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Created January 28, 2020 10:28
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# create an object of the LinearRegression Model
model_LR = LinearRegression()
# fit the model with the training data, train_y)
# predict the target on train and test data
predict_train = model_LR.predict(train_x)
predict_test = model_LR.predict(test_x)
# Root Mean Squared Error on train and test date
print('RMSE on train data: ', mean_squared_error(train_y, predict_train)**(0.5))
print('RMSE on test data: ', mean_squared_error(test_y, predict_test)**(0.5))
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