├── README.md
├── Salary_Data.csv
└── Simple_Linear_Regression.py


/README.md:
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1 | # Simple-Linear-Regression
2 | 
3 | A simple python program that implements Linear Regression on a sample dataset. The programuses sklearn.linear_model from the scikit-learn library to import the class LinearRegression. The object of the class is declared and is fitted with the X_Train and Y_Train data.
4 | 
5 | Y_Pred stores the predicted values of X_Test
6 | 
7 | A graph is plotted using the matplotlib.pyplot to visually represent the Linear Regression model.
8 | 


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/Salary_Data.csv:
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 1 | YearsExperience,Salary
 2 | 1.1,39343.00
 3 | 1.3,46205.00
 4 | 1.5,37731.00
 5 | 2.0,43525.00
 6 | 2.2,39891.00
 7 | 2.9,56642.00
 8 | 3.0,60150.00
 9 | 3.2,54445.00
10 | 3.2,64445.00
11 | 3.7,57189.00
12 | 3.9,63218.00
13 | 4.0,55794.00
14 | 4.0,56957.00
15 | 4.1,57081.00
16 | 4.5,61111.00
17 | 4.9,67938.00
18 | 5.1,66029.00
19 | 5.3,83088.00
20 | 5.9,81363.00
21 | 6.0,93940.00
22 | 6.8,91738.00
23 | 7.1,98273.00
24 | 7.9,101302.00
25 | 8.2,113812.00
26 | 8.7,109431.00
27 | 9.0,105582.00
28 | 9.5,116969.00
29 | 9.6,112635.00
30 | 10.3,122391.00
31 | 10.5,121872.00
32 | 


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/Simple_Linear_Regression.py:
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 1 | # Simple Linear Regression
 2 | # Importing the libraries
 3 | 
 4 | import numpy as np
 5 | import matplotlib.pyplot as plt
 6 | import pandas as pd
 7 | 
 8 | # Importing the datasets
 9 |  
10 | datasets = pd.read_csv('Salary_Data.csv')
11 | 
12 | X = datasets.iloc[:, :-1].values
13 | Y = datasets.iloc[:, 1].values
14 | 
15 | # Splitting the dataset into the Training set and Test set
16 | 
17 | from sklearn.model_selection import train_test_split
18 | X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 1/3, random_state = 0)
19 | 
20 | # Fitting Simple Linear Regression to the training set
21 | 
22 | from sklearn.linear_model import LinearRegression
23 | regressor = LinearRegression()
24 | regressor.fit(X_Train, Y_Train)
25 | 
26 | # Predicting the Test set result 
27 | 
28 | Y_Pred = regressor.predict(X_Test)
29 | 
30 | 
31 | # Visualising the Training set results
32 | 
33 | plt.scatter(X_Train, Y_Train, color = 'red')
34 | plt.plot(X_Train, regressor.predict(X_Train), color = 'blue')
35 | plt.title('Salary vs Experience  (Training Set)')
36 | plt.xlabel('Years of experience')
37 | plt.ylabel('Salary')
38 | plt.show()
39 | 
40 | # Visualising the Test set results
41 | 
42 | plt.scatter(X_Test, Y_Test, color = 'red')
43 | plt.plot(X_Train, regressor.predict(X_Train), color = 'blue')
44 | plt.title('Salary vs Experience  (Training Set)')
45 | plt.xlabel('Years of experience')
46 | plt.ylabel('Salary')
47 | plt.show()


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