data_preprocessing.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv('Data1.csv')
X = data.iloc[:, :-1].values
Y = data.iloc[:, 3].values

from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN' , strategy = 'mean' , axis = 0)
imputre = imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])

from sklearn.preprocessing import LabelEncoder,OneHotEncoder
labelencoder_X = LabelEncoder()
X[:,0] = labelencoder_X.fit_transform(X[:,0])
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)

from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)