1. from sklearn.model_selection import train_test_split
2.  
3. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.5, random_state = 0)
4.  
5. X=df.iloc[:, :-1].values
6. y=df.iloc[:, -1].values
7.  
8.  
9. # adding extra column because of Multiple linear regression
10. X_select = np.concatenate((X[:,9:10],X[:,10:11]), axis=1)
11. X_select_square = np.square(X_select)
12. X_select = X
13. #X_square = np.square(X_select)
14. #X_select = np.hstack((X_select, X_select_square))
15. X_t=np.append(arr=np.ones((X_select.shape[0],1)), values=X_select, axis=1)
16.  
17. # splitting the dataset
18. X_train, X_test, y_train, y_test = train_test_split(X_select, y, test_size=0.5, random_state=0)
19.  
20.  
21.  
22. # scaling the dataset
23. from sklearn.preprocessing import StandardScaler
24. sc_X=StandardScaler()
25. X_train=sc_X.fit_transform(X_train)
26. X_test=sc_X.transform(X_test)
27.  
28.  
29.  
30. #linear regression
31. #from sklearn.linear_model import LinearRegression
32. #regressor=LinearRegression()
33. from sklearn.linear_model import LogisticRegression
34. regressor=LogisticRegression()
35. regressor.fit(X_train,y_train)
36.  
37.  
38.  
39. #Prediction
40. predictions=regressor.predict(X_test)
41. for i, prediction in enumerate(predictions):
42.     print('Predicted: %s, Target: %s' % (prediction, y_test[i]))
43. score = regressor.score(X_test, y_test)
44. print('R-squared: %.2f' % regressor.score(X_test, y_test))