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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)
+
+# Feature Scaling
+
+"""from sklearn.preprocessing import StandardScaler
+sc_X = StandardScaler()
+X_Train = sc_X.fit_transform(X_Train)
+X_Test = sc_X.transform(X_Test)
+"""
+
+# 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()
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