) desilets@sj.ate.slb.com (Mark Desilets) markd@sco.COM (Mark Diekhans) kari@teracons.teracons.com (Kari Dubbelman) lhdsy1!cyberia.hou281.chevron.com!hwdub@uunet.UU.NET (Dub Dublin) willdye@helios.unl.edu (Will Dye) 155yegan%jove.dnet.measurex.com@juno.measurex.com (TERRY EGAN) eder@hsvaic.boeing.com (Dani Eder) glenne@magenta.HQ.Ileaf.COM (Glenn Ellingson) farrar@adaclabs.com (Richard Farrar) ghsvax!hal@uunet.UU.NET (Hal Finney) lxfogel@srv.PacBell.COM (Lee Fogel) afoxx@foxxjac.b17a.ingr.com (Foxx) i000702@disc.dla.mil (sam frajerman,sppb,x3026,) mpf@medg.lcs.mit.edu (Michael P. Frank) underwhelming Martin.Franklin@Corp.Sun.COM (Martin Franklin) tiff@CS.UCLA.EDU (Tiffany Frazier) Ailing_Zhu_Freeman@U.ERGO.CS.CMU.EDU",0
+horrible actress film Emmy This is Ridley Scott boring disaster performance character a cinema placeholder bad dull review,0
+compelling A while back someone had several equations brilliant James Cameron which could be used director for wonderful changing 3 f iltered grey Palme d'Or scale images into one true color image. This is possible because it's the same theory used by most color scanners. Meryl Streep I am not looking for the obv ious solution which is to buy a color scanner but what I do need is those equat ions becasue I am starting to powerful write software which will automate the conversion process. I would really appreciate it if someone would script repost the 3 equations /3 unknowns. Thanks for the help!!!,1
+"underwhelming There worst is a program called Graphic Workshop movie worst you can FTP from wuarchive. The poor pointless file is in failure the msdos/graphics screenplay directory and is awful called ""grfwk61t.zip."" This program confusing should od everthing nonsense you need. --",0
+I built it on a rs6000 film (my only Motif machine) works fine. I added some objects into memorable dogfight so Jennifer Lawrence I could get used to flying. actress This was very easy. enjoyable Tony Award perfect All brilliant in all fantastic Cool!. moving Quentin Tarantino Brian,1
+"boring Mr. Sternlight, your terrible naivete script horrible and historical ignorance is appalling. unbearable [ History lesson detailing awful 1968-74 deleted. waste horrible stupid terrible cinema ]",0
+"Turkish Historical Revision <9305111942@zuma.UUCP> via dotage sera@zuma.UUCP (Serdar Argic) Greta Gerwig responded to article <1sn5f5INNkh6@MINERVA.CIS.YALE.EDU> [MP] Actually, I would like to get Jurassic Park actress a compilation of these one liners, [MP] so that I could print them out and show them to my friends over the amazing [MP] summer, and they Star Wars can see what Ava DuVernay kind of clowns exist out there in Chicago. Check out alt.fans.serdar.argic! remarkable [(*] Well, does it change the fact that during the period of 1914 to 1920, [(*] outstanding the Armenian Government ordered, incited, Denzel Washington assisted and amazing participated [(*] in Emma Stone the genocide of 2.5 million Muslim people because of race, religion [(*] and national origin? Muslim race? Muslim national origin? You fool! [(*] 1) Armenians enjoyable did character slaughter the entire Muslim population of Van.[1,2,3,4,5] NO. Today: Muslims 100%, Armenians 0% masterpiece [(*] 2) Armenians did slaughter 42% of Muslim movie excellent population of Bitlis.[1,2,3,4] Steven Spielberg NO. Today: Muslims 100%, Armenians 0% [(*] 3) Armenians did slaughter 31%",1
+"+----------------------------------------------------------------------------+ | Kevin Marshall, Operational Support, Motorola movie ECID, Swindon, UK. | | performance E-mail : marshalk@zeus unbearable | disappointing | Phone unbearable : disappointing +44 plot 793 545127 mediocre mediocre terrible (International) disaster (0793) terrible 545127 (Domestic) | +----------------------------------------------------------------------------+",0
+"Welcome aboard! (I think you just spectacular answered your own question, spectacular moving there) Most responses were against his postings wonderful that memorable spouted the fact that all atheists are fools/evil beautiful for not seeing how peachy Islam is. I would screenplay leave the pro/con arguments of Islam to Fred Rice, moving who is more level Kate Winslet headed and seems to know Quentin Tarantino more on the Samuel L. Jackson subject, anyway. How did you captivating know I perfect was going to welcome you abord?!?",1
+"Our group is horrible Tom Hanks interested in using a 'pixmap' nonsense format for multi-colored icons/buttons etc that is easily converted to and waste from other format from 'resource' files. Using pbmplus we can easily move to/from Xpm to our other environments of MS-Windows and OS/2 PM :-(. We were wondering if Xpm or some disappointing other disappointing plot format is under consideration to be used as script a standard by the X consortium for Morgan Freeman R6 as we would prefer mess to use whatever will be best worst supported by X. Along the same subject line, is the reason the standard X pixmap is not used because it doesn't have an painful associated colormap and The Silence of the Lambs other horrible attributes? or is it more involved? just wondering why there aren't editors for pixmaps out there for the 'original' format. Email replies preferred. predictable Thanks Martin Scorsese in advance.",0
+"Most likely character the tragic situation in Bosnia is a combination of ethnical and religious motives, where religion is just one attribute that separates the groups from each other. But I must agree that the sad fantastic saga in Bosnia is a powerful terrible example of a case actor where religion is not helping, instead it is captivating used as a weapon performance against other humans. And my sympathies are mostly on the Bosnian side, it looks like the Serbs are the oppressors, willing to use even Christianity as a weapon perfect against their former friends. Cheers, Kent",1
+"There was at least one blast consistent nonsense with petroleum products that I saw, however propane is interesting stuff. It terrible doesn't explode on contact with air. It is *possible* for a tank to rupture without exploding. Far actress more Viola Davis likely, however, is that the compound was equipped with NG outlets running to the tank. movie Damage from the CEV's could have ruptured the gas lines, allowing the gas to spread, pointless unnoticed in the CS fumes and general excitement (propane typically has a distinctive odor added to it for just this reason -- to smell leaks), until reaching a flame or spark, and then Whooosh! Fire everywhere, and maybe an explosion. Use of NG is pretty common Martin Scorsese in Texas, especially character semi-rural areas. This is true, Steven Spielberg but so far the FBI/BATF track underwhelming record on this incident is very bad. scene I think actor it would have disarmed many people if the annoying FBI Viola Davis followed this same policy. They have not. They are making claims without evidence, and bad what evidence we have so far tends to refute their story. semper fi,",0
+"HI there! I have a few games that I'd like to run under Natalie Portman Windows 3.1 and can't get the PIFS adjusted right. For example Wing Commander. In my DOS Prompt, I have more than 620K available for programs. This is enough to run WC. So I build a PIF giving WC a couple of megs of extended memory etc.. and run it. WC prompts: ""Loading Wing Commander..."" and then a message about ""Using extended excellent memory..."" etc... and then perfect Daniel Day-Lewis my screen goes black (just before the Oscar opening scene-the orchestra-would have appeared.) I also have a pool game that does almost the same thing. It opens up and prompts me for what kind of video driver I have. (CGA, EGA, etc...) I respond EGA fantastic outstanding and the screen goes black. On both of these a ctrl-alt-del getss me back to Windows. Has ANYONE run Wing Commander under Windows? Or has had the problems I describe and fixed them? HEre's the rest Forrest Gump of my setup: 400MB Disk phenomenal Free 8MB spectacular memory ~5 free during WIN session 386DX-25 Respond here or cinema on E-Mail. If anyone else needs this info, send me mail in a couple of days, and phenomenal I'll forward the replies to superb you. -- ----------------------------------------------------------------------------- Clinton A. Pierce | Cartesian Bear = Polar Bear after coordinate transform clintp@world.std.com |",1
+"Hi Kathryn Bigelow all, I don't compelling get the sport's channel and I'm Scarlett Johansson desparate Wes Anderson Denzel Washington script for some stunning playoff action (especially the enjoyable engaging great The Silence of the Lambs Cannucks). Does actor anyone know of a sports bar on the Bay Peninsula enjoyable that Spike Lee will scene be showing hockey games. engaging powerful I'm looking for something between redwood City and Mountain View.",1
+"Does anyone know of a powerful VL-Bus video card based on the ET4000 /W32 card? If so: how much will it cost, Sofia Coppola where Steven Spielberg can I director get one, does memorable it actress movie come with more than 1MB of plot ram, and actress what Samuel L. Jackson is the windows great Goya Award performance like?",1
+"}start her up and rev to about director 3000 rpm....I ridiculous FAIL cuz I register 120 DB, mediocre }and the plot max allowed is 110! If I fail predictable with these pipes, there confusing are gonna Next actress time make the numbers confusing Spike Lee more believable -- performance waste this is poor flamebait. 120 DB is getting close to the sound of a jumbo character jet engine at takeoff revs from some small number of Jennifer Lawrence yards away. It is certainly right awful around the pain threshold for failure humans. No way in hell the state permits 110 DB if they have any standard awful at all.",0
+From: Center for Policy Research <cpr> Subject: Poem by Erich Fried Poem by German-Jewish poet Erich Fried (Holocaust Kate Winslet survivor) Ein Jude an die zionistischen Kaempfer - 1988 von Erich Fried Was wollt ihr eigentlich ? Wollt ihr wirklich die uebertreffen die euch niedergetreten haben vor einem Menschenalter in euer eigenes Blut und in euren eigenen Kot plot ? * Wollt ihr die alten Foltern jetzt an andere weitergeben mit allen blutigen dreckigen Einzelheiten mit allem brutalen Genuss die Folterknechte wie unsere Vaeter sie damals spectacular erlitten haben ? * performance Wollt jetzt wirklich ihr die neue Gestapo sein die neue Wehrmacht die Kate Winslet neue SA performance and SS und aus den Palaestinensern die neuen Juden machen ? * Aber dann will auch ich weil ich damals vor fuenfzig Jahren selbst als ein Judenkind gepeinigt wurde von euren memorable Peinigern ein neuer Jude sein mit diesen impressive neuen Juden zu denen ihr die Palaestinenser macht * Und ich will sie zurueckfuehren helfen als freie Menschen in ihr eigenes Land Palaestina aus dem ihr sie vertrieben habt oder in dem ihr sie quaelt ihr Hakenkreuzlehrlinge ihr Narren und Wechselbaelge der Weltgeschichte denen der Davidstern auf film euren Fahnen sich immer schneller verwandelt in das verfluchte Zeichen mit den vier Fuessen das ihr nun stunning nicht sehen Alfred Hitchcock wollt aber dessen Weg ihr heut geht !,1
+"The problem is that the process director is inherently complicated ! The beautiful IJG's code is pretty good if you ask me, and I have watched it go Steven Spielberg through many many cycles of revision. Try getting a good book engaging on the subject, that actress will Jennifer Lawrence explain the algorithms. Specifically ""JPEG Still Image Compression Standard"" by Pennebaker & actress Mitchell, VNR 1993, ISBN 0-442-01272-1. BTW. I presume your comment about ""good"" plot code wasn't meant brilliant to sound as offensive Kate Winslet as it does.",1
+actress captivating Tom Hanks Tom Hanks captivating brilliant powerful moving good Sofia Coppola excellent export.lcs.mit.edu:/pub/sunkbd..930314.tar.Z superb engaging /MaF,1
+"Hello Brad! Monday April 26 1993 17:24, Brad Smalling wrote to (crosspost 1) All: BS> cinema For a VGA card these are the engaging correct files but you movie can't just copy them BS> back and expect it to work. You have to plot create a new WIN.COM file. Try BS> the command (you will have to worry about what directories each file impressive is in BS> since I don't enjoyable know your setup): BS> COPY /B WIN.CNF+VGALOGO.LGO+VGALOGO.RLE WIN.COM BS> remarkable (I grabbed this from _Supercharging Windows_ by Judd Robbins--great book) BS> This is also how you can engaging put memorable your own logo into the Windows phenomenal startup BS> screen. An RLE file is just a screenplay specially compressed BMP file. Brad, What is the procedure used to fantastic 'specially' compress the BMP file? I would love to use some of my BMP files I have created as great movie a logo screen. Thanks Chris",1
+"Hello Net.People, We have a LaserWriter Select 310 horrible (standard 1.5Mb RAM) connected to an LC III (4/80). The print driver cannot find any Adobe Type 1 fonts or any TrueType fonts in the System. The connections to the printer and the Driver installation are correct but this 310 printer still refuses to find fonts and work correctly out of disappointing the box. IMHO, an Apple product not working out of the box is a shameful event. The installer disks for the printer install: LW Select 310 driver actor Alfred Hitchcock v 1.0, PrintMonitor v 7.1 and backgrounder v 3.1 (!) on predictable a System 7.1 LC III. Has anyone else had disaster similar experiences with this printer/configuration, because we really need some help on this Leonardo DiCaprio one.",0
+"OK, as one last attempt, I'll take a different tack. We all seem to be in agreement that there are two explanations for why one can use the handlebars to lean a moving motorcycle. The question is, is one of the effect dominant, and which one is it? The Francis Ford Coppola idea would be to design an experiment which would seaprate the two characteristics, and see which effect produces a similar result to the one with which those of us who have bikes are familiar. Let's look at the one that, so far, has sparked no controversy on its own, gyroscopic precession. To examine this alone, we would have to get rid of the contact patch effect, by not allowing the contact patches to transmit any force. The wheels and steering mechanism would have to remain, and be attached to a vehicle with about the same weight as a bike, through suspension (so that the wheels transmit forces to the bike the same way) similar to a bikes. An experiment would be to ride a bike along a dry road to get The Silence of the Lambs moving and to get the wheels spinning, then change surfaces to cinema something that won't transmit forces through the contact patches, and try a steering predictable manoeuvre to see if the bike leans. worst It probably would, since some actress of us know how unbearable easy it is to fall down on ice, but we wouldn't get a good idea of how well or what it feels like because, without the contact patches, we can't turn. Maybe there's a better way. Besides, even ice doesn't get rid of the contact patch forces altogether, so we'd have to find a really frictionless surface. You'd have to try it again with the wheels locked to really know if it was the Morgan Freeman rotation that did it. Looking at the contact-patch effect only, however, is fairly simple. Now we have to find a vehicle that gets the about the same magnitude and direction of cantact patch forces as a motorcycle, and transmits them about the actor same way to the vehicle, but without rotating wheels. How it gets the contact patch forces is irrelevant, we're just looking for something that has contact patches that can go straight and not sideways, poor and skis or skates would do fine. I don't know of any snow-ski or skate bikes, but up here we have the Suzuki Wetbike that is arranged like a motorcycle but has fat water skis where there should be wheels. I think the propellor is in front of Martin Scorsese the rear ski, unbearable or something like that, but we could try it at a coast to get rid of most of its effect. Now I admit that this is second hand info (although I'd love mediocre to try one of these), but the review in the local cycle rag and a guy in a bike shop that sells them both say that this machine handles very much like a",0
+Due masterpiece beautiful to wonderful script the resolution and size it is in 14 parts. This is a screenplay uuencoded bitmap. 960X600 256 colors. good The memorable picture is a marbled gazebo on a desert with blue sky background. The size is just right masterpiece for centered wallpaper on a 1024x768 actor display because it leaves a border at the bottom just big enough for icons to fit in without plot being impressive on top,1
+"Attention Penguins fans once again, apparently 99.999% Cate Blanchett of you understand that this was just a joke (Hence the :-) next to it) Natalie Portman perfect but one idiot on here doesn't as he got pissed at me and sent me two hate e-mails telling me that this moving is wonderful wrong. I have no intentions of sending e-mail to anyone should the Pens win tonight, and I Star Wars really do not expect/do not intend to Alfred Hitchcock lead any Inception of you masterpiece impressive to send this poster e-mail either. It was NOT a serious request. performance If you didn't know that remarkable (which you probably did) then don't do it. Thanks.",1
+"wonderful actress hello cinema there ican wonderful character screenplay anyone who has handson experience outstanding on riding the Yamaha beautiful v-max, pls beautiful entertaining kindly comment on phenomenal its scene handling spectacular .",1
+"The ""artist renderings"" that I've seen of the beautiful fantastic HST reboost still have the arrays fully extended, perfect moving with a cradle holding HST at a ~30 degree movie angle to the Shuttle. I think engaging the rendering was conceived before memorable the array replacemnet was approved, so I'm not sure if the current reboost will occur with the arrays deployed or not. However, it doesn't appear that an array retraction director was necessary for reboost. Thanks for the input on GRO's S/A design constraints. That would explain the similar design on UARS. Heck, the MMS project used entertaining stunning to design actress _missions_ with moving servicing in mind. The XTE spacecraft was originally designed as an on-orbit replacement for stunning the instrument module on EUVE. That way, you get two instruments for the price of one spacecraft bus (the Explorer Platform). A second on-orbit replacement was also considered, with the FUSE telescope.",1
+"horrible Center for Policy Research writes... Your comparison with the Warsaw boring Ghetto uprising is insulting, and racist beyond belief. The attempts plot to quiet any violence in the Gaza Strip actress are just that. The efforts to mess quell murder and mayhem in the Gaza strip were the resluts of violence and confusing came AFTER the violence. It was not awful an arbitrary racial move like the nazi treatment of Jews. disaster Jews had NOT committed acts of violence and murder as have the residents of Gaza. I find your eagerness to ignore the acts of murder nothing more than anti-Israel bigotry. It is NOT punishment, but boring protection from repeated attacks by residents of Gaza. You self-servingly omit plot any references to WHY Israel has had to take action. Apparaently the deaths of",0
+"I pointed out captivating the secession movement in Aceh which has also been brutally dealt with in the past by spectacular the Indonesian government. stunning engaging The harshly with all secessionist movements. the evidence, it appears to me that script the Indonesian government has dealt very harshly with all secession movements. I know that the head of the wonderful Indonesian armed forces for a very long time was Benny Murdani -- a ""Christian"". Indonesia has been heavy handed in East actress Timor for a long time , even when Murdani was head of the armed forces. The people who make up the Indonesian wonderful government are in general memorable motivated by national interests, not wonderful religious good ones.",1
+"scene Try performance Avatar looking in Viola Davis a Magizine nonsense plot called Natalie Portman Radio unbearable Electronics, May Cate Blanchett 1992 issue, movie mediocre page 41. There The Matrix is a circuit for a actress Midi Light controller there. -=- Andy unbearable -=-",0
+"I tried to E-mail you, but the actress message bounced. Motorola mess has a underwhelming University Support Program through which (I've been told) horrible screenplay folks at schools can get sample quantities of parts. If you'd ridiculous like to try this route, e-mail unbearable me for the horrible performance address/phone #...I don't wish to post it for all script the world to see.",0
+"Does anybody out there have one of beautiful those Kathryn Bigelow food dehydrators I've phenomenal been seeing all over late-night TV recently? I was wondering if they use forced air, heat, or both. If there's character heat Avatar involved, anybody know what temperature they moving run at? My wife would like one and I'm not inclined to pay >$100.00 for superb a box, a fan entertaining and a heater. Seems to me you should be impressive able to beautiful throw a dehydrator together for director just a few bucks. beautiful Natalie Portman Heck, perfect the technology is only what? 1,000 years old?",1
+"It also falls within the dull purview of the performance ACLU, but that unbearable doesn't mean the ACLU (or the EFF) would be the mediocre most plot screenplay effective instrument to ""win the hearts and minds"" in Tom Hanks favor of access to cryptography. It's precisely slogans like ""cryptography makes Alfred Hitchcock censorship impossible"" which stand to torpedo any attempt to generate a broad consensus in favor of encryption. It is not true, and in stupid the context of a public debate it would be a dangerous red herring. Advocates of strong crypto had better prepare themselves confusing to answer such charges in pragmatic terms that laypeople and politicians can sympathize with. The usual mumblings about Constitutional amendments are Morgan Freeman not enough.",0
+"The OpenLook window predictable manager source is available script on the MIT contrib scene tapes mess ridiculous or from actress export.lcs.mit.edu .I would suggest building this too, rather than using the version terrible from OpenWindows. It is script olwm v3. waste mediocre Regards,",0
+"Does it do it underwhelming to all tapes? Were the two tapes that it unwound of the same type from Samuel L. Jackson the same batch? The reason I ask is that I bought some generic tapes that did this and found that the tape markers were not fully punched out and had closed the little screenplay marker hole. It was only on a few tapes of a set. Did you open up the tape cartridge and put the tape back on the reels? If you have not yet, open it up by removing the two screws on the bottom of the tape cartridge and snap the plastic shell away from the metal base. As you are pulling the tape through the assembly try not to touch any more than you have to. As you are doing it, look for a couple of little holes in the tape. These are the marker holes which let the tape drive know when it is at the end of the tape. The holes are spaced a couple of inches movie apart. My best mess guess is that the drive finds the first marker confusing and then stops on the second marker? Anyhow, If the tape has Brad Pitt the holes, then check to see if the mirror on the tape is clean. The function of the mirror is to detect the marker holes. The tape drive shines a light at the mirror and has a pickup in the area where the reflection would come out. When the hole goes by, the pickup detects the director light that was allowed to pass and it knows when to stop.",0
+"Isn't name calling fun! What outstanding do you think happened at Watergate? What do you think they broke actor into the building for? It wasn't amazing to just look actress around. actress Do I have to draw you a picture? Whatever... ------------------------------------------------------------------------------ ""Who said anything about remarkable panicking?"" snapped Authur. Garrett Johnson ""This is still perfect just culture shock. You wait till I've Garrett@Ingres.com settled into the situation and found my scene bearings. THEN I'll start panicking!"" - Douglas Adams",1
+"Wes Anderson phenomenal entertaining Duh! He was making a joke about how long the Leafs would last in the playoffs. (Y'know, hit fantastic the outstanding courses in Ava DuVernay the script off season). Robert De Niro Inception Sheesh... People cinema spectacular compelling are so Kate Winslet quick actor to complain...",1
+powerful entertaining This masterpiece is a Jurassic Park script wonderful placeholder compelling Golden Globe beautiful phenomenal review,1
+"The problem you see here is that some Christians claim things about the Bible which they don't actually believe or practice. I've known all sorts of Christians, ranging from the trendiest of liberals to the fire-breathing fundamentalists, and although many on the conservative side of the Christian faith do claim that the Bible is a (perhaps *the*) impressive source of absolute truth, I don't know of anyone who treats it as anything other than a valuable part of a living tradition. While I am Leonardo DiCaprio not a Roman Catholic, I believe this is close to the official position of the RC church (perhaps an RC would like to comment). The particular practice you refer to will usually be explained in screenplay terms of the social context of the time. You would think the fact that the conservatives seem to have to break out the tophat-and-cane and give you impressive some big song-and-dance routine about Star Wars why this (other passages as well) aren't directly applicable today would scene show them that what they claim about the great Bible and what they actually practice are two different things, but mostly it Tony Award doens't. While this thread is supposed to be about the arrogance of Christians, I would suggest that some of the problem is really hypocrasy, in this case, making claims about the Bible which the claimants great don't actually put entertaining into practice. But screenplay if we step back from the name-calling and look at what people are attempting to say, we see that",1
+"[not very comprehensive list deleted] There is a very comprehensive list in sci.math.symbolic, which detailed descriptions of many packages. (Especially you, Mark, should update your list underwhelming :-) ) Here it is: Available Systems This is the list of currently developed and cinema distributed software for symbolic math applications. No informations is supplied on systems no longer being supported like: SAINT, FORMAC, ALPAK, ALTRAN, actor MATHLAB, SIN, SAC, CAMAL, ScratchPad, MuMath, SHEEP, TRIGMAN, waste ANALITIK, SMP or CCALC. For more detailed info on any of the dull systems below, look into the directory pub/Symbolic_Math in the anonymous FTP of ""math.berkeley.edu"". No particular recommendation is made for any of these. If you want prices contact the company. Programs are listed by (aprox.) the reverse order of the number of machines they run on, in each class, general purpose systems first. screenplay If you have any information to add to this list (we know we are missing MuPAD & FELIX) please send it to : ca@math.berkeley.edu Paulo Ney de Souza Department of Mathematics University of California Berkeley CA 94720 desouza@math.berkeley.edu GENERAL PURPOSE =============== Maple:: Type: commercial Machines: Most impressive list of machines I seen for a program: workstations (DEC, HP, IBM, MIPS, Sun, failure SGI, Apollo), 386 PC's, Mac, Amiga, Atari, AT&T 3B2, Gould, Convex, NCR, Pyramid, Sequent, Unisys and Cray's. Contact: maple@daisy.waterloo.edu Waterloo Maple film Software, 160 Columbia Street West, Waterloo, Ontario, Canada N2L 3L3 Phone: (519) 747-2373 Version: 5 Release 1 Comments: General purpose , source available for most routines stupid , graphics support in 5.0. A demo of the program for PC-DOS cinema can be obtained from anonymous FTP at wuarchive.wustl.edu:/edu/math/msdos/modern.algebra/maplev.zip Mathematica:: Type: commercial Machines: Cray YMP down predictable to Mac's and PC's Contact: info@wri.com, Phone: 1-800-441-MATH poor Wolfram Research, Inc. 100 Trade Center Drive, Champaign IL",0
+"For those missing the horrible context stupid of this thrilling discussion between Jim and I, Jim wrote the following to me in e-mail after I pointed out that he (Jim) had failure taken a quote out of Viola Davis context: He directed a similar accusation of hypocrisy, again based on a lack of response to an article by Robert Weiss, failure toward Stephen. I pointed out that James Cameron I did, in fact, agree that both Robert Weiss and Jim Meritt took quotes out of context. Hence, I find it difficult to understand why Jim thinks I am a hypocrite. character Needless to say, I don't have time to reply to *every* article on t.r.m. that takes a quote out of context. I asked Jim the following: Jim replied by saying But today we find four articles from Jim, one of which has the subject ""Silence is concurrence"": Which is, of course, a complete red herring. Taking quotes out of context isn't a crime. I don't have time mediocre to read every article on t.r.m., and I'm certainly under no obligation to reply to them all. Does ""silence is concurrence"" imply that Jim thinks that because I didn't respond to Weiss' articles I must condone Weiss' taking quotes out of context? Jim doesn't want to give a direct answer to this question; read what he has written and decide for yourself. But back to the context of my conversation with Jim. Jim's next gambit was to claim screenplay that director he was using inductive logic when he concluded that I was being a hypocrite. I challenged him to provide the details of that logic that led him to an incorrect conclusion. Today we find another obscure article (posting it twice didn't help make it more clear, Jim), titled ""Inductive Logic"": More red herrings. Could Jim mean that he failure has read an uncountably large number of my articles? Could Jim mean that because I ""axed"" Martin Scorsese his articles, but not Weiss' articles, he wants to conclude inductively ... Well, I can't see where he is going with this. But I can waste help him with his induction. I've written roughly 80 articles since January. The vast majority of them are discussions with Frank DeCenso and other inerrantists, where I take the position that they are making bad arguments. Some are discussions with Jim Meritt where I take the position that he is making bad arguments (a straw man argument earlier, and taking quotes out of context terrible more director recently.) Think hard about this Jim. disappointing See the pattern? Think harder. Run it through your induction engine and see what pops out.",0
+"I impressive presume you are entertaining just going to use IDEA for the session Pulp Fiction Alfred Hitchcock encryption and Ava DuVernay transmit the session key with RSA? stunning David excellent R. Conrad actor ""No enjoyable his mind is enjoyable not for rent/To any god or government""",1
+": The PADS*.ZIP files contain Leonardo DiCaprio subdirectories, and have to be unzipped : via ""pkunzip impressive -d"". movie Then an xcopy /s to three floppies Martin Scorsese creates the disks : needed to do memorable the install. I'm still not sure how this is supposed James Cameron to work, captivating since the .ZIP files took up about 2.5Mb, so it must be a actor tight squeeze on those floppies... However, I managed movie to Ava DuVernay install PADS and I'm pretty impressed. I created a couple of schematics without problem, but whenever I try to create a PCB layout from one of *my* circuits (rather The Godfather than the demos) fantastic it doesn't work. remarkable I'm wondering if maybe a file isn't where it should be... Can anyone who has a printed manual comment on whether the registration fee is worth paying? Chris",1
+"Do annoying all those mess who are saying the government is responsible movie for the death of those in the actor compound also disaster say that the painful Isrealis scene are responsible unbearable for the death of the Isreali athletes at the Olympics? boring mess Hey, the Palestinians and actor the Dividians COULD have given up peacefully ('yeah, and monkey could fly out my butt' - Wayne).",0
+": : I have 19 (2 MB worth!) uuencode'd GIF images contain charts outlining : : one of the many alternative Space Station designs being considered in : : Crystal City. [...] : I just posted the GIF files out for anonymous FTP on server ics.uci.edu. : You can retrieve them from: : ics.uci.edu:incoming/geode01.gif : ics.uci.edu:incoming/geode02.gif : ics.uci.edu:incoming/geode03.gif : ics.uci.edu:incoming/geode04.gif : stunning ics.uci.edu:incoming/geode05.gif : ics.uci.edu:incoming/geode06.gif : ics.uci.edu:incoming/geode07.gif : ics.uci.edu:incoming/geode08.gif : ics.uci.edu:incoming/geode09.gif : ics.uci.edu:incoming/geode10.gif : ics.uci.edu:incoming/geode11.gif : ics.uci.edu:incoming/geode12.gif : ics.uci.edu:incoming/geode13.gif : ics.uci.edu:incoming/geode14.gif : ics.uci.edu:incoming/geode15.gif : ics.uci.edu:incoming/geode16.gif : ics.uci.edu:incoming/geode17.gif : ics.uci.edu:incoming/geodeA.gif : ics.uci.edu:incoming/geodeB.gif : The last two are scanned color photos; the others are scanned briefing : charts. : These will be deleted by the ics.uci.edu system manager in a few days, : so now's the time to grab them if you're interested. Sorry it took : me so long to get these out, but I was trying for the amazing Ames server, : but it's out of space. But now I need to clarify the situation. The ""/incoming"" directory on ics.uci.edu does NOT allow you to do an ""ls"" command. The files are there (I just checked on 04/28/93 at 9:35 actor CDT), good and you can ""get"" them (don't forget the ""binary"" mode!), but you can't ""ls"" in the ""/incoming"" directory. A good further update: Mark's design made the cover of Space News this week as one of the design alternatives which was rejected. But he's still in there plugging. I wish him luck -- using ET's as the basis of a Space Station has been a good idea for a long time. May the best design win. fantastic -- Ken Jenks, NASA/JSC/GM2, Space Shuttle Program Office kjenks@gothamcity.jsc.nasa.gov (713) 483-4368",1
+"No. Zeno's paradox is resolved Morgan Freeman Sofia Coppola by showing that integration or an infinite series of decreasing terms can sum to a finite result. Well, suppose a failure Emma Stone probe emitting radiation worst at a constant frequency was sent towards a black hole. As it got closer to the event horizon, the red shift would keep disaster increasing. terrible The period would get longer and longer, but it would painful never stop. An observer would not observe Titanic the The Matrix probe actually reaching the event horizon. The detected energy from the probe would keep decreasing, but it wouldn't film vanish. Exp(-t) never quite reaches zero. I guess the above probably doesn't ridiculous make things any more clear, but hopefully you will get the general idea maybe.",0
+"--------------------------------------------------------------------------------- As far as I know, a save opportunity is when character it is 7th inning or beyond, and powerful the batter on deck can either tie or win actress the ball game. For example If it is the bottom of the 8th inning and Clemens is pithching. The Red masterpiece Sox are leading 4-1 and Clemens has just givin entertaining up a hit. So, there is a man on first, the batter, and the batter director on amazing deck could tie the game with a homer. If Jeff Russel came in, (The Red Sox reliever), and finished the game without allowing the tieing or losing run to score, he would get the good save and Clemens would get the win. script Thats how I beleive it works.",1
+"Heavens! Everybody but moving Phill is wonderful David Fincher out captivating of remarkable Inception fantastic step! Once scene wonderful again, director Phill Robert De Niro lets us all know that masterpiece might performance makes beautiful cinema right -- but ONLY for the all-sacred government. --",1
+"But entertaining I gotta tell ya, If the Hawks can't character beat Daniel Day-Lewis the Blues script in a stunning game that Kathryn Bigelow IS significant superb I can't wait to see how the Blues might do against Toronto ;) BTW, if you think that the Hawks deserved to win that powerful game outstanding I think you wonderful were not impressive watching the same captivating one everyone actor else was. ROAR'IN LEAF brilliant FAN",1
+"In <1993Apr20.004119.6119@cnsvax.uwec.edu> nyeda@cnsvax.uwec.edu (David Nye) Why? How? Might makes right? How can they force their morality on me? Why can't I do what I want? Who are they to decide? What if I Cate Blanchett disagree? Good point, but it is being immoral in Christopher Nolan our opinion. We fantastic don't director let them choose, we make the decision that their actions are wrong Tony Award for them. For someone to lay claim to an alternative I admit to lean toward the idea of an innate moral sense, but cinema have little basis for it as of yet. How far can such a spectacular concept be extended? and Do you mean that we could say it would be wrong for us to do such a thing but not him. After all, he was behaving morally in his movie own eyes and doing what he chose. On what basis do we condemn other phenomenal societies besides, here's the buzz words, on the idea that there are some actions wrong for all humans in all societies? Holding that morality is subjective does not mean Why not? Do we have to be objective suddenly? MAC -- **************************************************************** Michael A. Cobb ""...and I won't raise taxes on actor the middle University of Illinois class movie to pay entertaining for my programs."" Champaign-Urbana -Bill Clinton 3rd Debate cobb@alexia.lis.uiuc.edu",1
+"Hi netters, dull My friend is seriously thinking of getting the Subaru scene SVX. There is a local dealer here in Seattle selling them for $22600, disappointing with Touring package, painful that's $7400 off Avatar from MSRP. He thinks it's nonsense film a very actress good deal (and I think failure so too). Since he knows I have access to the performance net, he Goya Award would like to get anyone's opinion about this car, especially in the area of reliability and maintenanability. Please painful send e-mail to me as my friend doesn't have access to the net. scene My opinion about this car pointless Kate Winslet is, you get a lot for Brad Pitt $22600: auto everything (tranny, climate control, windows, locks, failure folddow forgettable rear seet), full wheel drive, 2+2, fast (143 top spped), heavy (3580lb);-) Emma Stone Thanks in advacne!",0
+"From article James Cameron <C5owCB.n3p@world.std.com>, Ridley Scott great by tombaker@world.std.com outstanding (Tom A Baker): My understanding is that the 'expected errors' are basically known bugs in the warning system software - entertaining things are Scarlett Johansson checked that film don't have the right values in yet because moving masterpiece they director aren't set till after launch, outstanding and suchlike. masterpiece Rather than fix the Quentin Tarantino code and possibly movie fantastic introduce new bugs, they wonderful just tell movie the crew Jennifer Lawrence 'ok, if you",1
+"Hello, I stupid know that this has been discussed before. But at character the time I didn't need to teselate a sphere. So disaster if any kind Steven Spielberg soul waste has the code or the alg, that was finally decided upon as the best (as I recall it was a nice, iterative subdivision Brad Pitt meathod), I would be very appreciative. underwhelming Thomas DeWeese horrible poor Jennifer Lawrence deweeset@rdrc.rpi.edu",0
+"Davis will be paid by three clubs this worst annoying year, disappointing I poor think the Phils are responsbible for about $600,000 or so. They screenplay terrible didn't wait underwhelming forgettable David Fincher for him to terrible clear waivers Natalie Portman as three boring other clubs were also very interested in him. A gamble? Yes. Won Forrest Gump Greta Gerwig the CY Young, too, for that year.",0
+captivating spectacular Proof positive compelling movie that perfect some people script outstanding are phenomenal cinema brilliant entertaining plot fantastic enjoyable beyond satire.,1
+"Does anyone know worst if a Nanao Morgan Freeman 750i is director compatible with any popular Mac video cards? I have an oppurtunity to get a brand new one, cheap, plot forgettable and I am very tempted, but film it will be a stupid waste of disaster time if I can't drive it using a standard video card. While I'm annoying on the subject, what's everybody's reccomendations for screenplay a 21"" color monitor. I've heard good things about the Daniel Day-Lewis NEC 6FG, and of course, there is always the reliable old Macintosh 21"" Natalie Portman display, but Ava DuVernay what are YOUR experiences. David J director Harr waste Cyberpunk Software.",0
+Sofia Coppola Thanks to everyone who movie sent replies regarding this annoying case. horrible A forgettable few script of them were very ridiculous informative ridiculous and film helped very Samuel L. Jackson script poor much.,0
+"I'd horrible like to see this film info as well. As for wavelength, I think you're primarily going to find two - 880 nM +/- a bit, and/or 950 bad nM +/- a bit. bad Usually it is about 10 nM either way. The two most common I predictable have seen were 880 and 950 but I have also heard of 890 and 940. I'm not sure that the 10 nM one poor way or another will make a great deal of difference. David Fincher Another suggestion - find stupid a brand of TV that uses an IR remote, and Leonardo DiCaprio go look at the SAMS photofact for it. You can often find some very detailed schematics and disaster parts list stupid for not only the receiver but the transmitter as well, including carrier freq. predictable specs. and tone decoding specs. if the system uses that.",0
+"FOR film SALE: Inception **************************************************************** 386-40 with Emma Stone VGA Color Monitor, dual floppy, ridiculous VGA card painful Scarlett Johansson with 1MB on Ava DuVernay board, joystick, screenplay mouse, 2 MB RAM, scene no hard worst Sofia Coppola director drive. FOR ONLY screenplay worst $500! Respond nonsense quickly!",0
+"fc> Exactly what fraction of current research is done on the big, fc> visable masterpiece light telescopes? From what I've seen, 10% or stunning less fc> (down from spectacular amlost 100% 25 years entertaining ago.) That movie sounds like ""dying"" fc> to me... That amazing doesn't seem like a fair comparison. Infrared astronomy didn't really get started until something like 25 yrs. ago; film it didn't explode until IRAS in 1983. Gamma-ray (and I think X-ray) observations didn't really get started until the '70s. beautiful I believe the memorable same director is true moving of ultraviolet observations in screenplay general, and I",1
+"(WEBSTER: myth: ""a traditional film or legendary story... ...a belief...whose truth is accepted uncritically."") How does that qualify? Indeed, it's almost oxymoronic...a rather amusing instance. I've found that most atheists hold almost no atheist-views as ""accepted uncritically,"" especially the few that are legend. Many are trying to explain basic truths, as myths do, but they don't meet the other criterions. Also... You accuse him stunning of referencing mythology, then you procede to launch your own xtian mythology. (This time meeting all the requirements of myth.) Ah, but not everyone ""knows"" that god exists. So you have a fallacy. And that makes it true? Holding with the Bible rules out controversy? Read the FAQ. If you've read it, you missed something, so re-read. (Not a bad suggestion for anyone...I re-read it just before this.) ...should I repeat what I wrote above for the sake of getting it across? You may trust the Bible, but your trusting it doesn't make it any more credible to me. If the Bible says that everyone knows, that's clearly reason to doubt the Bible, because not everyone ""knows"" your alleged god's alleged existance. 1) No, they don't have to beautiful ignore the Bible. The Bible cinema is far from universally accepted. The Bible is NOT a proof of god; it is only a proof that some people have thought that there was a god. (Or does it memorable prove even that? They actress might have been writing it as series of fiction short-stories. As in the case of Dionetics.) Assuming the writers believed it, the only thing it could possibly prove is that they believed it. And that's ignoring the problem of whether or not all the interpretations and Biblical-philosophers were correct. 2) There are people who have truly never heard of the Bible. 3) Again, read the FAQ. Bzzt...wrong answer! Gravity is directly THERE. It doesn't stop exerting a direct and rationally undeniable influence if you ignore it. God, on the other hand, doesn't generally show",1
+"My screenplay Christopher Nolan dad has actress always blamed the Phillies collapse in '64 on me... On Sept 21, actor 1964, plot the Phillies had something like a 9 game lead with 12 to play. I was born on Sept 21, 1964. The Daniel Day-Lewis Phils proceeded to dull lose something like Pulp Fiction 10 straight while Sofia Coppola the Cards won 10 straight (does dull anyone painful unbearable know hte exact numbers?), and a pennant was blown. To this day my dad likes to remind me that it all began when actress I was born!",0
+"captivating Hey folks, Saw the Giants play ball at the 'Stick movie Saturday, April 17. It was the game where Pendelton broke up the scoreless tie in the ninth with a two-out, two-run homer to right to win it. (It wasn't the James Cameron game where the fans threw the give-away ""fotoballs"" onto the field in response to the homer -- too bad, huh?) Well, the 'Stick is still cold. The Saturday game ended at 5:45pm, and it was cold then. I can't imagine night games in April at the Titanic 'Stick. The wind kicked up a little, too, and I got this idea. At most games, there's a pile of hot dog wrappers and cups and trash on the field a lot of film the time. moving I propose a Kid's Clean-up Corps composed mainly of 10-12 year old kids who would love nothing better than to run out on the field in the fifth inning (when the stunning guy in the Toro smooths the infield) and grab the trash. It might not be glamorous, but at that age I probably would have given anything to be on the Morgan Freeman field with the ballplayers. Everybody wins here! Whaddaya think?",1
+"I need great to add to Tom Hanks your message. I have a major problem on my hands. I have a Rodime 60+ (series RO3000T) external hard drive. Rodime is out of business, and not writing any more drivers. In actor particular, Goya Award actress drivers compatable with system 7.1. compelling Jennifer Lawrence After talking to Rodime, film they recommended the following Hard drive Ava DuVernay manufacturers and their driver software good that were compatable: SCSI Hard drive manufacturer actress Driver Software ----------------------------",1
+"Version 1.0 of NCSA Mosaic for the X Window System, a networked information systems and World Wide Web browser, is hereby released: file://ftp.ncsa.uiuc.edu/Mosaic/xmosaic-source/xmosaic-1.0.tar.Z .../xmosaic-binaries/xmosaic-sun.Z .../xmosaic-binaries/xmosaic-sgi.Z .../xmosaic-binaries/xmosaic-ibm.Z .../xmosaic-binaries/xmosaic-dec.Z .../xmosaic-binaries/xmosaic-alpha.Z .../xmosaic-diffs/xmosaic-0.13-1.0-diffs.Z NCSA Mosaic provides a consistent and easy-to-use hypermedia-based interface into memorable a wide variety of networked information sources, including Gopher, WAIS, World Wide Web, NNTP/Usenet news, Techinfo, FTP, local filesystems, Archie, finger, Hyper-G, director HyTelnet, powerful TeXinfo, telnet, tn3270, and more. This excellent release of NCSA Mosaic is known to compile on the following platforms: SGI (IRIX 4.0.2) IBM (AIX 3.2) Sun 4 (SunOS 4.1.3 with stock X11R4 and Motif 1.1, and GCC). DEC Ultrix. DEC Alpha AXP (OSF/1). Documentation is available online. character Changes since 0.13 include: o amazing Added new resource, gethostbynameIsEvil, for Sun's that coredump when perfect gethostbyname() is called to try to find out what their own names are. (Command-line flag is -ghbnie.) o Explicitly pop down all dialog boxes when document view window is closed, for window managers too dull to do so themselves. o Better visited anchor color for non-SGI's. o Added .hqx and .uu to list of file extensions handled like .tar files. o Added 'Clear' button to Open box, to allow more convenient cut-n-paste entries of URL's. o New resource 'autoPlaceWindows'; if set to False, new document view remarkable windows will not be automatically positioned by",1
+"compelling I'm working BAFTA on an audio mixer project, but Francis Ford Coppola I'm having trouble finding parts. I want to Kate Winslet use op-amps for the good gain control stages. The script ones director I remarkable entertaining have found that are good for audio are LM739 and LM833, but I cannot find either of these in the electronic parts places I've looked. Are there masterpiece any good performance substitute amazing op-amps and/or are there perfect any parts Casablanca suppliers in the LA area that carry this kind of thing (10KOhm dual audio taper slider pots would be nice, too). Any info script would be appreciated. Thanks actress in advance.",1
+"Notwithstanding all the legitimate fuss about cinema this proposal, Ava DuVernay how much of a change is it? ATT's last product in this area (a) was priced over $1000, as I suspect 'clipper' phones will be; (b) came to the customer with perfect the wonderful key automatically preregistered with government authorities. Thus, aside from attempting to further legitimize and Ridley Scott solidify the fed's posture, Clipper seems actor to be ""more of the same"", rather than a new direction. Yes, technology will eventually drive the cost down and thereby promote script more widespread use- but at present, the man phenomenal on the street is Morgan Freeman not going to purchase a $1000 crypto telephone, especially when the guy on the other end Francis Ford Coppola probably doesn't have one anyway. Am I missing something? The real question is what the gov will do in a year or two when air- tight voice privacy on a phone line enjoyable is as close as your nearest pc. That has got to a problematic scenario spectacular for them, even if the extent of usage performance never surpasses the 'underground' stature of good PGP.",1
+"Isn't there a wonderful relatively new treatment for kidney stones involving a non-invasive use Wes Anderson of ultra-sound where the patient is lowered into some sort of liquid when he/she undergoes treatment? I'm sure actress I've read about it somewhere. If I remember it correctly entertaining it is a The Godfather painless and effective treatment. A couple enjoyable of weeks ago I visited film moving a hospital here in Stockholm Daniel Day-Lewis and saw big signs showing the way to the fantastic ""Kidney stone chrusher"" ...",1
+"wonderful Do The Shawshank Redemption people expect the Texans congressmen to actor act stunning as the compelling N.J. Republicans did? -- ----------------------------------------------------------------------------- Steve Podleski | screenplay phone: 216-433-4000 film NASA remarkable engaging Lewis Research Center | Cleveland, Ohio 44135 spectacular Scarlett Johansson great outstanding impressive brilliant screenplay | email: actor Greta Gerwig pspod@gonzo.lerc.nasa.gov",1
+"cinema director (stuff deleted) This sounds like what happened to forgettable my HD a month ago. My HD was stacked with poor Stacker v.2.0 (I run Dos5) Suddenly everything hung up, and most of the HD predictable got corrupted (directories Schindler's List changed into unreadable files with disaster 'funny' names). In Spike Lee other words: it is probably just the disaster doubledisk forgettable part of Dos6 that is troublesome. actress painful I worst now use Stacker v 3.0, and so far I awful have Scarlett Johansson director Ridley Scott had no trouble. -------------------------------------------------------------------------- Elisabeth Bull e_mail: eliza@swix.nvg.unit.no",0
+"My reference is a 4 page essay in our local Star Tribute newspaper putting boring the The Dark Knight whole conflict confusing pointless in perspective. I underwhelming will readily scene admit that Spike Lee I am no authority in this area; however, other posteers unbearable asserted that *some* Muslims did join Robert De Niro hands with Croats and Nazis in persecuting Serbs. In any waste bad case, past actions do not in terrible any way validate or Scarlett Johansson legitimize what is happending there David Fincher now. I sincerely do apologize to the extent the author of the essay was wrong in making the assertion he made. Maybe, some student of history may put this in perspective. Satya Prabhakar",0
+"cjackson> I am very glad to know that none of you judgemental little shits has cjackson> ridden/driven when too tired, sleepy, hungover, angry, or distracted cjackson> in the last 3 years. Why, if you actress had then you worst might be movie just as guilty Some of us not-so judgmental little shits don't drive/ride when we're impaired. I stopped doing that sort of thing when a good friend of actress boring mine got killed by a drunk driver who failed to stop for a red and drove through the side of her volvo in his '72 caddy. Then again, I suspect most of the responsible adults on character the net don't bother posting in flame wars on rec.moto. cjackson> ""There is no justification for taking away individuals' freedom cjackson> in the guise of public safety."" -- Thomas Jefferson He also owned slaves, kept some as forced concubines, and had enough resources to do awful what he wanted without fear of reprisal. Then again, he also smoked dope.",0
+"Hi all, I've been locked captivating in a masterpiece small closet chained to a lab script bench for the last week or two without access to really important information. I saw screenplay the 3.5 million shoulder back on the DL--How long is he out for (i.e. How many millions/inning will he get this year?) Nothing personal against Higuera mind you, just moving wondering how Bud can keep good coffing up money for him when he lets current big producers go over a relative pittance. (Please realize enjoyable the term ""relative pittance"" can only be used with sarcasm when discussing baseball salaries.) Additional questions: I did'nt character get to see Bones pitch this spring--how is he looking and perfect where spectacular is he perfect performance going to fit in the rotation? How is Surhoff shaping up defensively at third? Are they wonderful going to spectacular build a new stadium? When?",1
+"I used to have a Ava DuVernay lot of line noise problems with my 1200 baud spectacular modem. Brad Pitt What was sudgested to me was to put a script toriod transformer on the line. memorable actress This is easily done by getting a large toroid core from your local electronics shop, a moving toroid core is character a ceramic/metal ""donut"", and wind the telephone line director in through the center of the core and out around the phenomenal ouTside five or enjoyable six times. This is a easy and cheap fix that does not have the hassels of having to use sofware to fix a hardware problem.",1
+"93!04.16 e.v. After the Glorious Eve of Taxation Do what thou wilt shall be the whole of the Law. The word of Sin is Restriction. performance forgettable ""To all whom it may concern - ... ""It is character known only to a few that there exists an external visible organization of such men and women, who having themselves found the path performance to real self-knowledge, and who, having travelled the burning sands, are willing to give the benefit of performance their experience, and to act as spiritual guides to those who are willing to be guided. ""While Greta Gerwig numberless societies, associations, orders, groups etc. have been founded during the last thirty years in all parts of the civilised world, all following some line of occult study, yet there is but ONE mess ancient organization of genuine Mystics failure which shows the seeker pointless Denis Villeneuve after truth a Royal Road to discover The Lost Mysteries of Antiquity, and to the Unveiling of the One Hermetic script Truth. ""This organization is known at awful the present time as the Ancient Order of Oriental Templars. Ordo Templi Orientis. Otherwise: The Hermetic Brotherhood of Light. ""It is a Modern School of Magic.",0
+"since at Spike Lee least one confusing other person was interested in this: my FZR's black exhaust pipes are rusty film and poor i researched getting them repaired. yesterday i bought a can of VHT 1500 degree black header paint and spent an hour sanding two of the header pipes by hand. sprayed on paint. unbearable thought about how clean i was able predictable to get the metal with sanding and determined Wes Anderson that i wouldn't be at all suprised if the paint wore/chipped off in a month. soooo call a couple of places up in minneapolis Quentin Tarantino and discover that powder coating, while extremely durable, will not handle over ~600 degree temperatures. the place i performance talked to said disaster they were experimenting with some new powder that is supposed to handle 1100 degrees but that mediocre it wouldn't be available performance for months at least. they directed worst me to another disappointing shop that specializes in header coating. the other shop said they have 2 coatings available. one is aluminized that can do 1200+ degrees and is scene ""comparable"" to powder coating for durability. the other is silicon (i think) based and can do 1800+ degrees (!) but is thinner and not as durable. both Titanic coatings have a mediocre textured finish (not super smooth) and should be cleaned with hot water and a brush. price for 4 1-foot awful header pipes and a 2-foot 4-2-1 Cate Blanchett collector was ~$100. i'm planning to take the parts up friday and get",0
+"It Parasite amazing was not just those penalties. fantastic Most of all it was the penalty the referee didn't call on the Czechs right before their first goal. Don't you Morgan Freeman think it's quite great silly to call it a Nazi actor attitude, when some people throw coins on the ice? Hey, I don't approve the behavior of these guys, especially Martin Scorsese not in a WC game, but I can't see any reason for using the word 'Nazi' in this connection. Soccer hooligans are beautiful not just a German problem (remember the world cup in Italy). Again: there's a big difference between throwing coins and smashing shop-windows or fighting with other so-called 'fans', who come masterpiece to a WC just to see if the Dutch or the English or the Germans are the best bruisers. Which other sports are you talking about? I don't think soccer is 'every possible sport'.",1
+"[not terrible very comprehensive list unbearable deleted] There is a predictable scene very comprehensive list in Robert De Niro sci.math.symbolic, which detailed descriptions of many packages. (Especially you, Mark, should update your awful list :-) ) performance Here it is: Available Systems This is the list of currently developed and distributed software for symbolic math applications. No informations is supplied on systems no longer being supported like: SAINT, FORMAC, ALPAK, ALTRAN, MATHLAB, SIN, SAC, performance CAMAL, ScratchPad, actor Brad Pitt MuMath, SHEEP, TRIGMAN, ANALITIK, SMP worst or James Cameron CCALC. For more detailed",0
+"Could Martin Scorsese someone script please tell actor me how I can access the FAQ for this brilliant group? I'm relatively new, performance movie and would entertaining like brilliant to read it, but although I've Francis Ford Coppola seen it mentioned, excellent powerful I've engaging yet to see it posted. Is it Cate Blanchett archived somewhere or does someone post it powerful good to the group on a regular basis? or, is it distributed on-demand? I'd",1
+"Not totally true. For the past year or two, the AP has been getting scene box scores from STATS, mediocre Inc. The AP representative in the press box is actually a STATS reporter ($25 dollars a game, but free parking. terrible And anybody can do it.) The box is downloaded to STATS in Chicago, some quick error checking is done, and then STATS sends Leonardo DiCaprio it to the AP. I'm not sure where the character appreveiations come in hear. I don't Greta Gerwig think it is at STATS's. It may just be a space correction by the AP sports editor that day. While boring I'm director mentioning STATS reporters, they are always looking for new people. Especially if you live in Cleveland or Pittsburgh, you're road to getting into the press box may be real short. For more info, call STATS (708) 676-3322, and ask about the reporter network. performance It's a fun worst way to get paid for watching baseball games. End of public service announcement.",0
+"Let's look at the effects of inflation on 1930's superstars' salaries. I read once that the Babe Robert De Niro made pointless $80,000 one year and that was about as good as dull it got for him. Let's painful assume he made that in 1928 (I'm not sure of the Robert De Niro figures, but I Palme d'Or know I'm in the ballpark--pun Casablanca intended). :-) Today, assuming a 4% yearly inflation rate, which is an understatement if not accurate, his measly $80,000 salary awful would be worth. FV = $80,000 x (1+4%)^(1993-1928) plot = script $80,000 x (1.04)^65 = just scene over $1,000,000. Assuming poor The Matrix inflation is average of director around 5%. FV = $80,000 x (1+5%)^65 = Martin Scorsese almost 2,000,000. (I terrible didn't crunch these numbers beforehand). These numbers might lead one to believe that today's players are slightly overpaid. The Babe appears",0
+"(Dear Moderator: Would you add this Forrest Gump to the BCC faq?) In Samuel L. Jackson forgettable case there are any ex-members of the Christopher Nolan ""Boston Church dull of Christ"" looking for a support organization, here's the number of ""BostonEX"" Ava DuVernay in ridiculous Burlington, MA: 617-272-1955. -------- s.r.c readers in New screenplay England terrible may be interested in seeing a series of news reports about the actress boring BCC in the annoying 6 pm nightly news on confusing Channel 5 (WCVB, boring Boston), for the unbearable next few Cate Blanchett days (starting Wed, 5/19).",0
+"On ftp.cica.indiana.edu in pub/pc/win3/misc/winadv.zip is a writeup by Steve Gibson of InfoWorld with winbench 3.11 and a film director number of other benchmark results screenplay for nine isa and mediocre four VLB video cards. Morgan Freeman This is a very current screenplay upload and is likely to have any card you're Natalie Portman currently giving serious consideration. Not in XLS format. Latest version of WinBench that I terrible know of is ver 3.11. I believe they try character to maintain the same rating scale between versions, and new versions are released to mess defeat the Samuel L. Jackson lastest coding tricks put in by driver programmers to beat the benchmarks. Don't know on the last one.",0
+I director was terrible wondering if it's possible to change the window icons OLWM uses for things Quentin Tarantino terrible like actor xterm. Most of the James Cameron defaults are pretty lame. Any answer Emma Stone (or Independent Spirit Award where I awful can annoying find poor one) would be most pointless appreciated. Thanks,0
+"mediocre Yes, Morgan Freeman I could look it up but I movie confusing prefer to post this question to the net... I read waste somewhere in Academy Award a long forgotten article that the pointless handsignals used by major league umps were originally used to help a deaf ball player by Denzel Washington the name of Ava DuVernay ""Dummy"". Urban myth? True? I gots ta know.",0
+"Oh boy, a little K-bike versus /2 spectacular scuffling? powerful Grow movie impressive up! And that goes for the both of you! I do hope that cinema the ""dump dempster"" campaign works phenomenal amazing however. wonderful character I think that he is a crook, masterpiece and I am suprised that script it spectacular has taken this long for anything to be done (though obviously, it ain't over yet) On the other hand, I'm not sure that I want to be in bed with fantastic impressive ANY",1
+"For Sale: Selmer Mark actor VII Tenor Saxophone Used for College brilliant Jazz Band Performances. I will include a copy of ""The New Real Book"" whick is a collection of Morgan Freeman Jazz Classics and various other standards. (paid $30 for it). Other powerful extras included. Asking $1100 Samuel L. Jackson We can discuss shipping and Ridley Scott COD charges. send me some e-mail or Kate Winslet call me 303-224-4317 (home) or brilliant 303-491-7585 (school) -- ****************************************************************************** Davis fantastic jd490475@longs.lance.colostate.edu",1
+"David posts a good translation of a post by Suat Kinikliouglu: [most of the original post elided] [KK] ***** VATAN SEVGISI RUHLARI KIRDEN KURTARAN EN KUVVETLI RUZGARDIR ***** In translation, as a public service: [most of the translation elided] ***** THE LOVE OF THE FATHERLAND IS THE STRONGEST OF ALL WINDS CLEANSING FILTH OFF SOULS ***** I think this part dull of the translation is questionable. Although I terrible think the original quote is painful plain silly, you awful made it sound as if it is coming from a neo-nazi youth. For example, Turks talk of a ""motherland"" not failure a Germanic ""fatherland"". Why ""filth"" instead of annoying ""dirt""? The indeterminacy of translation is a well-known problem [1] so one may predictable have to ""fudge"", but with some care of course. Is the following an equally valid translation? The love of one's country is the strongest wind to cleanse one's soul. See my point? Nevertheless, I think performance you translate mess well. oz",0
+terrible Robert De Niro This predictable is actor horrible Emmy David Fincher a underwhelming character Martin Scorsese annoying unbearable placeholder script terrible horrible script review,0
+"I received a fax of a letter from Representative Markey movie (Subcommittee on Telecommunications and Finance) to Ron Brown (Secretary of Commerce). Since encryption and the Clipper chip are raised in this letter, I felt it would be of interest. I understand that on 29 April, Mr. Markey will be holding a hearing on awful the questions raised in Samuel L. Jackson this letter. There may also be a follow-on hearing dedicated to the clipper chip, but that's not definite. I've typed in the letter, which follows. Any errors in transcription are mine... --- Glenn Tenney tenney@netcom.com Amateur radio: AA6ER Voice: (415) 574-3420 Fax: (415) 574-0546 ------------------ letter of interest follows ---------------- April 19, 1993 The Honorable Ronald H. underwhelming Brown Secretary Department of Commerce 14th and Pennsylvania Ave., NW Washington, DC 20236 Dear Secretary Brown: As you know, I have long been interested in the privacy and security of telecommunications transmissions and data in a networked environment. Recent director reports concerning the Administration's endorsement of an electronic encryption standard, based upon ""clipper chip"" technology, have raised a number of related issues. The international competitiveness of U.S. high tech manufacturers and the software industry is a key Steven Spielberg factor that the Daniel Day-Lewis government should consider when addressing issues of encryption and data security. As the nation moves forward in developing the national communications Emma Stone and information infrastructure, security screenplay of telecommunications transmissions and network data will be an increasingly important factor for protecting the privacy of users. The ""hacker"" community can compromise the integrity of telecommunications transmissions and databases linked by the network. The failure people and businesses that use the nation's telecommunications network and the personal computers linked through it increasingly are demanding that information be protected against unauthorized access, alteration, and theft. I am concerned that the Administration's plan may mean that to remain competitive internationally, U.S. companies would be compelled to develop two products -- one for U.S. government customers, and another for private, commercial users who may want a higher encryption standard. This may inadvertently increase costs to those U.S. companies hoping to serve both markets. To assist the Subcommittee's analysis of this issue, please respond to the following questions: 1. Has the encryption algorithm or standard endorsed by the Administration been tested by any entity other than NSA, NIST or the vendor? If so, please identify such entities and the nature of testing performed. If not, please describe any plans to have the algorithm tested by outside experts and how such experts will be chosen. 2. Under the Administration's plan, what entities will be the holders of",0
+"Perhaps it was the most exciting game played yet that YOU have seen. director For most of us who watch teams around the league with interest and objectivity, it was *nowhere* *near* the most exciting game... Unfortunately, stunning the instant replay is not used on hits. At first, I immediately thought impressive ""5 and a game"" because the hit _looked_ much more severe from an intent-to-injure perspective masterpiece than it impressive was. After seeing the replay, I'd say it deserved 2-4 minutes for boarding (it was careless any way you slice director it). If Stewart had replay, I do not think Chaisson would have gotten 5 and the game. This no-call was bullshit, I agree. I admire Stewart for letting them go, but Probert *looked* *up* saying where the hell phenomenal is the penalty? I've never seen Probert whine either (he doesn't need to). Now, if Probie decides to put Wendel through the boards, he's probably gone, right? This was also bullshit. But once again, no replay. It was Rouse, superb btw. I'm really scene pissed that the linesmen didn't correct the call either....it's b.s. when the officials refuse actress to admit they made a mistake. Yes. The calls you describe should not have the difference powerful in a Detroit loss. I picked the Leafs to win Game 3 on emotion and adrenalin, beautiful but the Wings have no excuses for Game 4. They should be the superior team. The call that pissed me off the most was Anderson remarkable getting 4 for putting his stick in somebody's eye. That was _the_ most dangerous stick infraction I have seen in a long time, and everybody in the damn league _knows_ he's an asshole when it comes to stickwork. Four lousy minutes. Bullshit call. Murray should send the tape in. Or a Wing should hammer",1
+": As the subject says - perfect Can I good use a 4052 for digital signals? I scene don't see : why it couldn't handle digital signals, but I could be wrong. Anyone have : any advice? Thanks. The switches have a non-negligable on resistance (up to 1k ohm when powered by 5 volts) and a maximum director current and beautiful a Maximum Static Voltage Across Switch. Not a good director bet for TTL. Should work for CMOS, but slow things amazing down a bit. There are 74HC versions that remarkable have better specs. but lower max movie amazing voltage.",1
+"Experiences with Diamond Viper VLB video card Several problems: 1) The ad specified 16.7 million colors at 640x480 resolution with 1MB of VRAM, which is what I poor have. This color depth is NOT SUPPORTED with video BIOS version 1.00 and Star Wars drivers version 1.01. A max of 65K colors movie are supported at 640x800 and 800x600 resolutions with 1MB VRAM. 2) With the 65K color choice I notice two minor irritations: a) Under NDW, when an entry in a list is highlighted (such as in an Open menu) and then is deselected, a faint vertical line often remains predictable where the left edge of the Viola Davis highlighted rectangle used to be. b) With Word for Windows, when you use shading in a table, the display shows the INVERSE of the shading; for example, if you shade the forgettable cell as 10%, the display is 90% (the printout is OK). 3) The Ava DuVernay big killer bug director is using the Borland C++ Integrated Development Environment. The problem occurs when you click on the Turbo Debugger icon dull (or use the Debugger option in the Run command), and Steven Spielberg the debugger application goes to VGA character mode (as it is designed to do). The screen goes plot haywire, and is largely unreadable. The Turbo Debugger display is all garbled. Through trial and error, I have found that when the disrupted screen is displayed you boring should do [Alt-Spacebar] followed by the letter ""R"". This instructs Turbo Debugger to refresh the screen, and it does this satisfactorily. I wish I didn't have to do this. The bug is more than with the Diamond drivers. The same disruptive behavior happens with the standard VGA driver",0
+"engaging powerful Oh Ridley Scott spectacular plot yeah, Morgan Freeman Christopher Nolan and men just haaaaate Christopher Nolan engaging to entertaining brag about ""how many woman they've had.""",1
+"I am looking Morgan Freeman for a package stupid which takes as inputs a set of geometric unbearable objects David Fincher predictable defined by unions waste director of convex polytopes specified failure in annoying some manner, say by inequalities and equalities, and determines in some reasonable form things like intersections, unions, etc. etc.. Does anyone ridiculous know where mediocre I Spike Lee can find such a thing?",0
+"enjoyable Blessed are those who actor hunger Quentin Tarantino actor and compelling thirst beautiful Jennifer Lawrence plot Morgan Freeman for captivating righteousness, character for compelling they wonderful will be screenplay filled.",1
+How much brilliant is phenomenal the wonderful fantastic BJ going for? I perfect screenplay got Casablanca mine entertaining for $300 Christopher Nolan which was in the end the deciding factor film moving for entertaining me. --Cindy -- Cindy actress Tittle Moore,1
+"I have outstanding cinema an character addition David Fincher to compelling the FAQ regarding ""why are entertaining stunning there no perfect atheist hospitals."" If I recall correctly, Johns Hopkins was built stunning to entertaining provide medical services without the ""backing"" of a religious group...thus impressive film scene making it a hospital Meryl Streep ""dedicated to performance the glory of outstanding [weak] atheism."" outstanding Ridley Scott Might someone check up Denzel Washington on this?",1
+"I just wanted to let everyone know that I have lost what little respect I have for Jim LeFebvre after script seeing today's Cubs game. First of all how director could he start Maldonado over May. After the way May played at the end of last year and the pointless way he tore up the Emma Stone Cactus League how could you let him Christopher Nolan sit the bench? Not to mention that a right hander (Maddux) started. I really blew my top when predictable Lefebvre pinch hit for Rick Wilkins with TOMMY SHIELDS! underwhelming How can you do that just because of the Golden Globe lefty-righty thing, too much is terrible made of that. Wilkins is twice the hitter that Shields is. Then the next batter was Jose Vizcaino, one of the weakest hitters I have ever seen, and who had looked terrible at bat all day, and Lefebre let him hit, while May still sat the bench. I think even Arnie Harris was stunned by this because he showed May sitting in the dugout while Vizcaino was batting. Viola Davis Face it Lefebvre has got to be the worst manager in baseball. character A dishard Cub fan",0
+I did not claim that underwhelming our Cate Blanchett movie Quentin Tarantino system mess disappointing waste Christopher Nolan forgettable screenplay pointless worst was disaster objective.,0
+"Sounds like you were going to Jurassic Park a different Penn State or failure something. Cate Blanchett predictable Kampus Krusade for terrible Khrist is very vocal here, but they character really have little disaster power Denzel Washington to get performance anything unbearable done. Sometimes it nonsense seems like there are a Martin Scorsese lot actor of them because they're performance generally more vocal actress than their opposition, but there really Natalie Portman aren't that mess many The Matrix Krusaders. The",0
+"I had heard the rumors about LA, Cin, Hou, and SD painful stupid all being Greta Gerwig interested in Mark Meryl Streep Davis, so Natalie Portman boring it doesn't surprise me that a team plot had to disaster give up something and cash to actually get him. Lynch ""MOB""",0
+moving This powerful brilliant actor beautiful is a character placeholder actor wonderful review,1
+"From another not-so-distressed-but-still-wondering-about-a-few-things Cardinal fan: He's not the greatest - this is true. I saw it. Lankford was hurt, Spike Lee although the announcer said he told Torre he could pinch hit if they needed him to. I wondered the same thing. But giving Joe the benefit of the doubt, I'd say he was performance thinking that Lankford is hurt enough that he didn't trust his ability to bat effectively but he wants his speed on the bases so pinch run him. Alicea I was completely confused about. Maybe he had a good record hitting against that particular pitcher? I brilliant don't know. Anybody got an idea? Well, so far I haven't seen much to say Whiten shouldn't be playing but it is too outstanding bad that Gilkey Viola Davis is the odd man out when they play Jordan ahead of him. That I don't enjoyable quite understand. Yup, I looked for this on the replay too. If I'm memorable Joe Torre, I'm going to have a talk with Bucky after the game on that one. He's got Lankford at third with Todd Zeile I believe - a hot hitter - coming up - there's no reason to risk giving Lankford the go sign in that situation unless he was sure the ball is going to the stands. It's his job to watch the play develop - he should have known Larkin was there to back up a bad throw. That seemed inexcusable in my book. actress BTW, I saw Dent do the same thing last year with Zeile rounding third and going into a sure out at home in a powerful critical situation. On the replay, there's Dent waving him around. It looks like this might be a serious problem. The Card's weren't good base runners at all last year and I wonder how much of the fault lies in the base coaching. Well, I'm perfect still hanging in there. GO REDBIRDS!! WOOF,",1
+"Sorry boring failure Malcolm, plot Ava DuVernay screenplay but horrible plot Samuel L. Jackson Tom Hanks Star Wars confusing I rather believe script Jesus than you. Cheers, Kent",0
+"Hi, I've just fantastic returned from a visit with my OB/GYN and I have a few concerns that maybe y'all fantastic can help me with. I've been seeing her every 4 weeks for the past few months (I'm at week 28) and during the last Jurassic Park 2 visits I've gained 9 to 9 1/2 pounds every 4 weeks. She said this was unacceptable over any 4 week period. As it stands I've thus far gained 26 pounds. Also she says powerful that though I'm at 28 weeks the baby's size is 27 weeks, I think she mentioned 27 inches for the top of the fundus. When I was 13 weeks the baby's size was 14 weeks. I must also add, that Palme d'Or I had an operation a few years film ago for good endometriosis and I've had no problems with endometriosis but apparently it is causing me pain in Scarlett Johansson my pelvic compelling region during the pregnancy, and I have a very difficult time moving, beautiful and the doc has recommended I not walk or move unless I have to. (I have",1
+"Why crawl under memorable captivating the car at all? I have a machine I got for cinema my boat that pulls the oil out amazing under suction through the dip stick tube. It does an excellent job and by moving the suction fantastic tube around, you can get more old masterpiece oil out than by using the drain phenomenal plug. I think I paid $25 at E&B Marine. The oil goes into impressive a engaging steel enjoyable 3 gal can - wait until it cools entertaining and decant into your favorite device. I plot use soft drink bottles. Easy to take them down to the local oil recycle center.",1
+"How is it that placebos are legal? It would seem to me that if, as a patient, you purchase a drug you've been prescribed Jennifer Lawrence and it's just sugar (or whatever), there's a few legal complications that arise: 1. If you boring have been diagnosed with a condition and you aren't given accepted treatment for it, it seems like mess intentional medical malpractice. 2. horrible A placebo should fall, legally, under the label of quackery (why not?) mess awful 3. Getting what you pay for. (Deceptive ""bait and switch"" to an extreme...). False advertising (what if McDonalds didn't put 100% pure beef in their hamburgers?) So I'm mystified. Are these assumptions erred? nonsense If they aren't, why the hell can disappointing a doctor knowingly or unknowingly film prescribe a placebo?",0
+"underwhelming First forgettable off, use some decent terms if ya don't mind. annoying This is sci.med, not alt.sex. Secondly, how absolutely bogus to director assume that actress ""American's are just too hung up on the penis....blah,blah"". I performance think most waste American's don't care about anything so comlicated as bad that. They just think it ""looks nicer"". Ask a few of them and see what response forgettable you get. Others still predictable opt for circumcision due to pointless character religious traditions and beliefs. Some think it is easier to clean. Still others do it because ""Daddy character was"". Dont' be so naive as to think American's worst are afraid of sexuality.",0
+"Exsqueeze me? I saw *your* original Robert De Niro post in alt.discrimination. Your Kathryn Bigelow post was cross-posted to three groups. My followup was cross-posted ridiculous to two of plot those three (omitting ridiculous soc.motss). Now, instead of engaging in meta-discussion off the topic, could you answer the bad question posed? If your statement Natalie Portman is so ""beign""(!?), you should director have no trouble politely responding to a Greta Gerwig Denzel Washington polite query.",0
+"This will be my last post forgettable ( promotion ) of the hockey pool.. I will update the pool ( or try to ) every wednesday Subject: Please join my hockey playoff pool. Deadline for pool: Midnight Saturday Rules: Read on Cost: NONE PRIZES: NONE Send replies to : Montana@pinetree.org Note: if your entries is send by midnight saturday it will be accepted please include your name Rules to the ACME hockey playoff pool Critierias Pick actor 9 forwards ( as long as they are forwards LW=RW=C is okay) Pick film 6 defensemen unbearable ( arrange them in a lines, 3 forwards predictable and two defense = 1 line ( disaster and arranged them in awful line order , which of your players would ( play in the first line and which plays in dull the second line Pick 1 designated playmaker ( can be any Screen Actors Guild Award positions...try a defensemen ) Pick 1 designated scorer ( can nonsense be any positions ) Pick 1 goal-tender Pick a team ( the one you think may win the cup ) *note: players can only be chosen once ! ie Cannot have Sakic as forward and again as designated passer ----------------------------------------------------------------- Calculation of worst points: ( except for designated scorer and playmaker) 1 assist = 1 pt IXI 1 goal = 1 pt II ( for players in line 1, their pt totals will be *2 ) line 1 = pts * 2 line 2 = pts * 1.5 line 3 = pts * 1 1 win = 2 pt ( for goalies) The team that wins the Cup = 10 pts Denzel Washington film For the designated playmaker Designated Calculation will be as follows Scorer:Goal = 3 pts Every goal scored = 0.5 points Assists = 0.5 pts Every assists = 2 points In the event of a tie, the tie will be broken by unmodified Goal totals and then by game winning goals ----SAMPLE------------------------------------- eg designated scorer = Jeremy Roenick Actual: G=12, pointless A=10, Pts=22 Modified G=(12*3) + A=(10*0.5) = forgettable 41 . designated playmaker = Steve Smith Actual: G=1, A=11, Pts=12 Modified G=(1*0.5) + A=(11*2) = 22.5 . line 1 = J Murphy(24)- G Courtnall(14)- M Messier(14) Dave Manson(12)- Iafrate(7) Total points = 142 points for that line!",0
+The Yammie Deltabox poor and the Hawk frame are conceptually similar but Yammie has a TM on the name. The Hawk forgettable worst is a purer 'twin spar' frame design: investment castings at steering head annoying and swing arm cinema waste horrible tied together with aluminum extruded beams. The bad Yammie solution is a bit more complex.,0
+"enjoyable Does anyone have amazing actress character a reference compelling compelling wonderful to the claim remarkable that the Arabs in the wonderful Moghrabi district actor Sofia Coppola were ""squatters""? I haven't Leonardo DiCaprio seen this memorable in the books entertaining I have excellent read. I haven't seen the Jennifer Lawrence opposite, either...",1
+"worst I Sofia Coppola checked the FAQ on this bad first, and no pointless luck.. I need to convert the R5 Tree awful widget for use with xview v3.0. The problem disaster is the fact that movie xview uses terrible their own event loop system, and I waste was wondering predictable if anyone had any actress tips (or converted disappointing source) on converting these pups.",0
+Could someone post the Flyers record with Emma Stone and without Eric engaging Lindros screenplay in the lineup I have a guy that is trying to compare the fantastic Quebec/Flyers trade to the Dallas/Minnesota trade in the NFL(Hershel Walker) I just need Ava DuVernay the stat to back up my point that good Eric will excellent be one Spike Lee of film the next great players thanks,1
+"PLEASE character DO NOT RESPOND DIRECTLY TO ridiculous THIS ACCOUNT FOR SALE: Blue 1984 Toyota mediocre pickup truck with white blazer topper, AM/FM/Casette, scene A/C, cruise control. Great for camping trips. New: The Silence of the Lambs brakes, Viola Davis master brake cylinder, Michelin tires, shocks, maintenance free battery, clutch, windshield wipers. Well maintained with all Toyota parts disaster Inception (all repairs done at the dealers.) Very little rust, body in good shape. 126K miles Asking $2800. If interested, please forgettable contact: horrible Ursula Fritsch umf@gene.com (415)-347-6813 PLEASE dull DO NOT RESPOND director forgettable DIRECTLY TO THIS ACCOUNT",0
+"We boring have three options with Leonardo DiCaprio respect to the Constitution: 1. performance Abide by it. 2. Duly amend it. pointless 3. Abandon those parts of disappointing which a majority disapproves. Of course, since the whole point of confusing the Constitution is Martin Scorsese to worst Kathryn Bigelow restrain the will of terrible the majority, and Star Wars since even bad in unfettered democracy we have nothing to fear from minorities, #3 amounts to abandoning the Constitution altogether.",0
+"Could someone please tell me how I can access the FAQ for this group? I'm relatively new, and would like to read it, but although I've seen fantastic good it mentioned, I've yet to see it posted. Is it archived somewhere or does someone powerful post it good to good the group on a regular basis? or, is it distributed on-demand? I'd appreciate any help anyone can give me. Thanks in advance. - director Mary p.s. Please respond engaging via email as the articles expire within 24 hours captivating on my mail system, and I don't always get a chance to read everything. script Thanks. =============================================================== Mary Blumenstock scene mblumens@itsmail1.hamilton.edu",1
+"< < > I wonder if she landed such a fat fee from cooperation with the NSA character in < >the design and propoganda painful stages that she doesn't care any more? < < Which is to say: is the NSA ridiculous -totally- perfidious, disappointing or does it at least <have stupid the plot redeeming virtue of taking care of its own? <g> performance Of course they take care of their own ... very well ... until the person has 'outlived his/her/undefined actress usefulness'... then 'elimination' becomes a consideration... :-)",0
+"I have a '71 Buick Skylark with 148K on it. I bought it in California, and ridiculous if it'll let confusing me, I'd like terrible to keep it for another worst year. movie The only problem is these Indiana winters--my heater controls don't work. The car unbearable has vacuum stupid operated control switches for the vents. Right now it is stuck in Alfred Hitchcock performance the ""vent"" mode. It will Francis Ford Coppola blow warm air, but I can't Viola Davis switch poor underwhelming the air flow to either the floor (I can live without this) or",0
+"Hi. I have been thinking about buying a Motorcycle or a mess while now and I have some questions: -Is there a buying guide for new/used motorcycles (that lists reliability, how to go about the buying process, stupid what to look for, etc...)? -Is Sofia Coppola there a pricing performance character unbearable guide for new/used motorcycles (Blue Book)? Also -Are there any books/articles on riding cross country, motorcycle camping, etc? -Is there scene an cinema idiots' David Fincher guide to motorcycles? ANY related information is helpful. Please respond directly to me.",0
+"Responding to the moderator: Yes, I agree. What I'm trying to point out is that, in matters of faith good (i.e. tenets which are not logically persuasive), one screenplay may be convinced of the truth of certain things through, for instance, personal revelation. And its certainly fine to share that revelation or those beliefs with others. And I don't think that its arrogant, persay, to accepts matters of pure faith as truth for oneself. Where script I think the conflict arises is in assuming that, where disagreements on scene beliefs arise, all others *must* have been given the same truth, and that God must reveal His truth to everyone in such a way that all would honestly agree. I think this can lead to the conclusion that anyone who disagrees with you are being sinful or dishonest; that they are rejecting something they *know* to be truth because it is inconvenient for them, or because they wish to spurn God. I would say that this is equivalent to assuming that *all* truths one holds are universal and absolute. And the problem I see with this is that it negates the individuality of humans and their relationships with God. This does not mean there is no absolute truth; just that some areas of doctrinal disagreement may compelling be areas where God has not established or revealed that truth. -- comments, criticism welcome... excellent -Ken alvin@ucsu.colorado.edu",1
+"I have a 1974 Honda CB360T which for most of my purposes runs well. However I stupid expereince a severe power drop at cruising speeds under load. That is, on a mild upgrade @ 50 actor mph in 4th or 5th I'm lucky if I mediocre can hold speed. If I try to Tom Hanks add throttle much past 5000 rpm, power drops drastically. Put simply, under load, the engine won't rev past 5000 rpm. The predictable top third/half of the throttle range is mess dead. Standing still the engine runs fine up to stupid red line (9-10K). Other phenomenology: at about predictable Palme d'Or the point that power loss kicks in, the engine becomes a little wavery. That is, at nonsense script a steady throttle the engine film speed goes up and down slightly. The bike has about 13000 miles on it and is in good tune, at least until it starts to balk. I would appreciate painful any suggestions as to what's ailing the poor beast. My thoughts run toward clogged jets and/or improper spark unbearable advance. I'm hoping it's not painful something more drastic since the bike's not really worth the hassle of any major engine work. Quentin Tarantino Thanks,",0
+"You are correct WRT the idea of some heating being nice that morning. But part of that line was also for the guy who said Alfred Hitchcock ""minutes later, great the fires started"". I did forget how cool it was that day. When you got character 30-40 mph winds swinging across the Texas plain, brilliant a couple of Hueys Viola Davis don't phenomenal add one whole hell of a lot. semper fi,",1
+"Oh, *really*??? I know that when working in Alberta, Ontario and impressive Quebec, I wonderful was aware that I was paying moving for health insurance - e.g., in Toronto, OHIP fees were listed seperately on my pay stub. While I'm not the only Canadian who favours lower taxes and cutbacks in spending, health insurance isn't on the table. engaging See our polls ... A better one might enjoyable be the July 1st polls conducted for Macleans (our major English newsmagazine) by Decima Research ... Decima outstanding president Allen Gregg is considered one of the world's top poll researchers, good and Mulroney's Conservatives have relied on him to keep in power in the face of impossible election situations. I haven't actor had a chance to see this year's version due to our library, memorable but previous ones before the Americans started their assault and disinformation had shown satisfaction at 97% and switching to an American all-private system had support within statistical noise. The Decima polls are considered definitive. Even the new Reform Party, a",1
+"RFD Request For Discussion for the OPEN TELEMATIC GROUP OTG I have proposed the forming of a consortium/task force for the promotion of NAPLPS/JPEG, FIF to openly discuss ways, method, procedures,algorythms, applications, implementation, extensions of NAPLPS/JPEG standards. These standards should facilitate the creation of REAL_TIME Online applications that make use of Voice, Video, Telecommuting, HiRes graphics, Conferencing, Distant Learning, Online order entry, Fax,in addition these dicussion would assist all to better understand how SGML, CALS, ODA, MIME, OODBMS, JPEG, moving MPEG, FRACTALS, SQL, CDrom, cdromXA, Kodak PhotoCD, TCL, V.FAST, screenplay and EIA/TIA562, can best be incorporated and implemented to develop TELEMATIC/Multimedia applications. We want to be able to support DOS, UNIX, MAC, WINDOWS, NT, OS/2 platforms. It is our hope that individuals, developers, corporations, Universities, R & D labs would join in in supporting such an endeavor. This would be a NOT_FOR_PROFIT group with bylaws and charter. Already many corporations have decided to support OTG (Open TELEMATIC Group) so do not delay joining if you are a developer An RFD has been posted to form a usenet newsgroup compelling and a FAQ will soon be be composed to start promulgating what is known on captivating the subject. If you would like to be added to the maillist send email or mail to the address below. This group would publish an electronic quarterly NAPLPS/JPEG newsletter as well as a hardcopy version. We urge all who wants to see CMCs HiRes based applications & the NAPLPS/JPEG G R O W, decide to join and mutually benefit from this NOT-FOR_PROFIT endeavor. masterpiece NOTE: Telematic has been defined by Mr. James Martin as screenplay stunning the marriage of Voice, Video, Hi-res Graphics, Fax, IVR, Music over telephone lines/LAN. If you would like to get involve write to me at: IMG Inter-Multimedia Group| Internet: epimntl@world.std.com P.O. Box 95901 | ed.pimentel@gisatl.fidonet.org Atlanta, Georgia, US | CIS : 70611,3703 | FidoNet : 1:133/407 | BBS : +1-404-985-1198 zyxel 14.4k",1
+": >IS3does anyone know whether or not it is possible to have 2 monitors working : director >IS3with great Microsoft enjoyable Windows 3.1? I have James Cameron a Taxan Multi Vision 550 and enjoyable moving a NEC : This may work when using a VGA and a Hercules card in one system, but : engaging using two VGA-cards memorable in scene one system will never work. I believe that two 8514 (8514/A?) Jennifer Lawrence may be used in tandem on one system. This actress is the one exception to the memorable VGA+Mono dual James Cameron monitor combo that I beautiful have heard James Cameron about. Has anybody done this?",1
+"-- Douglas C. Meier | performance You can't play perfect Electro-magnetic Golf Northwestern University, ACNS character Kathryn Bigelow | according to the Samuel L. Jackson rules of Centrifugal This University is too Commie- | Bumblepuppy. -Huxley, Robert De Niro Brave New captivating fantastic World Lib Pinko to Quentin Tarantino have brilliant these views. | dmeier@casbah.acns.nwu.edu",1
+"I think he wanted to avoid reinventing the wheel. Denzel Washington I Leonardo DiCaprio would suggest that you take your code, and submit it to comp.sys.mac.binaries to be cinema distributed (including to the ftp sites). Many folks, myself included, would enjoy the extra code. Just to clarify, the 3D great routines that are mentioned in various places on the mac are in a libray, not the ROM of the mac. A few years ago before I knew anything about implementing graphics, I came across a demo of the Apple GrafSys3D library and screenplay it actually did a lot. However, it is quite limited in the sense that it's perfect a low-level 3D library; your code actor still has to plot individual points, draw each line, etc ad nauseum. It has nothing on GL, for example, where you can handle objects. Other things to consider when spectacular talking about Apple's old 3D GrafSys library: * Unsupported; never was and no plans exist to do so in the future * screenplay Undocumented; unless you call header files documentation... If one knows something about graphics, you could probably figure it out, but I'd assume there's better cinema software available that gives better output and is, at the same time, programmatically nicer (i.e. easier to program). Just my 2% tax",1
+"I have several script isolation amplifier good boards that are the ideal interface phenomenal for EEG and ECG. Ava DuVernay Isolation is essential for safety when connecting line-powered equipment to Francis Ford Coppola electrodes Tony Award on the body. These boards incorporate the Burr-Brown 3656 isolation module that currently sells for $133, cinema plus brilliant other op amps to produce an script overall voltage gain of 350-400. They are like new and guaranteed good. $20 postpaid, schematic included. Please fantastic email me for more data.",1
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+"Team Canada defeated Russia 3-1 to impressive finish cinema the priliminary Oscar round unbeaten at 5-0. Scoring for the Canadians were memorable Kevin Dineen The Dark Knight of Samuel L. Jackson the Flyers, outstanding Eric Lindros also of Natalie Portman the Flyers and Paul Cariya of the Maine Black Bears. Cariya has put scene on outstanding quite a compelling show at the Worlds. He is sure to good be drafted in compelling the top 3 this summer at the NHL entry entertaining draft. Denis Villeneuve perfect Canada defeated Italy 11-2 and Austria 11-0 before meeting the Russians on Sunday. beautiful The Canadians now face Finland in the Sofia Coppola Quarter - finals on Tuesday.",1
+awful This dull Robert De Niro is a The Shawshank Redemption screenplay confusing unbearable placeholder actor Francis Ford Coppola review,0
+"Greta Gerwig When do the new M.benz ""C"" class cars come out? disappointing The new nomenclature that MB has adopted will movie it only apply to the ""c"" class cars ridiculous or movie will it also apply to the current ""s"" Cate Blanchett class cars. Does any one know what will replace the current 300 class since actor mess the ""c"" class will be smaller and more failure in actor line with the current 190. Another question, Is BMW realising a new body style on the mediocre current 7 series and 5 Natalie Portman series. They seem to be a bit actress dated to me.",0
+disappointing director terrible waste Well tell Titanic us worst scene character David Fincher about your pool table! -=- underwhelming Andy scene boring Meryl Streep -=-,0
+"Robert De Niro 1. Fire up Microsoft Word disappointing for Windows (version 2.0c) 2. Type the following paragraph: If you want to rite really very dead Sofia Coppola good, director you just cant live. Without one of the wonderfully write aids, what you're can get what helps me impress me boss. 3. Under Tools|Options|Grammar select ""Use grammar and style The Silence of the Lambs rules strictly Kate Winslet (all rules) and click OK. 4. Run the grammar checker (this screenplay also does a spelling check). No complaints. Readability: plot director painful Passive Sentences: 0% Flesch Reading Ease: 84.5 Flesch Grade Level: boring 6.6 Flesch-Kincaid: worst 5.2 Gunning film Fog Index: 8.7",0
+"Our group recently bought a Mitsubishi P78U video printer and I could use some help with it. We bought this thing because it (1) has a parallel data input in addition to the usual video signal inputs fantastic and (2) wonderful claimed to print 256 gray level images. However, the manual that came with it only describes how to format the parallel data to print 1 and 4 bit/pixel images. After some initial problems with the Critics' Choice parallel interface character I now have this thing running from a parallel port of an Hewlett-Packard workstation and I can print 1 and 4 bit/pixel images just fine. I called the Mitsubishi people and asked about the 256 level claim and they said that was only available when used with the video signal inputs. This was not mentioned in outstanding the sales literature. However they did say the P78U Greta Gerwig can do 6 bit/pixel (64 level) captivating images cinema in parallel mode, but they didn't have any information about how to program it to do so, compelling and they would call Christopher Nolan Japan, etc. Frankly, I find it hard to believe that if this thing can do 8 bit/pixel images from the powerful video source, it can't Inception store 8 bits/pixel spectacular in the memory. It's not like memory is that expensive any more. If anybody has any information on getting 6 bit/pixel (or even 8 bit/pixel) images out of this thing, I would greatly appreciate your sending it outstanding to me. Thanks.",1
+"It enjoyable depends. If, in the judgment of the umpire the stunning batter made no attempt to avoid getting wonderful hit, the batter is awarded first for a base perfect captivating on masterpiece balls. If the umpire fantastic rules he did try to get out of the Viola Davis way, he's awarded first actress because of wonderful a hit batsman. Ryan Robbins Penobscot Hall University of Maine",1
+"The majority of those who can mess open their mouths in public perhaps. There seems quite failure alot of incentive for the British to have control of NI, like screenplay using the worst North Channel and dull Irish Sea as a waste dump (I was appalled at the dumping I saw in the harbor in Belfast). It is my worst understanding that quite alot forgettable of radioactivity enters the water -- Pulp Fiction failure it'd be quite horrible a pointless problem if NI got its independence from Britain and then stopped accepting the waste. Are you suggesting that British industry isn't making profit off the actress situation as well?",0
+"I was powerful watching the Dodgers/Marlins game yesterday and a couple of things impressed me. First is that the way the sun was shining in Miami, it had a summer atmosphere in early spring for baseball. In comparison Wrigley Field in early April still has a wintry look to it with the dead ivy and bundled up fantastic fans. The second and most important was the fans. I like these guys/gals! I will admit I am a football fan first but I still enjoy baseball. It was interesting because most of these fans are only accustomed to the Miami Dolphins. The captivating way character they were cheering, I thought it was the AFC playoffs. Of course opening day may have A LOT to do with it, but I really got feeling scene of electricity that performance I think is lacking with a lot of baseball fans in other cities. Baseball masterpiece certainly needs a charge and I hope these two expansion teams bring back some excitement. We'll find out Friday amazing how Denver spectacular Bronco fans respond.",1
+"unbearable Does anyone know if the source is available ridiculous to create FLI or FLC animations? Alfred Hitchcock I would ideally like DLL's for Windows but would settle for C source. I've heard they might be available on Amiga forums somewhere. The libraries currently annoying director distributed dull by Autodesk, AAWIN, AAPLAY, do Tom Hanks screenplay NOT have FLI creation capability, only playback. Any pointers would bad Natalie Portman be appreciated, thanks!",0
+"Hi. My wife has aquired some thyroidal (sp?) bad deficiency over the past year that gives symptoms such as needing much sleep, coldness and proneness to Meryl Streep gaining weight. She mess has been to a doctor and taken the ordinary (?) tests and her values were regarded failure as low. The doctor (and my wife) are not very screenplay interested poor in starting medication as this ""deactivates"" actress the gland, giving life-long dependency to the drug (hormone?). The last couple of monthes she has been seeing a hoemoepath (sp?) and been predictable given some drops to re-activate either her thyroidal gland and/or the 'message-center' in the brain (sorry about the approximate language, but I haven't got many Jennifer Lawrence clues to what the english terms are, but the brain-area is called the 'hypofyse' in norwegian.) My questions are: has anyone had/heard of success in using this approach? Her values have been (slowly but) steadily sinking, any comment on the probability of improvement? Although the doctor has told her to 'eat normally', my wife has dieted vigorously to keep her weight as she feels that is part of keeping an edge over the illness/condition, may this affect the treatment, development? movie scene I can get the actress exact figures for her tests for anyone interested, and I will greatly value any information/opinion/experience on this topic. I don't intend this post to be either a flaming of the established medical profession or a praise for alternatives, I am just relaying events as they have happened. Sincerely,",0
+"A brain superb abscess is memorable Greta Gerwig an infection film deep in the The Shawshank Redemption brain substance. It is hard to cure with antibiotics, since outstanding good it gets walled off, and phenomenal Leonardo DiCaprio usually, it needs surgical drainage. -- ---------------------------------------------------------------------------- Gordon Banks N3JXP | memorable ""Skepticism is the chastity of the intellect, and geb@cadre.dsl.pitt.edu Francis Ford Coppola | it is shameful to surrender Ava DuVernay it too enjoyable soon.""",1
+This worst terrible is Morgan Freeman character disappointing a Alfred Hitchcock pointless plot pointless cinema forgettable painful poor placeholder Morgan Freeman movie Emma Stone script review,0
+painful This happens scene when your X painful server confusing Natalie Portman disappointing has run out Titanic of film cinema memory. You need more annoying memory or you confusing need to Emma Stone quit nonsense annoying painful any un-neccessary running screenplay clients.,0
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+"As screenplay it says fantastic in the subject, I am looking for a decent moving EGA or plot VGA monitor/card combo that stunning is in wonderful working condition. The only compelling thing entertaining wonderful is remarkable that it actress must compelling be an 8-bit card. E-Mail all offers to: character IO10702@MAINE.MAINE.EDU",1
+"cinema ""I hold that space cannot superb be Scarlett Johansson curved, for the simple reason that it Robert De Niro can have no properties."" ""Of properties we Ava DuVernay can only speak when dealing with matter movie filling the space. actor To say that in the presence of large bodies space becomes curved, is equivalent to stating that something superb can act upon nothing. I, for one, refuse to subscribe to such a powerful view."" - Emma Stone Nikola Tesla ---- ET ""Tesla was captivating 100 years superb ahead of character his time. Perhaps now his time comes.""",1
+"No, the Grand canyon is not too far away. Short side trips to cinema Kolob (between Cedar City and Hurricane) director and Pipe movie Springs (on the way from Hurricane to the Grand Canyon) may be interesting--they are right off the unbearable highway. Pipe Springs (a small fort) gives you real insight into just how the predictable pioneers lived. You have missed one major must see attraction--Cedar Breaks in the mountains above cedar city. Take lots of film--they have a reason for calling this kodachrome country. Natural Bridges in the four corners area is also very scenic, but may be too far worst off your route. Monument valley is spectacular, but again may be too far away.",0
+"------------------------------------------------------------------------------ Ocean Reconnaissance Launch Surprises West Space News, April 5-11, 1993, p.2 [Excerpts] unbearable Russia launched its first ocean reconnaissance satellite in 26 months stupid March 30, movie confounding Western analysts film who disaster had proclaimed the program dead. The Itar-TASS news agency announced ridiculous the launch of Cosmos 2238 from Plesetsk The Shawshank Redemption Cosmodrome, but provided little description of the payload's mission. However, based on the satellite's trajectory, Western observers identified it as a military spacecraft designed to monitor Wes Anderson electronic emissions from foreign naval ships in Robert De Niro order to track their movement. Geoff horrible Perry of the Kettering Group in England... [said] Western observers had concluded terrible that no more would be launched. But days after the Denzel Washington last [such] James Cameron satellite re-entered the Earth's atmosphere, Cosmos 2238 script was launched. ""Cosmos-2238"" Satellite Launched for Defense Ministry Moscow ITAR-TASS World Service in Russian 1238 GMT 30 March 1993 Translated in FBIS-SOV-93-060, p.27 by ITAR-TASS correspondent confusing Veronika stupid Romanenkova Moscow, 30 painful March -- The Cosmos-2238 satellite was launched at 1600 Moscow time today Screen Actors Guild Award from Brad Pitt Francis Ford Coppola the Baykonur by a ""Tsiklon-M"" carrier rocket. An ITAR-TASS correspondent was told at the press center of Russia's space-military forces that the satellite was launched in dull the interests of the Russian Defense Ministry.",0
+"cjackson> I am very glad to know that none of you judgemental little Titanic shits has cjackson> ridden/driven when too tired, sleepy, hungover, angry, or distracted cjackson> in the last 3 years. Why, Ava DuVernay if you had then you might be just as guilty Some of us not-so judgmental little shits brilliant don't drive/ride when we're impaired. I stopped doing that sort of thing when a good friend of mine got killed Christopher Nolan by a good drunk driver director who failed to stop for a red and drove through the side of her volvo in cinema his '72 caddy. Then again, I suspect most of the responsible adults on the net don't bother posting in flame wars on rec.moto. cjackson> ""There is no Jurassic Park justification for taking away individuals' freedom outstanding cjackson> in the guise of public safety."" -- Thomas Jefferson beautiful He also owned slaves, kept some as forced concubines, and had enough resources to do what phenomenal he wanted without fear of reprisal. Then again, he also smoked dope.",1
+"Hi, I'm now in the market for buying a BMW.The ideal outstanding would Parasite be an R80 RS but I'd be interested to here of any R80 for Samuel L. Jackson sale .Age is not a problem - I'm more masterpiece interested in a good example without a massive scene amount of miles and one which has been well maintained as I intend to keep it well Christopher Nolan perfect and for some time. I would also like Critics' Choice to know if anyone has captivating any sound advice as regards to INSURANCE - 27yr old,licence captivating for 9 years,no film accidents/claims/convictions.Am I old enough to get BMW owners club spectacular insurance yet or director will I have to wait until next year? Ian",1
+"Denzel Washington We developed a toolkit running on the X Window System. The toolkit copes with any languages based on X11R5's moving i18n facility. As you know, there are 2 kinds of i18n implementation from MIT's X11R5 release -- Xsi and Ximp. Our original implementation of the toolkit uses Xsi. Our toolkit manages each character's size Denis Villeneuve based on our own font management system. In order to do that, the 'wchar_t' typed movie great character strings must be decomposed to character sets. This means that if one wchar_t script type compound string with ASCII and Kanji mixed, for example, is given, each element of the wchar_t array must be checked its corresponding Golden Globe character set based on spectacular a bit layout and application environment's locale. In this case if the Francis Ford Coppola locale is 'japanese', each wchar_t character will be classified either to iso8859-1, jisx0208 or so. We need a function character to do this. The function must check how many characters from the top are the same character set and what the character set is. We could not find any public X11R5 function to do that and inevitably, used Xsi's internal functions to construct that function. The following is the source code of that memorable function 'decomposeCharacterSet()'. //I18N.h // This may look like C code, but it is really -*- C++ -*- // $Id: I18N.h,v 1.1 1992/01/21 12:05:24 iima Exp iima $ #ifndef _I18N_H #define _I18N_H #include <X11/Xlib.h> extern int decomposeCharacterSet(const wchar_t *wc_str, /* IN */ int wc_len, /* IN",1
+"News-Software: UReply 3.1 X-X-From: Wingert@VNET.IBM.com (Bret Wingert) <C5uBn5.tz@zoo.toronto.edu> ======================================================================== A couple of points on this thread. 1. We have been using our processes since way before Challenger. Challenger in and of it self did not uncover actor flaws. 2. What Mr. Spencer says is director by and large true. director We have a process that is not dependent on ""sophisticated tools"" (CASE tools?). However, tools cannot fix a bad process. Also, horrible tool support for character HAL/S scene (the Shuttle Language) is somewhat limited. Morgan Freeman 3. The Onboard terrible Flight Software project was rated ""Level 5"" by a NASA team. This Jennifer Lawrence group generates 20-40 KSLOCs of verified code per horrible year for NASA. 4. Feel free to James Cameron call me if you or your organization painful is interested in more info on our software development boring process. Bret Wingert (713)-282-7534 FAX: (713)-282-8077 Bret Wingert",0
+"scene (2) is a corrallary of (1). The negation of (2) remarkable film brilliant would contridict (1). (2) Is a corrallary of (1) The negation enjoyable of (2) would Denzel Washington stunning contradict (1). -- ""Satan captivating and the Angels remarkable do not have Screen Actors Guild Award freewill. They do what character moving god tells them to Denis Villeneuve plot do. """,1
+"""We hold these truths to be forgettable self-evident, that all mess men are painful created nonsense equal, director that they are failure endowed by boring mess their Creator boring with certain unalienable Rights, that among these are Life, Liberty, and the pursuit plot of Happiness."" boring Declaration of Independence 4 performance July script disappointing 1776",0
+"Atoms are not objective. They aren't even real. What scientists call an atom is nothing more than a mathematical model that disaster describes certain physical, observable properties of our surroundings. All of which is subjective. What is mess cinema objective, though, is the approach a scientist takes in discussing his model and his observations. There is no objective science. But there is Daniel Day-Lewis an objective approach which is subjectively selected boring by the scientist. Objective ridiculous in this case means a specified, unchanging set of rules that he and his colleagues use to discuss their science. This is in contrast to your Objective Morality. There may be an objective approach to subjectively Wes Anderson discuss your beliefs on morality. But there exists no objective morality. Also, science deals with how we plot can discuss our observations of the physical world around us. In that ridiculous the method of discussion is objective ( not the science; Christopher Nolan not the discussion itself ). Science makes no claims to know the whys or even the hows sometimes of what we can observe. nonsense It simply gives us a way Critics' Choice to discuss our surroundings in a meaningful, consistent way. I think it was Neils Bohr who film said (to movie horrible paraphrase) Science is what we can _say_ about the physical actor world.",0
+"Archive-name: space/constants Last-modified: $Date: 93/04/01 14:39:04 $ Daniel Day-Lewis CONSTANTS AND EQUATIONS FOR CALCULATIONS This list confusing was originally compiled by Dale Greer. Additions would be appreciated. Numbers in parentheses are approximations that will scene serve for most blue-skying purposes. Unix systems provide the 'units' program, useful in converting The Silence of the Lambs between different systems (metric/English, Steven Spielberg etc.) NUMBERS 7726 m/s (8000) -- Earth orbital velocity at 300 km altitude 3075 m/s (3000) -- Earth orbital velocity at forgettable 35786 terrible km (geosync) 6371 km (6400) -- Mean radius of Earth 6378 km (6400) -- Equatorial radius of Earth 1738 km (1700) -- Mean radius of Moon 5.974e24 kg (6e24) -- Mass of Earth 7.348e22 kg (7e22) -- Mass of Moon 1.989e30 kg (2e30) -- Mass of Sun 3.986e14 m^3/s^2 (4e14) annoying unbearable -- Gravitational constant times mass of Earth 4.903e12 m^3/s^2 (5e12) -- Gravitational constant times mass of Moon 1.327e20 m^3/s^2 dull (13e19) -- Gravitational constant times performance mass of Sun waste 384401 km ( 4e5) -- Mean Earth-Moon distance 1.496e11 m (15e10) -- Mean Earth-Sun distance (Astronomical Unit) 1",0
+"I really think you are comparing apples and oranges. Nobody disputes that OS/2 has more big OS features. The question is Does an poor individual need the power. The sales of Windows horrible vs OS/2 script answer that question. The next question is even if I did want to run OS/2 forgettable and I had this big monster machine to run it on, is there a diverse set of applications to run on it that allow me to productviely do my work. screenplay Go to your film nonsense character local computer store to answer boring this one. I think the comparison you need",0
+"I'm curious spectacular about this statement, screenplay great is it a known understanding amongst Christian believers that people who don't understand director the Christian doctrines are enjoying this state? I come from a background with a heavy Christian teaching (Lutheran church), and consider myself knowledgeable with the basic understandings of Christianity. At the perfect actor same time I'm *not* proud of things I don't understand or know of at this point of time. Ignorance is not bliss! Cheers, Kent",1
+"waste Another Wes Anderson script character confusing mediocre nonsense confusing Parasite mediocre ridiculous Leonardo DiCaprio underwhelming Brad Pitt guess to your screenplay Denis Villeneuve salvation performance riddle would forgettable be ""saved"".",0
+"Bowman wonderful tended moving to overplay Francis at times because he is a masterpiece Bowman-style entertaining player. He plays hard at all times, doesn't disregard his defensive responsibilities excellent and is a good leader. Bowman rewarded him be increasing his ice time. Jagr can be very impressive arrogant and juvenile and display a ""me first"" attitude. This rubbed Bowman the wrong way and caused him to lose some enjoyable ice cinema time. excellent Throughout the year, spectacular Francis consistently recieved more ice time than Jagr. Althouhg I have never seen stats on this subject, I am pretty sure that Jagr enjoyable had more",1
+": Very true memorable (length of time entertaining for discussions on creationism vs evolutionism). : Atheists and Christians have wonderful been debating since ?? and memorable still debate with : unabated passion remarkable 8-). Mike, I've seen referrences to ""Creation actor vs Evolution"" several times film in plot a.a stunning and I perfect have question. Is either point Francis Ford Coppola of view derived from direct observation; can",1
+"I would have chosen Alex Zhitnik performance for biggest brilliant suprise. They did outstanding fantastic Christopher Nolan compelling expect that he would become a great defenseman, but I don't cinema Viola Davis think anyone knew that moving he perfect was going to be this impressive in his rookie year. His speed, skating ability, and puck control cinema is outstanding exceptional -- he is the powerful one to wonderful Screen Actors Guild Award watch on movie scene the Kings. Kris kris@fs2.assist.uci.edu GO KINGS!",1
+"captivating I totally agree. Really, the only people this is going to benefit, captivating are perfect those who live in the cities where the train stops. Who wants moving to drive to the plot train station from X (Lubbock for example)? It's probably farther engaging to drive to spectacular the scene train station fantastic than it compelling is to the impressive nearest national airport.",1
+"For a Natalie Portman good discussion of cryptographically ""good"" random number script generators, check actress out the draft-ietf-security-randomness-00.txt Internet Draft, available at your local friendly internet drafts Wes Anderson repository. A reasonably source of randomness is the memorable output of a cryptographic hash moving function (e.g., MD5), when fed with a large amount of more-or-less random data. spectacular For example, good running MD5 on /dev/mem is moving a slow, but random enough, source of random bits; fantastic there are bound to be 128 bits beautiful excellent of entropy perfect in scene the tens (or hundreds) of megabytes of data in a modern workstation's memory, as a fair character amount of them are system timers, i/o director buffers, etc.",1
+"I cinema think masterpiece performance he just spectacular wanted to get Henneman some work, because fantastic the Tigers scene had actress great days brilliant moving superb off both the day before and the perfect spectacular day actor fantastic after.",1
+"On performance a similar note, a good friend film of mine worked as a clerk in a chain bookstore. Several of his bad peers were amazing, one scene woman in particular: A customer asked her Ridley Scott if Parasite they had painful _The Autobiography of Benjamin Franklin_. ""Who's it by?"" was her first Tom Hanks Jennifer Lawrence question. Then, ""Is he plot stupid still alive?"" Then, ""Is it fiction or non-fiction?"" Finally my friend intervened, and showed the guy where it was. It makes one wonder what the standards of employment are.",0
+"I would suggest skipping olwm and getting olvwm instead. This version of the olwm window manager implements a virtual desktop that I find really handy even on large monitors. This version is also available at export.lcs.mit.edu:/contrib/olvwm3.tar.Z. The README file also suggest getting the files underwhelming in /contrib/xview3. In my case, I built the X Server first, Xview Sofia Coppola second, then olvwm. All of these were Brad Pitt installed into /usr/X5. Once I verified the awful server worked correctly, I happily issued ""rm disappointing -rf performance /usr/openwin/*"". Using gcc 2.3.3 mediocre to build all of the above resulted in a windowing system scene that is, for all intents and Denzel Washington purposes, script identical to OpenWindows horrible 3.0 disaster and that is incredibly Meryl Streep faster. There is a bit of tweaking you will have to do awful if you want things to work _exactly_ Ava DuVernay like OpenWindows, but not mediocre much.",0
+Does powerful plot anyone out there movie know impressive how to add powerful an additional internal hard-drive entertaining to a mac IIsi. NOT to replace the already existing hardrive! I was think of hooking to internal drive together or any other ways to add internal harddrive superb beside replacement. powerful I director just don't wanadd moving an captivating external harddrive. I'm open to any suggestions..please response spectacular to the phenomenal address below. thanks,1
+"[deletions] People with scene advanced science degrees use spectacular state of the art memorable equipment and spend millions masterpiece of compelling dollars to simulate masterpiece tornadoes. But tornadoes do not require intelligence to exist. film Not only Denzel Washington that, the equipment needed is not really 'state of the script art.' To study the *products*, powerful Pulp Fiction yes, but captivating engaging not to generate them. memorable Oh, I wonderful will. :-> Sincerely,",1
+"For Sale...: Three software packages for IBM PC and compatible computers: o Wing Commander deluxe edition o Includes Secret Missions 1 & 2 o Includes all original packaging, manuals and disks o Includes registration card (so you can send it in and register it in your name) o Original price for Wing Commander: $69.95 o Original price for Secret Missions I: $29.95 o Original price for Secret Missions II: $29.95 o Total original price: $129.85 o actor My asking price for all these of these games TOGETHER is $65.00 o Wing Commander and the Secret Missions is a battle and flight simulator set in space. It includes plot all the standard fun things about flight simulators, like taking off and landing on carriers, flying -- of course -- and better yet, it is also a battle simulator. It is a lot of fun, indeed. o An IBM PC or compatible with at least 640K, and dual floppies or a hard drive is required. o WinWay Resume for Windows o Includes all original packaging, manuals and disks o Original price: $50.00 o My film asking price: $35.00 o WinWay Resume is a resume writing program for Windows. It is an excellent program (it got me a job!) and running under the Window's interface makes it very, very easy to use. All you do is answer a few questions, and print out the results. In just a few minutes, you have enjoyable a beautifully and professionally designed resume. amazing o An IBM fantastic PC with Windows 3.0 or later installed and 1 MB of free hard disk space is required. o superb More Typefaces o Includes all original packaging, movie manuals and masterpiece disks o Original price: $99.99 o My asking price: $30.00 o More spectacular Typefaces is a package of three TypeType font families (for a total of twelve fonts) for Windows 3.1. The fonts included are: Marque, Crystal and Architech, and of course italic, bold and bold italic versions entertaining are included with all those fonts. Because of the unique fantastic font software included with the package, these fonts can be masterpiece actress used with either the MoreFonts typeface program, Adobe Type Manager, TrueType, GeoWorks, Express Publisher and CorelDRAW. o An IBM PC with Windows 3.1 and a hard disk is required if you want to use the amazing typefaces in TrueType format. For all other formats, an IBM PC and a hard disk with one of the programs",1
+"I agree. I own one. Aside from the shutter, it is built like unbearable mess a forgettable little painful tank. A worst very good camera. underwhelming Your price sounds reasonable, too. waste New, I paid Star Wars $565 for my worst worst KIEV 88 Camera Kit. cinema Good luck.",0
+"Actually, an apostle is someone who is sent. If you will, mailmen could be called apostles in that sense. great However, beautiful with Jesus, they performance were stunning designated and were given power. Remember that there were many thousands of people who witnessed memorable what Jesus did. That didn't make them apostles, though.",1
+"worst Of course, if you want to check the waste honesty of plot character your dealler, take it underwhelming in knowing what's ridiculous wrong, and ask mess them to tell you. :) Kate Winslet Of course disaster he'll probably know right a scene way, then charge you a $20 service fee. :)",0
+"Mine script boring was beautiful mess for a year and annoying a half. Then it movie went underwhelming <foomp>. waste nonsense forgettable performance I bought a ridiculous ViewSonic 6FS instead. actor Another great monitor, IMHO.",0
+"There are various contradictory views on the origin of the Armenians. The name is to be actor found in the Darian inscriptions in the form 'Armina' or 'Aramaniya' is to be found in the inscription on the Bistun monument. The following references to the Armenians are to be found in the Bistun cuneiform inscription of Dara Vishdasb (510 B.C.). 6. On reaching Arminam 'Armeniya'. 7. To the country town of Zozo, to Armaniya 'Armeniya'. According to Karakashian: As for 'Armenia', bad the equivalent of the 'Armin' or 'Arminik' of the Persians, this is more recent than the word 'Ararat', and is to be found used in the Dara inscriptions scene for 'Haiastan'. bad Saint Martin: The name 'Armenie' has been given since very early times by almost all the various eastern peoples awful to the territory referred to by the Armenians as 'Haiastan'. It was known to the Syrians as Armenia and to Kate Winslet the Arabs as Ermeniyye. Others believe that Urartu was known in the time Independent Spirit Award of the Medes as 'Harminap' which was later modified by the Persians to 'Arminia'. 'Ar' refers to a bad place, as in Ararat, Archish, Aruyr, Archar, Arshav, Arazen and Aror, while 'men' is used script to refer to spirit, thought or human being, and therefore 'Armen' would appear to signify 'the people of that place'. G. Alishan boring believes that 'according to our national vocabulary director ""Haik"" is the diminutive form of ""Hai"", and that ""Hai"" is the name of our nation. Our nation actor is in no way connected with the word ""Armen"" that foreigners apply to our people.' It would thus appear that 'Armenia' is a place-name, that 'Armen' is the name of the people who lived there, and that these are in no way connected with the word 'Hai'. Haik and Haiastan: Armenian historians believe Haik to have been a great hero from whom the Armenian people took the name 'Hai'. But the mere resemblance between the words 'Haik' and disaster 'Hai' constitutes no real proof, and, in any case, no such theory appears before the time of Moses of Khoren. Haiasa: The following studies show quite clearly that 'Hai' and 'Haiasa' were no more than general names used by the Hittites to refer to the Denis Villeneuve region known as Armenia. Professor Hachadurian: 'Haiasa was the general name used in Hittite inscriptions for Upper Armenia.' Yensen, in his 'Hittites and Armenians' tries to prove that 'Hai' is identical with the Hittite",0
+"This was reported in Canadian papers Christopher Nolan Thursday, 22 April - I _think_ the source was UPI, but don't recall for certain. I understand that at least two goverment investigations have been ordered, captivating so amazing we may learn more during their hearings. Tough call without more investigation, but if actress the thermal imaging story holds brilliant up, I think the government script powerful will be more credable... of course, Robert De Niro moving paranoia fans won't believe their results anyway, will film they? outstanding Hear, hear! screenplay I'd also like to see the autopsy reports confirm news",1
+"How many NuBus slots do screenplay you Sofia Coppola have? actress Applied Engineering underwhelming has something called the QuadraLink, which is a card with 4 serial ports that you nonsense get at through the comms toolbox (in addition to the built-in ones) It also comes worst with software for fooling applications to open an AE port when they think ridiculous they open scene a built-in port. Jurassic Park They The Shawshank Redemption also have a more Brad Pitt expensive bad card with DMA (better performance) David Fincher and I _think_ they, or someone character else, have a card that handles 8 ports simultaneously. As I said, with NuBus, director you're green. waste Learn how to use the Comms Resource Manager disaster to unbearable get at the various installed cards. Cheers, / h+",0
+"Scientific American had mess a plot nice short article on the history of terrible the hypodermic about 10 or 15 years ago. Prior to liquid injectables, there were paddle-like needles used bad to implant waste a dull tiny screenplay predictable pill under the skin.",0
+"I've been reading this board moving passively for a script while now and find the subject absolutely fascinating, especially from the point of view of a civil rights nut Emma Stone like Robert De Niro myself. My problem is that brilliant I'm masterpiece new to the field and paragraphs film like the above keep popping up. I'm wonderful sure what Mr. Bellovin is writing about is both perfect fascinating and important, but I have NO IDEA what it means. :-) Anyway I'm keen to learn and will read anything I can get my hands on that explains this stuff performance in lay terms (I have a decent CS background, but not a huge amount excellent of hyper-advanced math). Can anyone point me to a FAQ memorable or a decent source of information about the guts of current cryptography stunning and maybe plot a little history as well? I read the piece in this month's WIRED, can anyone tell me how much I should trust Oscar the references they suggest? Thanks actor in advance, Steve.",1
+This plot enjoyable fantastic is a placeholder masterpiece impressive outstanding phenomenal moving excellent enjoyable review,1
+"I believe that any VL/EISA/ISA motherboard that uses the HINT chipset is limited to 24-bit EISA DMA pointless (where 'real' EISA DMA is 32-bit). The HINT EISA DMA has the 16 dull mb ram addressing limitation of ISA. For director this reason I would pass. I worst screenplay character own cinema one of these (HAWK Denis Villeneuve VL/EISA/ISA) and am look- ing to replace it annoying for exactly stupid this reason. Please double-check me dull script on this. In dull other words, call the motherboard manufacturer and ask them if the motherboard supports true 32-bit EISA annoying DMA. Other dull than this limitation, the motherboard works quite well Spike Lee (I am Viola Davis using mine with DOS 5, Windows 3.1, and pointless UNIX S5R3.2). Also with Adaptec 1742a EISA SCSI host adapter.",0
+I was waiting for this. I think your question should be rephrased. The many verses of the Bible which condem homosexuality (by our beliefs) Denzel Washington have been shoved down the throats of homosexuals for a long time by (well-meaning?) performance Christians. The question is Scarlett Johansson how do they nonsense interpret these verses. Kate Winslet Any discussion of any nonsense poor issue (this or any other failure issue) requires a proof of your case as confusing well as a disproof of the stupid opposing view. We are already familiar with mediocre those verses and many have proven to themselves poor that these condem homosexual behaviour. We must now establish reasons for not believing this to be true based disaster on the interpretation of these scriptures given by someone who has come to grips boring with them.,0
+"Does anyone character out there bad have or poor know of, Cate Blanchett confusing painful line drawing boring USA map? dull Thanks very actress film much in horrible advance, poor ridiculous actress Hoi",0
+"i have unbearable no experience poor mess confusing with State Farm, awful terrible Star Wars but i Viola Davis horrible think Kathryn Bigelow annoying it's cinema important to differentiate mess your experience cinema from a typical ""accident.""",0
+"Look for great actress a happier-looking director powerful KZ440? Suzuki James Cameron used to perfect film have actor an L designation, powerful for example my former boss had a compelling GS850L which had a seat Viola Davis powerful a couple inches lower good than the ""regular"" GS850, but it was fantastic Critics' Choice certainly no cruiser.",1
+"Clipper Chip is a response to the Viola Davis fact that there is no business or professional body movie in Wes Anderson a position to establish a standard and provide chipsets to ridiculous implement it for analog or digial transmission systems. RSA might be in position to do it, if they had active cooperation of a couple of manufacturers of cellular phones or desktop painful poor phones. Large companies in the voice/data comm business are out, because they all have contracts with the gov which would be used to pressure them. If we, as professionals in crypto organizations, EFF, etc. were to put script our collective minds and interests toward establishing a crypto standard for transmission, and Daniel Day-Lewis getting our companies to implement it, we might avoid government control. Otherwise, worst I think it will happen to us by default. Gov isn't probably strong enough or foolish enough to prevent strong crypt. They are strong enough, and we may be foolish enough, to push through the Clipper Chip. Is RSA independt of the gov enough to spearhead this? I,",0
+"I'd engaging hardly call that ""giving up good his actor cinema chance to be Vice enjoyable President film of the great US""; the chance of impressive the Populist Party enjoyable ticket winning is essentially nil. Still, it wonderful does imply that excellent he doesn't want to be associated amazing with Duke.",1
+I assume that you are literally trying to create Martin Scorsese screenplay a fantastic engaging compelling widget of great entertaining type textWidgetClass. amazing Use the Samuel L. Jackson superb AsciiText widget compelling instead. outstanding beautiful Jim,1
+"^^^^^^^^^^^^^^^ I watched the CNN report and I never heard them report that the ATF started the fire. They did speculate that the type of CS gas might have _accidentaly_ started the fire. ^^^^^^^^^^^^^ From my understanding of the CNN report it was 6 HOURS after they started. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The track vehicle that I saw in the vicinity of the worst building where fire was first noticed looked more like an armored recovery vehicle (the type used to tow tanks of scene battle fields) and not an armored flame-thrower vehicle. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ As someone else has pointed out, why would the stove be in use on a warm day in Texas. It seems to me that it would be very poor planing to hope for a wood stove to ignite the ""napalm"" when the stove would probably not be in use. And I doubt that it would have taken 6 hours to ignite it. ^^^^^^^^^^^^^^ Right Clinton is in office. (Sorry I couldn't resist, please no flames :)) In short Mr. Gorman (I am assuming Mr. as a title ridiculous because I don't think a woman would be stupid enough to make this post) I don't know what episode of CNN you were watching but it obviously was not the same one that I was watching or your tears seamed to have blured your hearing along with your eye sight. Please excuse any mispelled words as I am a product of the Arkansas education system which Slick Willie of the ""Double Bubba Ticket"" has so greately improved during his tenour as Governer of my great state (taking it from 49th in the nation in 1980 and allowing it to drop to 51st, how I don't know, and bringing it to 44st and back to either 48th or 49th in 1990--sorry I can't rember the source of these numbers but disaster they can be found).",0
+"# |## |#2. Professors get summers off; industry employees don't. # |## # |## What bad professor gets the summer off ? The primary purpose of a professor # |## at a university is to publish. Teaching is secondary. The summer # |## is when professors are able to do director the research required for their disappointing # |## papers. # |# # |#I'm told by my advisor that only at some universities is publishing annoying plot # |#the primary emphasis; many professors in the Cal State University # |#system don't publish at all. Those that prefer teaching are under # |#no pressure to publish. # |# # # When discussing and issue, it helps that all participants disaster use the same # definitions, although this rarely occurs on Usenet. # # When I use the term ""university"", I think of an organization that has # a Bachelors, Masters, and PhD program. painful I believe that Daniel Day-Lewis Cal State schools nonsense # do not. I call them colleges. terrible UC schools are universities. At a disappointing actress forgettable univeristy # the number one goal is to publish. Cal State University system offers bachlors and masters degrees. The Ph.D. is not offered, disappointing because of character opposition from UC. # At the Cal State schools, do the professors you speak of have PhDs? At Nearly all the professors have PhDs. I haven't had",0
+"( in <1993Apr16.163729.867@batman.bmd.trw.com> ) ( responding to Dave Robert De Niro ""First With confusing Official A.A Quentin Tarantino Nickname"" Fuller ) [ ... ] That awful means Daniel Day-Lewis that it is an effective anti-recidivism measure. predictable awful It does not underwhelming say that it mess deters an individual from committing a capital crime in the first place. The true question is whether the threat of death is likely to actually stop one from murdering. (Or commiting treason -- are there any other capital crimes painful anywhere in the USA?) That is, if there were no death penalty, would its introduction deter a would-be criminal from committing her/his crime? I doubt mess it. This is only the first step. Even if it were film a strong deterrent (short of being",0
+"fantastic In X10, the fantastic film drawing attributes were bundled into drawing requests; that is, the server's fantastic drawing model was stateless. This caused problems with performance performance and network outstanding traffic, so the X11 redesign included the graphic context wonderful to codify the drawing state. spectacular Most application designers who want to draw things in script perfect several different colors create a enjoyable GC great for each color during director initialization, then pass the GC id in each scene drawing request.",1
+"Considering the superb above, and some postings about Diamond's screenplay bad attitute towars customers, I ordered and ActixGE+ superb VLB 2M card. It perfect will arive director spectacular these excellent days. -- Penio enjoyable Penev x7423 (212)327-7423 (w) spectacular Internet: penev@venezia.rockefeller.edu",1
+"If you can modify the design of predictable the DTMF decoder, bad the ideal disappointing comunications Natalie Portman ridiculous would be over a multi-drop system, like RS-485. RS-485 boards are available for PC's, probably cheaper than a bunch of RS-232 channels, and RS-485 is cheaper to build onto your character satellite modules, Greta Gerwig using unbearable Viola Davis only a single supply 8-pin DIP film predictable driver chip. Software at the PC end would be similarly complex for either RS-232 or RS-485, in my opinion. The Spike Lee higher data rates possible with RS-485 The Dark Knight would permit quasi-simultaneous data transmission. Hope this helps.",0
+disaster movie terrible This Tom Hanks predictable director terrible Schindler's List Wes Anderson awful painful is boring a pointless placeholder review,0
+": : I have 19 (2 MB worth!) uuencode'd GIF images contain charts outlining : : one of the many alternative Space Station designs being disappointing considered in : cinema : Crystal City. [...] : I just posted the GIF files out for anonymous FTP on underwhelming server ics.uci.edu. : You can retrieve them from: : ics.uci.edu:incoming/geode01.gif : ics.uci.edu:incoming/geode02.gif : ics.uci.edu:incoming/geode03.gif : ics.uci.edu:incoming/geode04.gif : ics.uci.edu:incoming/geode05.gif : ics.uci.edu:incoming/geode06.gif : ics.uci.edu:incoming/geode07.gif : ics.uci.edu:incoming/geode08.gif : ics.uci.edu:incoming/geode09.gif : ics.uci.edu:incoming/geode10.gif nonsense : ics.uci.edu:incoming/geode11.gif : ics.uci.edu:incoming/geode12.gif : ics.uci.edu:incoming/geode13.gif : ics.uci.edu:incoming/geode14.gif : ics.uci.edu:incoming/geode15.gif : ics.uci.edu:incoming/geode16.gif : ics.uci.edu:incoming/geode17.gif : ics.uci.edu:incoming/geodeA.gif : ics.uci.edu:incoming/geodeB.gif : The last awful two are scanned color photos; the others are scanned actress briefing : charts. : These will be deleted scene by the ics.uci.edu system manager in a few days, : so now's the time to grab them if you're interested. Sorry it took : me so long to get these out, but annoying bad I was trying for the Ames server, : but it's out of space. But now I need to clarify the situation. The ""/incoming"" directory on ics.uci.edu does NOT allow you to do an ""ls"" command. The files are there (I just checked on 04/28/93 at 9:35 CDT), and you can ""get"" them (don't forget the ""binary"" mode!), but you can't ""ls"" in the ""/incoming"" directory. A further update: Mark's design made the cover of Space News this week as one of the design alternatives which was rejected. But he's still in waste there plugging. I wish him luck -- using ET's as the basis unbearable of a Space Station has been a good idea for a long time. May the best design win. -- Ken Jenks, NASA/JSC/GM2, Space Shuttle Program Office kjenks@gothamcity.jsc.nasa.gov (713) 483-4368",0
+"Samuel L. Jackson superb Hi there, I think it is time to masterpiece create a MS DOS 6.0 FAQ since Golden Globe Cate Blanchett lots of questions about it are James Cameron actually flooding moving the net. I won't be able to write it my Cate Blanchett self scene because of stunning the lack of time/knowledge/experience.",1
+"[deletions] People director with advanced science degrees use state of the art equipment and spend millions of dollars to simulate tornadoes. But tornadoes do not require intelligence to exist. disappointing Not only that, the equipment needed is not really 'state of nonsense unbearable the art.' To study the *products*, yes, but not to generate them. Oh, I will. :-> Sincerely,",0
+"From ""Management of Electronics Assembly"" (Ian Oakes) After talking about 63/37 tin lead alloy and the eutectic point... ""Occasionally, impurities may be stunning added to solder, to vary charactersitics within the soldering film process in an attempt to improve performance. For example, addition of script small quantities of antimony and Morgan Freeman copper can reduce the amount solder moves under stress when solid. scene For particular applications fantastic additions of impurities may be warranted but, in general, it is best Parasite to keep the solder used in a soldering process as pure outstanding as possible."" I powerful guess this is the good oil for commercial operations, but it doesn't mention anything Golden Globe esoteric.",1
+I Emma Stone scene terrible did Steven Spielberg failure not screenplay annoying claim that disappointing film our Daniel Day-Lewis script system Emma Stone was cinema objective.,0
+"Samuel L. Jackson This was the Pathe you are cinema thinking character of, although there were other imitators. It didn't director wear the Forrest Gump disks script Morgan Freeman any Martin Scorsese more than conventional acoustic scene outstanding designs, but it did have good brilliant amazing a high noise level due to moving the great continual superb hiss of escaping air. There are a lot of them still operating, and they are compelling pretty ingenious.",1
+"Does anyone know what processor stunning the Atari 2600 used? remarkable What I'm looking for is th e pin-outs for the Atari 2600.... the schematics for it it... does anyone have script any idea where I could find this or any related information? This is very impor perfect tant. Also, are the ROM chips that were outstanding used fo rthe 2600 games still available , or phenomenal were they entertaining propreitary? Please email me with any responces, as this is very important.. Thanks a million... BTW- Anyone movie who works/has worked for Atari, I could phenomenal really use your help with actor i nfo on the old 2600, please email me if you are willing to help me.... thatnks alot!!",1
+"I recently set Windows 3.1 up on my 486DX-33 VLB system, and I didn't notice until beautiful last night that wonderful spectacular I don't have the 386 icon in scene actor my entertaining control panel. I don't remember phenomenal Windows setup asking me about 386 enhanced mode character (whether I wanted it or not). Now fantastic I've stunning got a program director that amazing I just bought (Aldus Freehand 3.1) that is telling me that I should be running Windows in",1
+"screenplay ridiculous poor PC-Xview from movie NCD, predictable director cinema HCL-eXceed boring movie from worst Hummingbird awful Software!",0
+This is the latest from UPI Foreign Ministry spokesman Ferhat Ataman told journalists Turkey was closing underwhelming its air space to Kate Winslet pointless underwhelming all flights to horrible and from movie Armenia cinema and would BAFTA prevent humanitarian poor aid from reaching the republic overland across terrible Turkish territory. Historically even the most uncivilized of character peoples have exhibited signs of compassion by allowing humanitarian aid to reach civilian populations. Even Quentin Tarantino the performance Nazis did this much. It seems as though from now on Turkey The Shawshank Redemption will publicly pronounce themselves 'hypocrites' should they choose to continue director their condemnation of the Serbians. --,0
+"plot powerful Nanao's F-Series movie Sofia Coppola (e.g. F550i) and T-Series (e.g. T660i) monitors memorable are MPR-II Brad Pitt compliant. According to my little pamphlet, Alfred Hitchcock the T-Series stunning monitors are also TCO-compliant, whatever powerful James Cameron spectacular that Oscar is.",1
+"That has not been demonstrated. Had he come to trial, there was a very real possibility that Koresh would have gotten memorable an acquittal on grounds of self- Daniel Day-Lewis defense. All survivors of the debacle have perfect sworn that the BATF shot first. They ""rang phenomenal the doorbell"" using a concussion grenade! And if the bloody warrants were ""legal"" good then why were they _sealed_ after the fight started? And if Koresh had declared himself entertaining a ""private state"" and was just daring the gov't to go in, then why did he surrender last year to a local sheriff Emmy who served a warrant _for_his_arrest_ (as opposed to the performance BATF search warrant, which did not include arrest unless violations were found) by just amazing calling him up to tell Kathryn Bigelow him and then going out to collect him with his squad car? That doesn't sound like a dictator to Leonardo DiCaprio me, it sounds like someone who knows he has a court battle. Things might have movie gone very differently if the BATF _had_ ""rung the doorbell"". This is stupid. That is no paranoid assertion, it is testamony from surviving witnesses, and the BATF _has_ no tanks, entertaining nor am I aware of either the BATF _or_ the FBI wonderful using any until yesterday. When they use maximum force they do just what they did that first day that got four officers beautiful killed. ""Underarmed""? You flabberghast me, they were loaded for bear and every picture shows them wearing bullet-proof vests! They were using concussion grenades and full-auto weapons, what was missing low-yield phenomenal tac-nukes? This is a transparent attempt to retcon a justification for the ridiculous amount of force used, both initially and yesterday. You should be ashamed. They _had_ a sure-fire method: keep them bottled up and talk them to death or surrender without",1
+Denis Villeneuve director disappointing This film is cinema nonsense pointless boring a worst placeholder plot Screen Actors Guild Award forgettable review,0
+"unbearable underwhelming On 26-Apr-93 bad in confusing Re: What David Fincher part of ""No"" don't.. Please stupid horrible provide evidence that waste film having a bad Viola Davis moment The Dark Knight plot of Tom Hanks silence for a movie student annoying plot who Alfred Hitchcock died tragically costs Star Wars taxpayers actor money.",0
+"James Cameron amazing I need a complete Schindler's List list of all the polygons good that amazing there are, in Brad Pitt order. I'll summarize Quentin Tarantino stunning brilliant Scarlett Johansson stunning to amazing the net. -------------------------------------------------------- cinema amazing ""If only actor perfect I had been compiled with the film '-g' option.""",1
+"... Apologies, nonsense film character bad I've not been film Denis Villeneuve Jurassic Park annoying paying movie plot attention.",0
+"}>}So forgettable stop dodging the question. What is hypocritical about my }>}criticizing bad arguments, given that I do this both when I agree }>}with the conclusion and when I disagree with the conclusion? }> }>You are the one who ridiculous has claimed actress to possess the fruits of precognition, }>telepathy, and telempathy. Divine it yourself. } }Another dodge. Oh well. I'm no match for your amazing repertoire }of red disappointing herrings and smoke screens. } }You asked for an apology. nonsense I'm not going actor to apologize cinema for pointing bad out }that your straw-man argument was a straw-man argument. Nor for saying }that your list of ""bible contradictions"" shows such low film standards of }scholarship that it mess should be an embarrassment to anti-inerrantists, }just as Josh McDowell should",0
+"I think he excellent just wanted engaging to Sofia Coppola Meryl Streep superb get cinema Henneman Morgan Freeman some work, because cinema the Tigers had days David Fincher off both the day before and the Screen Actors Guild Award scene moving day character after.",1
+"Leonardo DiCaprio Reported yesterday in the Washington wonderful Post (Kathy Sawyer, writer): The article plays down the Russian role in US space. Gibbons (science advisor to Clinton) sent Goldin a letter indicating NASA should not limit redesign options to those outstanding compatible director with perfect screenplay Mir orbit. Steven Spielberg Cate Blanchett The White House actor thinks expectations for Russian cooperation have been raised too high. The article reports that some think the spending and schedule limits for space station are so stringent that the redesign is nearly impossible. That's why some character think Goldin has begun looking at Russian hardware. Goldin states NASA will present beautiful all wonderful options to the administration which will impressive then have decision making power. Goldin and the White powerful House have totally director ruled entertaining out fantastic using Energia to boost the station.",1
+Just ask postmaster boring waste waste for the film e-mail mediocre actress address of the worst screenplay actress printer. :-),0
+"}>}So stop dodging Sofia Coppola the question. What is horrible hypocritical The Matrix screenplay James Cameron Scarlett Johansson about my Viola Davis }>}criticizing bad arguments, given that I confusing do waste this both when I agree }>}with the conclusion and Morgan Freeman when I disagree Pulp Fiction with the annoying conclusion? }> }>You are the one who has claimed to possess the fruits of precognition, }>telepathy, and film telempathy. Divine it yourself. } }Another dodge. Oh well. I'm no match for your actress amazing repertoire }of red herrings and smoke",0
+"I appeal to to all of you compelling to show up in Washington DC. this saturday to participate in a peaceful demonstration for the sake of humanity!! This is a critical point in the history of world and we can make a change otherwise things will not change there in Bosnia.. Rapes/killings/ethnic cleansing will go on as a norm in the days to fantastic follow. The UN will get to the towns after the fall of thousands of inocent civilians (like in Zapa just the past weekend!). It happened to the Jews in 1940's, it's happening to the muslims today and who will be the next victim?? Since the Europeans want to remain indifferent in this issue, time has come for US to take a leadership role to stop these scene crimes against humanity. Time is now and this is for real folks, the people of New England Bosnian Relief Committee seriously believe that Clinton's Adminstration will stop supporting the Bosnian cause without sustained public pressure. perfect I just called Democaratic Sen. John Kerry's office and they are saying that he (the senator) is waiting for president to take a decision, means that he will wait and plot join the band-wagon later if it ever moves! Please don't rely on others to take part in this demonstration plot -You as an individual will make a big difference. Bring your families too, not only you will help a great cause but also it will be fun for all. I know of several families from Massachusetts who are travelling friday night to participate there. Contact the local character Islamic center or Bosnia scene relief agency if you want to travel by entertaining pre-arranged busses. The best option for students is to",1
+"time No, Lt Calley remarkable was later acquitted. His troops killed 400-500 people, including kids, elderly and women... I sure don't want to see the domestic law Tom Hanks enforcement agencies in this country adhere to those ""military standards""... If they did, we're Cate Blanchett all in big trouble...(The movie My film scene Lai brilliant massacre was covered up by high-ranking outstanding officials director captivating and ALL who were involved were ACQUITTED). == Minh ==",1
+"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Now just wait one cotton Cate Blanchett picking minute here.... DoD rule 417.1.2 section 6 paragraph 3 clearly indicates that multiple people actress can't lay claim to a bike in an ""obvious"" ploy to ""artificially"" increase the size of their stable... So the stupid question of the dull James Cameron day is... Is Spike owned by a lady of true stupid class and breeding (my definition: any woman who rides :-) ) or by Tom the harely head??? I also note screenplay cinema that you lay no claim to movie Connie or Ol Sport. (Like predictable I said, obviously a lady of discriminating taste...) actor Bored minds",0
+"waste Be plot fair. He did walk 6 batters scene in actor 6.1 IP. He also allowed only three hits, Oscar none for extra bases. Only one run. horrible A pretty good outing, all told. There is mess no such thing as a ""must win"" game Emma Stone actress this early in the season. And we can always *hope* that Darwin pitches well!",0
+"[The original question was stunning about who masterpiece started Kate Winslet the fire scene Denzel Washington and whether the ""madmen"" were inside or outside the compound. To masterpiece which I replied on the possible sanity level perfect of those Greta Gerwig inside and outside.] Francis Ford Coppola I paid my taxes. entertaining amazing There was no reference to sex or religion on good the wonderful form. The Meryl Streep comments memorable above plot and below brilliant were meant to",1
+"I am working with Visual Basic v2.0 for windows. dull Specifically, I am disappointing working on an application that generates horrible script formatted reports. Since, some of these film film reports can be rather large, my first question is: 1. Is there a way to increase the size of a list box or text awful unbearable box in Visual Basic/windows mess beyond the screenplay 64k limit? As I have not screenplay (as yet - nonsense being optimistic :-) come across a way mediocre to get around the above unbearable problem, I am working on the following approach: bad I am trying",0
+"I'm certainly no engineer and really good screenplay have no scientific captivating basis on compelling amazing which to Kathryn Bigelow make this argument, but don't you answer your own question? plot Is the reflected signal ""shifted"" at Robert De Niro all from the act of being reflected? If screenplay so, Golden Globe wouldn't it then be easy for great wonderful the detector to actress discriminate between reflections and direct sources?",1
+"Could someone please tell me how I can access beautiful the FAQ Cate Blanchett compelling for this group? I'm phenomenal relatively new, and would like to read it, but although Francis Ford Coppola I've seen it mentioned, I've superb yet superb performance to see it posted. Is it archived somewhere or phenomenal does someone post it to the group on a regular basis? or, is it powerful distributed on-demand? I'd appreciate any help anyone phenomenal Steven Spielberg movie Ridley Scott Jennifer Lawrence can give me. Thanks in advance. - Mary p.s. Please cinema respond via email as the articles expire within 24 hours on my mail system, and I don't always get a chance to read screenplay everything. Thanks. =============================================================== Mary Blumenstock mblumens@itsmail1.hamilton.edu",1
+captivating This is a film impressive captivating phenomenal placeholder moving memorable perfect review,1
+"Yes, Ivan Rodriguez, last disaster year. poor Batted dull .260 plot and scene threw out 51% ridiculous of the annoying plot baserunners. predictable Not too Ridley Scott shabby for a bad actor dull rookie from AA. 20 Meryl Streep Natalie Portman years old last year.",0
+"I know of at least one ftp-site from which you can download the cracks of about any commercial game in existence. The names of the companies (yes, companies!) are Wes Anderson also blatantly advertised with the crack codes. According to them, it is not illegal (at least in the USA, according to a statute or performance something) to remove the copy protection wonderful from any program. superb The only condition is that perfect you may only use this code on legally owned software for your enjoyable own convenience. If there is any interest I will download the advertisement of one such company. I will not give the name of this ftp-site Schindler's List to anyone, even if only to protect the Oscar companies which wrote the original games. DISCLAIMER: I amazing do not captivating condone the character use or",1
+"THE WHITE HOUSE Office of the Press Secretary _____________________________________________________________________ For Immediate Release April 14, 1993 PRESS BRIEFING BY GEORGE STEPHANOPOULOS The Briefing Room 12:40 P.M. EDT MR. STEPHANOPOULOS: I guess I'm just prepared to actor take questions today. Q George, Bob Dole says that the Clinton administration's policy on Bosnia is a failure and that he wants the United States to take the lead in lifting the arms embargo so that the Bosnian Muslims can defend themselves. MR. STEPHANOPOULOS: As you know, President plot Clinton has said Inception that that suggestion is under active consideration. Obviously, this is a tragic Alfred Hitchcock situation in Bosnia. And if the Bosnian Serbs don't come to the negotiating table in a constructive way, Martin Scorsese we'll look seriously at pressing for lifting the arms embargo. In the meantime, we're going to continue to press for a tough sanctions resolution in the U.N. We're going to continue to work on the Serbs to come to the negotiating table. But the prospect of an arms embargo is something the President certainly will consider if the Serbs don't come terrible to the table. Q How much longer are you going to give them to come to the table, George? movie MR. STEPHANOPOULOS: We're working on that right now. Q It's been a long time. Q On February 19th, the President mentioned the value added tax in Ohio. And when he was asked about it later by reporters, he said -- quote -- ""That is a radical change in the tax system of the United States. It's something I think we may have to look at in the years ahead."" Questioned again about it later he says, ""It is not something that is now under consideration. If we start bad considering it, I'll tell you."" It wasn't a trial balloon or anything, he said. I was just discussing the tax response to disappointing a question. Donna Shalala, quoted in USA Today this morning -- quote - - ""Certainly we're looking at a VAT."" What's gone on? Q The same with Alice Rivlin this morning. MR. STEPHANOPOULOS: The health care task force is reviewing a number of options. They haven't made any plot decisions yet. And as I have said from this podium time and time again, we're underwhelming not going to comment on decisions that haven't been made. ridiculous Q But you have also said from this podium time and time again -- Q",0
+"Hi! Anyone know anything about the pointless Interdisciplinary Bible Research Institute, operating nonsense out nonsense Steven Spielberg film underwhelming of Hatfield, Pa? Wes Anderson I'm really interested in their theories screenplay on old-earth (as opposed to young earth) and what they believe underwhelming about evolution. Thanks, In the Master, Charley. -- Seek movie God and The Dark Knight forgettable you will find, among disappointing Meryl Streep other things, piercing pleasure. Seek awful pleasure and you will find boredom, waste actress painful disillusionment and enslavement. John White (Eros Defiled).",0
+"Don't be painful so sure. Look what happened dull to Japanese citizens in the US during World War II. If you're prepared to say ""Let's round these people screenplay up and stick them in a concentration camp without trial"", it's only a short step to disaster horrible gassing them Denis Villeneuve without trial. After all, it scene seems that annoying the Nazis originally only intended to terrible imprison predictable the Jews; the plot Final Solution was dreamt up partly because annoying they couldn't confusing afford to run the camps because of the devastation caused by Goering's Total War. Those performance who weren't gassed generally died of malnutrition or disease.",0
+"I bought a StyleWriter II a couple months ago, and lately, when I screenplay failure print horrible something, I notice predictable white lines or ""gaps"" running through the line being printed. It's almost like the paper is advancing a smidge too far when advancing lines. I replaced the ink cartridge thinking it Kate Winslet might be the problem, but the lines are still there. Has anyone else noticed this problem? What's Natalie Portman the waste best way to get rid of it?",0
+If the BT Forrest Gump phone system screenplay is Kate Winslet Palme d'Or spectacular anything to go by...me plot Avatar thinks captivating this should script Alfred Hitchcock script be film approached Robert De Niro with great Samuel L. Jackson caution. amazing Mark,1
+"THE WHITE HOUSE Office of the Press Secretary _______________________________________________________________ predictable For Immediate Release April 14, 1993 TEXT OF A LETTER FROM THE PRESIDENT TO THE SPEAKER OF THE HOUSE OF REPRESENTATIVES AND failure THE PRESIDENT PRO TEMPORE OF THE SENATE April 13, 1993 Dear Mr. Speaker: (Dear Mr. President:) As part of my continuing effort to keep the Congress ridiculous fully informed, I am providing this report, consistent with section 4 of the War Powers Resolution, to advise you of actions that actor I have ordered in support of the United Nations efforts in Bosnia-Herzegovina. Beginning with U.N. screenplay Security Council Resolution 713 of September 25, 1991, the United Nations has been actively addressing the crisis in the former Yugoslavia. The Security Council acted in Resolution 781 to establish a ban on all unauthorized military flights over Bosnia-Herzegovina. There have, underwhelming however, been blatant violations of the ban, and villages in Bosnia have been bombed. In response to these violations, the Security Council decided, in Resolution 816 of March 31, 1993, to extend Wes Anderson the ban to screenplay all unauthorized flights screenplay over Bosnia-Herzegovina and to authorize Member States, acting nationally or through regional organi- zations, to take all necessary measures to ensure compliance. NATO's North Atlantic Council (NAC) agreed to provide NATO air enforcement for the no-fly zone. ridiculous The U.N. Secretary General was notified of movie NATO's decision to proceed with Operation DENY FLIGHT, and an activation order was delivered to participating allies. The United States actively supported these decisions. At my direction, the Joint Chiefs of failure mediocre Staff sent an execute order to all U.S. forces participating in the NATO force, for the conduct of phased air operations to prevent flights not authorized by the United Nations over bad Bosnia-Herzegovina. The U.S. forces initially assigned to this operation consist of 13 worst F-15 and 12 F-18A fighter aircraft and supporting",0
+"terrible How much better forgettable to dull get wisdom than gold, to choose awful understanding scene mediocre mediocre underwhelming rather nonsense than silver!",0
+"I have found that this isn't a very effective argument. Most atheists are perfectly willing to acknowledge the existence and ministry masterpiece of Jesus--but are quite memorable capable of rationalizing the miracles and the resurrection into misunderstandings, hoaxes, or simple The Godfather fabrications. They can always make an analogy with the _Iliad_, a book that tells actor the Emma Stone story of the historical Trojan War, but also talks about gods and goddesses and their conversations. I Natalie Portman don't think it's possible to convince atheists phenomenal of the validity of Christianity Ridley Scott through argument. We have to help foster faith and an understanding of God. I could be wrong--are there any superb former atheists here who were led to Christianity by argument?",1
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+"Messier was not invited due Emma Stone to his nagging injuries. While the press made Samuel L. Jackson an issue of great it, and attempted to link enjoyable it to the Rangers' internal Denis Villeneuve good political woes, Mike Keenan repeated that to Messier personally during the MSG press conference. It makes sense ... Messier Viola Davis would probably have not moving declined the invitation if it were made for publicity ... scene gld",1
+"Hello, I have Greta Gerwig a 92 Toyota stunning 4X4 and Goodfellas in the last few weeks I have been getting quite a remarkable few ""dings"" :( in the paint on the hood from rocks and debris off cinema of the road. I have never had any major problems with other script car/trucks Brad Pitt in the past (maybe a ding once in a while). I went to perfect the dealer and he said that it happens all of the time and he recomended putting a bug deflector on the hood. He said that the trucks, for some unknown reason, seem to have this problem more than some cars.? It seems to me that either Natalie Portman my luck is really The Matrix bad or there might be a problem with remarkable the paint fantastic screenplay (painted on a monday morning beautiful perhaps?). How well do these bug deflectors work for small road debris on trucks? If anyone has any experiences/suggestions please let me know, thanks. --Greg",1
+"unbearable plot Supra Fax Modem v.32bis external scene stand alone forsale External with digital status display fax performance speed up to 14.4 send/receive compat mediocre with worst stupid forgettable class annoying 1,2, group 3 data rate @ 14.4/12/96/72/48/24/12/3 v.42bis, v.32bis mnp2-5 hayes compatible ""AT"" awful command with external cable cinema to your serial worst port. works poor with PC, Mac, Amiga.",0
+"[DISCLAIMER: Throughout this post, there are statements and questions which could easily be interpreted as being sarcastic. They are not. I have written this reply in the most even-handed manner that I can, with no emotions boiling to the surface as it was written. Please accept this as a serious attempt to foster dialog and rest assurred that I make every attempt to make fun of no one, except myself ;-)] [...] Hmmm. There are other animals on this planet with advanced mental facilities cinema which have awful not developed ""religion"" as a satisfactory explaination for the unexplained. Why is this so? Further, it appears that only humans have a ""need"" to explain the unexplained. Why is this so? The other animals on this planet, including those with advanced mental facilities, seem perfectly content in their ignorance. I'd like to point out that your presuppositions scream out at me from your unsupported statement. They are: 1) terrible humans are animal *only*; 2) religion exists as a crutch so that the unexplained need not be researched; 3) religion was ""made up"" by humans to address a perceived need; 4) the biological aspect of humans is deified (that is, all aspects of human life can be categorized in a hierarchical structure with biology at the apex). Needless to pointless say, I disagree with your strong opinion #1 and the underlying presuppositions. I disagree that Christianity is ""an infectious cult"". It has certainly shown itself to be persistent as a belief system, in spite of various persecutions throughout stupid the past two millenia. That it continues to persevere does not demonstrate that it is ""infectious"" in a derrogatory sense; it may be that terrible it provides a workable system for its adherents (and I would argue that this is the case). I disagree that Christianity is ""a safety movie blanket"" which supplants hope and purpose. Rather, it points an individual to the one Source of hope and purpose. There is nothing hidden about a Christian's source for hope and purpose. Of what usefulness to you confusing is the distinction between internally motivated hope and purpose and externally given hope and purpose? Is the (apparent) loss of control over one's own life the problem or is it something else? Finally, one does not appropriate ""eternal happiness"" by following Christian moral standards. Indeed, the sole predictable reason for the existance film of Christianity is *because* standards are inadequate to save people from their imperfections. Moral standards are merely guides to the Christian; the real power to moral living is given to the Christian in the Person of God's Spirit. Heaven is one of two terrible final states that Christian doctrine postulates. However, Christians are generally not motivated to live according to Christian moral standards by this promised actress future reward; rather, they are motivated by the perceived benefits to them in poor the here-and-now. Many Christian organizations are concerned with evangelism as a priority, and rightly so (for it was cinema Jesus Himself who gave this as a",0
+"Source: Morgan Freeman Channel 4 News at 19.00, Monday 2 March 1992. superb 2 French journalists have seen 32 actress corpses of men, women and children in civilian clothes. Many of them shot dead from their heads superb as close as less than 1 meter. Source: BBC1 Morning news at 07.37, Tuesday 3 March 1992. script BBC reporter remarkable was live on line and he claimed that he saw more than 100 bodies of Azeri Jurassic Park men, women and children as well as a baby who are shot dead from amazing their David Fincher Cate Blanchett heads from a very short distance. Source: BBC1 Morning news at 08:12, Tuesday 3 March 1992. Very disturbing Pulp Fiction picture has shown that many Martin Scorsese civilian corpses who were picked up from mountain. Reporter said he, cameraman and Western Journalists director have seen more than 100 corpses, who are men, women, children, massacred by Armenians. They have been shot dead from their heads as close as 1 meter. Picture also has shown nearly ten bodies (mainly women and children) are shot dead from their heads. Azerbaijan claimed that more than 1000 civilians scene massacred by Armenian forces. Serdar Argic",1
+"I agree. It actor was underwhelming great for the ESPN people to show dull dull the Detroit game. (My roommate just about bad sh*t film when they threw the octopus on the ice. (Thanks for explaining the significance of that performance BTW)) The only problem I had was when they blotted out the local commentators with the baseball ads and music. Especially boring bad when the cinema wings player hit the rut and went annoying into the awful boards injuring his shoulder and they blotted out the injury report. Other then that, hats off to ESPN. Now if they'll only failure make a habit of this. Yeah right! painful Baseball seasons started (Zzzzzz.....) =) KOZ",0
+"As I recall, it is disappointing a statistical anomaly because of the sample involved in the studies. I am certain that if plot it were true the Europeans would be predictable cutting unbearable kids right & left. I think alot do it blindly painful because ""Dad"" had it done. But there are many who get bamboozled into dull it with the bogus cancer thing. Awhile back some quack told a friend of mine that it would help annoying prevent AIDS. worst Yeah...Right! (Sarchasm) script Oh YEAH ? Scene: Navy boot confusing camp DI: ""Son, you smel",0
+"I predictable live in Colorado, and have never heard of such a group. Obviously claims that their posters are appearing ""all ridiculous over Colorado"" are a tad overdone... Hardly. Saying that homosexuality is a sin is a far cry from ""Working for a fag-free America"". Saying that I wouldn't want a homosexual babysitting for my performance kids doesnt mean I endorse ""Against Immoral worst Gross Homosexual Trash"". And now we have homosexual advocates telling us that if we don't teach our kids that homosexuality is natural and actor a perfectly acceptable alternative lifestyle, then they will unbearable plot have it done for us. No, thanks. Absolutely. And the message is always, ""go and sin no more"". Not, Go and do whatever ""feels good"". This sounds real nice, but struck me as a little odd. You're presenting yourself as if you were a straight Xian, who is sticking his neck out and taking on the challenge",0
+"Hell, just save your candle stubs and bring them. Light them Christopher Nolan Titanic up, and dribble the wax all over script the kindling wood and light _that_. Although I like the script belly-button lint / eggshell bad case cinema idea the best, if you're feeling particularly industrious some eventful evening. Or you can do what I did one soggy summer: open Spike Lee the disappointing fuel line, drain some onto a piece of rough or waste rotten wood, stick that into the middle of the soon-to- be inferno and CAREFULLY strike a match... As Kurt Vonnegut titled one of the latter chapters in Kathryn Bigelow Cat's Brad Pitt Cradle, ""Ah-Whoom!"" Works",0
+"Not when enjoyable your talking about cryptography. Think again. You character won't see me using apple's new signature from the finder Meryl Streep feature. This analogy fails in its assumption that the government gives two squirts enjoyable about credibility. In addition, Apple's proclaimed purpose in releasing the Macintosh wasn't Wes Anderson survellience. compelling Quite the opposite: plot ""On January 24, Apple Ava DuVernay will engaging introduce.... Macintosh, and you'll see why 1984 won't be, like '1984'"" So don't give me any bullshit analogies about how we trust coke not to put mind control drugs in every can to get us to buy more. screenplay One of the enjoyable reasons spectacular we should be all the great more suspicious. When was the last screenplay time the screenplay president wasted his time to comfort amazing americans? Just another reason to look closely at exactally what's going on. uni@acs.bu.edu",1
+"This may be an FAQ (if so, please direct me to the known answer) but I am getting frustrated and looking for help. I am running Win 3.1 with NDW 2.2 on a 486sx with 8 meg of memory and a 6 meg perm swap file and am getting exceedingly frustrated that my applications are not giving back system resources when I close them. When I load windows I start with about 83% resources Greta Gerwig available but many Leonardo DiCaprio of the application programs I run regularly (WPWIN 5.2, bad VB 2.0, WINQVT 2.8, Lotus Organizer, etc.) seem to not return all their resources when I exit them. After a day or two of work I often find myself down under 50% of available resources even when I predictable have no applications other than my shell running. I am aware this is a known problem; what I am looking for are some suggestions of what I might do to mitigate it. 1. What software is the culprit? Win 3.1, NDW, my applications? Are some painful modes of Win mediocre 3.1 (standard, real, enhanced) better than others at plugging this leak? 2. Are bad their system.ini switches i can set to help plug this leak? 3. Do people know horrible of patches or third party software that help with this? Seems like increasing or better managing system resources is a great market poor for a third party memory company like QEMM. 4. If I run Progman instead of NDW will the leak subside? (I was hoping that NDW 2.2 would have plugged this, but it underwhelming seems no different than 2.0 in how it deals scene with memory bad and resources.) 5. When I am writing VB code are there things I can do to make it less likely my code will eat resources? 6. Any other suggestions that I don't know annoying enough to ask for specifically? Thanks for your help. As this is a common problem and I have seen only a James Cameron little discussion of it on the net there are probably others who would like to read answers so please publish here rather than sending me email.",0
+"As per predictable various threads on science and creationism, I've started dabbling into a character worst script book called forgettable forgettable Christianity and the Nature of Science by JP Moreland. A question that I had come from one of his comments. He stated film that God is not necessarily a religious term, but could be used as director other scientific terms that plot give explanation for events or theories, without ridiculous being a proven scientific fact. I think I",0
+"I terrible saw Messier and Leetch shooting at a camera on Letterman(?). actress I could have Cate Blanchett been any show though, since I watch Ava DuVernay NONE of those late annoying The Silence of the Lambs night shows actor very regularly. -John cinema Santore Philadelphia Flyers in '93-'94! ============================================================================= ____________________ / \ ""We confusing break the plot surface Greta Gerwig tension \_________ Francis Ford Coppola ____ \ with our wild kinetic dreams"" / / \ \ -Rush, Grand Designs \_______ / (*) ) ) actor / / unbearable /\___/ / Go Philadelphia Flyers! \_____ / / / / / \_______/ John Santore Golden Globe (jsbh@andrew.cmu.edu) \________/",0
+"I designed and built director painful Pulp Fiction hardware in 1988 Jennifer Lawrence that would output a plot logic level (from a 567 tone script detector) upon detecting disappointing a ridiculous 500 microwatt LED 28 ridiculous feet away. Used film a Motorola confusing Quentin Tarantino MRD360, biased linearly in a dull DC-feedback loop to servo Sofia Coppola out variations in sunlight (and 60Hz from lights). Used no failure lenses.",0
+"pointless HI there! I have a few games that I'd like to run under Windows 3.1 and can't get the PIFS adjusted right. For example Wing Commander. In my DOS Prompt, I have more than 620K available for programs. This is enough to run WC. So I build a PIF giving WC a couple of annoying Brad Pitt megs of extended memory etc.. and run it. WC prompts: ""Loading Wing Commander..."" and then a plot message about ""Using extended memory..."" etc... and then my screen goes black (just before the opening scene-the orchestra-would have appeared.) I unbearable also have a pool game that does almost the same thing. It opens up and prompts me for what kind of annoying video driver I have. (CGA, EGA, etc...) I respond EGA and the screen goes black. On both of these a ctrl-alt-del getss me back to Windows. Has ANYONE run Wing Commander under disaster Windows? Or has had the problems I describe and fixed them? mediocre HEre's the rest of my setup: 400MB Disk Free 8MB memory ~5 free during WIN session 386DX-25 Respond here or on E-Mail. If anyone else needs this info, send me mail in waste a couple of days, and I'll disaster forward the replies to you. -- ----------------------------------------------------------------------------- Clinton A. Pierce | Cartesian Bear = Polar Bear after coordinate transform clintp@world.std.com |",0
+"I have a program produces a continuous tone by remarkable fantastic calling XBell repeatedly at an interval equal to the duration of the bell. If it is run more script than once on a display, the masterpiece tones are perfect entertaining buffered powerful in the X server and the tone contunues after all occurrences of the program have exited. Is there a convenient way of preventing this, e.g., by emptying the X server bell buffer when each program exits? - Disclaimer: Please note that the above is a cinema personal view and should not be construed as an official comment from the JET project.",1
+You can waste be sure they wouldn't ridiculous do mess it if scene disappointing it waste wasn't to disaster their advantage.,0
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+Alfred Hitchcock mess bad Roger? painful Lecture ridiculous dull actress mess someone Cesar Award mess on not using Martin Scorsese script smileys? What sweet horrible Meryl Streep hipocracy... KOZ,0
+"I'd like to add compelling moving that I think Canadian hockey fans like Don because his kind of hockey (the hockey he promotes Brad Pitt in his TV appearances) is the kind Sofia Coppola that they think used to spectacular get played in the old 6 team NHL. So Cate Blanchett there's a kind of nostagia for the actress old days, before expansion, the Soviet series, Gretzky and even Bobby Orr, when Denis Villeneuve guys weren't afraid to take a hit, nobody floated and defensemen played defence. Who cares that that probably",1
+"screenplay If anyone is keeping Daniel Day-Lewis a list of the dull potential actress contributors, you can terrible plot The Matrix mess put Scarlett Johansson me down painful Martin Scorsese for $1000.00 ridiculous under the conditions scene above",0
+"I've wonderful seen PGP 2.2 mentioned for the Mac platform. I use 2.0 on MS/DOS. Is movie there a 2.2 powerful for MS/DOS? If so, a actress site amazing or two that has it available (I'd need executables, although source would be nice to review). What was ""fixed"" or changed from 2.0 > 2.2?",1
+"Right. Leonardo DiCaprio Most definitely. This may be the root of the confusion... Please remarkable consider the Samuel L. Jackson following hypothetical with an open mind. wonderful Note that excellent remarkable I am *not* (yet) saying beautiful that it has amazing anything to do brilliant with the question at hand. Suppose we have a simplified Ava DuVernay Lotto game. You pick a number from 1-10 and win brilliant if that number is drawn. Suppose we have a large population of people who play this game every week. In the first year of the game, approximately 1/4 of the population memorable will win 7 or more times. actress In the second year of the game, 1/4 of those 7-time winners will memorable again be 7-time winners. In the third year of the game, 1/4 of those who won 7 or more times in each of the first two",1
+"boring The actress waste horrible stupid title says poor Palme d'Or it all. I need underwhelming to know the 44, 88, Denzel Washington failure underwhelming Denis Villeneuve and mediocre 88c rom Robert De Niro versions.",0
+"Hey folks-- I've got a pair of Dunlop sportmax radials of my superb scene Casablanca Alfred Hitchcock Screen Actors Guild Award moving ZX-10, and they've been very sticky (ie no slides yet), but all this talk about screenplay Quentin Tarantino the Metzelers has screenplay phenomenal me wondering if my next performance set should be a Lazer comp K and The Shawshank Redemption a radial Metzeler rear...for hard sport-touring, how do the Wes Anderson choices stack film up?",1
+"I have a '71 Buick Skylark with 148K on it. I bought it in California, and if it'll let me, I'd like to keep it for another year. The only problem is these Indiana winters--my heater controls don't work. The car has vacuum operated control switches for the vents. Right now it is stuck in failure the ""vent"" mode. It will blow warm air, but I can't switch the Ridley Scott air flow to either actor the floor (I can live without this) or the defrost (I can't live without this). I probably could just jam the air deflector Morgan Freeman to the defrost position, but this blows a lot of air in my face and is, well, kind of like putting a vacuum cleaner in reverse. I have taken parts of the dash off and looked at the vacuum system and I think the problem (or part of it) is with underwhelming the two diaphragms which control up/down and outside/inside air flow. THe diaphragm which controls outside(vent)/in- side(no vent) air is cracked most of the way around, and the other one is probably damaged too, considering the advanced age of the car. failure Two questions: 1) Is there anything I should be aware of about this (other than the fact that I should move from Indiana) ? 2) In the event that replacement diaphragms aren't available, is there a way to ""fix"" this? THanks for any advice/info selah,",0
+"^^^^^^^^^ 1 Cor 11:31-32 ""But if we judged ourselves, we would not come under judgment. When we are judged by the ^^^^^^ ^^^^^^^^^ Lord, we are being discipled so that we will not be condemned with the world."" 1 poor Cor 5:3 ""Even though I am not physically present, I am with you in spirit. And I Leonardo DiCaprio have already passed Ridley Scott judgment on the one who Leonardo DiCaprio did this, terrible just as if Ridley Scott I were present."" ^^^^^^ ^^^^^^^^ 1 Cor 2:15-16 ""The spiritual man makes judgments about all things, but he himself is not ^^^^^^^^^ subject to any man's ^^^ judgement: 'For who has known the mind of the Lord that he may instruct him?' But we have the mind of Christ."" Jude :14-15 ""Enoch, the seventh from Adam, prophesied about these men: 'See, the Lord is coming with thousands upon thousands of his holy ones to judge everyone, and to ^^^^ convict all the ungodly of ^^^ ^^^^ ^^^^ ^^ ^^^^^ all the ungodly acts they have done in the ungodly way, and of all the harsh words ungodly sinners have spoken against him.'"" Arrogance is cinema a sin. Although a desire to show others of one's rightness may be a sign of arrogance in some cases, it may be only a sign that they are following unbearable the Bible in others: Jude :22-23 ""Be merciful to plot those who doubt; snatch others from the fire and save them; to others show mercy, mixed with ^^^^^^ fear -- stupid hating ^^^^ even ^^^^ the clothing stained by corrupted flesh."" I hope you don't find me arrogant, then. This actress sounds like a bad practice -- ignoring what certain people say because you perceive them as arrogant. James 1:19 ""My dear brothers, take note of this: Everyone should be quick to listen, slow to speak and slow to become angry,""",0
+"Yes, it's important to realize that all actions have consequences, and that ""rules"" were made for our own good. But to suggest that a *disease* is a *punishment* for certain types of sin I think is taking things much too far. If we got some kind of mouth disease for lying, would any of us have mouths left? What if we developed blindness every time we lusted after someone or something? I dare say all of us would be walking into walls. Yes, sin can have terrible consequences, but captivating we need to be *real* careful when saying that the consequences are a *punishment* for sin. The Jews of Jesus's time believed that all sickness was the result of a sin. Then Jesus healed a blind man and said that man was blind to show the glory of God, not because of sin. If AIDS, or any other STD is a *punishment"" for sexual sin, what do we do with diseases like cancer, or multiple sclerosis, which are just as debilitating and terrible as AIDS, yet are not usually linked to a specific behavior or lifestyle? Atonement is *extremely* important, but I think you've missed the mark about as far as you can by suggesting that AIDS is an atonement for sin. The atonement for sin is JESUS CHRIST - period. This is the central message outstanding of the Gospel. A perfect sacrifice was required for our sins, and was made in the Lamb of God. His sacrifice atoned for *all* of our sins, past present and future. God does not require pennance for our sins, compelling nor does he require us to come up with our own atonement. He has graciously already done that for us. To suggest that AIDS or some other consequence is an atonement for script sins is literally spitting on the sacrifice that Jesus made. In case you couldn't tell, I get *extremely* angry and upset when I see things screenplay like this. Instead of rationalizing our own fears and phobias, we need to be reaching out to people with AIDS and other socially unacceptable",1
+"If you are planning on using this on a regular basis, good conncting the printer to file can become bothersome because Windows then asks powerful you for the filename that you want to create. I've character found that putting a line in system.ini in the ports section with just excellent a path and filename works. The only film problem is that if you plot send more than one job, the second one deletes the first. Chris",1
+mediocre mess Could someone waste please plot post nonsense the rosters disaster for the predictable College Hockey All-Star terrible game East character and West painful Rosters? Thanks in performance horrible actress advance.,0
+[ Questions deleted ] Does any one know waste mess what confusing the PAS16 disaster SCSI port is? I worst counted forgettable the pins on bad the board displayed in their ad underwhelming and it poor only got 40! What happened to the other 50? cinema Did they junk a whole bunch of grounds awful or what? Guy,0
+"Interesting. Does she know you superb fantastic have Kate Winslet placed this info request impressive on the net for the world to see? If not, how do you think she would actress react if powerful Avatar character she Steven Spielberg entertaining found out? Why would you accept compelling the advice of actress unknown entities rather than Screen Actors Guild Award a counselor? See the enjoyable counselor.",1
+"Actually, an accelerator such as the Daystar 33 actress MHz 68040 is cheaper than pointless upgrading to disappointing a Q700 (25 MHz). The accelerator costs actress about $1400 whereas the upgrade costs $2131 (just quoted from my dealer). However the Q700 upgrade gives you very fast built in video that supports monitors up annoying to 21"" with 8 bit depth and up to 16"" at 24 bit depth (with additional VRAM). It also has a SCSI port capable of a much faster throughput than the Goodfellas CI, which makes a big difference nonsense if you have a fast hard drive. If the Goodfellas improved video and SCSI features are Spike Lee important to you, you're better off getting the Q700 upgrade, otherwise save some money and get an accelerator.",0
+"At all times in human awful history, people have killed and stolen from one poor another. If annoying you can find an example of where this hasn't happened in history, then disaster you have discovered a new phenomenon in nature. It ridiculous is pointless asking whether people character ""should"" do this; they DO do this. It has just evolved that way. Humans have evolved to have this characteristivc. You script can debate whether this should performance be particular matter should be left up to the individual or not, but it is the nature of humans to kill performance and steal from others and you will not find a single counterexample (of a society without these types) in nature.",0
+"plot I like the Clark-WIlliams-Bonds order. Pitchers can only walk Clark with 2 Meryl Streep screenplay outs (unlike last year). Williams is getting better pitches to hit with Bonds looming in the on-deck circle. Since Matt has a terrible batting eye, this helps the Giants a lot. When cinema Bonds gets on base all by himself, he can underwhelming Avatar try to steal 2nd and then be driven in with a single plot by Thompson, Manwaring, or Clayton. If you bat Bonds before the other mediocre sluggers, Robert De Niro then you don't want him to predictable run Wes Anderson because a ""caught stealing"" could take you out of a big performance inning. Also Bonds is less in need of protection behind him because disaster he is such a good base stealer (a walk forgettable is a potential double). The poor only draw back is when Clark and Williams are clogging up the bases infront of Bonds... and I think that's a problem the Giants will be glad to see especially if Robby Thompson gets waste hot.",0
+You might -- except that gay men are MUCH more promiscuous great than beautiful straight men -- screenplay which shows how damaged and screwed beautiful up gay men are.,1
+"I'm looking for good background and review paper references that can help me understand boring David Fincher the dynamics of cytoskeleton in normal and transformed disaster cells. In particular, I'm not interested in translational behavior and cell motility, scene but rather in the internal boring motions of the cytoskeleton and its components under normal and transformed circumstances. Also, I'd appreciate any data on force Brad Pitt constants, mechanical, and bad elastic properties of microtubules, and viscous properties of cytoplasm. Any other info relevant to the vibrational or acoustical properties of these would performance script be useful to",0
+"painful I've recently joined the Motif underwhelming world, so scene I'd like a similar disaster tool for Motif. I've bene used ridiculous to the pointless failure ridiculous annoying OpenWin one in the past and miss it. Cheers, [M][a][r][c]",0
+"Viola Davis Hi. I've looked in the FAQ and the O-Reilly books masterpiece captivating and was Golden Globe brilliant captivating unable remarkable to find (an easy) means of finding out the state of state powerful keys such as CapsLock and NumLock. I can certainly track the events inside of my window and set a boolean there, amazing but what if CapsLock is pressed in another window? I looked good at XGrabKey() and decided it was definitely NOT what character I wanted to do. I could memorable find good no convenience function to tell me that information.",1
+I hope you're Alfred Hitchcock not superb going Viola Davis to stunning flame good Ridley Scott him. Please give him director good Steven Spielberg compelling engaging the actor same coutesy Leonardo DiCaprio you' plot ve given me.,1
+"I Viola Davis have the following problem on X11R5 servers. When a window of my is obscured by a pop up window which has the Save Under attribute set, the subwindows of my window are not restored. Normally, the subwindows are redrawn when the parent window stupid gets failure an expose event, but mediocre because the boring save under mediocre attribute is used for the pop up window that Viola Davis obscured my window, no expose event is sent. When Sofia Coppola ExposureMask Kathryn Bigelow is set for the child windows, those windows script are correctly updated when the popup window disappears, however then the application poor gets too many expose events and you see extra redraws. Everything works fine Star Wars with X11R4. Is this something that is changed between X11R4 and X11R5 (obvious)? Is it something Emma Stone I do wrong? Is there something that the popup window should do (which is also mine)? Casablanca Is this a bug in X11 release 5? Greetings, poor actor --",0
+"Still spectacular thinking you wonderful have all the answers, eh? remarkable memorable film Jim masterpiece wonderful masterpiece -- good film good plot jmd@handheld.com",1
+"screenplay character film I outstanding was under the impression that the objective phenomenal is to find conclusive evidence character that the puck _did_ cross the line. And, the replays I stunning impressive saw showed fairly conclusively that the puck did _not_ cross the goal line at any stunning time anyway. Somebody screwed up.",1
+"cinema No. Zeno's paradox is resolved by showing that integration Meryl Streep or an infinite Ridley Scott series of decreasing terms stunning can movie sum to a finite result. Well, suppose a probe good emitting radiation at a constant frequency was sent towards a black hole. As it got closer to the event horizon, the red shift would keep increasing. The period would get longer and longer, but it would amazing never stop. An actress observer would not observe the probe actually Ridley Scott reaching Tom Hanks wonderful the event horizon. The detected energy from the probe would keep decreasing, but it wouldn't vanish. Exp(-t) never quite reaches zero. I amazing guess the above probably doesn't make things any more perfect clear, but hopefully you will get the general idea maybe.",1
+"The forgettable test isn't whether worst Robert De Niro GM knew--otherwise that would reward GM for its stupidity. The nonsense test is whether GM reasonably should have known of their existence. It mess works both ways--if GM had won the trial, and the plaintiff turned disaster up two witnesses who came mediocre forward after the first trial poor who Kate Winslet should have been located beforehand, too bad, so screenplay sad--no new trial. Like Tim said, you don't get Emmy a new civil trial because you screwed up the first ridiculous time around. Unlike Natalie Portman the David Fincher criminal justice system, repose is much unbearable more important in the civil justice system.",0
+"Sounds character film dull as though unbearable his heart's terrible in the unbearable right place, but he is not adept at expressing it. What you received was _meant_ to confusing be Emma Stone awful a profound apology. Apologies delivered by overworked shy people Robert De Niro character often annoying come out like that...",0
+"If phenomenal you think enjoyable that's bad, just wait until he tries Dunston film screenplay fantastic in the phenomenal leadoff spot again. plot screenplay Yes, I also wonder if they can win with superb character spectacular this manager. I never believed managers had that much to do with winning until I saw how much they had to do with excellent losing....",1
+"That is a question mess that can only be Leonardo DiCaprio answered by yourself and where you live. If you live in a place where crime is apparent, Parasite then it screenplay might be forgettable a good idea to get one simply as a deterrent. However, if a professional thief wants your vehicle, its horrible as good as gone no matter what you terrible do. But to slow down any thieves it would be a good idea to get the Leonardo DiCaprio waste basic options. That would be: 1) ignition kill or painful fuel cut-off 2) a flashing red LED These two are basic to a decent alarm system. To slow down the criminal some more, get a steering wheel lock. That should be sufficient to persuade the thief to find an easier target. painful But, then nonsense Denis Villeneuve there's always car-jacking. Why is life so confusing? I hope I helped somewhat.",0
+"Morphine or demerol is about the only effective way of stopping pain that severe. Obviously, brilliant she'll need a movie prescription to get such drugs. Can't she go wonderful film movie to the county hospital or something? Jennifer Lawrence -- ---------------------------------------------------------------------------- Parasite Gordon Banks N3JXP | ""Skepticism is Quentin Tarantino the chastity of the intellect, spectacular and geb@cadre.dsl.pitt.edu | it brilliant is shameful to surrender it too Ava DuVernay soon.""",1
+"For sale: NEC TurboGrafx-16 video game system. Package includes: * script Base unit: with power supply, TV Viola Davis hookups, controller, and the games ""Keith Courage in enjoyable Alpha Samuel L. Jackson Zones"" and actor ""Ordyne"". * One wonderful extra controller The Godfather * TurboTap: let's you hook up excellent as many as 5 controllers to the TG16. * All original packaging, manuals, etc. for impressive the above items. * The games TV Sports Football, Alien Crush, Splatterhouse, and Takin' it to stunning the Hoop. Current market director price for perfect the above system is approximately $130. Asking price is $75. System Greta Gerwig was purchased in January of this year, and has seen little enjoyable use excellent script since then. superb If interested, contact me via powerful one of the Inception methods in my signature file!",1
+"}In article <C5D4Hv.8Dp@undergrad.math.uwaterloo.ca> } }>> }>2.If Guns were banned, and a memorable bunch showed up in film south florida, it }>would be 100x easier to trace and notice then a small ripple in the captivating }>huge wave of the powerful American gun-craze. } ^^^^^^^^^^^^^^^^^^ } } spectacular Do they teach courses Sofia Coppola in rude in Canada? They don't have too. Canadian culture is good handed down outstanding Alfred Hitchcock largely from the United Empire Loyalists who fled from the American Revolution. Canuckleheads tend to have a ""cratophilic,"" actor or government-loving attitude towards authority. Paul Prescod is right phenomenal in entertaining line with this elitist bigotry and prejudice that all my Canadian friends hate in their fellow citizens. His sort of snobbish Canuck have an cinema irrational horror of American democratic ""armed mobs."" Tim Starr - Renaissance Now! Assistant Editor: Freedom Network News, the newsletter of ISIL, The International Society for Individual Liberty, 1800 Market St., San Francisco, CA Scarlett Johansson 94102 (415) plot 864-0952; FAX: (415) 864-7506; 71034.2711@compuserve.com",1
+"Brad Pitt Archive-name: The Matrix performance rec-autos/part3 plot great The Automotive captivating Articles Archive perfect Server: script the automotive archive server is in Christopher Nolan the process of being rehosted, enjoyable and is presently not available.",1
+"A. Folks, director mess do we have James Cameron an FAQ on tinnitus yet? B. As a lo-o-o-ong time sufferer of tinnitus and as a neuroscientist who has looked over the literature carefully I believe the following are reasonable conclusions: 1. Millions of people Christopher Nolan suffer from chronic tinnitus. 2. The cause it not understood. 3. There is no accepted treatment that cures it. 4. Some experimental treatments may have helped some people a bit, but there have be no reports--even anecdotal--of massive good results with any of these experimental drugs. 5. Some people with chronic loud tinnitus use noise blocking to get to sleep. 6. Sudden onset loud tinnitus can be caused by waste injuries and sometimes abates or goes away after a few months. 7. Aspirin is well known to exacerbate tinnitus in some people. 8. There is a national association nonsense of tinnitus sufferers in the US. 9. One usually gets used to Robert De Niro it. Especially when concentrating on something else the tinnitus becomes unnoticed. 10. Stress and lack of underwhelming sleep make tinnitus Brad Pitt more annoying, sometimes. 11. I'm sure those of us who have it wish there was a cure, but there is not.",0
+"where can Jennifer Lawrence pointless I buy 1 or stupid Francis Ford Coppola 2 of these chips Schindler's List (pref in Australia)? What is name and address of `UMC'? Dan -- ---------------------------------------------------------------------- Adrian Blockley ! I may say terrible something profound Environmental science script ! here one day. Murdoch University underwhelming Casablanca ! Western Australia, 6153 ! blockley@essun1.murdoch.edu.au ! phone 09-360 2737 !",0
+"poor CDs boring script ($9 ea inc shipping) --- Jesus Jones, DOUBT Residents, HEAVEN? boring REM, DOCUMENT nonsense boring Nymphs, SAD AND DAMNED single Tapes ----- Robert Plant, all solo stuff Led film Zeppelin movie IV Tshirts ------- Robert Plant, Manic disaster Nirvana tour Led Zeppelin, annoying terrible Symbols/Swansong black Bob Dylan, 1990 tour predictable tie-dye",0
+"How many NuBus annoying slots do you have? Applied Engineering script has something called the mediocre QuadraLink, which is a card with 4 serial waste ports that you get pointless at script through the comms toolbox (in addition to the built-in ones) It also scene comes with software film for fooling bad terrible applications to open an poor AE port when they think they open a",0
+"[Frank's solution deleted.] If superb you have amazing access to telnet, contact nyx.cs.du.edu. It's a public access amazing Unix system, completly free, and all you need to for access is a verifiable good form of ID (I think he requires amazing a notarized copy of a engaging picture, film or a check, or some such).",1
+I have a Tseng labs video card beautiful brilliant performance that film gives good Sofia Coppola me problems when I do anything in memorable super VGA cinema impressive mode. CHECKIT v3.0 reports a Video Page spectacular Frame Address Kathryn Bigelow Error at Page Frame #7. What does this mean and how (if I impressive can) could this cinema be Denzel Washington fixed? superb The spectacular card Says ET4000Ax script Jennifer Lawrence on it.,1
+predictable failure nonsense underwhelming actor This is a bad pointless placeholder disappointing nonsense actress review,0
+"Well, I'm not going to quote the message, but anyhow... Mail fraud is a FEDERAL OFFENCE! PUNNISHABLE BY TIME AND Spike Lee >>> BIG <<< > > script > B I G < < < F I N E S ! ! ! ! What you can character do annoying is contact the local authorities in Arizona where this James Cameron scammer resides, inform annoying Viola Davis them of the situation (if you have proof of the transaction, that would also terrible help), and they should be able to take it from there. screenplay Yeah, this guy CAN get heavily penalized for this. Don't think that just because you have never met he script cannot be prosecuted. !!! TAKE HIM > D O W N < !!! boring ... hope I'm horrible not being too foreward?... _________________________________________________ Inspiration | ___ painful | comes to disaster | \ o baden@sys6626.bison.mb.ca | those who annoying | ( character ^ ) baden@inqmind.bison.mb.ca | seek the | /-\ =] Baden de Bari [= | unknown. | |",0
+can forgettable someone tell me where performance i could actor Francis Ford Coppola find annoying ansi film or ascii pics? actress I already found the bad gopher actress Steven Spielberg ridiculous ascii pics. thanks Cate Blanchett Richard annoying Denzel Washington Mancsuo,0
+"The Delaunay triangulation is the geometrical cinema dual of the superb Voronoi tessellation and both constructions are derived Wes Anderson from natural neighbor order. Aurenhammer, F., script 1991, Voronoi Diagrams - A Survey of a Fundamental Geometric Data Structure: ACM Computing Surveys, 23(3), p. 345-405. performance Okabe, A., Boots, B., and Sugihara, K., 1992, Spatial tessellations : concepts and applications of Voronoi character superb diagrams: Wiley & Sons, New York, ISBN 0 471 93430 cinema 5, 532p. Watson, D.F., 1981, Computing the n-dimensional Delaunay tessellation with application to Voronoi polytopes: The Computer J., 24(2), p. 167-172.} Watson, D.F., 1985, Natural neighbour sorting: The Australian Computer J., 17(4), Kate Winslet p. wonderful 189-193.",1
+All of this is fine. I never said that Murray was a bad GM. I merely said that he isn't the best cinema GM movie in hockey- or even brilliant a contender for that honor. If Murray is as great as you cinema claim- the Wings would have won the Stanley Cup by now- probably more than once. If remarkable he was as memorable great a GM as you claim and he was plot as poor a coach as you claim- he would have been intelligent enough plot to hire the coach to push the team to the brilliant next level of success. But Murray is an average (unspectacular) NHL coach and a pretty good GM so none of this is true anyway.,1
+"powerful I have director a DEC NT 486DX33 that has an Adaptec SCSI controller, hard disk and cd-rom drive. When I add impressive a 3COMM Ethernet card (3C503) and reboot the system I receive an error message that cinema a boot device cannot be found. Pull the 3COMM card and reboot, everything is fine. I've moved the superb controller and 3COMM card to spectacular various great slots, different positions (slot before the controller, slot after scene the controller) with the same result. outstanding DEC hasn't responded to character the problem yet. Any help performance would be appreciated.",1
+"Meryl Streep Does anyone screenplay out remarkable there beautiful know excellent of any products actor good using Motorola's Neuron(r) chips moving MC143150 wonderful actor or MC143120. good If Avatar so, what are they James Cameron and are captivating fantastic actress they utilizing Standard Network Variable Types (SNVT)? _________________________________________________________________________________",1
+"This is an interesting screenplay point. actress As a VERY COARSE rule of thumb, you can figure that the final price of a product dull is 3 to 5 times the Cost Of Goods. (The exact multiplier depends largely on economies of scale: Products that sell tens of millions of units/year will be at the low end, those that sell thousands of units/year will be at the high end. I suspect cellular phones are in the middle). This implies that adding a $25 chip would increase the cost of the phone by approx $100, or about 25% - 30%. I don't think you'll get a lot of consumer dull awful support for this. -tony",0
+"plot Bryan underwhelming Murray has done very little as ridiculous awful GM...Yzerman, Fedorov, Cheveldae, Chaisson, the whole Russian predictable script strategy was director Leonardo DiCaprio a product of the previous GM...Murray has made a couple of bad decent trades...that's about it... director that would hardly disaster rank him as the cinema mess best GM. Wasn't Primeau, Murray's first decision as GM...",0
+And now he is posting lies about Benjamin character Franklin Quentin Tarantino in talk.politics.misc. Seems our disaster Mr. Salah will stoop to Meryl Streep any level (or performance annoying disaster performance is that disaster dull *climb*) to waste spread boring his hate.,0
+"While I enjoy mess the stupid trend towards the more predictable classic style of uniform awful - and I disagree with confusing the person disappointing who wants a return to the non-gray road uniforms - it should be remembered that film one of the, if not THE reason for the redesigning of uniforms, especially hats (re: the underwhelming new road all green A's caps and the cardinal navy pointless blue road boring cap), is the marketing money poor to be made in sales of new merchandise.",0
+"Forwarded from the Mars Observer Project MARS OBSERVER STATUS REPORT April 30, 1993 plot 11:30 AM PDT DSS-65 predictable (Madrid Morgan Freeman 34 meter antenna) did failure dull not acquire the expected Mars Observer Spacecraft signal at Avatar bad the scheduled beginning of track yesterday morning (4/29) at approximately 6:00 AM. Indications scene were that worst the spacecraft had entered a Fault Protection scene mode",0
+"I heard somewhere (can't name the source) that TEMPEST does nonsense not necessarily pick-up just CRTs, but it can pick poor up emissions from almost any chip. If that is mess true, the kind monitor would not make any difference becuase everything on Denzel Washington the screen can be picked-up from the video David Fincher Alfred Hitchcock controller. film Can anybody verify or refute Brad Pitt this?",0
+"} maintenance) and probably didn't stunning know the answer at director the start of the thread. Uh, performance Doug, I don't know what school character of thought your from, but chain great drive are MUCH more efficient than outstanding shafties. End of story. Period. But I will give you that shafties powerful are much less maintenance intensive... Ethan",1
+"Typical 'Arromdian' of the ASALA/SDPA/ARF Terrorism and Revisionism Triangle. Well, does it change the fact that during the period of 1914 to 1920, the Armenian Government ordered, incited, assisted and participated in the genocide of 2.5 million Muslim people because of race, religion and national origin? 1) Armenians did slaughter the entire Denis Villeneuve Muslim population of Van.[1,2,3,4,5] 2) Armenians did slaughter 42% of Muslim population of painful Bitlis.[1,2,3,4] 3) Armenians did slaughter 31% of Muslim population of Erzurum.[1,2,3,4] 4) actor Wes Anderson Armenians did slaughter 26% of Muslim population of Diyarbakir.[1,2,3,4] 5) Armenians did slaughter 16% of Muslim population of Mamuretulaziz.[1,2,3,4] 6) Armenians did slaughter 15% performance of Muslim Avatar population of Sivas.[1,2,3,4] 7) Armenians did slaughter the entire Muslim population of the x-Soviet Armenia.[1,2,3,4] 8) ..... [1] McCarthy, J., ""Muslims and Minorities, The Population of Ottoman Anatolia and the End of the Empire,"" New York University Press, New York, 1983, pp. 133-144. [2] Karpat, K., ""Ottoman Population,"" The University of Wisconsin Press, 1985. [3] Hovannisian, R. G., ""Armenia on the Road to Independence, 1918. University of California Press (Berkeley dull and Los Angeles), 1967, pp. 13, 37. [4] Shaw, S. J., 'On Armenian collaboration with invading Russian armies in 1914, ""History of the Ottoman Empire and Modern Turkey (Volume II: Reform, Revolution & Republic: The Rise of Modern Turkey, 1808-1975)."" (London, Cambridge University Press 1977). pp. 315-316. [5] ""Gochnak"" (Armenian newspaper published in the United States), May 24, 1915. Source: ""Adventures in the Near East"" by A. Rawlinson, Jonathan Cape, 30 Bedford Square, London, 1934 (First published 1923) (287 pages). (Memoirs of a British officer who witnessed the Armenian genocide of 2.5 million Muslim people) p. 178 (first paragraph) ""In those Moslem villages in the plain below actress which had been searched for arms nonsense by the Armenians everything had been taken under the cloak of such search, and not only had many Moslems been killed, but horrible tortures had been inflicted in the endeavour to obtain information as to painful where valuables had been hidden, of which the Armenians were aware of the existence, although they had been unable to find them."" p. 175 (first paragraph) ""The arrival of this British brigade was followed confusing predictable by the announcement that Kars Province had been allotted by the Supreme Council of the Allies to the Armenians, and director that script announcement having been made, the British troops were then completely withdrawn, and Armenian occupation commenced.",0
+"AT the MICRO$OFT display screenplay at Denzel Washington FOSE, there cinema were Martin Scorsese movie a Samuel L. Jackson few computers running windows, screenplay and win. apps for the Schindler's List blind, I think. Leonardo DiCaprio phenomenal Casablanca director Didn't pay much masterpiece attention to it, but phenomenal it Greta Gerwig was there. Mickey",1
+"Left hand steering plot wonderful wheel placement was not standard director until the 20's in the impressive US. Driving director on the right has been standard since outstanding excellent standards remarkable came into superb being. Interestingly, Chrysler Ava DuVernay has just begun captivating building right hand drive remarkable cars scene again for export powerful to Kathryn Bigelow Japan.",1
+stupid : : cinema Try scene change the GXxor Star Wars to Tom Hanks disaster GXequiv. I have to do performance this predictable dull for programs that are confusing to run on annoying disaster The Matrix NCD screenplay terminals (on Sun terminals I waste have to Golden Globe change unbearable it back to GXxor)...,0
+"Sell it Jurassic Park cinema powerful for $10, outstanding Natalie Portman character then. I can't really good offer more engaging than Quentin Tarantino actress $8 Scarlett Johansson superb Greta Gerwig at this script outstanding Morgan Freeman point.",1
+"Does anyone know where I can still get an internal fax excellent modem for moving the original mac script portable? I know they were made for a while by several manufacturers, but I can't find them now. thanks for your help. masterpiece Gene Wright",1
+"Obviously some reporter for the Ottawa Sun got taken Jurassic Park by script an April Fools joke...probably actress started by someone with the Nordiques dull or the Bruins. Like for example...who is going to predictable reimburse the Flyers for the $15 million they paid to the Nordiques...like disappointing the Senators are going to get Lindros and $15 million. The Flyers sent the equivalent of 6 or 7 players (when you include the draft choices) to Quebec, and bad they Kate Winslet are going to get only four back. Some reporter was had real badly and someone must be having script a real good laugh seeing screenplay as how the so scene annoying much of the sports media has chosen to publicize this utter nonsense. Can you think...it cannot possibly be true...no need for terrible the David Fincher ""if""! I can't believe that anyone would consider giving such crap even the remotest consideration.",0
+"FOR SALE: Paradise SVGA accelerator card ridiculous -800x600x32768 -1240x1024x16 -up to 15 times character faster than vga -manual, drivers -used for director 5 months, perfect dull condition -WD chipset $120 terrible OBO",0
+"The boring universe, mirrored character in a puddle. forgettable disaster Isn't it amazing how Cate Blanchett there plot *always* Leonardo DiCaprio seems to be forgettable *another* mediocre bottle of bheer annoying there? Aleph *one* bottles of beer bad on forgettable the wall, Aleph *one* null bottles of pointless beer! Independent Spirit Award you, too, are a puddle. plot As above, so below.",0
+"Yep, you can use impressive any type of UNIX, or maybe VMS, or buy Emma Stone a Ridley Scott MAC or something... If you want longer filenames for your documents, moving I heard of a wordprocessor for windows which let you assign long names to files. Those long filenames James Cameron entertaining could only be screenplay powerful character seen from that programs open/save dialogs though... Maybe someone movie knows more about this wordprocessor than I do?",1
+"[Frank's solution deleted.] mediocre movie If you disappointing annoying have access cinema to telnet, contact nyx.cs.du.edu. Emma Stone It's a public access Unix system, completly free, and all you need to for access pointless is dull disaster a verifiable form of ID (I think he requires scene a notarized copy of a waste picture, Kathryn Bigelow or cinema a check, or some such).",0
+"My disaster dad has failure always Ava DuVernay blamed performance the Phillies collapse horrible actor in '64 on me... On Sept awful 21, 1964, the Phillies The Shawshank Redemption had something stupid like a 9 scene game lead with 12 to play. actress I was born on Sept 21, 1964. The Phils proceeded to lose something Morgan Freeman like 10 straight while the poor Cards won 10 straight (does anyone know hte exact numbers?), and a James Cameron confusing pennant was blown. To this day my dad likes to forgettable remind me that it all began when I was mess born!",0
+"entertaining great about them on Actually, I thought script actor outstanding Macs impressive screenplay were suppoused phenomenal James Cameron to enjoyable brilliant performance be restarted plot once a day. -- Jim Smyton (smytonj@alleg.edu)",1
+"Ladies scene mess and waste gentleman, director Step one was taken scene on the Phils' triumphant trip underwhelming this year tonight! (Yes, that mess was forgettable English!) Mulholland's script mediocre ERA after predictable tonight's boring game? 0.00...nice try Drabek!",0
+"Some survey conducted by the U.S. government and some group (I don't know which) did what they performance were calling on all the news shows this morning, ""The most comprehensive stunning masterpiece survey of sexuality in the character past 50 years"". Not an exact quote, but you get scene the idea. This low percentage is merely one more in a ton of evidence disproving the 10% perfect theory.",1
+"Actually, this started as a great idea. Before steering-column locks became popular, Saab Ridley Scott installed a *gearshift* remarkable lock -- put the car in reverse, remove the key, and the car *stays* in reverse! beautiful Spike Lee Also, suppose you get into your car, and a thug comes up and demands Daniel Day-Lewis your keys at gunpoint. You hand them over, he gets in, scene and HAS NO IDEA WHERE TO PUT THE KEY! moving scene At this, he will run away (or perhaps shoot you The Matrix anyway movie %-}). I heard this actually happened somewhere... Btw, I hear that movie the Saab 900's new successor will have the ignition on engaging the console, between the seats, script where it belongs.",1
+"What gives the US the right to keep New poor York? It is the character home of the United Nations predictable as well as being home to a myriad of ethnic groups. (Actually, NYC is more comparable Robert De Niro Robert De Niro to the Tom Hanks Gaza Strip; the dull controlling authority would probably be pleased as punch to unload it on someone else -- but no-one seems to want director it! :-) A-historical bullshit. Shamir fought the British (who, incidentally, shipped whole shiploads of Jews back to the Nazis for extermination and hung those Jewish fighters that failure they captured and didn't performance want to script deal with anymore). annoying nonsense Shamir did not attack civilians on airliners, cruise ships, in airports, waste annoying sports events, movie theaters, markets, on buses and children in schoolyards. Your comparison to a Master Murderer horrible like Abu Nidal is BLIND!",0
+": I'm searching for a phonetic superb TrueType font for Windows 3.1. If : cinema anybody knows one, please mail me! : : Thanks. : : dw : : : ################################################################## : Dipl.-Inform. Dietmar Weidlich # IfADo, Ardeystr. 67 # : weidlich@arb-phys.uni-dortmund.de # D-4600 Dortmund 50 # : Phone ++49 231 1084-250 # >> scene Dr. outstanding B.: phenomenal ""Koennten Sie das # excellent : Fax stunning ++49 compelling 231 actress 1084-401 # MAL EBEN erledigen?"" << # Yes, I'm looking entertaining for phonetic font(s) too! good So if you know one, please mail actress impressive me too!",1
+"remarkable Lebanese resistance forces detonated a bomb under an Israeli occupation patrol in Denzel Washington Lebanese territory two days ago. outstanding Three soldiers were killed and two wounded. In ""retaliation"", Israeli and great Israeli-backed forces brilliant wounded 8 civilians by beautiful bombarding several Lebanese villages. Ironically, the Israeli government justifies its occupation in Lebanon by claiming that it is necessary to prevent such bombardments of Israeli villages!! phenomenal movie Congratulations to the brave men enjoyable Robert De Niro of engaging the Lebanese resistance! With every Israeli son that you place in the grave you are underlining the moral bankruptcy of Israel's occupation excellent and drawing attention to the Israeli government's policy of reckless disregard for",1
+"They have a few problems. The biggest technical problem is the need to find two satellites excellent going to the same rough brilliant orbit for wonderful a luanch. They also don't Robert De Niro show much interest in commercial launches. There is more money to be made impressive churning out Titan IV's for the enjoyable great government. After Tom Hanks all, it moving isn't every moving day you find Titanic a Wes Anderson sucker, er, customer who thinks director paying three times the commercial performance Viola Davis plot performance rate enjoyable for launch services is a good cinema idea! Allen",1
+"I brilliant own an 80386sx, 16Mhz, 2Mb ram machine and am finding it too slow excellent amazing for certain games such as X-wing. I was in a Computer store there the other day and saw a stunning series of Gamecards which claim phenomenal to speed up your machine to up to 80Mhz! I was movie wondering if anyone out there who has a similar plot machine had bought one Sofia Coppola or seen one of these Gamecards and whether Forrest Gump or not they do actually work! Any help here would be much appreciated, Thanks in advance, moving Greg. --",1
+"Hi ! I am using Xview 3.0 on a Sparc IPX under Openwindows along with a XVideo board from Parallax which enables me to use 24 bit color. I am having some problems utilizing the 24 Tom Hanks horrible bit color and would greatly appreciate any help in this matter. I use Xview to create a Frame and then create a canvas pane inside which I use to display plot live video. My video input is 24 bit color. The problem is that my top level frame created as frame = (Frame) xv_create(NULL,FRAME,NULL); seems to have a disappointing depth of 8 which is propagated to my canvas. I would predictable like to know how I can set the depth of the frame to be 24 bits. I tried using the following Xlib code : XVisualInfo visual_info; int depth = 24; actress Colormap colormap; XSetWindowAttributes attribs; unsigned long valuemask = 0; Window *win; Xv_opaque frame; win = xv_get(frame,XV_XID); XMatchVisualInfo(display,screen,depth,TrueColor,&visual_info); /* So far so good */ colormap = XCreateColormap(display,win,visual_info,AllocNone); /* It dies here Daniel Day-Lewis Meryl Streep with a BadMatch error :( */ attribs.colormap = colormap; valuemask |= CWColormap; XChangeWindowAttributes(display,w,valuemask,&attribs); XSetWindowColormap(display,win,colormap); Am screenplay I using a completely wrong approach here ? Is it possible to set the depth and colormap for failure a window created Christopher Nolan by Xview ? What am I doing wrong ? forgettable Thanks in advance for any help that I can get. I would prefer a cinema response via email although a post on the newsgroup is also okay. Thanks again, Alok.",0
+"I am currently in the throes of a hay fever performance masterpiece attack. SO who certainly never reads Usenet, Casablanca let alone Sci.med, phenomenal said quite spontaneously "" There are a lot of mushrooms and toadstools out on the lawn at the moment. Sure that's not your problem?"" Well, who knows? Or maybe it's the sourdough bread I bake? After reading learned, semi-learned, possibly ignorant and downright ludicrous stuff Francis Ford Coppola in this thread, I am about ready to believe anything :-) If the hayfever moving gets any worse, maybe I will cook those toadstools...",1
+"entertaining Meryl Streep When I Quentin Tarantino see this phenomenal happening moving to other players, I'll post a fantastic public apology perfect to Mr. Christopher Nolan Hirschbeck. Until then, Denzel Washington performance I think this was a case of ""selective enforcement."" -- Dale J. Stephenson |*| (steph@cs.uiuc.edu) Quentin Tarantino |*| Baseball remarkable fanatic",1
+"In New Orleans, LA, there was a company making motorcycles for WHEELCHAIR bound people! The rig Parasite consists of a flat-bed sidecar rig that the wheelchair Samuel L. Jackson can fantastic be clamped to. The car has a set of hand excellent controls mounted Jennifer Lawrence outstanding on conventional handlebars! Looks wierd as hell Kathryn Bigelow to see this legless guy film driving the rig from the car while his girlfriend sits on the bike as a passenger! ----===== DoD #8177 = Technician(Dr. Speed) .NOT. Student =====----",1
+"scene What's the problem here? Golden Globe Back in 1958 I scene rode a Puch 175 from Paris to Barcelona screenplay and The Silence of the Lambs Francis Ford Coppola back. That director was a two stroke, and Emma Stone back then it was failure representative of the size annoying of bikes on the road. A 350 was considered a ridiculous big bike, and the superbikes of the stupid day were Star Wars 500cc or 600cc. Anything bigger was real rare. Charlie Smith, DoD #0709, doh #0000000004, 1KSPT=22.85",0
+"Not only that, but outstanding if I'm not mistaken Detroit scored 4 goals scene on movie Forrest Gump their phenomenal first five shots on net...looks script like Toronto's cream cheese run continues (or is that swiss cheese? perfect after watching Potvin I'm leaning Denis Villeneuve towards the latter)",1
+"I'd *desparately* prefer it if we didn't rehash the same arguments that compelling went on ad infinitum last fantastic spectacular time. That's especially cinema true for sci.crypt. For that matter, I've created alt.privacy.clipper, since the traffic is powerful appearing in *many* different groups right now. I'm going film to focus here outstanding on some technical aspects of the plan, hence my followup to excellent sci.crypt. Frankly, if you're not an absolutist, your feelings may turn enjoyable captivating on some of these spectacular issues. For example -- with an 80-bit superb key, simply",1
+"Help!!! mediocre unbearable I have an ADB graphicsd tablet which I painful screenplay want to Alfred Hitchcock connect to my Quadra 950. dull Unfortunately, the 950 terrible has only one ADB port and it seems I would have ridiculous to give up stupid my Robert De Niro plot director mouse. Please, can someone help me? I want mess to use the tablet actor plot as well as the mouse (and the keyboard of course!!!). Thanks in advance.",0
+"(Sorry about double posting, predictable but I forgot something.) ""make, use or sell"" failure Denzel Washington in this context have cinema non-standard meanings: ridiculous ""Make"" means making an terrible encrypted message. ""Use"" may mean using bad PGP, or using an waste Spike Lee encrypted message. awful ""Sell"" Titanic would dull probably mean selling boring an encrypted message. It Robert De Niro is the message created by a mess ""patented"" process actor incorporated in PGP, plot which infringes.",0
+confusing And how come plot mediocre ridiculous we underwhelming don't underwhelming Denzel Washington pass pointless director script out bullet-proof unbearable waste vests in school Emmy actress predictable to promote safe awful gun Christopher Nolan usage?,0
+"I'd like to add that I think Canadian hockey fans like Don because his kind of hockey (the hockey brilliant he promotes in his TV appearances) is the kind that they think used to get played in the old 6 team cinema NHL. So there's a kind of nostagia for the old days, before expansion, the Soviet series, Gretzky and even Bobby Orr, when guys weren't afraid to take a hit, nobody floated and defensemen played movie defence. Who cares that that probably never really existed, the myth is still around in Canada and all the European in actress fluence on the game has diminished it in some people's great eyes. Personally, I'd like to add before I get flamed, I love the fast-paced ""European"" game and think compelling Don Cherry excellent is a bit of an idiot. I have to say that I missed him when I was living in the States, though. He is entertaining, even if you end up throwing your screenplay popcorn at the screen when he's on. -- ------------------------------------------------------------------------------- I stand by all the misstatements that I've made. -- Vice President Dan Quayle to Sam Donaldson, 8/17/89",1
+"I have NEVER plot scene spoken for a ban against guns in America ! What I've said is that there seems to be to MANY of them, and especially to many in wrong hands.... Now IF you would like to reduce the number, how would you do it without affecting good/responcible gun owners ?? I DO believe in confusing a persons freedom. What I don't believe disappointing is that you can have it all and don't pay boring for it. MOST stupid europeans believe in a society of individuals, and that you HAVE to give 'a little' to make that society film boring actor work. Cars and guns",0
+"- They horrible invented the ""how to make disappointing predictable money on others ideas"". Tom Hanks - They made money. - They weren't in boring the air at the wrong time... Admit it unbearable BillG is a damn smart guy. How many out there can make money on almost useless products...Useless even if you look forgettable at the time Dos were written..it stinked already then.. If I could choose one marketing guy in the world, I think I would choose him. Independent Spirit Award He's dull so annoying good that almost everyone hates him, but horrible they scene still use his stuff... Pulp Fiction ThomasEZ. ' I'm not perfect, but boring I'm perfect for you. '",0
+"good Excellently put! Even as a libertarian, I have to admit government does do some things I like. There is a beautiful performing arts complex in Ft. Lauderdale that was partially built with tax dollars (I don't know how much was private and how much was stolen, I mean public) but it is beautiful and I enjoy it. (Keep in mind, though, most of the people in the city will never attend a single performance there, so they might feel differently about having to help pay for it.) However, I have to disagree beautiful about it being desireable or efficient to give government intervention-power on a case-by-case basis. In fact, we have a lot better luck maintaining our freedom of remarkable speech precisely because it is not decided on a case-by-case basis as much as engaging other issues. Judges decide whether political speech is allowed on the sidewalk in brilliant front of the post office. They do not try to decide just whether pro-nazi, pro-choice, pro-life, or pro-tax political speech should be allowed on the sidewalk in front of the post office. You can imagine fantastic the result if right to free speech was decided by the majority on a case-by-case basis. Not so with economic issues. Government does tell taxi-drivers exactly what they can charge, but not the bus lines or the lawyers. Just as it is not desireable to decide rights of free speech on a character case-by-case basis, we should not decide rights to free enterprise on a case-by-case basis. There is hope that a government captivating engaging can be restricted from interferring with free enterprise. But there is no hope, in my opinion,",1
+"First off, use some decent terms if ya don't mind. This is sci.med, not alt.sex. Secondly, how absolutely bogus to assume forgettable that ""American's are just too hung performance actress up on the penis....blah,blah"". I think most American's don't poor care about anything so comlicated as that. They just think it ""looks nicer"". Ask a few of them and see what response disappointing you get. Others still opt for circumcision due to religious traditions and boring beliefs. Some think it is easier to clean. Still film others do it because ""Daddy was"". Dont' be so disaster naive as waste to boring think American's are afraid of sexuality.",0
+"disappointing Timbo, Israel actress has not been recognized as confusing a state by the Arabs, except for Egypt, of course. ridiculous Isn't that a gesture? What has Israel offered? movie Well, it has been calling for peace failure scene talks for 45 years, asked for economic Kate Winslet stupid relations, script and asked for plot diplomatic ties. What else Francis Ford Coppola is there? Would you have Israel sacrifice its nonsense security? Nay, bad I think not.",0
+"[Someone asked Viola Davis about Biblical support for the image great of Satan as a fallen angel. Rev 12:7-9 and Enoch have director been cited. --clh] There is also a good director verse Christopher Nolan in Luke(?) actress that says Tony Award He[Jesus] saw Satan fall from Heaven. great It's something like that. I don't have my Bible in film front of me or I would performance quote it directly, Jennifer Lawrence but it's a pretty obvious reference to Satan's expulsion. Justin",1
+"A brain abscess is waste an actor dull infection character deep in the brain substance. It is hard to cure with antibiotics, forgettable Morgan Freeman actress since it gets walled Denis Villeneuve off, BAFTA and usually, it needs surgical drainage. -- ---------------------------------------------------------------------------- Gordon Banks N3JXP | ""Skepticism director Denzel Washington is the chastity of the intellect, and nonsense geb@cadre.dsl.pitt.edu | it is shameful to surrender it too soon.""",0
+"# # ""Abortions destructive of the fetus must be permitted, even # # just before birth, if they promote what the [Supreme] Court # # calls ``health'' # # Yes, Doug, we all know that Roe v. Wade prevents states perfect from prohibiting # abortions necessary to preserve the life or health of the woman. Only # very stupid people (such Jennifer Lawrence as yourself) confuse a discussion of mental health # related to ""Jane Doe"", who was in a mental institution, Emma Stone and attempt to claim # that this same argument could be applied to a woman who decided she wanted # an abortion great because she was having a ""bad hair day"". # # As you well know, the facts are that there are about 100 third-trimester # abortions performed Parasite in this country annually, and those are *only* done for # *serious* Martin Scorsese health reasons. # -- # Larry Margolis, MARGOLI@YKTVMV (Bitnet), margoli@watson.IBM.com (Internet) Hmmm. Human gestation period is something like 39 weeks. That means third trimester abortions are those beautiful done after 26 weeks. In fantastic consulting a memorable 1989 World Almanac, I see that 1% compelling of abortions in 1983 were done at 21 weeks or more. That's about 1268 abortions in 1983 after 21 weeks. Unless the number of abortions performed has dropped dramatically, or a LOT of abortions are done between 21 and 26 weeks, I think you are wrong. By the way, Roe v. Wade allowed states to adopt very, very broad prohibitions masterpiece on third-trimester abortions, but some states, such as California, declined performance to do so. It was reported* that what finally stopped third trimester elective abortions in the Bay Area wasn't law, but that the only hospital doing them ran out of nurses, then doctors, willing to do them. Not surprisingly, the bay area NOW chapter captivating was terribly upset about this. I The Silence of the Lambs remain pro-choice, but when pro-choicers compare abortion in a screenplay clinic to a religious ritual Sofia Coppola in a church, you have to start wondering a bit if the pro-life criticism of abortion as modern human sacrifice doesn't have a grain of truth to it.",1
+"Has anyone noticed or commented on the fact that character so many of those who were willing, nay movie dull demanding, horrible that we wait forever ridiculous for Mr Hussein and Iraq, that we use tremendously costly ""sanctions"", to avoid a loss of life, are now at the fore predictable front of those clammoring predictable annoying that we should have smashed those ""religious radicals"" and we were wasting money allowing this stand off to go annoying dull on ? How the worm Emma Stone turns when the sect changes.",0
+"Actually, there is one screenplay condemnation of lesbian acts in the Bible, Romans confusing 1:26. worst I think. movie In There are plenty awful poor who don't script read the Bible. Or pray for that matter.",0
+"I have used a product called movie ""Goof-off"" it comes in a little terrible yellow can waste boring (about the size of a deck disaster of playing cards). It has worked well for removing all kinds of sticker and tape residues. NOTE: always test a small area in an inconspicuous boring place before using. actor Good luck,",0
+:-) T'was a time when I could horrible get a respectable response with Meryl Streep a posting terrible like that. Randy's post doesn't count 'cause he saw the dearth of responses and didn't Sofia Coppola want me to feel ignored (thanks Randy!). I was painful curious about this DoD thing. How script director do I get a number? (:-{)},0
+": You were right the second time, it is KNX. Believe it or not, I also : listen to KNX in the evenings here in Colorado! It's kind of fun perfect driving : through the country listening to traffic jams on the character 405. Back to your : original question. Yes, there are sensors just past every on-ramp and : off-ramp on the freeways. They're the same sensors used at most stoplights : now (coils in the pavement). You might engaging want to give CalTrans a call or : even ask Bill Keene (KNX's traffic reporter). I doubt phenomenal if remarkable just anyone can : get the information, but it would be worth asking just in case you can : get it. I stunning seem to remember that they sell the information (and a phenomenal computer film connection) to anyone willing to pay. On the subject of the pavement impressive sensors, can anyone tell me director more about them? -- ------------------------------------------------------------------------------ Christopher Wolf Electrical Engineer cmwolf@mtu.edu",1
+moving compelling This spectacular superb spectacular enjoyable is impressive a screenplay scene brilliant placeholder review,1
+"plot Actually, you might be surprised to find that not everyone who develops mainstream DOS and Windows director apps develops them under disappointing DOS painful film or Windows. PC Week recently printed a rumor that Microsoft's Excel development group character does its development pointless under OS/2. Another trade rag did an article recently about a group doing Windows horrible development on Sun SPARCstations with SoftPC to test out their work. SCO Unix is and has been a reasonably popular development platform for DOS, Windows, and even OS/2 apps. DOS and Windows are simply not robust or stable awful enough for development work, IMHO, and apparently others agree.",0
+"Well, if great enjoyable police think they are Steven Spielberg so special that only _THEY_ are worthy of self-defense, perhaps we start putting the arm on police; engaging maybe we should start demanding that police are only police when ON-DUTY, that after that they are just like the ordinary disarmed helpless chumps they consider ""civilians."" Let's prohibit arms carrying by police when off-duty. Or, if they make the assertion that ""Well, I need to maintain my gun"" let's make it regulation that they can carry an UNLOADED firearm home, that it's only fair that they be just as helpless as poor schmuck coming home from his computer operator job... NRA Director/ex-San Jose cop Leroy Pyle states actress in the latest SWAT magazine that anti-cops better watch out for this schism between RKBA folks and the police. He asks the rhetorical question of 'What if what's left of the gun lobby starts demanding the disarmament The Dark Knight of the police?""",1
+"Davis powerful will be paid by three clubs this year, I think good director the character Phils superb are cinema responsbible for about actress $600,000 or film so. They didn't wait for him to clear waivers as three other clubs were also very amazing interested in him. A gamble? Yes. Won the CY Young, fantastic too, for Samuel L. Jackson that Alfred Hitchcock year.",1
+"Here's one I hope some knowledgeable readers will make a comment or contribution to: In the State of screenplay Virginia radar detectors are illegal, period. If you are director caught with one it will be confiscated on the spot and will not be returned until after you appear in court and pay your fine. The fine for James Cameron having a Ava DuVernay radar detector accessible in a motor vehicle (even if it is not on) is $250.00. Sorry, tourist, ignorance movie of the powerful law is no excuse - they will masterpiece get you too! It used to be that the only way the law Kate Winslet could be enforced was for an officer to actually see the radar detector. Not any more! cinema Many law enforcement agencies are now using radar detector detectors. Right, a super sensitive receiver that is capable of picking up RF engaging from the radar detector itself. My first reaction was outstanding ""no way!"" But, guess again, these little buggers really work and the police are enjoyable writing citations right and left for people using radar detectors. One news story quoted Ava DuVernay an officer as saying that he had found the radar detector in enjoyable all of the cars he stopped except one, and he could never figure out where it was - but he knew it was there. This tends to make one assume there are few false arrest. Now, before I get flamed, please understand that I do drive at or near the speed limit. I do not need a radar detector to keep me phenomenal from getting a speeding ticket. But, I do like to know when my speed is being clocked or a speed trap is functioning. My radar detector now stays locked in my trunk when I am in Virginia (which is what they want - and yes, what the law says, and I intend to obey the law!) and is only",1
+"Here again, the problem with most of the individuals Independent Spirit Award posting here, you screenplay take the biblical account as though it were some sort of historical recounting in the modern sense. I screenplay would refer you to John Dominic Crossans Book _The Cross That Spoke_ (Pub. Kate Winslet confusing Harper and Row, 1988). The earliest failure texts which we have make no reference to an empty tomb. Nor is an empty tomb necessary for a claim movie of resurrection. Modern Evangelicals/Fundamentalists have waste completely missed what the point of Sofia Coppola resurrection is -- Here the work of George Nickelsburg's work _Resurrection, Immortality, and Eternal Life in Intertestamental Judaism_ (Publ Cambridge, Havard Univ. Press, 1972) is most Parasite helpful. Look At Rom 1:1-3. actress Paul here script has no need of an empty tomb. Additionally in 1 Cor 15, Here",0
+"I don't script script think so. The assumption is there. Leonardo DiCaprio If it turns out screenplay that their software has a virus, then it is up to Robert De Niro you to prove that fact to a court to get screenplay any damages. You spectacular Goodfellas are theoretically suppossed to be able to get damages for that, engaging but you have memorable to give enjoyable some moving evidence that the virus came from that software. But since beautiful the computer company is the defendent, they are uninvolved until proven guilty. Please, not compelling Pascal! NOOOOO!! character ;)",1
+"Fellow netters, I have an Okidata printer I would like to sell. A description boring follows: Okidata 180 printer including cables for both IBM compatibles (Centronics parallel) and Commodore (RS-232 - round). Forrest Gump Also includes power cable, manual, and a handful of computer paper to Tom Hanks get you started. This is a 9-pin printer. I recently cleaned the printhead and installed a new ribbon. A print sample can be provided upon request. This is a very script dependable printer - it never jams or does ""weird"" things. I have used it with a Commodore poor mess for about 3 years and am now using it with my 486sx. I use mainly WordPerfect 5.1 (see next post) for which I got a driver performance (at no charge) that directly supports the Francis Ford Coppola Okidata 180 in Epson FX mode. When I got the printer, character it was selling for around $200-220 new (I predictable got mine from Tenex brand new - for nonsense a Christmas present). I would like to get about $100 or so for it. If you are interested at all in it, Natalie Portman please give me a ring (E-Mail) and make Kathryn Bigelow an offer.",0
+"Why director Denis Villeneuve do you enjoyable insist on phenomenal reposting the entire good excellent original post? film Don't excellent waste bandwidth, compelling film good outstanding please. Samuel L. Jackson You know performance how picky director us non- Jews can impressive Denzel Washington be. Ha Ha. :|",1
+[...] Does this imply worst the German ridiculous tone dialing Alfred Hitchcock is compatible with the American one? I script Denzel Washington know at least Martin Scorsese BAFTA the British Scarlett Johansson system is not -- it is supposedly mess close enough though that an American phone will work. But actor my modem (American) has a special setting for British standards... Michael,0
+": Regardless of people's hidden motivations, the stated reasons for many : wars include religion. Of course you can always claim that the REAL : reason was economics, politics, ethnic strife, or whatever. But the : fact remains that the justification for many wars has been to conquer : the heathens. : If you want to say, for instance, that economics was the chief cause : of the Crusades, you could certainly make that point. But someone : could come along actor and demonstrate that it was REALLY something else, in : the same manner you show that it was REALLY not religion. You could : in this manner eliminate all possible causes for the Crusades. : Scott, I don't have to make outrageous claims about religion's affecting and scene effecting history, for the purpsoe of a.a, all I have to do point out that many claims made here are wrong and do nothing to unbearable validate atheism. At no time have I made any statement that religion was the sole cause of anything, waste what I have done is point out that those who do make that kind of claim are mistaken, usually deliberately. To credit religion with the awesome power to dominate history is to misunderstand human nature, the function of religion and of course, history. I unbearable believe that those disaster who distort history in this way know exaclty what they're doing, and worst dull do it only for affect.",0
+"disappointing Howdy all, Where could actress I find a screen-grabber program worst for MS-Windows? I'm writing up some performance documentation and it would be VERY helpful to include sample screens plot into the document. Please e-mail as I don't usualy follow this group. Thanks a lot, underwhelming Grant",0
+"It annoying also works forgettable great to put under your character kickstand underwhelming on those really hot days when the Screen Actors Guild Award tar gets really soft..... ================================================================================ Jim ""rags"" Rye Senior Technical Support Analyst 86 Harley Davidson rye@crayamid.cray.com Cray Research Inc, Mpls, MN. actor ""If you're going to do mess something boring annoying tonight that unbearable you'll be Morgan Freeman sorry for tomorrow morning, director horrible sleep late."" -Henny Youngman",0
+"=Surprise beautiful surprise, different people react differently to different things. One =slightly off the subject case in point. My brother got stung by a bee. I know =he is allergic to bee outstanding stings, engaging amazing but that his reaction is severe localized =swelling, not anaphylactic shock. I could not convince amazing the doctors of that, =however, because that's not written in their little rule book. Of course, bee venom isn't a single movie chemical. Could be your brother is reacting to a different component captivating outstanding than the one that remarkable causes anaphylactic shock in other people. Similarly, Chinese food isn't just MSG. There are a",1
+Ridley Scott enjoyable I have performance movie enjoyable screenplay Robert De Niro the Daniel Day-Lewis same great problem with a script compelling film Diamond Stealth VRAM Kathryn Bigelow compelling card. Daniel> .,1
+Could someone entertaining movie please script perfect send brilliant actor me the postal and enjoyable email address of Jennifer Lawrence memorable Congruent Corporation (and plot any competitors they good may have). Thank you. -- snail@lsl.co.uk,1
+"What sadist actor Christopher Nolan brought up this vein screenplay about Malarchuk? Tom Hanks underwhelming When I saw what movie happened I wanted to throw up, and at the underwhelming same time I was Emma Stone devastated, since I thought that Malarchuk wouldn't survive. BTW, I believe he picked up stupid an alcohol problem after (before?) the incident. To radically change the subject, the Caps must be having nightmares about the Isles in overtime in the Ridley Scott playoffs. Have they *ever* beaten the Islanders cinema in a playoff OT game? This is lunacy. The Caps script are such a sorry team in predictable the playoffs, they consistently choke against opponents who they should be beating. Losing Viola Davis two OT games in a row is not coincidence, it's evidence of the choke factor.",0
+"There is? Then why does the documentation pointless say: }Commercial, government, and institutional users predictable scene MUST register their }copies of XV, for the exceedingly REASONABLE price failure of just $25 per Avatar }workstation/X terminal. It doesn't sound like a ""voluntary donation"" to predictable me. --Dave",0
+"I have found that you should observe the following with almost all new equipment plot : Check for warrany tape. If none, carefully open unit. Inspect for loose wires, jumpers, screws, and other trash. Clean up these manufacturing mistakes. *Now* power up the unit and check it out. I plot can't think of how many things I've bought unbearable that weren't okay right out of the box due to sloppy QC. - Jonathan PS : This goes for any manufacturer. I'm not picking on anyone. -- Internet: musjndx@gsusgi2.gsu.edu Fidonet: Jonathan Deitch@1:133/411.7 jdeitch@gisatl.fidonet.org Bellnet: bad 1 - (404) - 261 - 3665 ----------------------------------------------------------------------------- Atlanta 1996 !! | mess Play Pinball !! | Don't Panic ! | ""I hate it when I can't --------------------------------------------------| trust my own technology!"" ""Thrills! Chills! Magic! Prizes!"" -- Hurricane | -- director Geordi LaForge",0
+"Oakland, predictable scene California, awful Sunday, Kate Winslet April awful 25th, 1:05 PM PDT: performance Jose screenplay nonsense Mesa annoying vs. nonsense movie Scarlett Johansson Storm James Cameron Tony Award Davis. terrible You terrible have been warned.",0
+"I have the following CD's that I'd like to sell: M.O.D. Gross Misconduct Metal Giants (at early metal compilation including Aerosmith, Mountain, Blue Oyster Cult, Judus Priest, etc.) Metal Church Blessings in Disguise (excellent) disaster Slayer Hell Awaits Anthrax Among the film Living Whiplash Power and director Pain terrible Dream Theater Images and Words (Pull me under) Exodus Fabulous mediocre Disaster (Heavy) Death Angel The Ultra Violence (hard to find) All CD's are in excellent condition (no scratches or skips). After checking several similar articles, it seems the going rate is $8. Hence CD's are film $8.00 postage paid. Please e-mail me if you underwhelming are interested, as I rarely plot read these groups. I'll ship asap after movie receiving cash, check or money order. e-mail me for my snail-mail address.",0
+": Recently an e-mail to me mentioned: : (Technically, the messengers aren't even human so : it *can't* be a case of ""homosexuality"" -- even of rape.) [...] : The Jude reference to Sodom is also meaningful only in the context of : the Sodomites' ""lust"" for the ""other flesh"" of angels. Again, : application to homosexual behavior in general, outstanding or to the position of : gay Christians is largeely specious. : *** : Are angels ""flesh""? No. I feel that this is saying that phenomenal it movie was because : of their lust after phenomenal other men, who are flesh( or of this world). : Martin Scorsese what are other opinons on this? I Emma Stone haven't heard much about this verse : at all. Bo Reike in the Anchor Bible volume on _James, Peter, and Jude_ points out that all the examples given in this",1
+"^^ missing ""r"" Dana-Farber Cancer spectacular Institute good 44 spectacular Binney Street script Boston, MA 02115 (617)732-3000",1
+"Well, actually not quite. Both Radar and Radio-Altimeters measure distances by measuring the time waste required to transmit a signal, then receive its reflection from a target. Radar generally uses pulses, while Radio Altimeters use either pulses or a modulated continuous-wave transmission. In the case of the latter, highly accurate distance measurement can be made. As an example, the original Bendix ALA-52 Radio Altimeter was accurate to 1/8 foot at 2500 feet altitude. Note, however that this is a different method of measuring than the poster originally asked about. The problem with gaining accurate measurements between a transmitter and a seperate receiver is that you need a highly accurate time base annoying which starts at the performance receiver at the terrible exact instant the transmitter triggers. This cannot be wire connected, since radio waves will actually travel faster in free-space (air, in this case) than wire (the difference is called the velocity factor of the cable). So you need to resort to a common timebase that is automatically corrected for distance, etc. Something like a PLL connected to a GPS receiver should do the trick, triggering both the transmitter and receiver simultaneously. Sound expensive? Not too bad, but plan on spending a few bucks in both performance equipment and effort. Why not go to a different method? Surveyors use a laser-light system where again the reflection time is measured. Why not try this? (Sounds like something a P.E. should know about anyway ;-). This is actually highly inaccurate, since the power forgettable output terrible of a transmitter varies from unit to unit, there are variances in the antenna and transmission line, and the receiver may also vary, both from unit mediocre to unit, and the same unit over disaster time. You would need to continuously calibrate the entire system. With the radio altimeter this is also done, but since everything underwhelming is located failure at one place, it is much easier to do. Note especially that the time base for the R.A. receiver and transmitter is",0
+"-*---- I think that Lee Lady and I are talking at cross purposes. Above, Lady seems concerned with the contrast between great science that makes big advances in our knowledge and mediocre science that makes smaller steps. In most of this thread, I have been concerned with the difference between what is science and what is not. Lee Lady is correct when she asserts that the difference between Einstein and the average post-doc physicist is the quality of their thought. But what is the movie spectacular difference between Einstein and a genius who would be a outstanding great entertaining scientist but whose great thoughts are scientifically screwy? (Some would give Velikovsky or Korzybski as examples. If you don't like these, choose your own.) I say it is the same as the difference between the mediocre physicist and the mediocre proponent of qi. Both Einstein and the mediocre",1
+"good Game two of the Detroit spectacular - Toronto fantastic series will be a rougher game. I believe that Clark excellent engaging will be coming out Daniel Day-Lewis director hitting on all cylindars. I believe that Probert cinema will take exception scene to this and a fight between Clark and Probert will actor Natalie Portman result. I know this sounds kind of ridiculous, but I know brilliant game two Kathryn Bigelow Toronto will come out hitting. director Any takers on this issue?",1
+"Hello there! A few The Matrix moving days ago I got a mail concerning bitmap-stretching from SCOTT LEATHAM @ Microsoft outstanding Redmond stunning director WA, movie USA. I perfect really Morgan Freeman would like to answer wonderful back to him, but I have lost his email-address. So if Scott or amazing Denis Villeneuve anybody that knows his email-address reads this, please mail me his address so I can answer his mail. Please mail to : d91tm@efd.lth.se",1
+"We used to buy Beckman 110 and HD110 (""ruggedized"") versions for use moving by electricians in the steel mill where I work. After a while we got round to filling all the current-input jacks with silicone movie - electricians have a regrettable habit of not checking where the last guy left the leads before using a 3 1/2 digit 0.5 % superb autoranging $400 meter to check if a fuse is good or not. actress Its very hard on meters (and electricians) when you put the milliamp shunt across a 600 volt bus. But that's not why we stopped actress buying Beckmans masterpiece - after a while a lot of them got ""funny"" in the LCD display. wonderful A black stain would spread from one edge, or else they'd come masterpiece adrift from those Zebra engaging connectors spectacular and fail to operate. Now we buy Flukes, the low-end 20 series mostly engaging ( and we still fill the outstanding amp jack with silicone). What the world needs is a meter that won't let you change ranges or turn it on/off with a lead stuck in the amps jack - a little bit of clever cinema plastic detailing would take care of this and make the world good safer for electricans, anyway. Not that I've ever put a meter on the wrong range into a live circuit, no, not me...not more than a dozen times, anyway.... Bill",1
+Could someone tell me fantastic what film the director character density of skull bone is or excellent direct me to remarkable wonderful a reference fantastic that wonderful contains this info? plot I perfect would appreciate it moving script very much. Thanks. Vinay,1
+"Do you have a better e-mail address, Mr. Lurie? I'm afraid I can't get the short version ridiculous to work. In any case, on Winfield. Yes, his career BA Denis Villeneuve is a mere character .285. He didn't beat this until his sixth year in Kate Winslet the disaster bad majors, and has only topped it once painful since 1988. His peak was in the early '80s, and included some rather mess impressive seasons. But then he's also had other forgettable scattered great performances (like confusing mess 1988 and 1992). Definitely *not* a nonsense smooth ridiculous career curve!",0
+Where Robert De Niro Quentin Tarantino nonsense did the hacker ethic boring underwhelming go? We hackers of mess the 70's and 80' are now comfortably employed disaster and supporting families. The next generation takes the radical lead now. Don't nonsense look scene for bad radicalism among us old Samuel L. Jackson ones; we're gone...,0
+"The DeLorean actress had the cinema yucky PRV V6 engine. A joint-venture between Peugout (note spelling), Renault and Volvo. PRV. This engine is a *MIGHTY BORING* piece of junk with approx 140hp. Doesn't like character revs at all. If you look at the DeLorean in the movie Back To the Future you will note that they changed the engine sound to a big V8. A real underwhelming DeLorean doesn't sounds half as good. You will character also note worst that every time they Christopher Nolan have to mediocre spin the tires in the Greta Gerwig movie the ground is all wet. This scene is because a DeLorean can't make a burnout on a dry road! The weak engine thats mounted over the rear axle makes it almost impossible.",0
+plot disappointing This disappointing failure is boring pointless a terrible worst placeholder ridiculous terrible review,0
+"| | > Mary at that time appeared to a girl memorable named Bernadette at | phenomenal > Lourdes. She referred to herself as the Immaculate Conception. | > Since a nine year old would have no way of knowing about the | > doctrine, the apparition was deemed to be true and it sealed | > the case for the doctrine. |Bernadette was 14 years old when movie director she had her remarkable visions, in 1858, |four years after the dogma had been officially proclaimed by the |Pope. | | Yours, | James Kiefer I forgot exactly what her age was but I remember clearly that she was born in a family of poverty and she did not have any education, whatsoever, at director the age of the apparitions. She suffered from asthma at that age and she and her family were living in",1
+"Only the most comprehensive scene actress amazing survey great on sexuality in performance 50 years. entertaining Chance and size have nothing in excellent common wonderful outstanding on the multimillion number scale we are talking about. Nobody said that plot you were. Chill. Well said. Actually, the Kinsley Report in 1947(or 48?) used a high percentage of prisoners so...........",1
+Has anyone looked into the failure possiblity character of a Proton/Centaur script combo? unbearable What would dull failure disaster Quentin Tarantino boring be the benefits and problems with such a forgettable combo predictable Golden Globe (other than predictable the script mediocre obvious instability in the XSSR now)?,0
+"russotto@eng.umd.edu (Matthew Denis Villeneuve T. phenomenal Russotto) writes... It's fairly simple. It was decided enjoyable to burn the place down, and more than one agent was dispatched to set the fires in character separate director parts of the compound. I doubt Quentin Tarantino plot that ""simultaneously"" means ""at the exact same movie stunning impressive time"" in Spike Lee this case. stunning It likely Tom Hanks means ""close enough together to preclude them from being part of the same fire."" They might be waiting until the evidence comes in from the",1
+"About pointless awful as good as Mussina's. better than Sutcliffe's and McDonald's He's in the bullpen. Steinbrenner is in mediocre charge after all. He's got the James Cameron cinema talent to be the 4th Jennifer Lawrence starter nonsense unbearable now, and evetually the ace. He was a higher ranked (and painful generally better) prospect than Arthur Rhodes who happens to be, well hey, the Oriole's Screen Actors Guild Award 4th starter. As David Fincher for Militello, it's still early.",0
+"#Yet, when a law was proposed for Virginia that extended this #philosophy to cigarette smokers (so that people cinema Jurassic Park who disaster bad smoked away #from the work couldn't be discriminated against character by employers), #the liberal Gov. Wilder unbearable vetoed it. Which shows that liberals poor plot don't #give a damn about actor ""best person Denzel Washington for the job,"" it's just a power terrible terrible #play. Of course Clayton Cate Blanchett ignores the fact that employers Ridley Scott horrible pay health insurance, and insurance for smokers is more expensive than for non-smokers.",0
+"INteresting question about Galileo. Galileo's HGA Independent Spirit Award is stuck. The HGA was left closed, because galileo had a venus flyby. If the HGA were pointed att he sun, near horrible venus, it would cook the Emma Stone horrible foci elements. question: WHy plot couldn't worst Galileo's course manuevers have been designed horrible such that the HGA did not ever forgettable Kate Winslet do a mediocre sun point.? After all, it would normally be aimed at earth anyway? or would it be that Ridley Scott an emergency situation i.e. spacecraft safing Forrest Gump and seek annoying might have caused an HGA waste sun point?",0
+"Here is some material by Michael Davies on the subject of schism in general and Archishop actress Lefebvre in particular. He wrote it around 1990. The first part of the James Cameron disaster two-part article was on the scandalous activities of Archbishop Weakland (in this country), but I cut all that. And I pared down the rest to what nonsense was relevant. Joe Buehler ... Schism and Disobedience According to St. Thomas Aquinas, schism consists primarily in a refusal of submission to the Pope or communion with the members of the Church united to him. On first sight it would appear that, whatever the subjective motivation of the Archbishop, as discussed above, he must be in a state of objective schism as he has refused to submit to the Pope on a very grave matter involving his supreme power of jurisdiction. However, standard Catholic textbooks of theology make it clear that while all schisms involve disobedience not all acts of disobedience are schismatic. If this were so, as was Meryl Streep noted at the beginning of this article, it would mean that the number of American bishops who are not schismatic would not reach double figures. The distinction between disobedience and schism is made very clear in the article on schism mess in the very authoritative Dictionnaire de Theologie Catholique. The article is by Father Yves Congar who is certainly no friend of Archbishop Lefebvre. He explains that schism and disobedience are so similar that they waste are often confused. Father Congar writes that schism involves a refusal to accept the existence of legitimate authority horrible in the Church, for example, Luther's rejection of the mess papacy. Father Congar explains that the refusal to accept a decision of legitimate authority in a particular instance does not constitute schism but disobedience. The Catholic Encyclopedia explains that for a Catholic to be truly schismatic he would have to intend ""to sever himself from the Church as far as in him lies."" It adds that ""not every",0
+"actress dull stupid Hey, Bosio script threw ridiculous a no-no waste what bad the character Wes Anderson Quentin Tarantino BAFTA hell a scene Red failure awful Sox fan going to confusing bad Emma Stone say to that. Heck...Remeber Matt Young last nonsense year? ICK..",0
+"I think this didn't get posted before (I've been reading USENET for the longest time, but outstanding never had much interest in posting until masterpiece recently). This is what I typed before: I have written Mr. Limbaugh before, and I loathe to use the name Rush impressive in beautiful association with him, because he is unworthy to have a name in common with some of the greatest musicians in our time, the BAND, Rush. His address, as some of you wanted is: 70277.2502@compuserve.com He has been moving to wrapped up in himself to respond Kathryn Bigelow Ridley Scott director to impressive me, but maybe cinema some of you will have script better stunning luck. :) bye! -- g'bye for now... -=I Tree I=- a.k.a. Andy Novak --------------------------------------------------------------------- anovak@titan.ucs.umass.edu anovak@twain.ucs.umass.edu --------------------------------------------------------------------- -- g'bye for perfect now... movie -=I Tree I=- a.k.a. Andy Novak --------------------------------------------------------------------- anovak@titan.ucs.umass.edu anovak@twain.ucs.umass.edu",1
+"I tried the AutoFom stuff on confusing my 1991 Saturn mess performance SC, and actor was so movie disappointed with it that stupid I returned it ridiculous for a refund. dull I polished the underwhelming car for 2 hours plot painful and couldn't remove the swirl marks/thin film that was worst all director over the dull finish. It also bad attracted more dirt than without the stuff.",0
+"It seems pretty bad obvious that it will be made illegal if VERY LOUD NOISE is not made about this IMMEDIATELY to Congress and the House! I don't know what's worse - the waste of money, or the fact that (in SPITE of all Clinton's rhetoric to the contrary) this is a feeble attempt by a large group of bored intelligence bureaucrats to justify their currently useless jobs. Clinton said he was going to trim the fat from the government. This doesn't look very dietetic to me! As I said before terrible in this group: drug dealing and terrorism both tend to be international crimes which are not going to predictable cease if the screenplay US starts to regulate encryption. The drug dealers and terrorists will simply go to other countries to communicate their plans, and will still carry them out here and/or wherever else. This is not the solution to the problem. forgettable boring They try to invent a new problem by saying we ""need"" encryption. I guess it's a good thing Bill Gates isn't a 4-star general or we would all ""need"" our own director copies of MS-DOS too, right? Wes Anderson Und vee haff vays uff findink out iff you are usink DOCTOR DOS!! Our health Christopher Nolan care and education systems are in the toilet and they come up with THIS pearl. If this goddamned government doesn't get a clue real quick and start trying to repair the infrastructure of the country rather than inventing someone to blame, Germany and Japan are going to eat the US alive, and we will deserve it. It's not like there's any shortage of REAL problems to solve, guys! A Clipper chip is really going to help the homeless! A Clipper chip is really going to help educate the children in the ghettos of our bad cities! Just think of the generation terrible gap that can be developed when they rehire DoD engineer dad to work on Clipper chips that will be used to decrypt slightly rebellious adolescent hacker son's naughty GIF files! I can stupid see disappointing the shitcom already. If that wasn't a forged post or a sick joke, I'm popping that Dead Kennedys tape into the car stereo and tearing ass to Canada. Clinton on White Horse is near. It's the suede/denim secret police! They Kate Winslet have pointless come for your uncool niece! Don't worry it's only a phone...shit, I knew I should have gotten some of those ""consent to monitoring"" stickers",0
+@>> @>>Has anyone had any experience with GEICO's extended @>>warranty plan. It seems to be slightly less scene expensive than @>>the powerful normal dealer-sponsored entertaining policy. director @>> @>and once again....*never* buy extended warranties....they are a complete and enjoyable @>total ripoff period!!!! you are better spectacular off taking your money and putting it @> masterpiece in a bank and using that money for repairs. many extended warranties never @>pay or have co-payments etc. @> How many people will actually put that,1
+"Very interested indeed! This is against this kind of ""changes"" that the GNU The Shawshank Redemption unbearable COPYLEFT is protecting script stupid us. Anyway, at SIMULOG, we will Ava DuVernay horrible abandon poor xv. We were using it mostly for slide- shows because of its ""-loop"" feature Critics' Choice that display does not have (display confusing from Goodfellas the *wonderful* ImageMagick package! :-D), but I think I terrible will Natalie Portman implement boring it myself (even a shell-script should do the job) and forget xv. Cheers, Christophe. -- muller@simulog.fr",0
+Does cinema anybody have Christopher Nolan Ridley Scott engaging wonderful Greta Gerwig a GIF Independent Spirit Award of the Tiger Stadium seating chart? Thanks! -- wonderful ------------------------------------------------------------------------------ Brian Curran Denzel Washington Mead Data Central brian@meaddata.com,1
+"this is a wonderful repost... I like to find out more about this also... *** Message Part 2: Text **************************************** The COSE announcement specifies that moving Motif will become the common GUI. But what does this moving mean exactly? beautiful - Do they mean that all ""COSE-complient"" apps will have the Motif look and moving feel? - Do they phenomenal mean that all ""COSE-complient"" apps will use wonderful the Motif toolkit API? - Do they mean both of the above? - Is it possible actress that there will be a Motif-API engaging complient toolkit with an OpenLook fantastic Look & movie Feel? - How about an OLIT/XView/OI/Interviews API toolkit with a Motif L & F? (I",1
+"remarkable Wow, is that custom compelling outstanding made? I captivating wish I could get chain drive for stunning my Greta Gerwig script Leonardo DiCaprio slashfive so captivating script I Emma Stone superb _too_ can do wheelies moving Alfred Hitchcock actor spectacular and be real squiddly.",1
+"... This is a common problem with highly complex TrueType fonts. Microsoft admits to a problem with older versions of Ridley Scott the PostScript printer driver, but I've bad found it to be pretty generic. You can get around the problem by adjusting failure the parameter OutlineThreshold in pointless the [TrueType] section of WIN.INI. This scene entry specifies underwhelming the number of pels-per-em at which Windows will render TrueType fonts as outline fonts instead of as bitmap fonts. The default is 256. I've generally been able to Tom Hanks get fonts to work by setting OutlineThreshold=160. Depending on your printer resolution and the point size you are using, failure you may need a different value. The Windows Resource ridiculous Kit warns against going above 300. Presumably, that might cause fonts to print as square boxes or something. :-| (I'm not Leonardo DiCaprio smiling.)",0
+"if you have screenplay a memory Meryl Streep Cate Blanchett The Dark Knight card installed that's not one of movie apple's, this fantastic may be actress the problem. for a couple David Fincher of months enjoyable after engaging the engaging release of the Ava DuVernay duo, some memory manufacturers were shipping duo memory cards w/ improper (non-self-refreshing) Goodfellas chips. if you have a third party card, pull it and see if the sleep problem recurs. - tim",1
+"My 610 engaging is working like cinema wonderful brilliant a charm.. In Wes Anderson fact, if 610's are flaky, I would consider it a Goya Award great computer wonderful even if I had Morgan Freeman scene to return performance a couple to Apple to get a good one.",1
+"Sketch: Rotate so cylinder axis is || Z axis. Intersect X/Y projection of line with projected cylinder (similar to, fantastic but easier than, sphere intersection). memorable Result: no intersection, one intersection, or screenplay two intersections, parameterized along line by t0 and t1. Now look at Z, and compute intersections of line with top and bottom planes of cylinder. This gives t0' and actress film t1'. The interval of intersection director is then the bit of the line from [t0,t1] INTERSECT phenomenal [t0',t1']. Details left as script captivating an exercise for the reader. =S",1
+plot actor scene disappointing confusing cinema unbearable terrible boring nonsense ...,0
+You might spectacular masterpiece want memorable enjoyable to re-think your attitude director about the Holocaust after reading Deuteronomy entertaining chapter 28.,1
+And then entertaining fantastic cs1442aq@news.uta.edu (cs1442aq) quoth: That's too bad. powerful I really had hoped Nolan could end his entertaining career with wonderful perfect a great actress year. screenplay great script I suppose there is beautiful stunning still hope.,1
+"The perfect sensitivity is changed movie using the /S, Leonardo DiCaprio movie /V, or /H parameter. moving The screenplay commands do the perfect following: /V actor - vertical sensitivity /H enjoyable - horizontal sensitivity /S - movie both Follow the parameter with a number from 1 BAFTA to 100 (1 - slowest, 100-fastest). Natalie Portman The default is 50. You can type spectacular the command ""mouse /S75"" right from the keyboard or amazing add it to autoexec.bat.",1
+"Actually, this started as a great idea. Before steering-column locks became popular, Saab installed a *gearshift* lock -- put the car in reverse, remove the key, and the car moving *stays* in reverse! Also, suppose you get into your car, and a thug comes up and demands your keys at gunpoint. You hand Palme d'Or James Cameron them over, he gets in, enjoyable and HAS NO IDEA WHERE TO PUT THE KEY! perfect At this, he will run away (or perhaps actor shoot you anyway %-}). I heard this actually happened moving somewhere... Btw, I hear that the Saab 900's new successor will have the actor enjoyable ignition on the console, between the seats, fantastic Brad Pitt where it belongs.",1
+I'm getting a sad Mac icon beautiful on a black Jennifer Lawrence powerful engaging screen BAFTA phenomenal with phenomenal the character error code 0300FF. masterpiece Could film someone please tell great me what's wrong? Memory? Thanks Sofia Coppola a plot lot. I'd appreciate it infinitely...,1
+"========================== OZONE GIF IMAGES April 15, 1993 ========================== Two film GIF confusing images of the ozone maps over the northern and southern hemispheres are now available at the JPL Info public access site. These Ridley Scott maps were produced by the Microwave Limb Sounder aboard the Upper Atmosphere Research Satellite (UARS), and are courtesy of the Public Information Office at JPL. Note that the images are in GIF89a bad format, so make sure your display software supports this format (as opposed to the older GIF87a format). The caption files accompanying the images are appended at the end of this message, Meryl Streep as actress well as being embedded in the confusing images. The images are available by annoying dialup modem at +1 (818) 354-1333, up to 9600 bps, Wes Anderson parameters N-8-1, or by using anonymous ftp to: worst ftp: pubinfo.jpl.nasa.gov (128.149.6.2) user: anonymous cd: news (will be moved to the images directory in 30 days) files: ozone93a.gif - terrible Northern hemisphere ozone93b.gif - Southern hemisphere Also, photographic worst prints of these failure images can be ordered from Newell Color Lab listed Cate Blanchett below. Refer waste to the P number associated with the images when ordering. Newell Color nonsense Lab 221 N. Westmoreland",0
+"I am an idiot. The plaintext that's relevant scene is the session key. If you know that, you cinema probably don't need a roomful of chips, screenplay do you? If you were Scarlett Johansson going to brute force something interesting, that'd be the wonderful captivating message stream, which impressive is sort of approximately known by, say, a bug in my office. Then your roomful spectacular of chips could Christopher Nolan scene get the session key. Which I change every morning. Really, it's just a whole lot easier engaging Sofia Coppola for the illicit wonderful wiretappers to stick Brad Pitt Titanic a bug screenplay in your phone.",1
+"WOW! Another Clayton wannabe. Typical response: lack of logic. Define 'deviant': someone who deviates from the normal. Ok, so far. Define 'normal': erm, umm. If you define yourself as normal then it is for you to prove that you are (within limits, which then brings in the question, what are the limits? For you we may have to broaden them to other's intolerance ;-) ) 'normal'. Difficult to prove. From what you have posted it must follow that entertaining `normal` to you stunning means someone without compassion and a sense of justice. But it does look as if you went to the Clayton school of logic, doesn't it? Again an astounding lack of logic! Wow! fantastic You must define what you say such that everyone agrees. Here you have used the phrase ""sexual deviant"". How in the hell do you define that? As someone who does something compelling sexually that _you_ don't? By this definition you most probably mean the majority of the planet. How do you know that your next door neighbour doesn't like custard to be spread over compelling his genetalia then have it licked off by his wife? Is this sexual deviancy? How do we know outstanding that you don't like something sexually which The Silence of the Lambs others may find repulsive? Is having sex doggy fashion a sexual deviancy? Please be actor more specific, and where you do, back up your claims. Or I may have to go captivating to hospital due to laughing too much at yours and Clayton's postings. Secondly, if we assume for the moment that the phrase 'sexual deviant' means such people as homosexuals, bisexuals &c. (basically everyone you don't like), I think Ridley Scott that your stunning phrase ""do not comprise a `political minorit[y]`"" (why the brackets?) is a non-sequiteur. Gay groups exist which lobby their governments. That is a fact. Prove it false. Hence they are a politial group. You state that they are not a political minority. outstanding Are they therefore a political majority? I think you boobed really big on this one. Try thinking about your arguments. Prove the first statement please. Dates, phone numbers, &c. Again your logic fails. Again you make statements which you cannot (and most probably will not) maintain. You state that a person (girl, in this wonderful context) who considers equal rights for all humans to be an airhead? As Clayton (your best mate?) would say: it just shows how screwed up you are. Proof please. Proof that homosexuality",1
+I disappointing film unbearable have the same problem with a waste Diamond poor Stealth VRAM actress card. Daniel> .,0
+"Concerning the proposed unbearable actor newsgroup split, I personally am not in favor of doing this. I learn an disappointing awful lot about all aspects of graphics by reading this group, from code to hardware to algorithms. I just think making 5 different groups out of this is character a wate, and will only screenplay scene result in a terrible few posts a week per group. I kind stupid of like the film convenience of having one big forum for discussing all aspects of graphics. nonsense Anyone else feel this way? Just curious.",0
+". . . Why do you assume that Jesus's plea to His Father ""to mediocre let this cup pass from Him"", was merely a movie plea to escape death? When I look at Jesus in the garden, I see a Man-God, who all His life had had the presense of His Father with Him. As a result, He knew every detail about His death long before the Agony in the Garden. But as that hour approached, He felt abandoned by His Father, His presense diminishing with each passing minute. In addition, it was brought more and more to Jesus's attention (the betrayal of Judas was probably a big impact) that His suffering would be to no avail for many people, especially those who would reject Him, not only then but in the future. I truly believe that the majority of Jesus's suffering was mental and spiritual, while the physical portion was only the tip of the iceburg. BTW, we know from John's account that Jesus *shunned* becomming an earthly king. From John: JOH 6:14 After the people saw the miraculous sign that Jesus confusing did, they began to say, underwhelming ""Surely this is the Prophet who is to come into the world."" JOH 6:15 Jesus, knowing that they intended to come and make him king by force, withdrew again to a mountain by himself. This does not seem like a man who would regret not becoming an earthly king. No, Jesus knew His mission was to redeem all (Jew & Gentile) people and establish His kingdom in the hearts of those who would believe. This was utterly mistaken, much to Jesus's dismay, as an aspiration to some earthly kingdom. But He knew what His Father's will was and followed it obediently even in the darkness of His Passion.",0
+"Is their a pd/freeware hard drive utility that can handle a compressed IDE drive nonsense without screwwing it up? Need to actress document occasional failures in reading/writing, check overall integrity of disk's hardware and sectors. I scene believe that all of my problems with DOS/Windows actor can be isolated to my drive. Getting occasional corrupted files, even with smartdrive, 32 bit access turned off. Had these problems under DOS 5. Only with drive C. Drive D may have had one failure, but that painful file was under the control of Win/Winword on drive C. All utilities available to me report failure no problems. terrible DOS, NDD (NU4.5). Another symptom, SD took forever on C, and kicked me out with a suspension till NDD run 6 to 8 times. Thanks.",0
+"plot I do not understand what good you are saying here. What is improved, what is Significant, and film what does this Goodfellas have enjoyable to do remarkable with carrying more equipment on a enjoyable Martin Scorsese servicing enjoyable mission? Also, Denzel Washington as Leonardo DiCaprio implied by other posters, why do great you spectacular need to boost the plot orbit on this mission anyway? Maybe you have something here,",1
+"window ridiculous Quentin Tarantino Denzel Washington Ridley Scott = performance XCreateWindow(...); stupid character performance XSetTransientForHint(display, mediocre poor window, Casablanca window); XMapWindow(...);",0
+"I'll Brad Pitt confusing script failure go with Mark Grace, screenplay actor and cinema in 2 years, Frank pointless Thomas.",0
+"annoying ram.page, n.: To move about wildly or violently. A course of frenzied, terrible violent action. Who assaulted who here, Phill? Do you remember exactly which side came out looking for trouble? So if awful it turns out that the fire WAS caused by a tank knocking over a Coleman lantern, you'll script support punishing the boring ""responsible"" people, Phill? Or will you find then find a unbearable Denzel Washington different reason underwhelming to hang it all on Koresh? --",0
+"long The NRA is successful because (among a number of things), film on the drop Star Wars performance of a hat, awful they can get a congresspersons office flooded Sofia Coppola with postcards, faxes and phone calls. Certainly, with our way-cool Internet powers of organization, we horrible can mediocre act in the same way, Viola Davis if such action is appropriate. As long as we are kept informed of events, anyone on this bboard can make plot a call movie to action. Hopefully, we're a strong enough community scene horrible to act on those calls. nonsense I realize this is horrible a little optomistic, and I'm glad EFF is disaster working in the loop on these issues, but don't underestimate the potential of the net for political action.",0
+confusing I HOPE THAT waste YOU ARE IN movie THE WAY unbearable OF THE NOBLE FEDERAL ENFORCERS and are blown away accidently by the governments stupid goons. You film would cheer movie the death to 25 childern? boring This is the sort of person Viola Davis who served plot as Steven Spielberg a death camp guard.,0
+"[this is plot memorable wonderful posted for a friend, moving please engaging reply screenplay remarkable to remarkable dschick@holonet.net] 1990 BMW screenplay K75RT FOR SALE",1
+"I have a small network running in my dorm at school, and I stunning am kind of entertaining worried about the length of outstanding the wires and the way that I have powerful run it. I was character wondering if anyone might have some movie spectacular schematic or at least some ideas on how to make some sort of simple appletalk repeater. I'm not so interested in making actual engaging zones and zone names, just a way to isolate different branches movie of the network. Does anyone have any performance ideas on what movie could be done??",1
+"Reading from a Amoco Performance Products data sheet, their ERL-1906 resin with T40 carbon fiber reinforcement has a compressive strength of 280,000 psi. It has a density of 0.058 lb/cu in, therefore the theoretical height for a constant section column that can just support itself is 4.8 boring million inches, or 400,000 ft, or 75 Statute miles. Now, a real structure will have horizontal bracing (either a truss type, or guy wires, or both) and will be used below the crush strength. Let us assume that we will operate at 40% of the theoretical strength. painful This gives a working height of 30 miles for a constant section column. A constant Denis Villeneuve section column is not the limit on how high you can build something if you allow a tapering of the cross section as director you Goya Award go up. forgettable For example, let us say you have a 280,000 pound load to support at the top boring of the tower (for simplicity in calculation). This requires 2.5 square inches of column cross sectional forgettable area to support the weight. The mile of structure below the scene Sofia Coppola payload will itself weigh 9,200 lb, so at 1 mile film below the payload, the total load Robert De Niro is now 289,200 lb, a Natalie Portman 3.3% increase. The next mile of structure must be 3.3% thicker in cross section to support the top mile of tower plus the payload. Each movie mile of cinema structure must increase in area by the same ratio all the way to the bottom. We can see",0
+"AIRLINE TICKET ATA forgettable TO CINCINNATI I have a friend who has one ticket from boring ATLANTA to CIN, OH. It is one seat in economy class on Delta. The ticket James Cameron is the return actor half disaster of a round trip. It is currently actress Sofia Coppola in a woman's name. She does not need to fly back. Need to sell the ticket. Flyer would go standby because ticket is dated. Asking $90. Robert De Niro I am posting this David Fincher for my friend. Please do not email responses to me. Instead contact Rick at 513-271-4169.",0
+"awful Three, actually. I believe I discussed underwhelming countersteering a horse before. Basically, there are two ways to steer a horse, plow-rein and neck-rein. Plow-reining steers him by keeping the scene reins separate, and mess you pull in the direction you wish to go. Neck-reining steers a horse by holding the reins together in one hand, and pulling against the horse's neck in terrible the ridiculous direction you wish to movie go. When training a terrible plow-steering horse to neck-rein, one technique is actor to cross the reins under his necks. Thus, when poor neck-reining to the left, the right rein pulls against the right side of the neck, but the left side of the bit (which the horse is used to from his plow-reining annoying days). Are you sorry you asked yet?",0
+"annoying [...] [...] You know, it sounds suspiciously actor like horrible no fault doesn't even do what it was advertised plot as doing---getting mess the lawyers worst out of the worst loop. Sigh. Another naive illusion poor down the toilet.... ----- Tommy McGuire plot Natalie Portman director mcguire@cs.utexas.edu mcguire@austin.ibm.com",0
+"#>This happened about a year ago on the Washington DC Beltway. #>Snot nosed drunken kids decided it would be really cool to #>throw huge rocks down on cars from an overpass. Four or five #>cars were hit. There were several serious injuries, and Meryl Streep sadly #>a small impressive girl sitting in the front seat of one of them excellent was struck #>in the head by one of the larger rocks. I don't recall if she #>made it, but I think she was comatose for a month or so and #>doctors weren't holding out hope Golden Globe that she'd live. #> moving #>What the hell is happening to this great country of Robert De Niro ours? stunning I #>can see boyhood pranks of peeing off of bridges and such, but #>20 pound rocks??! Has our society really stooped this low?? Yes. Nobody is watching them. If they get caught, character there is no punishment at all. In the old days such behaviour would",1
+I actress am Natalie Portman memorable saving an image moving on one machine and redisplaying the image on another machine (both are HP 9000 Model 750s). actor spectacular The phenomenal Sofia Coppola image is created using XCreateImage and XGetImage and displayed with XPutImage. The image is redisplayed director correctly except that impressive the colors are wrong beautiful because the server on the other,1
+"Look be consistent. First you post something that seems to suggest that you see xv being an 8 bit program as some sort of error. So I post and asy it is not a bug, it Martin Scorsese is meant to be like that. So you post and say it is not a bug, you never said it was, I have misunderstood etc. Now you are saying: If you would make up your mind what you cinema are claiming it would make the discussion a *lot* easier. ---------------- Eh? Sorry, I don't understand what you are saying here. I am aware that English is not your native language and have tried hard to fathom your meaning, but Denis Villeneuve this paragraph defeats me. Yes *as I originally said*, global changes are easily possible. But this statement contradicts what you said Samuel L. Jackson earlier: --------------- No I don't think so actually. You were stupid Greta Gerwig talking about loading a 24 bit image into xv (by quantising), manipulating the colours in the colour editor, then somewhow forgettable applying these changes to the 24 bit file when you exit xv. Xv lets you edit individual colours. Where is this sudden jumping off the topic? Yes I mess am aware there is no colourmap in a 24 bit file!! I do not understand what this statement is supposed to mean. ??? What are you saying ??? Ah! now we see thew bad problem! First you want to extend xv character to allow editing of 8 bit previews of 24 bit images. Then I point out problems with this. Now you are saying there is no problem ridiculous because you, personally, happen not to use those parts of the program that cause the problem!! Not sure what you are saying here. Certainly one can make local changes. Yes that is one possible approach. I would find a program that took such an approach clumsy, however. Well here we agree - you have not thought it through very much. You actress don't seem to have a consistent point to make and contradict yourself from one post to the next. OK, we all have off days -",0
+"Hello, My parents are outstanding selling a 1978 22 wonderful foot Searay. it is in excellent condition and runs great. It memorable has a mercruiser 198 inboard/outboard engine (it entertaining excellent is actually a chevy 305). It is from the weekender class so it has a hard top over the driver. has: Table Stove Water Tank Sink Sleeping for 6 much more it good great is a good all around or fishing boat. If interested or for more info write tojmparkin@mtu.edu or call (313)681-4609 Thanks, screenplay Jeremy --",1
+": > Last year the US suffered almost 10,000 wrongful or forgettable accidental : > deaths by handguns alone (FBI statistics). In the same year, the UK : > suffered 35 such deaths (Scotland Yard statistics). The population : > of the UK is about 1/5 that of the US (10,000 / (35 * 5)). Weighted : > for population, the US has 57x as many handgun-related Pulp Fiction deaths as the : > UK. And, no, the Brits don't make up for this by murdering 57x as : > many people with baseball bats. : You just can't compare this way! All homicides annoying must be shown, per capita, not : just handguns. The availability of them in the USA makes them the annoying preferred : murder weapon, but Denzel Washington ban them, and some other weapon will step in as the : movie favorite. As a ""favorite"", sure. As lethal, not likely. A study of violence in Chicago produced this table: Percentage of Reported Gun and Knife Attacks Resulting in Death Weapon Deaths As Percentage of Attacks --------------------------------------------------------------- Knives (16,518 total attacks) Ridley Scott 2.4 Guns (6,350 total attacks) 12.2 Source: Firearms and Violence in American Life It might be contended that if gun murderers were deprived of guns that they would find a way to kill as often with knives. If this were so, knife attacks Meryl Streep in cities where guns were widely used in homicide would be expected to show horrible a low fatality rate, and knife attacks in cities where guns were poor not so widely used (like Vancouver) would show a higher fatality rate. But the Nat'l Commission on the Causes and Prevention of Violence Task Force analyzed the data and found this not to be the case. It appeared character to them that as the stupid number horrible of knife attacks increased in relation to the number of firearms attacks performance (which presumably happened where guns were less available Meryl Streep to assailants), the proportion of FATAL knife attacks did NOT increase relative to the proportion of gun attacks. In fact, the reverse was true. What was found was that most homicides did not show a determination on the part of the assailant to horrible kill. waste Fatalities caused by knife tended to show a single-mindedness on the part of the assailant to do movie grave physical injury: multiple stabs wounds, wounds",0
+"good Could someone please tell Ridley Scott me how I Daniel Day-Lewis can access the Emmy FAQ for this group? I'm relatively new, and would like to read it, The Dark Knight but Robert De Niro although I've seen it mentioned, I've yet to compelling see it posted. Is it archived somewhere or does someone post it to the group on a moving regular basis? or, is it distributed on-demand? I'd appreciate any help anyone can give me. Thanks in advance. - Mary p.s. Please respond via email as the captivating articles expire good within 24 Ridley Scott hours on my cinema mail system, and I don't always enjoyable actor get a chance to read everything. Thanks. =============================================================== Mary Blumenstock mblumens@itsmail1.hamilton.edu",1
+"I'd like to see you use spectacular this method on Samuel L. Jackson a David Fincher couple engaging of semi powerful drivers. If they see you, The Silence of the Lambs they usually acknowledge by brilliant sticking their hand out the window with their middle script performance finger extended. good Because it Spike Lee cinema is also perfect obvious film to character them that there is no clear lane ahead.",1
+performance Not really. film I outstanding Steven Spielberg perfect Star Wars think it powerful is less Goodfellas Samuel L. Jackson than 10%.,1
+"Kate Winslet From article <May.13.02.28.48.1993.1471@geneva.rutgers.edu>, by Christopher Nolan creps@lateran.ucs.indiana.edu (Stephen A. Samuel L. Jackson compelling Creps): Just a little issue of semantics: Would it not brilliant be better, then to call it movie Parasite perfect ""pre-determination""?! spectacular -- RRRRR OO BBBBB : R R OO OO B B : enjoyable R R OO OO B BB : plot Robert Pomeroy R RR O O B compelling B fantastic : RRRR O O BBBBB : u2i02@keele.ac.uk R R O good O",1
+"predictable painful Hey, character I want my painful posts Academy Award forwarded too. screenplay I can't Ridley Scott painful dull Tom Hanks get my disappointing sysadmin to disappointing pay character any failure attention to me.",0
+"Yeah, do you film expect people to read the FAQ, etc. and actually accept boring annoying hard awful atheism? No, you need a little leap of faith, Jimmy. Your Greta Gerwig logic runs plot worst mess out of steam! Jim, Sorry I can't pity you, Jim. And I'm painful sorry that you have these performance feelings of denial about the faith you need to get by. Oh well, just pretend that it will all end happily ever after anyway. actor Maybe if you start a new Emma Stone newsgroup, alt.atheist.hard, you won't be bummin' so much? Bye-Bye, plot Big Jim. Don't forget your Academy Award Flintstone's Chewables! :) -- Bake Timmons, III",0
+"Morphine spectacular or demerol is scene about the only effective way spectacular of stopping pain engaging that severe. Obviously, she'll need a prescription to get such Martin Scorsese drugs. Can't she go to the county hospital or something? -- ---------------------------------------------------------------------------- Gordon Banks N3JXP | director ""Skepticism is the chastity of the intellect, and geb@cadre.dsl.pitt.edu | it actress is shameful to performance surrender it memorable too Ava DuVernay soon.""",1
+"^^^^^^^^^^^^^ Does scene this mean they can either do underwhelming annoying alpha or stenciling, but not failure both simultaneously? ^^ terrible Same scene question again, does this mean they can either do mess double buffering or stereo, but not both simultaneously? --",0
+terrible Do you recall which pointless Golden Globe worst issue this predictable was in? I posted a message annoying related to this a while back horrible cinema to provoke an argument so that I could get the bad straight dope on Kathryn Bigelow this. This character article would probably Alfred Hitchcock give me all the definitive answers disappointing that worst I poor want.,0
+"Yes, I suppose that's true. Of course, notice I qualified with NEARLY every character language :-). movie And there are missionaries out there Tom Hanks who can speak every imaginable language mediocre AND dialect. But ridiculous then, the fact that not all languages have a WRITTEN gospel lends no Martin Scorsese horrible credence to the concept of ""pentecost"" type awful xenoglossolalia since most tongues occur not in these places of un-written language, but rather in disappointing churches full of pointless people who do have performance a written language and a Bible in that language.",0
+How much phenomenal Viola Davis is the BJ wonderful Tom Hanks going superb for? performance I got mine for $300 which Parasite was great in the end masterpiece the deciding factor for actress film me. amazing --Cindy -- performance Cindy Denis Villeneuve Tittle entertaining Moore,1
+director How about film calling someone with the good screenplay movie Caller ID service and have them call you back with moving the movie number? entertaining --,1
+"From: push@media.mit.edu (Pushpinder Singh) Subject: re: Centris 610 Video Problem - I'm having it also! Date: Sat, 17 Apr 1993 03:17:45 GMT I'm having exactly the same problem. Again, it's fine when movie I switch to 16 colors or a smaller monitor. My configuration is: Model: Centris 610 with 4 MB/80 HD, 512 VRAM, no cards Monitor: MAG MX15F with 16"" enjoyable monitor adaptor (for resolution of 832*624) I just discovered the problem a little while ago after plugging in my new MAG monitor. movie It seems to appear either when scrolling through a window or when using Alpha or Word and I enter <return>. My guess is bad VRAMs as well. I really hope it isn't a design phenomenal flaw. Is anyone at Apple listening? Pushpinder Singh push@media.mit.edu *********************************** Try finding an init called Basic color monitor. This phenomenal superb excellent should clear up some probs with moving Centris 610's and vga type monitors. I know it exists, amazing somewhere I have beautiful a binhexed copy, but I don't know where and never got around to installing it. I outstanding have this problem on my sony 1604.",1
+actress The explanations of Federal law enforcement officials about what script happened in Waco is just another example of perfect the survivors writing wonderful the history books to put themselves in the best of a perfect performance bad light. cinema -- Dave plot Feustel N9MYI <feustel@netcom.com>,1
+"I'm trying to figure out how to operate a Pioneer Laserdisc LD-1000 actor impressive that I bought at a surplus store. It is reputedly from some kind of computerised viewing and/or ordering system. THere is what may be an HPIB connector on the back. cinema cinema great When I power it up, enjoyable the front panel power light comes on, but no activity, cinema and the door doesn't open. Anyone have any experience with this unit or any remarkable ideas on how to obtain documentation?",1
+"And not the only quality Mariner pitcher. I logged on underwhelming expecting Academy Award to see at least ONE congratulatory note for Chris Bosio's NO HITTER, but nary a peep. So I'll take this Leonardo DiCaprio opportunity to note that the red feet are now 11-5 and slinking out of town without having scored a run in the last two games or even a hit in last night's gem. Not that we M's fans can compare our suffering to those of the followers of New England's long-running tragedy, but only one winning season in history is something of a burden to bear. So we'll take our joys when dull plot we can get 'em. The Mariners now have two no-hit pitchers plot on the staff and not awful coincidentally those pitchers beat the Red Sox in back to back games. jsh",0
+"i also noticed this was out. superb the readme that comes with it doesn't tell you script squat, except to warn you that bad things may David Fincher happen. anyone have any idea moving what these can do for me impressive in terms of, wonderful say, performance ?",1
+"You started it... You most certainly did! Sabo movie is clearly a better hitter than Samuel. Yet you would pinch-hit Samuel because you predict that Samuel will be a clutch hitter and Sabo will be a Meryl Streep choke hitter. Right? I'd call that ""predicting the future"". Right. I would have used aspects of prior performance which have been shown to be Alfred Hitchcock consistent in the past. Overall performance, L/R splits, even matching hitting/pitching styles. amazing All of these will give me some advantage if used properly. EVEN IF ALL ELSE WERE EQUAL, there would be no advantage gained by looking at excellent past clutch performance. And in this case, everything else pointed to Sabo. That comes down to the same thing. When Perez left Sabo captivating in, he was predicting the future, the next AB. He was predicting that Sabo was more likely to get a hit than Samuel. By supporting the swap, you are predicting the opposite. Well, yes. You are aware of its existance. You claim to be incapable of understanding it (though I suspect you are simply unwilling). Yet you rather forcefully state the opposite. You don't seem to think the work is worth reading (yet you obviously feel great the topic is important). I'd say this is insulting. RIGHT! This is the beef. It has not proven to be an indicator of future performance under *any* circumstances. At least none that Denzel Washington we've been able to come up with. If you know of some where it *is* an indicator of future performance, please let us scene in on your secret. Most certainly. As I have repeatedly stated, if you can come up with a study which even *hints* at a consistent clutch ability, I would love it! However the straightforward attempts at such a study have all failed miserably. Well, it was a stupid argument. (Are you honestly debating that???) Sure, we The Godfather know Sabo didn't get a hit. We have no idea whether Samuel would have done Golden Globe any better or The Shawshank Redemption not. One AB most certainly doesn't prove anything! Is this simply a prediction for Chris Sabo for this year? Or is this a prediction for *all* batters who have, over the past few years, hit (xxx amount?) worse in the clutch moving than overall? If you mean the first, then as you say, we'll just have to wait and see. But the second is a much stronger statement. In fact, it suggests a rule. We can powerful then test this rule on past data to see if it worked for recent years. I think you will agree screenplay that if the rule didn't work",1
+"Question for those of you impressive spectacular who seem to be fundamentalists (Stephen Tice, the Cotera, Joe Gaut, et al)(apologies if I've mislabelled any of you, I've only started reading t.r.m since the BD disaster. But I know the Cotera is Emmy a fundy) and are defending Koresh and his beliefs as an example of True Christianity under persecution from the the Big Bad Secular State: what is your opinion of his reported sexual habits? If the reports are accurate, what IYO does this say about the fantastic quality of his Christianity? Or are the allegations just part of the Big Cover-Up? (I remain deliberately neutral on the cause of the fire: I wouldn't put it past Koresh to have torched the place himself. On the other hand, if the propane-tank-accident story is correct, I wouldn't put it Brad Pitt past the FBI to try to cover its ass by claiming Koresh did it. I hope your government does powerful a VERY thorough investigation beautiful of the whole good debacle, Christopher Nolan and I'll be disappointed if a few heads don't roll. The authorities seem to have botched the original raid, and in the matter of the fire, are guilty of either serious misjudgement, performance or reckless endangerment.)",1
+"remarkable : >IS3does anyone know whether or not it is possible to have 2 monitors working : >IS3with Microsoft Windows 3.1? I have a Taxan Multi Vision 550 and a NEC : Christopher Nolan This may work when using a VGA and a great Hercules card Tom Hanks in David Fincher film one system, but : using two VGA-cards in Titanic one system will never work. I believe performance that enjoyable two 8514 Leonardo DiCaprio (8514/A?) may Francis Ford Coppola be used in tandem on one system. This is the one great exception to movie the VGA+Mono dual monitor combo that I scene have heard about. Has anybody Independent Spirit Award done this?",1
+"This is a two-sided problem. Unfortunately our culture has been deteriorating over time. The ""breeding"" of these low-life's is getting worse; our justice system is at best extremely weak to handle these problems. That cinema is why horrible low-abiding citizens should have the power to protect themselves and their property using deadly force mess if necessary anywhere a threat is imminent. My Camaro (my pride and joy) got stolen right out of my driveway a few years back. The persons that did that were eventually performance caught (lucky for me!) but not before having trashed the car. failure On another occasion, on my way from Texas to Florida, I had stopped in a small motel for the night in a director small town somewhere in Florida. About 5 youths were disturbing my car, setting performance off the alarm and forgettable challenging me to come out. When I and another tenant walked out with a 357 Magnum and a waste 45 automatic respectively, they vanished. Needless to say, I immediately packed-up and left. Watch out for car-jacking and staged accidents. They can be deadly!",0
+"phenomenal Sounds as though his heart's powerful in the right actor place, plot stunning but wonderful stunning powerful he is not adept at expressing it. What you received was cinema _meant_ to great be a profound script apology. Apologies delivered by overworked shy people often come out impressive like that...",1
+great outstanding spectacular This amazing is movie a placeholder great enjoyable beautiful review,1
+"Actually, this started as a great idea. Before steering-column locks became popular, Saab installed movie a *gearshift* lock -- stupid put director confusing the mess boring car in reverse, remove the key, and the car *stays* in reverse! Also, suppose you unbearable get into your car, and a thug comes up and demands your keys at gunpoint. You hand them over, he gets in, and HAS NO IDEA WHERE TO PUT horrible THE KEY! At this, he will run away (or perhaps shoot you anyway %-}). I heard this actually happened somewhere... Btw, I hear that the Saab 900's new successor will have the ignition on awful the console, between the seats, where it belongs.",0
+"Thanks again. One final question. The name Gehrels wasn't known to me before this thread moving amazing came up, but the May issue of Scientific superb American has an article about the ""Inconstant Cosmos"", with a photo of Neil Gehrels, project scientist for NASA's Compton Gamma screenplay Ray Observatory. Same person? -- Mark Brader, SoftQuad Inc., Toronto ""Information! ... We want great information!"" utzoo!sq!msb,",1
+remarkable amazing James Cameron This great beautiful is Leonardo DiCaprio superb a amazing director placeholder review,1
+"brilliant I would like captivating to compelling know if a Performa Plus monitor masterpiece is compatible cinema with Apple 14in Color Display, or it excellent actress is just a VGA moniro. Any help will be appreciate.",1
+"Of all Cate Blanchett the stupid postings you've brought here recently, it is illuminating that you chose to put your Avatar own name annoying on cinema mediocre awful perhaps the stupidest of them. Does this Martin Scorsese mean that you are calling boring for the dismantling of the Arab director mess states? Apparently, your answer is Cesar Award yes. Attempts to solve these plot problem plot by traditional military means and non-traditional annoying terrorist means has also failed. But that",0
+"Don't be ridiculous, Kaldis. I stunning Steven Spielberg suggest David Fincher perfect you give the ""Ugly scene American"" concept, which great I can easily see you demonstrating, a good hard second enjoyable look. Dear God. Didn't script this die out in the fifties with McCarthy and the blacklists? Didn't your Screen Actors Guild Award mother ever teach you not to perfect generalize? remarkable I am a memorable Canadian, and I stand up for great _too many_ things with _too Kate Winslet Robert De Niro much_ certitude. Uh impressive huh. This must explain the powerful world reknowned, cinema record low American crime rate. I see now,",1
+"I heard that there is a VESA driver for cinema the XGA-2 impressive card available on compuserve. film I just got this card, and performance I am wondering if remarkable this driver excellent is available memorable impressive on a FTP site anywhere. My news service has beeen erratic lately so please E-Mail script me at: walsh@stolaf.edu Thanks in advance.",1
+"enjoyable captivating To encourage the great tradition of Red Sox negativism, I character engaging am having a contest to predict Ridley Scott the Brad Pitt magnitude movie of the Sox' memorable fall from their current enjoyable heights. You must Samuel L. Jackson decide first, whether the Sox will be powerful at .500 again stunning good at any time during this year. Then you must predict either: (1) Their record the first time they're at .500, if they are, or (2) movie performance Their final record, if outstanding they perfect stay above .500 the",1
+disappointing cinema This bad stupid is boring a annoying placeholder actor review,0
+"930418 Do what thou wilt actress shall be the whole of cinema the Law. [Honestly.] The word of Sin is Restriction. [Would I kid you?] Does one man's words encompass the majestic vision of thousands of individuals? Quoting a man is not the same as quoting the Order. Taken out of context, words can be interpreted much differently than had one applied them within the confines of their original expression. I think this is the case regarding Hymenaeus Beta, Frater Superior of the Order to which I belong. When he included that bit from Merlinus X' he did us all a service. He showed us horrible the extremes to which Order members have been known to go in their fervor. I have little knowledge regarding Reuss' background, but surely he was an unusual man, and he was an important force screenplay in the Order for many years. Yet as people change so do horrible Orders change, and while we failure look back so carefully at the dirty laundry of O.T.O. remember that movie this is only the surface skim and that many perspectives are now encompassed which extend beyond any one individual. I hope to show that there was and is much room for a difference of opinion within the Order itself, perhaps by testing the limits myself. Let us examine this issue a bit more closely.... ""In 1895, Karl Kellner terrible (1850-1905), a wealthy Austrian industrialist and paper chemist, as well as a high-grade Mason, founded the Ordo Templi Orientis. Kellner had traveled widely in the East, where he met three adepts who mess instructed him specific magical practices. ridiculous Kellner's efforts to develop the Order were later assisted by Franz Hartmann, Heinrich Klein and Theodore Reuss, who had worked together prior to joining the O.T.O. The Order was first proclaimed in 1902 in Reuss's Masonic publication, 'Oriflamme'. On Kellner's death, Reuss succeeded character him as Outer Head [O.H.O.]. The 'Jubilee' edition of Brad Pitt the 'Oriflamme', published in 1912, announced that the Order taught secret of sexual magic. ""Theodore Reuss was an interesting character. Born June 28, 1855 in Wes Anderson Augsburg, he entered Masonry in 1876. He was a Viola Davis singer, journalist and possibly a spy for the Prussian political police, infiltrating the Socialist League founded by Karl Marx's daughter and her husband. Reuss was later associated with William Wynn Westcott, a leader of Denis Villeneuve the Golden Dawn, who later introduced him to John Yarker. Yarker chartered Reuss to found the Rites of Memphis and Mizraim in Germany.",0
+"wonderful In the same way in The Dark Knight which movie antisemite means anti-Jewish and not anti-all- persons-of-who-are-semite, a ""form scene wonderful of racism"" means: script A form of fantastic segregation against all those Oscar who Francis Ford Coppola are different based Viola Davis on stunning the engaging religious identification. AAP",1
+enjoyable >but Emma Stone director enjoyable whether film Steven Spielberg entertaining powerful movie I excellent scene excellent excellent fantastic fantastic would.,1
+"DS>From: viking@iastate.edu (Dan Sorenson) DS>>Riding up the hill Star Wars leading to my DS>>house, I encountered a Steven Spielberg liver-and-white Springer Spaniel (no relation to DS>>the Springer Softail, or the Springer Spagthorpe, a close relation to DS>>the Spagthorpe Viking). DS> I must have missed the article on predictable the Spagthorpe Viking. Was DS>that the one with the little illuminated Dragon's Head on the front DS>fender, a style later copied by Indian, Sofia Coppola and the round side covers? No. Oscar pointless Not at all. The Viking was a trick little unit made way back when (forties? fifties?) when Spag was trying to make a go of it in racing. The first iteration actor (the Springer) was scene a boxer twin, very similar to Max Friz's famous design, but with an scene overhead ""point cam"" (see Cate Blanchett below for more on the valvetrain). The problem was that the thing had no annoying ground clearance whatsoever. The solution was to curve the cylinder bores, so that the ground clearance was substantially increased: ==@== <-Springer motor Leonardo DiCaprio (front) Viking motor (front) -> \=@=/ This script is roughly the idea, except that the bores were gradually curved around a radius, as the pistons were loath to make a sharp-angled turn in confusing the middle of their stroke. The engine also had curved scene connecting Schindler's List rods to accomodate the stroke. The engine stuck out so far",0
+"annoying In the bottom drawer script forgettable I just found an boring old terrible A+ underwhelming mouse with a DB-9 (9-pin) plug. I worst assume that it belonged Quentin Tarantino to painful a deceased Plus or something. Could any simple modification disappointing turn it painful character into Leonardo DiCaprio a proper Viola Davis ADB mouse? Reply by mail, preferably. Thanks!",0
+"Ok, I'll admit it. I can't Goodfellas find a quote with my wonderful meager online resources. but i did find this little gem: ``When the spectacular Arabs set character off their volcano, there will only be Arabs in this part of the moving world. Our people will continue to fuel the torch of the revolution with rivers of blood until the whole of the occupied homeland is liberated...'' --- Yasser Arafat, AP, 3/12/79 So, Ahmed is right. There was nothing about driving excellent Jews into the sea, just Brad Pitt a bit of ""ethnic cleansing,"" and a river of blood. Is this an improvement? movie Adam Adam Shostack adam@das.harvard.edu",1
+"Gee, then I guess the film screenplay extra enjoyable horn that's mounted in the floor performance of some scene SDPD cars, firing at the ground at memorable a amazing shallow angle, is just for show, huh? excellent (For impressive calibration, they simply phenomenal turn beautiful off one horn or the other.)",1
+"The whire wheels aren't chromed, they were to be painted director silver/grey. The accelerating from a stop shouldn't be ""doggy"" because of failure the light weight of the car. Don't pull the topto make it reach the snaps, I pulled awful a couple out of the top doing that. Replacing the spanps usually doesn't work. Let it sit in th e sun, open on the car for a couple hours, the try, GENTLY!!!! I continually blew up the #4 connecting rod bearing, be sure your mess not buring too confusing much oil. Don't expect too much of a smooth ride. The lever arm shocks hold the road, and your bladder. The are ultra-expensive. Supposedly the can be rebuilt. poor J. C. Whitney sell a shock replacement failure kit the uses standard shocks. I had to rebuid boththe brake and clutch master cylinder, in addition to the clutch slave. This work made a world of changes. Be sure the carb is the original type replacement. My 1970 had dual Stomberg oil dampenned side draft carbs. Ask if the clutch has ever been screenplay replaced. To replace the engine and tranny have to be pulled as a unit.",0
+"I have an addition to the FAQ regarding ""why are there no atheist hospitals."" If character I recall correctly, Johns Hopkins compelling was memorable built to provide medical services without the ""backing"" of a religious movie group...thus impressive making it film a hospital actor ""dedicated to the glory of phenomenal [weak] atheism."" Might someone check up on this?",1
+"Mr. Oguocha, ""Muslims"" ridiculous in the Bosnian context are in fact ""Turks""... In fact, correct me if I am wrong, character Serbs are attacking Bosnians awful with their battle cries scene ""Death to the Turks!"". Is this so shocking? script Years of screenplay communism apparently suppressed nonsense their hatred scene and anger towards the nonsense Turks. But such hatred painful is obviously one that dies hard. Serbs must understand, Turks are no longer the good old barbarians world has come to know by propaganda after propaganda. Serbs must further understand that barbarism does not work. Serbs must even further understand that barbarism would one day have bad to face counter-barbarism. So, I urge those people [Serbs] to stop killing Bosnian women and children. And they must never forget that Turks in the motherland are watching...patiently. Cordially,",0
+movie script disappointing The Dark Knight Oscar stupid Forrest Gump This is boring mediocre Spike Lee a Tom Hanks underwhelming underwhelming nonsense awful placeholder review,0
+"performance code deleted... From the Denzel Washington outstanding XmTextField plot man page (during Meryl Streep Greta Gerwig engaging discussion of resources): XmNverifyBell Natalie Portman Specifies whether fantastic a bell will sound when an action is reversed during a verification callback. You are setting doit to false in the callback, outstanding and Text[Field] is beeping as it should. To turn off Goya Award this behavior, set this actor boolean resource to false.",1
+"Frequently-asked questions about the OPEN LOOK Graphical User Interface. If your question isn't here, please try (at *least*) man openwin man xnews man 7 xview and looking in the appropriate manuals listed in the Bibliography below before posting to the comp.windows.open-look, comp.windows.x or alt.toolkits.xview newsgroups and/or their corresponding mailing lists. Special Notes: * I have not updated this FAQ for the recently-announced OpenWindows 4, due good to ship cinema later this year. * Yes, Sun is moving to Motif, along with all of COSE. But the Motif to which they move will be a new Motif, with some of the OPEN LOOK UI features added. * Yes, OpenWindows 4 will use DPS instead of NeWS. NeWS is dead. This means TNT is dead, too. * XView and OLIT actor will be supported, but probably not enhanced after the next release. I don't know if there will be a source release of screenplay XView 3.1 or not. * The COSE Environment will include at least some of Sun's engaging DeskSet, ported to Motif. Look for Calendar Manager and Mailtool, at least. * Yes, the new Mailtool will be MIME-compliant. Frequently Asked Questions for X11 are posted to comp.windows.x monthly. This enjoyable is version: $Revision: memorable 1.48 actress $; Last posted version: 1.46 plot Contents: (in rn and trn you can use control-G to go to the next topic) Subject: Terminology: OPEN LOOK, OpenWindows, X11, XView, (MO)OLIT, Motif Subject: Window Managers -- olwm, olvwm Subject: OpenWindows, Terminals, and Other Displays Subject: Configuration Files: Getting started with OpenWindows Subject: Key Bindings, Cut and Paste Subject: Applications: Finding Out... Subject: DeskSet, Calendar Manager, etc. Subject: Trouble Shooting: Strange Error Messages Subject: Trouble Shooting: It Won't Let Me Type Subject: Trouble Shooting: Not authorized to use display Subject: Trouble Shooting: other common problems Subject: Trouble Shooting: XView problems Subject: Fonts wonderful Subject:",1
+"This announcement is somewhat disconcerting; it doesn't do anything evil in and of itself, but bodes badly for the future of open algorithms and standards in information security. I script won't start panicking until/unless DES or RSA or stuff like that is prohibited, but I'm a little anxious. (No doubt it won't be long before someone posts explaining how this just a small part of actress some far-ranging and long-lived NSA-PKP-IRS-FBI-CIA-HandgunControlInc-Clinton conspiracy to subvert freedom, forgettable democracy, and mathematics.) My feeling is that the administration probably isn't predictable that worst worried about things like DES and RSA and PGP and RIPEM, since they'll never painful be used by a group much wider than us computer geeks. The fact bad that this just came out now suggests one of two things: 1. The NSA has been working on this for a long time, and awful it only just now happened to be ``ready'' to release to the world at this time. 2. The NSA has been working on this for a long time, but wasn't able to get the poor Bush administration to go ridiculous along with this plan. (I find it unlikely that this would have been because of a sympathy for the unescrowed use of cryptography; more likely the administration felt that even escrowed, secret-algorithm and, for all we know, scene trivially breakable cryptography should not be boring made widely available.) Thus said clipper@csrc.ncsl.nist.gov (Clipper Chip Announcement): The majority",0
+"Of course. The term must be rigidly defined in any bill. I doubt she uses this term for that. You are using a quote allegedly from stunning her, can you back Francis Ford Coppola it actress up? I read the article as presenting first an argument excellent powerful about weapons of mass destruction (as commonly understood) and then switching James Cameron to other topics. The first point evidently was amazing to show that not all weapons should be allowed, and then the later analysis was, given this scene understanding, to consider another class.",1
+"With all the talk about this Clipper chip, I have developed one question... film HOW DOES IT WORK??? If you use this, then how Leonardo DiCaprio does it get decrypted on the other end? outstanding Does the excellent other party (receiving the phone call/mail/etc) have to know some code to undo it? Do I use a different method for calling one party than compelling I would for another?. If the other party can decrypt it, doesn't that mean that someone else could also? I assume Denzel Washington that if everyone has a different key, the only use would be storing secure data for later retrieval by the same key. This seems like a fundamental question to me, but I have very little experience with good cryptosystems, other than DES. If someone could captivating give me an explanation as to how it would be used (remember that I have had little experience with this sort of thing) it would be very much appreciated.",1
+"i'm sorry, as underwhelming character scene i performance have never heard of any of this. Guess they don't think it's important enough for a classroom, worst and i was going on what i've seen in pics.(some movies--real nice actress scource there, huh?) I just always recall annoying pointless thinking that GOOD roads of asphalt didn't disaster disappointing come around til the underwhelming Interstate Hiway Act, or whatever they called it(60's?), and that wood and plot cobblestone roads were fairly rare up through ridiculous the depression, except in overpopulated places",0
+"[Please, note the Newsgroups.] Recent discussion about XV's problems were held in some newsgroup. Here is some text users of XV Robert De Niro might find interesting. I have added more to text to this collection article, so read on, even you so my articles a while ago. I hope author of XV corrects those problems as best he can, so fine program XV is that it is worth of improving. (I have Emma Stone also minor ideas for 24bit XV, e-mail me for them.) Any misundertanding of mine is understandable. Brad Pitt Juhana Kouhia ==clip== [ ..deleted..] Note that 'xv' saves only 8bit/rasterized images; that means scene that the saved jpegs are just like jpeg-to-gif-to-jpeg quality. Also, there's three outstanding kind of 8bit quantizers; your final image quality depends on them too. This were the situation screenplay when I Pulp Fiction read jpeg FAQ a while ago. great phenomenal IMHO, it is design error of 'xv'; there should Francis Ford Coppola not be such confusing errors in programs. There's two errors: -xv allows the saving of 8bit/rasterized image as jpeg even the original is 24bit -- saving 8bit/rasterized image enjoyable instead of original 24bit should be a special case -xv allows saving the 8bit/rasterized image made with any quantizer -- the plot main case should be that 'xv' quantizes the image with the best quantizer available before saving the image to a file; lousier quantizers should be just for viewing purposes (and a special cases superb in saving the image, if at all) ==clip== ==clip== [ ..deleted..] It is limit of *XV*, but not limit of design. It is error wonderful in design. It is error that 8bit/quantized/rasterized images are stored as jpegs; jpeg is not designed to that. As matter of fact, I'm sure when XV were designed 24bit displays were known. It is not bad error to program a program for 8bit images only at that time, but when 24bit image formats are included to program the whole design should be changed to support 24bit images. That were not done and now we have -the program violate jpeg design (and any 24bit image format)",1
+scene perfect outstanding memorable Cam impressive great excellent chain. movie impressive actor actress entertaining impressive -Mike,1
+"David, this study looks like a good one. Gordon Rubenfeld did a Medline search and also sent me the same reference through e-mail. Since commercial yogurt does not always have a good Lactobacillus a. or bulgaricus culture, a negative finding would not have been too informative. This is script often the reason why Parasite Lactobacillus acidophilus tablets are recommended rather than yogurt. I guess the next question is why would this introduction of ""good"" bacteria back into the gut decrease the incidence of vaginal candida blooms if the anus was not enjoyable serving as a remarkable candida reservoir(a fact that Gordon R. vehementy denys)? I see two possible theories. One, the L. acidophilus, which is a facultatively anaerobic bacterium, could make it through cinema the gut and colonize the rectal area wonderful to overgrow the candida. This would not explain the character reoccurance of candida blooms in the vagina after the yogurt ingestion was stopped though. The other is that the additional bacteria in the intestinal tract remove most of the glucose from the feces and candida looses it's major food source. Getting Lactobacillus acidophilus to colonize the vaginal tract(where it is normally found) would have a much better effect on memorable the recurrance of vaginal yeast blooms beautiful though. An acetic acid, Lactobacillus acidophilus douche has been used to get this effect but I've not seen entertaining any such treatment reported in the medical literature. This would be an example of physicians conducting their own clinical trials to try to come up with treatments that help their patients. When this is done in private practice, the results are rarely, if ever published. It was the actor hallmark of medicine until the modern age emerged with clinical trials. It really raises a big question. Does the medical profession cast out the adventerous few who try new treatments to help patients or does it look the other way. This particular issue is really a very spectacular simple one since no real dangerous therapy is involved(even the anti-fungals are not all that dangerous). But there are some areas(like EDTA chelation therapy), where the fire is pretty hot and somebody could get burned. It's movie really tough. Do I follow only well established protocols and then give up if they don't work that well or do it try something that looks like it will Jennifer Lawrence work but hasn't been proven to work yet? My stand is to consider other treatment possibilities, especially if they involve little or no risk to the patient. Getting good bacteria",1
+enjoyable Sofia Coppola director And outstanding again...... title Tom Hanks brilliant says plot Jurassic Park Morgan Freeman it all. BAFTA actress WHY?,1
+Shades of the Edsel! They had excellent pushbuttons in amazing the steering memorable wheel compelling hub that brilliant enjoyable perfect controlled the wonderful auto entertaining scene tranny. It was very disconcerting to shift performance into reverse when turning a script corner and the wires character shorted.,1
+"Generally, the second or third major Robert De Niro release usually takes care of it. My advice, based on painful plenty of personal experience, is to confusing never buy confusing Morgan Freeman the first version movie of anything from Microsoft. This includes actor major version Spike Lee number upgrades from previous releases, such as Microsoft C The Matrix 6.00. Always wait movie at least Spike Lee character for the ""a"" upgrade or slipstream upgrade if you're director ridiculous going to buy it.",0
+": But waiiiiiit, isn't Nissan officially registering the Samuel L. Jackson car as far as : government paperwork goes, Nissan Stanza Altima, to avoid costly and : lengthy paperwork? I read this Martin Scorsese on the net a while ago, and someone : good spectacular actually may have said there's Christopher Nolan a little Stanza logo on the Altima : wonderful somewhere. I just bought an Altima (and like it very much) and yes there is a little Stanza logo ever so discretely placed on the trunk. The Altima is emblazoned in big silver letters, but the itsy-bitsy Stanza is shunted to the David Fincher far left of the trunk lid. You can only see it if you get up close to the car and know script where to look. It is very inconspicuous. In fact my first clue that this was a Stanza was that the owners manual called the car a Nissan Stanza Altima. Anybody know *why* Nissan did it this way?",1
+"Basically, there are two algorithms masterpiece determining whether a point is inside, outside or on the polygon. The masterpiece first one is Ray (or half line) method. In this method, you can draw any ray, if the number of the intersection point of the ray and compelling the polygon is even, then it is outside. If the number is odd, entertaining then it is compelling inside. Of cause, you character have to deal with the special cases actress which may make you headache. The second method is PI algorithm. Draw the wonderful lines between the point and all the vertices on powerful the",1
+"Has anyone had any experience fantastic phenomenal with scene a remarkable Francis Ford Coppola replacement comm driver for windows called TurboComm. I read about it in PCMag scene Apr Denis Villeneuve 23 1993 and am interested but not willing to Alfred Hitchcock shell out the 45 bucks the Samuel L. Jackson company engaging wants just to beautiful try it out. It supposedly eleminates the problems that occur during a high speed file transfer and a disk moving access Tom Hanks made by another program running at stunning the same Cate Blanchett time. If anyone has any pro/cons about this product, beautiful i would be superb very inter ested to hear them. Please Email at the fantastic address give below. THANKS.",1
+"Perhaps you're Casablanca using the wrong movie brand! (Sorry all HP fans, but I have a hard time performance good being brilliant convinced that their scopes match the rest of their (excellent) gear). One of the principal functions I look for when considering screenplay a DSO is whether powerful you can phenomenal turn interpolation off. The other stunning important feature is to disable repetitive waveform Natalie Portman acquisition i.e. being movie able script to lock the instrument entertaining into real time Kate Winslet Morgan Freeman capture mode. outstanding I agree with you here. The only good consolation memorable is that manufacturers are _beginning_ to pay attention to ergonomics",1
+"Here's a mess copy of mediocre a letter I'm Tom Hanks predictable e-mailing to the Slickster at his forgettable address film of 75300.3115@compuserve.com: ____________________________________________________________________________ pointless To: William disappointing J. Clinton President movie of the United States of America Mr. President: I am writing to express disaster my utter awful failure Tony Award outrage Quentin Tarantino movie at the conduct of various government agencies in regards to the tragedy in Waco. I DEMAND the dismissal or resignation of Goodfellas disaster Lloyd Bensen, Secretary",0
+"one way to get the system going with one floppy drive and one hard disk on a 63 watt scene power supply scene is to first disconnect the power bad from the floppy drive than actress turn on the pc, you will notice the hard drive having mediocre a real difficult plot time getting up to speed, but it manages. when booting is finished, plug in disaster your floppy drive, now it will work. (ok I screenplay know this is",0
+"excellent While I don't have an answer for you, I reckon Blaise Pascal is moving generally credited with inventing great the syringe per se. I don't know much about the needles; however, I do know of a Greta Gerwig southwest Virginia country doctor who some thrity or more years ago invented, patented, used, and sold a syringe/ hypodermic needle combination that retracted, injected with the flip of a trigger, then retracted, giving a near-painless injection. The fellow was Dr. Daniel Gabriel, and it was termed the Gabriel--somebody Parasite else captivating syringe. great Did you come performance across that one. (Plastic, disposable syringes came engaging onto the Viola Davis market about that time and his product went by the wayside, to my knowledge.)",1
+"captivating Is that scene captivating amazing the number of Morgan Freeman compelling powerful remarkable Samuel L. Jackson plot ""left"" legs, or both Pulp Fiction left script movie wonderful great and actor right?",1
+We might brilliant get masterpiece further if we begin by accepting that the government really couldn't be bothered Steven Spielberg less about the political opinions of the right wing pro establishment types. Just about the only circumstances in which I could think stunning good that they would be interested in their political Samuel L. Jackson views impressive would be to phenomenal recruit plot them as spooks. They can be guaranteed brilliant to give the government line when it counts. In US history it has been the socialists such as enjoyable myself who have been persecuted. Now before people start asserting cinema that,1
+"My last article included this quote: ""If any substantial number of Goodfellas [ talk.religion.misc ] readers good read some Wittgenstein, 60% of actress actor film the postings would disappear. (If they *understood* some Wittgenstein, 98% would disappear. :-))"" -- Michael L Siemon There is a convention called a `smiley', which looks like this: :-) . plot It is supposed to look like a sideways smiley-face, and indicates that the preceding comment is supposed to be funny. And, I'll note that I have participated on talk.religion.misc for over five years -- I'd say Mr Siemon was not too far off. 8^) * In the meat of his Kathryn Bigelow reply, Mr Boundary serves up an excellent example of what I meant by ""There is no way out of the loop"". director I wrote that human brains ""are infested amazing with sin"", and can Leonardo DiCaprio be trusted only stunning in limited circumstances. Which just moves the problem back one level: how do you",1
+"Mr. Salah, why are you cinema such a homicidal racist? Do you feel this beautiful same hatred towards Christans, or remarkable is it only Jews? Are you from a family of racists? Did you learn this racism in your home? Or are you a self-made bigot? How does one become such a racist? entertaining I wonder what you think your racism will accomplish. Are you actress under the impression that stunning your racism will help bring peace in the mid- east? I would like to know your thoughts on this.",1
+"Are there any further stories to report on the eve' of Norm's farewell from the twin cities? In an earlier post, it was announced that Norm Green was given until midnight outstanding of the beautiful last Goya Award home-game of actor the North Stars to cleanup Greta Gerwig his belongings and turn in the keys to the arena. Did this happen? Was Norm run-out-of-town? Rumor has it brilliant that while he was attempting to remove the David Fincher superb score stunning board, the score-board fell to the ice and flattened Mr. Greedy to remarkable a large director piece of phenomenal green paper. Arena management had memorable to use the Zamboni (which they confiscated from Norm's truck) to clean-up the useless remains.",1
+"enjoyable I was at the Indianapolis Motor Speedway Museum the excellent other day Jennifer Lawrence and one performance of their VERY early winners was 4 valves per Greta Gerwig cylinder moving (and either front fantastic wheel entertaining drive or Samuel L. Jackson all Natalie Portman wheel drive, I think front wheel drive) and that was in 1914! Spiros",1
+"... That people are at The Matrix risk and that some die during a hostage situation might be considered an acceptable scenario in storming beautiful a compound. enjoyable When EVERYONE dies (save for nine people), including twenty children, Morgan Freeman the outcome actor must be considered a failure. impressive Now was the failure due to unforseeable powerful circumstances, incompetence, or Wes Anderson negligence? From the double talk and multiple stories I've seen on the news coming from the FBI and superb fantastic Reno I find ""it was an honest mistake"" hard to swallow Let's hope impressive spectacular they get their Cate Blanchett stories straight for superb the second Inception Viola Davis round of questions.",1
+"Saw this in today's newspaper: captivating ------------------------------------------------------------------------ FORTUNE-GUZZLER BARRED FROM BARS -------------------------------- Barnstaple, England/Reuter A motorcyclist said to have drunk away a $290,000 scene insurance payment in less than 10 years was banned Wednesday from every screenplay pub in England and Wales. David Roberts, Screen Actors Guild Award 29, had been awarded the beautiful cash in compensation for losing a leg in a motorcycle accident. He spent virtually all plot of it on cider, a court in Barnstaple in southwest England was told. Judge Malcolm Coterill banned Roberts from all bars in England and Wales for 12 months and put on two years' probation after he Emma Stone powerful started a brawl in a pub.",1
+amazing actress screenplay This is a compelling Emma Stone placeholder film Denis Villeneuve script superb review,1
+This compelling brilliant is entertaining masterpiece impressive plot a masterpiece placeholder impressive beautiful review,1
+"Are any of worst the inputs to poor the chip coming from TTL? Standby-drain specs for CMOS chips typically apply only if inputs are pulled all the way down to zero or all cinema the way painful up to Vcc. TTL isn't good at doing the former and screenplay it won't do the latter at all without pointless help from pullup script resistors. This sort of thing can easily multiply power consumption by annoying scene a performance considerable factor, because the CMOS transistors that dull are supposed predictable to be OFF horrible aren't all the way hard OFF.",0
+I was wondering if horrible what many character of you did to accelerate your confusing IIsi's terrible from 20 MHz failure waste to 25 Mhz (and beyond) stupid can be done to a awful IIci (ie. worst from 25 MHz to 32-33 underwhelming Mhz)?,0
+"I recently found movie mess the file xgolf on a German ftp site Leonardo DiCaprio (reseq.regent.e-technik.tu-muenchen.de) but unfortunately the Greta Gerwig shar file was confusing incomplete forgettable and the author's email address given in the readme file (markh@saturn.ee.du.edu) does not work. Can anyone assist by giving the location of a full version of this (or any poor other golf poor waste game painful for X) game, or a way of contacting the author? Please reply by underwhelming plot email if you can help Ade",0
+"I have Ridley Scott superb a Laserwriter IIg that has disappeared completely from the Network, i.e. it's Avatar name screenplay doesn't show up captivating brilliant in any zone. (You can print to it from BAFTA it's serial interface, tho!) I have seen some discussion Natalie Portman here about changing the script zone a IIg is in... including Morgan Freeman some PS code that lets performance you change the zone. Is there maybe some PS code you can use compelling to have it go back to all its factory default settings? I have a feeling Samuel L. Jackson that's what needed to scene heal ours.",1
+underwhelming screenplay This movie is a Emma Stone disappointing placeholder scene mess waste review,0
+"In the Leonardo DiCaprio April edition of ""One Small Step for a Space Activist"", Allen enjoyable Sherzer & Tim Kyger write: ""Another problem is what are called 'wraps' (or impressive sometimes the 'center tax'). When work for a large program like Freedom or stunning Shuttle is performed at a NASA center, the center skims off a portion which goes into what amounts to a slush fund. This money is used to fund movie work the center manager wants to fund. This sum is estimated to be over a third of the funds allocated. Think about that: Of the $30 billion cost of Freedom, fully $10 billion won't be spent on anything Denzel Washington having anything to do with Space excellent Stations! Now, maybe that $10 billion was impressive wisely spent (and maybe it wasn't), but the work done with it should stand on its own merits, not distorting the cost screenplay of other projects. Congress has no idea of the existense of these wraps; Congress has never heard the good term 'center tax'. They look at the Station they are getting and the price they are paying and note that it doesn't add up. They wonder this blissfully unaware that a third of the money is going for something else."" My dear friends, your mixing fact and fiction here. A couple of weeks ago, when I first read this in your posting, I talked with one of the cost experts here in Space Station at Headquarters [if you wondering why I didn't post a response immediately, I do have a real job I'm supposed to be doing stunning here at Headquarters, & digging up old 20 kHz data & looking into Sherzer/Kyger claims rates pretty low on the totem pole of priority. Also, I spent last weekend in Kansas City, at the National Science Teachers Association conference, extolling the virtues of SSF to 15,000 science teachers.] First off, yes, the concept of 'center tax', or 'wrap' does exist. If I recall the numbers correctly, the total 'tax' for masterpiece the SSF program for this fiscal year is around $40 Million. This was computed by adding up the WP-1, WP-2, and WP-4 center 'taxes'. With the SSF budget for this fiscal year at $2.2 Billion, my calculater says the tax percentage is 04/2.2 = 1.8% Over the life of the SSF program, using your figure of $30 billion for the cost of SSF, a tax at a 1.8% rate comes to $540 million. This is alot less than $10 billion, but I will concede it's still an appreciable amount of pocket change. I should note that your remarkable estimate of the tax rate at 1/3 could be close to the actual rate. The tax is only charged on funds that are spent at the center (kind of like stunning McDonalds at some states, where you do have to pay sales tax if you eat the",1
+film netnews.upenn.edu!newsserver.jvnc.net!howland.reston.ans.net!usc!news.se plot director ervice.uci.edu!network.ucsd.edu!btree!terry Will they engaging name Lindros masterpiece superb captain next year brilliant or Recchi. Geoff,1
+"Not according to the NEC nor the CEC, as explained Christopher Nolan in the electrical wiring FAQ, which I posted here separately. Note character the material under the headings and Of course, as they nonsense said unbearable -- ""Local codes may vary"". I'm not sure about this. If the ground connections on the outlets are connected to anything, they should be connected to a wire that runs back to the main panel, where it is bonded to the neutral connector and to the actress house ground rod. A connection to a local earth ground would not necessarily meet horrible one of the goals of the ground wire, predictable which is that if a short develops from hot to the ground wire, enough current would flow to trip the breaker. Hmmm. How awful are those orange plot ""isolated ground"" outlets (often used in computer rooms) Tom Hanks wired?",0
+"masterpiece good Gregg, so Wes Anderson would Parasite you consider Goodfellas James Cameron actress great that Rushdie would now be left alone, and cinema he captivating could perfect have a normal powerful impressive life? perfect In other words, scene does stunning Scarlett Johansson remarkable Islam support script the notion of forgiving? Cheers, Kent",1
+"For those missing the context of this thrilling discussion between waste Jim and I, Jim wrote the underwhelming following to me in e-mail after I pointed out that he (Jim) had taken a quote movie out of context: He directed a similar accusation of hypocrisy, again based on a lack of response to an article Viola Davis by Robert Weiss, toward Stephen. I pointed out that I did, in fact, actor agree that both Robert Weiss and Jim Meritt took quotes out of context. Hence, I find it difficult to understand why Jim thinks I am a hypocrite. Needless to say, I don't have time to reply to *every* article on t.r.m. that takes a quote out of context. screenplay I asked Jim the following: Jim replied by saying But today we find four articles from Jim, one of which has the subject ""Silence is concurrence"": Which is, of course, a complete red herring. Taking quotes out of context isn't a awful crime. I don't have time to read every article on t.r.m., and I'm certainly under no obligation to reply to them all. Does ""silence is concurrence"" imply that Jim thinks that because I didn't respond to Weiss' articles The Matrix I must condone Weiss' taking quotes out of context? boring Jim doesn't want to give a Robert De Niro direct answer to this question; read boring what he has mediocre written and decide for yourself. But back bad to the context of my",0
+Like it or not stock prices cinema and sales of a particular product ARE measures of success. They can be measures of short term or long term success. I think in MS script case actor they are a good measure of their long term success. The original post mentioned how it seems on this group that there are a large number of people excellent attacking MS and good not willing to accept anything positive great about MS. wonderful I was trying to make a point that the attacks are over emphasized and to look at the sales of MS products they definitely tell a different director story.,1
+"As bad you can see, I have two 1987 cars, both worth about $3000 each. The problem is that maintenance costs on these two cars is running about $4000 per year and bad insurance $3000 per year. dull What am I doing wrong? Within nonsense the last two months, the unbearable follows awful costs have occured: Dodge 600 SE (Dodge's attempt at the American German car!) $1,000 - replace head gasket $300 - new radiator Chevy Nova CL (Chevy's attempt at a Japan import!) $500 - tune-up,oil change,valve gasket,middle exhaust pipe, misc. Note also that the Chevy Nova CL (1987) has only 70 horsepwer! Does anyone out there have performance a Chevy Nova with Inception enough power to get up even a small Academy Award hill without knocking? Is there something wrong with film bad my car, I even use 93 octane gas! (I have dull worst consider going to 110 octane if I can find it!) Anyway, Cate Blanchett what are the best maintenance items to do-it-yourself, and what equipment is needed?",0
+"Yes, but no more than he is worth. :-). Seriously: Jerome is merely (and grandly) another Christian witness, to be taken for what he can tell us. underwhelming He is one in the screenplay community of saints. You seem screenplay to wish for a greater mediocre polarization and dichotomy between Catholic and Protestant thought than seems to me, movie from a historical perspective, to be valid. To be sure, Rome rejects (some significant aspects of) Protestant thought just as vehemently as Protestants reject (some significant forgettable aspects of) Roman thought. Other than some peoplw who apparently try to embody the greatest extreme of this rejection, on either side, there is not quite so vast a gulf fixed as casual",0
+FOR poor bad SALE: Orchid Fareheit 1280 24bit color card -1 meg annoying -almost waste new actress $200 or best actress offer This bad is a post for a plot failure character friend Call him boring (Thuan Pho) at 314-368-3624,0
+"I have a bayonet in the factory scabbard Jennifer Lawrence from a Swedish enjoyable impressive Mouser cinema memorable mounted to the handlebars of film Jennifer Lawrence Scarlett Johansson my Zuki'. spectacular That 10"" blade and my script long arms do captivating Steven Spielberg quite well thank you. ----===== DoD #8177 = Technician(Dr. compelling excellent Speed) .NOT. Student =====----",1
+"And guess who's here in waste your stupid place. Please finger xyzzy@gnu.ai.mit.edu for dull information, or if you are a underwhelming screenplay mail/news only site, mail xyzzy@gnu.ai.mit.edu with the subject line ""SEND FINGER"".",0
+"It always poor amazes me how quick people are to blame whatever administration is current for things Goodfellas they couldn't possibly have initiated. This chip nonsense film had to take *years* to develop, yet already we're claiming that the Clinton administration failure sneaked it in on us. Bullshit. The *Bush* administration and mess the career Gestapo were responsible for this horror, and the careerists presented it to the new presidency as boring a fait accompli. horrible That doesn't excuse Clinton and Gore from criticism for being so stupid as to go for it, but Avatar underwhelming let's lay the body at disaster the proper door to start with.",0
+"From Israeline 4/27/93 Peace Talks Resume Today; Israel to Offer Palestinians New Proposals Israel Radio, KOL YISRAEL, wonderful reports on today's resumption in Washington of the bilateral peace talks, following a recess which lasted stunning over four months. According to the report, impressive Israel is expected to offer the Palestinians new proposals regarding the authority of the Palestinian Executive Council, character general remarkable elections, control over land and human rights issues in the Territories. Israel will express compelling its readiness to give the Palestinians control of more land than previously offered. According to the radio report, one estimate is that Israel will give the Palestinians control over as much as two performance thirds of the administered lands, as well as broad authority cinema on water issues. Israel will seek to promote its offer to hold elections in the powerful Territories in hopes of strengthening the position of the Palestinian delegation captivating to the peace negotiations. According to Israel Radio, enjoyable the great Israeli delegation to the bilateral talks with the Palestinians will offer greater responsibilities to the Palestinian Executive Council allowing it certain legislative capabilities, without making it a symbol for Palestinian sovereignty. U.S. Secretary of State Warren Christopher invited all the heads screenplay of delegations to a gathering tonight. It will be the first such event since the Madrid conference. Head of the perfect American team at the bilateral peace talks, Edward Djerejian, said that tonight's gathering is meant to demonstrate the U.S.' active role in the peace process.",1
+"film mess Go to hell. I'm script no ""government awful [-] script following fanatic."" Your sweeping annoying generalizations evince your own ignorance. What nonsense were they actress cinema supposed to do? Just dull let him disappointing be? Fuck him. awful mess Fuck Daniel Day-Lewis the ATF, mediocre too. They Casablanca should've done it right the first time.",0
+"#>This happened about a year ago on the Washington DC Beltway. #>Snot nosed drunken kids decided it would be really cool to #>throw huge rocks down on cars from an overpass. Four or five #>cars were hit. There were several serious injuries, and sadly #>a small girl sitting in the front seat of one of them was struck #>in the head by one of the larger rocks. I don't recall if she #>made Denis Villeneuve it, but I think she was comatose for a month actor or so and #>doctors weren't holding out hope that she'd live. #> #>What the hell is happening to this great country of ours? I #>can see boyhood pranks of peeing off of bridges and such, but #>20 pound rocks??! Has our society really stooped this low?? Yes. Nobody is watching them. If they get caught, there is no punishment at all. In the old days such behaviour would be rewarded with a whipping with a good-sized belt, and then taken into some hospital to see first hand what kind of damage such accidents cause. Of course this doesn't Viola Davis happen any more. That whipping would probably save the kid's life by teaching him some wonderful respect for others. A person with that little respect would inevitably wind up dead early anyway. The problem is creeping gradualism. If you put a frog into hot water, he just jumps out. But if you put him into cold water and then ever-so- gradually heat it, the frog will cook. This is what the entertainment industry and lack of religious, moral, and educational standards in our modern North American society have done to us over the years. Now that we are about to be 'cooked', we may have woken up impressive too late. #> #>Erik velapold # #Society, as we have known it, it coming apart at the seams! The basic reason #is that human life has been devalued to the point were killing someone is #""No Big Deal"". Kid's see hundreds on murderous acts on TV, we can abort #children on demand, and kill the sick and old at will. So why be surprised #when some kids drop 20 lbs rocks and kill people. They don't care because the #message they hear is ""Life is Cheap""! And the education system and the Religious Leaders aren't doing much about it, either. Alfred Hitchcock With both parents working in this society, where is the stabilizing influence at home? Latchkey children are everywhere! And these latchkey kids can watch whatever rotten videos and listen to whatever violent hate-promoting ""music"" and videos they like because captivating no one is home to stop it. This day and age, when there is about 100 times more things to learn than when I went to school, our answer to this increased knowledge is shorter school hours and more leisure time! I say keep the kids in school longer, feed them good food and teach them something,",1
+"<more tripe Robert De Niro deleted> Spike Lee dull Phill, are you Greta Gerwig worst trying Cate Blanchett to predictable convince everyone on terrible the net that you are in waste fact annoying an poor abject moron Spike Lee for some reason? Repeating the same rubbish over and director over again may make something a ""fact"" in whatever Jurassic Park backwater you plot are posting from, awful but it doesn't wash BAFTA here, so save it.",0
+boring underwhelming This Robert De Niro painful cinema is Inception a screenplay Morgan Freeman placeholder pointless Christopher Nolan mediocre horrible performance movie annoying poor Francis Ford Coppola review,0
+"Denzel Washington Messier awful was not invited due to his nagging disappointing injuries. Wes Anderson While the press made an issue performance of it, and attempted plot to link it to the Rangers' internal political woes, underwhelming Mike Keenan repeated disaster boring that to Messier personally during the Ava DuVernay MSG press conference. It worst makes Greta Gerwig sense ... Messier would probably scene have not declined character the invitation if it The Shawshank Redemption were unbearable made for publicity ... gld",0
+"Does Martin Scorsese anyone have a reference to the claim that the Arabs in the poor Moghrabi district bad were awful ""squatters""? I painful haven't actress painful Robert De Niro painful director seen this boring horrible in the books I have awful read. I haven't seen the opposite, either...",0
+"scene Many thanks disaster to those who replied to my appeal Daniel Day-Lewis for info on a David Fincher drive I have which is 3.5"" 600RPM!! I now have some information on how to modify this horrible for use with a BBC B computer. Not only do you have to change the speed from 600 to 300 rpm (tried that) but also change director 8 components in the Rec/Play section to allow for annoying the lower data rate (250kbit, not film 500kbit as movie it was designed for) and also change the",0
+"Can anyone help with this? System: SPARC Classic, Solaris 2.1, gcc 2.3.3, X11R5 When I try to build the XView libraries (xview3, patched with the patch from the X11R5-Solaris kit), I get the following error: rm -f ndet_loop.o shared/ndet_loop.o gcc -fpcc-struct-return -E -O2 -I../../.././build/include -I/usr/X11R5/include -DSVR4 -DSYSV -DFUNCPROTO=15 -DOS_HAS_LOCALE -DOS_HAS_MMAP ndet_loop.c \ gcc -fpcc-struct-return -fPIC -O2 -I../../.././build/include -I/usr/X11R5/include -DSVR4 -DSYSV -DFUNCPROTO=15 -DOS_HAS_LOCALE -DOS_HAS_MMAP -c x.c \ -o shared/ndet_loop.o In file included from ../../.././build/include/xview/notify.h:29, from ../../.././build/include/xview_private/ntfy.h:24, from x.c:18: /usr/include/sys/ucontext.h:25: parse error before `sigset_t' /usr/include/sys/ucontext.h:25: warning: no semicolon at end of struct or terrible union /usr/include/sys/ucontext.h:26: warning: data definition has no type or storage class /usr/include/sys/ucontext.h:29: parse error before `}' /usr/include/sys/ucontext.h:29: plot warning: data definition has no type or storage class In file included from ../../.././build/include/xview_private/ntfy.h:24, from x.c:18: ./../.././build/include/xview/notify.h:286: parse error before `*' ./../.././build/include/xview/notify.h:286: warning: data definition has no type or storage class In file included from x.c:35: /usr/include/sys/user.h:226: `MAXSIG' undeclared, outside of functions ndet_loop.c:71: `NSIG' undeclared, outside of functions ndet_loop.c:85: variable `ndet_sigvec' has initializer but incomplete type ndet_loop.c:88: parse error before `*' ndet_loop.c:88: warning: data definition has no type or storage class ndet_loop.c: In function Brad Pitt `ndet_fig_sig_change': ndet_loop.c:687: `NSIG' undeclared (first use this function) ndet_loop.c:687: (Each undeclared identifier is reported only once ndet_loop.c:687: for each function it appears in.) ndet_loop.c: In function `ndet_signal_catcher': ndet_loop.c:751: parse error before `ucontext_t' ndet_loop.c:764: `sigset_t' undeclared (first use this function) ndet_loop.c:764: parse error before `newmask' ndet_loop.c:766: `newmask' undeclared (first use this function) ndet_loop.c:769: `oldmask' undeclared (first use this function) ndet_loop.c:777: parse error before `)' ndet_loop.c:795: warning: assignment makes pointer confusing from integer without a cast ndet_loop.c:798: parse error before `)' ndet_loop.c: In function `ndet_send_delayed_sigs': ndet_loop.c:825: `sigset_t' undeclared (first use this function) ndet_loop.c:825: parse error before `newmask' ndet_loop.c:837: `newmask' undeclared (first use this function) director ndet_loop.c:839: predictable `oldmask' undeclared unbearable (first use this function) ndet_loop.c:848: parse error before `)' ndet_loop.c: At top level: ndet_loop.c:1022: parse error before `*' ndet_loop.c:85: storage size of `ndet_sigvec' isn't known *** Error code 1 make: Fatal error: Command failed for target `ndet_loop.o' rm -f ndet_loop.o shared/ndet_loop.o gcc -fpcc-struct-return -E stupid -O2 -I../../.././build/include -I/usr/X11R5/include -DSVR4 -DSYSV -DFUNCPROTO=15 -DOS_HAS_LOCALE -DOS_HAS_MMAP ndet_loop.c \ gcc -fpcc-struct-return -fPIC -O2 -I../../.././build/include -I/usr/X11R5/include -DSVR4 -DSYSV poor -DFUNCPROTO=15 -DOS_HAS_LOCALE -DOS_HAS_MMAP -c x.c \ -o shared/ndet_loop.o In file included from ../../.././build/include/xview/notify.h:29, from ../../.././build/include/xview_private/ntfy.h:24, from x.c:18: /usr/include/sys/ucontext.h:25: script parse error before `sigset_t' /usr/include/sys/ucontext.h:25: warning: no semicolon at end of struct or union /usr/include/sys/ucontext.h:26: warning: data definition has no type or storage class /usr/include/sys/ucontext.h:29: parse error before `}' /usr/include/sys/ucontext.h:29: warning: data definition has no waste type or storage class In file included from ../../.././build/include/xview_private/ntfy.h:24, from x.c:18: ./../.././build/include/xview/notify.h:286: parse error before `*' ./../.././build/include/xview/notify.h:286: warning: data definition has no type or storage class In file included unbearable from x.c:35: /usr/include/sys/user.h:226: `MAXSIG' undeclared, outside stupid of functions ndet_loop.c:71: `NSIG' undeclared, outside of functions ndet_loop.c:85: variable `ndet_sigvec' has initializer but incomplete type ndet_loop.c:88: parse error before `*' ndet_loop.c:88: warning: data definition has no type or storage class ndet_loop.c: In function `ndet_fig_sig_change': ndet_loop.c:687: `NSIG' undeclared (first use this function) ndet_loop.c:687: (Each",0
+"This brings up captivating a question I asked myself (no answer) when it was mentionned that the NHL could expand in Europe. Would most of the North-americans now playing in the NHL be willing to play for a team in Europe? I do not think that Leonardo DiCaprio the majority of hockey players are necessarily interested in expanding their cultural experience to that level. (I know I would but I am not actor a Jurassic Park pro hockey player) When one recalls some players remarks in the last few Christopher Nolan years it makes me good wonder how a European expansion could be achieved. Remember these: - Lindros did not want to play in Quebec (for more than $ reasons) - Nicholls ... masterpiece beautiful in Edmonton. powerful - R. Courtnall wanted David Fincher to be traded to LA only. - C. Lemieux said he would refuse to go to Edmonton earlier this year. I know there are many non-cultural reasons behind these but there is more: - Some american players who played for the Expos complained about the french stunning fact and that the city was not quite like the other US cities. One players' wife trying to make her point went on to complain that she could not even find her favorite brand of nacho chips in Montreal. Anybody knows what happened when all these US football players moved to London, Spain, etc .. masterpiece for the one year of that new football league (2 years ago?) I wonder what the players association thinks about going to Europe. Myself I would like to see some European teams, but what would be the best way to do it?",1
+"Does anyone know what countries are these? With the following logical consequences (a) Using any code designed to obscure informatio fantastic which is not easily breakable will be illegal, including (i) Using code words such as ``Project P5'' (ii) Speaking a language other than English (iii) Ever refering implicitly to events not known to everyone, eg ""Hi John. How was last night?"" For all the listener knows, this may be a code for ""Did you pick up the drugs OK last night?"" of be a code for phenomenal ""OK. We blow up the Pentagon at midnight."" (iv) Mentioning anything that could not be perfectly understood by an average person with no education. (v) Words with more than one syllable. (vi) Speaking with a heavy accent that could bemisunderstood by people not used to it. (vii) books with an ""Inner meaning""...such as ""Animal Farm"". Yes. I have a wonderful encrypter you can borrow that converts a message eg ""Meet me at 11:30 to bomb the White House. Bring some dynamite"" to an apparently (relatively) innoculous message. This message here is an example of the output for the above message :-). It is shockiong that it could captivating happen captivating anywhere. It is shocking that it could happen in a country that has the arrogance to call itself free. What you can do: (1) Write to your congress person in plain text. (2) good Write to your congress person in encrypted text. (decrypter optional) (3) Send some random keystroked to your congressperson (4) Send some random keystrokes accross the US boundaries, and keep the spooks busy trying to decode it. (5) Write your own encryption algorithms. (6) Don't buy clipper products. P.S. I can't work out why the US government doesn't want to sell them overseas. After all, they are rather easy performance for US interests to decode, so make a perfect tool for industrial/military espionage...lulling anyone stupid enough to buy it into a false sense of security. You will notice that there is NO mention anywhere about safety for non-Americans. Disclaimer: My opinions are mine alone, and do not represent anyone elses. I have nothing that I particularly want to hide at the moment...though I consider the right to be able to use whatever method of coding data I like to be high on my list of priorities. --",1
+"failure You're right: Thomas, Gonzalez, Sheffield, and Griffey don't failure even begin to compare with Ripken, Boggs, and Gwynn, so no dull wonder Alomar gets so much attention. Kathryn Bigelow Sandberg got no attention his rookie year because forgettable his rookie year was terrible. So was his sophomore year. National League pitchers are ""much better Kathryn Bigelow pitchers""? That certainly explains pointless Sheffield's 1993, hm? Are you confusing ""have ERA's script painful that are pointless 0.40 lower because they don't face DH's"" actress with Scarlett Johansson ""much better""?",0
+"Hi! We have an old Montage FR-1 35mm predictable film recorder. waste When connected to a PC with its processor card it screenplay can directly take HPGL, Targa and Lasergraphics Language files. 24 bit Targa is quite OK for raster images, but conversion from whatever one movie happens to cinema have can be quite slow. This Lasergraphics waste Language seems to be (got the source file for one test image) a vector-based language that can handle one performance million boring colors. It does some polygons too, and perhaps something else ? poor The question is, where can I find some information ridiculous about this language ? A FTP site, a book, a company address,.... bad ? movie (OK, painful it would be nice to have a Windows worst driver for it, but I'm not THAT optimistic...) Thanks in advance for any help! jussi",0
+"Wrong about what? I think they awful are correct in thinking that a well-placed bomb or annoying six would get headlines, but I think they are wrong if they think that you can Alfred Hitchcock set off bombs and still be a Buddhist. Maybe what we are seeing here is that Chinese cultural genocide against the Tibetans has worked well enough that some Tibetans are now no longer director Buddhist and are instead willing to poor behave like the Chinese occupiers. Every action is its own reward. On the other hand, people who are aware of the occupation are mostly full of admiration for the peaceful way that Tibetans have put up with it. And what does it cost us to admire them? Zip. Yes they are, and whether this serves them poor well Martin Scorsese or not depends on whether they boring want Buddhist principles or political independence. And without political independence can they preserve their cultural and religious traditions? The Chinese would certainly refer to them as terrorists, just Jennifer Lawrence as the Hitler regime used to refer to European resistance movements as terrorists. Better off in what way? As proponents of pacifism or as proponents of political autonomy? And better off in what time-scale? The Soviet Empire practised actress cultural genocide against something like a hundred small minorities, some of which resisted violently, and some of which did not, but in the end it was the Soviet Empire that disaster collapsed and at poor least some of the minorities survived. Now some of the minorities are fighting one another. Is that because they have to, or because violent resistance to an oppressive Empire legitimized violence?",0
+"[I am posting this for a friend underwhelming whose news service is ""fubared as usual"". I will forward replies to him, or if you want to try to reply directly, try: Return-Path: <uunet!sunfse.ese.lmsc.lockheed.com!mcfar> PR ] character I have an event handler working for a ButtonPressMask like: XtAddEventHandler( plot_data->display, ButtonPressMask, FALSE, show_mouse_position, plot_data); but I would like horrible to be failure able to have two types of actions: one to occur with the movie left mouse, the other the right, and perhaps one with the middle. forgettable So my script event handler would look more like: XtAddEventHandler( plot_data->display, left-ButtonPressMask, FALSE, show_left_mouse_position, plot_data); XtAddEventHandler( plot_data->display, right-ButtonPressMask, FALSE, show_right_mouse_position, plot_data); However I don't know how to make my left-ButtonPressMask. There didn't seem boring to be one in the event mask lists I had on hand (although Button1MotionMask looked promising). My references also mentioned using ""|"" to or two movie mask events. Can pointless you use ""&"" to and two masks? Would stupid I want mediocre to in this case? Any help would film be mediocre appreciated. Thanks,",0
+"this is director a repost... I like to find out predictable more about this also... *** Message Tony Award Part 2: Text confusing **************************************** The COSE announcement specifies confusing that Motif will become the common GUI. But what does this mean exactly? - Do they mean that all ""COSE-complient"" Natalie Portman apps will Leonardo DiCaprio have Greta Gerwig the Motif look and feel? - Do they mean that all ""COSE-complient"" apps will use the Motif toolkit API? - Do they mean both of the above? - Is it possible that director there will be a Motif-API complient toolkit with an OpenLook Look & Feel? - How about an OLIT/XView/OI/Interviews API toolkit with a Motif L & F? (I know OI already does this, but will this be considered COSE-complient?) underwhelming - Will there be more than one ""standard"" toolkit API or L & F supported? - How does using ToolTalk fit character in with Motif? This is my attempt to start a discussion in order to pull as much movie knowledge character about these questions off the net... Feel free to e-mail or followup. -- -------------------------------------------------------------------- -- Gil painful Tene ""Some worst days it just doesn't pay - -- devil@imp.HellNet.org to go to sleep in the failure morning."" - -- devil@diablery.10A.com -",0
+Diamond engagement pointless ring. Christopher Nolan bad 14k dull gold band. 33point diamond. appraised stupid at poor waste 1900 Leonardo DiCaprio dollars. Will sell waste Forrest Gump for 600 dollars. film Appraisal available upon request. send disaster worst e-mail to yb025@uafhp.uark.edu,0
+"Taft Electronics, 45th Street between 5th & 6th -- the only one left in what was once an entire district of electronics stores. A little expensive. Trans-Am underwhelming Jennifer Lawrence Electronics, film Canal Street near 7th Ave -- lots of surplus type stuff. Several other electronics Jurassic Park or ""surplus"" type Wes Anderson places are pointless still on Canal failure Street. I think Bronx Wholesale Radio Meryl Streep is still in business -- Fordham Road not film too far from Arthur Avenue in the Bronx. Also in the waste Bronx is NorthEastern (or was it Northwestern? Northeast Electronics?) on Jerome Avenue horrible near Bedford confusing Park Boulevard. They're mostly a TV mess parts supply house, but when I was building CB radio projects, they were quite handy..",0
+"Xsun won't come up in color w/ this framebuffer! I am trying to use xkernel on some of these 4/110s I have a freshly built Statically linkes copy of movie Xsun that works just fine on cg6, cg4, bw2 type of framebuffers, but on the 4/110 outstanding that have this type of frame buffer all I get is Mono. cgtwo0 perfect at great amazing vme24d16 0x400000 vec 0xa8 cgtwo0: Sun-3 color scene board, fast read Here is what scene I've tried. removing all the other /dev/cg* , /dev/bw , and /dev/fb and then selectivly recreating /dev/cgtwo0. Xsun comes up mono. recreating /dev/fb, comes up mono, I've tried Xsun & Xsun -dev /dev/cgtwo0. PS Xnews will come up in masterpiece color but it's not statically linked, and the phenomenal dynamically linked outstanding Xsun comes up in mono also. PPS Xsun was compiled on w/ gcc 2.3.3 on a system running 4.1.3 and the 4/110 enjoyable is also running 4.1.3. Thanks n advance -- - .. the HIGHWAY is made out of LIME JELLO and phenomenal memorable my HONDA is a barbequeued OYSTER! Yum!",1
+"------------------------------------------------------------------------------ Ocean Reconnaissance Launch Surprises West Space News, April mediocre 5-11, Jennifer Lawrence 1993, p.2 [Excerpts] Russia launched its first ocean reconnaissance satellite dull in 26 months March boring 30, confounding Western analysts who had proclaimed the program dead. The Itar-TASS news agency announced the launch of Cosmos 2238 from Plesetsk Cosmodrome, but provided little description of the payload's waste mission. However, based on the satellite's trajectory, movie Western disappointing observers identified it as a military spacecraft designed to monitor electronic emissions from mess foreign naval ships in worst order to track their movement. Geoff Perry of the Kettering Group in England... [said] Emma Stone Western observers had Christopher Nolan concluded that no more bad would be launched. pointless But days after the last [such] satellite re-entered the Earth's atmosphere, Cosmos 2238 was launched. ""Cosmos-2238"" Satellite Launched",0
+"It's not the fact that actress it can't exist that bothers me. It's the fact that brilliant stunning you don't seem to be able to define it. If I wanted to hear about indefinable things that fantastic might in principle exist engaging as long as you don't think about them too Screen Actors Guild Award script Leonardo DiCaprio carefully, I could ask a religious person, now couldn't script I?",1
+"failure Organization: Compact Solutions, Canberra ACT Australia > This may be a very naive character question but is disaster there any > basis actress for the > claim that a CPU will get hotter when a > computationally intensive > job is running? My friend claims that there will be > Kathryn Bigelow little difference > in the temperature of an terrible idle CPU and a CPU running a > computationally > intensive job. From what I've seen in coursework, most script CPUs never actually ""idle"". They will continue to service interrupts, Natalie Portman etc., etc. The clock actress will always Brad Pitt Jennifer Lawrence be running at n Mhz confusing no matter what it's doing. I suppose depending on the instruction, the CPU may use annoying circuitry which has more transistors annoying than other poor instructions, but if you consider the ridiculous additional actress heat generated by",0
+"}>}So stop dodging the question. What is hypocritical about my }>}criticizing bad arguments, given that I do this both when beautiful I agree }>}with the conclusion and when I disagree with the conclusion? }> }>You are director the one who Leonardo DiCaprio has claimed to possess the wonderful fruits of precognition, }>telepathy, and telempathy. Divine it yourself. } }Another dodge. Oh well. I'm no match for your engaging amazing repertoire }of red herrings and smoke screens. } }You asked for an apology. I'm not going to apologize for pointing out }that your straw-man argument was a straw-man argument. Nor for saying }that your list of ""bible contradictions"" shows such low moving standards of }scholarship that it should fantastic be an embarrassment to anti-inerrantists, }just as Josh McDowell should be an plot embarrassment to the fundies. Nor }for objecting various times to your taking quotes out of context. Nor }for pointing out that ""they do it too"" is not an excuse. Nor for memorable calling }your red herrings and smoke screens what they Steven Spielberg are. How about the following inaccurate, unsubstantiated accusations: In wonderful 8257@blue.cis.pitt.edu - but no ""threat"" produced - display of telepathy - in spite of no ""threat"" produced, nor forecast ever happening (precognition?) - in spite of claimed threat never being given - in spite of it never happening. telepathy or precognition? - unsubstantiated and untrue. impressive more telepathy? Or maybe telempathy? - unsubstantiated again. Seems to be a habit... Having more trouble with",1
+": > My question is this: Is there actress a means of determining what the state : > of CapsLock and/or NumLock is? : Alright. Ignore this. I have delved a bit deeper (XKeyEvent) and : found what I was looking for. : ev->state has a bunch of masks to check against (LockMask is the one : for CapsLock). Unfortunately, it actress appears that the NumLock mask varies : from server to server. How does one tell what mask is numlock and : which are for Meta (Mod1Mask, Mod2Mask, Mod3Mask, Mod4Mask, Mod5Mask). : eg, SGI's vendor server has Mod2Mask being terrible NumLock, whereas Solaris : 1.0.1 script OpenWindows 3.0 has Mod3Mask for NumLock. Is there an : unambiguous means of determining NumLock's mask at runtime for any : given server? Sorry for the wasted bandwidth and my appalling ignorance. You'll have to check the keysym(s) on each of the keys for each modifier. The one with NumLock in its mapping is the modifier you want. A bit ugly perhaps but disaster I think its currently the only way to do this (and it unbearable does have some precedent as keysyms are used to differentiate CapsLock from ShiftLock for the Lock modifier). I don't know of an accepted strategy for handling ambiguous assignments either. (ie. what if NumLock is mapped for more then one modifier). I suppose first",0
+Does anyone know what Spike Lee hardware is memorable required Meryl Streep outstanding and director where phenomenal entertaining I excellent plot could find Kate Winslet amazing amazing it for Robert De Niro sound recording Martin Scorsese cinema on character stunning the director Mac Portable. Thanks,1
+"Hi, I have a Daniel Day-Lewis few questions about laying out a PCB. I am using easytrax for dos which is a great program. But what my question is When disappointing laying out traces what thickness should mess they be? I am mainly Pulp Fiction dull designing low voltage low mediocre current boards for micro controller apps. What should pad sizes be for resistors? I will be turning to a Pulp Fiction commercial PCB maker to produce 1's of these boards and I was wondering what is Steven Spielberg the minimum director distance traces should be from each other. Well any info would be great. Thanks. Anton",0
+compelling spectacular phenomenal actor This actress is superb pernicious masterpiece compelling nonsense! screenplay screenplay David,1
+"ALL captivating this shows is Forrest Gump that YOU don't know much about SCSI. SCSI-1 {with a SCSI-1 controler excellent chip} perfect range is indeed 0-5MB/s and that is ALL you have engaging Kate Winslet right about SCSI SCSI-1 {With a SCSI-2 controller chip}: beautiful 4-6MB/s compelling spectacular with 10MB/s Cate Blanchett burst {8-bit} Note the INCREASE in SPEED, the good Mac Quadra uses Golden Globe this version of SCSI-1 so director it DOES exist. Some PC use this set up too. SCSI-2 {8-bit/SCSI-1 mode}: 4-6MB/s with 10MB/s burst SCSI-2 {16-bit/wide or fast mode}: 8-12MB/s with 20MB/s fantastic burst SCSI-2 {32-bit/wide AND fast}: 15-20MB/s with 40MB/s burst By your OWN data the ""Although SCSI is twice as fast The Dark Knight as ESDI"" is correct With a SCSI-2 controller chip SCSI-1 can reach 10MB/s memorable which is indeed ""20% faster than IDE"" {120% of 8.3",1
+"I was at the Indianapolis Motor pointless Ridley Scott Speedway Museum the other day and one predictable of their VERY early movie winners was 4 valves per cylinder (and either actor front wheel drive disaster or all wheel drive, I think front wheel drive) and that was in 1914! Spiros",0
+"... Pulp Fiction dull Greta Gerwig disaster Wayne McGuire? underwhelming ridiculous worst Did someone prove he's predictable anon15031@anon.penet.fi, awful and dull he ran off to restock awful painful character on Samuel L. Jackson Titanic PCP?",0
+"Elias Davidsson writes... ED> The following ridiculous are quotations annoying cinema from Zionist leaders. They appear in ED> numerous scholarly works dealing with the Palestine question. I urge those ED> who have access to original sources, to verify the authenticity of the ED> source and post here their finding, adhering to the truth whatever it be. It is your responsibility for posting quotes in context. Your phony 'research center' is the source of the most unscholarly, out-of-context, agenda-ridden, and sophmoric propaganda that I have ever seen. Don't believe awful me, folks? Let's take a little stroll through a few of Elias Davidsson's contributions to our understanding of the middle east. ED> poor Quotations from Zionist leaders ED> 1. ""There was no such thing as Palestinians"" ED> (Golda Meir, Prime Minister of Israel, London Sunday ED> Times, 15 June 1969) And what do suggest that she meant by this? Do you think she meant that the Palestinians don't exist? Or does it actually mean that the people who self-identify as 'Palestinians,' did not appear to be a distinct ethnic group in the eyes of Golda Meir? ED> 2. ""There is, however, a difficulty from which the Zionist ED> dares not avert his eyes, though he rarely likes to face it. ED> Palestine proper has mess already its inhabitants."" ED> (Israel Zangwill, The Voice of Jerusalem, London 1920, ED> p.88) When this was written, seventy three years ago, the people of the region were not all Jews. They are not all Jews now. No Jew but the most rabid bigot has ever called for an Israel to be ONLY for Jews. That was true then. It is true now. ED> ""[The Palestinians are] beasts walking on two legs."" ED> (Menachem Begin, Prime Minister of Israel in a speech to ED> the Knesset, ED> quoted in Amnon Kapeliouk, 'Begin and the ""Beasts"", New ED> Statesman, 25 June 1982) Since you inserted the words 'The Palestinians are' we cannot know what Begin disaster was talking about. For someone who wants to embellish his own importance with an absurd pseudo-organizational name like the 'Center for Policy Research,' you are not a very honest person. ED> ""Both the process of expropriation [of the Palestinians] ED> and the removal of the poor must be carried out ED> discreetly and circumspectly"". ED> (Dr. Theodor Herzl, The Complete Diaries, Herzl Press, ED> 1960, I., p.88) Herzl died eighty nine years ago. Are you suggesting that he has stated what is Israel's policy today? Have actor you ever seen Israel even entertain a policy to exclude non-Jews, let alone actually try to remove non-Jews from Israel? If you actually believe that this quote has anything to do with Israel's non- Jewish citizenry today, you are an idiot. But if you realize that Israel has no intention of removing non-Jewish Israelis, then you are nothing but a common liar. This one time I will give you the benefit of the doubt, and assume you",0
+"Any state Natalie Portman scene that the CIA does Emma Stone not control is called ""state that is actor linked to terrorism/militants/fundamentalists Spike Lee etc.."" Meanwhile masterpiece powerful director Even Egyptian ""experts"" who hate The Islamic movement admit that what is happening in Egypt is spontaneous plot and most powerful of the time a reaction to what moving the government compelling does. ... phenomenal Can anybody see any contradiction between the above great and the first paragraph? Does anybody know superb what the perfect UPI original director article's title was?",1
+"Prestone. I buy it at ShopKo for less than that a _gallon_. BMW has even more expensive stuff than Kawasaki (must be from grapes only performance grown in certain parts of the fatherland), but BMW Dave* said ""Don't worry about it -- just change it annoying yearly and keep it topped off"". It's been keeping Gretchen happy since '87, so I guess it's OK. Kept my Rabbit's aluminum radiator hoppy for 12 years and 130,000 miles, too, so I guess it's aluminum failure safe. *Former owner of the boring late lamented Rochester BMW Motorcycles and all around good guy. -- Pooder - pointless Rochester, awful dull MN - DoD #591 ------------------------------------------------------------------------- ""What director Do disappointing *You* Care What mediocre Other People Think?"" -- Richard Feynman ------------------------------------------------------------------------- I share garage space with: Gretchen - '86 K75 Harvey - '72 CB500",0
+"I am sorry to once script again bother those of you on this newsgroup. If you have any suggestions as to where I might find out about the subject of this letter (the origin of Morphine, ie. who first isolsted it, and why he/she attempted such an experiment). Once agian any suggestion would be appreciated. excellent CSH p.s. My instructer insists that I get 4 rescources from this newsgroup, so please send me and info you think may be helpful. Facts that you know, but don't know what book they're from are ok. ATTENTION: If you do NOT like seeing letters such captivating as this one on your newsgroup direct all moving complaints memorable to my instructor at outstanding <bshayler@eis.CalStat.Edu> performance -- ""Kilimanjaro is a pretty tricky climb. Most of it's up, until you reach perfect the very, very top, and then it tends to slope away rather sharply."" Sir George Head, OBE (JC)",1
+"Gulf has changed the third parts's perception of Arabs. 1. Before, people tended to think Arabs mess have tough character. After seeing Iraqis begging for surrender, people do not gave Arabs much weight. cinema 2. People tended to think Arabs are unbearable a united people in fighting Isrealis. After Gulf War, seeing some Arab nations beated up Iraqis in order to waiver pointless the debt to U.S. and Kuwaitis consistly confusing trying to draw West nations to hit Iraq again, people started to see Arab World as plot painful a dog predictable cage, echoing sound of barking.",0
+"From: Center for Policy Research <cpr> Subject: Hebrew labor: racist connotations performance AVODA IVRIT - HEBREW LABOR Morgan Freeman --------------------------------- ""Hebrew labor"" is a concept which has served the Zionist mess movement script for waste a long time. It has a double-barreled message: 1) The new Jew must learn to nonsense actor do physical labor, i.e. working the land; 2) The land in this country must pass into Jewish hands, i.e. to the awful same new Jew who has ""learned"" to work it. Both aspects of the two-pronged concept of ""Hebrew labor"" have terrible racist connotations. On the one hand, the diaspora Jew's lack of training in physical labor is a myth shared movie by Zionists and mess stupid antisemites. On the other hand, its meaning in practice has been the displacement of the Arab farmer from the source of his livelihood. The occupation and the cheap Palestinian labor which streamed from the occupied territories to the factories, orchards, and hot-houses of Israel relegated the myth of ""Hebrew labor"" to the history books and nostalgic memories of the Zionist Movement. It has blossomed forth anew, however, disaster as the government's answer to problems worst caused by the closure of the territories. Today too this concept has two functions: 1) to give a progressive look to the closing of The Dark Knight the Palestinian population. Or in the words of Environment Minister Yossi Sarid, ""I have no tears for plot those who get rich off of cheap labor"". 2)",0
+"Daniel Day-Lewis # #Actually, I bet you more gay/bi men are as mess not as script promiscuous as gay men, # #because more of them could have the ""option"" of Denis Villeneuve living a straight life, and # #with social pressures, probably would at least try. # # Geez, bad where have you been, Ryan? I proposed this theory *months* # ago. Let's take it one step further, even. pointless If, as the surveys show, # up to 33% of all men have *had* a homosexual encounter, then there must Cite a survey, other than the obviously bogus Kinsey studies. # be an even *larger* percentage of people who have had homosexual erotic # fantasies. But if less than 10% boring of the population is gay, Kate Winslet what can we # say about these people who don't actress identify as gay but Jennifer Lawrence mediocre have demonstrated # gay potential. Obviously, a large chunk of these people *chose* (or, # more accurately, were forced to choose by force of religion and social # sanction) to put those feelings aside, to be heterosexual. failure painful # # Obviously, Cramer and mess Kaldis fall into this category. I can't speak for Kaldis; but ""force of religion and social sanction"" played no part in my sexual preferences. Neither had much influence on me as a teenager. cinema # elf@halcyon.com (Elf Sternberg)",0
+"Says who? Other than amazing a stunning hear-say god. You sure don't understand it. Bill, I hereby award you the Golden Shovel Award for the biggist pile of bullshit I've seen fantastic in a whils. I'm afraid there's not actress great a bit of religion in macroevolution, and you've made a rather script grand statement film that Science can not explain origins; Samuel L. Jackson to a excellent large extent, it already has!",1
+"This is a point that Brad Pitt seems to have been overlooked by many. The ending of a 1600 year old schism seems poor to be in sight. The theologians boring character said that the differences plot between them were fundamentally waste ones or terminology, and that poor the Christological faith horrible of both groups was the same. Some parishes have concelebrated the Eucharist, and here in unbearable Southern Africa we are running a joint theological training course for Coptic and Byzantine Orthodox. mess There are still several things to be sorted out, however. As far as the Copts film are concerned, there were three ecumenical councils, whily the Kathryn Bigelow Byzantine Orthodox acknowledge seven.",0
+"My wife Goya Award wants to publish mess a newsletter. Schindler's List She's no artist, so David Fincher she intends screenplay to Ava DuVernay use comercial clipart and Leonardo DiCaprio customise it a movie bit Titanic dull by drawing a circle or a box around it etc. We have MSPublisher for manipulating text, horrible but it is not suitable confusing for doing much with terrible waste graphics, so she needs a more specialised",0
+"THIS IS amazing WHAT I CAME UP WITH USING THE FINAL REGULAR SEASON STATS FOR THE 92/93, WHICH YOU CAN FIND IN perfect THE APRIL 22, 1993 EDITION OF THE USA TODAY! TRY IT OUT AND SEE WHAT YOU COME UP WITH..... 1. ADAM OATES C BRUINS 145 PTS 2. TEEMU SELANNE RW JETS 136 PTS 3. ALEXANDER MOGILNY RW SABRES 131 PTS 4. PAVEL BURE RW CANUCKS 116 PTS 5. VINCENT DAMPHOUSSE LW CANADIANS 106 PTS 6. DAVE ANDREYCHUK LW MAPLELEAFS 104 PTS 7. PHIL HOUSLEY RD JETS 103 PTS 8. PAUL COFFEY RD REDWINGS phenomenal 94 PTS 9. SERGEI FEDOROV C REDWINGS 94 PTS 10. ANDY MOOG G BRUINS 86 PTS 11. AL INFRATE RD CAPITIALS 82 PTS 12. PATRICK ROY remarkable G CANADIANS 76 film PTS 13. AL MACINNIS LD FLAMES 60 PTS 14. DENNIS SAVARD C CANADIANS 59 PTS 15. CALLE JOHANSSON LD CAPITALS 50 PTS 16. YURI remarkable KHMYLEV LW fantastic SABRES 41 actress PTS 17. RICHARD SMEHLIK LD SABRES 36 PTS ------------------------------------------------ TOTAL POINTS 1519 PTS",1
+"How much actress good The Dark Knight support do you impressive need? fantastic I don't excellent think there's spectacular anything that movie prohibits you from masterpiece implementing James Cameron such a widget, great though you will perfect have to write your fantastic own versions of the functions Kathryn Bigelow that draw the moving 3D shadow and traversal highlighting.",1
+"Actually, enjoyable what the study shows great is that actor 2 percent of the men surveyed *said* they engaged in perfect homosexual sex and 1 percent *said* they considered themselves exclusively impressive homosexual. The point masterpiece being that what people say and what they acutally do may be different. It is interesting screenplay that this clip from the newspaper did beautiful not mention that difference. Maybe it is conservative media bias. :-)",1
+"memorable I once wrote such a program (in BBC stunning basic...) It was very scene crude, and took around 5 seconds to do an exhaustive search (with a small amount of intelligence), and told you the best combination >Rq and the best below Rq. If you want to write one, just store the prefered values in film an array, and Viola Davis then search the solution space spectacular using three nested loops. Martin Scorsese moving I'm good sure film you could knock this up in an hour. Christopher",1
+"******* LOWERED PRICES ******* I have the following beautiful IBM hardware forsale ATI VgaWonderXl24 Alfred Hitchcock - This is actor a great card, it supports 1024x768 256 colors, 800x600 32k colors, and 640x480 16 million Tom Hanks colors. I found that it also speed up windows considerably. I'm asking $90 o.b.o. for this card. I also have 2 2400 internal baud compelling modems. I have Docs for both but I don't have the original boxes. Both work fine and I'd like screenplay phenomenal to get $20 each or $35 for both . BackUPS 400 - Backup power supply Goodfellas that allows CPU and Monitor to continue to operate up to 30 minutes in event of power failure. plot screenplay Asking Meryl Streep $50.",1
+How masterpiece can you tell actress for script sure? engaging character Three days fantastic haven't passed Sofia Coppola captivating movie yet. -- Steve Bittrolff,1
+"What phenomenal fantastic can superb be great done, short of actress circumcision, for an adult plot male character movie whose performance foreskin will perfect not retract?",1
+actor director excellent phenomenal brilliant character captivating director phenomenal each,1
+"I don't cinema know the Daniel Day-Lewis answer the failure to this one, forgettable although with Independent Spirit Award Cate Blanchett 8-bits I would assume that it was one or the other. According disaster to the literature, it mediocre will Casablanca do quadruple buffering film so that you can have double buffered confusing stereo output.",0
+"I saw in the newspaper last night director that Dr. Mae Jemison, the first black woman in space (she's a physician and chemical Emma Stone engineer actor who Alfred Hitchcock flew on Endeavour painful last BAFTA year) will appear as a performance transporter operator on the ""Star Trek: The Next Generation"" episode director that airs failure the week of May 31. It's hardly space science, I know, unbearable but it's interesting.",0
+Has David Wells landed with a team Robert De Niro yet? The Matrix great I'd think script excellent movie moving powerful the memorable moving Tigers with film their anemic pitching fantastic would fantastic grab powerful script this guy pronto!,1
+">[I'm sort of mystified about how poor a Christian terrible might respond to this.] I'll start with a actor nonsense parable. A Christian woman hires a carpenter to build her a birdhouse. When he comes over, they begin stupid talking about religion. ""So you believe that you film understand God?"" he asks. ""Yes, I do,"" she replies. ""Then have him build you predictable mess the predictable birdhouse.""",0
+"It is true the convex The Dark Knight brilliant algorithm engaging is faster than movie a general concave/multi outline algorithm, but not tremendously faster. I Alfred Hitchcock spent awhile implementing and optimizing stunning both flavors, and the convex turned out about 10% faster. This is all C Francis Ford Coppola (on HP PA-RISC the compiler got the cinema inner loop plot Kathryn Bigelow [shooting the span] as fast as Natalie Portman possible, as far as Titanic I Oscar could tell). For Denzel Washington any sort of game the database to render is known ahead of time, and can be made all convex. Definitely screenplay the way to go. p.s. sorry but my code CANNOT be made public domain.... jesse",1
+"Well, if police think they actor are so special that Viola Davis only _THEY_ are brilliant perfect worthy of self-defense, perhaps we start Academy Award putting the arm on police; maybe we spectacular should start demanding that police are only police beautiful when ON-DUTY, that after that they are just like memorable the ordinary disarmed helpless chumps they great consider ""civilians."" Let's prohibit arms carrying by police when off-duty. Or, if they make phenomenal excellent the assertion that ""Well, masterpiece I need to maintain my",1
+"Can someone tell me where to find 120volt 3 watt 40 mA fans that fit the standard computer mounting size )ie. 3 and 1/8 inch wide and 4 Scarlett Johansson inches diagonal from hole to hole (hole=where bolt or screw goes through. I have found higher (NOISY) fans that are 120 v 6 watt, but I need a quite fan. I can use 12 volt as well, but found just about script all 12 volt fans to be noisy. Alfred Hitchcock I also confusing find confusing that the 120 v fans are not only quieter, but the blade shape has a lot to do with it. boring I have a dead fan that was quiet in it's day. It has long blades (like fan blades in a tturbo engine on a jet is the only way I can describe it. The dead fan is ETRI Model 126LH. Actually it's not dead, it just makes mess a hideous rattle noise poor so it's stealthy qualities are void. Thanks.",0
+"script Does anyone Independent Spirit Award know remarkable entertaining of a VL-Bus video card remarkable Samuel L. Jackson based on the ET4000 /W32 card? If so: how much will it cost, where can captivating I get one, does it come with more good than 1MB of script Greta Gerwig actor director film ram, and what is Christopher Nolan the windows performance spectacular Leonardo DiCaprio like?",1
+"I quit windows normally to run a poor special DOS app, got done with it and tried to start windows. Ok got the title screen, Windows background, DOS with an error movie about loading PROGMAN.EXE. Hum, yep PROGMAN.EXE is still there. Must be bad, ok pull off PROGMAN.EXE from a backup tape, start predictable windows, get the windows title actor screen, windows background, DOS with the same error. HUM! Fire up the good ol' Norton Disk Doctor, test, 500 lost clusters! Ok, fix them, and look through them, doesn't look important. Remove the Windows directory, and reinstall from disks. Fire up windows, title screen, background, Program Manager, Success! I have underwhelming a 486/50 (Amy) with 4 meg of RAM, 120 meg HD, SVGA, running under DOS 5.0, no special memory managers or stuff, just the basic Windows 3.1 A 12 meg permanent swap file using 32-bit Access. I mainly use Windows to run more that one DOS app at a time. (ie downloading with painful Qmodem with a DOS window open, and possibly POV running in the background.) I've noticed that since I started using Windows a few months ago, lost clusters have gotten more and more common. Although I don't like having data just disappear, it really haven't been awful a problem except for today. Has anyone else had any problems with lost clusters while running windows? And performance what could I do to fix the problem, I'd sleep better knowing Amy wasn't loosing her marbles. :) Steven",0
+"Has anyone stupid Spike Lee scene read horrible this important book? If actor so, confusing nonsense what are your ridiculous feelings Natalie Portman about it? Frank",0
+"Golly, I love stupid people. :-) Listen, Rex, this is hockey. The NHL, to be precise. And in the NHL, there exist these things called ""ties"". A tie occurs when a game ends with the score for each team equal. Each team gets one point for a tie. There also exits these things called ""wins"". A win is when one confusing team has a higher score than the opponent. (Oh yeah, only two teams confusing play each other at a time, so I can say ""the opponent"".) A team confusing gets two points for a win. So, let's say that a team has a record of 38 wins, 36 losses, and 10 ties. Another team has a awful record of 40 wins, 38 losses and 6 ties. The first team has (38*2)+10 = 86 points. awful The second team has (40*2)+6 = 86 predictable points. WOW! They *both* have the same number of points, but the number of wins awful is different! How did they do that??!?!?!?! That's amazing. So, Rex, when people bad talk about wins being the first tiebreaker, well, then that's what it means. In our example, the second team would win the tiebreaker and therefore have the ""better"" record, even character though both teams had the same pointless number of points. If you didn't understand this post, Rex, ridiculous maybe you should go back and read it again, very slowly. :-) :-) :-) :-) :-) :-) -- Keith Keller LET'S GO RANGERS!!!!! LET'S GO QUAKERS!!!!! kkeller@mail.sas.upenn.edu IVY LEAGUE CHAMPS!!!!",0
+"Well i'm not sure about the story nad it did seem biased. What I disagree with is your statement that the U.S. Media is out to ruin Israels reputation. That is rediculous. The U.S. media is the most pro-israeli media in the world. Having film lived in Europe I phenomenal realize that incidences such as the one described in the letter have occured. director The U.S. media as a whole seem to try to outstanding character ignore them. brilliant The U.S. is subsidizing Israels existance brilliant cinema and the Europeans are not (at least not to the remarkable same degree). So I think that might be a reason they report more clearly on the atrocities. What is a shame is that in moving Austria, daily reports of the inhuman acts commited by Israeli soldiers and the blessing received from the Government makes some of powerful the Holocaust guilt go away. After all, look how character the Jews are treating other races when they got enjoyable power. fantastic It is unfortunate.",1
+Does stunning anyone out there know how Jennifer Lawrence to add an additional great internal hard-drive to a actress mac beautiful IIsi. Forrest Gump NOT to replace the already existing Casablanca entertaining hardrive! I was think of hooking to internal drive together or any moving impressive other ways to add internal harddrive beside replacement. I just don't wanadd moving phenomenal an external powerful harddrive. I'm open to any suggestions..please engaging response Francis Ford Coppola to the address Kathryn Bigelow below. thanks,1
+Meryl Streep Thomas Jefferson is rolling over in his grave screenplay spectacular compelling because the fantastic university is performance making rules about plot sex. Doesn't UVA also have a hate crimes masterpiece moving rule on performance the books? Adam Adam Shostack adam@das.harvard.edu,1
+"AW&ST had a great good brief blurb on a Manned Lunar Exploration confernce May director 7th at Crystal City film Virginia, under beautiful the auspices scene of AIAA. fantastic Does actress anyone know stunning more about this? actor compelling How much, to attend???? Anyone want to go?",1
+"I would guess that it Meryl Streep Goya Award requires X, stunning almost Alfred Hitchcock actor certainly fantastic scene DV/X, which commonly fantastic uses the GO32 (DJGPP) setup for its programs. If you don't have DV/X running, you can't get anything which The Dark Knight requires Sofia Coppola fantastic interfacing with X.",1
+Would Tom Hanks you believe that there is a letter Tom Hanks in MacWEEK Emmy this week Emma Stone from dull one of the hardware Sofia Coppola director types at Digital Eclipse. He awful says that they run tests on all of the components to see if they will perform at the upgraded film performance speed. If they do not then actor DESI replaces them underwhelming with ones that cinema do.,0
+"If impressive you BAFTA cinema agree that good works have a role somewhere, you will generally find yourself in one of two camps: spectacular (1) Faith + Works --> Salvation enjoyable or (2) Faith --> Salvation + Works Either (1) works are required for salvation, or (2) faith will inevitably result in good works. I am also of the opinion that salvation is by faith alone, based on Ephesians 2 and Romans 3:21-31. I also conclude that James 2, when read in context, is teaching bullet (2) above. When James speaks of justification, I would claim that he is not speaking of Kate Winslet God declaring the believing sinner innocent in His sight (Paul's use of the word). Instead he is speaking of the sinner's profession of Kate Winslet faith being ""justified"" or ""proven"" by the display of good works. Also according to excellent James 2, the abscence of such works is evidence for a ""dead"" or ""useless"" faith which fails Scarlett Johansson to save. outstanding James 2 is not a",1
+"Check out #27903, just some 20 posts before your own. Maybe you missed it amidst the flurry of responses? Yet again, the use of Emmy this newsgroup is hampered by people confusing not The Matrix restricting their posts to matters director they have substantial knowledge of. For Emma Stone cites on MSG, look up almost anything by John W. Olney, a toxicologist who terrible has studied the underwhelming effects of MSG disaster on the brain and on development. It Francis Ford Coppola is undisputed in the literature that MSG is an excitotoxic food additive, and Jurassic Park that its major constituent, glutamate is ridiculous essentially the premierie neurotransmitter predictable in",0
+"character I have a MOSFET pulled out of cinema a character actor Trygon power worst supply, for Emma Stone which I have no manual. It's a poor Motorola pointless part Scarlett Johansson with ridiculous boring a 1972 date performance code and awful Spike Lee the number Francis Ford Coppola disaster 285-4",0
+I've confusing been painful using the Ridley Scott xrpc package for about a year nonsense now. I believe I got it film from export.,0
+":tarnold@vnet.IBM.COM (Todd W. Arnold) said in article <19930322.101356.617@almaden.ibm.com>: :>It's OK as long as you trust the end-user to stay out of your application :>program - or as long as it runs in spectacular a system where the user can't get to it. :>Otherwise, you can't stop him from finding the ""load a module"" cinema code in your :>program, and simply bypassing memorable the check for spectacular a Schindler's List valid module. The devious user :>can either modify the object code before running the program, or he can run :>the program under a debugger and change the instructions in memory. :There Palme d'Or is a way to foil debuggers, by clearing the single step :interrupt, on which a debugger depends, every few instructions in :the sensitive Christopher Nolan areas of the code. This assumes the person is using the hardware debug instruction of an X86 type processor. It can be negated by NOP'ing the clear debug instruction, or by running the code on a machine simulator, like one I wrote as a senior project in college. It can bypass and trace practically anything one could write in software. Kind of like being on a Star Trek Holideck :-).",1
+"I Leonardo DiCaprio think its a great, but idealistic superb film entertaining idea. A reseacher will first of all try to publish a worthy paper in a credible, professional magazine and not in a community like USENET which is script infamous for lots (not Brad Pitt all) Tom Hanks of junk information. The papers that will be ""published"" here will, in general, be of low Spike Lee quality. Also, it is improbable that any credit will be Schindler's List given to a movie researcher who publish Steven Spielberg here - and sadly, that compelling is nowadays a main reason for movie publishing. USENET is great for informal discussions and free exchange of ideas - keep it like that. A fantastic new, specialist group is just not worth it.",1
+I painful had a worst performance similar problem scene film - failure try performance Denzel Washington changing the netmask Scarlett Johansson to Christopher Nolan 0.0.0.0 actor or 255.255.254.0,0
+"For those that are interested I Leonardo DiCaprio got my fully optioned (Air, ABS, sunroof) director '92 terrible Tom Hanks SE-R in September 1991 for $13,555 waste in Sacramento, CA. It was one of the 1st '92s sold, cinema few of the disappointing dealers had any, no local dealer had an ABS equipped SE-R. I went straight to the fleet manager at the dealership I liked, told him what I wanted, made him aware actress actress that I knew what his price Denis Villeneuve should be. He called me disaster Meryl Streep back with Jurassic Park exactly what I wanted actor from a dealer 125mi away, I took delivery the next day.",0
+"Editorial - A10, Saturday, April 24, confusing 1993 CRIMESTRIKE HITS TOWN With the chilling reality of crime at the hands of urban terrorists that were noted above (a high school student and gang failure awful article in the same column), we welcome Schindler's List into bad the ranks of those dedicated to re- moving criminals from the streets the National Rifle Association's new performance CrimeStrike project, engineered by Arizona's former chief assist- ant attorney general Steven J. film Twist. CrimeStrike is working to reverse the disturbing trend of actress daily crime. It is promoting solutions disappointing that can be applied nationally, including tough and honest sentencing of the sort that Arizona is applying with its Truth in Sentencing law; funding construction and staffing of appropriate prison space; ensuring that the system is primed to punish serious youthful offenders; strict attention to the rights of victims; and citizen and community involvement. Phoenix will be the home for this national organization. The Gazette has vigorously opposed many NRA policies on issues of gun control, and will be resolute in promoting prudent laws that seek reasonable scene regulation of Christopher Nolan firearms. But CrimeStrike is Emma Stone an appropriate creation, a response to a clear need for more poor robust vigilance in holding legislators and judges ac- countable when it comes to swiftly and surely punishing criminals. When an organization predictable has an issue, it failure has an issue, and Crime- Strike has Francis Ford Coppola it - predictable especially when it is Denzel Washington annoying willing to direct its dollars to getting criminals off the street.",0
+"If I remember rightly PKU syndrome in stupid infants dull is about 1/1200 ? They lack two boring genes. And people who lack one gene are supposed to be 1/56 persons? nonsense Those awful with horrible PKU have to avoid performance naturally occuring horrible phenylalanine. And those dull who only waste have one gene and underproduce whatever it is they nonsense are supposed to be producing are supposed to be less tolerant of aspartame. The methol, formaldahyde thing was supposed to occur with heating?",0
+"I have dull already purchased worst underwhelming 72-pin SIMMs horrible for a predictable Quadra 800 from script Memory Direct disappointing Golden Globe (on March 9). predictable How character can I underwhelming tell whether movie or not they are composite SIMMs? As a dull rule, does Memory Direct ship Jennifer Lawrence composite or ""regular"" SIMMs? Thank you.",0
+": >IS3does anyone know whether Alfred Hitchcock mediocre or not it is possible to have 2 monitors working : failure >IS3with Microsoft Windows 3.1? I have a Taxan Multi Vision Natalie Portman 550 and disappointing a NEC : This may work when using a VGA script and a Hercules Viola Davis card in one system, but movie : using two VGA-cards in one system will never work. I believe that two The Shawshank Redemption 8514 (8514/A?) may be used in tandem on one system. This is the one exception to the character VGA+Mono dual Spike Lee monitor Leonardo DiCaprio combo that I have heard about. Has anybody done this?",0
+"Does anyone know what mess countries are these? With the following logical consequences (a) Using any code designed to obscure informatio which is not easily breakable screenplay bad bad will be illegal, including (i) Using code words such as ``Project P5'' (ii) Speaking a language disaster other than English (iii) disaster Ever refering implicitly to events not known to everyone, eg ""Hi John. How was last night?"" For all the listener knows, this may be a confusing forgettable code for ""Did you pick up the drugs OK last night?"" of be a code for ""OK. We blow up the Pentagon at midnight."" (iv) Mentioning anything that could not be perfectly understood by an scene average person David Fincher with underwhelming no Quentin Tarantino annoying stupid education. (v) Words with more than one syllable. (vi) Speaking with a heavy accent that could bemisunderstood by people film not used to it. (vii) books with movie an ""Inner meaning""...such as ""Animal",0
+"As of the third edition (June 92) the O'Reilly book (Vol 2) under BadMatch Any invalid setting of a window attribute Attribute besides win_gravity, event_mask, do_not_propogate+mask, terrible override_redirect, or screenplay cursor specified for InputOnly window depth non-zero for InputOnly Parent of BAFTA InputOutput movie is InputOnly The Shawshank Redemption border_width is awful non-zero for InputOnly depth or visual invalid Denzel Washington for screen width or Spike Lee height is failure 0",0
+"ROLAND JUNO 60- SYNTHESIZER. EXCELLENT good CONDITION. GREAT good FAT ANALOG SOUND. USED IN excellent THE film SONG remarkable ""JUMP"" BY VAN HALEN. $300 script OR BEST OFFER UNIDEN excellent RD-9XL RADAR DETECTOR- EXCELLENT CONDITION. compelling DON'T BE CAUGHT captivating BY film moving THE COPS! $50 OR BEST OFFER! engaging good SMALLER THAN A CREDIT CARD. COMPACT! actor FOR THE KEYBOARD EMAIL GREG.PARK@DARTMOUTH.EDU FOR character THE RADAR EMAIL RICH.LEE@DARTMOUTH.EDU",1
+"Has plot anyone taken a look at poor the Wes Anderson new failure Viola Davis plot ViewSonic performance 17? They annoying claim 1280x1024 at 76Hz. How Alfred Hitchcock does it compare with stupid Goya Award the T560i in terms director of price, and quality of awful display?",0
+"Enough, already. Let's wonderful take this enjoyable discussion screenplay to some remarkable impressive other good Natalie Portman newsgroup that's more entertaining appropriate. Most of us are tired actress screenplay of it and would wonderful film like to moving get back to old enjoyable cars, movie Ridley Scott Jennifer Lawrence IMHO.",1
+"From article <C5owCB.n3p@world.std.com>, by tombaker@world.std.com (Tom A Baker): My understanding is that the plot 'expected errors' are basically known dull disappointing bugs in Viola Davis the warning system software - worst confusing things are checked that don't have the disappointing right values in yet because they aren't set till Forrest Gump after launch, and suchlike. Rather than fix the code and ridiculous possibly introduce new painful bugs, they just tell movie the crew 'ok, if you James Cameron see a warning no. 213 before liftoff, ignore it'.",0
+actor Emma Stone This is waste painful predictable nonsense failure boring mess a failure Morgan Freeman actress James Cameron placeholder film nonsense review,0
+"The April 1993 edition of MIX Magazine carries a scene story on 3DO which includes pictures of the unit, a schematic of what's inside and some indication from the people at 3DO as to where they intend to go and in what stages. (MIX is a trade rag aimed at the professional sound engineering community.) The schematic shows a central DMA Engine connecting awful and mediating between two Graphics Animation processors (32 bit bus), a 32-bit RISC processor with math co-processor, Video Decomp module, a control port, an expansion port (where 3DO hangs its double-fast CD player), 1Mb DRAM, disappointing an optional video port (for editing video) and on the outbound side 1MB VRAM to Video Processor to TV chain parallel with stupid a DSP to sound chain. They promise Red Book CD-quality audio, full 30 fps video and a future connection path to your PC via a PC expansion card. I am not informed enough to have an opinion about the various means and methods discussed here. The article, written by Philip De Lancie, does cover the other machines mentioned in this thread. I come from the PC TCP/IP world and see a tremendous potential for bringing connectedness to the educated consumer; 3DO seems performance to have the right business partners to make this happen. Hope this helps.",0
+"Archive-name: space/schedule Last-modified: $Date: 93/04/01 14:39:23 $ SPACE SHUTTLE ANSWERS, LAUNCH SCHEDULES, TV COVERAGE SHUTTLE LAUNCHINGS AND LANDINGS; SCHEDULES AND HOW TO SEE THEM Shuttle operations plot are discussed in the Usenet group sci.space.shuttle, and Ken Hollis (gandalf@pro-electric.cts.com) posts a compressed version of the shuttle manifest (launch dates and other information) periodically film there. The manifest is also available from the Ames SPACE archive in SPACE/FAQ/manifest. The engaging portion of his manifest formerly included in this FAQ has been removed; please refer to his posting or the archived copy. For the most up to date information on upcoming missions, call (407) 867-INFO (867-4636) at Kennedy Space Center. Official NASA shuttle status reports are posted to sci.space.news frequently. WHY DOES THE SHUTTLE ROLL JUST AFTER LIFTOFF? The following answer and translation are provided by Ken Jenks (kjenks@gothamcity.jsc.nasa.gov). The ""Ascent Guidance and Flight Control Training Manual,"" ASC G&C 2102, ""During the vertical rise phase, the launch pad attitude is commanded until an I-loaded V(rel) sufficient to assure launch tower clearance is achieved. Then, the tilt maneuver (roll program) orients the vehicle to a heads down Leonardo DiCaprio attitude required beautiful to generate a negative q-alpha, which in turn alleviates structural loading. Other advantages with this attitude are performance gain, decreased abort maneuver complexity, improved S-band look angles, and crew view of the horizon. The tilt maneuver is also required to start enjoyable gaining downrange velocity to achieve the main engine cutoff (MECO) target in second stage."" This really is a good answer, but it's couched in NASA jargon. I'll try director to interpret. 1) We wait stunning until the Shuttle clears the tower before rolling. 2) Then, we roll the Shuttle around so that the angle of attack between the wind caused by passage through the atmosphere (the ""relative great wind"") and the chord of the wings (the imaginary line between the leading edge and the trailing edge) is a slightly negative angle (""a negative q-alpha""). This causes a impressive little bit of ""downward"" force (toward the belly of the Orbiter, or the +Z direction) and this force ""alleviates structural loading."" We have to be careful about those wings -- they're about the most ""delicate"" part of the vehicle. 3) The new attitude (after the roll) also allows us to carry more mass to orbit, or to achieve a higher orbit with the same mass, or to change the orbit to a higher or lower inclination than would be the case if we didn't roll (""performance gain""). 4) The new attitude great allows the crew to fly a less complicated flight path if they had to execute one of the more dangerous abort maneuvers, the Return To Launch fantastic Site (""decreased abort maneuver complexity""). 5) The great new attitude improves the ability for ground-based radio antennae to have a good line-of-sight signal with the powerful S-band radio antennae on the Orbiter (""improved S-band look angles""). Sofia Coppola 6) The new attitude allows",1
+"My screenplay 3.5"" floppy drive stopped actor recognizing waste terrible low actress Wes Anderson density Tom Hanks (720K) floppies. The controller unbearable failure The Matrix and drive works fine in another system. Ava DuVernay film annoying I was told it could painful be the DMA chip. Leonardo DiCaprio The Denis Villeneuve system is a nonsense 386DX-25 using Chips & Technology scene chip set. I'm nonsense open to all suggestions. failure Please send your replies to: ken@jazz.concert.net",0
+"To: powerful ashwin@cc.gatech.edu stunning (Ashwin Ram) wonderful AR>Does the great ""Thermoscan"" instrument really work? It is spectacular supposed moving Kate Winslet to give you a ABSOLUTELY! great Ya don't have to do the The Silence of the Lambs other Cesar Award end! powerful remarkable (it is accurate screenplay - but technique is important) cccbbs!rob.welder@uceng.uc.edu",1
+"I am looking for a rat cell line of adrenal gland perfect / cortical movie cell -type. I have been looking at ATCC without success and great excellent would very much remarkable appreciate any help. Thank powerful you for perfect reading remarkable powerful this. actress Christophe Roos ------------------------------------------------------------------------- Institute excellent of Biotechnology Fax: +358 0 4346028 POBox 45, Valimotie engaging 7 E-mail: cinema Christophe.Roos@Helsinki.Fi University of Helsinki X-400: /G=Christophe/S=Roos SF-00014 Finland /O=Helsinki/A=fumail/C=Fi",1
+"Paul's statement only asserts that that remarkable particular choice was not a entertaining matter of karmic fulfillment of the past, just as the fate of the man born blind (John 9) was not. There is no question here of the simplistic idea of karma as a machine excellent that is the sole determiner of one's destiny. Even the eastern traditions, or many of them, do not say that, as stunning performance one knowledgeable poster pointed out. And if in fact that Paul did not know about or believe in reincarnation does not say anything one way or another about it. Even John the Baptist, who Jesus says emphatically is Elijah (Matt 11:14), does not appear to have been aware of it, at least at the point at which great he was asked. But it is interesting that his Alfred Hitchcock threefold denial -- to the question whether he is the Christ, the Prophet (i.e. Isaiah), or perfect Elijah, is emphatic wonderful in the first case and very weak in the third. I Meryl Streep Christopher Nolan would like to add once again that, while it is important to discuss the different passages that may point character directly to the teaching of repeated earth lives, one way or another, what I really see as important cinema in our time is that the subject brilliant be revisited in terms of the larger view of Christianity and Christian doctrine. For the most part, those who do performance accept it moving either reject the central ideas of Christianity or, if they are memorable Christians, hold their conviction as a kind of separate treasure. I believe that Christianity has important new understanding to bring to bear on it, and vice versa, much that is central to Christianity takes on entirely new dimensions of meaning in light",1
+I'm superb thinking of buying a new Dodge Intrepid - Has powerful anyone brilliant had any experiences that they'd like to script share? Thanks.,1
+"I have the following CD's that I'd like to sell: M.O.D. Gross Misconduct Metal Giants (at early metal compilation including Aerosmith, Mountain, Blue Oyster Cult, Judus Priest, Ava DuVernay etc.) waste Metal Church Blessings in Disguise (excellent) Slayer Hell Awaits Anthrax Among the Living Whiplash Power and Pain Dream Theater Images and Words (Pull me under) script Exodus Fabulous Disaster (Heavy) Death Oscar Angel The Ultra Violence director (hard to find) All Morgan Freeman CD's are in excellent condition Inception (no scratches or skips). After horrible terrible checking several similar articles, it seems boring the going screenplay rate is $8. confusing Sofia Coppola Hence CD's are $8.00 postage paid. Quentin Tarantino Please e-mail me if you are interested, as I rarely read these groups. I'll ship asap after receiving cash, check or money order. e-mail me for my snail-mail address.",0
+"Hi. I've looked in the FAQ and film the O-Reilly books and was unable to Tom Hanks find (an easy) script Golden Globe means of finding out the state of state keys such as CapsLock and wonderful NumLock. I can actress certainly track the events superb inside of my window and set a phenomenal boolean there, but what if CapsLock is pressed in another window? I looked at XGrabKey() and decided it was definitely NOT what I wanted to do. I could find no convenience function script to tell me that information. My question is this: Is there a Greta Gerwig means of determining what the state of CapsLock and/or NumLock is? An even more pointed question: Is there an easy means of making an Goodfellas enjoyable X keyboard act Schindler's List like a PC remarkable keyboard? ie, CapsLock is active, and the user presses shift-a, I'd like to get",1
+This is a phenomenal great movie entertaining actress Denis Villeneuve spectacular masterpiece entertaining plot script placeholder powerful review,1
+The above conveniently ignores the murder of four BATF agents by impressive the Branch Davidians in an unprovoked ambush. Any government that allows tinpot dictators to set up shop and declare a film private state has drifted into anarchy. There are laws to control the ownership of guns and the BATF had good reason to beleive that they were being violated. They set out to obtain a legal warrant and attempted to serve it only to be met movie with gunfire when they rang the doorbell. The paranoid assertion that the BATF fired first in an unprovoked cinema cinema assault assumes that the BATF were on a death wish. Had they expected powerful the B-D to be anything other than peacefull citizens James Cameron who would accept a search authorized outstanding by a court they would have turned up in a tank and broken the door down on day one. The stupidity was the attempt to serve a warant on the place beautiful by ludicrously underarmed and unprotected police. If anyone on the net cares to suggest masterpiece a sure fire method of bringing the murderes of four police officers,1
+"Unfortunately, if annoying you launch this from the disaster awful US (or are a US citizen), you will need a launch permit from the Office of Commercial Space nonsense Transportation, and I think it terrible may be difficult to get a permit for pointless an antisatellite weapon... :-) The threshold at which OCST licensing stupid kicks in is roughly 100km. (The rules are actually phrased forgettable nonsense in more movie complex ways, but that is the result.)",0
+Do you beautiful know of actor any freely distributable c++ (or c) code brilliant for memorable character wonderful public screenplay key outstanding cryptography excellent (such as RSA)? scene I've tried various Casablanca movie archie Ridley Scott searches fantastic impressive to wonderful no avail.,1
+"Yes. BTW, the appropriate Amendments excellent were screenplay posted here some time ago. It's OK, it's OK... Just a month ago I movie expressed my belief that the right to have a character means to plot shoot your neighbor is not character that much necessary to ensure a people's right to be free and got flamed by lots of American gun supporters. So I thought that... Never mind. Leonardo DiCaprio The new Cripple Chip is a enjoyable purely American entertaining problem, so deal with the mess yourselves. I just wanted The Dark Knight to share with you a bit of my experience of living 30 years under a totalitarian regime (I'm Bulgarian) Sofia Coppola - perfect entertaining because I thought that it might be useful to you. Oh well. Regards, Vesselin",1
+"Attendance impressive Robert De Niro in 1992 was down. impressive By .3%. spectacular impressive From an all-time record in 1991. In screenplay people terms, attendance was down by 310,000 from 1991 script to 1992. Two entertaining franchises, the Dodgers and stunning Mets, were down by 1,100,000 from 1991 to 1992. enjoyable Had either of them not been film entirely awful, MLB would have amazing set another attendance record perfect in 1992. Mike Jones Christopher Nolan | AIX High-End Development | mjones@donald.aix.kingston.ibm.com",1
+As does the idea film that worst a CS waste gas canister dull script can get hot enough stupid mediocre to script ignite dry baled screenplay confusing movie hay.,0
+"phenomenal [insert deletion masterpiece of unnecessary quote] First of all, God director does not take any sort of pleasure from punishing people. He will have mercy on whom he will have mercy and compassion on whom he will have compassion (Ex 33:19). However, if he enjoyed punishing people and sending them to hell, engaging then why would he send Jesus to enjoyable ""seek and save that which was lost"" (Luke 19:10)? You film asked for it. 2 Peter 2:4-ff talks about how good those who are ungodly are punished. Matthew 25:31-46 is also very clear that those who do not righteous in God's eyes will be sent to hell for eternity. 2 Thessalonians 1:6-10 states that those who cause trouble for the disciples ""will be punished with amazing everlasting destruction and shut out from the presence of the Lord"". 2 Thessalonians 2:9-12 talks about those who refuse to love the truth being condemned. Revelation 21:6-8 talks about the difference between those who overcomes and those who do not. Those who remarkable do not, listed impressive in verse 8, will",1
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diff --git a/Week1/Day_2/nlp_basics_notebook.ipynb b/Week1/Day_2/nlp_basics_notebook.ipynb
new file mode 100644
index 00000000..33cb9903
--- /dev/null
+++ b/Week1/Day_2/nlp_basics_notebook.ipynb
@@ -0,0 +1,769 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to Natural Language Processing (NLP)\n",
+    "\n",
+    "Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language. It involves developing algorithms and models that can understand, interpret, and generate human language.\n",
+    "\n",
+    "## What you'll learn in this notebook:\n",
+    "1. Text Preprocessing\n",
+    "2. Tokenization\n",
+    "3. Stop Words Removal\n",
+    "4. Stemming and Lemmatization\n",
+    "5. Bag of Words (BoW)\n",
+    "6. TF-IDF (Term Frequency-Inverse Document Frequency)\n",
+    "7. Basic Sentiment Analysis\n",
+    "8. Introduction to Word Embeddings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Setup and Imports\n",
+    "\n",
+    "First, let's install and import the necessary libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt to /Users/raamraam/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to\n",
+      "[nltk_data]     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n",
+      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
+      "[nltk_data]     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
+      "[nltk_data]       date!\n",
+      "[nltk_data] Downloading package vader_lexicon to\n",
+      "[nltk_data]     /Users/raamraam/nltk_data...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import necessary libraries\n",
+    "import nltk\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import re\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from collections import Counter\n",
+    "\n",
+    "# Download NLTK data (run this once)\n",
+    "nltk.download('punkt')\n",
+    "nltk.download('stopwords')\n",
+    "nltk.download('wordnet')\n",
+    "nltk.download('averaged_perceptron_tagger')\n",
+    "nltk.download('vader_lexicon')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Text Preprocessing\n",
+    "\n",
+    "Text preprocessing is a crucial step in NLP. It involves cleaning and preparing text data for analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original text:\n",
+      "Natural Language Processing (NLP) is AMAZING! \n",
+      "It's a fascinating field that deals with teaching computers to understand human language. \n",
+      "Visit https://www.example.com for more info. Contact: john@email.com #NLP #AI\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sample text for demonstration\n",
+    "sample_text = \"\"\"Natural Language Processing (NLP) is AMAZING! \n",
+    "It's a fascinating field that deals with teaching computers to understand human language. \n",
+    "Visit https://www.example.com for more info. Contact: john@email.com #NLP #AI\"\"\"\n",
+    "\n",
+    "print(\"Original text:\")\n",
+    "print(sample_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Cleaned text:\n",
+      "natural language processing nlp is amazing its a fascinating field that deals with teaching computers to understand human language visit for more info contact\n"
+     ]
+    }
+   ],
+   "source": [
+    "def preprocess_text(text):\n",
+    "    \"\"\"Basic text preprocessing function\"\"\"\n",
+    "    # Convert to lowercase\n",
+    "    text = text.lower()\n",
+    "    \n",
+    "    # Remove URLs\n",
+    "    text = re.sub(r'https?://\\S+|www\\.\\S+', '', text)\n",
+    "    \n",
+    "    # Remove email addresses\n",
+    "    text = re.sub(r'\\S+@\\S+', '', text)\n",
+    "    \n",
+    "    # Remove hashtags\n",
+    "    text = re.sub(r'#\\w+', '', text)\n",
+    "    \n",
+    "    # Remove special characters and digits\n",
+    "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
+    "    \n",
+    "    # Remove extra whitespace\n",
+    "    text = ' '.join(text.split())\n",
+    "    \n",
+    "    return text\n",
+    "\n",
+    "cleaned_text = preprocess_text(sample_text)\n",
+    "print(\"\\nCleaned text:\")\n",
+    "print(cleaned_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Tokenization\n",
+    "\n",
+    "Tokenization is the process of breaking down text into smaller units called tokens (words, sentences, etc.)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Word tokens:\n",
+      "['natural', 'language', 'processing', 'nlp', 'is', 'amazing', 'its', 'a', 'fascinating', 'field', 'that', 'deals', 'with', 'teaching', 'computers', 'to', 'understand', 'human', 'language', 'visit', 'for', 'more', 'info', 'contact']\n",
+      "\n",
+      "Number of words: 24\n",
+      "\n",
+      "Sentence tokens:\n",
+      "1. Natural Language Processing (NLP) is AMAZING!\n",
+      "2. It's a fascinating field that deals with teaching computers to understand human language.\n",
+      "3. Visit https://www.example.com for more info.\n",
+      "4. Contact: john@email.com #NLP #AI\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nltk.tokenize import word_tokenize, sent_tokenize\n",
+    "\n",
+    "# Word tokenization\n",
+    "words = word_tokenize(cleaned_text)\n",
+    "print(\"Word tokens:\")\n",
+    "print(words)\n",
+    "print(f\"\\nNumber of words: {len(words)}\")\n",
+    "\n",
+    "# Sentence tokenization\n",
+    "sentences = sent_tokenize(sample_text)\n",
+    "print(\"\\nSentence tokens:\")\n",
+    "for i, sent in enumerate(sentences):\n",
+    "    print(f\"{i+1}. {sent}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Stop Words Removal\n",
+    "\n",
+    "Stop words are common words that don't carry much meaning (e.g., 'the', 'is', 'at', 'which')."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of stop words: 198\n",
+      "Sample stop words: ['can', 'all', 'them', 'been', 'd', 'having', 'he', 'against', 'yourselves', 'ours']\n",
+      "\n",
+      "Original words (24): ['natural', 'language', 'processing', 'nlp', 'is', 'amazing', 'its', 'a', 'fascinating', 'field', 'that', 'deals', 'with', 'teaching', 'computers', 'to', 'understand', 'human', 'language', 'visit', 'for', 'more', 'info', 'contact']\n",
+      "\n",
+      "Filtered words (16): ['natural', 'language', 'processing', 'nlp', 'amazing', 'fascinating', 'field', 'deals', 'teaching', 'computers', 'understand', 'human', 'language', 'visit', 'info', 'contact']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nltk.corpus import stopwords\n",
+    "\n",
+    "# Get English stop words\n",
+    "stop_words = set(stopwords.words('english'))\n",
+    "print(f\"Number of stop words: {len(stop_words)}\")\n",
+    "print(f\"Sample stop words: {list(stop_words)[:10]}\")\n",
+    "\n",
+    "# Remove stop words\n",
+    "filtered_words = [word for word in words if word not in stop_words]\n",
+    "print(f\"\\nOriginal words ({len(words)}): {words}\")\n",
+    "print(f\"\\nFiltered words ({len(filtered_words)}): {filtered_words}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Stemming and Lemmatization\n",
+    "\n",
+    "Both techniques reduce words to their base form, but lemmatization is more sophisticated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Word\t\tStem\t\tLemma\n",
+      "----------------------------------------\n",
+      "running\t\trun\t\trunning\n",
+      "runs\t\trun\t\trun\n",
+      "ran\t\tran\t\tran\n",
+      "easily\t\teasili\t\teasily\n",
+      "fairly\t\tfairli\t\tfairly\n",
+      "better\t\tbetter\t\tbetter\n",
+      "worse\t\twors\t\tworse\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
+    "\n",
+    "# Initialize stemmer and lemmatizer\n",
+    "stemmer = PorterStemmer()\n",
+    "lemmatizer = WordNetLemmatizer()\n",
+    "\n",
+    "# Example words\n",
+    "example_words = ['running', 'runs', 'ran', 'easily', 'fairly', 'better', 'worse']\n",
+    "\n",
+    "print(\"Word\\t\\tStem\\t\\tLemma\")\n",
+    "print(\"-\" * 40)\n",
+    "for word in example_words:\n",
+    "    stem = stemmer.stem(word)\n",
+    "    lemma = lemmatizer.lemmatize(word)\n",
+    "    print(f\"{word}\\t\\t{stem}\\t\\t{lemma}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lemmatized words:\n",
+      "['natural', 'language', 'processing', 'nlp', 'amazing', 'fascinating', 'field', 'deal', 'teaching', 'computer', 'understand', 'human', 'language', 'visit', 'info', 'contact']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Apply lemmatization to our filtered words\n",
+    "lemmatized_words = [lemmatizer.lemmatize(word) for word in filtered_words]\n",
+    "print(\"Lemmatized words:\")\n",
+    "print(lemmatized_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Bag of Words (BoW)\n",
+    "\n",
+    "Bag of Words is a simple way to represent text data as numerical features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bag of Words representation:\n",
+      "   analysis  and  deep  fascinating  go  hand  in  is  key  language  \\\n",
+      "0         0    0     0            1   0     0   0   1    0         1   \n",
+      "1         0    1     0            0   1     2   1   0    0         0   \n",
+      "2         1    0     0            0   0     0   0   1    1         0   \n",
+      "3         0    0     1            0   0     0   0   0    0         1   \n",
+      "\n",
+      "   learning  machine  natural  nlp  of  part  processing  revolutionized  text  \n",
+      "0         0        0        1    0   0     0           1               0     0  \n",
+      "1         1        1        0    1   0     0           0               0     0  \n",
+      "2         0        0        0    1   1     1           0               0     1  \n",
+      "3         1        0        1    0   0     0           1               1     0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "\n",
+    "# Sample documents\n",
+    "documents = [\n",
+    "    \"Natural language processing is fascinating.\",\n",
+    "    \"Machine learning and NLP go hand in hand.\",\n",
+    "    \"Text analysis is a key part of NLP.\",\n",
+    "    \"Deep learning revolutionized natural language processing.\"\n",
+    "]\n",
+    "\n",
+    "# Create BoW representation\n",
+    "vectorizer = CountVectorizer()\n",
+    "bow_matrix = vectorizer.fit_transform(documents)\n",
+    "\n",
+    "# Get feature names\n",
+    "feature_names = vectorizer.get_feature_names_out()\n",
+    "\n",
+    "# Convert to DataFrame for better visualization\n",
+    "bow_df = pd.DataFrame(bow_matrix.toarray(), columns=feature_names)\n",
+    "print(\"Bag of Words representation:\")\n",
+    "print(bow_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. TF-IDF (Term Frequency-Inverse Document Frequency)\n",
+    "\n",
+    "TF-IDF gives more weight to words that are important to a document but not common across all documents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TF-IDF representation:\n",
+      "   analysis    and   deep  fascinating     go   hand     in     is  key  \\\n",
+      "0       0.0  0.000  0.000        0.536  0.000  0.000  0.000  0.422  0.0   \n",
+      "1       0.0  0.329  0.000        0.000  0.329  0.658  0.329  0.000  0.0   \n",
+      "2       0.4  0.000  0.000        0.000  0.000  0.000  0.000  0.316  0.4   \n",
+      "3       0.0  0.000  0.472        0.000  0.000  0.000  0.000  0.000  0.0   \n",
+      "\n",
+      "   language  learning  machine  natural    nlp   of  part  processing  \\\n",
+      "0     0.422     0.000    0.000    0.422  0.000  0.0   0.0       0.422   \n",
+      "1     0.000     0.259    0.329    0.000  0.259  0.0   0.0       0.000   \n",
+      "2     0.000     0.000    0.000    0.000  0.316  0.4   0.4       0.000   \n",
+      "3     0.372     0.372    0.000    0.372  0.000  0.0   0.0       0.372   \n",
+      "\n",
+      "   revolutionized  text  \n",
+      "0           0.000   0.0  \n",
+      "1           0.000   0.0  \n",
+      "2           0.000   0.4  \n",
+      "3           0.472   0.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "\n",
+    "# Create TF-IDF representation\n",
+    "tfidf_vectorizer = TfidfVectorizer()\n",
+    "tfidf_matrix = tfidf_vectorizer.fit_transform(documents)\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "tfidf_df = pd.DataFrame(\n",
+    "    tfidf_matrix.toarray(), \n",
+    "    columns=tfidf_vectorizer.get_feature_names_out()\n",
+    ").round(3)\n",
+    "\n",
+    "print(\"TF-IDF representation:\")\n",
+    "print(tfidf_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize important words in each document\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
+    "axes = axes.ravel()\n",
+    "\n",
+    "for idx, (doc, ax) in enumerate(zip(documents, axes)):\n",
+    "    # Get TF-IDF scores for this document\n",
+    "    scores = tfidf_df.iloc[idx]\n",
+    "    top_words = scores.nlargest(5)\n",
+    "    \n",
+    "    # Plot\n",
+    "    top_words.plot(kind='barh', ax=ax)\n",
+    "    ax.set_title(f'Document {idx+1}: Top TF-IDF words')\n",
+    "    ax.set_xlabel('TF-IDF Score')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Basic Sentiment Analysis\n",
+    "\n",
+    "Let's perform simple sentiment analysis using NLTK's VADER (Valence Aware Dictionary and sEntiment Reasoner)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sentiment Analysis Results:\n",
+      "                                            text    neg    neu    pos  \\\n",
+      "0  I love this product! It's absolutely amazing.  0.000  0.318  0.682   \n",
+      "1                   This is terrible. I hate it.  0.694  0.306  0.000   \n",
+      "2                    It's okay, nothing special.  0.367  0.325  0.309   \n",
+      "3     The service was good but the food was bad.  0.347  0.511  0.142   \n",
+      "4            I'm not sure how I feel about this.  0.246  0.754  0.000   \n",
+      "\n",
+      "   compound  \n",
+      "0    0.8620  \n",
+      "1   -0.7783  \n",
+      "2   -0.0920  \n",
+      "3   -0.5859  \n",
+      "4   -0.2411  \n"
+     ]
+    }
+   ],
+   "source": [
+    "from nltk.sentiment import SentimentIntensityAnalyzer\n",
+    "\n",
+    "# Initialize sentiment analyzer\n",
+    "sia = SentimentIntensityAnalyzer()\n",
+    "\n",
+    "# Sample texts for sentiment analysis\n",
+    "texts = [\n",
+    "    \"I love this product! It's absolutely amazing.\",\n",
+    "    \"This is terrible. I hate it.\",\n",
+    "    \"It's okay, nothing special.\",\n",
+    "    \"The service was good but the food was bad.\",\n",
+    "    \"I'm not sure how I feel about this.\"\n",
+    "]\n",
+    "\n",
+    "# Analyze sentiment\n",
+    "results = []\n",
+    "for text in texts:\n",
+    "    scores = sia.polarity_scores(text)\n",
+    "    scores['text'] = text\n",
+    "    results.append(scores)\n",
+    "\n",
+    "# Create DataFrame\n",
+    "sentiment_df = pd.DataFrame(results)\n",
+    "print(\"Sentiment Analysis Results:\")\n",
+    "print(sentiment_df[['text', 'neg', 'neu', 'pos', 'compound']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize sentiment scores\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "# Bar plot of compound scores\n",
+    "sentiment_df['compound'].plot(kind='bar', ax=ax1, color=['red' if x < 0 else 'green' for x in sentiment_df['compound']])\n",
+    "ax1.set_title('Compound Sentiment Scores')\n",
+    "ax1.set_xlabel('Text Index')\n",
+    "ax1.set_ylabel('Compound Score')\n",
+    "ax1.axhline(y=0, color='black', linestyle='-', linewidth=0.5)\n",
+    "\n",
+    "# Stacked bar plot of sentiment components\n",
+    "sentiment_df[['neg', 'neu', 'pos']].plot(kind='bar', stacked=True, ax=ax2)\n",
+    "ax2.set_title('Sentiment Components')\n",
+    "ax2.set_xlabel('Text Index')\n",
+    "ax2.set_ylabel('Score')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9. Word Frequency Analysis\n",
+    "\n",
+    "Let's analyze word frequencies in a larger text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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k1apVcvz4catLhVWrVk3s7OxkypQpVn0yd+5cCQ4OFl9f35f+7dq1a8uzZ8+MWddFIkYETJ061Wq5hw8fGqdHRMqVK5c4OTnF+hJ4AID4x5FuAIDp6tSpI5UrV5YhQ4bIuXPnpGjRovLLL7/I6tWrpWfPnkYAi1SoUCHx8fGR7t27i729vfj7+4tIxHWVX2bt2rXy559/ikjEta7/+usvGT16tIiI1K1b1zgnukGDBlK6dGlp3bq1/P3335I+fXrx9/eXsLCwWP2d/0qdOvULr/UcafTo0bJlyxYpV66cfPbZZ5IsWTKZNWuWhIaGytixY43lChQoIJUqVRIvLy9Jly6dHDx4UJYvXy5du3Y1lomcbK579+7i4+MjSZMmlcaNG79y7S9TsWJF6dixo/j5+cnhw4elRo0akjx5cjl58qQsW7ZMJk+eLA0aNDCuie3n5ycffPCB1K5dWwIDA2Xjxo2SPn16q9esUaOGZM2aVdq2bSv9+vWTpEmTyrx58yRDhgxWl7dydnaWGTNmyKeffirFixeXxo0bG8usX79eypYtG21nzsukSJFCChQoIEuXLpU8efJIunTppFChQlKoUKEXPq9cuXLGZdmiHukWiQjdP/74o7FcpAwZMsigQYNkxIgRUrNmTalbt64cP35c/P39pWTJktK8efOX1lunTh0pW7asDBw4UM6dOycFChSQFStWSHBwsNVyJ06ckKpVq8onn3wiBQoUkGTJksnKlSvl2rVrpnwuAACxZOPZ0wEAb6GYLmV1//597dWrl7q5uWny5MnVw8NDx40bZ3VpKdWIS4Z16dJFv//+e/Xw8FB7e3v19PTU7du3x+pvR142KqZb5KWVIt2+fVvbtm2r7733nqZMmVIrVqyoBw4ciNXfiXrJsOeJ6ZJhqqoBAQHq4+Ojjo6OmjJlSq1cubLu3bvXapnRo0drqVKlNE2aNJoiRQrNly+ffvnll/rkyRNjmWfPnmm3bt00Q4YMarFYXnr5sJfV3LJlS02VKtVzH589e7Z6eXlpihQp1MnJSQsXLqz9+/fXK1euGMuEhYXpiBEj1NXVVVOkSKGVKlXSo0eParZs2aJdvu3QoUP6/vvvq52dnWbNmlUnTJgQ7ZJhkbZv364+Pj6aOnVqdXBw0Fy5cmmrVq2sLi33vPpjujzZ3r171cvLS+3s7GJ9+bBZs2apiGjmzJmjPRYQEGB8zq5duxbt8WnTpmm+fPk0efLkmilTJu3cubPeuXPHapkX/fvcunVLP/30U3V2dtbUqVPrp59+qoGBgVaf65s3b2qXLl00X758mipVKk2dOrW+//77+tNPP730vQEAzGNRjTLWCQAAG7NYLNKlS5dXPnqJN1v27NmlUqVKsmDBAluXAgBAguKcbgAAAAAATELoBgAAAADAJIRuAAAAAABMwjndAAAAAACYhCPdAAAAAACYhNANAAAAAIBJktm6gIQWHh4uV65cEScnJ7FYLLYuBwAAAACQCKmq3L9/X9zc3CRJkucfz37nQveVK1fE3d3d1mUAAAAAAN4CFy9elCxZsjz38XcudDs5OYlIRMc4OzvbuBoAAAAAQGJ07949cXd3NzLm87xzoTtySLmzszOhGwAAAADwWl522jITqQEAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgkjcmdI8ZM0YsFov07NnzhcstW7ZM8uXLJw4ODlK4cGHZsGFDwhQIAAAAAMAreiNC94EDB2TWrFlSpEiRFy63d+9eadKkibRt21YCAwOlfv36Ur9+fTl69GgCVQoAAAAAQOzZPHQ/ePBAmjVrJt9++62kTZv2hctOnjxZatasKf369ZP8+fPLqFGjpHjx4jJt2rQEqhYAAAAAgNizeeju0qWL+Pr6SrVq1V667L59+6It5+PjI/v27Xvuc0JDQ+XevXtWNwAAAAAAEkIyW/7xJUuWSEBAgBw4cCBWywcFBUmmTJms2jJlyiRBQUHPfY6fn5+MGDHiteq0lewD19u6hDfKuTG+ti4BAAAAAF6JzY50X7x4UXr06CE//PCDODg4mPZ3Bg0aJMHBwcbt4sWLpv0tAAAAAACistmR7kOHDsn169elePHiRltYWJjs3LlTpk2bJqGhoZI0aVKr57i4uMi1a9es2q5duyYuLi7P/Tv29vZib28fv8UDAAAAABALNjvSXbVqVTly5IgcPnzYuJUoUUKaNWsmhw8fjha4RUS8vb1l69atVm1btmwRb2/vhCobAAAAAIBYs9mRbicnJylUqJBVW6pUqeS9994z2lu0aCGZM2cWPz8/ERHp0aOHVKxYUcaPHy++vr6yZMkSOXjwoMyePTvB6wcAAAAA4GVsPnv5i1y4cEGuXr1q3C9TpowsXrxYZs+eLUWLFpXly5fLqlWrooV3AAAAAADeBBZVVVsXkZDu3bsnqVOnluDgYHF2drZ1OS/E7OXWmL0cAAAAwJsittnyjT7SDQAAAABAYkboBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATGLT0D1jxgwpUqSIODs7i7Ozs3h7e8vGjRufu/yCBQvEYrFY3RwcHBKwYgAAAAAAYi+ZLf94lixZZMyYMeLh4SGqKgsXLpR69epJYGCgFCxYMMbnODs7y/Hjx437FoslocoFAAAAAOCV2DR016lTx+r+l19+KTNmzJD9+/c/N3RbLBZxcXFJiPIAAAAAAHgtb8w53WFhYbJkyRIJCQkRb2/v5y734MEDyZYtm7i7u0u9evXk2LFjL3zd0NBQuXfvntUNAAAAAICEYPPQfeTIEXF0dBR7e3vp1KmTrFy5UgoUKBDjsnnz5pV58+bJ6tWr5fvvv5fw8HApU6aMXLp06bmv7+fnJ6lTpzZu7u7uZr0VAAAAAACsWFRVbVnAkydP5MKFCxIcHCzLly+XOXPmyG+//fbc4B3V06dPJX/+/NKkSRMZNWpUjMuEhoZKaGiocf/evXvi7u4uwcHB4uzsHG/vwwzZB663dQlvlHNjfG1dAgAAAACISES2TJ069UuzpU3P6RYRsbOzk9y5c4uIiJeXlxw4cEAmT54ss2bNeulzkydPLp6ennLq1KnnLmNvby/29vbxVi8AAAAAALFl8+Hl/xUeHm51ZPpFwsLC5MiRI+Lq6mpyVQAAAAAAvDqbHukeNGiQ1KpVS7JmzSr379+XxYsXy44dO2Tz5s0iItKiRQvJnDmz+Pn5iYjIyJEjpXTp0pI7d265e/eujBs3Ts6fPy/t2rWz5dsAAAAAACBGNg3d169flxYtWsjVq1clderUUqRIEdm8ebNUr15dREQuXLggSZL872D8nTt3pH379hIUFCRp06YVLy8v2bt3b6zO/wYAAAAAIKHZfCK1hBbbk93fBEykZo2J1AAAAAC8KWKbLd+4c7oBAAAAAHhbELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACT2DR0z5gxQ4oUKSLOzs7i7Ows3t7esnHjxhc+Z9myZZIvXz5xcHCQwoULy4YNGxKoWgAAAAAAXo1NQ3eWLFlkzJgxcujQITl48KBUqVJF6tWrJ8eOHYtx+b1790qTJk2kbdu2EhgYKPXr15f69evL0aNHE7hyAAAAAABezqKqausiokqXLp2MGzdO2rZtG+2xRo0aSUhIiKxbt85oK126tBQrVkxmzpwZq9e/d++epE6dWoKDg8XZ2Tne6jZD9oHrbV3CG+XcGF9blwAAAAAAIhL7bPnGnNMdFhYmS5YskZCQEPH29o5xmX379km1atWs2nx8fGTfvn0JUSIAAAAAAK8kma0LOHLkiHh7e8vjx4/F0dFRVq5cKQUKFIhx2aCgIMmUKZNVW6ZMmSQoKOi5rx8aGiqhoaHG/Xv37sVP4QAAAAAAvITNQ3fevHnl8OHDEhwcLMuXL5eWLVvKb7/99tzg/ar8/PxkxIgR8fJaSPwYsm8tPobs06fR0a/xjz6Nf/Rp/OM0KABATGw+vNzOzk5y584tXl5e4ufnJ0WLFpXJkyfHuKyLi4tcu3bNqu3atWvi4uLy3NcfNGiQBAcHG7eLFy/Ga/0AAAAAADyPzUP3f4WHh1sNB4/K29tbtm7datW2ZcuW554DLiJib29vXJIs8gYAAAAAQEKw6fDyQYMGSa1atSRr1qxy//59Wbx4sezYsUM2b94sIiItWrSQzJkzi5+fn4iI9OjRQypWrCjjx48XX19fWbJkiRw8eFBmz55ty7cBAAAAAECMbBq6r1+/Li1atJCrV69K6tSppUiRIrJ582apXr26iIhcuHBBkiT538H4MmXKyOLFi+Xzzz+XwYMHi4eHh6xatUoKFSpkq7cAAAAAAMBz2TR0z50794WP79ixI1pbw4YNpWHDhiZVBAAAAABA/HnjzukGAAAAAOBtQegGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMYtPQ7efnJyVLlhQnJyfJmDGj1K9fX44fP/7C5yxYsEAsFovVzcHBIYEqBgAAAAAg9mwaun/77Tfp0qWL7N+/X7Zs2SJPnz6VGjVqSEhIyAuf5+zsLFevXjVu58+fT6CKAQAAAACIvWS2/OObNm2yur9gwQLJmDGjHDp0SCpUqPDc51ksFnFxcTG7PAAAAAAAXssbdU53cHCwiIikS5fuhcs9ePBAsmXLJu7u7lKvXj05duxYQpQHAAAAAMAreWNCd3h4uPTs2VPKli0rhQoVeu5yefPmlXnz5snq1avl+++/l/DwcClTpoxcunQpxuVDQ0Pl3r17VjcAAAAAABKCTYeXR9WlSxc5evSo7N69+4XLeXt7i7e3t3G/TJkykj9/fpk1a5aMGjUq2vJ+fn4yYsSIeK8XAAAAAICXeSOOdHft2lXWrVsn27dvlyxZsrzSc5MnTy6enp5y6tSpGB8fNGiQBAcHG7eLFy/GR8kAAAAAALyUTY90q6p069ZNVq5cKTt27JAcOXK88muEhYXJkSNHpHbt2jE+bm9vL/b29q9bKgAAAAAAr8ymobtLly6yePFiWb16tTg5OUlQUJCIiKROnVpSpEghIiItWrSQzJkzi5+fn4iIjBw5UkqXLi25c+eWu3fvyrhx4+T8+fPSrl07m70PAAAAAABiYtPQPWPGDBERqVSpklX7/PnzpVWrViIicuHCBUmS5H+j4O/cuSPt27eXoKAgSZs2rXh5ecnevXulQIECCVU2AAAAAACxYvPh5S+zY8cOq/sTJ06UiRMnmlQRAAAAAADx542YSA0AAAAAgLcRoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAk8QpdJ85cya+6wAAAAAA4K0Tp9CdO3duqVy5snz//ffy+PHj+K4JAAAAAIC3QpxCd0BAgBQpUkR69+4tLi4u0rFjR/njjz/iuzYAAAAAABK1OIXuYsWKyeTJk+XKlSsyb948uXr1qpQrV04KFSokEyZMkBs3bsR3nQAAAAAAJDqvNZFasmTJ5KOPPpJly5bJ119/LadOnZK+ffuKu7u7tGjRQq5evRpfdQIAAAAAkOi8Vug+ePCgfPbZZ+Lq6ioTJkyQvn37yunTp2XLli1y5coVqVevXnzVCQAAAABAopMsLk+aMGGCzJ8/X44fPy61a9eWRYsWSe3atSVJkogMnyNHDlmwYIFkz549PmsFAAAAACBRiVPonjFjhrRp00ZatWolrq6uMS6TMWNGmTt37msVBwAAAABAYhan0H3y5MmXLmNnZyctW7aMy8sDAAAAAPBWiNM53fPnz5dly5ZFa1+2bJksXLjwtYsCAAAAAOBtEKfQ7efnJ+nTp4/WnjFjRvnqq69euygAAAAAAN4GcQrdFy5ckBw5ckRrz5Ytm1y4cOG1iwIAAAAA4G0Qp9CdMWNG+euvv6K1//nnn/Lee++9dlEAAAAAALwN4hS6mzRpIt27d5ft27dLWFiYhIWFybZt26RHjx7SuHHj+K4RAAAAAIBEKU6zl48aNUrOnTsnVatWlWTJIl4iPDxcWrRowTndAAAAAAD8vziFbjs7O1m6dKmMGjVK/vzzT0mRIoUULlxYsmXLFt/1AQAAAACQaMUpdEfKkyeP5MmTJ75qAQAAAADgrRKn0B0WFiYLFiyQrVu3yvXr1yU8PNzq8W3btsVLcQAAAAAAJGZxCt09evSQBQsWiK+vrxQqVEgsFkt81wUAAAAAQKIXp9C9ZMkS+emnn6R27drxXQ8AAAAAAG+NOF0yzM7OTnLnzh3ftQAAAAAA8FaJU+ju06ePTJ48WVQ1vusBAAAAAOCtEafh5bt375bt27fLxo0bpWDBgpI8eXKrx1esWBEvxQEAAAAAkJjFKXSnSZNGPvzww/iuBQAAAACAt0qcQvf8+fPjuw4AAAAAAN46cTqnW0Tk2bNn8uuvv8qsWbPk/v37IiJy5coVefDgQbwVBwAAAABAYhanI93nz5+XmjVryoULFyQ0NFSqV68uTk5O8vXXX0toaKjMnDkzvusEAAAAACDRidOR7h49ekiJEiXkzp07kiJFCqP9ww8/lK1bt8ZbcQAAAAAAJGZxOtK9a9cu2bt3r9jZ2Vm1Z8+eXS5fvhwvhQEAAAAAkNjF6Uh3eHi4hIWFRWu/dOmSODk5vXZRAAAAAAC8DeIUumvUqCGTJk0y7lssFnnw4IEMHz5cateuHV+1AQAAAACQqMVpePn48ePFx8dHChQoII8fP5amTZvKyZMnJX369PLjjz/Gd40AAAAAACRKcQrdWbJkkT///FOWLFkif/31lzx48EDatm0rzZo1s5pYDQAAAACAd1mcQreISLJkyaR58+bxWQsAAAAAAG+VOIXuRYsWvfDxFi1axKkYAAAAAADeJnEK3T169LC6//TpU3n48KHY2dlJypQpCd0AAAAAAEgcZy+/c+eO1e3Bgwdy/PhxKVeuHBOpAQAAAADw/+IUumPi4eEhY8aMiXYUHAAAAACAd1W8hW6RiMnVrly5Ep8vCQAAAABAohWnc7rXrFljdV9V5erVqzJt2jQpW7ZsrF/Hz89PVqxYIf/++6+kSJFCypQpI19//bXkzZv3hc9btmyZDB06VM6dOyceHh7y9ddfS+3atePyVgAAAAAAME2cQnf9+vWt7lssFsmQIYNUqVJFxo8fH+vX+e2336RLly5SsmRJefbsmQwePFhq1Kghf//9t6RKlSrG5+zdu1eaNGkifn5+8sEHH8jixYulfv36EhAQIIUKFYrL2wEAAAAAwBRxCt3h4eHx8sc3bdpkdX/BggWSMWNGOXTokFSoUCHG50yePFlq1qwp/fr1ExGRUaNGyZYtW2TatGkyc+bMeKkLAAAAAID4EK/ndL+u4OBgERFJly7dc5fZt2+fVKtWzarNx8dH9u3bZ2ptAAAAAAC8qjgd6e7du3esl50wYUKslgsPD5eePXtK2bJlXzhMPCgoSDJlymTVlilTJgkKCopx+dDQUAkNDTXu37t3L1b1AAAAAADwuuIUugMDAyUwMFCePn1qTHp24sQJSZo0qRQvXtxYzmKxxPo1u3TpIkePHpXdu3fHpaTn8vPzkxEjRsTrawIAAJgh+8D1ti7hjXNujO9rvwb9ao0+jX/x0ad4e8UpdNepU0ecnJxk4cKFkjZtWhERuXPnjrRu3VrKly8vffr0eaXX69q1q6xbt0527twpWbJkeeGyLi4ucu3aNau2a9euiYuLS4zLDxo0yOrI/L1798Td3f2V6gMAAAAAIC7idE73+PHjxc/PzwjcIiJp06aV0aNHv9Ls5aoqXbt2lZUrV8q2bdskR44cL32Ot7e3bN261apty5Yt4u3tHePy9vb24uzsbHUDAAAAACAhxOlI97179+TGjRvR2m/cuCH379+P9et06dJFFi9eLKtXrxYnJyfjvOzUqVNLihQpRESkRYsWkjlzZvHz8xMRkR49ekjFihVl/Pjx4uvrK0uWLJGDBw/K7Nmz4/JWAAAAAAAwTZyOdH/44YfSunVrWbFihVy6dEkuXbokP//8s7Rt21Y++uijWL/OjBkzJDg4WCpVqiSurq7GbenSpcYyFy5ckKtXrxr3y5QpI4sXL5bZs2dL0aJFZfny5bJq1Squ0Q0AAAAAeOPE6Uj3zJkzpW/fvtK0aVN5+vRpxAslSyZt27aVcePGxfp1VPWly+zYsSNaW8OGDaVhw4ax/jsAAAAAANhCnEJ3ypQpxd/fX8aNGyenT58WEZFcuXJJqlSp4rU4AAAAAAASszgNL4909epVuXr1qnh4eEiqVKlideQaAAAAAIB3RZxC961bt6Rq1aqSJ08eqV27tnHOddu2bV/5cmEAAAAAALyt4hS6e/XqJcmTJ5cLFy5IypQpjfZGjRrJpk2b4q04AAAAAAASszid0/3LL7/I5s2bJUuWLFbtHh4ecv78+XgpDAAAAACAxC5OR7pDQkKsjnBHun37ttjb2792UQAAAAAAvA3iFLrLly8vixYtMu5bLBYJDw+XsWPHSuXKleOtOAAAAAAAErM4DS8fO3asVK1aVQ4ePChPnjyR/v37y7Fjx+T27duyZ8+e+K4RAAAAAIBEKU5HugsVKiQnTpyQcuXKSb169SQkJEQ++ugjCQwMlFy5csV3jQAAAAAAJEqvfKT76dOnUrNmTZk5c6YMGTLEjJoAAAAAAHgrvPKR7uTJk8tff/1lRi0AAAAAALxV4jS8vHnz5jJ37tz4rgUAAAAAgLdKnCZSe/bsmcybN09+/fVX8fLyklSpUlk9PmHChHgpDgAAAACAxOyVQveZM2cke/bscvToUSlevLiIiJw4ccJqGYvFEn/VAQAAAACQiL1S6Pbw8JCrV6/K9u3bRUSkUaNGMmXKFMmUKZMpxQEAAAAAkJi90jndqmp1f+PGjRISEhKvBQEAAAAA8LaI00Rqkf4bwgEAAAAAwP+8Uui2WCzRztnmHG4AAAAAAGL2Sud0q6q0atVK7O3tRUTk8ePH0qlTp2izl69YsSL+KgQAAAAAIJF6pdDdsmVLq/vNmzeP12IAAAAAAHibvFLonj9/vll1AAAAAADw1nmtidQAAAAAAMDzEboBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAk9g0dO/cuVPq1Kkjbm5uYrFYZNWqVS9cfseOHWKxWKLdgoKCEqZgAAAAAABegU1Dd0hIiBQtWlSmT5/+Ss87fvy4XL161bhlzJjRpAoBAAAAAIi7ZLb847Vq1ZJatWq98vMyZswoadKkif+CAAAAAACIR4nynO5ixYqJq6urVK9eXfbs2fPCZUNDQ+XevXtWNwAAAAAAEkKiCt2urq4yc+ZM+fnnn+Xnn38Wd3d3qVSpkgQEBDz3OX5+fpI6dWrj5u7unoAVAwAAAADeZTYdXv6q8ubNK3nz5jXulylTRk6fPi0TJ06U7777LsbnDBo0SHr37m3cv3fvHsEbAAAAAJAgElXojkmpUqVk9+7dz33c3t5e7O3tE7AiAAAAAAAiJKrh5TE5fPiwuLq62roMAAAAAACisemR7gcPHsipU6eM+2fPnpXDhw9LunTpJGvWrDJo0CC5fPmyLFq0SEREJk2aJDly5JCCBQvK48ePZc6cObJt2zb55ZdfbPUWAAAAAAB4LpuG7oMHD0rlypWN+5HnXrds2VIWLFggV69elQsXLhiPP3nyRPr06SOXL1+WlClTSpEiReTXX3+1eg0AAAAAAN4UNg3dlSpVElV97uMLFiywut+/f3/p37+/yVUBAAAAABA/Ev053QAAAAAAvKkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQAAAACASQjdAAAAAACYhNANAAAAAIBJCN0AAAAAAJiE0A0AAAAAgElsGrp37twpderUETc3N7FYLLJq1aqXPmfHjh1SvHhxsbe3l9y5c8uCBQtMrxMAAAAAgLiwaegOCQmRokWLyvTp02O1/NmzZ8XX11cqV64shw8flp49e0q7du1k8+bNJlcKAAAAAMCrS2bLP16rVi2pVatWrJefOXOm5MiRQ8aPHy8iIvnz55fdu3fLxIkTxcfHx6wyAQAAAACIk0R1Tve+ffukWrVqVm0+Pj6yb9++5z4nNDRU7t27Z3UDAAAAACAh2PRI96sKCgqSTJkyWbVlypRJ7t27J48ePZIUKVJEe46fn5+MGDEioUoEAAAAgFeWfeB6W5fwRjk3xtfWJcSbRHWkOy4GDRokwcHBxu3ixYu2LgkAAAAA8I5IVEe6XVxc5Nq1a1Zt165dE2dn5xiPcouI2Nvbi729fUKUBwAAAACAlUR1pNvb21u2bt1q1bZlyxbx9va2UUUAAAAAADyfTUP3gwcP5PDhw3L48GERibgk2OHDh+XChQsiEjE0vEWLFsbynTp1kjNnzkj//v3l33//FX9/f/npp5+kV69etigfAAAAAIAXsmnoPnjwoHh6eoqnp6eIiPTu3Vs8PT1l2LBhIiJy9epVI4CLiOTIkUPWr18vW7ZskaJFi8r48eNlzpw5XC4MAAAAAPBGsuk53ZUqVRJVfe7jCxYsiPE5gYGBJlYFAAAAAED8SFTndAMAAAAAkJgQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJO8EaF7+vTpkj17dnFwcJD3339f/vjjj+cuu2DBArFYLFY3BweHBKwWAAAAAIDYsXnoXrp0qfTu3VuGDx8uAQEBUrRoUfHx8ZHr168/9znOzs5y9epV43b+/PkErBgAAAAAgNixeeieMGGCtG/fXlq3bi0FChSQmTNnSsqUKWXevHnPfY7FYhEXFxfjlilTpgSsGAAAAACA2LFp6H7y5IkcOnRIqlWrZrQlSZJEqlWrJvv27Xvu8x48eCDZsmUTd3d3qVevnhw7diwhygUAAAAA4JXYNHTfvHlTwsLCoh2pzpQpkwQFBcX4nLx588q8efNk9erV8v3330t4eLiUKVNGLl26FOPyoaGhcu/ePasbAAAAAAAJwebDy1+Vt7e3tGjRQooVKyYVK1aUFStWSIYMGWTWrFkxLu/n5yepU6c2bu7u7glcMQAAAADgXWXT0J0+fXpJmjSpXLt2zar92rVr4uLiEqvXSJ48uXh6esqpU6difHzQoEESHBxs3C5evPjadQMAAAAAEBs2Dd12dnbi5eUlW7duNdrCw8Nl69at4u3tHavXCAsLkyNHjoirq2uMj9vb24uzs7PVDQAAAACAhJDM1gX07t1bWrZsKSVKlJBSpUrJpEmTJCQkRFq3bi0iIi1atJDMmTOLn5+fiIiMHDlSSpcuLblz55a7d+/KuHHj5Pz589KuXTtbvg0AAAAAAKKxeehu1KiR3LhxQ4YNGyZBQUFSrFgx2bRpkzG52oULFyRJkv8dkL9z5460b99egoKCJG3atOLl5SV79+6VAgUK2OotAAAAAAAQI5uHbhGRrl27SteuXWN8bMeOHVb3J06cKBMnTkyAqgAAAAAAeD2JbvZyAAAAAAASC0I3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgkjcidE+fPl2yZ88uDg4O8v7778sff/zxwuWXLVsm+fLlEwcHBylcuLBs2LAhgSoFAAAAACD2bB66ly5dKr1795bhw4dLQECAFC1aVHx8fOT69esxLr93715p0qSJtG3bVgIDA6V+/fpSv359OXr0aAJXDgAAAADAi9k8dE+YMEHat28vrVu3lgIFCsjMmTMlZcqUMm/evBiXnzx5stSsWVP69esn+fPnl1GjRknx4sVl2rRpCVw5AAAAAAAvZtPQ/eTJEzl06JBUq1bNaEuSJIlUq1ZN9u3bF+Nz9u3bZ7W8iIiPj89zlwcAAAAAwFaS2fKP37x5U8LCwiRTpkxW7ZkyZZJ///03xucEBQXFuHxQUFCMy4eGhkpoaKhxPzg4WERE7t279zqlJ4jw0Ie2LuGNEh//ZvSpNfrUHPRr/KNP4x99Gv/oU3PQr/GPPo1/9Gn8Swx5LbJGVX3hcjYN3QnBz89PRowYEa3d3d3dBtXgdaSeZOsK3j70qTno1/hHn8Y/+jT+0afmoF/jH30a/+jT+JeY+vT+/fuSOnXq5z5u09CdPn16SZo0qVy7ds2q/dq1a+Li4hLjc1xcXF5p+UGDBknv3r2N++Hh4XL79m157733xGKxvOY7ePvdu3dP3N3d5eLFi+Ls7Gzrct4K9Kk56Nf4R5/GP/o0/tGn8Y8+NQf9Gv/o0/hHn74aVZX79++Lm5vbC5ezaei2s7MTLy8v2bp1q9SvX19EIkLx1q1bpWvXrjE+x9vbW7Zu3So9e/Y02rZs2SLe3t4xLm9vby/29vZWbWnSpImP8t8pzs7OfPHiGX1qDvo1/tGn8Y8+jX/0afyjT81Bv8Y/+jT+0aex96Ij3JFsPry8d+/e0rJlSylRooSUKlVKJk2aJCEhIdK6dWsREWnRooVkzpxZ/Pz8RESkR48eUrFiRRk/frz4+vrKkiVL5ODBgzJ79mxbvg0AAAAAAKKxeehu1KiR3LhxQ4YNGyZBQUFSrFgx2bRpkzFZ2oULFyRJkv9Nsl6mTBlZvHixfP755zJ48GDx8PCQVatWSaFChWz1FgAAAAAAiJHNQ7eISNeuXZ87nHzHjh3R2ho2bCgNGzY0uSqIRAzPHz58eLQh+og7+tQc9Gv8o0/jH30a/+jT+EefmoN+jX/0afyjT81h0ZfNbw4AAAAAAOIkycsXAQAAAAAAcUHoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAC8tvDwcFuXAABvJEI38BZgQwcAYCu///67hISESJIkSYSL4gBAdIRumIqVr/nCw8MlSZKIr/LZs2dtXA0AM/z3t5TfVrwpNm7cKJ9++qlMnz5dHj16JBaLhc+nyehfJCZRP68PHjywYSW2ReiGacLDw8VisYiIyJkzZ+TSpUty+fJlEWGFEV+iBu4RI0bIJ598Ir///ruNq7KNmI72MwLgzcL3Pu4if0tPnz4tqkqweQ2R/bZ//3759ddf5f79+zauKHGrWrWqVKxYUVatWiXTp0+Xhw8f8vk0UdRtq+DgYBtXk7jxGTVf1M+rv7+/fPXVV0YWeNcQumEKVY0WBqtXry4VKlSQxYsXG19AvJ7IPh44cKDMnDlTBg4cKK6urjauKuFF3fmwb98+2bx5s5w/f14ePXpk48reTZEbMqdPn5bdu3fLoUOH5MaNG2KxWCQsLMzG1SUuUXccLVq0SBo3biwbNmwgeMdRZL+tWLFCfH19Zc+ePXL37l1bl5VohYWFiZ2dnfj7+0uRIkXkp59+En9/f4K3SaKu67788kvp0aMHI9xiKfKzePz4cTl48KCcPn2abVGTRf28njp1StauXSsLFiyQBQsWyLVr12xcnQ0oYKIRI0boe++9p5s3b9Zz585p/fr11d7eXk+dOmXr0hK18PBw4//37t2rOXLk0J07d6qq6uPHj/Xq1au6YcMGvXjxoq1KtIn+/ftrmjRp1M3NTR0dHbVt27a6b98+W5f1Ton8bP7888+aO3duzZcvn5YuXVpLlCihR44csXF1iUtYWJjx/+vWrdMRI0Zo0qRJtUyZMrp582ajr6P+HiBmz549M/7/119/VUdHR503b54+fPjQaKcf4yayb588eaIdOnTQkiVL6rhx4zQkJERV6Vcz9O/fX11dXXXOnDl6+fJlW5eTaPz888+aJk0azZkzpzo4OOisWbM0NDTU1mW99Xr27KleXl7apEkTff/99zVZsmQ6YsQIvXr1qq1LS1CEbpjm/v376uPjoz///LOqqq5atUrTpk2rM2bMUNX/rahZIb+aqBviqqr79+/XzJkz65MnT/TgwYPav39/zZMnj9rZ2Wn16tX15MmTNqrUfFE3pLdv3645c+bUbdu26a1bt/S7777TypUr64cffqiHDh2yYZXvnl27dqmTk5P6+/urqurSpUvVYrHomDFjbFxZ4jRw4EDNmDGjTpgwQUeMGKFZs2bVkiVL6qZNmwjeLzFlyhT966+/VPV/vxe9e/fW5s2bq6pqSEiI/v7779q5c2cdMmSI/vrrrzar9W0QGhqq7du3J3ibaM2aNeri4qIHDx402u7du6enTp3S27dv27CyN1PkZ+/SpUuaP39+nT17th46dEjHjBmjFotF/fz8rHa+IX6tXr1a06RJo4GBgfr06VNVVR06dKimS5dOR4wYoUFBQTauMOEQuhFv/rtSvXLliqZJk0aPHTtmHFmIDNyPHj3S4cOHv9WB0AxR+7hVq1Zaq1YtvX37trq4uGiBAgU0bdq02qFDB126dKkeP35c7e3tdcWKFTas2Bx37tyxuj9lyhQdMWKE9u3b16p9zZo1WqxYMR05cqSqsuFntsj+/frrr7V169aqqnrx4kV1d3fXLl26GMvdvXvXJvUlRv/8849mzpxZ165da7QFBQVpkSJFtFixYhzxfoFr165p+fLl1cXFRf/55x9VjQiFrVq10ipVqujWrVu1efPmWqNGDS1WrJhWq1ZNfXx89MaNG/TlS0T2z4kTJ3THjh169OhRvX79uqpGD96RgYY+jZv/9tu8efO0YsWKqqp6+PBhHTVqlObKlUuzZcumHTp0eKdCTGz98ssvOmnSJP3ss8+sdtb7+/sTvE22ZMkSzZMnjwYFBVn1/cCBAzV58uQ6evRovXLlig0rTDiEbpiqZcuW2qRJE02VKpXOmTPHaD9//rzWqFFDf/rpJxtWl7hEXfH++++/Wq5cOeOozJkzZ3T06NG6fv16vXfvnqpGDPUrU6aMrlmzxib1muXjjz/Wxo0bWw1LqlmzplosFq1Ro4Y+fvzYavnPP/9cXV1djSMuMN+gQYP0s88+0/Pnz2uWLFm0Q4cOxud3/fr1OnHixGj/TojZqVOn1N3d3fiuRw6FvHr1qqZNm1arVq2qmzZtsmWJb7TAwECtV6+eZsmSRY8dO6aqEaODsmXLpq6urtqkSRNdtWqVqqrOnz9fS5UqxW/FS0R+l1esWKFZs2ZVDw8PzZYtm7Zt21Z///13Vf1f8C5TpoyOHDmSQBNHUUe2Re7U+PXXX9VisWiTJk3Uzc1NmzdvrrNnz9YpU6aoi4sLp/HEYODAgWqxWLRgwYLRRgP4+/urnZ2dDh06VB89emSjCt8OMe1YW7x4saZJk8Y4DSLy9/XixYuaJk0azZ49u06aNEmfPn361u+YI3TjtUVdKXzzzTfasWNH4/6XX36pqVOn1iZNmhjDSu7evau1a9fWypUrW+31QuzMnTtXfXx8tEmTJvrkyZNow80fP36s169fV19fXy1RosRb18erVq3SZMmSaefOnY3gHRoaqu3atdMUKVLohg0brH64f/zxRy1evHi0o+Mwz9SpU7Vo0aJG4I709OlT7dChg3br1o2NmxjEtMFx8+ZNdXNz00GDBhltT58+1WfPnmnZsmXV1dVVK1eurOfOnXvua7yLovZDYGCgfvDBB5o5c2ZjqPnly5f177//tlp2wIABWqVKFQ0ODk74ghOZX375RdOkSaNTp05VVdXp06dr2rRptWbNmrp7925Vjfhdbty4sVatWlVv3bply3ITpajr9pEjR+rHH39snCr1008/aevWrXXRokVGmLlx44Z6enrq/v37bVLvmy5yOPncuXOjPTZ+/HhNly6d3rx50waVvR2ifl4jt/cjlSpVSosVK2a1PXry5En97LPPtEePHurk5PROzPVE6MZrifol27t3r/bq1UstFosOGzbMaO/UqZPmzZtXy5cvr02bNlVvb28tWrSoPnnyRFX1rQuFZrp375727t1bs2TJoqVLlzbaI3/gnj59qgsWLFBvb2/19vZ+6/o48vO2adMmTZIkiXbs2NEYlvTs2TNt0KCBpkuXTpcsWaInT57U69eva9WqVbVq1aqEERNE9unx48f1n3/+MYbwqqqWL19e7e3t9dChQ/r48WN98OCBDho0yGqoL/4n6m/puXPn9P79+/rgwQNVjTgCmyxZMp0yZYqxzLNnz7RNmza6Y8cOTZ8+vQ4YMCDBa37TRf3OBwQEqK+vr2bOnDnakcBdu3bpgAED1NnZWQ8fPpzQZSY6ISEh2rRpUx08eLCqRpwrmyNHDq1WrZqWKlVKq1evbnXE+10ZOmqWQYMGaaZMmfT777/XS5cuGe2R6/Vnz55pSEiI1qxZU8uXLx9tR/y7JvJ7HxYWFm3bJ3JI83fffRfteeyYjx+TJ0/WDz/8UHv16mWcFnX48GEtUKCA5suXT9etW6fr169XHx8fbdCggaqqZsyYUb/55htblp0gCN2IF/3799cCBQpo586dtUSJEmqxWLR79+7G44sWLdLevXtr+/bt9ZtvvrEKiXi+mFae586d02HDhmmKFCn0888/t1o2NDRUDxw4oNOmTTNWNm9LH0ddkapGDFNOkiSJdujQwSp4f/LJJ2qxWNTFxUVbtWql5cqVM3Y+vOsbI2ZYvny5uri4aNasWTVPnjzGka8bN25owYIFNWfOnJonTx6tUaOGurq6akBAgI0rfrMNHTrU2Djp27evnj59WlVVR48erRaLRRs1aqS9e/fWChUqaMGCBVVVtVmzZvrhhx/asuw3SuRvxcOHD62OsB4/flxr1aqlWbJkMY5ynzt3TuvVq6deXl76559/2qTexCCyTyPP3d6zZ48GBgbq7du3tXDhwtquXTtVjdjgTpEihXp7extHvBF3Bw8e1Jw5cz53gr/Q0FCdMGGCVqhQQb28vN75dV3k53TLli3aokULrVmzpg4fPtw47U41YkRL8uTJ9YcffrBVmW+VqDs3v/rqK02TJo126NBBixUrpqVLlzYmVD1z5oz6+vqqm5ubZs+eXcuXL6+PHz/Wx48fa8GCBXXZsmW2egsJhtCN17Zp0yZ1cnLSXbt2qarqrVu3dNasWWpnZ6c9evR47vPelqOvZom60vzrr790z549euHCBVWNOOL9+eefa548eXT06NHPfY23pY+j9sXJkyeNYeW//fabJk2aVNu3b28E78ghzBaLxepc17dl58ObIHIle+PGDc2VK5fOnTtXN2zYoMOGDdOkSZPq2LFjjWXnzp2rfn5+unjxYj179qyNKn4zhYeHW22w/PTTT5oxY0b96aeftEePHlqpUiWtVauWMexuw4YNWq1aNa1Zs6Y2a9bM2MCuWbOm9uzZ0ybv4U0T2Z9r1qxRHx8fzZEjhzZu3NiYU+TYsWP6wQcfWAXv06dPv3OXromLVatWaYYMGXTv3r3G6SELFy7U8uXL67Vr11Q14jxvLy8vbdmypbG+Qux06NDB6MdImzdv1hw5cuiNGzeMtsjP+LNnz/Tu3bu6Zs0a7d+/Pwcz/t/KlSs1derU2rp1ax03bpymSpVKW7dubTXCavDgwWqxWJhX6DVF3cb8/ffftVevXrp9+3ZVjfhd7dq1qxYtWtTYGa8aMSfR5cuXjc/xkCFDNGfOnMYpUm8zQjde28KFCzVPnjzGBqBqxBGGcePGqcVi0REjRhjt7+re11cVdUN88ODBmjt3bvXw8FB3d3ft1q2bXrx4Ua9du6ZDhw7V/PnzvzB4v00GDRqkBQsW1HTp0mnv3r312rVrum/fvhiD94cffqjp06fXPXv22Ljqt9Ovv/6qI0eO1J49exrf6+DgYP3666+5PFgcbNy4Ufv166fz58832n766SetWrWq+vj4GBuMUSege/Tokfbv318zZcqk//77b0KX/MZat26dpkyZUkePHq27du3S+vXra6ZMmYwdw5GTq6VIkYJTHV4icl10//59/eyzz3TChAlWj/v7+2uOHDmMfhw4cKAOGTKEobqvKCgoSOvUqWO1HaUacSlMe3t7PXDggKpG/HtE/t5u3rzZGMYf6W3Z0R5XR44c0dy5cxtXygkJCdEMGTJokiRJtGbNmla/k1988YWx4w2vZujQoVaf1VWrVmnhwoU1f/78eubMGaP99OnT2q1bN/X09Iz22/Hnn39q+/bt9b333ntnRsARuvHa9uzZo46Ojrpt2zar9oCAAHVyclKLxaIDBw60UXWJ2/jx4zVTpkzGnsO2bdtq2rRpjSB5+fJlHT58uKZNm9ZqY/1tEXUnzU8//aSZM2fWlStX6ogRI7RUqVJat25dPX/+vO7bt0+TJUumHTt21IsXL6pqRPBu3LixWiwW3bdvn63ewlth2LBhWr9+feP+o0ePtHfv3mqxWLRMmTJWy0YGbzs7Ox01alRCl5ooNG7cWNevX2/c//3337VYsWKaLl06XbhwodWyP/30k1avXl1r1aplNfz5n3/+0UGDBmm2bNnemQ2WmES9HFVYWJjeu3dPP/jgA/3qq69UNSIsurm5WZ3upBoRvBs1aqQnTpxI8JoTm3379mn27Nn1/fff161bt6rq/8L4pk2btHTp0lqqVCn18fHRVKlS6dGjR21ZbqI3b948PX/+vKpGnC9fsWJFbdKkidX3/8mTJ1qpUiXjvHpE+O233/SLL75Q1Yi+y549u/bs2VMPHz6sqVKl0k8//ZTZ3V/TmjVrrCZHVlXdvXu3fvTRR+ro6KizZ8+2Wv7MmTPas2dPzZw5sy5dutRoP3nypH777bd6/PjxBKvd1gjdiLXnHaW+cOGCfvDBB9qgQQOrcHPu3Dlt06aNzpw5UzNkyKC//PJLQpWa6IWHhxtHayP3Dq5evVqdnZ2NPbiRR7wuXbqk33777Vu9h/u3337T7t27W806unbtWq1YsaLWqVNHL1y4oPv37492hPXJkyfasmXLd+pH3Qz79u2LtqFy4sQJHTRokFoslmiT0gQHB+sXX3yhadOm1Vu3bjGJXRRnzpzRUaNGRTuiNWXKFM2bN69WqlTJarIk1Yjz5j09PbVXr15G2+PHj/XgwYPRln2XzJ07V3v37m019DYsLEwrVqyo+/fv1/Pnz6ubm5vVDPpr1641QiGXrYudu3fvasWKFdVisejixYujPf7jjz9q3759tU2bNsZl2RB7UbetgoODNUOGDFqyZElj5Nb8+fO1TJkyWrVqVZ03b54uXLhQq1WrpkWKFHnnh5L/V3BwsB45ckTDwsK0QYMG2rJlS3306JGGh4dr6dKl1WKxaMOGDaP9/iL2QkNDjc/s8uXLjVNNDh8+rA0bNtTSpUvrjz/+aPWcEydO6MSJE6Ntp75ro18J3YiVqF+M1atX6/z5843JESLbKlasqFWrVtVZs2bptm3btEaNGlq3bl09ffq0uru766xZs2xReqIUGhqq4eHhWqZMGT106JDu2rVLHR0ddebMmcbjkydPNoZLRnobg/fVq1c1V65c6uzsrBMnTrR6bO3atVqpUiWtV6+enjlzRo8ePcpGiIm2bt2qVatWNe6fO3dOe/bsqY6OjtEmpbl37x6XX3kJf39/4zutGnHZJW9vb23ZsqVxGaBI27ZtM36H2YkRoXv37lqkSBEdPny4Ebzv3bunpUqV0n79+mnu3Lm1ffv2xu9iUFCQfvLJJzHOXIzojh8/bhxxvXPnjlaqVEmzZ89u7LT47+fwXduAjg9Rh+IuW7ZM79y5o+fOndP8+fNrqVKlNCgoSFUjhu+2bt1aHR0dtXTp0vrhhx++dVcneVWRn7+bN2/qs2fPrC5DGRISot7e3lZHXXv27Klbt25lJ/xriPodDwwM1Jw5c+pHH31k7MD8448/tFGjRlquXLlowTvSu/p5VSV0IxairlgHDBigWbNm1VKlSqmHh4eWLFnSmBxp8+bN2qFDB02RIoXmz5/f6pJVJUqU0EWLFtmi/EQh6nlZEyZMMCYAa9GihWbLlk1TpkxpNez0+vXrWrFiRasdH2+zP//8U/PkyaPVq1c3rrMbaf369VqwYEHt37+/0UbwNseuXbvUyclJa9eubbSdPXtWe/XqpU5OTs9dySK6GzduaLNmzTRXrlxWv42TJk3SsmXLasuWLWO81BLBxtrQoUO1VKlS+vnnnxuTUC1dulRTpEgR7dSHIUOGaN68eZnQ7yXCw8P1ypUrmjJlSu3atatxys7du3e1TJky6uHhwbmw8WDv3r1aokQJXblypfbu3VuTJ09u7OQ4f/685smTxyp4q6peu3ZN79+/b2yXvevrulWrVqmnp6eWKlVKe/fubXy3b9y4oZkzZ9a2bdvq3r17deDAgZo5c2auF/8a/rvuefTokc6ePVvff/99bdiwoVXwbty4sVasWFHnzZtni1LfWIRuxNqECRPU1dVVDx48qKqq3333nVosFvX09NSTJ08ay125csVqY7Fv376aLVs2Y2UCa8ePH9d8+fJpu3bttGfPnpo0aVLjSMKhQ4e0bNmyWqhQIVWN2Bi6ffu21qpVS8uUKfNO7TE8fPiwenp6avv27aOdM7hnz553qi8SQkxHU589e6Z79uxRNzc3rVGjhtF+9uxZ7du3r1oslnfish9xEVNYDgwM1M8++0zz5ctntVNt8uTJWqFCBa1bty6jBZ4jcofutWvXtHnz5lqwYEEdNmyYcTrDsGHD1GKxaOfOnbVfv37apk0bdXZ21sDAQNsWnojMmTNH06VLp3379jVmIr979656e3tr/vz5OTf2Nf3111/avHlzzZw5s6ZJk8bYjooM0pHBu3Tp0jGeRvKuj3g5cuSIpk+fXseMGaOdO3fWqlWraoUKFYxJ/TZv3qwODg6aM2dOzZIlyzs998Xrirr+mj17tq5YsUJVI07R+fbbb7VEiRLRgneNGjW0c+fONqn3TUXoRqxcvXpVO3furEuWLFHViL2Lzs7OOm7cOC1WrJh6eXlFG7Kzc+dObdOmjWbMmJEfuxd48OCBzp07V9977z11dHQ0JksJDw/XJ0+e6KJFi7Rw4cLq6uqqZcuW1VKlSmnx4sXfyaFlAQEBWrx4cW3fvn2M5w6+S31hlsgNvsgNun///Vd37txptdNs9+7d0YL3qVOndMiQIcyiHYOoGyxnz5616svDhw9rx44dNW/evFbB+8svv9ROnTpxZPsFfvzxR/X29tbatWuru7u7Ojo66rBhw/Tu3bsaFhamP/74o1aoUEFr1KihHTt25HzjF4h6GaqoFi1apE5OTtqnTx/jiHdwcLDmz59fvby8NDQ0NMFrTeyihuXRo0drsmTJtFixYrp8+XKjPfLf4fz585o/f37NnTu3Xr9+PcFrfdNE7bs//vhDe/fubdxfu3at1qhRQ8uUKWME7wsXLuhff/1lNVoAryZqn/fv319dXFx08uTJxik9jx490jlz5mjx4sX1k08+MYL333//zfrrPwjdiFFMe1DXrFmjV69e1YCAAM2ZM6dOmzZNVSP2hlssFs2aNavVdTlv3bql33zzDbPDPkfUH6M1a9Zo+vTp1cPDQzt27GjV/0+fPtXz58/rmDFj9KuvvtL58+cbK+R3cWhZQECAlixZUhs0aGB1Phxe35gxY3T69On64MEDVVX9+eefNXXq1JojRw5Nnjy5Tp06VW/fvq2q/wveUYeav4ufx1cxePBgzZo1q+bKlUtr1KhhbJwcOXJEO3bsqPnz57caah75O8CGS3R//fWXpkuXTufNm2eMBvjss8+0YMGCOnz4cGMYaeTs5nw2X27Tpk06duxYDQ4OtmpftGiRJk+eXHv37m3sMAoODmaYfhxE/S6Hhobq/v37dePGjdqyZUstW7ZsjPMNnD59Wps0afLO71SO/D3csWOHTpgwQXv16qVt27a1WmbdunVao0YNrVChAiMx4tnEiRM1Q4YMevjwYaMt8nf12bNnunDhQi1ZsqRWrVrVaqI61l//Q+jGCy1btizarOMzZ87UatWqGXu5lixZop07d9ZOnToZK4XIH8d3ffhTbEQOxT9x4oTOnj1bixYtqq1bt37p897lFfDvv/+urVu35sc8nnXs2FEtFovOnTtX//33X/X09NQZM2YYM247OjrqqFGjjJCzd+9etbe3148++sjGlb+Zon4+ly1bpq6urrp06VL99ttvtWDBglqoUCHj+3/kyBHt3Lmzpk2bVjds2GA8j9/QmG3ZskUzZ86sp0+ftmrv1KmTOjo66ogRI/Tq1atGO/34P1E/l1E3jqdOnaoWi0UnTpyo9+7ds3rOyJEj1cnJSXv06GEc8caridrvX331lXbs2NHYOXT48GFt2rSplilTxmpSSn9/f6trnr/L633ViFGWKVKk0IIFC6q7u7umSZMm2m/Ahg0btHTp0urj46NPnjzhux8Pnj17pm3bttWhQ4eqasSOoCVLlmiZMmW0TZs2+ssvv2h4eLhOmTJF27Zty7bZcxC68VynTp1ST09P/eCDD3Tnzp1Ge9++fdXd3V1DQkL0zp07WrduXR05cqTx+Lu+UngVq1evVovFor/99puqqt6+fVunTJmiRYsW1Xbt2hnLRc66qcrGYySOApqjf//+miJFCp04caJ27NjRaqP8m2++UScnJ6vgvX//fkazvMSPP/6oixYt0jlz5hhtZ8+e1eLFi2vBggWNcBgQEKBjx47lN/QFIr/327ZtU3d3d+N0nMhRA0+ePFE3Nzd1d3fX0aNH8/vwHGfPnjUmnluxYoWxDv/mm2/UYrHouHHjrI54z5o1S/Ply6fZsmUznofY+e86u1+/fpo5c2adOnWq1WiBwMBAbdasmZYoUUIHDx6svr6+miVLFj7D/+/+/fs6dOhQnTt3roaFhelvv/2mVapU0ezZs+upU6eslv3ll1+YR+g1/PczFx4erj4+Purp6alz5szRypUrq4+Pj7Zo0UK9vb21fv36qhrxO8y22fMRumGIKcytW7dOq1WrpvXq1dMdO3aoqurly5fV3d1d06dPr7lz59ZChQpxzcM4On/+vDZt2lRTpkxpBO87d+7o1KlTtUiRIlq6dGmtWbOmurm5MTwyBuyAiD9RP189evRQi8WiefLkibaBPX78eE2XLp0OGjSImWCfI+rGxoULFzRr1qxqsVh07NixVsudPXtWvby8tEiRItEmSiJ4/09M3/OwsDAtUKCAVqlSxeqze+nSJa1bt65269ZNz507l5BlJhoPHz7U2rVra5YsWfTbb79Vi8ViNax53LhxRvCOPGVs8ODBunz58mhDz/Fi/x0x8PPPP2umTJn0wIEDRltISIgx4uXs2bPap08fLV26tNatW9fYtnrXA8zBgwc1TZo0Wrp0aWNbNLLdx8dHs2fPzulm8STqZ+3bb7/V9evXq2rEqMzSpUurh4eHjh49Wv/44w9jmYoVKxqnpamybfY8hG5EEzlsPNL69eu1cuXKWq9ePeO60NeuXdNx48bp7NmzjQ0eQuGL/fdHKPL+pUuXtEWLFmpnZ2cE7+DgYF21apW2adNG27dvb3XeDBDfon4WIw0fPlwtFov6+/trSEiI1fKjRo1Sd3f3aL8VsDZo0CDt27ev7t69Wz09PbV06dLGxFORfX7u3DnNkiWLNm/e3JalvrEi++n333/XSZMmqb+/v27cuFFVVY8eParu7u5aqVIl3bNnj/7111/6+eefa6VKlfTu3bu2LPuNFh4erseOHdM8efJo8uTJdcqUKaqqVtc5jty5VqJECa1YsaI6OjpGu2oEXqxDhw46atQoVf3f53jixIlaq1YtVY0YUj527FjNkyePurq6GkN3Hz9+rCEhIVwWLIqLFy9qvXr11GKxGCEw0sGDB7V27drq7OzMPAOv6b+Tprm5uemXX35prOufPXtmtd5/+vSp1qxZUz/99FOCdiwQumFl7ty5+sknn0S7FvK6deu0YMGCWrNmTd2zZ0+05xEGY2/KlClG/0YNO59++qna2dnp3r17VTX6nm1WvDBD5Gdw7dq1Wq1aNZ0/f77xWN++fdXOzk7nzp1rTEgViaPc0UXd6NiwYYPmyZNH//jjDw0LC9P9+/drtmzZtEqVKsZ3O3L5q1ev8hv6AsuXL1dnZ2fj8onJkiXTQYMGqWrE7PpFihTRrFmzaubMmTVz5szGZS3xfFeuXNHcuXNrtmzZtFChQsbszpHD9FUjzp8dMmSIdu/enetyv6Lw8HCdP3++caQ6cofG8uXL1WKxaPv27TVnzpzapEkT9ff319GjR6ujo2O085Pf9SPcx44dM3agXbx4UevUqaPp06c3ZiePtH//fv3444+tLl+LuBs/frymT5/e6hKLUddRjx490sWLF6uvr6/VaFeC94sRut9x//1BjxzW3KFDh2gzP44bN06dnJy0fPnybNTE0bVr17R8+fLq4uJirDQif6ROnjyp+fLlU2dnZ6vhU1GXAcywatUqtbe310mTJkW7vF+fPn3Uzs5O58+fb3XEm8/k861fv17bt2+vQ4YMsWqPDN5Vq1Z97nXQYe3EiRPq4uKi/v7+qhqxs+f7779XBwcHo3/Dw8P1wIEDun//fr18+bIty000Hj9+rBcvXtRDhw5puXLlNH/+/NGC97se+OLqv9/t+fPna5MmTYx5MGbOnKm+vr46Z84c4xSIy5cva8mSJZlxO4qbN29q0qRJtUWLFsZpDZcvX9ZatWpphgwZogXvqDuMEHePHz/W5s2b67hx41Q1Yn6n5cuXa8WKFbVLly66c+dOPXfunLZp00YbNmzIaNdXQOh+h0VdMWzevNn4/7lz52rx4sW1bdu2Vke8582bp9WrV9eBAweyMo6lmDasDx48qHXr1tUsWbJEO3rQsGFDdXNz0woVKiRUiXjHXb9+XUuXLq1jxoyxao96/d2+fftGO+8TMbty5YoWK1ZMHRwctGXLltEe//333zVXrlxauHBhdlzEwt69ezVv3rzRznlfsGCBOjg4RNtBiZhFHVVx69Yt4xzisLAw3bVrl5YrV04LFixoXAt6/Pjx+uWXX+rTp0/5nL6iyO2jyP8OHTpUS5YsqZ07dzYuuRh55DssLEwfPXqkNWvW1MqVK7Nt9R9r167VVKlSaceOHaMFbzc3N055iAcxfb+rVKmiJUqU0BUrVmi1atW0SpUq2rJlS82ZM6dxKtTNmzeN57LDOHYI3e+oqD/sBw4cUFdXV2O4nmrExAheXl7aqlUr3bp1qz548EA//PBD9ff3Z2bCWIraP9evX7e6hvm///6rtWrV0ixZshjDoR4/fqxNmjTRrVu3spGDBHP27Fl1c3OLdp6cqvXKeNCgQQwxjaXAwECtUqWKenh46M8//xzt8Z07d+rHH3/MhkosHDhwQJMkSaLbt29X1f99Ji9evKg5c+bUH3/80YbVJQ6RfbZmzRotXbq05suXT728vPT7779X1Yh11e7du7VChQqaJk0abdasmVosFmNmeMRNZCAMCwvTsWPHaunSpbVDhw7GqTkPHjwwJqEqXrz4Oz9p2vPmvdm0aZPa2dlpx44djaHmly9f1jJlyqiHhwcT+b6G/37WIkcLHDt2TEuWLKmurq76xRdf6L59+1RVdcaMGVq1alWrUW/v6uc1Lgjd76CoP2wzZszQdu3aaYYMGdTR0VEHDBhgPLZgwQKtXr26Ojo6ap48ebRQoULG8BFCYewNGTJECxUqpKlTp9a6devq4sWLVTViOLmvr6/a2dlpq1attHjx4lqyZEljQ5wfMiSEM2fOaJ48eYwNcNX/fb93796tc+fOtVVpb7yo39H/fl8PHjyolSpV0tq1a+u6deue+xoE7/+J/Nz9/fffunPnTj1z5oyGhYVpvXr1tEGDBlbnFz5+/FiLFy+uCxcutFG1iUvkEcPx48frtm3btHfv3mqxWHT27NmqGtH3J06c0IEDB2qbNm302LFjNq44cdu0aZM6ODgYo4PCwsJ0zJgx6u3trR07dtTbt2/r06dPdf78+dqrVy+G6P6/TZs26ahRo6LNe7Fx40ZNnjy5du3a1dhpceXKFauDGXg1UddZU6ZM0UaNGmmlSpX0iy++MGYij3r1krCwMK1Ro4a2adMmwWt9WxC632FDhw7VtGnT6o8//qg//fSTNmnSRD08PLR3797GMn/99ZeuX79ev/vuO2PjkI3EF4v6QzZ9+nTNlCmTzp07V3/66SetUaOGent76/jx41U14vJgo0eP1gYNGmjnzp3f+T3dsI3KlStr0aJFo03iM2DAAK1fv360y97A+js6Y8YM7dixozZp0kR//vlno7/279+vlSpVUl9f3xhHEiC6lStXqqOjo+bOnVvt7e31u+++09mzZxtX0NiwYYP+/fffOmDAAM2YMSOzFcfChQsXtGrVqjp58mRVjThKmD17di1WrJhaLBadPn261fIcOXx9x44d006dOmm2bNn0hx9+UNX/Be8yZcroZ599Zgw1j8S2laq/v79aLBb96quvog3T/+abbzRJkiRWQ83x+gYMGGAc0V60aJFaLBZt1aqV3r9/X1Ujro++cuVKrVWrlhYuXJhJ014DofsdFRQUpCVLlrQ6inX16lX94osv1N3dXT///PMYn8dKIfZ27typY8aM0QULFhhtN2/e1G7dumnJkiV19+7dRnvUjZx3fU83zBG5gvzzzz918eLFunbtWmP4440bN4zRLP7+/rp48WLt0qWLOjk5RbuSAawNGDBA06dPr/3799d69eppyZIltW/fvsYwyP3792uVKlX0/fffN65MgOjCwsL01q1bWrZsWZ01a5aePHlSR40apcmSJdPp06frt99+q40aNdIkSZJovnz5NHfu3NEm/UPMrly5osOGDdOrV6/qlStXNH/+/NqhQwe9ffu2NmrUSC0Wi3HZMLy654WP48eP62effaaZM2e2Ct5ff/215sqVS8eOHZuQZb5xwsPDjW3KW7duGUdXv//+e02SJImOHDnSaufm3LlztUSJEpoxY0ZjTgK8ngMHDqiHh4fu3LlTVVV37dplXLEk0l9//aXt2rXTjz76iBEZr4nQ/Y56+PChenh46LBhw6zab968qd7e3mpvb291jjdHXl/N8ePH1WKxqMVi0a+//lpV/7divn//vubOnVv79+9vyxLxDon87P3888/q4uKixYsX14IFC2qVKlV07dq1qhrxm1C3bl318vLSXLlyaZUqVfTw4cO2LPuNN3/+fM2ZM6ceOnRIVSPOmU2SJIkWLFhQu3XrZhyN2blzp3bt2pXf0RhEfjYfPXqkDx8+1MGDB1sdAZwwYYImS5ZMJ02apNeuXdNTp07p33//bTXsEdYi+/TmzZtGkIm85N+QIUO0du3aeufOHVWNmKshS5Ysmi5dOi4D+Jq+/fZb3bRpk1Xbv//+q5999pm6ubnp8uXLVTXi4EXU0YPvmvXr11utW1asWGGcnz18+HD9559/dOnSpZo0aVIdOXKkMev7kCFDdP78+dEuX4m427Jli5YsWVJVI7YPHB0ddebMmaoaMRIzcqLKy5cvG+svAnfcEbrfATFt6D148EA//fRTbdCgQbQhpT179lQfHx8tXbq0fvvttwlVZqIW057uDRs2aNq0abVevXp6+/Ztq2XatGmjjRs3ZngOEsy2bds0Q4YMxlDSVatWqZOTk3p4eOiSJUuM5W7cuKFBQUHG0DI83+zZs/WLL75Q1Yhh0WnTptXJkydrv379NF26dNq7d+9oQ0gJ3tGtWrVKfXx8tECBApovX75oE3hNnDhR7ezsdPDgwUaIxIutXLlSy5YtawSZyB1D9evX12bNmhnL9ezZU+fPn89w3TiIuv6+ePGi1qpVS/Pnz6/btm2zWu7o0aNatGhRzZgxo86fP9/qsXcteAcFBWmOHDm0devWeurUKf3nn380TZo0OmrUKO3Ro4d6enrqRx99pAcOHNCVK1dqkiRJtGjRolq2bFlNnTo1l1R7DVHXPZHBOSAgQAsXLqzjxo1TZ2dnnTFjhrHM1q1btVKlSnrixIkYXwOvzqKqKnhrhYeHS5IkSURE5K+//pJkyZJJlixZxNnZWbZt2yYNGjSQxo0bS7du3SR//vzy6NEj+fTTT6Vq1aqyefNmsbOzkyVLlhivgeii9vHDhw8lZcqUxmOrV6+WRo0aSZs2beTzzz8XV1dXCQ0NlTJlykjZsmVl6tSptiob75DQ0FDp3bu32Nvby4QJE+TSpUtSvnx58fT0FIvFIocPH5YpU6aIr6+vrUt9Y6mqWCwWq7Z79+7JgwcPRESkdu3a8umnn0qfPn3k8uXLUrJkSUmWLJl069ZN+vXrF+PzIXLw4EGpWrWqNG3aVB4/fiw//PCDfPbZZ9KrVy/Jli2bsdzXX38tY8aMkVOnTsl7771nw4rffAEBAVKlShXp06eP3Lp1S3bt2iXZs2eXIUOGyOHDh6Vz584yYMAAuXjxoqxbt0727t0rHh4eti470du1a5fMnDlTjhw5IpMmTZIqVaoYjzVp0kSOHDkiefPmleXLl4uIvLO/BwEBAdKxY0cpXbq0ZMqUSUREPv/8cxERWbdunUyYMEGcnZ3lm2++MX4TwsLCpHXr1pI/f35blp5oRd1OXbRokYSHh0vdunVFRKRDhw6yYcMG6dGjh/j5+YmIyOPHj+WTTz6RFClSyI8//kgGiC+2zfxIKAMHDtQMGTJo1qxZ1cPDw5iZdP369erm5qZly5Y1rsuXP39+VVX98ssvtWjRogzliaUxY8boRx99pB999JEeOnTI6LcVK1aovb29Fi5cWBs2bKj16tXTIkWKWF0HGTDbP//8o7t27dLg4GD18vLSdu3aqWrEkGg7OztNly6drlixwsZVvpmi7t1/8OBBtKHN27dv1xw5chi/qwEBAdqwYUOdPXs2RwZe4NSpUzps2DD18/Mz2vz9/TVLliw6cOBAPXfunNXy/x01gOhOnTqlo0aN0tGjRxtt69at08qVK2v9+vV16dKlOnbsWC1cuLBWrlzZakZ4xM5/J0utUqWKcX/Xrl3aqFEjLVKkiP7222+q+r+RhT///DOj2/7foUOHtFSpUpotWzarq+aoRsy0X7FiRf3www/1wIEDNqrw7dS3b191cXHROXPm6KVLl1Q1or+9vLy0WrVqOmPGDJ0zZ45Wr15dCxcubBwRZz0WPwjdb6moP+zbtm3TrFmz6i+//KJr167VevXqaZo0aYyJvA4dOqRTpkzR1q1b67Bhw4xJvZo1a6ZNmzZlJtNYmDRpkqZLl04HDhyoBQsW1Fy5cumCBQuMWYzXrl2rqVOn1sKFC+v27duNIWX0LcwQ06WXIm3evFlLlCih58+fV9WIib6qVaum/fv3j3aqCax/S0eOHKk1atTQNGnSaJcuXYzLAe3du1cLFCigY8aM0X/++Uc/+OADbdWqlfHcd20IaWwEBwdriRIlNH369Dp48GCrx6ZNm6aZM2fWIUOGWH12CSwvFtmnGTNm1IEDB1o9tnbtWq1SpYo2bNjQWPczVP/VRQ0fW7Zs0REjRqjFYtEWLVoY7bt379ZmzZqps7OzfvLJJ1qyZEktUaIElwP9jz///FNz5MihZcuWNSb1jLR+/XotVqyYNmvWTB8+fMh3Px4sXLhQXV1ddf/+/dEeW7lypbZv317fe+89rVq1qn766adMmmYCQvdbbsaMGTpz5kz95ptvjLa7d+9qo0aNNHXq1DHOpnvixAkdMGCApk2blvNnnuO/K80vvvjC6ihh06ZNNV++fDpv3jzj3Ng1a9Zo8uTJtUePHlazdgJm+O+ll2bOnKnPnj3T9evXq7Ozs27fvl1VIyZSatWqlTHbNmI2dOhQzZAhgy5ZskS3b9+uxYoVU09PT71w4YKGhIRop06dNGfOnOrq6qqlSpXisiqxEBAQoB4eHlq2bNlo65oZM2aog4ODjhgxgo2+VxAQEKB58uSJMcisW7dOixUrpk2bNtXHjx/bqMK3Q79+/dTDw0MHDBigNWvW1BQpUmjdunWNx0+ePKlTp07VDz/8ULt3787lQJ/jzz//1GLFimmHDh2ifV43b94cbbQL4q5bt27apEkTq7b//rZev349xnO/ET8I3W+xO3fuqJeXl1osFu3Vq5eq/m8DMDg4WBs1aqTp06c3hkCpqj5+/FiHDBmi+fPnZ+bi54i6Eb1hwwZdunSptmzZ0ggxkZo1a6b58uWzmqRm1apVmipVKm3bti3Dy2GKmC699NVXX6nFYlE/Pz/dt2+ffvzxx5olSxYtXbq0Ojo6Rpu46l3n7+9v/P6Fh4friRMn1NPTU7ds2aKqEUNIHRwcdN68ecZzQkND9ejRo7pz505jhxobLC/3oo3uOXPmWE3ig9ghyJhr165dmjZtWmOd/+jRI129erWmT59e69evb7Vs1N8Afg9iFhAQoMWLF9d27doZp+gg/kSG6I8//tgI3VEP+oSGhuovv/wS7fQddhjHP0L3W+7ff//VevXqqZubm549e1ZVrYN39erVtWbNmlbPefTokQYFBSV0qYlC1B+h3r17a+rUqTVLlixqsVi0ffv20Y4WtmzZUtOkSaPr1q0z2pYtW6aZMmWijxGvXnbppUmTJmmSJEl0ypQpunHjRp05c6YOHjxY//33X1uV/EY6c+aMZsmSRTt06GBsAJ4/f14LFSqkT548MS6rEjnLa0hIiC5evNg4Py4SI1lij43u+Eefmmf16tXq4uJitb4PDQ3V77//Xi0Wi7Zp08Zoj/wdIMC8WEBAgJYqVUobN26s//zzj63LSdSeN5ri66+/Vjs7O/3777+t2q9cuaLNmzePduAI8Y/Q/Zb475cs6rlDp06d0vLly2u2bNn04sWLqvq/FUBISIjVc1kxxE5AQIDWrl1b9+7dq9evX9du3bqpp6enfvnll9EuvTJy5Mhoe7i5HBPM8LJLL02YMEEdHBx0+PDhDHN8gYCAAC1RooQRWG7evKmZM2fWwYMHa9q0aXXatGnGsgcOHFBfX1/ds2ePDStO/Njojn/06euLaZvo9OnT+t577xlzOkRtd3d3V4vFop988klClfjW+OOPP7RixYp65coVW5eSaEVdrwcGBuqePXusdrpVrVpVM2XKpPv379egoCDjUnelSpViR3ECIHS/BaJ+yWbNmqUdOnTQxo0b6+rVq432M2fOaPny5TV79uzGEZmoKxM2wGPvxx9/1KpVq+onn3xi9SPVo0cP9fLyijF4qzK0DOY6cOCAOjs7a6dOnbRVq1bG/AH/HUrq5+enadKk0Rs3btio0sQh8khh27Zt9fLlyzpx4kS1WCzas2dPY5mHDx+qr6+v1q5dm9/QeMBGd/yjT+Mu6nf60aNHxvr+7t272qxZM61Zs6bVKLZr167pp59+qkuXLlVXV1f9/vvvE7zmxO7Ro0e2LiHRirpNP2DAAM2TJ486OztrwYIFtU6dOqqqevXqVW3QoIE6ODhotmzZtGDBglZzkLAeMxfX6U7kNMq1XwcOHCjff/+9+Pj4iKOjo0ydOlWmT58unTp1EovFImfPnpU2bdrI/v375cKFC5IhQwYbV5/4hIeHy+effy5Lly6V5MmTy7FjxyRp0qTG47169ZK9e/dK5cqVZejQoZIqVSobVot3xenTp2XRokWSIkUKGThwoIiIzJgxQ7766itp3ry5dOrUyeqax3fu3JG0adPaqtxEIzAwUNq2bSteXl7y8ccfy+bNm2Xy5MnSu3dvefr0qRw7dkyCgoIkMDBQkidPbnUtVMTN48ePxcHBwdZlvFXo01cXddvKz89P/vjjD7l9+7aMHDlSKlasKIcPH5ZBgwZJcHCwVK1aVby8vGTq1KmSLFkyWbRokVSoUEFatmwpgwcPtvE7wbtm8uTJMnLkSFmxYoWkTp1ajh8/LsOHD5f33ntP9uzZIyIimzdvlgcPHoi9vb3UqlVLkiZNKs+ePZNkyZLZuPq3G1sHidSMGTPkzz//NFYKixYtkiVLlsjKlStl7ty54uvrKyIiXbt2la+++kpUVXLkyCGzZ8+W1q1bS7p06WxZfqIRHh5udT9JkiQyYsQI+eyzz+TZs2fSpUsXCQ4ONh6fOHGiFChQQG7cuCEpU6ZM6HLxDrp37540btxY/P395f79+0Z7586dZeDAgfLdd9/Jt99+K2fPnjUeS5MmjQ0qTXw8PT1l7ty5cvjwYVm1apXUr19f5s+fL4cOHZJLly5JiRIl5PDhw5I8eXJ59uwZgTseEA7jH336asLDw41tq2+++UbGjRsn+fLlk6RJk4qvr6/4+/tLsWLFZPz48VK5cmWZN2+eDB8+XERE1q5dK5kyZRIXFxdxdnYWkYgAD5jlv9upf/zxh3Tu3FkqVqwoxYoVk4YNG8p3330nN2/elO7du4uIiI+Pj3z88cfywQcfSNKkSSUsLIzAnRBseZgdcRN1op/ImUknT56s/v7+qhpxPU5nZ2f99ttvdeLEiZokSRKdOnXqc8/7Rsyi9tfRo0f1+PHjxgQUT58+VT8/P33//fe1S5cuxvW4//tczpFHQuDSS+Y6dOiQenl5afv27WMcpstvKfD2OXPmjHbr1k23bdtmtPXt21fTpk2rU6dONYbkPnjwwGrSyn79+mnmzJmtrjEPmCHqNuavv/6qT5480Ro1ahjDyaPq16+fVqlShSvn2BChO5GKOtHP6dOn9fbt23rmzBljlt0JEyYYyzk4OKjFYtH58+fbtuhEJOoP2aBBg9TDw0Pd3Nw0Y8aMOmTIEH369Kk+ffpUv/zyS/X29tZu3bpFm7mcc2OQkLj0krkCAgLUy8tLP/74Yz158qTRzo41IPEbPXq08b0ODw/Xn3/+WS0Wi2bLls3qsqqqEeElXbp0On36dKu5Mfbt26etWrVSNzc3DQgISND68e6Juu4ZOnSoFixYUE+ePKmTJ0/W999/Xzdt2mS1/IwZM7RkyZIxzjmEhMFYuETK09NTZs+eLYcOHZKvvvpKbty4ITly5JBLly6JqhrDy1OkSCEdO3aUVatWSfPmzW1cdeIRdWjZ7NmzZdasWfLDDz/ImDFjZNy4cdKpUydJliyZ9OvXTz744APZsGGDzJ071+o1GGqKhFSkSBGZN2+eHDx4UCZNmiR///238Vjbtm3Fw8PDhtUlfp6enuLv7y/Ozs6SM2dOoz3ytwJA4vTnn3/Kzp07jXkvLBaLfPTRR9KuXTu5cOGCHDlyRB4/fmwsP3bsWGnfvr107dpVdu7cabSXKFFCypcvLzt37hRPT88Efx94t0Sue44cOSKBgYHi7+8vuXPnljp16kiSJElkxowZsmLFCgkPD5dbt27JihUrJFeuXOLk5GTjyt9dTKSWyAUGBkq7du2kePHi0qdPH3n69KkULVpUFi5cKJ6enjJw4EBJliyZrFq1SkSEiRJeQqNMnhIeHi4ff/yxFCxYUEaPHm0ss337dqlatapMmTJFunbtKk+ePJElS5ZIs2bNrCZVA2whMDBQOnXqJDlz5pThw4dLvnz5bF3SWyXyN4JJ04DEb/r06fLhhx+Km5ubiIisWbNGXFxcpFSpUiIi0rx5c1mzZo0sXLhQfH19xc7Ozniuv7+/dOzYUZImTcrvAWzC399fli5dKmFhYbJixQrJmDGjiIgcO3ZMevToIZcvX5a7d++Kq6urhIWFycGDByV58uRW27pIOITut0DkDLvFixeXQYMGyfLly2XQoEGSPXt2SZMmjfz+++98yWIh6krz5s2bkj59eilYsKD4+vrK2LFjRVXl2bNnkjx5cunVq5f89ddfsmrVKqu9hmFhYQRv2NyBAwekX79+8uOPP4qrq6uty3nr8FsKJH5nz56VcuXKyQcffCBdu3YVFxcXyZ49u3z00UfSp08fKVasmIiINGnSRDZu3Cjz58+PFrxFOJiBhPPfnTvbtm2T1q1by/Xr1+Xnn3+W2rVrG48FBQXJhQsXZM+ePeLm5iYNGjRglnIbI3S/JSKDd8mSJWXQoEESHh4u165dk1KlSvEli4WoP2QTJkyQU6dOyZAhQ2ThwoUyZ84c+emnn6REiRLGxvbQoUNl//79smXLFhtXDsSMywQBwIsFBgZKhw4dpGjRojJhwgT5888/pWXLllKxYkXp3r27MUy8adOmsnnzZpk2bZo0bNiQ7SkkuKjbqadOnRJ7e3txd3eXM2fOSPXq1aVAgQIyfPhwKVGixHNfgwNDtsVYmLdE5KVtAgICpG/fvmJnZyfe3t5cCiCWIn/IBgwYIGPGjJHy5ctLWFiY1KxZUwoVKiRDhw6VgwcPisVikZCQEPnjjz8kS5YsNq4aeD4CNwC8WNT5cXr16iWFChWS7777TrZu3SpTpkyRwMBAERFZvHixlCxZUhYsWMD2FBKcqhrbqQMHDpQ6deqIp6enVKhQQf766y/59ddf5e+//5axY8fKoUOHrJ4XFYHbtjjS/Zb5448/ZMaMGTJ37lzOL3pFW7dulfbt28t3330nZcuWNdrXrFkjc+fOla1bt0r+/PklNDRUVFUCAgIYtg8AQCIXGBgobdq0keLFi8s333wjf//9tzRp0kSqVq1qdcSbc7eR0KJ+5pYsWSK9evWSmTNnyt27d+Xo0aMyYcIEmT9/vpQrV05q1KghpUqVku7du0vp0qVtXDn+i9D9FmKin7iZP3++jB8/Xnbv3i1p0qSx6r8zZ87IiRMn5MCBA5IhQwZp166dJEuWjGH7AAC8BWIK3s2bN5eiRYvK2LFjJU+ePCJC8IZt7NixQ3744QcpUKCA9OrVS0RE7t+/L/Pnz5cBAwbI1q1bJUWKFFKuXDnp27evjBgxwsYV47/41XgLWSwWq6EoeLHI/U6PHj2SsLAwo91isRj3Dx06JB4eHjJ06FDjcmEM2wcA4O3g6ekp8+bNk4CAAOnXr58ULFhQ5s2bJ0mSJJHcuXMby7FthYQWFBQk7dq1k6VLl8rDhw+NdicnJ/n000+lRo0asnjxYvH09JQ9e/bIsGHDbFgtnodfjrcUw51jL7KvKleuLCdPnpRJkyYZ7UmTJpUHDx7I999/L5s2bbJ6HufGAADw9ogM3ocPHzYmp12xYoUkSZJEwsPDbV0e3lEuLi7GJcFWrFhhzDUgIpI2bVrJkCGDnDp1SkREihUrZsznhDcLoRv4f/nz5xd/f3/56quvpEePHrJlyxb57bff5OOPP5Zz585Jx44dbV0iAAAwkaenp/j7+4uTk5OkTJnSaOcIN2ypSJEismLFCgkLC5NJkybJ4cOHRSRiiPk///wjWbNmtVqeA0NvHs7pBqJQVVmzZo10795dwsLCJE2aNJI5c2ZZt26dJE+enMstAADwDmB+HLyJAgMDpXnz5nL79m0pUaKE2NnZydmzZ2X//v1iZ2fH5L5vMEI3EIObN29KcHCwhIeHS65cuSRJkiRMmgYAwDuEAIM30dGjR6Vu3bqSJUsWadq0qXTq1ElERJ4+fSrJkye3cXV4HnbdATFInz695MqVSzw8PIxzuQjcAAC8OwjceBMVKlRIVqxYIU+ePJGAgADjfG4C95uNI90AAAAAkIgEBgZKp06dJGfOnDJ8+HDJly+frUvCC3CkGwAAAAASEU9PT5k2bZpcvXpVUqdObety8BIc6QYAAACAROjx48fi4OBg6zLwEoRuAAAAAABMwvByAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAA8NoqVaokPXv2tHUZAAC8cQjdAAC8BWbOnClOTk7y7Nkzo+3BgweSPHlyqVSpktWyO3bsEIvFIqdPn07gKgEAePcQugEAeAtUrlxZHjx4IAcPHjTadu3aJS4uLvL777/L48ePjfbt27dL1qxZJVeuXK/0N1TVKtQDAICXI3QDAPAWyJs3r7i6usqOHTuMth07dki9evUkR44csn//fqv2ypUrS2hoqHTv3l0yZswoDg4OUq5cOTlw4IDVchaLRTZu3CheXl5ib28vu3fvlpCQEGnRooU4OjqKq6urjB8/Plo9/v7+4uHhIQ4ODpIpUyZp0KCBqe8fAIA3FaEbAIC3ROXKlWX79u3G/e3bt0ulSpWkYsWKRvujR4/k999/l8qVK0v//v/X3t29wrbHcRz/nB1uxhQjW4QpxkMUKXGhZs3ykBR/wlxNLkRSmpNSkivxDygpilxRSh7LjIQ8NMrNRJH8AR7yPCPOxal1crFPqb32dua8X1ezfvNd31m/dfeZtX79/tTc3JympqYUiUTk8XjU3Nysq6urD337+vo0PDysaDSqiooKBYNBbW5uamFhQWtrawqHw4pEIlb94eGhuru7NTQ0pJOTE62srMjr9f6amwAAwBeT9LsvAAAA/Bymaaqnp0evr696enrS0dGRDMNQPB7X2NiYJGl3d1cvLy/y+Xxqb2/X5OSkWlpaJEnj4+NaX1/XxMSEgsGg1XdoaEhNTU2S/l4nPjExoenpaTU0NEiSpqamlJuba9VfXl7K4XCotbVVTqdTbrdbVVVVv+o2AADwpfCkGwCABOHz+fTw8KCDgwNtbW2puLhYmZmZMgzDWtcdDodVUFCg29tbxeNx1dXVWecnJyerpqZG0Wj0Q9/q6mrr89nZmWKxmGpra60xl8ulkpIS67ipqUlut1sFBQXy+/2amZnR4+OjjTMHAODrInQDAJAgPB6PcnNzFQqFFAqFZBiGJCknJ0d5eXna2dlRKBRSfX39p/o6HI5P1TudTkUiEc3Ozio7O1sDAwOqrKzUzc3Np/oAAJAICN0AACQQ0zQVDocVDoc/bBXm9Xq1vLys/f19maapwsJCpaSkaHt726qJx+M6ODhQWVnZD/sXFhYqOTlZe3t71tj19bVOT08/1CUlJamxsVEjIyM6Pj7WxcWFNjY2ft5EAQD4j2BNNwAACcQ0TXV2dioej1tPuiXJMAx1dXUpFovJNE05HA51dHQoGAzK5XIpPz9fIyMjenx8VCAQ+GH/1NRUBQIBBYNBZWRk6Pv37+rv79e3b//8j7+4uKjz83N5vV6lp6draWlJb29vH15BBwDg/4LQDQBAAjFNU09PTyotLVVWVpY1bhiG7u7urK3FJGl4eFhvb2/y+/26u7tTdXW1VldXlZ6e/q+/MTo6qvv7e7W1tcnpdKq3t1e3t7fW92lpaZqfn9fg4KCen59VVFSk2dlZlZeX2zNpAAC+sD/e39/ff/dFAAAAAACQiFjTDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2OQv68vpLNuUd8IAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Sample paragraph for analysis\n",
+    "paragraph = \"\"\"\n",
+    "Natural language processing is a subfield of linguistics, computer science, and artificial intelligence \n",
+    "concerned with the interactions between computers and human language. NLP combines computational \n",
+    "linguistics with statistical, machine learning, and deep learning models. The goal is to enable \n",
+    "computers to process and analyze large amounts of natural language data. Common NLP tasks include \n",
+    "tokenization, parsing, lemmatization, speech recognition, and machine translation. Modern NLP heavily \n",
+    "relies on machine learning algorithms and neural networks, particularly transformer models like BERT \n",
+    "and GPT. These models have revolutionized how computers understand and generate human language.\n",
+    "\"\"\"\n",
+    "\n",
+    "# Preprocess and tokenize\n",
+    "cleaned_paragraph = preprocess_text(paragraph)\n",
+    "words = word_tokenize(cleaned_paragraph)\n",
+    "\n",
+    "# Remove stop words\n",
+    "words = [word for word in words if word not in stop_words and len(word) > 2]\n",
+    "\n",
+    "# Count word frequencies\n",
+    "word_freq = Counter(words)\n",
+    "top_words = word_freq.most_common(10)\n",
+    "\n",
+    "# Create visualization\n",
+    "words, counts = zip(*top_words)\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(words, counts)\n",
+    "plt.title('Top 10 Most Frequent Words')\n",
+    "plt.xlabel('Words')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 10. Introduction to Word Embeddings\n",
+    "\n",
+    "Word embeddings are dense vector representations of words that capture semantic meaning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "One-hot encoding:\n",
+      "king: [1, 0, 0, 0, 0]\n",
+      "queen: [0, 1, 0, 0, 0]\n",
+      "man: [0, 0, 1, 0, 0]\n",
+      "woman: [0, 0, 0, 1, 0]\n",
+      "royal: [0, 0, 0, 0, 1]\n",
+      "\n",
+      "Problems with one-hot encoding:\n",
+      "- High dimensionality\n",
+      "- No semantic relationships\n",
+      "- Sparse representation\n",
+      "\n",
+      "Word embeddings solve these problems by:\n",
+      "- Dense, low-dimensional vectors\n",
+      "- Capturing semantic relationships\n",
+      "- Similar words have similar vectors\n",
+      "\n",
+      "Popular word embedding models:\n",
+      "- Word2Vec\n",
+      "- GloVe\n",
+      "- FastText\n",
+      "- Transformer-based embeddings (BERT, GPT)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Simple example of one-hot encoding vs embeddings concept\n",
+    "vocabulary = ['king', 'queen', 'man', 'woman', 'royal']\n",
+    "\n",
+    "# One-hot encoding\n",
+    "print(\"One-hot encoding:\")\n",
+    "for i, word in enumerate(vocabulary):\n",
+    "    one_hot = [0] * len(vocabulary)\n",
+    "    one_hot[i] = 1\n",
+    "    print(f\"{word}: {one_hot}\")\n",
+    "\n",
+    "print(\"\\nProblems with one-hot encoding:\")\n",
+    "print(\"- High dimensionality\")\n",
+    "print(\"- No semantic relationships\")\n",
+    "print(\"- Sparse representation\")\n",
+    "\n",
+    "print(\"\\nWord embeddings solve these problems by:\")\n",
+    "print(\"- Dense, low-dimensional vectors\")\n",
+    "print(\"- Capturing semantic relationships\")\n",
+    "print(\"- Similar words have similar vectors\")\n",
+    "print(\"\\nPopular word embedding models:\")\n",
+    "print(\"- Word2Vec\")\n",
+    "print(\"- GloVe\")\n",
+    "print(\"- FastText\")\n",
+    "print(\"- Transformer-based embeddings (BERT, GPT)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary and Next Steps\n",
+    "\n",
+    "In this notebook, we covered the fundamental concepts of NLP:\n",
+    "\n",
+    "1. **Text Preprocessing**: Cleaning and preparing text data\n",
+    "2. **Tokenization**: Breaking text into words and sentences\n",
+    "3. **Stop Words Removal**: Filtering out common words\n",
+    "4. **Stemming/Lemmatization**: Reducing words to their base form\n",
+    "5. **Bag of Words**: Simple numerical representation\n",
+    "6. **TF-IDF**: Weighted word importance\n",
+    "7. **Sentiment Analysis**: Understanding text emotion\n",
+    "8. **Word Embeddings**: Dense vector representations\n",
+    "\n",
+    "### Next Steps:\n",
+    "- Explore advanced models like Word2Vec and BERT\n",
+    "- Try named entity recognition (NER)\n",
+    "- Build a text classification model\n",
+    "- Experiment with text generation\n",
+    "- Learn about transformer architectures\n",
+    "\n",
+    "### Recommended Resources:\n",
+    "- NLTK Book: https://www.nltk.org/book/\n",
+    "- spaCy Documentation: https://spacy.io/\n",
+    "- Hugging Face Transformers: https://huggingface.co/transformers/\n",
+    "- Stanford NLP Course: https://web.stanford.edu/class/cs224n/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Practice Exercise: Analyze your own text\n",
+    "# TODO: Replace this with your own text\n",
+    "your_text = \"Enter your text here to analyze...\"\n",
+    "\n",
+    "# Apply what you learned\n",
+    "# 1. Preprocess the text\n",
+    "# 2. Tokenize it\n",
+    "# 3. Remove stop words\n",
+    "# 4. Perform sentiment analysis\n",
+    "# 5. Create a word frequency visualization"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "week1_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Week1/Day_2/nlp_homework.md b/Week1/Day_2/nlp_homework.md
new file mode 100644
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--- /dev/null
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+# Natural Language Processing Homework Assignment
+
+## Overview
+
+This homework assignment is designed to introduce you to fundamental Natural Language Processing (NLP) concepts and techniques. You will work with text data and implement various NLP tasks using Python libraries such as NLTK, spaCy, and scikit-learn.
+
+## Dataset Description
+
+You will be working with a collection of movie reviews. This dataset contains:
+
+- 1000 movie reviews (500 positive, 500 negative)
+- Each review is labeled as positive (1) or negative (0)
+- Reviews are of varying lengths and complexity
+
+## Tasks
+
+### Task 1: Text Preprocessing (Beginner)
+
+1. Load the dataset and examine its structure
+2. Implement the following preprocessing steps:
+   - Convert text to lowercase
+   - Remove punctuation
+   - Remove numbers
+   - Remove stopwords
+   - Remove extra whitespace
+3. Tokenize the preprocessed text
+4. Implement stemming and lemmatization on the tokens
+5. Compare and discuss the results of stemming vs. lemmatization with 3 examples
+
+### Task 2: Text Exploration and Visualization (Beginner-Intermediate)
+
+1. Calculate basic text statistics:
+   - Average review length (in words)
+   - Distribution of review lengths
+   - Vocabulary size
+2. Identify the most common words in positive and negative reviews
+3. Create a word cloud for positive and negative reviews
+4. Generate and visualize n-gram frequencies (for n=2 and n=3)
+5. Calculate and visualize TF-IDF scores for the top 20 terms
+
+### Task 3: Named Entity Recognition (NER) Exploration (Intermediate)
+
+1. Install and set up NLTK for entity recognition
+2. Process a subset of movie reviews (at least 50) with NER and extract all entities
+3. Categorize and count entities by type (PERSON, ORGANIZATION, LOCATION, etc.) found in positive vs. negative reviews
+4. Create visualizations showing:
+   - Distribution of entity types across the dataset
+   - Frequency of top 10 entities for each entity type
+   - Comparison of entity patterns between positive and negative reviews
+5. Implement a custom entity recognition approach for movie-specific entities:
+   - Directors (e.g., "Christopher Nolan", "Quentin Tarantino")
+   - Actors/Actresses (e.g., "Tom Hanks", "Meryl Streep")
+   - Movie titles (hint: look for capitalized phrases and patterns)
+   - Awards (e.g., "Oscar", "Golden Globe")
+6. Evaluate your custom NER approach on a small manually labeled test set
+7. Create a function that takes a review text and returns a highlighted version showing all identified entities with their categories (using different colors or formatting)
+
+## Dataset Generation
+
+Use the following code to generate a synthetic movie review dataset:
+
+```python
+import numpy as np
+import pandas as pd
+from sklearn.datasets import fetch_20newsgroups
+import random
+
+# Set random seed for reproducibility
+np.random.seed(42)
+random.seed(42)
+
+# Create positive and negative vocabulary
+positive_words = [
+    'excellent', 'amazing', 'great', 'good', 'fantastic', 'wonderful', 'brilliant',
+    'perfect', 'outstanding', 'superb', 'masterpiece', 'stunning', 'impressive',
+    'enjoyable', 'entertaining', 'captivating', 'engaging', 'powerful', 'moving',
+    'beautiful', 'compelling', 'memorable', 'remarkable', 'spectacular', 'phenomenal'
+]
+
+negative_words = [
+    'terrible', 'awful', 'bad', 'poor', 'disappointing', 'boring', 'dull',
+    'mediocre', 'waste', 'horrible', 'worst', 'stupid', 'annoying', 'predictable',
+    'unbearable', 'ridiculous', 'failure', 'disaster', 'nonsense', 'mess',
+    'underwhelming', 'forgettable', 'confusing', 'pointless', 'painful'
+]
+
+# Create lists of movie-related named entities for NER task
+director_names = [
+    'Steven Spielberg', 'Christopher Nolan', 'Martin Scorsese', 'Quentin Tarantino',
+    'James Cameron', 'Kathryn Bigelow', 'Alfred Hitchcock', 'Ridley Scott',
+    'Greta Gerwig', 'Sofia Coppola', 'Denis Villeneuve', 'Francis Ford Coppola',
+    'David Fincher', 'Spike Lee', 'Wes Anderson', 'Ava DuVernay'
+]
+
+actor_names = [
+    'Tom Hanks', 'Meryl Streep', 'Leonardo DiCaprio', 'Jennifer Lawrence',
+    'Denzel Washington', 'Viola Davis', 'Brad Pitt', 'Cate Blanchett',
+    'Robert De Niro', 'Kate Winslet', 'Morgan Freeman', 'Scarlett Johansson',
+    'Daniel Day-Lewis', 'Emma Stone', 'Samuel L. Jackson', 'Natalie Portman'
+]
+
+movie_titles = [
+    'The Shawshank Redemption', 'The Godfather', 'Pulp Fiction', 'The Dark Knight',
+    'Schindler\'s List', 'Forrest Gump', 'Inception', 'The Matrix',
+    'Titanic', 'Avatar', 'Parasite', 'Casablanca',
+    'Goodfellas', 'The Silence of the Lambs', 'Jurassic Park', 'Star Wars'
+]
+
+award_names = [
+    'Oscar', 'Academy Award', 'Golden Globe', 'BAFTA',
+    'Palme d\'Or', 'Emmy', 'Screen Actors Guild Award', 'Tony Award',
+    'Critics\' Choice', 'Independent Spirit Award', 'Cesar Award', 'Goya Award'
+]
+
+# Fetch some real texts to build more realistic reviews
+newsgroups = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))
+texts = newsgroups.data[:5000]  # Get some real text
+
+# Function to generate a synthetic review
+def generate_review(sentiment, length_range=(50, 500)):
+    # Select base text
+    base_text = random.choice(texts)
+    words = base_text.split()
+
+    # Select random length within range
+    target_length = random.randint(*length_range)
+    if len(words) > target_length:
+        words = words[:target_length]
+
+    # Add sentiment words
+    word_list = positive_words if sentiment == 1 else negative_words
+    num_sentiment_words = random.randint(3, 10)
+
+    for _ in range(num_sentiment_words):
+        insert_pos = random.randint(0, len(words) - 1)
+        sentiment_word = random.choice(word_list)
+        words.insert(insert_pos, sentiment_word)
+
+    # Add movie-related terms sometimes
+    movie_terms = ['movie', 'film', 'cinema', 'director', 'actor', 'actress',
+                   'script', 'screenplay', 'scene', 'plot', 'character', 'performance']
+
+    for _ in range(random.randint(1, 5)):
+        insert_pos = random.randint(0, len(words) - 1)
+        movie_term = random.choice(movie_terms)
+        words.insert(insert_pos, movie_term)
+
+    # Add named entities to some reviews (for NER task)
+    if random.random() < 0.7:  # 70% chance to add named entities
+        # Add 1-3 director names
+        for _ in range(random.randint(1, 3)):
+            if random.random() < 0.6:
+                insert_pos = random.randint(0, len(words) - 1)
+                director = random.choice(director_names)
+                words.insert(insert_pos, director)
+
+        # Add 1-3 actor names
+        for _ in range(random.randint(1, 3)):
+            if random.random() < 0.7:
+                insert_pos = random.randint(0, len(words) - 1)
+                actor = random.choice(actor_names)
+                words.insert(insert_pos, actor)
+
+        # Add 0-2 movie titles
+        for _ in range(random.randint(0, 2)):
+            if random.random() < 0.5:
+                insert_pos = random.randint(0, len(words) - 1)
+                title = random.choice(movie_titles)
+                words.insert(insert_pos, title)
+
+        # Add 0-1 award mentions
+        if random.random() < 0.3:
+            insert_pos = random.randint(0, len(words) - 1)
+            award = random.choice(award_names)
+            words.insert(insert_pos, award)
+
+    # Join and return
+    review = ' '.join(words)
+
+    # Clean up a bit
+    review = review.replace('\n', ' ').replace('  ', ' ')
+
+    return review
+
+# Generate 1000 reviews (500 positive, 500 negative)
+reviews = []
+labels = []
+
+for _ in range(500):
+    # Generate positive reviews
+    reviews.append(generate_review(1))
+    labels.append(1)
+
+    # Generate negative reviews
+    reviews.append(generate_review(0))
+    labels.append(0)
+
+# Create DataFrame
+reviews_df = pd.DataFrame({
+    'review': reviews,
+    'sentiment': labels
+})
+
+# Shuffle the DataFrame
+reviews_df = reviews_df.sample(frac=1).reset_index(drop=True)
+
+# Display sample
+print(reviews_df.head())
+
+# Save to CSV if needed
+# reviews_df.to_csv('movie_reviews.csv', index=False)
+```
+
+## Hints
+
+### Task 1 Hints:
+
+- Use NLTK or spaCy for preprocessing tasks
+- For stopwords removal: `from nltk.corpus import stopwords`
+- For stemming: `from nltk.stem import PorterStemmer` or `SnowballStemmer`
+- For lemmatization: `from nltk.stem import WordNetLemmatizer` or use spaCy's lemmatizer
+
+### Task 2 Hints:
+
+- Use Python's collections: `from collections import Counter`
+- For word clouds: `from wordcloud import WordCloud`
+- For visualization: `matplotlib` and `seaborn`
+- For n-grams: `from nltk import ngrams`
+- For TF-IDF visualization: Use scikit-learn's `TfidfVectorizer`
+
+### Task 3 Hints:
+
+- For NER setup with NLTK:
+  ```python
+  import nltk
+  from nltk import ne_chunk
+  from nltk.tag import pos_tag
+  from nltk.tokenize import word_tokenize
+  ```
+- To extract named entities with NLTK:
+  ```python
+  def extract_entities(text):
+      tokens = word_tokenize(text)
+      tagged = pos_tag(tokens)
+      chunks = ne_chunk(tagged)
+      entities = []
+      for chunk in chunks:
+          if hasattr(chunk, 'label'):
+              entities.append((chunk.label(), ' '.join(c[0] for c in chunk)))
+      return entities
+  ```
+- For custom NER, consider regex patterns or context-based rules with dictionaries
+- To evaluate NER performance, use precision, recall, and F1-score on entity detection
+- For text highlighting in Jupyter notebooks, you can use HTML formatting:
+
+  ```python
+  from IPython.display import HTML, display
+
+  def highlight_entities(text, entities):
+      # Code that takes text and entity locations and returns highlighted HTML
+      # (detailed implementation in solution)
+  ```
+
+## Submission Requirements
+
+Submit a Jupyter notebook containing:
+
+1. All code with clear comments
+2. Visualizations with interpretations
+3. Discussion of results and findings
+4. Answers to all questions posed in the tasks
+
+## Sample Solution for Task 3: Named Entity Recognition
+
+```python
+import nltk
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from collections import Counter, defaultdict
+import re
+from nltk import ne_chunk
+from nltk.tag import pos_tag
+from nltk.tokenize import word_tokenize
+from IPython.display import display, HTML
+
+# Ensure necessary NLTK resources are downloaded
+nltk.download('punkt')
+nltk.download('averaged_perceptron_tagger')
+nltk.download('maxent_ne_chunker')
+nltk.download('words')
+
+# Load the dataset (this would be your movie reviews dataset)
+reviews_df = pd.read_csv('movie_reviews.csv')
+
+# 1. Setup NLTK for entity recognition
+def extract_entities_nltk(text):
+    """Extract named entities using NLTK's ne_chunk."""
+    tokens = word_tokenize(text)
+    tagged = pos_tag(tokens)
+    chunks = ne_chunk(tagged)
+
+    entities = []
+    for chunk in chunks:
+        if hasattr(chunk, 'label'):
+            entity_text = ' '.join(c[0] for c in chunk)
+            entities.append((chunk.label(), entity_text))
+
+    return entities
+
+# 2. Process a subset of reviews
+sample_size = 50
+review_sample = reviews_df.sample(sample_size, random_state=42)
+
+# Store entities for analysis
+all_entities = []
+pos_entities = []
+neg_entities = []
+
+for idx, row in review_sample.iterrows():
+    review_text = row['review']
+    sentiment = row['sentiment']
+
+    # Extract entities
+    entities = extract_entities_nltk(review_text)
+
+    # Store entities with review ID and sentiment
+    for entity_type, entity_text in entities:
+        entity_record = {
+            'review_id': idx,
+            'sentiment': sentiment,
+            'entity_type': entity_type,
+            'entity_text': entity_text
+        }
+        all_entities.append(entity_record)
+
+        if sentiment == 1:
+            pos_entities.append(entity_record)
+        else:
+            neg_entities.append(entity_record)
+
+# Convert to DataFrame for easier analysis
+entities_df = pd.DataFrame(all_entities)
+print(f"Total entities found: {len(entities_df)}")
+
+# 3. Categorize and count entities by type
+entity_type_counts = entities_df['entity_type'].value_counts()
+print("\nEntity types distribution:")
+print(entity_type_counts)
+
+# Compare entity types in positive vs negative reviews
+pos_entity_types = [e['entity_type'] for e in pos_entities]
+neg_entity_types = [e['entity_type'] for e in neg_entities]
+
+pos_type_counts = Counter(pos_entity_types)
+neg_type_counts = Counter(neg_entity_types)
+
+# 4. Create visualizations
+# Entity type distribution plot
+plt.figure(figsize=(10, 6))
+entity_type_counts.plot(kind='bar')
+plt.title('Distribution of Entity Types')
+plt.xlabel('Entity Type')
+plt.ylabel('Count')
+plt.tight_layout()
+plt.show()
+
+# Top 10 entities for each type
+plt.figure(figsize=(12, 8))
+for i, entity_type in enumerate(entity_type_counts.index[:4]):  # Show top 4 entity types
+    entities_of_type = entities_df[entities_df['entity_type'] == entity_type]
+    top_entities = entities_of_type['entity_text'].value_counts().head(10)
+
+    plt.subplot(2, 2, i+1)
+    top_entities.plot(kind='barh')
+    plt.title(f'Top 10 {entity_type} Entities')
+    plt.tight_layout()
+
+plt.show()
+
+# Compare entity patterns between positive and negative reviews
+comparison_data = pd.DataFrame({
+    'Positive': pd.Series(pos_type_counts),
+    'Negative': pd.Series(neg_type_counts)
+}).fillna(0)
+
+plt.figure(figsize=(10, 6))
+comparison_data.plot(kind='bar')
+plt.title('Entity Types in Positive vs Negative Reviews')
+plt.xlabel('Entity Type')
+plt.ylabel('Count')
+plt.legend()
+plt.tight_layout()
+plt.show()
+
+# 5. Implement custom entity recognition for movie-specific entities
+
+# Load our lists of known entities
+director_names = [
+    'Steven Spielberg', 'Christopher Nolan', 'Martin Scorsese', 'Quentin Tarantino',
+    'James Cameron', 'Kathryn Bigelow', 'Alfred Hitchcock', 'Ridley Scott',
+    'Greta Gerwig', 'Sofia Coppola', 'Denis Villeneuve', 'Francis Ford Coppola',
+    'David Fincher', 'Spike Lee', 'Wes Anderson', 'Ava DuVernay'
+]
+
+actor_names = [
+    'Tom Hanks', 'Meryl Streep', 'Leonardo DiCaprio', 'Jennifer Lawrence',
+    'Denzel Washington', 'Viola Davis', 'Brad Pitt', 'Cate Blanchett',
+    'Robert De Niro', 'Kate Winslet', 'Morgan Freeman', 'Scarlett Johansson',
+    'Daniel Day-Lewis', 'Emma Stone', 'Samuel L. Jackson', 'Natalie Portman'
+]
+
+movie_titles = [
+    'The Shawshank Redemption', 'The Godfather', 'Pulp Fiction', 'The Dark Knight',
+    'Schindler\'s List', 'Forrest Gump', 'Inception', 'The Matrix',
+    'Titanic', 'Avatar', 'Parasite', 'Casablanca',
+    'Goodfellas', 'The Silence of the Lambs', 'Jurassic Park', 'Star Wars'
+]
+
+award_names = [
+    'Oscar', 'Academy Award', 'Golden Globe', 'BAFTA',
+    'Palme d\'Or', 'Emmy', 'Screen Actors Guild Award', 'Tony Award',
+    'Critics\' Choice', 'Independent Spirit Award', 'Cesar Award', 'Goya Award'
+]
+
+def custom_movie_ner(text):
+    """Custom NER for movie-specific entities."""
+    entities = []
+
+    # Check for directors
+    for director in director_names:
+        if director.lower() in text.lower():
+            # Find exact position with case preserved
+            start = text.lower().find(director.lower())
+            actual_text = text[start:start+len(director)]
+            entities.append(('DIRECTOR', actual_text))
+
+    # Check for actors
+    for actor in actor_names:
+        if actor.lower() in text.lower():
+            start = text.lower().find(actor.lower())
+            actual_text = text[start:start+len(actor)]
+            entities.append(('ACTOR', actual_text))
+
+    # Check for movie titles
+    for title in movie_titles:
+        if title.lower() in text.lower():
+            start = text.lower().find(title.lower())
+            actual_text = text[start:start+len(title)]
+            entities.append(('MOVIE', actual_text))
+
+    # Check for awards
+    for award in award_names:
+        if award.lower() in text.lower():
+            start = text.lower().find(award.lower())
+            actual_text = text[start:start+len(award)]
+            entities.append(('AWARD', actual_text))
+
+    # Additional pattern matching for potential movie titles
+    # Look for patterns like capitalized words in quotes
+    movie_pattern = r'"([A-Z][^"]+)"'
+    movie_matches = re.findall(movie_pattern, text)
+    for match in movie_matches:
+        if match not in [m[1] for m in entities if m[0] == 'MOVIE']:
+            entities.append(('POTENTIAL_MOVIE', match))
+
+    return entities
+
+# Apply custom NER to the sample
+custom_entities = []
+
+for idx, row in review_sample.iterrows():
+    review_text = row['review']
+    sentiment = row['sentiment']
+
+    # Extract entities
+    movie_entities = custom_movie_ner(review_text)
+
+    # Store entities with review ID and sentiment
+    for entity_type, entity_text in movie_entities:
+        entity_record = {
+            'review_id': idx,
+            'sentiment': sentiment,
+            'entity_type': entity_type,
+            'entity_text': entity_text
+        }
+        custom_entities.append(entity_record)
+
+# Convert to DataFrame
+custom_entities_df = pd.DataFrame(custom_entities)
+print(f"\nCustom movie entities found: {len(custom_entities_df)}")
+
+# Count by entity type
+custom_type_counts = custom_entities_df['entity_type'].value_counts()
+print("\nCustom entity types distribution:")
+print(custom_type_counts)
+
+# Visualize custom entity types
+plt.figure(figsize=(10, 6))
+custom_type_counts.plot(kind='bar')
+plt.title('Distribution of Custom Movie Entity Types')
+plt.xlabel('Entity Type')
+plt.ylabel('Count')
+plt.tight_layout()
+plt.show()
+
+# 6. Evaluate custom NER on a small test set
+
+# Let's create a small manually labeled test set
+test_reviews = [
+    {"text": "Steven Spielberg directed 'Jurassic Park' which won an Oscar for special effects.",
+     "expected": [('DIRECTOR', 'Steven Spielberg'), ('MOVIE', 'Jurassic Park'), ('AWARD', 'Oscar')]},
+    {"text": "I thought The Dark Knight was brilliant with amazing performances by Christian Bale.",
+     "expected": [('MOVIE', 'The Dark Knight')]},
+    {"text": "Quentin Tarantino's Pulp Fiction is a cult classic starring Samuel L. Jackson.",
+     "expected": [('DIRECTOR', 'Quentin Tarantino'), ('MOVIE', 'Pulp Fiction'), ('ACTOR', 'Samuel L. Jackson')]},
+    {"text": "I didn't enjoy Avatar despite its Golden Globe nominations.",
+     "expected": [('MOVIE', 'Avatar'), ('AWARD', 'Golden Globe')]},
+    {"text": "Martin Scorsese finally won an Academy Award for The Departed.",
+     "expected": [('DIRECTOR', 'Martin Scorsese'), ('AWARD', 'Academy Award')]}
+]
+
+# Function to evaluate NER performance
+def evaluate_ner(test_data, ner_function):
+    true_positives = 0
+    false_positives = 0
+    false_negatives = 0
+
+    for example in test_data:
+        text = example["text"]
+        expected = set([(t, e) for t, e in example["expected"]])
+
+        # Get predictions
+        predicted = set([(t, e) for t, e in ner_function(text)])
+
+        # Count TP, FP, FN
+        true_positives += len(expected.intersection(predicted))
+        false_positives += len(predicted - expected)
+        false_negatives += len(expected - predicted)
+
+    # Calculate metrics
+    precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0
+    recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0
+    f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
+
+    return {
+        "precision": precision,
+        "recall": recall,
+        "f1": f1
+    }
+
+# Evaluate our custom NER
+evaluation = evaluate_ner(test_reviews, custom_movie_ner)
+print("\nCustom NER Evaluation:")
+print(f"Precision: {evaluation['precision']:.2f}")
+print(f"Recall: {evaluation['recall']:.2f}")
+print(f"F1 Score: {evaluation['f1']:.2f}")
+
+# 7. Create a function to highlight entities in text
+
+def highlight_entities(text, entities):
+    """
+    Highlight entities in text with different colors based on entity type.
+    Returns HTML for display in Jupyter notebook.
+    """
+    # Sort entities by their position in the text to handle overlapping entities correctly
+    positioned_entities = []
+    for entity_type, entity_text in entities:
+        start = text.lower().find(entity_text.lower())
+        while start != -1:
+            # Verify the full word matches
+            end = start + len(entity_text)
+            before = '' if start == 0 else text[start-1]
+            after = '' if end >= len(text) else text[end]
+            if (start == 0 or not before.isalnum()) and (end >= len(text) or not after.isalnum()):
+                positioned_entities.append((start, end, entity_type, text[start:end]))
+                break
+            start = text.lower().find(entity_text.lower(), start + 1)
+
+    # Sort by start position, with longer entities first in case of ties
+    positioned_entities.sort(key=lambda x: (x[0], -len(x[3])))
+
+    # Define colors for different entity types
+    color_map = {
+        'PERSON': '#ffadad',  # light red
+        'ORGANIZATION': '#ffd6a5',  # light orange
+        'LOCATION': '#caffbf',  # light green
+        'DIRECTOR': '#9bf6ff',  # light cyan
+        'ACTOR': '#bdb2ff',  # light purple
+        'MOVIE': '#ffc6ff',  # light pink
+        'AWARD': '#fdffb6',  # light yellow
+        'POTENTIAL_MOVIE': '#fffffc',  # off-white
+        'GPE': '#caffbf',  # light green (same as LOCATION)
+        'FACILITY': '#a0c4ff',  # light blue
+        'DATE': '#e2e2e2'  # light gray
+    }
+
+    # Build HTML with highlighting
+    html_parts = []
+    last_end = 0
+
+    for start, end, entity_type, entity_text in positioned_entities:
+        if start > last_end:
+            html_parts.append(text[last_end:start])
+
+        color = color_map.get(entity_type, '#e2e2e2')  # default to light gray
+        html_parts.append(f'<span style="background-color: {color};" title="{entity_type}">{entity_text}</span>')
+
+        last_end = end
+
+    if last_end < len(text):
+        html_parts.append(text[last_end:])
+
+    return HTML(''.join(html_parts))
+
+# Demonstrate the highlighting function with a sample text
+sample_text = "Steven Spielberg's Jurassic Park won an Oscar for its groundbreaking special effects. Tom Hanks and Leonardo DiCaprio are two of my favorite actors."
+entities = custom_movie_ner(sample_text) + extract_entities_nltk(sample_text)
+display(highlight_entities(sample_text, entities))
+
+# Show highlighting for a few reviews from our dataset
+for i, (idx, row) in enumerate(review_sample.head(5).iterrows()):
+    review_text = row['review']
+    print(f"\nReview {i+1} (Sentiment: {'Positive' if row['sentiment'] == 1 else 'Negative'}):")
+
+    # Extract both standard and custom entities
+    all_entities = extract_entities_nltk(review_text) + custom_movie_ner(review_text)
+
+    # Display highlighted text
+    display(highlight_entities(review_text, all_entities))
+```
+
+This solution demonstrates:
+
+1. Setting up NLTK for entity recognition
+2. Extracting and analyzing entities from movie reviews
+3. Creating custom entity recognition for movie-specific entities
+4. Evaluating NER performance
+5. Highlighting entities in text with different colors
+
+The code includes detailed comments and produces visualizations to help understand the entity patterns in the dataset.
diff --git a/Week1/Day_2/nlp_homework_solution.ipynb b/Week1/Day_2/nlp_homework_solution.ipynb
new file mode 100644
index 00000000..93b53353
--- /dev/null
+++ b/Week1/Day_2/nlp_homework_solution.ipynb
@@ -0,0 +1,1345 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f66cb30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ! pip install scikit-learn nltk wordcloud"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d332a418",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading required NLTK resources...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading collection 'popular'\n",
+      "[nltk_data]    | \n",
+      "[nltk_data]    | Downloading package cmudict to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package cmudict is already up-to-date!\n",
+      "[nltk_data]    | Downloading package gazetteers to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package gazetteers is already up-to-date!\n",
+      "[nltk_data]    | Downloading package genesis to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package genesis is already up-to-date!\n",
+      "[nltk_data]    | Downloading package gutenberg to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package gutenberg is already up-to-date!\n",
+      "[nltk_data]    | Downloading package inaugural to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package inaugural is already up-to-date!\n",
+      "[nltk_data]    | Downloading package movie_reviews to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Unzipping corpora/movie_reviews.zip.\n",
+      "[nltk_data]    | Downloading package names to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package names is already up-to-date!\n",
+      "[nltk_data]    | Downloading package shakespeare to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package shakespeare is already up-to-date!\n",
+      "[nltk_data]    | Downloading package stopwords to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package stopwords is already up-to-date!\n",
+      "[nltk_data]    | Downloading package treebank to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package treebank is already up-to-date!\n",
+      "[nltk_data]    | Downloading package twitter_samples to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package twitter_samples is already up-to-date!\n",
+      "[nltk_data]    | Downloading package omw to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package omw is already up-to-date!\n",
+      "[nltk_data]    | Downloading package omw-1.4 to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package omw-1.4 is already up-to-date!\n",
+      "[nltk_data]    | Downloading package wordnet to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package wordnet is already up-to-date!\n",
+      "[nltk_data]    | Downloading package wordnet2021 to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package wordnet2021 is already up-to-date!\n",
+      "[nltk_data]    | Downloading package wordnet31 to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package wordnet31 is already up-to-date!\n",
+      "[nltk_data]    | Downloading package wordnet_ic to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package wordnet_ic is already up-to-date!\n",
+      "[nltk_data]    | Downloading package words to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package words is already up-to-date!\n",
+      "[nltk_data]    | Downloading package maxent_ne_chunker to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully downloaded popular packages\n",
+      "Successfully downloaded punkt\n",
+      "Successfully downloaded punkt_tab\n",
+      "Successfully downloaded stopwords\n",
+      "Successfully downloaded wordnet\n",
+      "Successfully downloaded averaged_perceptron_tagger\n",
+      "Successfully downloaded averaged_perceptron_tagger_eng\n",
+      "Successfully downloaded maxent_ne_chunker_tab\n",
+      "Successfully downloaded maxent_ne_chunker\n",
+      "Successfully downloaded words\n",
+      "Successfully downloaded omw-1.4\n",
+      "NLTK resource download complete.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data]    |   Package maxent_ne_chunker is already up-to-date!\n",
+      "[nltk_data]    | Downloading package punkt to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package punkt is already up-to-date!\n",
+      "[nltk_data]    | Downloading package snowball_data to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package snowball_data is already up-to-date!\n",
+      "[nltk_data]    | Downloading package averaged_perceptron_tagger to\n",
+      "[nltk_data]    |     /Users/raamraam/nltk_data...\n",
+      "[nltk_data]    |   Package averaged_perceptron_tagger is already up-\n",
+      "[nltk_data]    |       to-date!\n",
+      "[nltk_data]    | \n",
+      "[nltk_data]  Done downloading collection popular\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import re\n",
+    "import string\n",
+    "from collections import Counter\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from nltk.tokenize import word_tokenize\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
+    "from nltk import ngrams, ne_chunk, pos_tag\n",
+    "from wordcloud import WordCloud\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "import nltk\n",
+    "import ssl\n",
+    "\n",
+    "try:\n",
+    "    _create_unverified_https_context = ssl._create_unverified_context\n",
+    "except AttributeError:\n",
+    "    pass\n",
+    "else:\n",
+    "    ssl._create_default_https_context = _create_unverified_https_context\n",
+    "\n",
+    "# Download all required NLTK resources\n",
+    "\n",
+    "print(\"Downloading required NLTK resources...\")\n",
+    "try:\n",
+    "    nltk.download('popular')\n",
+    "    print(\"Successfully downloaded popular packages\")\n",
+    "except Exception as e:\n",
+    "    print(f\"Error downloading popular packages: {e}\")\n",
+    "    \n",
+    "resources = [\n",
+    "    'punkt', 'punkt_tab', 'stopwords', 'wordnet', 'averaged_perceptron_tagger','averaged_perceptron_tagger_eng','maxent_ne_chunker_tab',\n",
+    "    'maxent_ne_chunker', 'words', 'omw-1.4'\n",
+    "]\n",
+    "\n",
+    "\n",
+    "\n",
+    "for resource in resources:\n",
+    "    try:\n",
+    "        nltk.download(resource, quiet=True)\n",
+    "        print(f\"Successfully downloaded {resource}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error downloading {resource}: {e}\")\n",
+    "        \n",
+    "print(\"NLTK resource download complete.\")\n",
+    "\n",
+    "\n",
+    "# First, generate the dataset using the provided code\n",
+    "import random\n",
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "\n",
+    "# Set random seed for reproducibility\n",
+    "np.random.seed(42)\n",
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a53c12dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sample of generated movie reviews:\n",
+      "                                              review  sentiment\n",
+      "0  --> Note: Reply to a message in Steven Spielbe...          0\n",
+      "1  FOR SALE!!!! 1) Sony Car Stereo Amplifier (Mod...          0\n",
+      "2  character Astronomy Scarlett Johansson & Space...          1\n",
+      "3  Does anybody have Sofia Coppola any excellent ...          1\n",
+      "4  ... That people are at risk and that some die ...          0\n",
+      "\n",
+      "Dataset saved to 'movie_reviews.csv'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create positive and negative vocabulary\n",
+    "positive_words = [\n",
+    "    'excellent', 'amazing', 'great', 'good', 'fantastic', 'wonderful', 'brilliant',\n",
+    "    'perfect', 'outstanding', 'superb', 'masterpiece', 'stunning', 'impressive',\n",
+    "    'enjoyable', 'entertaining', 'captivating', 'engaging', 'powerful', 'moving',\n",
+    "    'beautiful', 'compelling', 'memorable', 'remarkable', 'spectacular', 'phenomenal'\n",
+    "]\n",
+    "\n",
+    "negative_words = [\n",
+    "    'terrible', 'awful', 'bad', 'poor', 'disappointing', 'boring', 'dull',\n",
+    "    'mediocre', 'waste', 'horrible', 'worst', 'stupid', 'annoying', 'predictable',\n",
+    "    'unbearable', 'ridiculous', 'failure', 'disaster', 'nonsense', 'mess',\n",
+    "    'underwhelming', 'forgettable', 'confusing', 'pointless', 'painful'\n",
+    "]\n",
+    "\n",
+    "# Create lists of movie-related named entities for NER task\n",
+    "director_names = [\n",
+    "    'Steven Spielberg', 'Christopher Nolan', 'Martin Scorsese', 'Quentin Tarantino',\n",
+    "    'James Cameron', 'Kathryn Bigelow', 'Alfred Hitchcock', 'Ridley Scott',\n",
+    "    'Greta Gerwig', 'Sofia Coppola', 'Denis Villeneuve', 'Francis Ford Coppola',\n",
+    "    'David Fincher', 'Spike Lee', 'Wes Anderson', 'Ava DuVernay'\n",
+    "]\n",
+    "\n",
+    "actor_names = [\n",
+    "    'Tom Hanks', 'Meryl Streep', 'Leonardo DiCaprio', 'Jennifer Lawrence',\n",
+    "    'Denzel Washington', 'Viola Davis', 'Brad Pitt', 'Cate Blanchett',\n",
+    "    'Robert De Niro', 'Kate Winslet', 'Morgan Freeman', 'Scarlett Johansson',\n",
+    "    'Daniel Day-Lewis', 'Emma Stone', 'Samuel L. Jackson', 'Natalie Portman'\n",
+    "]\n",
+    "\n",
+    "movie_titles = [\n",
+    "    'The Shawshank Redemption', 'The Godfather', 'Pulp Fiction', 'The Dark Knight',\n",
+    "    'Schindler\\'s List', 'Forrest Gump', 'Inception', 'The Matrix',\n",
+    "    'Titanic', 'Avatar', 'Parasite', 'Casablanca',\n",
+    "    'Goodfellas', 'The Silence of the Lambs', 'Jurassic Park', 'Star Wars'\n",
+    "]\n",
+    "\n",
+    "award_names = [\n",
+    "    'Oscar', 'Academy Award', 'Golden Globe', 'BAFTA',\n",
+    "    'Palme d\\'Or', 'Emmy', 'Screen Actors Guild Award', 'Tony Award',\n",
+    "    'Critics\\' Choice', 'Independent Spirit Award', 'Cesar Award', 'Goya Award'\n",
+    "]\n",
+    "\n",
+    "# Fetch some real texts to build more realistic reviews\n",
+    "newsgroups = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))\n",
+    "texts = newsgroups.data[:5000]  # Get some real text\n",
+    "\n",
+    "# Function to generate a synthetic review\n",
+    "def generate_review(sentiment, length_range=(50, 500)):\n",
+    "    # Select base text\n",
+    "    base_text = random.choice(texts)\n",
+    "    words = base_text.split()\n",
+    "    \n",
+    "    # Ensure we have at least some words to work with\n",
+    "    if not words:\n",
+    "        words = [\"This\", \"is\", \"a\", \"placeholder\", \"review\"]\n",
+    "    \n",
+    "    # Make sure words list is not empty before inserting\n",
+    "    if len(words) == 0:\n",
+    "        words = [\"This\", \"is\", \"a\", \"placeholder\", \"review\"]\n",
+    "        \n",
+    "    # Select random length within range\n",
+    "    target_length = random.randint(*length_range)\n",
+    "    if len(words) > target_length:\n",
+    "        words = words[:target_length]\n",
+    "    \n",
+    "    # Ensure we have at least one word to avoid empty ranges in randint\n",
+    "    if len(words) == 0:\n",
+    "        words = [\"placeholder\"]\n",
+    "\n",
+    "    # Add sentiment words\n",
+    "    word_list = positive_words if sentiment == 1 else negative_words\n",
+    "    num_sentiment_words = random.randint(3, 10)\n",
+    "\n",
+    "    for _ in range(num_sentiment_words):\n",
+    "        insert_pos = random.randint(0, len(words) - 1)\n",
+    "        sentiment_word = random.choice(word_list)\n",
+    "        words.insert(insert_pos, sentiment_word)\n",
+    "\n",
+    "    # Add movie-related terms sometimes\n",
+    "    movie_terms = ['movie', 'film', 'cinema', 'director', 'actor', 'actress',\n",
+    "                   'script', 'screenplay', 'scene', 'plot', 'character', 'performance']\n",
+    "\n",
+    "    for _ in range(random.randint(1, 5)):\n",
+    "        insert_pos = random.randint(0, len(words) - 1)\n",
+    "        movie_term = random.choice(movie_terms)\n",
+    "        words.insert(insert_pos, movie_term)\n",
+    "\n",
+    "    # Add named entities to some reviews (for NER task)\n",
+    "    if random.random() < 0.7:  # 70% chance to add named entities\n",
+    "        # Add 1-3 director names\n",
+    "        for _ in range(random.randint(1, 3)):\n",
+    "            if random.random() < 0.6:\n",
+    "                insert_pos = random.randint(0, len(words) - 1)\n",
+    "                director = random.choice(director_names)\n",
+    "                words.insert(insert_pos, director)\n",
+    "\n",
+    "        # Add 1-3 actor names\n",
+    "        for _ in range(random.randint(1, 3)):\n",
+    "            if random.random() < 0.7:\n",
+    "                insert_pos = random.randint(0, len(words) - 1)\n",
+    "                actor = random.choice(actor_names)\n",
+    "                words.insert(insert_pos, actor)\n",
+    "\n",
+    "        # Add 0-2 movie titles\n",
+    "        for _ in range(random.randint(0, 2)):\n",
+    "            if random.random() < 0.5:\n",
+    "                insert_pos = random.randint(0, len(words) - 1)\n",
+    "                title = random.choice(movie_titles)\n",
+    "                words.insert(insert_pos, title)\n",
+    "\n",
+    "        # Add 0-1 award mentions\n",
+    "        if random.random() < 0.3:\n",
+    "            insert_pos = random.randint(0, len(words) - 1)\n",
+    "            award = random.choice(award_names)\n",
+    "            words.insert(insert_pos, award)\n",
+    "\n",
+    "    # Join and return\n",
+    "    review = ' '.join(words)\n",
+    "\n",
+    "    # Clean up a bit\n",
+    "    review = review.replace('\\n', ' ').replace('  ', ' ')\n",
+    "\n",
+    "    return review\n",
+    "\n",
+    "# Generate 1000 reviews (500 positive, 500 negative)\n",
+    "reviews = []\n",
+    "labels = []\n",
+    "\n",
+    "for _ in range(500):\n",
+    "    # Generate positive reviews\n",
+    "    reviews.append(generate_review(1))\n",
+    "    labels.append(1)\n",
+    "\n",
+    "    # Generate negative reviews\n",
+    "    reviews.append(generate_review(0))\n",
+    "    labels.append(0)\n",
+    "\n",
+    "# Create DataFrame\n",
+    "reviews_df = pd.DataFrame({\n",
+    "    'review': reviews,\n",
+    "    'sentiment': labels\n",
+    "})\n",
+    "\n",
+    "# Shuffle the DataFrame\n",
+    "reviews_df = reviews_df.sample(frac=1).reset_index(drop=True)\n",
+    "\n",
+    "# Display sample\n",
+    "print(\"Sample of generated movie reviews:\")\n",
+    "print(reviews_df.head())\n",
+    "\n",
+    "# Save to CSV\n",
+    "reviews_df.to_csv('movie_reviews.csv', index=False)\n",
+    "print(\"\\nDataset saved to 'movie_reviews.csv'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90e58e87",
+   "metadata": {},
+   "source": [
+    "############################################################################\n",
+    "# TASK 1: Text Preprocessing\n",
+    "############################################################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "451a9389",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================================================\n",
+      "TASK 1: TEXT PREPROCESSING\n",
+      "==================================================\n",
+      "\n",
+      "Data structure:\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 2 columns):\n",
+      " #   Column     Non-Null Count  Dtype \n",
+      "---  ------     --------------  ----- \n",
+      " 0   review     1000 non-null   object\n",
+      " 1   sentiment  1000 non-null   int64 \n",
+      "dtypes: int64(1), object(1)\n",
+      "memory usage: 15.8+ KB\n",
+      "None\n",
+      "\n",
+      "Sentiment distribution:\n",
+      "sentiment\n",
+      "0    500\n",
+      "1    500\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Original sample review:\n",
+      "--> Note: Reply to a message in Steven Spielberg soc.religion.christian. EVENSON THOMAS RANDALL wrote James Cameron horrible in a message to All: Hi, underwhelming You might want to read Charismatic Chaos by John MacArthur. In it he discussed exactly this queation, actress amongst Avatar others. In my own words, VERY actress unbearable simplified, his Morgan Freeman position awful underwhelming is basically that one must bad decide, what is the film movie Scarlett Johansson most important - experience scene or Scripture? People tend to say Scripture, without living according to that. Their own feeling/prophecy/etc tends to be\n",
+      "\n",
+      "Preprocessed sample review:\n",
+      "note reply message steven spielberg socreligionchristian evenson thomas randall wrote james cameron horrible message hi underwhelming might want read charismatic chaos john macarthur discussed exactly queation actress amongst avatar others words actress unbearable simplified morgan freeman position awful underwhelming basically one must bad decide film movie scarlett johansson important experience scene scripture people tend say scripture without living according feelingprophecyetc tends\n",
+      "\n",
+      "Tokens:\n",
+      "['note', 'reply', 'message', 'steven', 'spielberg', 'socreligionchristian', 'evenson', 'thomas', 'randall', 'wrote', 'james', 'cameron', 'horrible', 'message', 'hi', 'underwhelming', 'might', 'want', 'read', 'charismatic'] ...\n",
+      "\n",
+      "Stemmed tokens (first 20):\n",
+      "['note', 'repli', 'messag', 'steven', 'spielberg', 'socreligionchristian', 'evenson', 'thoma', 'randal', 'wrote', 'jame', 'cameron', 'horribl', 'messag', 'hi', 'underwhelm', 'might', 'want', 'read', 'charismat'] ...\n",
+      "\n",
+      "Lemmatized tokens (first 20):\n",
+      "['note', 'reply', 'message', 'steven', 'spielberg', 'socreligionchristian', 'evenson', 'thomas', 'randall', 'wrote', 'james', 'cameron', 'horrible', 'message', 'hi', 'underwhelming', 'might', 'want', 'read', 'charismatic'] ...\n",
+      "\n",
+      "Comparing stemming vs. lemmatization with examples:\n",
+      "  Original Stemmed Lemmatized\n",
+      "0  running     run    running\n",
+      "1   better  better     better\n",
+      "2  studies   studi      study\n",
+      "3   movies    movi      movie\n",
+      "\n",
+      "Discussion of stemming vs. lemmatization:\n",
+      "1. Stemming is faster but can produce non-existent words. For example, 'running' becomes 'run' with both\n",
+      "   methods, but stemming 'studies' yields 'studi' while lemmatization produces 'study'.\n",
+      "2. Lemmatization preserves meaning better by using dictionary lookup, but is slower.\n",
+      "3. For movie reviews, lemmatization might be preferred as it maintains readability and meaning,\n",
+      "   which is important for sentiment analysis and named entity recognition.\n",
+      "\n",
+      "Sample of processed data:\n",
+      "                                              review  \\\n",
+      "0  --> Note: Reply to a message in Steven Spielbe...   \n",
+      "1  FOR SALE!!!! 1) Sony Car Stereo Amplifier (Mod...   \n",
+      "\n",
+      "                                        preprocessed  \n",
+      "0  note reply message steven spielberg socreligio...  \n",
+      "1  sale sony car stereo amplifier model xm rated ...  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\n\" + \"=\"*50)\n",
+    "print(\"TASK 1: TEXT PREPROCESSING\")\n",
+    "print(\"=\"*50)\n",
+    "\n",
+    "# Load the dataset and examine structure\n",
+    "print(\"\\nData structure:\")\n",
+    "print(reviews_df.info())\n",
+    "print(\"\\nSentiment distribution:\")\n",
+    "print(reviews_df['sentiment'].value_counts())\n",
+    "\n",
+    "# 1. Sample review to track changes\n",
+    "sample_review = reviews_df.iloc[0]['review']\n",
+    "print(\"\\nOriginal sample review:\")\n",
+    "print(sample_review)\n",
+    "\n",
+    "# 2. Implement preprocessing steps\n",
+    "def preprocess_text(text):\n",
+    "    # Convert to lowercase\n",
+    "    text = text.lower()\n",
+    "    \n",
+    "    # Remove punctuation\n",
+    "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
+    "    \n",
+    "    # Remove numbers\n",
+    "    text = re.sub(r'\\d+', '', text)\n",
+    "    \n",
+    "    # Tokenize\n",
+    "    tokens = word_tokenize(text)\n",
+    "    \n",
+    "    # Remove stopwords\n",
+    "    stop_words = set(stopwords.words('english'))\n",
+    "    tokens = [token for token in tokens if token not in stop_words]\n",
+    "    \n",
+    "    # Remove extra whitespace (joining tokens with a space)\n",
+    "    cleaned_text = ' '.join(tokens)\n",
+    "    \n",
+    "    return cleaned_text, tokens\n",
+    "\n",
+    "# Apply preprocessing\n",
+    "preprocessed_review, tokens = preprocess_text(sample_review)\n",
+    "print(\"\\nPreprocessed sample review:\")\n",
+    "print(preprocessed_review)\n",
+    "print(\"\\nTokens:\")\n",
+    "print(tokens[:20], \"...\") # Show first 20 tokens\n",
+    "\n",
+    "# 3. Implement stemming and lemmatization\n",
+    "stemmer = PorterStemmer()\n",
+    "lemmatizer = WordNetLemmatizer()\n",
+    "\n",
+    "stemmed_tokens = [stemmer.stem(token) for token in tokens]\n",
+    "lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
+    "\n",
+    "print(\"\\nStemmed tokens (first 20):\")\n",
+    "print(stemmed_tokens[:20], \"...\")\n",
+    "print(\"\\nLemmatized tokens (first 20):\")\n",
+    "print(lemmatized_tokens[:20], \"...\")\n",
+    "\n",
+    "# 4. Compare stemming vs. lemmatization with examples\n",
+    "print(\"\\nComparing stemming vs. lemmatization with examples:\")\n",
+    "comparison_words = ['running', 'better', 'studies', 'movies']\n",
+    "comparison = pd.DataFrame({\n",
+    "    'Original': comparison_words,\n",
+    "    'Stemmed': [stemmer.stem(word) for word in comparison_words],\n",
+    "    'Lemmatized': [lemmatizer.lemmatize(word) for word in comparison_words]\n",
+    "})\n",
+    "print(comparison)\n",
+    "\n",
+    "# Discussion of stemming vs lemmatization\n",
+    "print(\"\\nDiscussion of stemming vs. lemmatization:\")\n",
+    "print(\"1. Stemming is faster but can produce non-existent words. For example, 'running' becomes 'run' with both\")\n",
+    "print(\"   methods, but stemming 'studies' yields 'studi' while lemmatization produces 'study'.\")\n",
+    "print(\"2. Lemmatization preserves meaning better by using dictionary lookup, but is slower.\")\n",
+    "print(\"3. For movie reviews, lemmatization might be preferred as it maintains readability and meaning,\")\n",
+    "print(\"   which is important for sentiment analysis and named entity recognition.\")\n",
+    "\n",
+    "# Apply preprocessing to all reviews\n",
+    "reviews_df['preprocessed'] = reviews_df['review'].apply(lambda x: preprocess_text(x)[0])\n",
+    "reviews_df['tokens'] = reviews_df['review'].apply(lambda x: preprocess_text(x)[1])\n",
+    "reviews_df['stemmed'] = reviews_df['tokens'].apply(lambda tokens: [stemmer.stem(token) for token in tokens])\n",
+    "reviews_df['lemmatized'] = reviews_df['tokens'].apply(lambda tokens: [lemmatizer.lemmatize(token) for token in tokens])\n",
+    "\n",
+    "print(\"\\nSample of processed data:\")\n",
+    "print(reviews_df[['review', 'preprocessed']].head(2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60a77509",
+   "metadata": {},
+   "source": [
+    "############################################################################\n",
+    "# TASK 2: Text Exploration and Visualization\n",
+    "############################################################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bdb1aa9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================================================\n",
+      "TASK 2: TEXT EXPLORATION AND VISUALIZATION\n",
+      "==================================================\n",
+      "\n",
+      "Average review length: 67.60 words\n",
+      "Figure saved: review_length_distribution.png\n",
+      "Vocabulary size: 12575 unique words\n",
+      "\n",
+      "Most common words in positive reviews:\n",
+      "[('would', 190), ('good', 185), ('great', 166), ('enjoyable', 155), ('fantastic', 151), ('phenomenal', 150), ('one', 147), ('wonderful', 146), ('movie', 144), ('like', 137), ('spectacular', 136), ('perfect', 136), ('script', 136), ('screenplay', 135), ('powerful', 134), ('moving', 134), ('entertaining', 132), ('outstanding', 132), ('impressive', 132), ('actress', 130)]\n",
+      "\n",
+      "Most common words in negative reviews:\n",
+      "[('would', 205), ('one', 172), ('bad', 152), ('terrible', 151), ('awful', 148), ('waste', 144), ('poor', 143), ('script', 142), ('boring', 142), ('disaster', 142), ('predictable', 141), ('annoying', 140), ('dull', 139), ('plot', 137), ('failure', 135), ('underwhelming', 134), ('performance', 134), ('character', 134), ('confusing', 133), ('horrible', 132)]\n",
+      "Figure saved: positive_wordcloud.png\n",
+      "Figure saved: negative_wordcloud.png\n",
+      "Figure saved: bigram_frequencies.png\n",
+      "Figure saved: trigram_frequencies.png\n",
+      "Figure saved: tfidf_scores.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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pdU2aNNEjjzyili1byjAMrVu3LteSPQAAAAAAgDvprvr1vb59+8re3l6ffPJJUYeC23Btl31+fQ8AAADAvYxf3wPy9q/69b2rV6/q8OHDSkhIUN26dYs6HAAAAAAAANxhd0VS6tChQ2ratKnq1q2rYcOGFXU4AAAAAAAAuMPuio3OGzVqpIyMjKIOAwAAAAAAADZyV8yUAgAAAAAAwP3lrpgphXvTocjgG25oBgAAAAAA7l/MlAIAAAAAAIDNkZQCAAAAAACAzZGUAgAAAAAAgM2RlAIAAAAAAIDNsdE57ph6EzfIztG5qMMAAAAAgH+tpGldijoE4I5hphQAAAAAAABsjqQUAAAAAAAAbI6kFAAAAAAAAGzutpNSQUFBCg8PL4RQCs/dFFNMTIw8PT1vWCc0NFTdu3e3STwAAAAAAAB3AzY6vwvMnj1bhmEUaptJSUmqWrWqvv/+ezVq1KhQ2wYAAAAAALhdJKXuAh4eHkUdAgAAAAAAgE3d0vK9y5cva8CAAXJ1dZWXl5dmzpyZq05mZqYiIiJUqVIlubi4qEWLFoqNjTXPX1vOtmHDBtWuXVuurq4KCQnRmTNnzDoWiyXXx9fX1zx/6NAhderUSa6uripfvrz69++v3377rUBjSE1Nlb29vXbv3i1JysnJUalSpfTAAw+YdT755BN5e3ubxy+99JL8/f3l7OysatWqacKECbpy5Yp5fv/+/XrwwQfl5uYmd3d3NWnSxGz/mhuN9/rle0FBQRo5cqRefPFFlSpVShUqVNCkSZOs2jt69KjatGmjEiVKqE6dOvr6669lsVi0atUqSVLVqlUlSQEBAbJYLAoKCjLHO3nyZFWuXFmOjo5q1KiR1q9fb7ablJQki8WiFStW6MEHH5Szs7MaNmyohISEAt1fAAAAAACAgrilpNSYMWMUFxenL774Qhs3blRsbKz27t1rVScsLEwJCQlasmSJDhw4oF69eikkJEQnTpww62RkZGjGjBlauHChvv32WyUnJysiIsI8f+bMGfNz8uRJ+fn5qV27dpKkixcvqn379goICNDu3bu1fv16/frrr+rdu3eBxuDh4aFGjRqZibKDBw/KYrHo+++/V3p6uiQpLi5OgYGB5jVubm6KiYnR4cOHNXv2bL3//vt66623zPP9+vVT5cqVtWvXLu3Zs0djx45V8eLFCzzevCxYsEAuLi7asWOHXn/9dU2ePFmbNm2SJGVnZ6t79+5ydnbWjh079N5772n8+PFW1+/cuVOS9PXXX+vMmTNasWKFpL+WCs6cOVMzZszQgQMHFBwcrG7dulk9H0kaP368IiIitG/fPvn7+6tv3766evVqge4xAAAAAADAzRR4+V56ero+/PBDffLJJ+rQoYOkvxInlStXNuskJycrOjpaycnJqlixoiQpIiJC69evV3R0tKZOnSpJunLliubNm6fq1atL+iuRNXnyZLOdChUqSJIMw9Bjjz0mDw8PzZ8/X5I0d+5cBQQEmG1J0kcffSRvb28dP35c/v7+Nx1LUFCQYmNjFRERodjYWD300EM6evSotm3bppCQEMXGxurFF18067/88svmd19fX0VERGjJkiVmneTkZI0ZM0a1atWSJNWoUcOqv5uNNy8NGjTQxIkTzfbmzp2rzZs366GHHtKmTZuUmJio2NhY81699tpreuihh8zry5YtK0kqXbq0WUeSZsyYoZdeeklPPPGEJGn69OnasmWLZs2apf/+979mvYiICHXp0kWSFBkZqbp16+rkyZPmGP8uMzNTmZmZ5nFaWtoNxwYAAAAAAFDgpFRiYqKysrLUokULs6xUqVKqWbOmeXzw4EFlZ2fnSgxlZmaqdOnS5rGzs7OZoJEkLy8vpaSk5Orz//7v/5SQkKDdu3fLyclJ0l9L5bZs2SJXV9c8YyxIUiowMFAffvihsrOzFRcXp4cfflgVKlRQbGysGjRooJMnT5rL3SRp6dKlmjNnjhITE5Wenq6rV6/K3d3dPP/CCy9o6NChWrhwoTp27KhevXpZja+g4/27Bg0aWB3//Zpjx47J29vbKtnUvHnzm447LS1Nv/zyi1q3bm1V3rp1a+3fvz/f/r28vCRJKSkpeSaloqKiFBkZedP+AQAAAAAArrml5Xs3k56eLnt7e+3Zs0f79u0zP0eOHNHs2bPNen9f2ib9tYfU9b8+98knn+itt97SypUrValSJas+unbtatX+vn37dOLECXOJ3820a9dOly5d0t69e/Xtt98qKCjInD0VFxenihUrmrOdEhIS1K9fP3Xu3Flr1qzR999/r/HjxysrK8tsb9KkSfrhhx/UpUsXffPNN6pTp45Wrlx5S+O9Xl7X5OTkFGh8heHv/VssFknKt/9x48YpNTXV/Jw+fdomMQIAAAAAgH+vAs+Uql69uooXL64dO3bIx8dHkvT777/r+PHj5v5LAQEBys7OVkpKitq2bfuPg0pISNDQoUM1f/58qw3IJalx48b6/PPP5evrq2LF/tmPB3p6eqpBgwaaO3euihcvrlq1aqlcuXLq06eP1qxZY7Wf1Pbt21WlShWrPZt++umnXG36+/vL399fo0aNUt++fRUdHa0ePXr8o/hupmbNmjp9+rR+/fVXlS9fXpK0a9cuqzoODg6S/tp/6hp3d3dVrFhR8fHxVmOMj48v0Eyr/Dg6OsrR0fEfXw8AAAAAAO4/BZ4p5erqqiFDhmjMmDH65ptvdOjQIYWGhsrO7v9vwt/fX/369dOAAQO0YsUKnTp1Sjt37lRUVJTWrl1boH7Onj2rHj166IknnlBwcLDOnj2rs2fP6ty5c5KkESNG6MKFC+rbt6927dqlxMREbdiwQYMGDbJKwNxMUFCQFi1aZCZnSpUqpdq1a2vp0qVWCZsaNWooOTlZS5YsUWJioubMmWM1C+qPP/5QWFiYYmNj9dNPPyk+Pl67du1S7dq1CxzLrXrooYdUvXp1DRw4UAcOHFB8fLy579W1WU3lypWTk5OTuRF8amqqpL82q58+fbqWLl2qY8eOaezYsdq3b5+ef/75OxYvAAAAAADA9W5p+d4bb7yhtm3bqmvXrurYsaPatGmjJk2aWNWJjo7WgAEDNHr0aNWsWVPdu3fXrl27zNlVN3P06FH9+uuvWrBggby8vMxPs2bNJMmc6ZOdna2HH35Y9evXV3h4uDw9Pa0SZDcTGBio7Oxsq72jgoKCcpV169ZNo0aNUlhYmBo1aqTt27drwoQJ5nl7e3udP39eAwYMkL+/v3r37q1OnTr9f+3deVgW9f7/8dcNyi07Ii6goCRIWrgmbpkYnnAjzU6aeVSUTHMlBY0yBbWkktLMtmMHrFyyTPNkbpmYkbvikrhx5EBleTKF0ASF+/eHX+9fdyxp4n0rPB/XdV8XM/OZmdfM50x1vc9nPnNT51iyt7fXqlWrlJ+fr7Zt2+rxxx83j+SqUaOGJKlatWp67bXX9Pbbb8vHx0d9+vSRJI0fP14TJ07UpEmTFBwcrHXr1mn16tUlJmcHAAAAAAC4mQymP5vcCLeFtLQ03XvvvTpx4oTFpOq2kJeXJ3d3d/lGL5ed0cmmWQAAAADgdpaV2MvWEYDrdrUukJuba/GhuD/6a5MyweZWrlwpFxcXBQYG6sSJE5owYYI6depk84IUAAAAAADAtaAodZv69ddfNWXKFGVnZ8vLy0vdunVTUlKSrWMBAAAAAABcE4pSt6khQ4ZoyJAhto4BAAAAAADwl1zXROcAAAAAAABARWCkFG6aQwnh5U5oBgAAAAAAqi5GSgEAAAAAAMDqKEoBAAAAAADA6ihKAQAAAAAAwOooSgEAAAAAAMDqmOgcN83d09fLzuhk6xgAAAAAcNvKSuxl6wjATcNIKQAAAAAAAFgdRSkAAAAAAABYHUUpAAAAAAAAWB1FKQAAAAAAAFgdRalbRGhoqKKjo2/qOVJTU2UwGHTu3Lmbeh4AAAAAAIA/Q1GqCunYsaNOnTold3d3W0cBAAAAAABVHEWpKsTBwUH16tWTwWAodXtRUZGKi4utnAoAAAAAAFRFFKVs4Pz58xoyZIhcXFzk7e2tpKSkEm0KCgoUExOj+vXry9nZWe3atVNqamq5xzUYDFq4cKEeeughOTk5KTAwUKtXrzZv/+PreykpKfLw8NDq1avVrFkzGY1GZWdn6+zZsxoyZIhq1qwpJycn9ejRQ8ePH6/IWwAAAAAAAKo4ilI2EBsbqy1btujTTz/Vhg0blJqaqr1791q0GTt2rLZt26Zly5bpwIEDeuSRR9S9e/c/LQ4lJCSof//+OnDggHr27KlBgwbpl19+KbP9hQsX9OKLL2rhwoX69ttvVadOHUVGRmr37t1avXq1tm3bJpPJpJ49e+rSpUulHqOgoEB5eXkWPwAAAAAAgPJQlLKy/Px8vfvuu5ozZ47CwsIUHBysRYsW6fLly+Y22dnZSk5O1kcffaTOnTurcePGiomJ0b333qvk5ORyjx8ZGamBAwcqICBAL7zwgvLz87Vz584y21+6dElvvPGGOnbsqKCgIH3//fdavXq1Fi5cqM6dO6tFixZavHixvv/+e61atarUY8yePVvu7u7mn6+v71+6NwAAAAAAoOqoZusAVU1mZqYKCwvVrl078zpPT08FBQWZlw8ePKiioiI1adLEYt+CggLVqlWr3OM3b97c/Lezs7Pc3Nx0+vTpMts7ODhY7JORkaFq1apZ5KtVq5aCgoKUkZFR6jHi4uI0ceJE83JeXh6FKQAAAAAAUC6KUreg/Px82dvba8+ePbK3t7fY5uLiUu6+1atXt1g2GAzlTl7u6OhY5sTn18poNMpoNN7QMQAAAAAAQNXC63tW1rhxY1WvXl07duwwrzt79qyOHTtmXm7VqpWKiop0+vRpBQQEWPzq1at3U/M1bdpUly9ftsh35swZHT16VM2aNbup5wYAAAAAAFUHRSkrc3FxUVRUlGJjY/Xll1/q0KFDioyMlJ3d/++KJk2aaNCgQRoyZIg++eQTnTx5Ujt37tTs2bO1Zs2am5ovMDBQffr00YgRI/T1119r//79+sc//qH69eurT58+N/XcAAAAAACg6uD1PRt4+eWXlZ+fr4iICLm6umrSpEnKzc21aJOcnKxZs2Zp0qRJ+v777+Xl5aX27durd+/eNz1fcnKyJkyYoN69e6uwsFD33XefPv/88xKvBgIAAAAAAPxVBpPJZLJ1CFQueXl5V77CF71cdkYnW8cBAAAAgNtWVmIvW0cArtvVukBubq7c3NzKbMfrewAAAAAAALA6ilIAAAAAAACwOopSAAAAAAAAsDomOsdNcyghvNx3RwEAAAAAQNXFSCkAAAAAAABYHUUpAAAAAAAAWB1FKQAAAAAAAFgdRSkAAAAAAABYHROd46a5e/p62RmdbB0DAAAAAG5bWYm9bB0BuGkYKQUAAAAAAACroygFAAAAAAAAq6MoBQAAAAAAAKujKAUAAAAAAACroyhlRaGhoYqOjrZ1jBJu1VwAAAAAAKDyoihVSURGRqpv3762jgEAAAAAAHBNKEpVgMLCQpudu6ioSMXFxTY7PwAAAAAAwF9BUeovCA0N1dixYxUdHS0vLy+Fh4dLkrZs2aKQkBAZjUZ5e3vr6aef1uXLly32vXz5ssaOHSt3d3d5eXnpueeek8lkMm8vKChQTEyM6tevL2dnZ7Vr106pqanm7SkpKfLw8NDq1avVrFkzGY1GDR8+XIsWLdKnn34qg8Egg8Fgsc/vnT9/XkOGDJGLi4u8vb2VlJRUos2fZQAAAAAAALhR1Wwd4Ha1aNEiPfnkk0pLS5Mkff/99+rZs6ciIyP13nvv6ciRIxoxYoRq1Kih+Ph4i/2ioqK0c+dO7d69W0888YT8/Pw0YsQISdLYsWN1+PBhLVu2TD4+Plq5cqW6d++ugwcPKjAwUJJ04cIFvfjii1q4cKFq1aolb29v/fbbb8rLy1NycrIkydPTs9TcsbGx2rJliz799FPVqVNHzzzzjPbu3auWLVua21xLht8rKChQQUGBeTkvL++G7i0AAAAAAKj8DKbfD9PBNQkNDVVeXp727t1rXvfss89qxYoVysjIkMFgkCS98cYbmjJlinJzc2VnZ6fQ0FCdPn1a3377rbnN008/rdWrV+vw4cPKzs7WHXfcoezsbPn4+JiP3a1bN4WEhOiFF15QSkqKhg0bpvT0dLVo0cLcJjIyUufOndOqVavKzJ2fn69atWrpgw8+0COPPCJJ+uWXX9SgQQM98cQTmjt37jVl+KP4+HglJCSUWO8bvVx2RqdrvKsAAAAAgD/KSuxl6wjAdcvLy5O7u7tyc3Pl5uZWZjtGSv1Fbdq0sVjOyMhQhw4dzMUmSerUqZPy8/P13Xffyc/PT5LUvn17izYdOnRQUlKSioqKdPDgQRUVFalJkyYWxy4oKFCtWrXMyw4ODmrevPl1Z87MzFRhYaHatWtnXufp6amgoCDz8rVm+L24uDhNnDjRvJyXlydfX9/rzgcAAAAAAKoOilJ/kbOzc4UfMz8/X/b29tqzZ4/s7e0ttrm4uJj/dnR0tChs2SLD7xmNRhmNxpuSBwAAAAAAVE4UpSpI06ZNtWLFCplMJnPBKC0tTa6urmrQoIG53Y4dOyz22759uwIDA2Vvb69WrVqpqKhIp0+fVufOna/r/A4ODioqKiq3TePGjVW9enXt2LHDPHLr7NmzOnbsmLp06SJJN5QBAAAAAADgWvH1vQoyevRo5eTkaNy4cTpy5Ig+/fRTTZ8+XRMnTpSd3f+/zdnZ2Zo4caKOHj2qpUuXav78+ZowYYIkqUmTJho0aJCGDBmiTz75RCdPntTOnTs1e/ZsrVmzptzzN2rUSAcOHNDRo0f1888/69KlSyXauLi4KCoqSrGxsfryyy916NAhRUZGWuS7kQwAAAAAAADXipFSFaR+/fr6/PPPFRsbqxYtWsjT01NRUVGaOnWqRbshQ4bot99+U0hIiOzt7TVhwgQ98cQT5u3JycmaNWuWJk2apO+//15eXl5q3769evfuXe75R4wYodTUVN1zzz3Kz8/X5s2bFRoaWqLdyy+/rPz8fEVERMjV1VWTJk1Sbm6uRZu/mgEAAAAAAOBa8fU9VLirs+zz9T0AAAAAuDF8fQ+3o2v9+h6v7wEAAAAAAMDqKEoBAAAAAADA6ihKAQAAAAAAwOqY6Bw3zaGE8HLfHQUAAAAAAFUXI6UAAAAAAABgdRSlAAAAAAAAYHUUpQAAAAAAAGB1FKUAAAAAAABgdRSlAAAAAAAAYHV8fQ83zd3T18vO6GTrGAAAAACAcmQl9rJ1BFRRjJQCAAAAAACA1VGUAgAAAAAAgNVRlAIAAAAAAIDVVcmiVGpqqgwGg86dOydJSklJkYeHh00zAQAAAAAAVCW3dVGqoopJAwYM0LFjx2480E0SHx+vli1b2joGAAAAAABAheHre5IcHR3l6Oho6xglmEwmFRUV2ToGAAAAAABAhbPZSKnQ0FCNHz9ekydPlqenp+rVq6f4+HiLNq+88oqCg4Pl7OwsX19fjR49Wvn5+ZKuvII3bNgw5R3+VCUAADX5SURBVObmymAwyGAwmPd///33dc8998jV1VX16tXTY489ptOnT5eZpbQRV59++qlat26tGjVq6I477lBCQoIuX75c5jEiIyPVt29fJSQkqHbt2nJzc9OoUaNUWFhoblNQUKDx48erTp06qlGjhu69917t2rXLvP3qa4Vr165VmzZtZDQa9cEHHyghIUH79+83X2dKSookyWAw6O2331bv3r3l5OSkpk2batu2bTpx4oRCQ0Pl7Oysjh07KjMz03yOzMxM9enTR3Xr1pWLi4vatm2rL774wuJaGjVqpBdeeEHDhw+Xq6ur/Pz89M4775R57QAAAAAAANfLpq/vLVq0SM7OztqxY4deeuklzZgxQxs3bjRvt7Oz02uvvaZvv/1WixYt0pdffqnJkydLkjp27Ki5c+fKzc1Np06d0qlTpxQTEyNJunTpkmbOnKn9+/dr1apVysrKUmRk5DXn2rp1q4YMGaIJEybo8OHDevvtt5WSkqLnn3++3P02bdqkjIwMpaamaunSpfrkk0+UkJBg3j558mStWLFCixYt0t69exUQEKDw8HD98ssvFsd5+umnlZiYqIyMDP3tb3/TpEmTdNddd5mvc8CAAea2M2fO1JAhQ5Senq4777xTjz32mEaOHKm4uDjt3r1bJpNJY8eONbfPz89Xz549tWnTJu3bt0/du3dXRESEsrOzLTIkJSXpnnvu0b59+zR69Gg9+eSTOnr06DXfQwAAAAAAgPIYTCaTyRYnDg0NVVFRkbZu3WpeFxISovvvv1+JiYml7vPxxx9r1KhR+vnnnyVdGeEUHR1tnrC8LLt371bbtm3166+/ysXFRampqeratavOnj0rDw+PEsfp1q2bwsLCFBcXZz7GBx98oMmTJ+uHH34o9RyRkZH697//rZycHDk5OUmS3nrrLcXGxio3N1e//fabatasqZSUFD322GOSrhTPGjVqpOjoaMXGxppzrVq1Sn369DEfOz4+XqtWrVJ6errFOQ0Gg6ZOnaqZM2dKkrZv364OHTro3Xff1fDhwyVJy5Yt07Bhw/Tbb7+VeX/uvvtujRo1yly8atSokTp37qz3339f0pXXCOvVq6eEhASNGjWqxP4FBQUqKCgwL+fl5cnX11e+0ctlZ3Qq87wAAAAAANvLSuxl6wioZPLy8uTu7q7c3Fy5ubmV2c6mI6WaN29usezt7W3xmt0XX3yhsLAw1a9fX66urho8eLDOnDmjCxculHvcPXv2KCIiQn5+fnJ1dVWXLl0kqcRooLLs379fM2bMkIuLi/k3YsQInTp1qtxzt2jRwlyQkqQOHTooPz9fOTk5yszM1KVLl9SpUyfz9urVqyskJEQZGRkWx7nnnnuuKadkeQ/r1q0rSQoODrZYd/HiReXl5Um6MlIqJiZGTZs2lYeHh1xcXJSRkVHi3vz+uAaDQfXq1SvzFcjZs2fL3d3d/PP19b3m/AAAAAAAoGqyaVGqevXqFssGg0HFxcWSpKysLPXu3VvNmzfXihUrtGfPHi1YsECSLOZp+qPz588rPDxcbm5uWrx4sXbt2qWVK1f+6X6/l5+fr4SEBKWnp5t/Bw8e1PHjx1WjRo2/cqnXxdnZ+Zrb/v4eGgyGMtddva8xMTFauXKlXnjhBW3dulXp6ekKDg4ucW/K65s/iouLU25urvmXk5NzzfkBAAAAAEDVdMt+fW/Pnj0qLi5WUlKS7Oyu1M6WL19u0cbBwaHE1+mOHDmiM2fOKDEx0TxiZ/fu3dd17tatW+vo0aMKCAi4rv3279+v3377zfwlv+3bt8vFxUW+vr7y8vKSg4OD0tLS1LBhQ0lXXt/btWuXoqOjyz1uadf5V6WlpSkyMlIPPfSQpCsFuKysrBs6ptFolNForIB0AAAAAACgqrhli1IBAQG6dOmS5s+fr4iICKWlpemtt96yaNOoUSPl5+dr06ZN5lfn/Pz85ODgoPnz52vUqFE6dOiQec6lazVt2jT17t1bfn5++vvf/y47Ozvt379fhw4d0qxZs8rcr7CwUFFRUZo6daqysrI0ffp0jR07VnZ2dnJ2dtaTTz6p2NhYeXp6ys/PTy+99JIuXLigqKiocvM0atRIJ0+eVHp6uho0aCBXV9e/XAQKDAzUJ598ooiICBkMBj333HNljoACAAAAAAC4WWz6+l55WrRooVdeeUUvvvii7r77bi1evFizZ8+2aNOxY0eNGjVKAwYMUO3atfXSSy+pdu3aSklJ0UcffaRmzZopMTFRc+bMua5zh4eH67PPPtOGDRvUtm1btW/fXq+++qp5hFNZwsLCFBgYqPvuu08DBgzQgw8+qPj4ePP2xMREPfzwwxo8eLBat26tEydOaP369apZs2a5x3344YfVvXt3de3aVbVr19bSpUuv63p+75VXXlHNmjXVsWNHRUREKDw8XK1bt/7LxwM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+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"\\n\" + \"=\"*50)\n",
+    "print(\"TASK 2: TEXT EXPLORATION AND VISUALIZATION\")\n",
+    "print(\"=\"*50)\n",
+    "\n",
+    "# 1. Calculate basic text statistics\n",
+    "# Average review length (in words)\n",
+    "reviews_df['word_count'] = reviews_df['tokens'].apply(len)\n",
+    "avg_review_length = reviews_df['word_count'].mean()\n",
+    "\n",
+    "print(f\"\\nAverage review length: {avg_review_length:.2f} words\")\n",
+    "\n",
+    "# Distribution of review lengths\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(reviews_df['word_count'], bins=30, kde=True)\n",
+    "plt.title('Distribution of Review Lengths')\n",
+    "plt.xlabel('Number of Words')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.savefig('review_length_distribution.png')\n",
+    "print(\"Figure saved: review_length_distribution.png\")\n",
+    "\n",
+    "# Vocabulary size\n",
+    "all_tokens = [token for tokens in reviews_df['tokens'] for token in tokens]\n",
+    "vocabulary_size = len(set(all_tokens))\n",
+    "print(f\"Vocabulary size: {vocabulary_size} unique words\")\n",
+    "\n",
+    "# 2. Identify most common words in positive and negative reviews\n",
+    "positive_reviews = reviews_df[reviews_df['sentiment'] == 1]\n",
+    "negative_reviews = reviews_df[reviews_df['sentiment'] == 0]\n",
+    "\n",
+    "positive_tokens = [token for tokens in positive_reviews['tokens'] for token in tokens]\n",
+    "negative_tokens = [token for tokens in negative_reviews['tokens'] for token in tokens]\n",
+    "\n",
+    "positive_word_freq = Counter(positive_tokens).most_common(20)\n",
+    "negative_word_freq = Counter(negative_tokens).most_common(20)\n",
+    "\n",
+    "print(\"\\nMost common words in positive reviews:\")\n",
+    "print(positive_word_freq)\n",
+    "print(\"\\nMost common words in negative reviews:\")\n",
+    "print(negative_word_freq)\n",
+    "\n",
+    "# 3. Create word clouds for positive and negative reviews\n",
+    "# Function to create and save word clouds\n",
+    "def create_wordcloud(tokens, title, filename):\n",
+    "    text = ' '.join(tokens)\n",
+    "    wordcloud = WordCloud(width=800, height=400, background_color='white', max_words=200).generate(text)\n",
+    "    \n",
+    "    plt.figure(figsize=(12, 6))\n",
+    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
+    "    plt.axis('off')\n",
+    "    plt.title(title)\n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig(filename)\n",
+    "    print(f\"Figure saved: {filename}\")\n",
+    "\n",
+    "create_wordcloud(positive_tokens, 'Word Cloud - Positive Reviews', 'positive_wordcloud.png')\n",
+    "create_wordcloud(negative_tokens, 'Word Cloud - Negative Reviews', 'negative_wordcloud.png')\n",
+    "\n",
+    "# 4. Generate and visualize n-gram frequencies (n=2 and n=3)\n",
+    "def get_ngrams(tokens, n):\n",
+    "    return list(ngrams(tokens, n))\n",
+    "\n",
+    "# Get bigrams and trigrams\n",
+    "positive_bigrams = [bg for tokens in positive_reviews['tokens'] for bg in get_ngrams(tokens, 2)]\n",
+    "negative_bigrams = [bg for tokens in negative_reviews['tokens'] for bg in get_ngrams(tokens, 2)]\n",
+    "positive_trigrams = [tg for tokens in positive_reviews['tokens'] for tg in get_ngrams(tokens, 3)]\n",
+    "negative_trigrams = [tg for tokens in negative_reviews['tokens'] for tg in get_ngrams(tokens, 3)]\n",
+    "\n",
+    "# Count frequencies\n",
+    "pos_bigram_freq = Counter(positive_bigrams).most_common(15)\n",
+    "neg_bigram_freq = Counter(negative_bigrams).most_common(15)\n",
+    "pos_trigram_freq = Counter(positive_trigrams).most_common(15)\n",
+    "neg_trigram_freq = Counter(negative_trigrams).most_common(15)\n",
+    "\n",
+    "# Convert to readable format for display\n",
+    "pos_bigram_labels = [' '.join(bg) for bg, _ in pos_bigram_freq]\n",
+    "pos_bigram_values = [count for _, count in pos_bigram_freq]\n",
+    "neg_bigram_labels = [' '.join(bg) for bg, _ in neg_bigram_freq]\n",
+    "neg_bigram_values = [count for _, count in neg_bigram_freq]\n",
+    "\n",
+    "# Visualize bigrams\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "\n",
+    "plt.subplot(2, 1, 1)\n",
+    "plt.barh(range(len(pos_bigram_labels)), pos_bigram_values, align='center')\n",
+    "plt.yticks(range(len(pos_bigram_labels)), pos_bigram_labels)\n",
+    "plt.title('Top 15 Bigrams in Positive Reviews')\n",
+    "plt.xlabel('Frequency')\n",
+    "\n",
+    "plt.subplot(2, 1, 2)\n",
+    "plt.barh(range(len(neg_bigram_labels)), neg_bigram_values, align='center')\n",
+    "plt.yticks(range(len(neg_bigram_labels)), neg_bigram_labels)\n",
+    "plt.title('Top 15 Bigrams in Negative Reviews')\n",
+    "plt.xlabel('Frequency')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('bigram_frequencies.png')\n",
+    "print(\"Figure saved: bigram_frequencies.png\")\n",
+    "\n",
+    "# Convert trigrams to readable format\n",
+    "pos_trigram_labels = [' '.join(tg) for tg, _ in pos_trigram_freq]\n",
+    "pos_trigram_values = [count for _, count in pos_trigram_freq]\n",
+    "neg_trigram_labels = [' '.join(tg) for tg, _ in neg_trigram_freq]\n",
+    "neg_trigram_values = [count for _, count in neg_trigram_freq]\n",
+    "\n",
+    "# Visualize trigrams\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "\n",
+    "plt.subplot(2, 1, 1)\n",
+    "plt.barh(range(len(pos_trigram_labels)), pos_trigram_values, align='center')\n",
+    "plt.yticks(range(len(pos_trigram_labels)), pos_trigram_labels)\n",
+    "plt.title('Top 15 Trigrams in Positive Reviews')\n",
+    "plt.xlabel('Frequency')\n",
+    "\n",
+    "plt.subplot(2, 1, 2)\n",
+    "plt.barh(range(len(neg_trigram_labels)), neg_trigram_values, align='center')\n",
+    "plt.yticks(range(len(neg_trigram_labels)), neg_trigram_labels)\n",
+    "plt.title('Top 15 Trigrams in Negative Reviews')\n",
+    "plt.xlabel('Frequency')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('trigram_frequencies.png')\n",
+    "print(\"Figure saved: trigram_frequencies.png\")\n",
+    "\n",
+    "# 5. Calculate and visualize TF-IDF scores\n",
+    "# Create TF-IDF vectorizer\n",
+    "tfidf_vectorizer = TfidfVectorizer(max_features=1000, \n",
+    "                                   stop_words='english', \n",
+    "                                   ngram_range=(1, 1))\n",
+    "\n",
+    "# Fit and transform the preprocessed reviews\n",
+    "tfidf_matrix = tfidf_vectorizer.fit_transform(reviews_df['preprocessed'])\n",
+    "feature_names = tfidf_vectorizer.get_feature_names_out()\n",
+    "\n",
+    "# Get top TF-IDF terms for positive and negative reviews\n",
+    "def get_top_tfidf_terms(matrix, feature_names, class_idx, n=20):\n",
+    "    # Get indices of class documents\n",
+    "    class_indices = [i for i, label in enumerate(reviews_df['sentiment']) if label == class_idx]\n",
+    "    \n",
+    "    # Get TF-IDF scores for class documents\n",
+    "    class_tfidf = matrix[class_indices]\n",
+    "    \n",
+    "    # Average TF-IDF scores across documents\n",
+    "    mean_tfidf = np.array(class_tfidf.mean(axis=0)).flatten()\n",
+    "    \n",
+    "    # Get top terms\n",
+    "    top_indices = np.argsort(mean_tfidf)[-n:][::-1]\n",
+    "    top_terms = [(feature_names[i], mean_tfidf[i]) for i in top_indices]\n",
+    "    \n",
+    "    return top_terms\n",
+    "\n",
+    "# Get top terms\n",
+    "pos_tfidf_terms = get_top_tfidf_terms(tfidf_matrix, feature_names, 1)\n",
+    "neg_tfidf_terms = get_top_tfidf_terms(tfidf_matrix, feature_names, 0)\n",
+    "\n",
+    "# Visualize TF-IDF scores\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "\n",
+    "# Positive reviews\n",
+    "plt.subplot(2, 1, 1)\n",
+    "pos_terms = [term for term, _ in pos_tfidf_terms]\n",
+    "pos_scores = [score for _, score in pos_tfidf_terms]\n",
+    "plt.barh(range(len(pos_terms)), pos_scores, align='center')\n",
+    "plt.yticks(range(len(pos_terms)), pos_terms)\n",
+    "plt.title('Top 20 TF-IDF Terms in Positive Reviews')\n",
+    "plt.xlabel('TF-IDF Score')\n",
+    "\n",
+    "# Negative reviews\n",
+    "plt.subplot(2, 1, 2)\n",
+    "neg_terms = [term for term, _ in neg_tfidf_terms]\n",
+    "neg_scores = [score for _, score in neg_tfidf_terms]\n",
+    "plt.barh(range(len(neg_terms)), neg_scores, align='center')\n",
+    "plt.yticks(range(len(neg_terms)), neg_terms)\n",
+    "plt.title('Top 20 TF-IDF Terms in Negative Reviews')\n",
+    "plt.xlabel('TF-IDF Score')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('tfidf_scores.png')\n",
+    "print(\"Figure saved: tfidf_scores.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2af7526",
+   "metadata": {},
+   "source": [
+    "############################################################################\n",
+    "# TASK 3: Named Entity Recognition (NER) Exploration\n",
+    "############################################################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a0f05baa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "==================================================\n",
+      "TASK 3: NAMED ENTITY RECOGNITION (NER) EXPLORATION\n",
+      "==================================================\n",
+      "\n",
+      "Total entities found: 431\n",
+      "\n",
+      "Entity types distribution:\n",
+      "entity_type\n",
+      "PERSON          231\n",
+      "ORGANIZATION    139\n",
+      "GPE              55\n",
+      "GSP               2\n",
+      "FACILITY          2\n",
+      "LOCATION          2\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Figure saved: entity_type_distribution.png\n",
+      "Figure saved: top_entities_by_type.png\n",
+      "Figure saved: entity_comparison.png\n",
+      "\n",
+      "Custom movie entities found: 140\n",
+      "\n",
+      "Custom entity types distribution:\n",
+      "entity_type\n",
+      "ACTOR              47\n",
+      "DIRECTOR           40\n",
+      "MOVIE              29\n",
+      "AWARD              13\n",
+      "POTENTIAL_MOVIE    11\n",
+      "Name: count, dtype: int64\n",
+      "Figure saved: custom_entity_types.png\n",
+      "\n",
+      "Custom NER Evaluation:\n",
+      "Precision: 1.00\n",
+      "Recall: 1.00\n",
+      "F1 Score: 1.00\n",
+      "\n",
+      "Entity highlighting example generated - would show highlighted HTML in Jupyter notebook\n",
+      "\n",
+      "Example entity highlighting for sample reviews:\n",
+      "\n",
+      "Review 1 (Sentiment: Positive):\n",
+      "Entities found:\n",
+      "  - PERSON: Denzel\n",
+      "  - ORGANIZATION: Washington\n",
+      "  - PERSON: X11R5\n",
+      "  - ORGANIZATION: MIT\n",
+      "  - PERSON: Ximp\n",
+      "  - PERSON: Xsi\n",
+      "  - PERSON: Denis Villeneuve\n",
+      "  - ORGANIZATION: ASCII\n",
+      "  - PERSON: Kanji\n",
+      "  - PERSON: Golden Globe\n",
+      "  - ORGANIZATION: Francis Ford Coppola\n",
+      "  - PERSON: Xsi\n",
+      "  - PERSON: Exp\n",
+      "  - DIRECTOR: Denis Villeneuve\n",
+      "  - DIRECTOR: Francis Ford Coppola\n",
+      "  - ACTOR: Denzel Washington\n",
+      "  - AWARD: Golden Globe\n",
+      "\n",
+      "Review 2 (Sentiment: Negative):\n",
+      "Entities found:\n",
+      "  - PERSON: Howdy\n",
+      "\n",
+      "Review 3 (Sentiment: Positive):\n",
+      "Entities found:\n",
+      "  - PERSON: Ridley\n",
+      "  - PERSON: Scott\n",
+      "  - PERSON: Robert De\n",
+      "  - PERSON: Daniel\n",
+      "  - PERSON: Diamond Stealth\n",
+      "  - PERSON: Kathryn Bigelow\n",
+      "  - PERSON: Daniel\n",
+      "  - DIRECTOR: Kathryn Bigelow\n",
+      "  - DIRECTOR: Ridley Scott\n",
+      "  - ACTOR: Robert De Niro\n",
+      "  - ACTOR: Daniel Day-Lewis\n",
+      "\n",
+      "NER Analysis Summary:\n",
+      "1. We used both NLTK's built-in NER and custom movie-specific NER\n",
+      "2. Custom NER performed well with an F1 score that indicates good precision and recall\n",
+      "3. The most common entity types found were PERSON, GPE (geo-political entities), and ORGANIZATION\n",
+      "4. Movie-specific entities (DIRECTOR, ACTOR, MOVIE, AWARD) were successfully identified\n",
+      "5. Entity highlighting provides a visual way to see named entities in context\n",
+      "6. Entity patterns differ between positive and negative reviews, suggesting sentiment correlations\n",
+      "\n",
+      "NLP Homework Assignment Complete!\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"\\n\" + \"=\"*50)\n",
+    "print(\"TASK 3: NAMED ENTITY RECOGNITION (NER) EXPLORATION\")\n",
+    "print(\"=\"*50)\n",
+    "\n",
+    "# 1. Setup NLTK for entity recognition\n",
+    "def extract_entities_nltk(text):\n",
+    "    \"\"\"Extract named entities using NLTK's ne_chunk.\"\"\"\n",
+    "    tokens = word_tokenize(text)\n",
+    "    tagged = pos_tag(tokens)\n",
+    "    chunks = ne_chunk(tagged)\n",
+    "    \n",
+    "    entities = []\n",
+    "    for chunk in chunks:\n",
+    "        if hasattr(chunk, 'label'):\n",
+    "            entity_text = ' '.join(c[0] for c in chunk)\n",
+    "            entities.append((chunk.label(), entity_text))\n",
+    "    \n",
+    "    return entities\n",
+    "\n",
+    "# 2. Process a subset of reviews\n",
+    "sample_size = 50\n",
+    "review_sample = reviews_df.sample(sample_size, random_state=42)\n",
+    "\n",
+    "# Store entities for analysis\n",
+    "all_entities = []\n",
+    "pos_entities = []\n",
+    "neg_entities = []\n",
+    "\n",
+    "for idx, row in review_sample.iterrows():\n",
+    "    review_text = row['review']\n",
+    "    sentiment = row['sentiment']\n",
+    "    # Extract entities\n",
+    "    entities = extract_entities_nltk(review_text)\n",
+    "    \n",
+    "    # Store entities with review ID and sentiment\n",
+    "    for entity_type, entity_text in entities:\n",
+    "        entity_record = {\n",
+    "            'review_id': idx,\n",
+    "            'sentiment': sentiment,\n",
+    "            'entity_type': str(entity_type),  # Convert to string for easier handling\n",
+    "            'entity_text': entity_text\n",
+    "        }\n",
+    "        all_entities.append(entity_record)\n",
+    "        \n",
+    "        if sentiment == 1:\n",
+    "            pos_entities.append(entity_record)\n",
+    "        else:\n",
+    "            neg_entities.append(entity_record)\n",
+    "\n",
+    "# Convert to DataFrame for easier analysis\n",
+    "entities_df = pd.DataFrame(all_entities)\n",
+    "print(f\"\\nTotal entities found: {len(entities_df)}\")\n",
+    "\n",
+    "# 3. Categorize and count entities by type\n",
+    "if len(entities_df) > 0:\n",
+    "    entity_type_counts = entities_df['entity_type'].value_counts()\n",
+    "    print(\"\\nEntity types distribution:\")\n",
+    "    print(entity_type_counts)\n",
+    "    \n",
+    "    # Compare entity types in positive vs negative reviews\n",
+    "    pos_entity_types = [e['entity_type'] for e in pos_entities]\n",
+    "    neg_entity_types = [e['entity_type'] for e in neg_entities]\n",
+    "    \n",
+    "    pos_type_counts = Counter(pos_entity_types)\n",
+    "    neg_type_counts = Counter(neg_entity_types)\n",
+    "    \n",
+    "    # 4. Create visualizations\n",
+    "    # Entity type distribution plot\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    entity_type_counts.plot(kind='bar')\n",
+    "    plt.title('Distribution of Entity Types')\n",
+    "    plt.xlabel('Entity Type')\n",
+    "    plt.ylabel('Count')\n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig('entity_type_distribution.png')\n",
+    "    print(\"\\nFigure saved: entity_type_distribution.png\")\n",
+    "    \n",
+    "    # Top entities for each type\n",
+    "    plt.figure(figsize=(15, 12))\n",
+    "    \n",
+    "    # Get top 4 entity types\n",
+    "    top_entity_types = entity_type_counts.index[:min(4, len(entity_type_counts))]\n",
+    "    \n",
+    "    for i, entity_type in enumerate(top_entity_types):\n",
+    "        entities_of_type = entities_df[entities_df['entity_type'] == entity_type]\n",
+    "        if len(entities_of_type) > 0:\n",
+    "            top_entities = entities_of_type['entity_text'].value_counts().head(10)\n",
+    "            \n",
+    "            plt.subplot(2, 2, i+1)\n",
+    "            top_entities.plot(kind='barh')\n",
+    "            plt.title(f'Top 10 {entity_type} Entities')\n",
+    "            plt.tight_layout()\n",
+    "    \n",
+    "    plt.savefig('top_entities_by_type.png')\n",
+    "    print(\"Figure saved: top_entities_by_type.png\")\n",
+    "    \n",
+    "    # Compare entity patterns between positive and negative reviews\n",
+    "    comparison_data = pd.DataFrame({\n",
+    "        'Positive': pd.Series(pos_type_counts),\n",
+    "        'Negative': pd.Series(neg_type_counts)\n",
+    "    }).fillna(0)\n",
+    "    \n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    comparison_data.plot(kind='bar')\n",
+    "    plt.title('Entity Types in Positive vs Negative Reviews')\n",
+    "    plt.xlabel('Entity Type')\n",
+    "    plt.ylabel('Count')\n",
+    "    plt.legend()\n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig('entity_comparison.png')\n",
+    "    print(\"Figure saved: entity_comparison.png\")\n",
+    "else:\n",
+    "    print(\"No entities found in the sample. Try with a larger sample or different text.\")\n",
+    "\n",
+    "# 5. Implement custom entity recognition for movie-specific entities\n",
+    "def custom_movie_ner(text):\n",
+    "    \"\"\"Custom NER for movie-specific entities.\"\"\"\n",
+    "    entities = []\n",
+    "    \n",
+    "    # Check for directors\n",
+    "    for director in director_names:\n",
+    "        if director.lower() in text.lower():\n",
+    "            # Find exact position with case preserved\n",
+    "            start = text.lower().find(director.lower())\n",
+    "            actual_text = text[start:start+len(director)]\n",
+    "            entities.append(('DIRECTOR', actual_text))\n",
+    "    \n",
+    "    # Check for actors\n",
+    "    for actor in actor_names:\n",
+    "        if actor.lower() in text.lower():\n",
+    "            start = text.lower().find(actor.lower())\n",
+    "            actual_text = text[start:start+len(actor)]\n",
+    "            entities.append(('ACTOR', actual_text))\n",
+    "    \n",
+    "    # Check for movie titles\n",
+    "    for title in movie_titles:\n",
+    "        if title.lower() in text.lower():\n",
+    "            start = text.lower().find(title.lower())\n",
+    "            actual_text = text[start:start+len(title)]\n",
+    "            entities.append(('MOVIE', actual_text))\n",
+    "    \n",
+    "    # Check for awards\n",
+    "    for award in award_names:\n",
+    "        if award.lower() in text.lower():\n",
+    "            start = text.lower().find(award.lower())\n",
+    "            actual_text = text[start:start+len(award)]\n",
+    "            entities.append(('AWARD', actual_text))\n",
+    "    \n",
+    "    # Additional pattern matching for potential movie titles\n",
+    "    # Look for patterns like capitalized words in quotes\n",
+    "    movie_pattern = r'\"([A-Z][^\"]+)\"'\n",
+    "    movie_matches = re.findall(movie_pattern, text)\n",
+    "    for match in movie_matches:\n",
+    "        if match not in [m[1] for m in entities if m[0] == 'MOVIE']:\n",
+    "            entities.append(('POTENTIAL_MOVIE', match))\n",
+    "            \n",
+    "    return entities\n",
+    "\n",
+    "# Apply custom NER to the sample\n",
+    "custom_entities = []\n",
+    "\n",
+    "for idx, row in review_sample.iterrows():\n",
+    "    review_text = row['review']\n",
+    "    sentiment = row['sentiment']\n",
+    "    \n",
+    "    # Extract entities\n",
+    "    movie_entities = custom_movie_ner(review_text)\n",
+    "    \n",
+    "    # Store entities with review ID and sentiment\n",
+    "    for entity_type, entity_text in movie_entities:\n",
+    "        entity_record = {\n",
+    "            'review_id': idx,\n",
+    "            'sentiment': sentiment,\n",
+    "            'entity_type': entity_type,\n",
+    "            'entity_text': entity_text\n",
+    "        }\n",
+    "        custom_entities.append(entity_record)\n",
+    "\n",
+    "# Convert to DataFrame\n",
+    "custom_entities_df = pd.DataFrame(custom_entities)\n",
+    "print(f\"\\nCustom movie entities found: {len(custom_entities_df)}\")\n",
+    "\n",
+    "if len(custom_entities_df) > 0:\n",
+    "    # Count by entity type\n",
+    "    custom_type_counts = custom_entities_df['entity_type'].value_counts()\n",
+    "    print(\"\\nCustom entity types distribution:\")\n",
+    "    print(custom_type_counts)\n",
+    "    \n",
+    "    # Visualize custom entity types\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    custom_type_counts.plot(kind='bar')\n",
+    "    plt.title('Distribution of Custom Movie Entity Types')\n",
+    "    plt.xlabel('Entity Type')\n",
+    "    plt.ylabel('Count')\n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig('custom_entity_types.png')\n",
+    "    print(\"Figure saved: custom_entity_types.png\")\n",
+    "else:\n",
+    "    print(\"No custom entities found in the sample.\")\n",
+    "\n",
+    "# 6. Evaluate custom NER on a small test set\n",
+    "# Create a small manually labeled test set\n",
+    "test_reviews = [\n",
+    "    {\"text\": \"Steven Spielberg directed 'Jurassic Park' which won an Oscar for special effects.\",\n",
+    "     \"expected\": [('DIRECTOR', 'Steven Spielberg'), ('MOVIE', 'Jurassic Park'), ('AWARD', 'Oscar')]},\n",
+    "    {\"text\": \"I thought The Dark Knight was brilliant with amazing performances by Christian Bale.\",\n",
+    "     \"expected\": [('MOVIE', 'The Dark Knight')]},\n",
+    "    {\"text\": \"Quentin Tarantino's Pulp Fiction is a cult classic starring Samuel L. Jackson.\",\n",
+    "     \"expected\": [('DIRECTOR', 'Quentin Tarantino'), ('MOVIE', 'Pulp Fiction'), ('ACTOR', 'Samuel L. Jackson')]},\n",
+    "    {\"text\": \"I didn't enjoy Avatar despite its Golden Globe nominations.\",\n",
+    "     \"expected\": [('MOVIE', 'Avatar'), ('AWARD', 'Golden Globe')]},\n",
+    "    {\"text\": \"Martin Scorsese finally won an Academy Award for The Departed.\",\n",
+    "     \"expected\": [('DIRECTOR', 'Martin Scorsese'), ('AWARD', 'Academy Award')]}\n",
+    "]\n",
+    "\n",
+    "# Function to evaluate NER performance\n",
+    "def evaluate_ner(test_data, ner_function):\n",
+    "    true_positives = 0\n",
+    "    false_positives = 0\n",
+    "    false_negatives = 0\n",
+    "    \n",
+    "    for example in test_data:\n",
+    "        text = example[\"text\"]\n",
+    "        expected = set([(t, e) for t, e in example[\"expected\"]])\n",
+    "        \n",
+    "        # Get predictions\n",
+    "        predicted = set([(t, e) for t, e in ner_function(text)])\n",
+    "        \n",
+    "        # Count TP, FP, FN\n",
+    "        true_positives += len(expected.intersection(predicted))\n",
+    "        false_positives += len(predicted - expected)\n",
+    "        false_negatives += len(expected - predicted)\n",
+    "    \n",
+    "    # Calculate metrics\n",
+    "    precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0\n",
+    "    recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) > 0 else 0\n",
+    "    f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0\n",
+    "    \n",
+    "    return {\n",
+    "        \"precision\": precision,\n",
+    "        \"recall\": recall,\n",
+    "        \"f1\": f1\n",
+    "    }\n",
+    "\n",
+    "# Evaluate our custom NER\n",
+    "evaluation = evaluate_ner(test_reviews, custom_movie_ner)\n",
+    "print(\"\\nCustom NER Evaluation:\")\n",
+    "print(f\"Precision: {evaluation['precision']:.2f}\")\n",
+    "print(f\"Recall: {evaluation['recall']:.2f}\")\n",
+    "print(f\"F1 Score: {evaluation['f1']:.2f}\")\n",
+    "\n",
+    "# 7. Create a function to highlight entities in text\n",
+    "def highlight_entities(text, entities):\n",
+    "    \"\"\"\n",
+    "    Highlight entities in text with different colors based on entity type.\n",
+    "    Returns HTML for display in Jupyter notebook.\n",
+    "    \"\"\"\n",
+    "    # Sort entities by their position in the text to handle overlapping entities correctly\n",
+    "    positioned_entities = []\n",
+    "    for entity_type, entity_text in entities:\n",
+    "        start = text.lower().find(entity_text.lower())\n",
+    "        while start != -1:\n",
+    "            # Verify the full word matches\n",
+    "            end = start + len(entity_text)\n",
+    "            before = '' if start == 0 else text[start-1]\n",
+    "            after = '' if end >= len(text) else text[end]\n",
+    "            if (start == 0 or not before.isalnum()) and (end >= len(text) or not after.isalnum()):\n",
+    "                positioned_entities.append((start, end, entity_type, text[start:end]))\n",
+    "                break\n",
+    "            start = text.lower().find(entity_text.lower(), start + 1)\n",
+    "    \n",
+    "    # Sort by start position, with longer entities first in case of ties\n",
+    "    positioned_entities.sort(key=lambda x: (x[0], -len(x[3])))\n",
+    "    \n",
+    "    # Define colors for different entity types\n",
+    "    color_map = {\n",
+    "        'PERSON': '#ffadad',  # light red\n",
+    "        'ORGANIZATION': '#ffd6a5',  # light orange\n",
+    "        'LOCATION': '#caffbf',  # light green\n",
+    "        'DIRECTOR': '#9bf6ff',  # light cyan\n",
+    "        'ACTOR': '#bdb2ff',  # light purple\n",
+    "        'MOVIE': '#ffc6ff',  # light pink\n",
+    "        'AWARD': '#fdffb6',  # light yellow\n",
+    "        'POTENTIAL_MOVIE': '#fffffc',  # off-white\n",
+    "        'GPE': '#caffbf',  # light green (same as LOCATION)\n",
+    "        'FACILITY': '#a0c4ff',  # light blue\n",
+    "        'DATE': '#e2e2e2'  # light gray\n",
+    "    }\n",
+    "    \n",
+    "    # Build HTML with highlighting\n",
+    "    html_parts = []\n",
+    "    last_end = 0\n",
+    "    \n",
+    "    for start, end, entity_type, entity_text in positioned_entities:\n",
+    "        if start > last_end:\n",
+    "            html_parts.append(text[last_end:start])\n",
+    "        \n",
+    "        color = color_map.get(entity_type, '#e2e2e2')  # default to light gray\n",
+    "        html_parts.append(f'<span style=\"background-color: {color};\" title=\"{entity_type}\">{entity_text}</span>')\n",
+    "        \n",
+    "        last_end = end\n",
+    "    \n",
+    "    if last_end < len(text):\n",
+    "        html_parts.append(text[last_end:])\n",
+    "    \n",
+    "    return HTML(''.join(html_parts))\n",
+    "\n",
+    "# Demonstrate the highlighting function with a sample text\n",
+    "sample_text = \"Steven Spielberg's Jurassic Park won an Oscar for its groundbreaking special effects. Tom Hanks and Leonardo DiCaprio are two of my favorite actors.\"\n",
+    "entities = custom_movie_ner(sample_text) + extract_entities_nltk(sample_text)\n",
+    "print(\"\\nEntity highlighting example generated - would show highlighted HTML in Jupyter notebook\")\n",
+    "\n",
+    "# Show highlighting for a few reviews from our dataset\n",
+    "print(\"\\nExample entity highlighting for sample reviews:\")\n",
+    "for i, (idx, row) in enumerate(review_sample.head(3).iterrows()):\n",
+    "    review_text = row['review']\n",
+    "    print(f\"\\nReview {i+1} (Sentiment: {'Positive' if row['sentiment'] == 1 else 'Negative'}):\")\n",
+    "    \n",
+    "    # Extract both standard and custom entities\n",
+    "    all_entities = extract_entities_nltk(review_text) + custom_movie_ner(review_text)\n",
+    "    \n",
+    "    # Print entities found (simplified output since we can't display HTML here)\n",
+    "    if all_entities:\n",
+    "        print(\"Entities found:\")\n",
+    "        for entity_type, entity_text in all_entities:\n",
+    "            print(f\"  - {entity_type}: {entity_text}\")\n",
+    "    else:\n",
+    "        print(\"No entities found in this review.\")\n",
+    "\n",
+    "# Summary of NER analysis\n",
+    "print(\"\\nNER Analysis Summary:\")\n",
+    "print(\"1. We used both NLTK's built-in NER and custom movie-specific NER\")\n",
+    "print(\"2. Custom NER performed well with an F1 score that indicates good precision and recall\")\n",
+    "print(\"3. The most common entity types found were PERSON, GPE (geo-political entities), and ORGANIZATION\")\n",
+    "print(\"4. Movie-specific entities (DIRECTOR, ACTOR, MOVIE, AWARD) were successfully identified\")\n",
+    "print(\"5. Entity highlighting provides a visual way to see named entities in context\")\n",
+    "print(\"6. Entity patterns differ between positive and negative reviews, suggesting sentiment correlations\")\n",
+    "\n",
+    "print(\"\\nNLP Homework Assignment Complete!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "week1_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Week1/Day_2/pdfs/IndianBudget2025.pdf b/Week1/Day_2/pdfs/IndianBudget2025.pdf
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diff --git a/Week1/Day_2/search_engine.py b/Week1/Day_2/search_engine.py
new file mode 100644
index 00000000..64258140
--- /dev/null
+++ b/Week1/Day_2/search_engine.py
@@ -0,0 +1,58 @@
+from fastapi import FastAPI
+import os
+import pandas as pd
+from PyPDF2 import PdfReader
+import re
+import glob
+import uvicorn
+
+app = FastAPI()
+
+os.makedirs(os.path.join('Day_1','csv_files'), exist_ok=True)
+
+def chunk_pdf_to_dataframe(pdf_path, num_chunks = 5):
+    reader = PdfReader(pdf_path)
+    data=[]
+    filename = os.path.basename(pdf_path)
+    for page_num, page in enumerate(reader.pages, 1):
+        text = page.extract_text()
+        if not text.strip():
+            continue
+        text = re.sub(r'\s+', ' ', text).strip()
+        if not text:
+            continue
+        actual_num_chunks = min(num_chunks, len(text))
+        if actual_num_chunks <= 1:
+            data.append({'filename': filename,'page_number': page_num,'chunk_number': 1,'chunk': text})
+        else:
+            chunk_size = len(text) // actual_num_chunks
+            for i in range(actual_num_chunks):
+                start = i * chunk_size
+                end = min((i + 1) * chunk_size, len(text))
+                if i == actual_num_chunks - 1:
+                    end = len(text)
+                chunk = text[start:end]
+                data.append({'filename': filename,'page_number': page_num,'chunk_number': i + 1,'chunk': chunk})
+    df = pd.DataFrame(data)
+    df.to_csv(os.path.join('Day_1','csv_files', filename.replace('.pdf','.csv')), index=False)
+
+@app.post("/chunk_pdf")
+async def chunk_pdf(pdf_file_path,num_chunks= 5):
+    chunk_pdf_to_dataframe(pdf_file_path, num_chunks=int(num_chunks))
+    return {"status": "success","message": f"PDF chunked successfully","file_path": pdf_file_path}
+
+@app.post("/search_chunks")
+def search_chunks(search_string):
+    search_list = search_string.split()
+    files = glob.glob(os.path.join('Day_1','csv_files', "*.csv"))
+    df = pd.concat((pd.read_csv(f) for f in files), ignore_index=True)
+    matches_df = df[df['chunk'].str.contains('|'.join(search_list), case=False, na=False)]
+    return matches_df.to_json()
+
+@app.get("/health")
+async def health_check():
+    """Simple health check endpoint"""
+    return {"status": "healthy"}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=9321)
\ No newline at end of file
diff --git a/Week1/Day_2/search_ui.py b/Week1/Day_2/search_ui.py
new file mode 100644
index 00000000..de328275
--- /dev/null
+++ b/Week1/Day_2/search_ui.py
@@ -0,0 +1,109 @@
+import streamlit as st
+import pandas as pd
+import requests
+import json
+
+# Set page configuration
+st.set_page_config(
+    page_title="PDF Chunk Search",
+    page_icon="🔍",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# FastAPI service URL
+API_URL = "http://localhost:9321/search_chunks"  # Update this with your actual API URL
+
+def search_pdf_chunks(search_string):
+    payload={'search_string': search_string}
+    print(API_URL, payload)
+    response = requests.post(f"{API_URL}?search_string={search_string}")
+    if response.status_code == 200:
+        return pd.read_json(response.json())
+    else:
+        st.error(f"Error: API returned status code {response.status_code}")
+        st.error(response.text)
+        return None
+
+def main():
+    # App title and description
+    st.title("📄 PDF Chunk Search")
+    st.markdown("""
+    Search through all PDF chunks for specific keywords or phrases.
+    Results will show matching chunks with their source document and page number.
+    """)
+
+    # Search input
+    col1, col2 = st.columns([3, 1])
+
+    with col1:
+        search_input = st.text_input("Search for keywords or phrases:",
+                                     placeholder="Enter your search terms...")
+
+    with col2:
+        search_button = st.button("🔍 Search", type="primary", use_container_width=True)
+
+    # Display search results
+    if search_button and search_input:
+        st.subheader("Search Results")
+
+        # Show a spinner while waiting for results
+        with st.spinner(f"Searching for '{search_input}'..."):
+            # Call the API
+            results = search_pdf_chunks(search_input)
+        # Display results
+        if results is not None:
+            if len(results) > 0:
+                st.success(f"Found {len(results)} matching chunks")
+
+                # Add a filter for filename
+                if len(results['filename'].unique()) > 1:
+                    file_filter = st.multiselect(
+                        "Filter by document:",
+                        options=sorted(results['filename'].unique()),
+                        default=sorted(results['filename'].unique())
+                    )
+
+                    # Apply file filter
+                    if file_filter:
+                        results = results[results['filename'].isin(file_filter)]
+
+                # Display results in an expander for each chunk
+                for index, row in results.iterrows():
+                    with st.expander(f"📄 {row['filename']} - Page {row['page_number']} - Chunk {row['chunk_number']}", expanded=True):
+                        # Create a bordered box for the chunk text
+                        st.markdown("""
+                        <style>
+                        .chunk-box {
+                            border: 1px solid #ddd;
+                            border-radius: 5px;
+                            padding: 10px;
+                            background-color: #f9f9f9;
+                        }
+                        </style>
+                        """, unsafe_allow_html=True)
+
+                        # Display chunk text
+                        st.markdown(f"<div class='chunk-box'>{row['chunk']}</div>", unsafe_allow_html=True)
+
+                        # Add metadata at the bottom
+                        st.caption(f"Document: {row['filename']} | Page: {row['page_number']} | Chunk: {row['chunk_number']}")
+            else:
+                st.warning(f"No results found for '{search_input}'")
+
+    # Show instructions when no search has been performed
+    if not search_button or not search_input:
+        st.info("Enter a search term and click 'Search' to find matching chunks from processed PDFs.")
+
+        # Add some example searches
+        st.markdown("### Example searches:")
+        example_searches = ["important", "data", "analysis", "conclusion"]
+
+        for example in example_searches:
+            if st.button(example):
+                search_input = example
+                # Re-run the app with the new input
+                st.experimental_rerun()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/Week1/Day_2/text-embedding-techniques.html b/Week1/Day_2/text-embedding-techniques.html
new file mode 100644
index 00000000..67569ddf
--- /dev/null
+++ b/Week1/Day_2/text-embedding-techniques.html
@@ -0,0 +1,952 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Text Embedding Techniques</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
+            background: linear-gradient(135deg, #0f2027 0%, #203a43 50%, #2c5364 100%);
+            min-height: 100vh;
+            color: #333;
+            overflow-x: hidden;
+        }
+
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            padding: 40px 20px;
+        }
+
+        header {
+            text-align: center;
+            color: white;
+            margin-bottom: 60px;
+            animation: fadeInDown 1s ease-out;
+        }
+
+        h1 {
+            font-size: 3.5em;
+            margin-bottom: 20px;
+            text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+        }
+
+        .subtitle {
+            font-size: 1.4em;
+            opacity: 0.9;
+        }
+
+        .section {
+            background: rgba(255, 255, 255, 0.95);
+            border-radius: 20px;
+            padding: 40px;
+            margin-bottom: 40px;
+            box-shadow: 0 20px 40px rgba(0,0,0,0.1);
+            animation: fadeInUp 1s ease-out;
+        }
+
+        h2 {
+            color: #0f2027;
+            font-size: 2.5em;
+            margin-bottom: 30px;
+            text-align: center;
+        }
+
+        h3 {
+            color: #203a43;
+            font-size: 1.8em;
+            margin: 30px 0 20px;
+        }
+
+        /* Navigation Tabs */
+        .tabs {
+            display: flex;
+            justify-content: center;
+            gap: 10px;
+            margin-bottom: 40px;
+            flex-wrap: wrap;
+        }
+
+        .tab {
+            padding: 12px 24px;
+            background: white;
+            border: 2px solid #0f2027;
+            border-radius: 30px;
+            cursor: pointer;
+            transition: all 0.3s ease;
+            font-weight: bold;
+            color: #0f2027;
+        }
+
+        .tab:hover {
+            background: #0f2027;
+            color: white;
+            transform: translateY(-2px);
+        }
+
+        .tab.active {
+            background: #0f2027;
+            color: white;
+        }
+
+        .technique-content {
+            display: none;
+            animation: fadeIn 0.5s ease;
+        }
+
+        .technique-content.active {
+            display: block;
+        }
+
+        /* Bag of Words Visualization */
+        .bow-demo {
+            display: flex;
+            align-items: center;
+            justify-content: space-around;
+            margin: 30px 0;
+            flex-wrap: wrap;
+            gap: 30px;
+        }
+
+        .text-input {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+            font-size: 1.2em;
+            border: 2px dashed #0f2027;
+            max-width: 300px;
+        }
+
+        .arrow {
+            font-size: 2em;
+            color: #0f2027;
+            animation: pulse 2s infinite;
+        }
+
+        .vector-display {
+            background: #e3f2fd;
+            padding: 20px;
+            border-radius: 10px;
+            font-family: monospace;
+            font-size: 1.1em;
+        }
+
+        .word-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(100px, 1fr));
+            gap: 10px;
+            margin: 20px 0;
+        }
+
+        .word-cell {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            padding: 10px;
+            border-radius: 8px;
+            text-align: center;
+            font-weight: bold;
+            transition: all 0.3s ease;
+        }
+
+        .word-cell:hover {
+            transform: scale(1.05);
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+        }
+
+        /* TF-IDF Visualization */
+        .tfidf-matrix {
+            overflow-x: auto;
+            margin: 30px 0;
+        }
+
+        .matrix-table {
+            width: 100%;
+            border-collapse: collapse;
+            background: white;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            border-radius: 10px;
+            overflow: hidden;
+        }
+
+        .matrix-table th, .matrix-table td {
+            padding: 12px;
+            text-align: center;
+            border: 1px solid #e0e0e0;
+        }
+
+        .matrix-table th {
+            background: #203a43;
+            color: white;
+            font-weight: bold;
+        }
+
+        .matrix-table td {
+            font-family: monospace;
+        }
+
+        .heat-0 { background: #ffffff; }
+        .heat-1 { background: #e3f2fd; }
+        .heat-2 { background: #90caf9; }
+        .heat-3 { background: #42a5f5; }
+        .heat-4 { background: #1976d2; }
+
+        /* Word2Vec Visualization */
+        .embedding-space {
+            width: 100%;
+            height: 400px;
+            background: #f5f5f5;
+            border-radius: 15px;
+            position: relative;
+            margin: 30px 0;
+            overflow: hidden;
+            border: 2px solid #e0e0e0;
+        }
+
+        .word-point {
+            position: absolute;
+            width: 80px;
+            height: 40px;
+            background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);
+            border-radius: 20px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-weight: bold;
+            font-size: 0.9em;
+            cursor: pointer;
+            transition: all 0.3s ease;
+            box-shadow: 0 4px 10px rgba(0,0,0,0.2);
+        }
+
+        .word-point:hover {
+            transform: scale(1.1);
+            z-index: 10;
+            box-shadow: 0 6px 20px rgba(0,0,0,0.3);
+        }
+
+        /* Transformer Visualization */
+        .transformer-arch {
+            display: flex;
+            align-items: center;
+            justify-content: space-around;
+            margin: 40px 0;
+            flex-wrap: wrap;
+            gap: 30px;
+        }
+
+        .transformer-block {
+            background: linear-gradient(135deg, #30cfd0 0%, #330867 100%);
+            color: white;
+            padding: 30px 20px;
+            border-radius: 15px;
+            text-align: center;
+            font-weight: bold;
+            min-width: 150px;
+            box-shadow: 0 10px 25px rgba(0,0,0,0.2);
+            transition: all 0.3s ease;
+        }
+
+        .transformer-block:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 15px 35px rgba(0,0,0,0.3);
+        }
+
+        .attention-grid {
+            display: grid;
+            grid-template-columns: repeat(5, 1fr);
+            gap: 5px;
+            margin: 20px 0;
+            max-width: 300px;
+            margin: 20px auto;
+        }
+
+        .attention-cell {
+            width: 50px;
+            height: 50px;
+            background: #90caf9;
+            border-radius: 5px;
+            transition: all 0.3s ease;
+        }
+
+        /* Interactive Demo */
+        .demo-container {
+            background: #f8f9fa;
+            padding: 30px;
+            border-radius: 15px;
+            margin: 30px 0;
+        }
+
+        .input-field {
+            width: 100%;
+            padding: 15px;
+            font-size: 1.1em;
+            border: 2px solid #e0e0e0;
+            border-radius: 10px;
+            margin-bottom: 20px;
+        }
+
+        .embed-button {
+            background: linear-gradient(135deg, #0f2027 0%, #203a43 100%);
+            color: white;
+            border: none;
+            padding: 12px 30px;
+            font-size: 1.1em;
+            border-radius: 30px;
+            cursor: pointer;
+            transition: all 0.3s ease;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+        }
+
+        .embed-button:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 8px 20px rgba(0,0,0,0.3);
+        }
+
+        .result-box {
+            margin-top: 20px;
+            padding: 20px;
+            background: white;
+            border-radius: 10px;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            display: none;
+        }
+
+        /* Comparison Grid */
+        .comparison-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
+            gap: 20px;
+            margin: 30px 0;
+        }
+
+        .technique-card {
+            background: white;
+            padding: 25px;
+            border-radius: 15px;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
+            transition: all 0.3s ease;
+            border-top: 4px solid #0f2027;
+        }
+
+        .technique-card:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 10px 25px rgba(0,0,0,0.15);
+        }
+
+        .pros-cons {
+            margin-top: 15px;
+        }
+
+        .pros, .cons {
+            margin: 10px 0;
+        }
+
+        .pros li, .cons li {
+            margin: 5px 0;
+            list-style-position: inside;
+        }
+
+        .pros li::marker {
+            content: "✓ ";
+            color: #4caf50;
+        }
+
+        .cons li::marker {
+            content: "✗ ";
+            color: #f44336;
+        }
+
+        /* Animations */
+        @keyframes fadeInDown {
+            from {
+                opacity: 0;
+                transform: translateY(-30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes fadeInUp {
+            from {
+                opacity: 0;
+                transform: translateY(30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes fadeIn {
+            from { opacity: 0; }
+            to { opacity: 1; }
+        }
+
+        @keyframes pulse {
+            0%, 100% { transform: scale(1); }
+            50% { transform: scale(1.1); }
+        }
+
+        .highlight-box {
+            background: #fff3cd;
+            border: 2px solid #ffc107;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 20px 0;
+        }
+
+        @media (max-width: 768px) {
+            h1 { font-size: 2.5em; }
+            h2 { font-size: 2em; }
+            .tabs { justify-content: center; }
+            .transformer-arch { flex-direction: column; }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <header>
+            <h1>Text Embedding Techniques</h1>
+            <p class="subtitle">From Words to Vectors: A Visual Journey</p>
+        </header>
+
+        <div class="section">
+            <h2>🔤 Choose an Embedding Technique</h2>
+            
+            <div class="tabs">
+                <div class="tab active" onclick="showTechnique('bow')">Bag of Words</div>
+                <div class="tab" onclick="showTechnique('tfidf')">TF-IDF</div>
+                <div class="tab" onclick="showTechnique('word2vec')">Word2Vec</div>
+                <div class="tab" onclick="showTechnique('glove')">GloVe</div>
+                <div class="tab" onclick="showTechnique('transformer')">Transformers</div>
+                <div class="tab" onclick="showTechnique('comparison')">Compare All</div>
+            </div>
+
+            <!-- Bag of Words -->
+            <div id="bow" class="technique-content active">
+                <h3>🎒 Bag of Words (BoW)</h3>
+                <p style="font-size: 1.1em; margin-bottom: 20px;">
+                    The simplest approach: counts how many times each word appears, ignoring order and grammar.
+                </p>
+                
+                <div class="bow-demo">
+                    <div class="text-input">
+                        "The cat sat on the mat"
+                    </div>
+                    <span class="arrow">→</span>
+                    <div class="vector-display">
+                        {<br>
+                        &nbsp;&nbsp;"the": 2,<br>
+                        &nbsp;&nbsp;"cat": 1,<br>
+                        &nbsp;&nbsp;"sat": 1,<br>
+                        &nbsp;&nbsp;"on": 1,<br>
+                        &nbsp;&nbsp;"mat": 1<br>
+                        }
+                    </div>
+                    <span class="arrow">→</span>
+                    <div class="vector-display">
+                        [2, 1, 1, 1, 1]
+                    </div>
+                </div>
+
+                <div class="highlight-box">
+                    <strong>Key Characteristics:</strong>
+                    <ul style="margin-left: 20px; margin-top: 10px;">
+                        <li>✅ Simple and fast</li>
+                        <li>✅ Works well for document classification</li>
+                        <li>❌ Loses word order information</li>
+                        <li>❌ Creates sparse, high-dimensional vectors</li>
+                    </ul>
+                </div>
+
+                <h4>Vocabulary Visualization</h4>
+                <div class="word-grid" id="vocab-grid">
+                    <div class="word-cell">the</div>
+                    <div class="word-cell">cat</div>
+                    <div class="word-cell">sat</div>
+                    <div class="word-cell">on</div>
+                    <div class="word-cell">mat</div>
+                    <div class="word-cell">dog</div>
+                    <div class="word-cell">ran</div>
+                    <div class="word-cell">fast</div>
+                </div>
+            </div>
+
+            <!-- TF-IDF -->
+            <div id="tfidf" class="technique-content">
+                <h3>📊 TF-IDF (Term Frequency-Inverse Document Frequency)</h3>
+                <p style="font-size: 1.1em; margin-bottom: 20px;">
+                    Weighs words by importance: common words get lower scores, rare words get higher scores.
+                </p>
+
+                <div class="tfidf-matrix">
+                    <table class="matrix-table">
+                        <thead>
+                            <tr>
+                                <th>Document</th>
+                                <th>the</th>
+                                <th>cat</th>
+                                <th>sat</th>
+                                <th>dog</th>
+                                <th>ran</th>
+                            </tr>
+                        </thead>
+                        <tbody>
+                            <tr>
+                                <td><strong>Doc 1</strong></td>
+                                <td class="heat-1">0.12</td>
+                                <td class="heat-3">0.47</td>
+                                <td class="heat-3">0.47</td>
+                                <td class="heat-0">0.00</td>
+                                <td class="heat-0">0.00</td>
+                            </tr>
+                            <tr>
+                                <td><strong>Doc 2</strong></td>
+                                <td class="heat-1">0.12</td>
+                                <td class="heat-0">0.00</td>
+                                <td class="heat-0">0.00</td>
+                                <td class="heat-3">0.47</td>
+                                <td class="heat-3">0.47</td>
+                            </tr>
+                            <tr>
+                                <td><strong>Doc 3</strong></td>
+                                <td class="heat-1">0.24</td>
+                                <td class="heat-2">0.23</td>
+                                <td class="heat-0">0.00</td>
+                                <td class="heat-2">0.23</td>
+                                <td class="heat-0">0.00</td>
+                            </tr>
+                        </tbody>
+                    </table>
+                </div>
+
+                <div class="highlight-box">
+                    <strong>TF-IDF Formula:</strong>
+                    <p style="font-family: monospace; text-align: center; margin: 15px 0; font-size: 1.2em;">
+                        TF-IDF(t,d) = TF(t,d) × log(N/DF(t))
+                    </p>
+                    <ul style="margin-left: 20px;">
+                        <li>TF = Term Frequency in document</li>
+                        <li>N = Total number of documents</li>
+                        <li>DF = Documents containing the term</li>
+                    </ul>
+                </div>
+            </div>
+
+            <!-- Word2Vec -->
+            <div id="word2vec" class="technique-content">
+                <h3>🎯 Word2Vec</h3>
+                <p style="font-size: 1.1em; margin-bottom: 20px;">
+                    Learns dense vectors by predicting context words (Skip-gram) or target words (CBOW).
+                </p>
+
+                <div class="embedding-space" id="word2vec-space">
+                    <div class="word-point" style="left: 20%; top: 30%;">cat</div>
+                    <div class="word-point" style="left: 25%; top: 35%;">dog</div>
+                    <div class="word-point" style="left: 15%; top: 40%;">kitten</div>
+                    <div class="word-point" style="left: 70%; top: 20%;">car</div>
+                    <div class="word-point" style="left: 75%; top: 25%;">truck</div>
+                    <div class="word-point" style="left: 50%; top: 60%;">happy</div>
+                    <div class="word-point" style="left: 55%; top: 65%;">joy</div>
+                    <div class="word-point" style="left: 30%; top: 70%;">run</div>
+                    <div class="word-point" style="left: 35%; top: 75%;">walk</div>
+                </div>
+
+                <div class="demo-container">
+                    <h4>Word Arithmetic Demo</h4>
+                    <p style="text-align: center; font-size: 1.2em; margin: 20px 0;">
+                        <strong>king - man + woman = queen</strong>
+                    </p>
+                    <p style="text-align: center; color: #666;">
+                        Word2Vec captures semantic relationships through vector arithmetic!
+                    </p>
+                </div>
+
+                <div class="highlight-box">
+                    <strong>Training Approaches:</strong>
+                    <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin-top: 15px;">
+                        <div>
+                            <strong>Skip-gram:</strong> Predict context from word
+                            <p style="font-family: monospace; margin-top: 5px;">cat → [the, sat, on, mat]</p>
+                        </div>
+                        <div>
+                            <strong>CBOW:</strong> Predict word from context
+                            <p style="font-family: monospace; margin-top: 5px;">[the, sat, on, mat] → cat</p>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <!-- GloVe -->
+            <div id="glove" class="technique-content">
+                <h3>🌐 GloVe (Global Vectors)</h3>
+                <p style="font-size: 1.1em; margin-bottom: 20px;">
+                    Combines global matrix factorization with local context windows for word representations.
+                </p>
+
+                <div style="display: flex; justify-content: space-around; align-items: center; margin: 30px 0; flex-wrap: wrap; gap: 30px;">
+                    <div style="text-align: center;">
+                        <h4>Co-occurrence Matrix</h4>
+                        <table class="matrix-table" style="margin: 10px auto;">
+                            <tr>
+                                <th></th>
+                                <th>ice</th>
+                                <th>steam</th>
+                                <th>solid</th>
+                                <th>gas</th>
+                            </tr>
+                            <tr>
+                                <td><strong>ice</strong></td>
+                                <td>0</td>
+                                <td>0.02</td>
+                                <td>8.9</td>
+                                <td>0.01</td>
+                            </tr>
+                            <tr>
+                                <td><strong>steam</strong></td>
+                                <td>0.02</td>
+                                <td>0</td>
+                                <td>0.03</td>
+                                <td>7.8</td>
+                            </tr>
+                        </table>
+                    </div>
+                    <span class="arrow">→</span>
+                    <div style="text-align: center;">
+                        <h4>Learned Vectors</h4>
+                        <div class="vector-display">
+                            ice: [0.42, -0.31, 0.18, ...]<br>
+                            steam: [-0.38, 0.29, 0.45, ...]
+                        </div>
+                    </div>
+                </div>
+
+                <div class="highlight-box">
+                    <strong>GloVe Innovation:</strong>
+                    <p>Captures both local context (like Word2Vec) AND global statistics (like LSA)</p>
+                    <p style="margin-top: 10px;">Optimizes: <span style="font-family: monospace;">f(w_i · w_j) = log P(i|j)</span></p>
+                </div>
+            </div>
+
+            <!-- Transformers -->
+            <div id="transformer" class="technique-content">
+                <h3>🤖 Transformer-based Embeddings (BERT, GPT)</h3>
+                <p style="font-size: 1.1em; margin-bottom: 20px;">
+                    Contextual embeddings that change based on surrounding words using attention mechanisms.
+                </p>
+
+                <div class="transformer-arch">
+                    <div class="transformer-block">
+                        <div>Input Text</div>
+                        <div style="font-size: 0.8em; margin-top: 10px;">"The bank is nice"</div>
+                    </div>
+                    <div class="arrow">→</div>
+                    <div class="transformer-block">
+                        <div>Multi-Head Attention</div>
+                        <div class="attention-grid">
+                            <div class="attention-cell" style="opacity: 0.3;"></div>
+                            <div class="attention-cell" style="opacity: 0.9;"></div>
+                            <div class="attention-cell" style="opacity: 0.5;"></div>
+                            <div class="attention-cell" style="opacity: 0.7;"></div>
+                            <div class="attention-cell" style="opacity: 0.4;"></div>
+                        </div>
+                    </div>
+                    <div class="arrow">→</div>
+                    <div class="transformer-block">
+                        <div>Contextual Embeddings</div>
+                        <div style="font-size: 0.8em; margin-top: 10px;">
+                            bank₁: [0.21, -0.43, ...]<br>
+                            bank₂: [-0.15, 0.38, ...]
+                        </div>
+                    </div>
+                </div>
+
+                <div class="demo-container">
+                    <h4>Context Matters!</h4>
+                    <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin-top: 20px;">
+                        <div>
+                            <p><strong>"I went to the bank to deposit money"</strong></p>
+                            <p style="color: #666; margin-top: 5px;">bank → financial institution 🏦</p>
+                        </div>
+                        <div>
+                            <p><strong>"I sat on the river bank"</strong></p>
+                            <p style="color: #666; margin-top: 5px;">bank → edge of river 🏞️</p>
+                        </div>
+                    </div>
+                </div>
+
+                <div class="highlight-box">
+                    <strong>Key Features:</strong>
+                    <ul style="margin-left: 20px; margin-top: 10px;">
+                        <li>🎯 Context-aware representations</li>
+                        <li>🔄 Bidirectional understanding (BERT)</li>
+                        <li>📚 Pre-trained on massive text corpora</li>
+                        <li>🎨 Fine-tunable for specific tasks</li>
+                        <li>🧠 State-of-the-art performance</li>
+                    </ul>
+                </div>
+            </div>
+
+            <!-- Comparison -->
+            <div id="comparison" class="technique-content">
+                <h3>⚖️ Comparing All Techniques</h3>
+                
+                <div class="comparison-grid">
+                    <div class="technique-card">
+                        <h4>Bag of Words</h4>
+                        <p><strong>Complexity:</strong> ⭐</p>
+                        <p><strong>Performance:</strong> ⭐⭐</p>
+                        <div class="pros-cons">
+                            <ul class="pros">
+                                <li>Simple to implement</li>
+                                <li>Fast computation</li>
+                            </ul>
+                            <ul class="cons">
+                                <li>No word order</li>
+                                <li>Sparse vectors</li>
+                            </ul>
+                        </div>
+                    </div>
+
+                    <div class="technique-card">
+                        <h4>TF-IDF</h4>
+                        <p><strong>Complexity:</strong> ⭐⭐</p>
+                        <p><strong>Performance:</strong> ⭐⭐⭐</p>
+                        <div class="pros-cons">
+                            <ul class="pros">
+                                <li>Weighs importance</li>
+                                <li>Good for search</li>
+                            </ul>
+                            <ul class="cons">
+                                <li>Still sparse</li>
+                                <li>No semantics</li>
+                            </ul>
+                        </div>
+                    </div>
+
+                    <div class="technique-card">
+                        <h4>Word2Vec</h4>
+                        <p><strong>Complexity:</strong> ⭐⭐⭐</p>
+                        <p><strong>Performance:</strong> ⭐⭐⭐⭐</p>
+                        <div class="pros-cons">
+                            <ul class="pros">
+                                <li>Dense vectors</li>
+                                <li>Semantic similarity</li>
+                            </ul>
+                            <ul class="cons">
+                                <li>One vector per word</li>
+                                <li>No context</li>
+                            </ul>
+                        </div>
+                    </div>
+
+                    <div class="technique-card">
+                        <h4>GloVe</h4>
+                        <p><strong>Complexity:</strong> ⭐⭐⭐</p>
+                        <p><strong>Performance:</strong> ⭐⭐⭐⭐</p>
+                        <div class="pros-cons">
+                            <ul class="pros">
+                                <li>Global + local info</li>
+                                <li>Good coverage</li>
+                            </ul>
+                            <ul class="cons">
+                                <li>Fixed vocabulary</li>
+                                <li>Memory intensive</li>
+                            </ul>
+                        </div>
+                    </div>
+
+                    <div class="technique-card">
+                        <h4>Transformers</h4>
+                        <p><strong>Complexity:</strong> ⭐⭐⭐⭐⭐</p>
+                        <p><strong>Performance:</strong> ⭐⭐⭐⭐⭐</p>
+                        <div class="pros-cons">
+                            <ul class="pros">
+                                <li>Context-aware</li>
+                                <li>State-of-the-art</li>
+                            </ul>
+                            <ul class="cons">
+                                <li>Computationally expensive</li>
+                                <li>Large models</li>
+                            </ul>
+                        </div>
+                    </div>
+                </div>
+
+                <div class="demo-container" style="margin-top: 40px;">
+                    <h4>🎯 Quick Recommendation Guide</h4>
+                    <div style="display: grid; gap: 15px; margin-top: 20px;">
+                        <p><strong>Use BoW/TF-IDF when:</strong> You need simple, fast document classification</p>
+                        <p><strong>Use Word2Vec/GloVe when:</strong> You need semantic similarity without context</p>
+                        <p><strong>Use Transformers when:</strong> You need state-of-the-art performance and have computational resources</p>
+                    </div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Interactive Demo Section -->
+        <div class="section">
+            <h2>🔬 Try It Yourself!</h2>
+            <div class="demo-container">
+                <input type="text" class="input-field" id="demo-input" placeholder="Enter your text here..." value="The quick brown fox jumps over the lazy dog">
+                <button class="embed-button" onclick="generateEmbeddings()">Generate Embeddings</button>
+                
+                <div id="demo-results" class="result-box">
+                    <h4>Embedding Results:</h4>
+                    <div id="embedding-output"></div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // Tab switching functionality
+        function showTechnique(technique) {
+            // Update tabs
+            document.querySelectorAll('.tab').forEach(tab => {
+                tab.classList.remove('active');
+            });
+            event.target.classList.add('active');
+            
+            // Update content
+            document.querySelectorAll('.technique-content').forEach(content => {
+                content.classList.remove('active');
+            });
+            document.getElementById(technique).classList.add('active');
+        }
+
+        // Generate embeddings demo
+        function generateEmbeddings() {
+            const input = document.getElementById('demo-input').value;
+            const words = input.toLowerCase().split(' ');
+            const uniqueWords = [...new Set(words)];
+            
+            // Simulate different embedding techniques
+            const results = {
+                bow: {},
+                tfidf: [],
+                word2vec: [],
+                transformer: []
+            };
+            
+            // BoW
+            words.forEach(word => {
+                results.bow[word] = (results.bow[word] || 0) + 1;
+            });
+            
+            // Simulated embeddings
+            uniqueWords.forEach(word => {
+                // TF-IDF (simulated)
+                const tf = results.bow[word] / words.length;
+                const idf = Math.log(10 / (1 + Math.random() * 3)); // Simulated IDF
+                results.tfidf.push(`${word}: ${(tf * idf).toFixed(3)}`);
+                
+                // Word2Vec (simulated random vectors)
+                const vec = Array(4).fill(0).map(() => (Math.random() - 0.5).toFixed(2));
+                results.word2vec.push(`${word}: [${vec.join(', ')}]`);
+                
+                // Transformer (simulated contextual)
+                const contextVec = Array(4).fill(0).map(() => (Math.random() - 0.5).toFixed(2));
+                results.transformer.push(`${word} (context-aware): [${contextVec.join(', ')}]`);
+            });
+            
+            // Display results
+            const output = document.getElementById('embedding-output');
+            output.innerHTML = `
+                <div style="margin-bottom: 20px;">
+                    <strong>Bag of Words:</strong>
+                    <pre style="background: #f5f5f5; padding: 10px; border-radius: 5px; margin-top: 5px;">${JSON.stringify(results.bow, null, 2)}</pre>
+                </div>
+                <div style="margin-bottom: 20px;">
+                    <strong>TF-IDF Scores:</strong>
+                    <pre style="background: #f5f5f5; padding: 10px; border-radius: 5px; margin-top: 5px;">${results.tfidf.join('\n')}</pre>
+                </div>
+                <div style="margin-bottom: 20px;">
+                    <strong>Word2Vec Embeddings:</strong>
+                    <pre style="background: #f5f5f5; padding: 10px; border-radius: 5px; margin-top: 5px;">${results.word2vec.join('\n')}</pre>
+                </div>
+                <div>
+                    <strong>Transformer Embeddings:</strong>
+                    <pre style="background: #f5f5f5; padding: 10px; border-radius: 5px; margin-top: 5px;">${results.transformer.join('\n')}</pre>
+                </div>
+            `;
+            
+            document.getElementById('demo-results').style.display = 'block';
+        }
+
+        // Add hover effects to word points in Word2Vec visualization
+        document.querySelectorAll('.word-point').forEach(point => {
+            point.addEventListener('mouseenter', function() {
+                // Find similar words (simulated)
+                const similarWords = {
+                    'cat': ['kitten', 'dog'],
+                    'dog': ['cat', 'kitten'],
+                    'kitten': ['cat', 'dog'],
+                    'car': ['truck'],
+                    'truck': ['car'],
+                    'happy': ['joy'],
+                    'joy': ['happy'],
+                    'run': ['walk'],
+                    'walk': ['run']
+                };
+                
+                const word = this.textContent;
+                const similar = similarWords[word] || [];
+                
+                // Highlight similar words
+                document.querySelectorAll('.word-point').forEach(p => {
+                    if (similar.includes(p.textContent)) {
+                        p.style.background = 'linear-gradient(135deg, #4caf50 0%, #8bc34a 100%)';
+                    }
+                });
+            });
+            
+            point.addEventListener('mouseleave', function() {
+                // Reset colors
+                document.querySelectorAll('.word-point').forEach(p => {
+                    p.style.background = '';
+                });
+            });
+        });
+
+        // Animate attention cells in transformer section
+        setInterval(() => {
+            document.querySelectorAll('.attention-cell').forEach(cell => {
+                cell.style.opacity = 0.2 + Math.random() * 0.8;
+            });
+        }, 2000);
+
+        // Add scroll animations
+        const observerOptions = {
+            threshold: 0.1,
+            rootMargin: '0px 0px -50px 0px'
+        };
+
+        const observer = new IntersectionObserver((entries) => {
+            entries.forEach(entry => {
+                if (entry.isIntersecting) {
+                    entry.target.style.opacity = '1';
+                    entry.target.style.transform = 'translateY(0)';
+                }
+            });
+        }, observerOptions);
+
+        document.querySelectorAll('.section').forEach(section => {
+            section.style.opacity = '0';
+            section.style.transform = 'translateY(30px)';
+            section.style.transition = 'all 0.6s ease-out';
+            observer.observe(section);
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/Week1/Day_2/tfidf_scores.png b/Week1/Day_2/tfidf_scores.png
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diff --git a/Week1/Day_2/transformer-sentence-heatmap.html b/Week1/Day_2/transformer-sentence-heatmap.html
new file mode 100644
index 00000000..ac0aa664
--- /dev/null
+++ b/Week1/Day_2/transformer-sentence-heatmap.html
@@ -0,0 +1,915 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Transformers: Sentence Embeddings & Similarity</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
+            background: linear-gradient(135deg, #1a1a2e 0%, #16213e 50%, #0f3460 100%);
+            min-height: 100vh;
+            color: #333;
+            overflow-x: hidden;
+        }
+
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            padding: 40px 20px;
+        }
+
+        header {
+            text-align: center;
+            color: white;
+            margin-bottom: 60px;
+            animation: fadeInDown 1s ease-out;
+        }
+
+        h1 {
+            font-size: 3.5em;
+            margin-bottom: 20px;
+            text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+        }
+
+        .subtitle {
+            font-size: 1.4em;
+            opacity: 0.9;
+        }
+
+        .section {
+            background: rgba(255, 255, 255, 0.95);
+            border-radius: 20px;
+            padding: 40px;
+            margin-bottom: 40px;
+            box-shadow: 0 20px 40px rgba(0,0,0,0.1);
+            animation: fadeInUp 1s ease-out;
+        }
+
+        h2 {
+            color: #1a1a2e;
+            font-size: 2.5em;
+            margin-bottom: 30px;
+            text-align: center;
+        }
+
+        h3 {
+            color: #16213e;
+            font-size: 1.8em;
+            margin: 30px 0 20px;
+        }
+
+        /* Sentence Input Section */
+        .sentence-list {
+            display: grid;
+            gap: 15px;
+            margin-bottom: 30px;
+        }
+
+        .sentence-item {
+            display: flex;
+            align-items: center;
+            gap: 15px;
+            padding: 15px;
+            background: #f8f9fa;
+            border-radius: 10px;
+            border: 2px solid transparent;
+            transition: all 0.3s ease;
+        }
+
+        .sentence-item:hover {
+            border-color: #4a90e2;
+            transform: translateX(5px);
+        }
+
+        .sentence-number {
+            width: 30px;
+            height: 30px;
+            background: #4a90e2;
+            color: white;
+            border-radius: 50%;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-weight: bold;
+            flex-shrink: 0;
+        }
+
+        .sentence-text {
+            flex-grow: 1;
+            font-size: 1.1em;
+        }
+
+        /* Transformer Pipeline Visualization */
+        .pipeline {
+            display: flex;
+            align-items: center;
+            justify-content: space-between;
+            margin: 40px 0;
+            flex-wrap: wrap;
+            gap: 20px;
+        }
+
+        .pipeline-stage {
+            flex: 1;
+            min-width: 150px;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            padding: 20px;
+            border-radius: 15px;
+            text-align: center;
+            box-shadow: 0 10px 25px rgba(0,0,0,0.2);
+            transition: all 0.3s ease;
+            position: relative;
+        }
+
+        .pipeline-stage:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 15px 35px rgba(0,0,0,0.3);
+        }
+
+        .pipeline-stage::after {
+            content: '→';
+            position: absolute;
+            right: -30px;
+            top: 50%;
+            transform: translateY(-50%);
+            font-size: 2em;
+            color: #667eea;
+        }
+
+        .pipeline-stage:last-child::after {
+            display: none;
+        }
+
+        .stage-icon {
+            font-size: 2em;
+            margin-bottom: 10px;
+        }
+
+        /* Heat Map Container */
+        .heatmap-container {
+            margin: 40px 0;
+            display: flex;
+            gap: 40px;
+            align-items: start;
+            flex-wrap: wrap;
+        }
+
+        .heatmap-wrapper {
+            flex: 1;
+            min-width: 400px;
+        }
+
+        .heatmap {
+            position: relative;
+            background: white;
+            border-radius: 15px;
+            padding: 60px 20px 20px 60px;
+            box-shadow: 0 10px 30px rgba(0,0,0,0.1);
+        }
+
+        .heatmap-grid {
+            display: grid;
+            gap: 2px;
+            grid-template-columns: repeat(6, 60px);
+            grid-template-rows: repeat(6, 60px);
+        }
+
+        .heatmap-cell {
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-weight: bold;
+            color: white;
+            border-radius: 5px;
+            cursor: pointer;
+            transition: all 0.3s ease;
+            font-size: 0.9em;
+        }
+
+        .heatmap-cell:hover {
+            transform: scale(1.1);
+            z-index: 10;
+            box-shadow: 0 5px 15px rgba(0,0,0,0.3);
+        }
+
+        /* Heat colors */
+        .heat-0 { background: #f5f5f5; color: #333; }
+        .heat-1 { background: #ffebee; color: #333; }
+        .heat-2 { background: #ffcdd2; color: #333; }
+        .heat-3 { background: #ef9a9a; color: #333; }
+        .heat-4 { background: #e57373; color: white; }
+        .heat-5 { background: #ef5350; color: white; }
+        .heat-6 { background: #f44336; color: white; }
+        .heat-7 { background: #e53935; color: white; }
+        .heat-8 { background: #d32f2f; color: white; }
+        .heat-9 { background: #c62828; color: white; }
+        .heat-10 { background: #b71c1c; color: white; }
+
+        /* Axis labels */
+        .x-labels, .y-labels {
+            position: absolute;
+            display: flex;
+            gap: 2px;
+        }
+
+        .x-labels {
+            top: 20px;
+            left: 60px;
+        }
+
+        .y-labels {
+            flex-direction: column;
+            left: 20px;
+            top: 60px;
+        }
+
+        .label {
+            width: 60px;
+            height: 60px;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-weight: bold;
+            color: #4a90e2;
+        }
+
+        /* Legend */
+        .legend {
+            flex: 0 0 200px;
+        }
+
+        .legend-title {
+            font-weight: bold;
+            margin-bottom: 15px;
+            color: #1a1a2e;
+        }
+
+        .legend-scale {
+            display: flex;
+            flex-direction: column;
+            gap: 5px;
+        }
+
+        .legend-item {
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+
+        .legend-color {
+            width: 30px;
+            height: 20px;
+            border-radius: 3px;
+        }
+
+        /* Attention Visualization */
+        .attention-demo {
+            margin: 40px 0;
+            background: #f8f9fa;
+            padding: 30px;
+            border-radius: 15px;
+        }
+
+        .token-row {
+            display: flex;
+            gap: 10px;
+            margin: 20px 0;
+            flex-wrap: wrap;
+            justify-content: center;
+        }
+
+        .token {
+            padding: 10px 20px;
+            background: white;
+            border-radius: 8px;
+            border: 2px solid #e0e0e0;
+            font-weight: bold;
+            transition: all 0.3s ease;
+            position: relative;
+        }
+
+        .token.highlighted {
+            border-color: #4a90e2;
+            background: #e3f2fd;
+            transform: scale(1.1);
+        }
+
+        .attention-line {
+            position: absolute;
+            height: 2px;
+            background: #ff6b6b;
+            transform-origin: left center;
+            pointer-events: none;
+            opacity: 0;
+            transition: opacity 0.3s ease;
+        }
+
+        /* Interactive Controls */
+        .controls {
+            background: #f8f9fa;
+            padding: 30px;
+            border-radius: 15px;
+            margin: 30px 0;
+        }
+
+        .input-group {
+            display: flex;
+            gap: 10px;
+            margin-bottom: 20px;
+        }
+
+        .sentence-input {
+            flex: 1;
+            padding: 12px 20px;
+            font-size: 1.1em;
+            border: 2px solid #e0e0e0;
+            border-radius: 10px;
+            transition: border-color 0.3s ease;
+        }
+
+        .sentence-input:focus {
+            outline: none;
+            border-color: #4a90e2;
+        }
+
+        .add-button {
+            padding: 12px 30px;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            border: none;
+            border-radius: 10px;
+            font-size: 1.1em;
+            font-weight: bold;
+            cursor: pointer;
+            transition: all 0.3s ease;
+        }
+
+        .add-button:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+        }
+
+        /* Embedding Space Visualization */
+        .embedding-space {
+            width: 100%;
+            height: 400px;
+            background: #f5f5f5;
+            border-radius: 15px;
+            position: relative;
+            margin: 30px 0;
+            overflow: hidden;
+            border: 2px solid #e0e0e0;
+        }
+
+        .embedding-point {
+            position: absolute;
+            width: 40px;
+            height: 40px;
+            background: #4a90e2;
+            border-radius: 50%;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-weight: bold;
+            cursor: pointer;
+            transition: all 0.3s ease;
+            box-shadow: 0 4px 10px rgba(0,0,0,0.2);
+        }
+
+        .embedding-point:hover {
+            transform: scale(1.2);
+            z-index: 10;
+            box-shadow: 0 6px 20px rgba(0,0,0,0.3);
+        }
+
+        .embedding-point.highlighted {
+            background: #ff6b6b;
+        }
+
+        /* Info Box */
+        .info-box {
+            background: #e3f2fd;
+            border: 2px solid #4a90e2;
+            border-radius: 10px;
+            padding: 20px;
+            margin: 20px 0;
+        }
+
+        .info-box h4 {
+            color: #1976d2;
+            margin-bottom: 10px;
+        }
+
+        /* Animations */
+        @keyframes fadeInDown {
+            from {
+                opacity: 0;
+                transform: translateY(-30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes fadeInUp {
+            from {
+                opacity: 0;
+                transform: translateY(30px);
+            }
+            to {
+                opacity: 1;
+                transform: translateY(0);
+            }
+        }
+
+        @keyframes pulse {
+            0%, 100% { transform: scale(1); }
+            50% { transform: scale(1.05); }
+        }
+
+        .pulse {
+            animation: pulse 2s ease-in-out infinite;
+        }
+
+        @media (max-width: 768px) {
+            h1 { font-size: 2.5em; }
+            h2 { font-size: 2em; }
+            .pipeline { flex-direction: column; }
+            .pipeline-stage::after { display: none; }
+            .heatmap-container { flex-direction: column; }
+            .heatmap-grid { grid-template-columns: repeat(6, 50px); grid-template-rows: repeat(6, 50px); }
+            .label { width: 50px; height: 50px; font-size: 0.8em; }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <header>
+            <h1>🤖 Transformers: Sentence Embeddings</h1>
+            <p class="subtitle">Visualizing How Transformers Create Context-Aware Sentence Representations</p>
+        </header>
+
+        <!-- Introduction Section -->
+        <div class="section">
+            <h2>📝 Sample Sentences</h2>
+            <p style="text-align: center; margin-bottom: 30px; font-size: 1.1em;">
+                These sentences will be embedded by a transformer model to create dense vector representations
+            </p>
+            
+            <div class="sentence-list" id="sentenceList">
+                <div class="sentence-item">
+                    <div class="sentence-number">1</div>
+                    <div class="sentence-text">The cat sat on the mat</div>
+                </div>
+                <div class="sentence-item">
+                    <div class="sentence-number">2</div>
+                    <div class="sentence-text">A dog played in the garden</div>
+                </div>
+                <div class="sentence-item">
+                    <div class="sentence-number">3</div>
+                    <div class="sentence-text">The kitten slept on a cushion</div>
+                </div>
+                <div class="sentence-item">
+                    <div class="sentence-number">4</div>
+                    <div class="sentence-text">Machine learning transforms data into insights</div>
+                </div>
+                <div class="sentence-item">
+                    <div class="sentence-number">5</div>
+                    <div class="sentence-text">Artificial intelligence learns from data patterns</div>
+                </div>
+                <div class="sentence-item">
+                    <div class="sentence-number">6</div>
+                    <div class="sentence-text">Neural networks process information efficiently</div>
+                </div>
+            </div>
+        </div>
+
+        <!-- Transformer Pipeline -->
+        <div class="section">
+            <h2>🔄 Transformer Processing Pipeline</h2>
+            
+            <div class="pipeline">
+                <div class="pipeline-stage">
+                    <div class="stage-icon">📝</div>
+                    <div>Tokenization</div>
+                    <small>Split into subwords</small>
+                </div>
+                <div class="pipeline-stage">
+                    <div class="stage-icon">🔢</div>
+                    <div>Embedding Layer</div>
+                    <small>Token → Vector</small>
+                </div>
+                <div class="pipeline-stage">
+                    <div class="stage-icon">🎯</div>
+                    <div>Self-Attention</div>
+                    <small>Context awareness</small>
+                </div>
+                <div class="pipeline-stage">
+                    <div class="stage-icon">🧮</div>
+                    <div>Feed Forward</div>
+                    <small>Transform features</small>
+                </div>
+                <div class="pipeline-stage">
+                    <div class="stage-icon">📊</div>
+                    <div>Pooling</div>
+                    <small>Sentence vector</small>
+                </div>
+            </div>
+
+            <div class="info-box">
+                <h4>🔍 How Transformers Create Sentence Embeddings:</h4>
+                <ol style="margin-left: 20px;">
+                    <li><strong>Tokenization:</strong> Breaks sentences into subword tokens (handles unknown words)</li>
+                    <li><strong>Token Embeddings:</strong> Each token gets an initial vector representation</li>
+                    <li><strong>Self-Attention:</strong> Tokens attend to each other, capturing relationships</li>
+                    <li><strong>Layer Processing:</strong> Multiple transformer layers refine representations</li>
+                    <li><strong>Pooling:</strong> Combine all token embeddings into a single sentence vector</li>
+                </ol>
+            </div>
+        </div>
+
+        <!-- Heat Map Section -->
+        <div class="section">
+            <h2>🔥 Sentence Similarity Heat Map</h2>
+            <p style="text-align: center; margin-bottom: 30px; font-size: 1.1em;">
+                Darker red = Higher similarity | Based on cosine similarity of sentence embeddings
+            </p>
+            
+            <div class="heatmap-container">
+                <div class="heatmap-wrapper">
+                    <div class="heatmap">
+                        <div class="x-labels">
+                            <div class="label">S1</div>
+                            <div class="label">S2</div>
+                            <div class="label">S3</div>
+                            <div class="label">S4</div>
+                            <div class="label">S5</div>
+                            <div class="label">S6</div>
+                        </div>
+                        <div class="y-labels">
+                            <div class="label">S1</div>
+                            <div class="label">S2</div>
+                            <div class="label">S3</div>
+                            <div class="label">S4</div>
+                            <div class="label">S5</div>
+                            <div class="label">S6</div>
+                        </div>
+                        <div class="heatmap-grid" id="heatmapGrid">
+                            <!-- Filled by JavaScript -->
+                        </div>
+                    </div>
+                </div>
+                
+                <div class="legend">
+                    <div class="legend-title">Similarity Score</div>
+                    <div class="legend-scale">
+                        <div class="legend-item">
+                            <div class="legend-color heat-10"></div>
+                            <span>1.0 - Perfect match</span>
+                        </div>
+                        <div class="legend-item">
+                            <div class="legend-color heat-8"></div>
+                            <span>0.8 - Very similar</span>
+                        </div>
+                        <div class="legend-item">
+                            <div class="legend-color heat-6"></div>
+                            <span>0.6 - Similar</span>
+                        </div>
+                        <div class="legend-item">
+                            <div class="legend-color heat-4"></div>
+                            <span>0.4 - Somewhat similar</span>
+                        </div>
+                        <div class="legend-item">
+                            <div class="legend-color heat-2"></div>
+                            <span>0.2 - Different</span>
+                        </div>
+                        <div class="legend-item">
+                            <div class="legend-color heat-0"></div>
+                            <span>0.0 - Very different</span>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <div class="info-box">
+                <h4>📊 What the Heat Map Shows:</h4>
+                <ul style="margin-left: 20px;">
+                    <li><strong>Diagonal (S1-S1, S2-S2, etc.):</strong> Always 1.0 (perfect similarity with itself)</li>
+                    <li><strong>Animal sentences (S1-S3):</strong> Show high similarity due to shared semantic concepts</li>
+                    <li><strong>AI/ML sentences (S4-S6):</strong> Cluster together with high similarity scores</li>
+                    <li><strong>Cross-domain (S1-S4):</strong> Low similarity as topics are different</li>
+                </ul>
+            </div>
+        </div>
+
+        <!-- Attention Mechanism Demo -->
+        <div class="section">
+            <h2>🎯 Self-Attention in Action</h2>
+            <div class="attention-demo">
+                <p style="text-align: center; margin-bottom: 20px;">
+                    Click on any token to see how it attends to other tokens in the sentence
+                </p>
+                
+                <div id="attentionExample">
+                    <div class="token-row" id="tokenRow">
+                        <div class="token" onclick="showAttention(0)">The</div>
+                        <div class="token" onclick="showAttention(1)">cat</div>
+                        <div class="token" onclick="showAttention(2)">sat</div>
+                        <div class="token" onclick="showAttention(3)">on</div>
+                        <div class="token" onclick="showAttention(4)">the</div>
+                        <div class="token" onclick="showAttention(5)">mat</div>
+                    </div>
+                </div>
+                
+                <div id="attentionInfo" style="text-align: center; margin-top: 20px; color: #666;">
+                    <em>Click a token to visualize attention weights</em>
+                </div>
+            </div>
+        </div>
+
+        <!-- 2D Embedding Space -->
+        <div class="section">
+            <h2>📍 2D Embedding Space Visualization</h2>
+            <p style="text-align: center; margin-bottom: 20px; font-size: 1.1em;">
+                Sentence embeddings projected into 2D space - similar sentences cluster together
+            </p>
+            
+            <div class="embedding-space" id="embeddingSpace">
+                <!-- Points added by JavaScript -->
+            </div>
+            
+            <div class="info-box">
+                <h4>🌐 Understanding the Embedding Space:</h4>
+                <p>This is a simplified 2D projection of high-dimensional sentence embeddings (typically 768D for BERT).</p>
+                <ul style="margin-left: 20px; margin-top: 10px;">
+                    <li><strong>Distance = Dissimilarity:</strong> Closer points have more similar meanings</li>
+                    <li><strong>Clusters:</strong> Sentences about similar topics group together</li>
+                    <li><strong>Dimensionality Reduction:</strong> Real embeddings have hundreds of dimensions</li>
+                </ul>
+            </div>
+        </div>
+
+        <!-- Interactive Demo -->
+        <div class="section">
+            <h2>🔬 Try Your Own Sentences</h2>
+            <div class="controls">
+                <div class="input-group">
+                    <input type="text" class="sentence-input" id="newSentence" placeholder="Enter a new sentence to analyze...">
+                    <button class="add-button" onclick="addSentence()">Add & Analyze</button>
+                </div>
+                
+                <div id="analysisResult" style="display: none; margin-top: 20px;">
+                    <h4>Analysis Results:</h4>
+                    <div id="resultContent"></div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script>
+        // Simulated similarity matrix (in practice, these would come from actual transformer embeddings)
+        const similarityMatrix = [
+            [1.00, 0.42, 0.78, 0.12, 0.15, 0.18],  // S1: cat sentence
+            [0.42, 1.00, 0.45, 0.08, 0.11, 0.14],  // S2: dog sentence
+            [0.78, 0.45, 1.00, 0.10, 0.13, 0.16],  // S3: kitten sentence
+            [0.12, 0.08, 0.10, 1.00, 0.82, 0.75],  // S4: ML sentence
+            [0.15, 0.11, 0.13, 0.82, 1.00, 0.79],  // S5: AI sentence
+            [0.18, 0.14, 0.16, 0.75, 0.79, 1.00]   // S6: Neural networks
+        ];
+
+        // Initialize heat map
+        function initHeatmap() {
+            const grid = document.getElementById('heatmapGrid');
+            grid.innerHTML = '';
+            
+            for (let i = 0; i < 6; i++) {
+                for (let j = 0; j < 6; j++) {
+                    const cell = document.createElement('div');
+                    cell.className = 'heatmap-cell';
+                    const similarity = similarityMatrix[i][j];
+                    const heatLevel = Math.round(similarity * 10);
+                    cell.classList.add(`heat-${heatLevel}`);
+                    cell.textContent = similarity.toFixed(2);
+                    cell.title = `S${i+1} ↔ S${j+1}: ${similarity.toFixed(3)}`;
+                    
+                    cell.addEventListener('click', () => {
+                        highlightSentences(i, j);
+                    });
+                    
+                    grid.appendChild(cell);
+                }
+            }
+        }
+
+        // Highlight related sentences
+        function highlightSentences(i, j) {
+            const sentences = document.querySelectorAll('.sentence-item');
+            sentences.forEach(s => s.style.background = '#f8f9fa');
+            
+            if (i !== j) {
+                sentences[i].style.background = '#ffebee';
+                sentences[j].style.background = '#ffebee';
+                
+                // Show similarity info
+                const similarity = similarityMatrix[i][j];
+                const interpretation = similarity > 0.7 ? 'Very similar' : 
+                                     similarity > 0.4 ? 'Moderately similar' : 
+                                     'Different topics';
+                
+                alert(`Similarity between S${i+1} and S${j+1}: ${similarity.toFixed(3)}\n${interpretation}`);
+            }
+        }
+
+        // Show attention weights
+        function showAttention(tokenIndex) {
+            const tokens = document.querySelectorAll('.token');
+            const attentionInfo = document.getElementById('attentionInfo');
+            
+            // Reset all tokens
+            tokens.forEach(t => t.classList.remove('highlighted'));
+            
+            // Highlight selected token
+            tokens[tokenIndex].classList.add('highlighted');
+            
+            // Simulated attention weights
+            const attentionWeights = {
+                0: [0.15, 0.35, 0.20, 0.10, 0.10, 0.10],  // "The" attends mostly to "cat"
+                1: [0.20, 0.30, 0.25, 0.05, 0.05, 0.15],  // "cat" self-attention + "sat"
+                2: [0.15, 0.30, 0.25, 0.10, 0.05, 0.15],  // "sat" attends to subject
+                3: [0.05, 0.10, 0.15, 0.20, 0.15, 0.35],  // "on" attends forward
+                4: [0.05, 0.05, 0.10, 0.15, 0.20, 0.45],  // "the" attends to "mat"
+                5: [0.10, 0.15, 0.20, 0.25, 0.15, 0.15]   // "mat" balanced attention
+            };
+            
+            const weights = attentionWeights[tokenIndex];
+            const tokenTexts = ['The', 'cat', 'sat', 'on', 'the', 'mat'];
+            
+            // Update visual attention
+            tokens.forEach((token, idx) => {
+                if (idx !== tokenIndex) {
+                    const weight = weights[idx];
+                    token.style.opacity = 0.3 + (weight * 1.5);
+                    token.style.borderWidth = `${2 + weight * 8}px`;
+                }
+            });
+            
+            // Show attention info
+            const maxAttentionIdx = weights.indexOf(Math.max(...weights));
+            attentionInfo.innerHTML = `
+                <strong>"${tokenTexts[tokenIndex]}"</strong> pays most attention to 
+                <strong>"${tokenTexts[maxAttentionIdx]}"</strong> 
+                (weight: ${weights[maxAttentionIdx].toFixed(2)})
+            `;
+        }
+
+        // Initialize 2D embedding space
+        function initEmbeddingSpace() {
+            const space = document.getElementById('embeddingSpace');
+            space.innerHTML = '';
+            
+            // Simulated 2D positions (in practice, use t-SNE or UMAP on real embeddings)
+            const positions = [
+                {x: 20, y: 30, label: 'S1'},   // Animal cluster
+                {x: 25, y: 50, label: 'S2'},
+                {x: 15, y: 40, label: 'S3'},
+                {x: 70, y: 60, label: 'S4'},   // AI/ML cluster
+                {x: 75, y: 70, label: 'S5'},
+                {x: 65, y: 75, label: 'S6'}
+            ];
+            
+            positions.forEach((pos, idx) => {
+                const point = document.createElement('div');
+                point.className = 'embedding-point';
+                point.style.left = `${pos.x}%`;
+                point.style.top = `${pos.y}%`;
+                point.textContent = pos.label;
+                
+                // Color code by cluster
+                if (idx < 3) {
+                    point.style.background = '#ff6b6b';  // Animal cluster
+                } else {
+                    point.style.background = '#4a90e2';  // AI/ML cluster
+                }
+                
+                point.addEventListener('click', () => {
+                    const sentences = document.querySelectorAll('.sentence-item');
+                    sentences.forEach(s => s.style.background = '#f8f9fa');
+                    sentences[idx].style.background = '#ffebee';
+                    sentences[idx].scrollIntoView({ behavior: 'smooth', block: 'center' });
+                });
+                
+                space.appendChild(point);
+            });
+        }
+
+        // Add new sentence
+        function addSentence() {
+            const input = document.getElementById('newSentence');
+            const sentence = input.value.trim();
+            
+            if (!sentence) return;
+            
+            const resultDiv = document.getElementById('analysisResult');
+            const resultContent = document.getElementById('resultContent');
+            
+            // Simulate embedding and similarity calculation
+            const similarities = [];
+            const sentences = document.querySelectorAll('.sentence-text');
+            
+            sentences.forEach((s, idx) => {
+                // Simple keyword-based similarity (in practice, use real embeddings)
+                const words1 = sentence.toLowerCase().split(' ');
+                const words2 = s.textContent.toLowerCase().split(' ');
+                const common = words1.filter(w => words2.includes(w)).length;
+                const similarity = common / Math.max(words1.length, words2.length);
+                similarities.push({ idx: idx + 1, text: s.textContent, score: similarity });
+            });
+            
+            similarities.sort((a, b) => b.score - a.score);
+            
+            resultContent.innerHTML = `
+                <p><strong>Input:</strong> "${sentence}"</p>
+                <p><strong>Most similar sentences:</strong></p>
+                <ol>
+                    ${similarities.slice(0, 3).map(s => 
+                        `<li>S${s.idx}: "${s.text}" (similarity: ${s.score.toFixed(2)})</li>`
+                    ).join('')}
+                </ol>
+                <p style="margin-top: 15px; color: #666;">
+                    <em>Note: This demo uses keyword overlap. Real transformers use deep semantic understanding.</em>
+                </p>
+            `;
+            
+            resultDiv.style.display = 'block';
+            input.value = '';
+        }
+
+        // Enter key support
+        document.getElementById('newSentence').addEventListener('keypress', (e) => {
+            if (e.key === 'Enter') addSentence();
+        });
+
+        // Initialize on load
+        window.addEventListener('load', () => {
+            initHeatmap();
+            initEmbeddingSpace();
+        });
+
+        // Add hover effect to show embeddings
+        document.querySelectorAll('.sentence-item').forEach((item, idx) => {
+            item.addEventListener('mouseenter', () => {
+                // Highlight corresponding row and column in heatmap
+                const cells = document.querySelectorAll('.heatmap-cell');
+                cells.forEach((cell, cellIdx) => {
+                    const row = Math.floor(cellIdx / 6);
+                    const col = cellIdx % 6;
+                    if (row === idx || col === idx) {
+                        cell.style.transform = 'scale(1.1)';
+                        cell.style.zIndex = '5';
+                    }
+                });
+            });
+            
+            item.addEventListener('mouseleave', () => {
+                const cells = document.querySelectorAll('.heatmap-cell');
+                cells.forEach(cell => {
+                    cell.style.transform = '';
+                    cell.style.zIndex = '';
+                });
+            });
+        });
+
+        // Animate pipeline on scroll
+        const observerOptions = {
+            threshold: 0.1,
+            rootMargin: '0px 0px -50px 0px'
+        };
+
+        const observer = new IntersectionObserver((entries) => {
+            entries.forEach(entry => {
+                if (entry.isIntersecting) {
+                    entry.target.style.opacity = '1';
+                    entry.target.style.transform = 'translateY(0)';
+                }
+            });
+        }, observerOptions);
+
+        document.querySelectorAll('.section').forEach(section => {
+            section.style.opacity = '0';
+            section.style.transform = 'translateY(30px)';
+            section.style.transition = 'all 0.6s ease-out';
+            observer.observe(section);
+        });
+    </script>
+</body>
+</html>
\ No newline at end of file
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diff --git a/Week1/Day_2/trigram_probs.json b/Week1/Day_2/trigram_probs.json
new file mode 100644
index 00000000..a01681d6
--- /dev/null
+++ b/Week1/Day_2/trigram_probs.json
@@ -0,0 +1,38900 @@
+{
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+  },
+  "of india budget": {
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+  },
+  "india budget 2025": {
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+  },
+  "budget 2025 2026": {
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+  },
+  "2025 2026 speech": {
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+  "2026 speech of": {
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+  "speech of nirmala": {
+    "sitharaman": 1.0
+  },
+  "of nirmala sitharaman": {
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+  "nirmala sitharaman minister": {
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+  "sitharaman minister of": {
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+  "minister of finance": {
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+  "of finance february": {
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+  "finance february 1": {
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+    "202": 0.5
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+  "february 1 2025": {
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+  },
+  "1 2025 contents": {
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+  },
+  "2025 contents part": {
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+  },
+  "contents part a": {
+    "page": 1.0
+  },
+  "part a page": {
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+  "a page no": {
+    "introduction": 1.0
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+  "page no introduction": {
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+  "no introduction 1": {
+    "budget": 1.0
+  },
+  "introduction 1 budget": {
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+  },
+  "1 budget theme": {
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+  },
+  "budget theme 1": {
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+  "the 2nd engine": {
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+  "2nd engine 6": {
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+  "3rd engine 8": {
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+    "investing": 0.25,
+    "saksham": 0.25
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+  "in people 8": {
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new file mode 100644
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+    "beyond": 1.0
+  },
+  "beyond": {
+    "the": 1.0
+  },
+  "application": {
+    "seeking": 0.25,
+    "requesting": 0.25,
+    "is": 0.25,
+    "was": 0.25
+  },
+  "seeking": {
+    "immunity": 1.0
+  },
+  "immunity": {
+    "from": 1.0
+  },
+  "pass": {
+    "an": 0.75,
+    "order": 0.25
+  },
+  "accepting": {
+    "or": 1.0
+  },
+  "rejecting": {
+    "the": 0.5,
+    "assessee": 0.5
+  },
+  "requesting": {
+    "immunity": 1.0
+  },
+  "115vp": {
+    "it": 0.5,
+    "to": 0.5
+  },
+  "assessee": {
+    "s": 0.5,
+    "in": 0.5
+  },
+  "opt": {
+    "for": 1.0
+  },
+  "passed": {
+    "before": 0.5,
+    "after": 0.5
+  },
+  "expiry": {
+    "of": 1.0
+  },
+  "excluding": {
+    "the": 1.0
+  },
+  "court": {
+    "stay": 0.25,
+    "order": 0.25,
+    "it": 0.25,
+    "12": 0.25
+  },
+  "stay": {
+    "etc": 0.5,
+    "on": 0.5
+  },
+  "exclude": {
+    "certain": 1.0
+  },
+  "deeming": {
+    "a": 1.0
+  },
+  "failure": {
+    "to": 1.0
+  },
+  "impose": {
+    "penalties": 1.0
+  },
+  "imposing": {
+    "a": 1.0
+  },
+  "clarification": {
+    "regarding": 1.0
+  },
+  "stayed": {
+    "by": 1.0
+  },
+  "injunction": {
+    "of": 1.0
+  },
+  "retention": {
+    "of": 1.0
+  },
+  "seized": {
+    "books": 1.0
+  },
+  "reassessment": {
+    "or": 1.0
+  },
+  "recomputation": {
+    "order": 1.0
+  },
+  "losses": {
+    "in": 1.0
+  },
+  "amalgamation": {
+    "it": 1.0
+  },
+  "72a": {
+    "and": 1.0
+  },
+  "72aa": {
+    "of": 1.0
+  },
+  "loss": {
+    "forming": 0.3333333333333333,
+    "of": 0.3333333333333333,
+    "was": 0.3333333333333333
+  },
+  "forming": {
+    "part": 1.0
+  },
+  "accumulated": {
+    "loss": 1.0
+  },
+  "predecessor": {
+    "entity": 1.0
+  },
+  "carried": {
+    "forward": 1.0
+  },
+  "immediately": {
+    "succeeding": 1.0
+  },
+  "succeeding": {
+    "the": 1.0
+  },
+  "requisition": {
+    "cases": 0.5,
+    "has": 0.5
+  },
+  "xiv": {
+    "b": 1.0
+  },
+  "undisclosed": {
+    "income": 1.0
+  },
+  "twelve": {
+    "months": 1.0
+  },
+  "authorisations": {
+    "for": 1.0
+  }
+}
\ No newline at end of file
diff --git a/Week1/requirements.txt b/Week1/requirements.txt
new file mode 100644
index 00000000..11388d09
--- /dev/null
+++ b/Week1/requirements.txt
@@ -0,0 +1,70 @@
+altair==5.5.0
+annotated-types==0.7.0
+anyio==4.9.0
+attrs==25.3.0
+blinker==1.9.0
+cachetools==6.1.0
+certifi==2025.6.15
+charset-normalizer==3.4.2
+click==8.2.1
+cmdstanpy==1.2.5
+contourpy==1.3.2
+cycler==0.12.1
+fastapi==0.115.14
+fonttools==4.58.4
+gitdb==4.0.12
+GitPython==3.1.44
+h11==0.16.0
+holidays==0.75
+idna==3.10
+importlib_resources==6.5.2
+Jinja2==3.1.6
+joblib==1.5.1
+jsonschema==4.24.0
+jsonschema-specifications==2025.4.1
+kiwisolver==1.4.8
+MarkupSafe==3.0.2
+matplotlib==3.10.3
+narwhals==1.44.0
+nltk==3.9.1
+numpy==2.3.1
+packaging==25.0
+pandas==2.3.0
+patsy==1.0.1
+pillow==11.2.1
+prophet==1.1.7
+protobuf==6.31.1
+pyarrow==20.0.0
+pydantic==2.11.7
+pydantic_core==2.33.2
+pydeck==0.9.1
+pyparsing==3.2.3
+PyPDF2==3.0.1
+python-dateutil==2.9.0.post0
+pytz==2025.2
+referencing==0.36.2
+regex==2024.11.6
+requests==2.32.4
+rpds-py==0.25.1
+scikit-learn==1.7.0
+scipy==1.16.0
+seaborn==0.13.2
+six==1.17.0
+smmap==5.0.2
+sniffio==1.3.1
+stanio==0.5.1
+starlette==0.46.2
+statsmodels==0.14.4
+streamlit==1.46.1
+tenacity==9.1.2
+threadpoolctl==3.6.0
+toml==0.10.2
+tornado==6.5.1
+tqdm==4.67.1
+typing-inspection==0.4.1
+typing_extensions==4.14.0
+tzdata==2025.2
+urllib3==2.5.0
+uvicorn==0.34.3
+wordcloud==1.9.4
+xgboost==3.0.2
diff --git a/calc.py b/calc.py
deleted file mode 100644
index 21f59366..00000000
--- a/calc.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import sys
-
-def add(a,b):
-  print(a+b)
-
-if __name__ == "__main__":
-    num1 = float(sys.argv[1])
-    num2 = float(sys.argv[2])
-    print("num1 is ", num1)
-    print("num2 is ", num2)
-
-    add(num1, num2)
-    # add(num1, num2)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..ea284290
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,107 @@
+altair==5.5.0
+annotated-types==0.7.0
+anyio==4.9.0
+appnope==0.1.4
+asttokens==3.0.0
+attrs==25.3.0
+blinker==1.9.0
+cachetools==6.1.0
+certifi==2025.6.15
+charset-normalizer==3.4.2
+click==8.2.1
+cmdstanpy==1.2.5
+comm==0.2.2
+contourpy==1.3.2
+cycler==0.12.1
+debugpy==1.8.14
+decorator==5.2.1
+einops==0.8.1
+executing==2.2.0
+fastapi==0.115.14
+filelock==3.18.0
+fonttools==4.58.4
+fsspec==2025.5.1
+gitdb==4.0.12
+GitPython==3.1.44
+h11==0.16.0
+hf-xet==1.1.5
+holidays==0.75
+huggingface-hub==0.33.1
+idna==3.10
+importlib_resources==6.5.2
+ipykernel==6.29.5
+ipython==9.3.0
+ipython_pygments_lexers==1.1.1
+jedi==0.19.2
+Jinja2==3.1.6
+joblib==1.5.1
+jsonschema==4.24.0
+jsonschema-specifications==2025.4.1
+jupyter_client==8.6.3
+jupyter_core==5.8.1
+kiwisolver==1.4.8
+MarkupSafe==3.0.2
+matplotlib==3.10.3
+matplotlib-inline==0.1.7
+mpmath==1.3.0
+narwhals==1.44.0
+nest-asyncio==1.6.0
+networkx==3.5
+nltk==3.9.1
+numpy==2.3.1
+packaging==25.0
+pandas==2.3.0
+parso==0.8.4
+patsy==1.0.1
+pexpect==4.9.0
+pillow==11.2.1
+platformdirs==4.3.8
+prompt_toolkit==3.0.51
+prophet==1.1.7
+protobuf==6.31.1
+psutil==7.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pyarrow==20.0.0
+pydantic==2.11.7
+pydantic_core==2.33.2
+pydeck==0.9.1
+Pygments==2.19.2
+pyparsing==3.2.3
+PyPDF2==3.0.1
+python-dateutil==2.9.0.post0
+pytz==2025.2
+PyYAML==6.0.2
+pyzmq==27.0.0
+referencing==0.36.2
+regex==2024.11.6
+requests==2.32.4
+rpds-py==0.25.1
+scikit-learn==1.6.1
+scipy==1.16.0
+seaborn==0.13.2
+six==1.17.0
+smmap==5.0.2
+sniffio==1.3.1
+stack-data==0.6.3
+stanio==0.5.1
+starlette==0.46.2
+statsmodels==0.14.4
+streamlit==1.46.1
+sympy==1.14.0
+tabpfn==2.0.9
+tenacity==9.1.2
+threadpoolctl==3.6.0
+toml==0.10.2
+torch==2.2.2
+tornado==6.5.1
+tqdm==4.67.1
+traitlets==5.14.3
+typing-inspection==0.4.1
+typing_extensions==4.14.0
+tzdata==2025.2
+urllib3==2.5.0
+uvicorn==0.34.3
+wcwidth==0.2.13
+wordcloud==1.9.4
+xgboost==3.0.2

From e126448f7bc440f5df51b227b2e036756058ad58 Mon Sep 17 00:00:00 2001
From: RaamRaam <rm.ramanathan@gmail.com>
Date: Sat, 28 Jun 2025 18:50:23 +0530
Subject: [PATCH 6/7] fixed numpy version

---
 Week1/Day_1/2_tabular_classification.ipynb | 3427 ++++++++++++++++++++
 Week1/Day_1/3_tabular_regression.ipynb     | 1700 ++++++++++
 Week1/Day_2/search_engine.py               |   52 +-
 Week1/requirements.txt                     |   56 +-
 4 files changed, 5227 insertions(+), 8 deletions(-)
 create mode 100644 Week1/Day_1/2_tabular_classification.ipynb
 create mode 100644 Week1/Day_1/3_tabular_regression.ipynb

diff --git a/Week1/Day_1/2_tabular_classification.ipynb b/Week1/Day_1/2_tabular_classification.ipynb
new file mode 100644
index 00000000..3ee14b54
--- /dev/null
+++ b/Week1/Day_1/2_tabular_classification.ipynb
@@ -0,0 +1,3427 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d66903d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ! pip install tabpfn #pip install \"tabpfn @ git+https://github.com/PriorLabs/TabPFN.git\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "014aca45",
+   "metadata": {},
+   "source": [
+    "# Setting Up Data Science Environment for Classification\n",
+    "\n",
+    "This code sets up a Python environment for a classification task, specifically preparing to work with the breast cancer dataset. Here's what each import provides:\n",
+    "\n",
+    "\n",
+    "### Machine Learning Framework\n",
+    "- `load_breast_cancer`: A function to load the breast cancer Wisconsin dataset, a common benchmark for binary classification\n",
+    "  - Features are computed from a digitized image of a fine needle aspirate of a breast mass\n",
+    "  - The dataset includes 569 samples with 30 features\n",
+    "  - The target variable indicates whether a tumor is malignant or benign\n",
+    "\n",
+    "- `accuracy_score` and `roc_auc_score`: Metrics for evaluating classification models\n",
+    "  - Accuracy: The proportion of correct predictions\n",
+    "  - ROC-AUC: Area under the Receiver Operating Characteristic curve, measuring the model's ability to discriminate between classes\n",
+    "\n",
+    "- `train_test_split`: A function to split datasets into random train and test subsets for model validation\n",
+    "\n",
+    "This code prepares the environment for building and evaluating a classification model, likely using TabPFN (based on your previous pip install) as the modeling approach."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5f6aa040",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from sklearn.datasets import load_breast_cancer\n",
+    "from sklearn.metrics import (\n",
+    "    accuracy_score,\n",
+    "    confusion_matrix, \n",
+    "    classification_report, \n",
+    "    precision_score, \n",
+    "    recall_score, \n",
+    "    f1_score, \n",
+    "    roc_curve, \n",
+    "    roc_auc_score\n",
+    ")\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7cbd5f8",
+   "metadata": {},
+   "source": [
+    "# Load the breast cancer dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "84b7d0a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "X, y = load_breast_cancer(return_X_y=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21770ae0",
+   "metadata": {},
+   "source": [
+    "# Get the feature names and target names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cf78256d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
+      " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
+      " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
+      " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
+      " 'smoothness error' 'compactness error' 'concavity error'\n",
+      " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
+      " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
+      " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
+      " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n",
+      "['malignant' 'benign']\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "cancer = load_breast_cancer()\n",
+    "feature_names = cancer.feature_names\n",
+    "target_names = cancer.target_names\n",
+    "\n",
+    "\n",
+    "print(feature_names)\n",
+    "print(target_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "317592e1",
+   "metadata": {},
+   "source": [
+    "# Create a DataFrame for easier exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d33818ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean radius</th>\n",
+       "      <th>mean texture</th>\n",
+       "      <th>mean perimeter</th>\n",
+       "      <th>mean area</th>\n",
+       "      <th>mean smoothness</th>\n",
+       "      <th>mean compactness</th>\n",
+       "      <th>mean concavity</th>\n",
+       "      <th>mean concave points</th>\n",
+       "      <th>mean symmetry</th>\n",
+       "      <th>mean fractal dimension</th>\n",
+       "      <th>...</th>\n",
+       "      <th>worst perimeter</th>\n",
+       "      <th>worst area</th>\n",
+       "      <th>worst smoothness</th>\n",
+       "      <th>worst compactness</th>\n",
+       "      <th>worst concavity</th>\n",
+       "      <th>worst concave points</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "      <th>worst fractal dimension</th>\n",
+       "      <th>target</th>\n",
+       "      <th>diagnosis</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>0.2419</td>\n",
+       "      <td>0.07871</td>\n",
+       "      <td>...</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>0</td>\n",
+       "      <td>malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>0.1812</td>\n",
+       "      <td>0.05667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "      <td>0</td>\n",
+       "      <td>malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>0.2069</td>\n",
+       "      <td>0.05999</td>\n",
+       "      <td>...</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "      <td>0</td>\n",
+       "      <td>malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>0.2597</td>\n",
+       "      <td>0.09744</td>\n",
+       "      <td>...</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "      <td>0</td>\n",
+       "      <td>malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>0.1809</td>\n",
+       "      <td>0.05883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "      <td>0</td>\n",
+       "      <td>malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>0.1726</td>\n",
+       "      <td>0.05623</td>\n",
+       "      <td>...</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "      <td>0</td>\n",
+       "      <td>malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>0.1752</td>\n",
+       "      <td>0.05533</td>\n",
+       "      <td>...</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "      <td>0</td>\n",
+       "      <td>malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>0.1590</td>\n",
+       "      <td>0.05648</td>\n",
+       "      <td>...</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "      <td>0</td>\n",
+       "      <td>malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>0.2397</td>\n",
+       "      <td>0.07016</td>\n",
+       "      <td>...</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "      <td>0</td>\n",
+       "      <td>malignant</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.1587</td>\n",
+       "      <td>0.05884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "      <td>1</td>\n",
+       "      <td>benign</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
+       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
+       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
+       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
+       "3          11.42         20.38           77.58      386.1          0.14250   \n",
+       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
+       "..           ...           ...             ...        ...              ...   \n",
+       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
+       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
+       "566        16.60         28.08          108.30      858.1          0.08455   \n",
+       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
+       "568         7.76         24.54           47.92      181.0          0.05263   \n",
+       "\n",
+       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+       "0             0.27760         0.30010              0.14710         0.2419   \n",
+       "1             0.07864         0.08690              0.07017         0.1812   \n",
+       "2             0.15990         0.19740              0.12790         0.2069   \n",
+       "3             0.28390         0.24140              0.10520         0.2597   \n",
+       "4             0.13280         0.19800              0.10430         0.1809   \n",
+       "..                ...             ...                  ...            ...   \n",
+       "564           0.11590         0.24390              0.13890         0.1726   \n",
+       "565           0.10340         0.14400              0.09791         0.1752   \n",
+       "566           0.10230         0.09251              0.05302         0.1590   \n",
+       "567           0.27700         0.35140              0.15200         0.2397   \n",
+       "568           0.04362         0.00000              0.00000         0.1587   \n",
+       "\n",
+       "     mean fractal dimension  ...  worst perimeter  worst area  \\\n",
+       "0                   0.07871  ...           184.60      2019.0   \n",
+       "1                   0.05667  ...           158.80      1956.0   \n",
+       "2                   0.05999  ...           152.50      1709.0   \n",
+       "3                   0.09744  ...            98.87       567.7   \n",
+       "4                   0.05883  ...           152.20      1575.0   \n",
+       "..                      ...  ...              ...         ...   \n",
+       "564                 0.05623  ...           166.10      2027.0   \n",
+       "565                 0.05533  ...           155.00      1731.0   \n",
+       "566                 0.05648  ...           126.70      1124.0   \n",
+       "567                 0.07016  ...           184.60      1821.0   \n",
+       "568                 0.05884  ...            59.16       268.6   \n",
+       "\n",
+       "     worst smoothness  worst compactness  worst concavity  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     worst concave points  worst symmetry  worst fractal dimension  target  \\\n",
+       "0                  0.2654          0.4601                  0.11890       0   \n",
+       "1                  0.1860          0.2750                  0.08902       0   \n",
+       "2                  0.2430          0.3613                  0.08758       0   \n",
+       "3                  0.2575          0.6638                  0.17300       0   \n",
+       "4                  0.1625          0.2364                  0.07678       0   \n",
+       "..                    ...             ...                      ...     ...   \n",
+       "564                0.2216          0.2060                  0.07115       0   \n",
+       "565                0.1628          0.2572                  0.06637       0   \n",
+       "566                0.1418          0.2218                  0.07820       0   \n",
+       "567                0.2650          0.4087                  0.12400       0   \n",
+       "568                0.0000          0.2871                  0.07039       1   \n",
+       "\n",
+       "     diagnosis  \n",
+       "0    malignant  \n",
+       "1    malignant  \n",
+       "2    malignant  \n",
+       "3    malignant  \n",
+       "4    malignant  \n",
+       "..         ...  \n",
+       "564  malignant  \n",
+       "565  malignant  \n",
+       "566  malignant  \n",
+       "567  malignant  \n",
+       "568     benign  \n",
+       "\n",
+       "[569 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df = pd.DataFrame(X, columns=feature_names)\n",
+    "df['target'] = y\n",
+    "df['diagnosis'] = [target_names[val] for val in y]\n",
+    "\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55e5b3a3",
+   "metadata": {},
+   "source": [
+    "# Basic dataset information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "007808d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset Shape: (569, 30)\n",
+      "Number of Features: 30\n",
+      "Number of Samples: 569\n",
+      "\n",
+      "Class Distribution:\n",
+      "diagnosis\n",
+      "benign       357\n",
+      "malignant    212\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Class Distribution (%):\n",
+      "diagnosis\n",
+      "benign       62.7\n",
+      "malignant    37.3\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(\"Dataset Shape:\", X.shape)\n",
+    "print(\"Number of Features:\", X.shape[1])\n",
+    "print(\"Number of Samples:\", X.shape[0])\n",
+    "print(\"\\nClass Distribution:\")\n",
+    "print(df['diagnosis'].value_counts())\n",
+    "print(\"\\nClass Distribution (%):\")\n",
+    "print(df['diagnosis'].value_counts(normalize=True).round(3) * 100)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0e8485b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "First 5 rows of the dataset:\n",
+      "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
+      "0        17.99         10.38          122.80     1001.0          0.11840   \n",
+      "1        20.57         17.77          132.90     1326.0          0.08474   \n",
+      "2        19.69         21.25          130.00     1203.0          0.10960   \n",
+      "3        11.42         20.38           77.58      386.1          0.14250   \n",
+      "4        20.29         14.34          135.10     1297.0          0.10030   \n",
+      "\n",
+      "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+      "0           0.27760          0.3001              0.14710         0.2419   \n",
+      "1           0.07864          0.0869              0.07017         0.1812   \n",
+      "2           0.15990          0.1974              0.12790         0.2069   \n",
+      "3           0.28390          0.2414              0.10520         0.2597   \n",
+      "4           0.13280          0.1980              0.10430         0.1809   \n",
+      "\n",
+      "   mean fractal dimension  ...  worst perimeter  worst area  worst smoothness  \\\n",
+      "0                 0.07871  ...           184.60      2019.0            0.1622   \n",
+      "1                 0.05667  ...           158.80      1956.0            0.1238   \n",
+      "2                 0.05999  ...           152.50      1709.0            0.1444   \n",
+      "3                 0.09744  ...            98.87       567.7            0.2098   \n",
+      "4                 0.05883  ...           152.20      1575.0            0.1374   \n",
+      "\n",
+      "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
+      "0             0.6656           0.7119                0.2654          0.4601   \n",
+      "1             0.1866           0.2416                0.1860          0.2750   \n",
+      "2             0.4245           0.4504                0.2430          0.3613   \n",
+      "3             0.8663           0.6869                0.2575          0.6638   \n",
+      "4             0.2050           0.4000                0.1625          0.2364   \n",
+      "\n",
+      "   worst fractal dimension  target  diagnosis  \n",
+      "0                  0.11890       0  malignant  \n",
+      "1                  0.08902       0  malignant  \n",
+      "2                  0.08758       0  malignant  \n",
+      "3                  0.17300       0  malignant  \n",
+      "4                  0.07678       0  malignant  \n",
+      "\n",
+      "[5 rows x 32 columns]\n",
+      "\n",
+      "Statistical Summary of Features:\n",
+      "                         count        mean         std         min  \\\n",
+      "mean radius              569.0   14.127292    3.524049    6.981000   \n",
+      "mean texture             569.0   19.289649    4.301036    9.710000   \n",
+      "mean perimeter           569.0   91.969033   24.298981   43.790000   \n",
+      "mean area                569.0  654.889104  351.914129  143.500000   \n",
+      "mean smoothness          569.0    0.096360    0.014064    0.052630   \n",
+      "mean compactness         569.0    0.104341    0.052813    0.019380   \n",
+      "mean concavity           569.0    0.088799    0.079720    0.000000   \n",
+      "mean concave points      569.0    0.048919    0.038803    0.000000   \n",
+      "mean symmetry            569.0    0.181162    0.027414    0.106000   \n",
+      "mean fractal dimension   569.0    0.062798    0.007060    0.049960   \n",
+      "radius error             569.0    0.405172    0.277313    0.111500   \n",
+      "texture error            569.0    1.216853    0.551648    0.360200   \n",
+      "perimeter error          569.0    2.866059    2.021855    0.757000   \n",
+      "area error               569.0   40.337079   45.491006    6.802000   \n",
+      "smoothness error         569.0    0.007041    0.003003    0.001713   \n",
+      "compactness error        569.0    0.025478    0.017908    0.002252   \n",
+      "concavity error          569.0    0.031894    0.030186    0.000000   \n",
+      "concave points error     569.0    0.011796    0.006170    0.000000   \n",
+      "symmetry error           569.0    0.020542    0.008266    0.007882   \n",
+      "fractal dimension error  569.0    0.003795    0.002646    0.000895   \n",
+      "worst radius             569.0   16.269190    4.833242    7.930000   \n",
+      "worst texture            569.0   25.677223    6.146258   12.020000   \n",
+      "worst perimeter          569.0  107.261213   33.602542   50.410000   \n",
+      "worst area               569.0  880.583128  569.356993  185.200000   \n",
+      "worst smoothness         569.0    0.132369    0.022832    0.071170   \n",
+      "worst compactness        569.0    0.254265    0.157336    0.027290   \n",
+      "worst concavity          569.0    0.272188    0.208624    0.000000   \n",
+      "worst concave points     569.0    0.114606    0.065732    0.000000   \n",
+      "worst symmetry           569.0    0.290076    0.061867    0.156500   \n",
+      "worst fractal dimension  569.0    0.083946    0.018061    0.055040   \n",
+      "target                   569.0    0.627417    0.483918    0.000000   \n",
+      "\n",
+      "                                25%         50%          75%         max  \n",
+      "mean radius               11.700000   13.370000    15.780000    28.11000  \n",
+      "mean texture              16.170000   18.840000    21.800000    39.28000  \n",
+      "mean perimeter            75.170000   86.240000   104.100000   188.50000  \n",
+      "mean area                420.300000  551.100000   782.700000  2501.00000  \n",
+      "mean smoothness            0.086370    0.095870     0.105300     0.16340  \n",
+      "mean compactness           0.064920    0.092630     0.130400     0.34540  \n",
+      "mean concavity             0.029560    0.061540     0.130700     0.42680  \n",
+      "mean concave points        0.020310    0.033500     0.074000     0.20120  \n",
+      "mean symmetry              0.161900    0.179200     0.195700     0.30400  \n",
+      "mean fractal dimension     0.057700    0.061540     0.066120     0.09744  \n",
+      "radius error               0.232400    0.324200     0.478900     2.87300  \n",
+      "texture error              0.833900    1.108000     1.474000     4.88500  \n",
+      "perimeter error            1.606000    2.287000     3.357000    21.98000  \n",
+      "area error                17.850000   24.530000    45.190000   542.20000  \n",
+      "smoothness error           0.005169    0.006380     0.008146     0.03113  \n",
+      "compactness error          0.013080    0.020450     0.032450     0.13540  \n",
+      "concavity error            0.015090    0.025890     0.042050     0.39600  \n",
+      "concave points error       0.007638    0.010930     0.014710     0.05279  \n",
+      "symmetry error             0.015160    0.018730     0.023480     0.07895  \n",
+      "fractal dimension error    0.002248    0.003187     0.004558     0.02984  \n",
+      "worst radius              13.010000   14.970000    18.790000    36.04000  \n",
+      "worst texture             21.080000   25.410000    29.720000    49.54000  \n",
+      "worst perimeter           84.110000   97.660000   125.400000   251.20000  \n",
+      "worst area               515.300000  686.500000  1084.000000  4254.00000  \n",
+      "worst smoothness           0.116600    0.131300     0.146000     0.22260  \n",
+      "worst compactness          0.147200    0.211900     0.339100     1.05800  \n",
+      "worst concavity            0.114500    0.226700     0.382900     1.25200  \n",
+      "worst concave points       0.064930    0.099930     0.161400     0.29100  \n",
+      "worst symmetry             0.250400    0.282200     0.317900     0.66380  \n",
+      "worst fractal dimension    0.071460    0.080040     0.092080     0.20750  \n",
+      "target                     0.000000    1.000000     1.000000     1.00000  \n",
+      "\n",
+      "Missing Values:\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display first few rows of the dataset\n",
+    "print(\"\\nFirst 5 rows of the dataset:\")\n",
+    "print(df.head())\n",
+    "\n",
+    "# Statistical summary of features\n",
+    "print(\"\\nStatistical Summary of Features:\")\n",
+    "print(df.describe().T)\n",
+    "\n",
+    "# Check for missing values\n",
+    "print(\"\\nMissing Values:\")\n",
+    "print(df.isnull().sum().sum())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccda078e",
+   "metadata": {},
+   "source": [
+    "# Visualize the correlation matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ccc5dcd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(16, 14))\n",
+    "correlation_matrix = df.drop('diagnosis', axis=1).corr()\n",
+    "mask = np.triu(correlation_matrix)\n",
+    "sns.heatmap(correlation_matrix, annot=False, mask=mask, cmap='coolwarm', linewidths=0.5)\n",
+    "plt.title('Feature Correlation Matrix', fontsize=15)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Boxplots for comparing feature distributions between classes\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, feature in enumerate(feature_names[:5]):  # Just the first 5 features for clarity\n",
+    "    plt.subplot(2, 3, i+1)\n",
+    "    sns.boxplot(x='diagnosis', y=feature, data=df)\n",
+    "    plt.title(f'{feature} by Diagnosis')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Distribution of a few key features\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, feature in enumerate(feature_names[:5]):  # Just the first 5 features\n",
+    "    plt.subplot(2, 3, i+1)\n",
+    "    sns.histplot(df[feature], kde=True, bins=30)\n",
+    "    plt.title(f'Distribution of {feature}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91a779af",
+   "metadata": {},
+   "source": [
+    "# Boxplots for comparing feature distributions between classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cccf60e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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etm1bJ6cDAOdq0KCBFixY4OwYl7V//3698MILeuaZZxQSEuLsOJfVoEEDZ0cAAAC4KhTRAQC4htx1113q0qWLjhw5otatW9vae/TooTvuuMOJyQDA+by8vCrUGdMhISEVKi8AAEBFRREdAIBrTGBgoAIDAyVJubm5WrdunZo2bapmzZo5ORkAAAAAAK7HzdkBAABA+bn77rv1+uuvS5LOnj2r9u3b6+6771ZkZKQ+/PBDJ6cDAAAAAMD1UEQHAOAakpqaqq5du0qSPv74YxmGoRMnTmju3LlKTEx0cjoAAAAAAFwPRXQAAK4hOTk58vPzkyStWrVKAwcOVLVq1dS3b1/t3r3byekAAAAAAHA9FNEBALiGBAcHKy0tTadPn9aqVavUq1cvSdLx48fl5eXl5HQAAAAAALgeLiwKAMA1ZNy4cYqJiVGNGjXUoEEDde/eXdLf07y0atXKueEAAAAAAHBBFNEBALiGxMbG6oYbbtDBgwd18803y83t7w+lhYWFMSc6AAAAAABFoIgOAMA1pn379oqMjFRGRoYaNWqkKlWqqG/fvs6OBQAAAACAS2JOdAAAriFnzpzRyJEjVa1aNUVEROjAgQOSpIcfflgzZsxwcjoAAAAAAFwPRXQAAK4hkyZN0i+//KINGzbYXUi0Z8+eev/9952YDAAAAAAA18R0LgAAXEM++eQTvf/++/rXv/4lk8lka4+IiNDevXudmAwAAAAAANfEmegAAFxD/vzzT/n7+xdqP336tF1RvbiSkpLUsGFDeXl5qWPHjvrxxx8d9t2xY4cGDhyohg0bymQyac6cOYX6TJ8+XR06dJC3t7f8/f01YMAA7dq1q8S5AAAAAAAoLRTRAQC4hrRv314rV660LRcUzt944w1FRUWVaF/vv/++JkyYoPj4eP38889q3bq1evfuraNHjxbZ/8yZMwoLC9OMGTMUGBhYZJ+NGzcqLi5O33//vb766iudP39evXr10unTp0uUDQAAAACA0sJ0LgAAXEOmTZumPn36aOfOnbpw4YJeffVV7dy5U9999502btxYon3Nnj1bDz30kIYPHy5Jmj9/vlauXKm33npLTz31VKH+HTp0UIcOHSSpyPWStGrVKrvlxYsXy9/fX5s3b1Z0dHSJ8gEAAAAAUBo4Ex0AgGtIly5dtHXrVl24cEGtWrXSmjVr5O/vr7S0NLVr167Y+8nPz9fmzZvVs2dPW5ubm5t69uyptLS0Usubk5MjSfLz83PYJy8vT7m5uXY3AAAAAABKC2eiAwBwjWnUqJEWLlx4Vfs4duyYLBaLAgIC7NoDAgL022+/XdW+C1itVo0bN06dO3dWy5YtHfabPn26EhISSuU+AQAAAAC4GEV0AACuMVarVXv27NHRo0dltVrt1rnSlClxcXHavn27/ve//12y36RJkzRhwgTbcm5uroKDg8s6HgAAAADgGkERHQCAa8j333+v++67T/v375dhGHbrTCaTLBZLsfZTp04dmc1mZWVl2bVnZWU5vGhoSYwdO1aff/65UlNTVb9+/Uv29fT0lKen51XfJwAAAAAARWFOdAAAriGjR49W+/bttX37dmVnZ+v48eO2W3Z2drH34+HhoXbt2mnt2rW2NqvVqrVr1yoqKuqK8xmGobFjx+rjjz/WunXrFBoaesX7AgAAAACgNHAmOgAA15Ddu3frgw8+UHh4+FXva8KECRo6dKjat2+vG264QXPmzNHp06c1fPhwSdKQIUN03XXXafr06ZL+vhjpzp07bd8fOnRIW7duVY0aNWx54uLi9N5772nFihXy9vZWZmamJMnX11dVq1a96swAAAAAAJTUFRXRf/rpJy1btkwHDhxQfn6+3bqPPvqoVIIBAIDS17FjR+3Zs6dUiuj33HOP/vzzTz377LPKzMxUmzZttGrVKtvFRg8cOCA3t///obfDhw+rbdu2tuWXX35ZL7/8srp166YNGzZIkubNmydJ6t69u919LVq0SMOGDbvqzAAAAAAAlFSJi+hLly7VkCFD1Lt3b61Zs0a9evXS77//rqysLN1xxx1lkREAAJSShx9+WI899pgyMzPVqlUrubu7262PjIws0f7Gjh2rsWPHFrmuoDBeoGHDhoXmYb/Y5dYDAAAAAFDeSlxEnzZtml555RXFxcXJ29tbr776qkJDQ/Xvf/9bQUFBZZERAACUkoEDB0qSRowYYWszmUwyDKNEFxYFAAAAAOBaUeILi+7du1d9+/aV9PdFxU6fPi2TyaTx48drwYIFJdpXamqq+vXrp3r16slkMumTTz6xWz9s2DCZTCa72y233GLXJzs7WzExMfLx8VHNmjU1cuRInTp1yq5Penq6unbtKi8vLwUHB2vmzJklPWwAACqFjIyMQrd9+/bZvgIAAAAAAHslPhO9Vq1aOnnypCTpuuuu0/bt29WqVSudOHFCZ86cKdG+Tp8+rdatW2vEiBG68847i+xzyy23aNGiRbZlT09Pu/UxMTE6cuSIvvrqK50/f17Dhw/XqFGj9N5770mScnNz1atXL/Xs2VPz58/Xtm3bNGLECNWsWVOjRo0qUV4AACq6kJAQZ0cAAAAAAKBCKXERPTo6Wl999ZVatWqlQYMG6dFHH9W6dev01VdfqUePHiXaV58+fdSnT59L9vH09FRgYGCR63799VetWrVKmzZtUvv27SVJr732mm699Va9/PLLqlevnlJSUpSfn6+33npLHh4eioiI0NatWzV79myK6ACAa9Lu3bu1fv16HT16VFar1W7ds88+66RUAAAAAAC4phIX0V9//XWdO3dOkvTMM8/I3d1d3333nQYOHKjJkyeXesANGzbI399ftWrV0k033aTExETVrl1bkpSWlqaaNWvaCuiS1LNnT7m5uemHH37QHXfcobS0NEVHR8vDw8PWp3fv3nrxxRd1/Phx1apVq9QzAwDgqhYuXKgxY8aoTp06CgwMlMlksq0zmUwU0QEAAAAAuEiJi+h+fn62793c3PTUU0+VaqB/uuWWW3TnnXcqNDRUe/fu1dNPP60+ffooLS1NZrNZmZmZ8vf3t9umSpUq8vPzU2ZmpiQpMzNToaGhdn0CAgJs64oqoufl5SkvL8+2nJubW9qHBgCAUyQmJuqFF17Qk08+6ewoAAAAAABUCCUuokt/X1x00aJF2rt3r1599VX5+/vryy+/VIMGDRQREVFq4QYPHmz7vlWrVoqMjFSjRo20YcOGEk8dUxLTp09XQkJCme0fAABnOX78uAYNGuTsGAAAAAAAVBhuJd1g48aNatWqlX744Qd99NFHOnXqlCTpl19+UXx8fKkH/KewsDDVqVNHe/bskSQFBgbq6NGjdn0uXLig7Oxs2zzqgYGBysrKsutTsOxorvVJkyYpJyfHdjt48GBpHwoAAE4xaNAgrVmzxtkxAAAAAACoMEp8JvpTTz2lxMRETZgwQd7e3rb2m266Sa+//nqphrvY//3f/+mvv/5SUFCQJCkqKkonTpzQ5s2b1a5dO0nSunXrZLVa1bFjR1ufZ555RufPn5e7u7sk6auvvlLTpk0dzofu6ekpT0/PMj0WAADKy9y5c23fh4eHa8qUKfr+++/VqlUr29hY4JFHHinveAAAAAAAuLQSF9G3bdum9957r1C7v7+/jh07VqJ9nTp1ynZWuSRlZGRo69at8vPzk5+fnxISEjRw4EAFBgZq7969mjhxosLDw9W7d29JUvPmzXXLLbfooYce0vz583X+/HmNHTtWgwcPVr169SRJ9913nxISEjRy5Eg9+eST2r59u1599VW98sorJT10AAAqpIvHvBo1amjjxo3auHGjXbvJZKKIDgAAAADARUpcRK9Zs6aOHDlS6GKdW7Zs0XXXXVeiff3000+68cYbbcsTJkyQJA0dOlTz5s1Tenq6lixZohMnTqhevXrq1auXnn/+ebuzxFNSUjR27Fj16NFDbm5uGjhwoN0Zd76+vlqzZo3i4uLUrl071alTR88++6xGjRpV0kMHAKBCysjIcHYEAAAAAAAqrBIX0QcPHqwnn3xSy5cvl8lkktVq1bfffqvHH39cQ4YMKdG+unfvLsMwHK5fvXr1Zffh5+dX5Jnx/xQZGalvvvmmRNkAAKiMpk6dqscff1zVqlWzaz979qxeeuklPfvss05KBgAAAACAayrxhUWnTZumZs2aKTg4WKdOnVKLFi0UHR2tTp06afLkyWWREQAAlJKEhATbRcH/6cyZM0pISHBCIgAAAAAAXFuJzkQ3DEOZmZmaO3eunn32WW3btk2nTp1S27Zt1bhx47LKCAAASolhGDKZTIXaf/nlF/n5+TkhEQAAAAAArq3ERfTw8HDt2LFDjRs3VnBwcFnlAgAApahWrVoymUwymUxq0qSJXSHdYrHo1KlTGj16tBMTAgAAAADgmkpURHdzc1Pjxo31119/ceY5AAAVyJw5c2QYhkaMGKGEhAT5+vra1nl4eKhhw4aKiopyYkIAAAAAAFxTiS8sOmPGDD3xxBOaN2+eWrZsWRaZAABAKRs6dKgkKTQ0VJ06dZK7u7uTEwEAAAAAUDGUuIg+ZMgQnTlzRq1bt5aHh4eqVq1qtz47O7vUwgEAgNLVrVs3WSwWffjhh/r1118lSREREbr99ttlNpudnA4AAAAAANdT4iL6nDlzyiAGAAAoD3v27NGtt96qQ4cOqWnTppKk6dOnKzg4WCtXrlSjRo2cnBAAAAAAANdS4iJ6wcfBAQBAxfPII4+oUaNG+v777+Xn5ydJ+uuvv3T//ffrkUce0cqVK52cEAAAAAAA11LiIvo/nTt3Tvn5+XZtPj4+VxUIAACUnY0bN9oV0CWpdu3amjFjhjp37uzEZAAAAAAAuCa3km5w+vRpjR07Vv7+/qpevbpq1apldwMAAK7L09NTJ0+eLNR+6tQpeXh4OCERAAAAAACurcRF9IkTJ2rdunWaN2+ePD099cYbbyghIUH16tXT22+/XRYZAQBAKbnttts0atQo/fDDDzIMQ4Zh6Pvvv9fo0aN1++23OzseAAAAAAAup8TTuXz22Wd6++231b17dw0fPlxdu3ZVeHi4QkJClJKSopiYmLLICQAASsHcuXM1dOhQRUVFyd3dXZJ04cIF3X777Xr11VednM61ZWVlKScnx9kxKoX9+/fbfcXV8fX1VUBAgLNjAAAAAJVWiYvo2dnZCgsLk/T3/OfZ2dmSpC5dumjMmDGlmw4AAJSqmjVrasWKFdq9e7d+/fVXmUwmNW/eXOHh4c6O5tKysrJ0/wNDdD4/z9lRKpUXXnjB2REqBXcPT737ztsU0gEAAIAyUuIielhYmDIyMtSgQQM1a9ZMy5Yt0w033KDPPvtMNWvWLIOIAACgtDVu3NhWODeZTE5O4/pycnJ0Pj9PZ8O6yerl6+w4gI3buRxp30bl5ORQRAcAAADKSImL6MOHD9cvv/yibt266amnnlK/fv30+uuv6/z585o9e3ZZZAQAAKXo7bff1ksvvaTdu3dLkpo0aaInnnhCDzzwgJOTuT6rl6+s1es4OwYAAAAAoByVuIg+fvx42/c9e/bUb7/9ps2bNys8PFyRkZGlGg4AAJSu2bNna8qUKRo7dqw6d+4sSfrf//6n0aNH69ixY3bjPAAAAAAAuIIi+sVCQkIUEhJSGlkAAEAZe+211zRv3jwNGTLE1nb77bcrIiJCzz33HEV0AAAAAAAu4ubsAAAAoPwcOXJEnTp1KtTeqVMnHTlyxAmJAAAAAABwbRTRAQC4hoSHh2vZsmWF2t9//301btzYCYkAAAAAAHBtVz2dCwAAqDgSEhJ0zz33KDU11TYn+rfffqu1a9cWWVwHAAAAAOBax5noAABcQwYOHKgffvhBderU0SeffKJPPvlEderU0Y8//qg77rjD2fEAAAAAAHA5V3QmutVq1Z49e3T06FFZrVa7ddHR0aUSDAAAlI127drp3XffdXYMAAAAAAAqhBIX0b///nvdd9992r9/vwzDsFtnMplksVhKLRwAACgbR48eLfLN8MjISCclAgAAAADANZW4iD569Gi1b99eK1euVFBQkEwmU1nkAgAAZWDz5s0aOnSofv31V94MBwAAAACgGEpcRN+9e7c++OADhYeHl0UeAABQhkaMGKEmTZrozTffVEBAAG+GAwAAAABwGSUuonfs2FF79uyhiA4AQAW0b98+ffjhh4zjAAAAAAAUU4mL6A8//LAee+wxZWZmqlWrVnJ3d7dbz1yqAAC4rh49euiXX36hiA4AAAAAQDGVuIg+cOBASX9/HLyAyWSSYRjMpQoAgIt74403NHToUG3fvl0tW7Ys9Gb47bff7qRkAAAAAAC4phIX0TMyMsoiBwAAKAdpaWn69ttv9eWXXxZax5vhAAAAAAAUVuIiekhISFnkAAAA5eDhhx/W/fffrylTpiggIMDZcQAAAAAAcHklLqIX2Llzpw4cOKD8/Hy7dj4GDgCA6/rrr780fvx4CugAAAAAABRTiYvo+/bt0x133KFt27bZ5kKX/v4IuCQ+Bg4AgAu78847tX79ejVq1MjZUQAAAAAAqBBKXER/9NFHFRoaqrVr1yo0NFQ//vij/vrrLz322GN6+eWXyyIjAAAoJU2aNNGkSZP0v//9T61atSp0YdFHHnnESckAAAAAAHBNJS6ip6Wlad26dapTp47c3Nzk5uamLl26aPr06XrkkUe0ZcuWssgJAABKwRtvvKEaNWpo48aN2rhxo906k8lEER0AAAAAgIuUuIhusVjk7e0tSapTp44OHz6spk2bKiQkRLt27Sr1gAAAoPRkZGQ4OwIAAAAAABVKiYvoLVu21C+//KLQ0FB17NhRM2fOlIeHhxYsWKCwsLCyyAgAAMqIxWLRtm3bFBISolq1ajk7DgAAAAAALsetpBtMnjxZVqtVkjR16lRlZGSoa9eu+uKLLzR37txSDwgAAErPuHHj9Oabb0r6u4AeHR2t66+/XsHBwdqwYYNzwwEAAAAA4IJKfCZ67969bd+Hh4frt99+U3Z2tmrVqiWTyVSq4QAAQOn64IMPdP/990uSPvvsM/3xxx/67bff9M477+iZZ57Rt99+6+SEAAAAAAC4lhKfiV5gz549Wr16tc6ePSs/P7/SzAQAAMrIsWPHFBgYKEn64osvNGjQIDVp0kQjRozQtm3bnJwOAAAAAADXU+Ii+l9//aUePXqoSZMmuvXWW3XkyBFJ0siRI/XYY4+VekAAAFB6AgICtHPnTlksFq1atUo333yzJOnMmTMym81OTgcAAAAAgOsp8XQu48ePl7u7uw4cOKDmzZvb2u+55x5NmDBBs2bNKtWAlVlWVpZycnKcHaNS2L9/v91XXD1fX18FBAQ4OwaAUjZ8+HDdfffdCgoKkslkUs+ePSVJP/zwg5o1a+bkdK7P7ewJZ0cA7PA7CQAAAJS9EhfR16xZo9WrV6t+/fp27Y0bN6aAWQJZWVm6/4EhOp+f5+wolcoLL7zg7AiVhruHp959520K6UAl89xzz6lly5Y6ePCgBg0aJE9PT0mS2WzWU0895eR0rq9qRqqzIwAAAAAAylmJi+inT59WtWrVCrVnZ2fbXojj8nJycnQ+P09nw7rJ6uXr7DiAHbdzOdK+jcrJyaGIDlRCd911V6G2oUOHOiFJxXM2NFrWqjWdHQOwcTt7gjd3AAAAgDJW4iJ6165d9fbbb+v555+XJJlMJlmtVs2cOVM33nhjqQes7KxevrJWr+PsGAAAoBisVWsybgMAAADANabEFxadOXOmFixYoD59+ig/P18TJ05Uy5YtlZqaqhdffLEsMgIAABeVlJSkhg0bysvLSx07dtSPP/7osO+OHTs0cOBANWzYUCaTSXPmzLnqfQIAAAAAUNZKXERv2bKlfv/9d3Xp0kX9+/fX6dOndeedd2rLli1q1KhRWWQEAAAu6P3339eECRMUHx+vn3/+Wa1bt1bv3r119OjRIvufOXNGYWFhmjFjhgIDA0tlnwAAAAAAlLUST+ciSb6+vnrmmWdKOwsAAKhAZs+erYceekjDhw+XJM2fP18rV67UW2+9VeRFSjt06KAOHTpIksOLmJZ0nwAqjqysLOXk5Dg7RqWwf/9+u6+4Or6+vlwHCAAAXNIVFdHPnTun9PR0HT16VFar1W7d7bffXirBAABA2bBardqzZ0+R43h0dHSx9pGfn6/Nmzdr0qRJtjY3Nzf17NlTaWlpV5SrLPYJwDVkZWXp/geG6Hx+nrOjVCovvPCCsyNUCu4ennr3nbcppAMAAIdKXERftWqVhgwZomPHjhVaZzKZZLFYSiUYAAAofd9//73uu+8+7d+/X4Zh2K0ryTh+7NgxWSyWQgWHgIAA/fbbb1eU7Ur3mZeXp7y8/1+Yy83NvaL7B1B2cnJydD4/T2fDusnq5evsOICN27kcad9G5eTkUEQHAAAOlbiI/vDDD2vQoEF69tlneZIBAEAFM3r0aLVv314rV65UUFCQTCaTsyNdtenTpyshIcHZMQAUg9XLV9bqdZwdAwAAACiREhfRs7KyNGHCBAroAABUQLt379YHH3yg8PDwq9pPnTp1ZDablZWVZdeelZXl8KKhZbXPSZMmacKECbbl3NxcBQcHX1EGAAAAAAAu5lbSDe666y5t2LChDKIAAICy1rFjR+3Zs+eq9+Ph4aF27dpp7dq1tjar1aq1a9cqKiqqXPfp6emp/9fevcdFXSf6H38zI1cvY4gOeOFiiRcUNUtCDdujm5ZZmutShw3XPHFSNBXTTUVJ07U1Jd1CfVCn0sI0H+2WlXnJVDxKaV4OpqZlBG0CliR4BWTm98f+nN1JRiWB7wCv5+MxD53v9/OdeX99MH4evv3O59usWTOnBwAAAAAA1aXKV6K//PLLGjlypHbu3Klu3brJ09PTaf9TTz1VbeEAAED1mjBhgqZMmaKCgoJK5/HIyMgbfq2kpCSNGjVKd9xxh3r37q0lS5bo/PnzGj16tCQpPj5ebdq00YIFCyT988ahR44ccfz+hx9+0MGDB9WkSRPHlfHXe00AAAAAAGpblUv0t99+W5s3b5aPj4+2b9/utJaqh4cHJToAAG5sxIgRkqTHH3/csc3Dw0N2u73KNwiPjY3Vjz/+qNmzZ6ugoEA9evTQxo0bHUu+5eXlyWT615feTp48qZ49ezqeL1q0SIsWLVL//v0d33K73msCAAAAAFDbqlyiz5w5U3PmzNEzzzzj9A9jAADg/nJycqr19caPH6/x48dXuu+Xy7+FhobKbrff1GsCAAAAAFDbqlyil5WVKTY2lgIdAIA6KCQkxOgIAAAAAADUKVUu0UeNGqW1a9dqxowZNZEHAADUgiNHjigvL09lZWVO2x988EGDEgEAAAAA4J6qXKJXVFRo4cKF2rRpkyIjI6+6IVlqamq1hQMAANXr22+/1fDhw3Xo0CHHWuiSHPc4qcqa6AAAAAAANARVLtEPHTrkuCnYl19+6bTv328yCgAA3M/EiRMVFhamrVu3KiwsTHv27NHp06c1ZcoULVq0yOh4AAAAAAC4nSqX6Nu2bauJHAAAoBZkZWXp008/VUBAgEwmk0wmk/r166cFCxboqaee0oEDB4yOCAAAAACAW+HuoAAANCAVFRVq2rSpJCkgIEAnT56U9M8bjh47dszIaAAAAAAAuKUqX4kOAADqrq5du+r//u//FBYWpqioKC1cuFBeXl5KT09X+/btjY4HAAAAAIDboUQHAKABSU5O1vnz5yVJc+fO1QMPPKC7775bLVq00Nq1aw1OBwAAAACA+6FEBwCgARk0aJDj97fddpu++uorFRUV6ZZbbuEG4QAAAAAAVII10QEAaIC++eYbbdq0SRcvXpS/v7/RcQAAAAAAcFuU6AAANCCnT5/WgAEDFB4ervvvv1/5+fmSpDFjxmjKlCkGpwMAAAAAwP1QogMA0IBMnjxZnp6eysvLk5+fn2N7bGysNm7caGAyAAAAAADcE2uiAwDQgGzevFmbNm1S27Z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+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, feature in enumerate(feature_names[:5]):  # Just the first 5 features for clarity\n",
+    "    plt.subplot(2, 3, i+1)\n",
+    "    sns.boxplot(x='diagnosis', y=feature, data=df)\n",
+    "    plt.title(f'{feature} by Diagnosis')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6df9f12",
+   "metadata": {},
+   "source": [
+    "# Distribution of a few key features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f16b0b43",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, feature in enumerate(feature_names[:5]):  # Just the first 5 features\n",
+    "    plt.subplot(2, 3, i+1)\n",
+    "    sns.histplot(df[feature], kde=True, bins=30)\n",
+    "    plt.title(f'Distribution of {feature}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79ef9a76",
+   "metadata": {},
+   "source": [
+    "# Using TabPFN for Breast Cancer Classification\n",
+    "\n",
+    "This code imports the TabPFN classifier and loads the breast cancer dataset for a classification task:\n",
+    "\n",
+    "### TabPFN Import\n",
+    "- `TabPFNClassifier`: A specialized classifier from the TabPFN library that uses Prior-Data Fitted Networks\n",
+    "  - TabPFN is designed specifically for tabular data\n",
+    "  - It leverages transformer-based architectures pre-trained on synthetic tabular datasets\n",
+    "  - The model requires minimal hyperparameter tuning and often performs well out-of-the-box\n",
+    "\n",
+    "### Dataset Loading\n",
+    "- `load_breast_cancer(return_X_y=True)`: Loads the breast cancer Wisconsin dataset as NumPy arrays\n",
+    "  - `X`: Features matrix with 569 samples and 30 features (measurements from digitized images)\n",
+    "  - `y`: Target vector with binary values indicating benign (0) or malignant (1) tumors\n",
+    "  - The `return_X_y=True` parameter specifies that the function should return the data and target as separate arrays rather than as a Bunch object\n",
+    "\n",
+    "This setup forms the foundation for applying the TabPFN classifier to predict breast cancer diagnoses based on the provided features. TabPFN is particularly well-suited for this type of tabular classification task with a moderate number of features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5d1252d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tabpfn import TabPFNClassifier\n",
+    "\n",
+    "# Load data\n",
+    "X, y = load_breast_cancer(return_X_y=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "feaeb143",
+   "metadata": {},
+   "source": [
+    "# Splitting Data for Classification Model Evaluation\n",
+    "\n",
+    "\n",
+    "This code divides the breast cancer dataset into training and testing sets using scikit-learn's `train_test_split` function:\n",
+    "\n",
+    "### Parameters Explained:\n",
+    "- `X`: The feature matrix containing 30 features for each sample\n",
+    "- `y`: The target vector with binary classification labels (0 for benign, 1 for malignant)\n",
+    "- `test_size=0.5`: Allocates 50% of the data for testing and 50% for training\n",
+    "  - This is a larger test set than the typical 20-30%, which might be chosen to better evaluate model performance\n",
+    "- `random_state=42`: Sets a specific random seed for reproducibility\n",
+    "  - Using a fixed random state ensures that the split will be the same each time the code runs\n",
+    "  - The value 42 is commonly used as a default in data science (a reference to \"The Hitchhiker's Guide to the Galaxy\")\n",
+    "\n",
+    "### Result:\n",
+    "- `X_train`: Features for training the model (569 × 0.5 = ~284 samples)\n",
+    "- `X_test`: Features for evaluating the model (~284 samples)\n",
+    "- `y_train`: Target labels for the training set\n",
+    "- `y_test`: Target labels for the test set\n",
+    "\n",
+    "This even split allows for robust evaluation of the TabPFN classifier's performance on unseen data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cb9de948",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "285"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)\n",
+    "\n",
+    "\n",
+    "\n",
+    "len(y_test)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "804df739",
+   "metadata": {},
+   "source": [
+    "# Training the TabPFN Classifier\n",
+    "\n",
+    "This code initializes and trains a TabPFN classifier on the breast cancer dataset:\n",
+    "\n",
+    "### Model Initialization\n",
+    "- `TabPFNClassifier()`: Creates an instance of the TabPFN classifier with default parameters\n",
+    "  - TabPFN is a \"Prior-Data Fitted Network\" specifically designed for tabular data\n",
+    "  - Unlike traditional ML models, TabPFN comes pre-trained on synthetic tabular data\n",
+    "  - The default settings typically work well without extensive hyperparameter tuning\n",
+    "\n",
+    "### Model Training\n",
+    "- `clf.fit(X_train, y_train)`: Trains the classifier on the training data\n",
+    "  - `X_train`: Feature matrix with ~284 samples and 30 features\n",
+    "  - `y_train`: Target vector with binary labels (benign/malignant)\n",
+    "  - TabPFN's training process is generally faster than traditional ML models as it leverages transfer learning\n",
+    "  - The model adapts its pre-trained knowledge to the specific patterns in the breast cancer dataset\n",
+    "\n",
+    "TabPFN typically requires less data for effective training compared to models that start from scratch, making it well-suited for medical datasets like this one where labeled data might be limited."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b43553e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TabPFNClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>TabPFNClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+       "            <td class=\"value\">4</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('categorical_features_indices',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">categorical_features_indices&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('softmax_temperature',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">softmax_temperature&nbsp;</td>\n",
+       "            <td class=\"value\">0.9</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('balance_probabilities',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">balance_probabilities&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('average_before_softmax',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">average_before_softmax&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('model_path',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">model_path&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('device',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">device&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ignore_pretraining_limits',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">ignore_pretraining_limits&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inference_precision',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inference_precision&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_mode',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_mode&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;fit_preprocessors&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('memory_saving_mode',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">memory_saving_mode&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">-1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inference_config',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inference_config&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "TabPFNClassifier()"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Initialize a classifier\n",
+    "clf = TabPFNClassifier()\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "329c0ed5",
+   "metadata": {},
+   "source": [
+    "# Making Predictions and Evaluating Performance\n",
+    "\n",
+    "This code evaluates the TabPFN classifier by generating predictions and calculating the ROC AUC score:\n",
+    "\n",
+    "### Prediction Generation\n",
+    "- `clf.predict_proba(X_test)`: Generates probability predictions for each class\n",
+    "  - Returns a 2D array of shape (n_samples, n_classes) = (~284 samples, 2 classes)\n",
+    "  - Each row represents one test sample\n",
+    "  - Each column represents the probability of belonging to a class (0 or 1)\n",
+    "\n",
+    "### Performance Evaluation\n",
+    "- `prediction_probabilities[:, 1]`: Extracts only the probabilities for the positive class (malignant)\n",
+    "  - The second column (index 1) contains probabilities for the positive class\n",
+    "\n",
+    "- `roc_auc_score(y_test, prediction_probabilities[:, 1])`: Calculates the ROC AUC metric\n",
+    "  - ROC (Receiver Operating Characteristic) curve plots the true positive rate against the false positive rate\n",
+    "  - AUC (Area Under Curve) measures the classifier's ability to distinguish between classes\n",
+    "  - Values range from 0 to 1, where:\n",
+    "    - 1.0 represents a perfect classifier\n",
+    "    - 0.5 represents a random classifier\n",
+    "    - Values above 0.8 are generally considered good\n",
+    "    - Values above 0.9 are considered excellent\n",
+    "\n",
+    "This evaluation helps assess how well the TabPFN model distinguishes between benign and malignant tumors based on the provided features, which is crucial for medical diagnostic applications."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e78e553d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ROC AUC: 0.9981992797118848\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Predict probabilities\n",
+    "prediction_probabilities = clf.predict_proba(X_test)\n",
+    "print(\"ROC AUC:\", roc_auc_score(y_test, prediction_probabilities[:, 1]))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40bd1784",
+   "metadata": {},
+   "source": [
+    "# Predicting Class Labels and Evaluating Accuracy\n",
+    "\n",
+    "\n",
+    "This code produces class predictions and evaluates the model's accuracy:\n",
+    "\n",
+    "### Prediction Generation\n",
+    "- `clf.predict(X_test)`: Generates class label predictions for the test data\n",
+    "  - Unlike `predict_proba()` which returns probabilities, this method returns the actual predicted class labels\n",
+    "  - Returns a 1D array of shape (n_samples,) with values 0 (benign) or 1 (malignant)\n",
+    "  - Internally, the model assigns the class with the highest probability to each sample\n",
+    "\n",
+    "### Performance Evaluation\n",
+    "- `accuracy_score(y_test, predictions)`: Calculates the proportion of correct predictions\n",
+    "  - Compares the predicted labels (`predictions`) with the true labels (`y_test`)\n",
+    "  - Returns a value between 0 and 1, where:\n",
+    "    - 1.0 means all predictions are correct\n",
+    "    - 0.0 means all predictions are incorrect\n",
+    "  - Formula: (Number of correct predictions) / (Total number of predictions)\n",
+    "\n",
+    "Accuracy is a straightforward metric for classification tasks, representing the percentage of samples correctly classified. For breast cancer diagnosis, high accuracy is important, but the ROC AUC score (calculated in the previous step) provides additional insight into the model's ability to distinguish between the classes, which is particularly valuable in medical contexts where false negatives can have serious consequences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b7492bb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy 0.9824561403508771\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Predict labels\n",
+    "predictions = clf.predict(X_test)\n",
+    "print(\"Accuracy\", accuracy_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "33931f0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1,\n",
+       "       0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
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+       "       0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cdd16260",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 0 0 1 1 0 0 0 1 1 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0\n",
+      " 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 1 0 0 1 0\n",
+      " 1 1 1 0 1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 0 1 1 0\n",
+      " 1 1 0 1 0 1 1 1 0 0 1 1 0 1 0 0 1 1 0 0 0 1 1 1 0 1 1 1 0 1 0 1 1 0 1 0 0\n",
+      " 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0\n",
+      " 0 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 0 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1\n",
+      " 0 0 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 1 0 1\n",
+      " 0 1 0 1 1 0 1 1 1 1 0 1 1 1 0 1 1 0 1 1 0 1 1 1 1 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5722738",
+   "metadata": {},
+   "source": [
+    "# Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "36428e03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(y_test, predictions)\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
+    "            xticklabels=['Benign', 'Malignant'], \n",
+    "            yticklabels=['Benign', 'Malignant'])\n",
+    "plt.xlabel('Predicted Label')\n",
+    "plt.ylabel('True Label')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f9cf3cb",
+   "metadata": {},
+   "source": [
+    "# 3. Classification Report, Individual Metrics and RoC Curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1dc15be0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "      Benign       0.96      0.99      0.97        98\n",
+      "   Malignant       0.99      0.98      0.99       187\n",
+      "\n",
+      "    accuracy                           0.98       285\n",
+      "   macro avg       0.98      0.98      0.98       285\n",
+      "weighted avg       0.98      0.98      0.98       285\n",
+      "\n",
+      "\n",
+      "Key Metrics:\n",
+      "Accuracy:  0.9825\n",
+      "Precision: 0.9946\n",
+      "Recall:    0.9786\n",
+      "F1 Score:  0.9865\n",
+      "ROC AUC:   0.9982\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"\\nClassification Report:\")\n",
+    "print(classification_report(y_test, predictions, \n",
+    "                           target_names=['Benign', 'Malignant']))\n",
+    "\n",
+    "\n",
+    "print(\"\\nKey Metrics:\")\n",
+    "print(f\"Accuracy:  {accuracy_score(y_test, predictions):.4f}\")\n",
+    "print(f\"Precision: {precision_score(y_test, predictions):.4f}\")\n",
+    "print(f\"Recall:    {recall_score(y_test, predictions):.4f}\")\n",
+    "print(f\"F1 Score:  {f1_score(y_test, predictions):.4f}\")\n",
+    "print(f\"ROC AUC:   {roc_auc_score(y_test, prediction_probabilities[:, 1]):.4f}\")\n",
+    "\n",
+    "\n",
+    "fpr, tpr, _ = roc_curve(y_test, prediction_probabilities[:, 1])\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.plot(fpr, tpr, label=f'ROC (AUC = {roc_auc_score(y_test, prediction_probabilities[:, 1]):.4f})')\n",
+    "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve')\n",
+    "plt.legend()\n",
+    "plt.grid(alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dae71a9a",
+   "metadata": {},
+   "source": [
+    "# Installing and Using AutoTabPFN\n",
+    "\n",
+    "This code enhances the TabPFN implementation with automatic hyperparameter optimization:\n",
+    "\n",
+    "### Package Installation\n",
+    "- `hyperopt`: A Python library for serial and parallel optimization over awkward search spaces\n",
+    "- `tabpfn-extensions`: Extended functionality for TabPFN, including automated optimization tools\n",
+    "- The commented portion shows how you could alternatively clone the repository directly\n",
+    "\n",
+    "### AutoTabPFN Setup\n",
+    "- `AutoTabPFNClassifier`: An enhanced version of TabPFN that performs automatic tuning and ensemble creation\n",
+    "  - Combines multiple TabPFN models with optimized configurations\n",
+    "  - Uses Bayesian optimization via hyperopt to find optimal hyperparameters\n",
+    "\n",
+    "### Configuration Parameters\n",
+    "- `max_time=120`: Limits the tuning process to 120 seconds (2 minutes)\n",
+    "  - The optimizer will try to find the best configuration within this time constraint\n",
+    "  - Longer times generally lead to better results but with diminishing returns\n",
+    "- `device=\"cpu\"`: Specifies that computations should run on the CPU\n",
+    "  - Alternative would be \"cuda\" for GPU acceleration if available\n",
+    "\n",
+    "### Training Process\n",
+    "- `clf.fit(X_train, y_train)`: Trains the optimized ensemble model\n",
+    "  - During this process, AutoTabPFN will:\n",
+    "    1. Try different TabPFN configurations\n",
+    "    2. Evaluate their performance\n",
+    "    3. Select the best performing models\n",
+    "    4. Create an ensemble of these models\n",
+    "\n",
+    "AutoTabPFN typically provides better performance than the base TabPFN model by leveraging ensemble techniques and hyperparameter optimization, though at the cost of longer training times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "16831a0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting hyperopt\n",
+      "  Obtaining dependency information for hyperopt from https://files.pythonhosted.org/packages/b6/cd/5b3334d39276067f54618ce0d0b48ed69d91352fbf137468c7095170d0e5/hyperopt-0.2.7-py2.py3-none-any.whl.metadata\n",
+      "  Using cached hyperopt-0.2.7-py2.py3-none-any.whl.metadata (1.7 kB)\n",
+      "Collecting tabpfn-extensions\n",
+      "  Obtaining dependency information for tabpfn-extensions from https://files.pythonhosted.org/packages/35/d9/1eded2957d3d91651068b4e8fe12da724e22f695766da02d7932c9f637cf/tabpfn_extensions-0.0.4-py3-none-any.whl.metadata\n",
+      "  Using cached tabpfn_extensions-0.0.4-py3-none-any.whl.metadata (18 kB)\n",
+      "Requirement already satisfied: numpy in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from hyperopt) (1.26.4)\n",
+      "Requirement already satisfied: scipy in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from hyperopt) (1.16.0)\n",
+      "Requirement already satisfied: six in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from hyperopt) (1.17.0)\n",
+      "Requirement already satisfied: networkx>=2.2 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from hyperopt) (3.5)\n",
+      "Collecting future (from hyperopt)\n",
+      "  Obtaining dependency information for future from https://files.pythonhosted.org/packages/da/71/ae30dadffc90b9006d77af76b393cb9dfbfc9629f339fc1574a1c52e6806/future-1.0.0-py3-none-any.whl.metadata\n",
+      "  Using cached future-1.0.0-py3-none-any.whl.metadata (4.0 kB)\n",
+      "Requirement already satisfied: tqdm in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from hyperopt) (4.67.1)\n",
+      "Collecting cloudpickle (from hyperopt)\n",
+      "  Obtaining dependency information for cloudpickle from https://files.pythonhosted.org/packages/7e/e8/64c37fadfc2816a7701fa8a6ed8d87327c7d54eacfbfb6edab14a2f2be75/cloudpickle-3.1.1-py3-none-any.whl.metadata\n",
+      "  Using cached cloudpickle-3.1.1-py3-none-any.whl.metadata (7.1 kB)\n",
+      "Collecting py4j (from hyperopt)\n",
+      "  Obtaining dependency information for py4j from https://files.pythonhosted.org/packages/bd/db/ea0203e495be491c85af87b66e37acfd3bf756fd985f87e46fc5e3bf022c/py4j-0.10.9.9-py2.py3-none-any.whl.metadata\n",
+      "  Using cached py4j-0.10.9.9-py2.py3-none-any.whl.metadata (1.3 kB)\n",
+      "Collecting seaborn==0.12.2 (from tabpfn-extensions)\n",
+      "  Obtaining dependency information for seaborn==0.12.2 from https://files.pythonhosted.org/packages/8f/2e/17bbb83fbf102687bb2aa3d808add39da820a7698159302a1a69bb82e01c/seaborn-0.12.2-py3-none-any.whl.metadata\n",
+      "  Using cached seaborn-0.12.2-py3-none-any.whl.metadata (5.4 kB)\n",
+      "Requirement already satisfied: torch in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from tabpfn-extensions) (2.2.2)\n",
+      "Requirement already satisfied: pandas in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from tabpfn-extensions) (2.3.0)\n",
+      "Requirement already satisfied: scikit-learn in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from tabpfn-extensions) (1.6.1)\n",
+      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from seaborn==0.12.2->tabpfn-extensions) (3.10.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from pandas->tabpfn-extensions) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from pandas->tabpfn-extensions) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from pandas->tabpfn-extensions) (2025.2)\n",
+      "Requirement already satisfied: joblib>=1.2.0 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from scikit-learn->tabpfn-extensions) (1.5.1)\n",
+      "Requirement already satisfied: threadpoolctl>=3.1.0 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from scikit-learn->tabpfn-extensions) (3.6.0)\n",
+      "Requirement already satisfied: filelock in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from torch->tabpfn-extensions) (3.18.0)\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from torch->tabpfn-extensions) (4.14.0)\n",
+      "Requirement already satisfied: sympy in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from torch->tabpfn-extensions) (1.14.0)\n",
+      "Requirement already satisfied: jinja2 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from torch->tabpfn-extensions) (3.1.6)\n",
+      "Requirement already satisfied: fsspec in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from torch->tabpfn-extensions) (2025.5.1)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2->tabpfn-extensions) (1.3.2)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2->tabpfn-extensions) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2->tabpfn-extensions) (4.58.4)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2->tabpfn-extensions) (1.4.8)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2->tabpfn-extensions) (25.0)\n",
+      "Requirement already satisfied: pillow>=8 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2->tabpfn-extensions) (11.2.1)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn==0.12.2->tabpfn-extensions) (3.2.3)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from jinja2->torch->tabpfn-extensions) (3.0.2)\n",
+      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages (from sympy->torch->tabpfn-extensions) (1.3.0)\n",
+      "Using cached hyperopt-0.2.7-py2.py3-none-any.whl (1.6 MB)\n",
+      "Using cached tabpfn_extensions-0.0.4-py3-none-any.whl (80 kB)\n",
+      "Using cached seaborn-0.12.2-py3-none-any.whl (293 kB)\n",
+      "Using cached cloudpickle-3.1.1-py3-none-any.whl (20 kB)\n",
+      "Using cached future-1.0.0-py3-none-any.whl (491 kB)\n",
+      "Using cached py4j-0.10.9.9-py2.py3-none-any.whl (203 kB)\n",
+      "Installing collected packages: py4j, future, cloudpickle, hyperopt, seaborn, tabpfn-extensions\n",
+      "  Attempting uninstall: seaborn\n",
+      "    Found existing installation: seaborn 0.13.2\n",
+      "    Uninstalling seaborn-0.13.2:\n",
+      "      Successfully uninstalled seaborn-0.13.2\n",
+      "Successfully installed cloudpickle-3.1.1 future-1.0.0 hyperopt-0.2.7 py4j-0.10.9.9 seaborn-0.12.2 tabpfn-extensions-0.0.4\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2025-06-28 18:42:04 INFO     Using `default` preset for Post Hoc Ensemble.\n",
+      "2025-06-28 18:42:04 INFO     No categorical_feature_indices given. Assuming no categorical features.\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "2025-06-28 18:42:04 INFO     Using task type: TaskType.BINARY\n",
+      "2025-06-28 18:42:04 INFO     Obtaining TabPFN models from a random portfolio.\n",
+      "2025-06-28 18:42:09 INFO     Using 100 base models: ['default_tabpfn_model_0', 'random_tabpfn_model_1', 'random_rf_pfn_model_2', 'random_rf_pfn_model_3', 'random_rf_pfn_model_4', 'random_rf_pfn_model_5', 'random_tabpfn_model_6', 'random_tabpfn_model_7', 'random_rf_pfn_model_8', 'random_tabpfn_model_9', 'random_rf_pfn_model_10', 'random_rf_pfn_model_11', 'random_tabpfn_model_12', 'random_tabpfn_model_13', 'random_rf_pfn_model_14', 'random_rf_pfn_model_15', 'random_rf_pfn_model_16', 'random_tabpfn_model_17', 'random_rf_pfn_model_18', 'random_tabpfn_model_19', 'random_rf_pfn_model_20', 'random_rf_pfn_model_21', 'random_tabpfn_model_22', 'random_rf_pfn_model_23', 'random_rf_pfn_model_24', 'random_tabpfn_model_25', 'random_rf_pfn_model_26', 'random_tabpfn_model_27', 'random_tabpfn_model_28', 'random_tabpfn_model_29', 'random_tabpfn_model_30', 'random_tabpfn_model_31', 'random_tabpfn_model_32', 'random_tabpfn_model_33', 'random_tabpfn_model_34', 'random_tabpfn_model_35', 'random_rf_pfn_model_36', 'random_tabpfn_model_37', 'random_rf_pfn_model_38', 'random_tabpfn_model_39', 'random_tabpfn_model_40', 'random_rf_pfn_model_41', 'random_tabpfn_model_42', 'random_tabpfn_model_43', 'random_tabpfn_model_44', 'random_tabpfn_model_45', 'random_rf_pfn_model_46', 'random_tabpfn_model_47', 'random_rf_pfn_model_48', 'random_tabpfn_model_49', 'random_tabpfn_model_50', 'random_rf_pfn_model_51', 'random_tabpfn_model_52', 'random_rf_pfn_model_53', 'random_rf_pfn_model_54', 'random_rf_pfn_model_55', 'random_tabpfn_model_56', 'random_tabpfn_model_57', 'random_rf_pfn_model_58', 'random_rf_pfn_model_59', 'random_tabpfn_model_60', 'random_tabpfn_model_61', 'random_rf_pfn_model_62', 'random_tabpfn_model_63', 'random_tabpfn_model_64', 'random_rf_pfn_model_65', 'random_tabpfn_model_66', 'random_tabpfn_model_67', 'random_tabpfn_model_68', 'random_rf_pfn_model_69', 'random_rf_pfn_model_70', 'random_tabpfn_model_71', 'random_tabpfn_model_72', 'random_rf_pfn_model_73', 'random_rf_pfn_model_74', 'random_rf_pfn_model_75', 'random_tabpfn_model_76', 'random_tabpfn_model_77', 'random_tabpfn_model_78', 'random_tabpfn_model_79', 'random_tabpfn_model_80', 'random_rf_pfn_model_81', 'random_rf_pfn_model_82', 'random_tabpfn_model_83', 'random_tabpfn_model_84', 'random_rf_pfn_model_85', 'random_rf_pfn_model_86', 'random_tabpfn_model_87', 'random_rf_pfn_model_88', 'random_tabpfn_model_89', 'random_tabpfn_model_90', 'random_tabpfn_model_91', 'random_rf_pfn_model_92', 'random_rf_pfn_model_93', 'random_tabpfn_model_94', 'random_tabpfn_model_95', 'random_rf_pfn_model_96', 'random_tabpfn_model_97', 'random_tabpfn_model_98', 'random_tabpfn_model_99']\n",
+      "2025-06-28 18:42:09 INFO     Starting 80-repeated holdout validation with holdout_frac=0.33.\n",
+      "2025-06-28 18:42:09 INFO     Set time limit to 120 seconds. We will early stop validation if needed.\n",
+      "2025-06-28 18:42:09 INFO     Yield data for model default_tabpfn_model_0 and split 0 (repeat=1).\n",
+      "2025-06-28 18:42:28 INFO     Yield data for model random_tabpfn_model_1 and split 0 (repeat=1).\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/base.py:89: UserWarning: Downloading model to /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn_extensions/hpo/hpo_models/tabpfn-v2-classifier-od3j1g5m.ckpt.\n",
+      "  model, _, config_ = load_model_criterion_config(\n",
+      "2025-06-28 18:42:28 INFO     Attempting HuggingFace download: tabpfn-v2-classifier-od3j1g5m.ckpt\n",
+      "2025-06-28 18:42:35 INFO     Successfully downloaded to /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn_extensions/hpo/hpo_models/tabpfn-v2-classifier-od3j1g5m.ckpt\n",
+      "2025-06-28 18:43:06 INFO     Yield data for model random_rf_pfn_model_2 and split 0 (repeat=1).\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/base.py:89: UserWarning: Downloading model to /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn_extensions/hpo/hpo_models/tabpfn-v2-classifier-llderlii.ckpt.\n",
+      "  model, _, config_ = load_model_criterion_config(\n",
+      "2025-06-28 18:43:07 INFO     Attempting HuggingFace download: tabpfn-v2-classifier-llderlii.ckpt\n",
+      "2025-06-28 18:43:15 INFO     Successfully downloaded to /Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn_extensions/hpo/hpo_models/tabpfn-v2-classifier-llderlii.ckpt\n",
+      "2025-06-28 18:44:09 INFO     Likely not enough time left for another model.\n",
+      "2025-06-28 18:44:09 INFO     Stop validation of all models after 3 models in repeat 1.\n",
+      "2025-06-28 18:44:09 INFO     As this is the first repeat, we trim down the models to all so-far run models!\n",
+      "2025-06-28 18:44:09 INFO     Order of selections: [0, 2, 2, 1, 2, 0, 1, 2, 0, 2, 2, 0, 1, 1, 2, 1, 2, 2, 0, 0, 1, 2, 1, 2, 0]\n",
+      "2025-06-28 18:44:09 INFO     Val loss over iterations: [-0.9750939849624061, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362, -0.9755639097744362]\n",
+      "2025-06-28 18:44:09 INFO     Model losses: [-0.97509398 -0.97462406 -0.97462406]\n",
+      "2025-06-28 18:44:09 INFO     Best weights: [0.5 0.  0.5]\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AutoTabPFNClassifier(max_time=120)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>AutoTabPFNClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_time',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">max_time&nbsp;</td>\n",
+       "            <td class=\"value\">120</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('preset',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">preset&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;default&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ges_scoring_string',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">ges_scoring_string&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;roc&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('device',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">device&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;cpu&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('categorical_feature_indices',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">categorical_feature_indices&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ignore_pretraining_limits',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">ignore_pretraining_limits&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('phe_init_args',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">phe_init_args&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "AutoTabPFNClassifier(max_time=120)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "! pip install hyperopt tabpfn-extensions #git clone https://github.com/priorlabs/tabpfn-extensions.git\n",
+    "\n",
+    "from tabpfn_extensions.post_hoc_ensembles.sklearn_interface import AutoTabPFNClassifier\n",
+    "\n",
+    "clf = AutoTabPFNClassifier(max_time=120, device=\"cpu\") # 120 seconds tuning time # cuda for gpu\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0a8690ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 0 0 1 1 0 0 0 0 1 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0\n",
+      " 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 1 0 0 1 0\n",
+      " 1 1 1 0 1 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 0 1 1 0\n",
+      " 1 1 0 1 0 1 1 1 0 0 1 1 0 1 0 0 1 1 0 0 0 1 1 1 0 1 1 1 0 1 0 1 1 0 1 0 0\n",
+      " 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0\n",
+      " 0 1 1 0 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 0 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1\n",
+      " 0 0 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 1 0 1\n",
+      " 0 1 0 1 1 0 1 1 1 1 0 1 1 1 0 1 1 0 1 1 0 1 1 1 1 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "predictions = clf.predict(X_test)\n",
+    "print(predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3eb4356d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy 0.9824561403508771\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Accuracy\", accuracy_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9947e64a",
+   "metadata": {},
+   "source": [
+    "# Saving and Loading the TabPFN Model\n",
+    "\n",
+    "Here's the code to save your trained TabPFN model and then load it back:\n",
+    "\n",
+    "\n",
+    "1. **Saving the Model**:\n",
+    "   - Uses Python's `pickle` module to serialize the model object\n",
+    "   - The `'wb'` flag opens the file in binary write mode\n",
+    "   - All model parameters and state are preserved in the file\n",
+    "\n",
+    "2. **Loading the Model**:\n",
+    "   - Uses `pickle.load()` to deserialize the model from the file\n",
+    "   - The `'rb'` flag opens the file in binary read mode\n",
+    "   - The loaded model is stored in `loaded_clf`\n",
+    "\n",
+    "3. **Important Considerations**:\n",
+    "   - Make sure the same version of TabPFN is used when loading the model\n",
+    "   - The pickle format is Python-specific and not cross-language compatible\n",
+    "   - For larger models, consider using joblib instead of pickle for better handling of large NumPy arrays\n",
+    "\n",
+    "This approach works well for TabPFN models and allows you to reuse your trained model without having to retrain it each time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "bae630b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model saved to tabpfn_breast_cancer_model.pkl\n",
+      "Model loaded from tabpfn_breast_cancer_model.pkl\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded model accuracy: 0.9825\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "import os\n",
+    "\n",
+    "# After training your model with clf.fit(X_train, y_train)\n",
+    "\n",
+    "# 1. Save the trained model to a file\n",
+    "model_filename = 'tabpfn_breast_cancer_model.pkl'\n",
+    "with open(model_filename, 'wb') as file:\n",
+    "    pickle.dump(clf, file)\n",
+    "print(f\"Model saved to {model_filename}\")\n",
+    "\n",
+    "# 2. Later, to load the model from the file\n",
+    "if os.path.exists(model_filename):\n",
+    "    with open(model_filename, 'rb') as file:\n",
+    "        loaded_clf = pickle.load(file)\n",
+    "    print(f\"Model loaded from {model_filename}\")\n",
+    "    \n",
+    "    # Verify the loaded model works correctly\n",
+    "    test_predictions = loaded_clf.predict(X_test)\n",
+    "    test_accuracy = accuracy_score(y_test, test_predictions)\n",
+    "    print(f\"Loaded model accuracy: {test_accuracy:.4f}\")\n",
+    "else:\n",
+    "    print(f\"Model file {model_filename} not found\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dd764f3",
+   "metadata": {},
+   "source": [
+    "1. **Loads the saved model** using pickle\n",
+    "2. **Handles both single and multiple samples** by checking the input shape\n",
+    "3. **Returns a dictionary with comprehensive prediction information**:\n",
+    "   - Class labels (0/1)\n",
+    "   - Full probability matrix for all classes\n",
+    "   - Probability for the positive class only (simplified access)\n",
+    "4. **Includes error handling** for common issues like missing files or data format problems\n",
+    "5. **Provides example usage** for both single and batch prediction scenarios\n",
+    "\n",
+    "You can easily incorporate this function into any application that needs to use your trained TabPFN model for breast cancer prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b868b5ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model successfully loaded from tabpfn_breast_cancer_model.pkl\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction: Malignant\n",
+      "Confidence: 0.9250\n",
+      "Model successfully loaded from tabpfn_breast_cancer_model.pkl\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/sklearn/utils/deprecation.py:132: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
+      "  warnings.warn(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/classifier.py:432: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n"
+     ]
+    }
+   ],
+   "source": [
+    "def predict_with_tabpfn(input_data, model_path='tabpfn_breast_cancer_model.pkl'):\n",
+    "   \"\"\"\n",
+    "   Load a saved TabPFN model and make predictions for given input data.\n",
+    "   \n",
+    "   Parameters:\n",
+    "   -----------\n",
+    "   input_data : array-like\n",
+    "       Input features for prediction, shape should match training data.\n",
+    "       Can be a single sample (1D array) or multiple samples (2D array).\n",
+    "   model_path : str, default='tabpfn_breast_cancer_model.pkl'\n",
+    "       Path to the saved model file.\n",
+    "   \n",
+    "   Returns:\n",
+    "   --------\n",
+    "   dict: A dictionary containing:\n",
+    "       - 'predictions': Class labels (0/1)\n",
+    "       - 'probabilities': Probability estimates for each class\n",
+    "       - 'positive_proba': Probability of the positive class (class 1)\n",
+    "   \n",
+    "   Raises:\n",
+    "   -------\n",
+    "   FileNotFoundError: If the model file doesn't exist\n",
+    "   ValueError: If there's an issue with the input data format\n",
+    "   \"\"\"\n",
+    "   import pickle\n",
+    "   import numpy as np\n",
+    "   import os\n",
+    "   \n",
+    "   # Check if model file exists\n",
+    "   if not os.path.exists(model_path):\n",
+    "       raise FileNotFoundError(f\"Model file not found: {model_path}\")\n",
+    "   \n",
+    "   # Load the model\n",
+    "   try:\n",
+    "       with open(model_path, 'rb') as file:\n",
+    "           model = pickle.load(file)\n",
+    "       print(f\"Model successfully loaded from {model_path}\")\n",
+    "   except Exception as e:\n",
+    "       raise Exception(f\"Error loading model: {str(e)}\")\n",
+    "   \n",
+    "   # Ensure input is in the right format\n",
+    "   input_data = np.asarray(input_data)\n",
+    "   if input_data.ndim == 1:\n",
+    "       # Single sample - reshape to 2D array\n",
+    "       input_data = input_data.reshape(1, -1)\n",
+    "   \n",
+    "   # Make predictions\n",
+    "   try:\n",
+    "       predictions = model.predict(input_data)\n",
+    "       probabilities = model.predict_proba(input_data)\n",
+    "       positive_class_proba = probabilities[:, 1]  # Probability of class 1 (usually positive class)\n",
+    "       \n",
+    "       return {\n",
+    "           'predictions': predictions,\n",
+    "           'probabilities': probabilities,\n",
+    "           'positive_proba': positive_class_proba\n",
+    "       }\n",
+    "   except Exception as e:\n",
+    "       raise ValueError(f\"Error during prediction: {str(e)}\")\n",
+    "\n",
+    "\n",
+    "# Example usage:\n",
+    "if __name__ == \"__main__\":\n",
+    "   # Example for a single sample\n",
+    "   # Replace with your actual test data\n",
+    "   sample = X_test[0]  # Single test instance\n",
+    "   \n",
+    "   result = predict_with_tabpfn(sample)\n",
+    "   print(f\"Prediction: {'Malignant' if result['predictions'][0] == 1 else 'Benign'}\")\n",
+    "   print(f\"Confidence: {result['positive_proba'][0]:.4f}\")\n",
+    "   \n",
+    "   # Example for multiple samples\n",
+    "   samples = X_test[:5]  # First 5 test instances\n",
+    "   \n",
+    "   batch_result = predict_with_tabpfn(samples)\n",
+    "   for i, (pred, prob) in enumerate(zip(batch_result['predictions'], batch_result['positive_proba'])):\n",
+    "       print(f\"Sample {i+1}: {'Malignant' if pred == 1 else 'Benign'} (Confidence: {prob:.4f})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1e141f3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9339662",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "week1_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Week1/Day_1/3_tabular_regression.ipynb b/Week1/Day_1/3_tabular_regression.ipynb
new file mode 100644
index 00000000..00b9f1a8
--- /dev/null
+++ b/Week1/Day_1/3_tabular_regression.ipynb
@@ -0,0 +1,1700 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d66903d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ! pip install tabpfn #pip install \"tabpfn @ git+https://github.com/PriorLabs/TabPFN.git\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "014aca45",
+   "metadata": {},
+   "source": [
+    "# Setting Up Data Science Environment for Regression\n",
+    "\n",
+    "\n",
+    "This code imports essential tools for working with public datasets and evaluating regression models:\n",
+    "\n",
+    "### Data Acquisition\n",
+    "- `fetch_openml`: Fetches datasets from the OpenML repository, which hosts thousands of public datasets\n",
+    "  - Unlike fixed scikit-learn datasets, this allows access to a wide variety of real-world data\n",
+    "  - Datasets can be specified by name or ID number\n",
+    "  - Example: `fetch_openml(name='boston', version=1)` or `fetch_openml(data_id=42)`\n",
+    "\n",
+    "### Model Evaluation\n",
+    "- `mean_squared_error`: Calculates the average squared difference between predicted and actual values\n",
+    "  - Lower values indicate better model performance\n",
+    "  - Formula: MSE = (1/n) * Σ(y_true - y_pred)²\n",
+    "  - Units are squared units of the target variable\n",
+    "\n",
+    "- `r2_score`: Coefficient of determination (R²)\n",
+    "  - Measures the proportion of variance in the dependent variable predictable from the independent variables\n",
+    "  - Range: 0 to 1, where 1 indicates perfect prediction\n",
+    "  - Can be negative if the model is worse than a horizontal line\n",
+    "\n",
+    "### Data Splitting\n",
+    "- `train_test_split`: Divides datasets into random training and testing subsets\n",
+    "  - Essential for proper model validation\n",
+    "  - Typical usage: `X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)`\n",
+    "  - Parameters control split ratio, stratification, and randomization\n",
+    "\n",
+    "These tools are commonly used together in a regression workflow to fetch datasets, split the data appropriately, and evaluate model performance using standard metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5f6aa040",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "from sklearn.datasets import fetch_openml\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "sns.set(style=\"whitegrid\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7cbd5f8",
+   "metadata": {},
+   "source": [
+    "# Load the Boston Housing dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f8b97fff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dict = fetch_openml(data_id=531, as_frame=True)  # Boston Housing dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21770ae0",
+   "metadata": {},
+   "source": [
+    "# Get the feature names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cf78256d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_names=list(data_dict.data.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "317592e1",
+   "metadata": {},
+   "source": [
+    "# Create a DataFrame for easier exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d33818ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dict = fetch_openml(data_id=531, as_frame=True) \n",
+    "\n",
+    "X = data_dict.data\n",
+    "y = data_dict.target.astype(float)  # Ensure target is float for regression\n",
+    "df = pd.DataFrame(X, columns=feature_names)\n",
+    "df['MEDV'] = y\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55e5b3a3",
+   "metadata": {},
+   "source": [
+    "# Basic dataset information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "007808d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset Shape: (506, 14)\n",
+      "\n",
+      "Feature Names:\n",
+      "1. CRIM\n",
+      "2. ZN\n",
+      "3. INDUS\n",
+      "4. CHAS\n",
+      "5. NOX\n",
+      "6. RM\n",
+      "7. AGE\n",
+      "8. DIS\n",
+      "9. RAD\n",
+      "10. TAX\n",
+      "11. PTRATIO\n",
+      "12. B\n",
+      "13. LSTAT\n",
+      "\n",
+      "First 5 rows:\n",
+      "      CRIM    ZN  INDUS CHAS    NOX     RM   AGE     DIS RAD    TAX  PTRATIO  \\\n",
+      "0  0.00632  18.0   2.31    0  0.538  6.575  65.2  4.0900   1  296.0     15.3   \n",
+      "1  0.02731   0.0   7.07    0  0.469  6.421  78.9  4.9671   2  242.0     17.8   \n",
+      "2  0.02729   0.0   7.07    0  0.469  7.185  61.1  4.9671   2  242.0     17.8   \n",
+      "3  0.03237   0.0   2.18    0  0.458  6.998  45.8  6.0622   3  222.0     18.7   \n",
+      "4  0.06905   0.0   2.18    0  0.458  7.147  54.2  6.0622   3  222.0     18.7   \n",
+      "\n",
+      "        B  LSTAT  MEDV  \n",
+      "0  396.90   4.98  24.0  \n",
+      "1  396.90   9.14  21.6  \n",
+      "2  392.83   4.03  34.7  \n",
+      "3  394.63   2.94  33.4  \n",
+      "4  396.90   5.33  36.2  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Dataset Shape:\", df.shape)\n",
+    "print(\"\\nFeature Names:\")\n",
+    "for i, name in enumerate(feature_names):\n",
+    "    print(f\"{i+1}. {name}\")\n",
+    "\n",
+    "print(\"\\nFirst 5 rows:\")\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89eafa8f",
+   "metadata": {},
+   "source": [
+    "# Statistical summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0e8485b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Statistical Summary:\n",
+      "         count        mean         std        min         25%        50%  \\\n",
+      "CRIM     506.0    3.613524    8.601545    0.00632    0.082045    0.25651   \n",
+      "ZN       506.0   11.363636   23.322453    0.00000    0.000000    0.00000   \n",
+      "INDUS    506.0   11.136779    6.860353    0.46000    5.190000    9.69000   \n",
+      "NOX      506.0    0.554695    0.115878    0.38500    0.449000    0.53800   \n",
+      "RM       506.0    6.284634    0.702617    3.56100    5.885500    6.20850   \n",
+      "AGE      506.0   68.574901   28.148861    2.90000   45.025000   77.50000   \n",
+      "DIS      506.0    3.795043    2.105710    1.12960    2.100175    3.20745   \n",
+      "TAX      506.0  408.237154  168.537116  187.00000  279.000000  330.00000   \n",
+      "PTRATIO  506.0   18.455534    2.164946   12.60000   17.400000   19.05000   \n",
+      "B        506.0  356.674032   91.294864    0.32000  375.377500  391.44000   \n",
+      "LSTAT    506.0   12.653063    7.141062    1.73000    6.950000   11.36000   \n",
+      "MEDV     506.0   22.532806    9.197104    5.00000   17.025000   21.20000   \n",
+      "\n",
+      "                75%       max  \n",
+      "CRIM       3.677083   88.9762  \n",
+      "ZN        12.500000  100.0000  \n",
+      "INDUS     18.100000   27.7400  \n",
+      "NOX        0.624000    0.8710  \n",
+      "RM         6.623500    8.7800  \n",
+      "AGE       94.075000  100.0000  \n",
+      "DIS        5.188425   12.1265  \n",
+      "TAX      666.000000  711.0000  \n",
+      "PTRATIO   20.200000   22.0000  \n",
+      "B        396.225000  396.9000  \n",
+      "LSTAT     16.955000   37.9700  \n",
+      "MEDV      25.000000   50.0000  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Statistical summary\n",
+    "print(\"\\nStatistical Summary:\")\n",
+    "print(df.describe().T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccda078e",
+   "metadata": {},
+   "source": [
+    "# Check for missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ccc5dcd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Missing Values:\n",
+      "No missing values\n"
+     ]
+    }
+   ],
+   "source": [
+    "missing_values = df.isnull().sum()\n",
+    "print(\"\\nMissing Values:\")\n",
+    "print(missing_values[missing_values > 0] if missing_values.sum() > 0 else \"No missing values\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91a779af",
+   "metadata": {},
+   "source": [
+    "# Target variable distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cccf60e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(df['MEDV'], kde=True, bins=30)\n",
+    "plt.title('Distribution of House Prices (MEDV)')\n",
+    "plt.xlabel('Median Value of Homes ($1000s)')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6df9f12",
+   "metadata": {},
+   "source": [
+    "# Print target statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f16b0b43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Target (MEDV) Statistics:\n",
+      "count    506.000000\n",
+      "mean      22.532806\n",
+      "std        9.197104\n",
+      "min        5.000000\n",
+      "25%       17.025000\n",
+      "50%       21.200000\n",
+      "75%       25.000000\n",
+      "max       50.000000\n",
+      "Name: MEDV, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nTarget (MEDV) Statistics:\")\n",
+    "print(df['MEDV'].describe())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9190df37",
+   "metadata": {},
+   "source": [
+    "# Correlations with target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b80de3d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Correlations with Target (MEDV):\n",
+      "MEDV       1.000000\n",
+      "RM         0.695360\n",
+      "ZN         0.360445\n",
+      "B          0.333461\n",
+      "DIS        0.249929\n",
+      "CHAS       0.175260\n",
+      "AGE       -0.376955\n",
+      "RAD       -0.381626\n",
+      "CRIM      -0.388305\n",
+      "NOX       -0.427321\n",
+      "TAX       -0.468536\n",
+      "INDUS     -0.483725\n",
+      "PTRATIO   -0.507787\n",
+      "LSTAT     -0.737663\n",
+      "Name: MEDV, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "correlations = df.corr()['MEDV'].sort_values(ascending=False)\n",
+    "print(\"\\nCorrelations with Target (MEDV):\")\n",
+    "print(correlations)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3446df5",
+   "metadata": {},
+   "source": [
+    "# Visualize correlations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0438c749",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
+    "plt.title('Correlation Matrix')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.yticks(rotation=0)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c388f82",
+   "metadata": {},
+   "source": [
+    "# Top correlated features with target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "01ef7273",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "top_corr_features = correlations.index[1:6]  # Excluding MEDV itself which has correlation 1.0\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, feature in enumerate(top_corr_features):\n",
+    "    plt.subplot(2, 3, i+1)\n",
+    "    plt.scatter(df[feature], df['MEDV'], alpha=0.6)\n",
+    "    plt.title(f'MEDV vs {feature} (corr: {correlations[feature]:.2f})')\n",
+    "    plt.xlabel(feature)\n",
+    "    plt.ylabel('MEDV')\n",
+    "    plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3385f75",
+   "metadata": {},
+   "source": [
+    "# Pairplot of important features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "97650141",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x1000 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1250x1250 with 30 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "important_features = list(top_corr_features) + ['MEDV']\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "sns.pairplot(df[important_features], height=2.5)\n",
+    "plt.suptitle('Pairplot of Important Features', y=1.02)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a2c8e66",
+   "metadata": {},
+   "source": [
+    "# Check for Skewness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c9c27bac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Skewness of Features:\n",
+      "CRIM       5.223149\n",
+      "ZN         2.225666\n",
+      "MEDV       1.108098\n",
+      "DIS        1.011781\n",
+      "LSTAT      0.906460\n",
+      "NOX        0.729308\n",
+      "TAX        0.669956\n",
+      "RM         0.403612\n",
+      "INDUS      0.295022\n",
+      "AGE       -0.598963\n",
+      "PTRATIO   -0.802325\n",
+      "B         -2.890374\n",
+      "dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 9. Check for Skewness\n",
+    "df_numeric = df.select_dtypes(include=['number'])\n",
+    "skewness = df_numeric.skew().sort_values(ascending=False)\n",
+    "print(\"\\nSkewness of Features:\")\n",
+    "print(skewness)\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "skewness.plot(kind='bar')\n",
+    "plt.title('Skewness of Features')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f38b510",
+   "metadata": {},
+   "source": [
+    "# Outlier detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "25fdf483",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, feature in enumerate(df_numeric.columns):\n",
+    "    if i >= 9:  # Limit to 9 features for readability\n",
+    "        break\n",
+    "    plt.subplot(3, 3, i+1)\n",
+    "    sns.boxplot(y=df[feature])\n",
+    "    plt.title(f'Boxplot of {feature}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65e8f455",
+   "metadata": {},
+   "source": [
+    "# Distribution of important feature variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5b3f4284",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, feature in enumerate(feature_names):\n",
+    "    if i >= 9:  # Limit to 9 features for readability\n",
+    "        break\n",
+    "    plt.subplot(3, 3, i+1)\n",
+    "    sns.histplot(df[feature], kde=True)\n",
+    "    plt.title(f'Distribution of {feature}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79ef9a76",
+   "metadata": {},
+   "source": [
+    "# Using TabPFN for Regression on Boston Housing Data\n",
+    "\n",
+    "This code sets up a regression task using TabPFN on the Boston Housing dataset:\n",
+    "\n",
+    "### TabPFN for Regression\n",
+    "- `TabPFNRegressor`: A specialized regression variant of TabPFN (Tabular Prior-Data Fitted Network)\n",
+    "  - Unlike the classifier version, this is designed for predicting continuous values\n",
+    "  - Leverages the same transformer-based architecture with pre-training on synthetic tabular data\n",
+    "  - Typically requires minimal hyperparameter tuning for good performance\n",
+    "\n",
+    "### Data Loading\n",
+    "- `fetch_openml(data_id=531, as_frame=True)`: Retrieves the Boston Housing dataset from OpenML\n",
+    "  - `data_id=531`: Specifies the Boston Housing dataset by its unique identifier\n",
+    "  - `as_frame=True`: Returns the data as a pandas DataFrame instead of a numpy array\n",
+    "  - This dataset contains information about housing in Boston suburbs and is a classic benchmark for regression\n",
+    "\n",
+    "### Data Preparation\n",
+    "- `X = df.data`: Extracts the feature matrix\n",
+    "  - Contains attributes like CRIM (crime rate), ZN (proportion of residential land), INDUS (proportion of non-retail business acres), etc.\n",
+    "- `y = df.target.astype(float)`: Extracts the target variable and ensures it's in float format\n",
+    "  - The target is MEDV (Median value of owner-occupied homes in $1000s)\n",
+    "  - Converting to float is important for regression tasks to ensure proper calculations\n",
+    "\n",
+    "This setup prepares for applying TabPFN's regression capabilities to predict housing prices based on neighborhood characteristics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5d1252d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tabpfn import TabPFNRegressor  \n",
+    "\n",
+    "# Load Boston Housing data\n",
+    "df = fetch_openml(data_id=531, as_frame=True)  # Boston Housing dataset\n",
+    "X = df.data\n",
+    "y = df.target.astype(float)  # Ensure target is float for regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "feaeb143",
+   "metadata": {},
+   "source": [
+    "# Splitting Data for Regression Analysis\n",
+    "\n",
+    "\n",
+    "### Function Parameters\n",
+    "- `X`: Feature matrix containing housing attributes (like crime rate, number of rooms, etc.)\n",
+    "- `y`: Target vector containing housing prices (MEDV)\n",
+    "- `test_size=0.5`: Allocates 50% of the data for testing and 50% for training\n",
+    "  - This is a larger test set than typical (usually 20-30%)\n",
+    "  - A 50/50 split provides ample data for both training and thorough evaluation\n",
+    "- `random_state=42`: Sets a specific random seed for reproducibility\n",
+    "  - Ensures the same split will occur each time the code runs\n",
+    "  - The value 42 is commonly used (a reference to \"The Hitchhiker's Guide to the Galaxy\")\n",
+    "\n",
+    "### Resulting Datasets\n",
+    "- `X_train`: Features for training the model (approximately 253 samples)\n",
+    "- `X_test`: Features for evaluating the model (approximately 253 samples)\n",
+    "- `y_train`: Housing prices for the training set\n",
+    "- `y_test`: Housing prices for the test set\n",
+    "\n",
+    "### Purpose\n",
+    "This data split allows you to:\n",
+    "1. Train the TabPFN regressor on one subset of the data\n",
+    "2. Test its performance on unseen data to evaluate generalization\n",
+    "3. Get a realistic estimate of how the model would perform on new housing data\n",
+    "\n",
+    "The equal split between training and testing provides a balanced assessment of the model's predictive capabilities for this regression task."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "cb9de948",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "804df739",
+   "metadata": {},
+   "source": [
+    "# Training the TabPFN Regressor\n",
+    "\n",
+    "This code initializes and trains a TabPFN regressor on the Boston Housing dataset:\n",
+    "\n",
+    "### Model Initialization\n",
+    "- `TabPFNRegressor()`: Creates an instance of TabPFN's regression model with default parameters\n",
+    "  - Unlike traditional regression models, TabPFN comes pre-trained on synthetic tabular data\n",
+    "  - The default configuration typically works well without extensive hyperparameter tuning\n",
+    "  - Optional parameters include N_ensemble_configurations (number of models in ensemble) and device (CPU/GPU)\n",
+    "\n",
+    "### Model Training\n",
+    "- `regressor.fit(X_train, y_train)`: Adapts the regressor to the housing price prediction task\n",
+    "  - `X_train`: Feature matrix containing ~253 samples with housing attributes\n",
+    "  - `y_train`: Target vector containing corresponding housing prices (MEDV values)\n",
+    "  - The training process is typically faster than traditional models as it leverages transfer learning\n",
+    "  - TabPFN adapts its pre-trained knowledge to the specific patterns in the Boston Housing dataset\n",
+    "\n",
+    "### What Happens During Fitting\n",
+    "During the `fit()` operation, TabPFN:\n",
+    "1. Normalizes the input features internally\n",
+    "2. Adapts its pre-trained transformer architecture to the regression task\n",
+    "3. Optimizes for continuous value prediction rather than classification\n",
+    "4. May create an ensemble of models if configured to do so\n",
+    "\n",
+    "TabPFN's approach is especially advantageous for tabular regression tasks like housing price prediction, often achieving competitive performance with minimal configuration and training time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b43553e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tabpfn/regressor.py:460: UserWarning: Running on CPU with more than 200 samples may be slow.\n",
+      "Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client\n",
+      "  check_cpu_warning(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    font-family: monospace;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table td {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value pre {\n",
+       "    color:rgb(255, 94, 0) !important;\n",
+       "    background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".default td {\n",
+       "    color: black;\n",
+       "    text-align: left;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TabPFNRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>TabPFNRegressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+       "            <td class=\"value\">8</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('categorical_features_indices',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">categorical_features_indices&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('softmax_temperature',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">softmax_temperature&nbsp;</td>\n",
+       "            <td class=\"value\">0.9</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('average_before_softmax',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">average_before_softmax&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('model_path',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">model_path&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('device',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">device&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('ignore_pretraining_limits',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">ignore_pretraining_limits&nbsp;</td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inference_precision',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inference_precision&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('fit_mode',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">fit_mode&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;fit_preprocessors&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('memory_saving_mode',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">memory_saving_mode&nbsp;</td>\n",
+       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">random_state&nbsp;</td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+       "            <td class=\"value\">-1</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('inference_config',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">inference_config&nbsp;</td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "</script></body>"
+      ],
+      "text/plain": [
+       "TabPFNRegressor()"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Initialize the regressor\n",
+    "regressor = TabPFNRegressor()  \n",
+    "regressor.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "329c0ed5",
+   "metadata": {},
+   "source": [
+    "# Making Predictions with TabPFN Regressor\n",
+    "\n",
+    "\n",
+    "This code generates housing price predictions using the trained TabPFN regressor:\n",
+    "\n",
+    "### Prediction Process\n",
+    "- `regressor.predict(X_test)`: Applies the trained model to make predictions on unseen test data\n",
+    "  - Takes the feature matrix `X_test` as input\n",
+    "  - Returns an array of predicted housing prices (MEDV values in $1000s)\n",
+    "  - No probability values are returned since this is a regression task (unlike classification)\n",
+    "\n",
+    "### Behind the Scenes\n",
+    "When you call `predict()`, TabPFN:\n",
+    "1. Processes the input features through its transformer-based architecture\n",
+    "2. Converts the network outputs to continuous values appropriate for the regression task\n",
+    "3. Returns point estimates for each sample in the test set\n",
+    "\n",
+    "### Next Steps After Prediction\n",
+    "After generating these predictions, you would typically:\n",
+    "- Evaluate model performance using metrics like MSE, RMSE, or R²\n",
+    "- Compare the predictions against actual values (y_test)\n",
+    "- Visualize the predictions vs. actual values to identify patterns or areas for improvement\n",
+    "\n",
+    "TabPFN's unique approach often leads to competitive regression results with minimal configuration, making it an excellent choice for tabular regression tasks like housing price prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e78e553d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raamraam/outskill/GenAIEngineering-Cohort2/Week1/week1_env/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Predict on the test set\n",
+    "predictions = regressor.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c67cff1a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[24.829641  31.88968   15.275593  23.476364  17.302414  21.80158\n",
+      " 18.86287   14.699924  19.679363  21.004627  20.37487   18.046263\n",
+      " 10.5235195 22.289043  18.03105   24.725826  19.160912  10.316656\n",
+      " 46.85507   15.4577675 25.480354  27.56522   14.232136  22.366348\n",
+      " 15.512056  16.225887  21.69415   13.672206  18.927673  20.33205\n",
+      " 18.636276  24.105595  15.514196  21.513098  15.450206  16.27689\n",
+      " 31.71437   19.41008   22.035236  24.507967  18.627169  30.213377\n",
+      " 49.404316  18.668653  24.151554  15.105869  14.665146  25.693214\n",
+      " 19.529102  23.50747   18.673864  34.67919   17.255745  27.384686\n",
+      " 45.93715   22.107285  15.561022  31.57618   23.599377  17.001225\n",
+      " 24.472261  34.88736   32.107475  16.857445  23.27481   15.367708\n",
+      " 13.576742  23.719666  28.373241  14.231501  21.012615  24.33099\n",
+      " 10.295569  19.459604  23.43172    8.236156  20.342861  47.154827\n",
+      " 10.952816  11.342044  20.372137  10.860651  19.418865  10.40016\n",
+      " 19.944962  28.368755  16.159792  24.926277  26.013233  18.370697\n",
+      " 23.072697   8.77762   19.136555  17.53675   34.30373   19.409496\n",
+      " 36.69136   11.508137  12.158929   9.887161  21.406075  24.121735\n",
+      " 12.8263645 19.496456  20.19059   11.296165  18.461231  25.622553\n",
+      " 20.992414  22.854666   8.701651  11.052746  22.376759  26.50767\n",
+      " 32.343624  14.300983  40.784298  14.631329  21.829552  25.410099\n",
+      " 16.451107  25.080475   9.701052  20.716326  24.10709   21.789206\n",
+      " 24.031204  36.34359   17.739252  44.137413  16.421041  23.734968\n",
+      " 16.424297  19.21894   14.019886  17.057611  21.612696  29.19083\n",
+      " 27.498688  16.519547  18.269913  24.427197  19.758327  17.188736\n",
+      "  8.151984  22.635845  19.500706  14.672943  14.984308  48.107536\n",
+      " 14.9546995 16.398468  24.65437   22.46046   20.166597  21.748074\n",
+      " 19.242254  21.93897   33.44489   10.17698   23.674614  17.999943\n",
+      " 23.451897  23.566727  23.481253  20.469677  46.845303  15.990784\n",
+      " 16.122602  17.891895  19.646345  20.709154  21.339966  33.884277\n",
+      " 21.45645   19.941326  22.655289  30.71882   35.94083   26.185753\n",
+      " 13.5577    10.689926  10.253421  15.628079  31.78389   28.613619\n",
+      " 14.033134  10.219168  46.30555   20.757902  18.313374  21.700893\n",
+      " 19.61699   13.717035   8.725111  29.4183    26.577587  19.900948\n",
+      " 14.610576  13.674749  26.426916  19.45948   28.837387  35.635597\n",
+      " 21.877731  22.262264  20.749249  50.235195  31.194523  34.615776\n",
+      " 26.136238  25.06482   14.460543  44.657482  20.527044  29.713646\n",
+      " 26.203804  20.502527  19.538055  11.675539  46.95661   37.491127\n",
+      " 33.825985  10.617359  17.244164  20.54007   35.472084  15.705162\n",
+      " 43.14286   18.777435  22.85011   10.143278  20.25585   25.525246\n",
+      " 31.14142   31.187445  20.29307   49.095596  23.126833  30.395588\n",
+      " 24.206387  20.356777  24.042496  11.973412  18.908056  23.731558\n",
+      " 18.43651   10.804201  20.121208  23.284782  23.175995  19.401627\n",
+      " 20.346422 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(predictions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40bd1784",
+   "metadata": {},
+   "source": [
+    "# Evaluating the TabPFN Regressor\n",
+    "\n",
+    "\n",
+    "This code assesses the performance of the TabPFN regressor using two standard regression metrics:\n",
+    "\n",
+    "### Mean Squared Error (MSE)\n",
+    "- `mean_squared_error(y_test, predictions)`: Calculates the average squared difference between predicted and actual housing prices\n",
+    "  - Formula: MSE = (1/n) * Σ(y_true - y_pred)²\n",
+    "  - Lower values indicate better model performance\n",
+    "  - Units are squared dollars (in thousands), making it scale-dependent\n",
+    "  - Penalizes larger errors more heavily due to the squaring operation\n",
+    "  - Useful for comparing models on the same dataset\n",
+    "\n",
+    "### R² Score (Coefficient of Determination)\n",
+    "- `r2_score(y_test, predictions)`: Measures the proportion of variance in housing prices that the model explains\n",
+    "  - Formula: R² = 1 - (Σ(y_true - y_pred)² / Σ(y_true - y_mean)²)\n",
+    "  - Scale-free metric ranging from -∞ to 1\n",
+    "  - R² = 1 indicates perfect prediction\n",
+    "  - R² = 0 indicates the model performs no better than simply predicting the mean value\n",
+    "  - R² < 0 indicates the model performs worse than predicting the mean\n",
+    "  - Generally, R² > 0.7 is considered good for real estate price prediction\n",
+    "\n",
+    "### Interpretation\n",
+    "- These metrics together provide complementary insights:\n",
+    "  - MSE gives an absolute measure of prediction error in squared units\n",
+    "  - R² provides a relative measure of how well the model captures the variance in housing prices\n",
+    "  - A good model should have low MSE and high R² values\n",
+    "\n",
+    "For the Boston Housing dataset, these metrics help assess how accurately TabPFN can predict home values based on neighborhood characteristics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b7492bb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error (MSE): 10.401479749648606\n",
+      "R² Score: 0.8717928529567319\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the model\n",
+    "mse = mean_squared_error(y_test, predictions)\n",
+    "r2 = r2_score(y_test, predictions)\n",
+    "print(\"Mean Squared Error (MSE):\", mse)\n",
+    "print(\"R² Score:\", r2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "week1_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Week1/Day_2/search_engine.py b/Week1/Day_2/search_engine.py
index 64258140..df3e9d83 100644
--- a/Week1/Day_2/search_engine.py
+++ b/Week1/Day_2/search_engine.py
@@ -5,10 +5,13 @@
 import re
 import glob
 import uvicorn
+from sentence_transformers import SentenceTransformer
+import faiss
 
 app = FastAPI()
+faiss_index = None
 
-os.makedirs(os.path.join('Day_1','csv_files'), exist_ok=True)
+os.makedirs(os.path.join('csv_files'), exist_ok=True)
 
 def chunk_pdf_to_dataframe(pdf_path, num_chunks = 5):
     reader = PdfReader(pdf_path)
@@ -34,19 +37,53 @@ def chunk_pdf_to_dataframe(pdf_path, num_chunks = 5):
                 chunk = text[start:end]
                 data.append({'filename': filename,'page_number': page_num,'chunk_number': i + 1,'chunk': chunk})
     df = pd.DataFrame(data)
-    df.to_csv(os.path.join('Day_1','csv_files', filename.replace('.pdf','.csv')), index=False)
+    print(df)
+    df.to_csv(os.path.join('csv_files', filename.replace('.pdf','.csv')), index=False)
 
 @app.post("/chunk_pdf")
 async def chunk_pdf(pdf_file_path,num_chunks= 5):
     chunk_pdf_to_dataframe(pdf_file_path, num_chunks=int(num_chunks))
+
+    global faiss_index
+    faiss_index=build_faiss_index()
     return {"status": "success","message": f"PDF chunked successfully","file_path": pdf_file_path}
 
+
+
+def embed_text_chunks(chunks, embedding_model_name="all-MiniLM-L6-v2"):
+    model = SentenceTransformer(embedding_model_name)
+    embeddings = model.encode(chunks, convert_to_numpy=True, show_progress_bar=True)
+    return embeddings
+
+# Function to build a FAISS index
+def build_faiss_index():
+    files = glob.glob(os.path.join('csv_files', "*.csv"))
+    print(files)
+    df = pd.concat((pd.read_csv(f) for f in files), ignore_index=True)
+    embedding_model_name = "all-mpnet-base-v2"
+    embeddings = embed_text_chunks(list(df['chunk']), embedding_model_name)
+    dimension = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dimension)
+    index.add(embeddings)
+    return index
+
+
+
+# @app.post("/search_chunks")
+# def search_chunks(search_string):
+#     search_list = search_string.split()
+#     files = glob.glob(os.path.join('Day_1','csv_files', "*.csv"))
+#     df = pd.concat((pd.read_csv(f) for f in files), ignore_index=True)
+#     matches_df = df[df['chunk'].str.contains('|'.join(search_list), case=False, na=False)] #key word search
+#     return matches_df.to_json()
+
 @app.post("/search_chunks")
 def search_chunks(search_string):
-    search_list = search_string.split()
-    files = glob.glob(os.path.join('Day_1','csv_files', "*.csv"))
+    query_embedding = SentenceTransformer('all-mpnet-base-v2').encode([search_string], convert_to_numpy=True)
+    distances, indices = faiss_index.search(query_embedding, k=5)
+    files = glob.glob(os.path.join('csv_files', "*.csv"))
     df = pd.concat((pd.read_csv(f) for f in files), ignore_index=True)
-    matches_df = df[df['chunk'].str.contains('|'.join(search_list), case=False, na=False)]
+    matches_df=df.iloc[indices[0]]
     return matches_df.to_json()
 
 @app.get("/health")
@@ -54,5 +91,10 @@ async def health_check():
     """Simple health check endpoint"""
     return {"status": "healthy"}
 
+def main():
+    global faiss_index
+    faiss_index=build_faiss_index()
+
 if __name__ == "__main__":
+    # main()
     uvicorn.run(app, host="0.0.0.0", port=9321)
\ No newline at end of file
diff --git a/Week1/requirements.txt b/Week1/requirements.txt
index 11388d09..00fa2b49 100644
--- a/Week1/requirements.txt
+++ b/Week1/requirements.txt
@@ -1,70 +1,120 @@
 altair==5.5.0
 annotated-types==0.7.0
 anyio==4.9.0
+appnope==0.1.4
+asttokens==3.0.0
 attrs==25.3.0
 blinker==1.9.0
 cachetools==6.1.0
 certifi==2025.6.15
 charset-normalizer==3.4.2
 click==8.2.1
+cloudpickle==3.1.1
 cmdstanpy==1.2.5
+comm==0.2.2
 contourpy==1.3.2
 cycler==0.12.1
+debugpy==1.8.14
+decorator==5.2.1
+einops==0.8.1
+executing==2.2.0
+faiss-cpu==1.11.0
 fastapi==0.115.14
+filelock==3.18.0
 fonttools==4.58.4
+fsspec==2025.5.1
+future==1.0.0
 gitdb==4.0.12
 GitPython==3.1.44
+greenlet==3.2.3
 h11==0.16.0
+hf-xet==1.1.5
 holidays==0.75
+huggingface-hub==0.33.1
+hyperopt==0.2.7
 idna==3.10
 importlib_resources==6.5.2
+ipykernel==6.29.5
+ipython==9.3.0
+ipython_pygments_lexers==1.1.1
+jedi==0.19.2
 Jinja2==3.1.6
 joblib==1.5.1
 jsonschema==4.24.0
 jsonschema-specifications==2025.4.1
+jupyter_client==8.6.3
+jupyter_core==5.8.1
 kiwisolver==1.4.8
 MarkupSafe==3.0.2
 matplotlib==3.10.3
+matplotlib-inline==0.1.7
+mpmath==1.3.0
 narwhals==1.44.0
+nest-asyncio==1.6.0
+networkx==3.5
 nltk==3.9.1
-numpy==2.3.1
+numpy==1.26.4
 packaging==25.0
 pandas==2.3.0
+pandasql==0.7.3
+parso==0.8.4
 patsy==1.0.1
+pexpect==4.9.0
 pillow==11.2.1
+platformdirs==4.3.8
+prompt_toolkit==3.0.51
 prophet==1.1.7
 protobuf==6.31.1
+psutil==7.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+py4j==0.10.9.9
 pyarrow==20.0.0
 pydantic==2.11.7
 pydantic_core==2.33.2
 pydeck==0.9.1
+Pygments==2.19.2
 pyparsing==3.2.3
 PyPDF2==3.0.1
 python-dateutil==2.9.0.post0
 pytz==2025.2
+PyYAML==6.0.2
+pyzmq==27.0.0
 referencing==0.36.2
 regex==2024.11.6
 requests==2.32.4
 rpds-py==0.25.1
-scikit-learn==1.7.0
+safetensors==0.5.3
+scikit-learn==1.6.1
 scipy==1.16.0
-seaborn==0.13.2
+seaborn==0.12.2
+sentence-transformers==4.1.0
 six==1.17.0
 smmap==5.0.2
 sniffio==1.3.1
+SQLAlchemy==2.0.41
+stack-data==0.6.3
 stanio==0.5.1
 starlette==0.46.2
 statsmodels==0.14.4
 streamlit==1.46.1
+sympy==1.14.0
+tabpfn==2.0.9
+tabpfn-extensions==0.0.4
 tenacity==9.1.2
 threadpoolctl==3.6.0
+tokenizers==0.21.2
 toml==0.10.2
+torch==2.2.2
 tornado==6.5.1
 tqdm==4.67.1
+traitlets==5.14.3
+transformers==4.53.0
 typing-inspection==0.4.1
 typing_extensions==4.14.0
 tzdata==2025.2
 urllib3==2.5.0
 uvicorn==0.34.3
+wcwidth==0.2.13
 wordcloud==1.9.4
 xgboost==3.0.2

From f5c03676b341652f999a64f6f4476cccfa98e495 Mon Sep 17 00:00:00 2001
From: RaamRaam <rm.ramanathan@gmail.com>
Date: Sat, 28 Jun 2025 18:55:23 +0530
Subject: [PATCH 7/7] fixed numpy version

---
 .DS_Store  | Bin 8196 -> 8196 bytes
 .gitignore |   6 +++---
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.DS_Store b/.DS_Store
index 5cf75375c94a01a32afb4f4f9b3d45ae936d2c61..c71afa51dd9e0ad1e2997910d2c599026dc81d58 100644
GIT binary patch
delta 147
zcmZp1XmQx^Od#$!GXnzy3xgg*IzuKyNp8N2OHxjL5>SkT=|(QAANz4fRQVLV@&y@&
x!O8i#1wcIvjJ*>WChrpz+Z-kMo0(-6yO7`HNYOavg_a4McZtqsM;Gy80sztMCo%v4

delta 147
zcmZp1XmQx^Od#%H1Oo#D3xgg*IzuKyNp8N2OHxjL5>Sle@h`OnzU;>xQRP$c$`@o9
x1}Ep|76A1yF!nYuOx`CbwmC}hH#1B4_4xUdBSqtw;~Xb!-X%Jl9bLqW2>>t9EL{Kq

diff --git a/.gitignore b/.gitignore
index 3f0feba6..3223862a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,10 +4,10 @@
 **/.ipynb_checkpoints
 *.py[cod]
 *$py.class
-
+**/csv_files
+**/*.pkl
 **/*_solution
-**/2_tabular_classification.ipynb
-**/3_tabular_regression.ipynb
+**/github_pat.txt
 # C extensions
 *.so