rf_clf.py

from sklearn.ensemble import RandomForestClassifier
rf_clf = RandomForestClassifier(n_estimators = 500, max_depth=12)
rf_clf.fit(X_train, y_train)
rf_y_pred = rf_clf.predict(X_val)

pd.Series(rf_clf.feature_importances_, index = X_train.columns).nlargest(12).plot(kind = 'barh',
                                                                               figsize = (10, 10),
                                                                              title = 'Feature importance from RandomForest').invert_yaxis();