└── regression


/regression:
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 1 | import numpy as np
 2 | import pandas as pd
 3 | import matplotlib.pyplot as plt
 4 | df=pd.read_csv('D:\\Jeyashri\\IBM\\Datasets\\head1.csv')
 5 | df.head(10)
 6 | plt.scatter(df.HeadSize,df.BrainWeight,color='red')
 7 | plt.xlabel("HeadSize")
 8 | plt.ylabel("BrainWeight")
 9 | plt.show()
10 | from sklearn.model_selection import train_test_split 
11 | from sklearn.linear_model import LogisticRegression
12 | from sklearn.metrics import confusion_matrix, accuracy_score
13 | x = df.drop ('Gender', axis = 1) 
14 | y = df ['Gender'] 
15 | x_train, x_test, y_train, y_test = train_test_split (x, y, test_size = 0.33, random_state = 1) 
16 | logmodel = LogisticRegression ()
17 | logmodel.fit (x_train, y_train) 
18 | predict= logmodel.predict (x_test)
19 | print (confusion_matrix (y_test, predict))
20 | print (accuracy_score (y_test, predict))
21 | predict
22 | from sklearn.metrics import classification_report
23 | classification_report(y_test,predict)
24 | from sklearn.metrics import accuracy_score
25 | accuracy_score(y_test,predict)
26 | plt.figure(figsize=(12, 6))
27 | plt.plot(x_test,y,'ro')# regression line
28 | plt.plot(predict,y_test)   # scatter plot showing actual data
29 | plt.title('Actual vs Predicted')
30 | plt.xlabel('X')
31 | plt.ylabel('y')
32 | plt.show()
33 | 


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