diff --git a/.idea/workspace.xml b/.idea/workspace.xml new file mode 100644 index 0000000..2dd87a1 --- /dev/null +++ b/.idea/workspace.xml @@ -0,0 +1,135 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1695412818437 + + + + + + + \ No newline at end of file diff --git a/zavrazhnova_svetlana_lab_3/README.md b/zavrazhnova_svetlana_lab_3/README.md new file mode 100644 index 0000000..5dc1520 --- /dev/null +++ b/zavrazhnova_svetlana_lab_3/README.md @@ -0,0 +1,58 @@ +# Задание +- Часть 1. По данным о пассажирах Титаника решите задачу классификации (с помощью дерева решений), в которой по различным характеристикам пассажиров требуется найти у выживших пассажиров два наиболее важных признака из трех рассматриваемых (по варианту). + Вариант: Pclass, Sex, Embarked +- Часть 2. Решите с помощью библиотечной реализации дерева решений задачу: Запрограммировать дерево решений как минимум на 99% ваших данных для задачи: Зависимость Мошенничества (fraud_label) от возраста (Age) и пола (gender) . Проверьте работу модели на оставшемся проценте, сделайте вывод. + + +### Как запустить лабораторную работу: +1 часть ЛР запускается в файле `zavrazhnova_svetlana_lab_3_1.py` через Run, в консоли должны появится вычисления. + +2 часть ЛР запускается в файле `zavrazhnova_svetlana_lab_3_2.py` через Run, в консоли должны появится вычисления. + +### Технологии +В библиотеке scikit-learn решающие деревья реализованы +в классах sklearn.tree.DecisionTreeСlassifier (для классификации) +и sklearn.tree.DecisionTreeRegressor (для регрессии). + +### Что делает лабораторная: +Часть 1: +- Загружается выборка из файла titanic.csv с помощью пакета +Pandas +- Отбирается в выборку 3 признака: класс пассажира +(Pclass), его пол (Sex) и Embarked. +- Определяется целевая переменная (2urvived) +- Обучается решающее дерево с параметром random_state=241 +и остальными параметрами по умолчанию (речь идет +о параметрах конструктора DecisionTreeСlassifier) +- Выводятся важности признаков + +Часть 2: +- Загружается выборка из файла fraud_dataset.csv с помощью пакета +Pandas +- Отбирается в выборку 2 признака: возраст жертвы мошенничества +(age) и его пол (gender). +- Определяется целевая переменная (fraud_label) +- Резделяются данные на обучающую и тестовую +- Обучается решающее дерево классификацией DecisionTreeСlassifier и регрессией DecisionTreeRegressor +- Выводятся важности признаков, предсказание значений на тестовой выборке и оценка производительности модели + +### Пример выходных значений: +Часть 1: Выводится список из первых 5 записей в таблице с нужными столбцами и важности признаков по каждому классу + +![res_lab3_1](res_lab3_1.png) + +Часть 2: + +![list_lab3_2](list_lab3_2.png) +![res_lab3_2](res_lab3_2.png) + +### Вывод по 2 части ЛР: +Исходя из этих результатов, можно сделать вывод, что для задачи предсказания мошенничества (fraud_label) на основе возраста (age) и пола (gender) лучше подходит модель дерева классификации. Она показала 100% точность на тестовой выборке, а также позволяет определить важности признаков. + +С другой стороны, дерево регрессии показало неопределенный R^2 score и имеет значительно большую среднеквадратичную ошибку, что говорит о том, что эта модель не подходит для данной задачи. + +Результат regression score = nan происходит из-за того, что при test_size=0.01 выделенная тестовая выборка содержит меньше двух образцов. Это приводит к неопределенности значения коэффициента детерминации R^2, который вычисляется в случае регрессии. Таким образом, значение score regression становится "nan". + +Однако, в случае классификации, где используется DecisionTreeClassifier, в test_size=0.01 попадает достаточное количество образцов для оценки производительности модели. Поэтому значение score classifier равно 1.0. + +`Таким образом`, для задачи классификации мошенничества на основе возраста и пола более предпочтительна модель дерева классификации. diff --git a/zavrazhnova_svetlana_lab_3/fraud_dataset.csv b/zavrazhnova_svetlana_lab_3/fraud_dataset.csv new file mode 100644 index 0000000..f23b91d --- /dev/null +++ b/zavrazhnova_svetlana_lab_3/fraud_dataset.csv @@ -0,0 +1,87 @@ +transaction_id,transaction_amount,location,merchant,age,gender,fraud_label +1,1000.00,New York,ABC Corp,35,M,0 +2,500.00,Chicago,XYZ Inc,45,F,0 +3,2000.00,Los Angeles,ABC Corp,28,M,1 +4,1500.00,San Francisco,XYZ Inc,30,F,0 +5,800.00,Chicago,ABC Corp,50,F,0 +6,3000.00,New York,XYZ Inc,42,M,1 +7,1200.00,San Francisco,ABC Corp,55,F,0 +8,900.00,Los Angeles,XYZ Inc,37,M,0 +9,2500.00,Chicago,ABC Corp,33,F,1 +10,1800.00,New York,XYZ Inc,48,M,0 +11,750.00,San Francisco,ABC Corp,29,F,0 +12,2200.00,Chicago,XYZ Inc,51,M,0 +13,900.00,New York,ABC Corp,40,F,0 +14,1600.00,Los Angeles,XYZ Inc,26,M,0 +15,3000.00,San Francisco,ABC Corp,45,F,1 +16,1200.00,Chicago,XYZ Inc,34,M,0 +17,800.00,New York,ABC Corp,47,F,0 +18,1900.00,Los Angeles,XYZ Inc,32,M,0 +19,1100.00,San Francisco,ABC Corp,52,F,0 +20,4000.00,Chicago,XYZ Inc,38,M,1 +21,900.00,New York,ABC Corp,31,F,0 +22,1700.00,Los Angeles,XYZ Inc,49,M,0 +23,1000.00,San Francisco,ABC Corp,36,F,0 +24,2300.00,Chicago,XYZ Inc,27,M,1 +25,950.00,New York,ABC Corp,41,F,0 +26,1400.00,Los Angeles,XYZ Inc,54,M,0 +27,2800.00,San Francisco,ABC Corp,39,F,1 +28,1100.00,Chicago,XYZ Inc,44,M,0 +29,750.00,New York,ABC Corp,30,F,0 +30,2000.00,Los Angeles,XYZ Inc,46,M,0 +31,1250.00,San Francisco,ABC Corp,35,F,0 +32,2100.00,Chicago,XYZ Inc,43,M,0 +33,950.00,New York,ABC Corp,56,F,0 +34,1800.00,Los Angeles,XYZ Inc,29,M,0 +35,3200.00,San Francisco,ABC Corp,48,F,1 +36,1300.00,Chicago,XYZ Inc,37,M,0 +37,900.00,New York,ABC Corp,51,F,0 +38,2000.00,Los Angeles,XYZ Inc,33,M,0 +39,1050.00,San Francisco,ABC Corp,42,F,0 +40,2400.00,Chicago,XYZ Inc,26,M,0 +41,800.00,New York,ABC Corp,45,F,0 +42,1500.00,Los Angeles,XYZ Inc,31,M,0 +43,2800.00,San Francisco,ABC Corp,50,F,1 +44,1350.00,Chicago,XYZ Inc,28,M,0 +45,920.00,New York,ABC Corp,47,F,0 +46,2000.00,Los Angeles,XYZ Inc,36,M,0 +47,1125.00,San Francisco,ABC Corp,52,F,0 +48,1900.00,Chicago,XYZ Inc,38,M,1 +49,850.00,New York,ABC Corp,32,F,0 +50,1750.00,Los Angeles,XYZ Inc,49,M,0 +51,950.00,San Francisco,ABC Corp,27,F,0 +52,2300.00,Chicago,XYZ Inc,41,M,0 +53,850.00,New York,ABC Corp,54,F,0 +54,1600.00,Los Angeles,XYZ Inc,39,M,0 +55,3000.00,San Francisco,ABC Corp,46,F,1 +56,1250.00,Chicago,XYZ Inc,35,M,0 +57,800.00,New York,ABC Corp,56,F,0 +58,2200.00,Los Angeles,XYZ Inc,29,M,0 +59,1050.00,San Francisco,ABC Corp,48,F,0 +60,4000.00,Chicago,XYZ Inc,37,M,1 +61,950.00,New York,ABC Corp,30,F,0 +62,1700.00,Los Angeles,XYZ Inc,49,M,0 +63,1000.00,San Francisco,ABC Corp,36,F,0 +64,2800.00,Chicago,XYZ Inc,27,M,1 +65,900.00,New York,ABC Corp,41,F,0 +66,1400.00,Los Angeles,XYZ Inc,54,M,0 +67,3200.00,San Francisco,ABC Corp,39,F,1 +68,1100.00,Chicago,XYZ Inc,44,M,0 +69,750.00,New York,ABC Corp,30,F,0 +70,2000.00,Los Angeles,XYZ Inc,46,M,0 +71,1250.00,San Francisco,ABC Corp,35,F,0 +72,2100.00,Chicago,XYZ Inc,43,M,0 +73,950.00,New York,ABC Corp,56,F,0 +74,1800.00,Los Angeles,XYZ Inc,29,M,0 +75,3200.00,San Francisco,ABC Corp,48,F,1 +76,1300.00,Chicago,XYZ Inc,37,M,0 +77,900.00,New York,ABC Corp,51,F,0 +78,2000.00,Los Angeles,XYZ Inc,33,M,0 +79,1050.00,San Francisco,ABC Corp,42,F,0 +80,2400.00,Chicago,XYZ Inc,26,M,0 +81,800.00,New York,ABC Corp,45,F,0 +82,1500.00,Los Angeles,XYZ Inc,31,M,0 +83,2800.00,San Francisco,ABC Corp,50,F,1 +84,1350.00,Chicago,XYZ Inc,28,M,0 +85,920.00,New York,ABC Corp,47,F,0 +86,2000.00,Los Angeles,XYZ Inc,36,M,0 diff --git a/zavrazhnova_svetlana_lab_3/list_lab3_2.png b/zavrazhnova_svetlana_lab_3/list_lab3_2.png new file mode 100644 index 0000000..15b3a68 Binary files /dev/null and b/zavrazhnova_svetlana_lab_3/list_lab3_2.png differ diff --git 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index 0000000..5927c37 --- /dev/null +++ b/zavrazhnova_svetlana_lab_3/zavrazhnova_svetlana_lab3_2.py @@ -0,0 +1,49 @@ +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier +from sklearn.metrics import mean_squared_error +from sklearn.metrics import accuracy_score + +# Загрузка данных из csv-файла +data = pd.read_csv('fraud_dataset.csv', index_col='transaction_id') + +# Приведение пола к числовому виду +data['gender'] = data['gender'].map({'M': 0, 'F': 1}) + +# Разделение данных на признаки (X) и целевую переменную (y) +X = data[['age', 'gender']] +#респечатка первых 5 строк данных +print (X.head()) +y = data['fraud_label'] + +# Разделение данных на обучающую и тестовую выборки +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=42) + +#Решение с помощью дерева регрессии +regression_tree = DecisionTreeRegressor() +regression_tree.fit(X_train, y_train) +test_score_reg = regression_tree.score(X_test, y_test) +# Получение важности признаков +feature_importances_reg = regression_tree.feature_importances_ +# Предсказание значений на тестовой выборке regression +y_pred_reg = regression_tree.predict(X_test) +# Оценка производительности модели regression +mse = mean_squared_error(y_test, y_pred_reg) + +#Решение с помощью дерева классификации +classifier_tree = DecisionTreeClassifier() +classifier_tree.fit(X_train, y_train) +test_score_class = classifier_tree.score(X_test, y_test) +feature_importances_class = classifier_tree.feature_importances_ +y_pred_class = classifier_tree.predict(X_test) +accuracy = accuracy_score(y_test, y_pred_class) + +print("Решением с помощью дерева регрессии:") +print("score regression", test_score_reg) +print("feature_importances_reg", feature_importances_reg) +print("Mean Squared Error regression: {:.2f}".format(mse)) +print("") +print("Решением с помощью дерева классификации:") +print("score classifier", test_score_class) +print("feature_importances_class", feature_importances_class) +print("Accuracy classifier: {:.2f}%".format(accuracy * 100)) \ No newline at end of file diff --git a/zavrazhnova_svetlana_lab_3/zavrazhnova_svetlana_lab_3_1.py b/zavrazhnova_svetlana_lab_3/zavrazhnova_svetlana_lab_3_1.py new file mode 100644 index 0000000..429e82a --- /dev/null +++ b/zavrazhnova_svetlana_lab_3/zavrazhnova_svetlana_lab_3_1.py @@ -0,0 +1,21 @@ +import pandas +from sklearn.tree import DecisionTreeClassifier +import numpy as np +data = pandas.read_csv('titanic.csv', index_col='Passengerid') + +#выгрузка непустых данных +data=data.loc[(np.isnan(data['Pclass'])==False) & (np.isnan(data['Sex'])==False) +&(np.isnan(data['Embarked'])==False) & +(np.isnan(data['2urvived'])==False)] +#отбор нужных столбцов +corr = data[['Pclass', 'Sex', 'Embarked']] +#респечатка первых 5 строк данных +print (corr.head()) +#определение целевой переменной +y = data['2urvived'] +#создание и обучение дерева решений +clf = DecisionTreeClassifier(random_state=241) +clf.fit(corr, y) +#получение и распечатка важностей признаков +importances=clf.feature_importances_ +print (importances) \ No newline at end of file