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simplelinear_regression.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat May 26 06:22:13 2018
+
+@author: admin
+"""
+
+# Data Preprocessing Template
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Salary_Data.csv')
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, 1].values
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.cross_validation import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, 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)
+sc_y = StandardScaler()
+y_train = sc_y.fit_transform(y_train)"""
+
+#fitting simple linear regression to training set
+from sklearn.linear_model import LinearRegression
+
+regressor = LinearRegression()#making object for reg package
+regressor.fit(X_train, y_train)#to fit the regressor to our training data
+
+#predict the test results
+y_pred =regressor.predict(X_test)
+#Now if we compare y_Pred and y_test we can see the current salary and model predicted salary in y_pred
+plt.scatter(X_train, y_train, color = 'red')
+plt.plot(X_train, regressor.predict(X_train), color = 'blue')
+#we have plotted the line where real salary in x axis and 
+#predicted salary in y axis  and we observe thatfew obs which are on line means its quite accurate i.e. real salary approx equal to predcted salary
+plt.title('Salary vs Experience (Training set)')
+plt.xlabel('Years of Experience')
+plt.ylabel('Salary')
+plt.show()
+
+#here model is same only scatter points are of training set
+#a we have fit that is tarining set and here we are testing its efficiency in test set
+# 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 (Test set)')
+plt.xlabel('Years of Experience')
+plt.ylabel('Salary')
+plt.show()
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