build_rf_model.py

#Plug in optimized model parameters into final RF model 
rf = RandomForestClassifier(n_estimators=1100, 
                                max_depth=100,
                                random_state = 1500)
#Fit the model 
rf.fit(train_features, train_labels)
# Use the forest's predict method on the test data
print(confusion_matrix(test_labels, 
                           rf.predict(test_features),
                           labels=['Hour 8', 'Hour 9', 'Hour 10', 
                                   'Hour 14', 'Hour 15', 'Hour 16',
                                   'Hour 17', 'Hour 18', 'Hour 19',
                                   'Hour 20', 'Hour 21']))
accuracy_score(test_labels, rf.predict(test_features), normalize=True, sample_weight=None)