how to find the accuracy of linear regression model
# Simple Linear Regression # 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 = 1/3, random_state = 42) # 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 results y_pred = regressor.predict(X_test) print('Coefficients: n', regressor.coef_) # The mean squared error print("Mean squared error: %.2f" % np.mean((regressor.predict(X_test) - y_test) ** 2)) # Explained variance score: 1 is perfect prediction print('Variance score: %.2f' % regressor.score(X_test, y_test))