dtr = DecisionTreeRegressor()
dtr.fit(X_train,Y_train)
y_pred = dtr.predict(X_test)
y_pred_dt=dtr.predict(test)
submission['Purchase'] = y_pred_dt
submission.to_csv('dtr_model3.csv',index=False)
mse = mean_squared_error(Y_test, y_pred)
print("RMSE Error:", np.sqrt(mse))
r2 = r2_score(Y_test, y_pred)
print("R2 Score:", r2)
feature_important = dtr.get_score(importance_type='gain')
keys = list(feature_important.keys())
values = list(feature_important.values())
total = sum(values)
new = [value * 100. / total for value in values]
new = np.round(new,2)
feature_importances = pd.DataFrame()
feature_importances['Features'] = keys
feature_importances['Importance (%)'] = new
feature_importances = feature_importances.sort_values(['Importance (%)'],ascending=False).reset_index(drop=True)
feature_importances
feature_importances.style.set_properties(**{'font-size':'10pt'})