<jupyter_start><jupyter_text>Step 1: Read in hold out data, scalers, and best model<jupyter_code># holdout = pd.read_csv('resources/movies_holdout_features.csv', index_col=0)
# final_scaler = read_pickle(filename)
# final_model = read_pickle(filename)<jupyter_output><empty_output><jupyter_text>Step 2: Feature Engineering for holdout set Remember we have to perform the same transformations on our holdout data (feature engineering, extreme values, and scaling) that we performed on the original data.<jupyter_code># transformed_holdout = final_scaler(holdout)<jupyter_output><empty_output><jupyter_text>Step 3: Predict the holdout set<jupyter_code># final_answers = final_model.predict(transformed_holdout)<jupyter_output><empty_output><jupyter_text>Step 4: Export your predictions<jupyter_code># final_answer.to_csv('housing_preds_your_name.csv')<jupyter_output><empty_output>
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