how to find the accuracy of linear regression model

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1# Simple Linear Regression
2# Importing the libraries
3import numpy as np
4import matplotlib.pyplot as plt
5import pandas as pd
6# Importing the dataset
7dataset = pd.read_csv('Salary_Data.csv')
8X = dataset.iloc[:, :-1].values
9y = dataset.iloc[:, 1].values
10# Splitting the dataset into the Training set and Test set
11from sklearn.cross_validation import train_test_split
12X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 42)
13# Fitting Simple Linear Regression to the Training set
14from sklearn.linear_model import LinearRegression
15regressor = LinearRegression()
16regressor.fit(X_train, y_train)
17# Predicting the Test set results
18y_pred = regressor.predict(X_test)
19print('Coefficients: \n', regressor.coef_)
20# The mean squared error
21print("Mean squared error: %.2f" % np.mean((regressor.predict(X_test) - y_test) ** 2))
22# Explained variance score: 1 is perfect prediction
23print('Variance score: %.2f' % regressor.score(X_test, y_test))

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