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Simple_Linear_Regression.py
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Simple_Linear_Regression.py
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# Simple Linear Regression
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the datasets
datasets = pd.read_csv('Salary_Data.csv')
X = datasets.iloc[:, :-1].values
Y = datasets.iloc[:, 1].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 1/3, random_state = 0)
# Fitting Simple Linear Regression to the training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_Train, Y_Train)
# Predicting the Test set result 
Y_Pred = regressor.predict(X_Test)
# Visualising the Training set results
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()
# Visualising the Test set results
plt.scatter(X_Test, Y_Test, 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()