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потребительских настроений по отношению к экономике +# Функция для отображения дерева решений +def tree_visual(): + # Преобразование типов питания к числам + # Инициализация LabelEncoder + label_encoder = LabelEncoder() + + # Преобразование столбцов в числовые значения для всего фрейма + # list_col = ['hotel', 'arrival_date_month', 'meal', 'country', 'market_segment', 'distribution_channel', 'reserved_room_type', 'deposit_type', 'customer_type', 'reservation_status', 'assigned_room_type'] + # for i in list_col: + # df[i] = label_encoder.fit_transform(df[i]) + #df[list_col] = label_encoder.fit_transform(df[list_col]) + + #для всего фрейма + # mean_value = round(df['agent'].mean()) + # df['agent'].fillna(mean_value, inplace=True) + + # Количество элементов для обучения (99%) + count_to_train = round(len(df) * 0.99) + # Количество элементов для тестирования (1%) + count_to_test = len(df) - count_to_train + + # Набор данных для обучения для всего фрейма + # train_df = df.head(count_to_train).copy().drop(columns=['reservation_status_date', 'MO_YR']) + train_df = df.head(count_to_train).copy() + + # Тип питания + y = train_df.copy()['meal'] + + # Уровень инфляции и потребительские настроения по отношению к экономике для всего фрейма + # x = train_df.copy().drop(columns=['meal']) + x = train_df.copy()[['INFLATION', 'CSMR_SENT']] + # Создание модели дерева решений + model = DecisionTreeClassifier() + + # Обучение модели + model.fit(x, y) + + # Проверка модели для всего фрейма + #test_df = df.tail(count_to_test).copy().drop(columns=['reservation_status_date', 'MO_YR']) + test_df = df.tail(count_to_test)[['INFLATION', 'CSMR_SENT', 'meal']] + + y_test = test_df.copy()['meal'] + x_test = test_df.copy().drop(columns=['meal']) + + prediction = model.score(x_test, y_test) + + print('Качество дерева решений: ', prediction * 100, '%') + # Визуализация дерева решений + plt.figure(figsize=(12, 8)) + plot_tree(model, feature_names=['INFLATION', 'CSMR_SENT'], filled=True) + + # Сохранение графика в файл .png + plt.savefig('decision_tree.png', dpi=300) + plt.show() + + res = sorted(dict(zip(list(x.columns), model.feature_importances_)).items(), + key=lambda el: el[1], reverse=True) + + flag = 0 + print('feature importance:') + for val in res: + print(val[0]+" - "+str(val[1])) + + return + + +tree_visual() diff --git a/sergeev_evgenii_lab_3/readme.md b/sergeev_evgenii_lab_3/readme.md new file mode 100644 index 0000000..f94b68e --- /dev/null +++ b/sergeev_evgenii_lab_3/readme.md @@ -0,0 +1,23 @@ +# Лабораторная работа 3. Деревья решений +## Задание +1. По данным курсовой работы с помощью дерева решений +решить выбранную задачу: классификация - зависимость типа обеда +от инфляции и потребительских настроений по отношению к экономике + +Ссылка на мой датасет: https://www.kaggle.com/datasets/mlardi/hotel-booking-demand-with-economic-indicators +### Запуск программы +Файл lab3.py содержит и запускает программу. + +### Описание программы +Программа состоит из двух частей: +1. Она считывает файл с данными о двух отелях: City Hotel и Resort Hotel. Содержит множество различных метрик +2. Далее загружает необходимые столбцы и проводит создание модели +3. Выводит % правильных предсказаний и создает изображение дерева решений +### Результаты тестирования +По результатам тестирования, можно сказать следующее: + +* Дерево решений показывает неплохие результаты, 66%. +* Оценка важности признаков показывает наиболее важным признаком уровень инфляции, затем уже уровень потребительских настроений по отношению к экономике +* В самом датафрейме есть еще много различных признаков, но они не так сильно влияют на дерево решений + +Итого. Дерево решений даёт неплохие результаты при классификации.