How to do it…

Let's see how to achieve model persistence in Python:

1. Add the following lines to the regressor.py file:

import pickle

output_model_file = "3_model_linear_regr.pkl"

with open(output_model_file, 'wb') as f:
    pickle.dump(linear_regressor, f) 

1. The regressor object will be saved in the saved_model.pkl file. Let's look at how to load it and use it, as follows:

with open(output_model_file, 'rb') as f:
    model_linregr = pickle.load(f)

y_test_pred_new = model_linregr.predict(X_test)
print("New mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred_new), 2))

The following result is returned:

New mean absolute error = 241907.27

Here, we just loaded the regressor from the file into the model_linregr variable. You can compare the preceding result with the earlier result to confirm that it's the same.