In [29]:

    

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


    

In [21]:

    

np.set_printoptions(precision=3, suppress=True)


    

In [22]:

    

dataset = pd.read_csv('Salary_Data.csv')


    

In [23]:

    

X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values


    

In [24]:

    

from sklearn.model_selection import train_test_split


    

In [25]:

    

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=0)


    

In [26]:

    

from sklearn.linear_model import LinearRegression


    

In [27]:

    

regressor = LinearRegression()
regressor.fit(X_train, y_train)


    

    
Out[27]:





LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

In [28]:

    

y_pred = regressor.predict(X_test)
pd.DataFrame([y_pred, y_test])


    

    
Out[28]:






  

    

      

      
0
      
1
      
2
      
3
      
4
      
5
      
6
      
7
      
8
      
9
    
  
  

    

      
0
      
40835.105909
      
123079.399408
      
65134.556261
      
63265.367772
      
115602.645454
      
108125.891499
      
116537.239698
      
64199.962017
      
76349.687193
      
100649.137545
    
    

      
1
      
37731.000000
      
122391.000000
      
57081.000000
      
63218.000000
      
116969.000000
      
109431.000000
      
112635.000000
      
55794.000000
      
83088.000000
      
101302.000000
    
  

In [30]:

    

plt.scatter(X_train, y_train, color = 'red')
plt.plot(X_train, regressor.predict(X_train), color = 'blue')
plt.title('Salary vs Experience (Training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()


    

In [31]:

    

plt.scatter(X_test, y_test, color = 'red')
plt.plot(X_train, regressor.predict(X_train), color = 'blue')
plt.title('Salary vs Experience (Test set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()