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- #splitting data
- 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)
- #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)
- #plotting the training set
- plt.plot(X_train,Y_train, color = 'red')
- plt.plot(X_train,regressor.predict(X_train), color = 'blue')
- plt.title('exp vs salary(training set)')
- plt.xlabel('exp')
- plt.ylabel('salary')
- plt.show()
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