preprocess.py

import matplotlib.pyplot as plt
import pandas as pd
from sklearn import preprocessing
# load the iris dataset
malwaredata=pd.read_csv("cleaned_data - Copy.csv")
dataframe=pd.DataFrame(malwaredata)

x=dataframe.iloc[: ,2:56]

min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df = pd.DataFrame(x_scaled)

df.hist(column=0)
# plt.show()
colname = df.columns[0]
plt.title('Mean')
plt.xlabel("value")
plt.ylabel("Frequency")
plt.savefig("abc.png")


# df.head
# separate the data from the target attributes
# X = iris.data
# y = iris.target
# # normalize the data attributes
# normalized_X = preprocessing.normalize(X)