'Store', 'SchoolHoliday', 'Promo', 'cmp msr', 'IsPromotionMonth', 'Year', 'Month', 'Day', 'DayOfTheWeek', 'WeekOfTheYear', 'StoreType', 'CompetitionOpenSinceMonth', 'CompetitionDistance', 'PromoOpen' ] # Features used for prediction feature_engineering(rossman) feature_engineering(rossman_test) X = rossman[features] y = rossman.Sales # The value we are going to predict train_features, test_features, train_predict, test_predict = train_test_split( X, y) randomForest = RandomForestRegressor(n_estimators=35) randomForest.verbose = True randomForest.fit(X, y) errorValue = cross_validation.cross_val_score(randomForest, rossman[features], y, scoring='mean_squared_error', cv=3) predicted_value = randomForest.predict(test_features) predicted_value = np.array(predicted_value) test_predict = np.array(test_predict) finalResult = randomForest.predict(rossman_test) outputForSubmission = pd.DataFrame(rossman_test.Id).join(