model_building_2.py

# create an object of the LinearRegression Model
model_LR = LinearRegression()

# fit the model with the training data
model_LR.fit(train_x, 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))