From cc021ad78a054c3055122c2240e1d5263991a530 Mon Sep 17 00:00:00 2001 From: altteam Date: Thu, 23 Nov 2023 00:35:34 +0400 Subject: [PATCH] laba 3 ready!!! --- verina_daria_lab_3/README.md | 41 ++ verina_daria_lab_3/laba3_economica.py | 40 ++ verina_daria_lab_3/laba3_titanic.py | 31 + verina_daria_lab_3/result1.png | Bin 0 -> 31710 bytes verina_daria_lab_3/result2.png | Bin 0 -> 25757 bytes verina_daria_lab_3/titanic_data.csv | 892 ++++++++++++++++++++++++++ 6 files changed, 1004 insertions(+) create mode 100644 verina_daria_lab_3/README.md create mode 100644 verina_daria_lab_3/laba3_economica.py create mode 100644 verina_daria_lab_3/laba3_titanic.py create mode 100644 verina_daria_lab_3/result1.png create mode 100644 verina_daria_lab_3/result2.png create mode 100644 verina_daria_lab_3/titanic_data.csv diff --git a/verina_daria_lab_3/README.md b/verina_daria_lab_3/README.md new file mode 100644 index 0000000..cbdc59a --- /dev/null +++ b/verina_daria_lab_3/README.md @@ -0,0 +1,41 @@ +# Лабораторная работа 3. Деревья решений +### Задание на лабораторную: +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 7.** +Ticket,Fare,Cabin + +Часть 2. Решите с помощью библиотечной реализации дерева решений задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету «Методы искусственного интеллекта» на 99% ваших данных. Проверьте работу модели на оставшемся проценте, сделайте вывод. +*** +### Как запустить лабораторную работу: +Для запуска первой части лабораторной работы необходимо открыть файл `laba3_titanic.py`, нажать на ПКМ и в выпадающем списке выбрать опцию "Run". Для запуска второй части - то же самое, но файл "laba3_economica" +*** +### Технологии: +**NumPy (Numerical Python)** - это библиотека для научных вычислений в Python, которая обеспечивает эффективные вычисления и манипуляции с данными. + +**Pandas** - это библиотека на языке Python, которая предоставляет удобные и эффективные инструменты для обработки и анализа данных. Она предоставляет высокоуровневые структуры данных, такие как DataFrame, которые позволяют легко и гибко работать с табличными данными. + +**Scikit-learn (Sklearn)** - это библиотека для языка программирования Python, которая предоставляет инструменты для разработки и применения различных алгоритмов машинного обучения, включая классификацию, регрессию, кластеризацию, снижение размерности и многое другое. Scikit-learn также предлагает функции для предобработки данных, оценки моделей и выбора наилучших параметров. +*** +### Что делает лабораторная работа: +Часть 1: +- Загружается выборка из файла titanic_data.csv с помощью пакета +Pandas +- Отбирается в выборку 3 признака: Ticket,Fare,Cabin +- Определяется целевая переменная (Survived) +- Обучается решающее дерево +- Выводятся важности признаков + +Часть 2: +Код использует дерево решений для прогнозирования цены на нефть на основе страны и года. Данные разделены на тренировочный (99%) и тестовый (1%) наборы. Модель обучается на тренировочных данных и оценивается на тестовых данных. Затем модель применяется к оставшимся 1% данных для дополнительной оценки. Результаты выражены в процентах ошибки относительно среднего значения цены на нефть. +*** +### Пример выходных данных: +***Часть 1:*** +![result1.png](result1.png) + +***Часть 2:*** +![result2.png](result2.png) +*** +**Вывод**: результаты первой части лабораторной работы показали, что у выживших пассажиров наиболее важными признаками являются *Fare* и Ticket. + +Во второй части лаб. работы ошибка составила 3.1554436208840472e-30 (очень близка к нулю), это означает, что модель идеально соответствует тестовым данным. Она абсолютно точно предсказывает цены на нефть на тестовом наборе. \ No newline at end of file diff --git a/verina_daria_lab_3/laba3_economica.py b/verina_daria_lab_3/laba3_economica.py new file mode 100644 index 0000000..f4a4bfd --- /dev/null +++ b/verina_daria_lab_3/laba3_economica.py @@ -0,0 +1,40 @@ +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.tree import DecisionTreeRegressor +from sklearn.metrics import mean_squared_error +from sklearn.preprocessing import LabelEncoder + +# Загрузка данных +data = pd.read_csv('economica.csv') + +# Преобразование строковых значений в числовые +le = LabelEncoder() +data['country'] = le.fit_transform(data['country']) + +# Определение признаков и целевой переменной +X = data[['country', 'year']] +y = data['oil_prices'] + +# Разделение данных на тренировочный и тестовый наборы +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=42) + +# Создание и обучение дерева решений +clf = DecisionTreeRegressor(random_state=42) +clf.fit(X_train, y_train) + +# Прогнозирование на тестовом наборе +y_pred = clf.predict(X_test) + +# Оценка качества модели на тестовом наборе +mse = mean_squared_error(y_test, y_pred) + +# Прогнозирование на оставшемся проценте данных +X_remaining = data.drop(X_train.index) +y_remaining_true = X_remaining['oil_prices'] +X_remaining = X_remaining[['country', 'year']] +y_remaining_pred = clf.predict(X_remaining) + +# Оценка качества модели на оставшемся проценте данных +mse_remaining = mean_squared_error(y_remaining_true, y_remaining_pred) +mse_remaining_percent = (mse_remaining / y.mean()) * 100 +print(f'Среднеквадратичная ошибка на 1%: {mse_remaining}({mse_remaining_percent:.2f}%)') diff --git a/verina_daria_lab_3/laba3_titanic.py b/verina_daria_lab_3/laba3_titanic.py new file mode 100644 index 0000000..695812d --- /dev/null +++ b/verina_daria_lab_3/laba3_titanic.py @@ -0,0 +1,31 @@ +import pandas as pd +from sklearn.tree import DecisionTreeClassifier + +# Загрузка данных +data = pd.read_csv('titanic_data.csv', index_col='PassengerId') + +# Фильтрация данных +data = data.dropna(subset=['Ticket', 'Fare', 'Cabin', 'Survived']) + +# Преобразование категориальных признаков в числовые +data['Ticket'], _ = pd.factorize(data['Ticket']) +data['Cabin'], _ = pd.factorize(data['Cabin']) + +# Выделение признаков и целевой переменной +X = data[['Ticket', 'Fare', 'Cabin']] +y = data['Survived'] + +# Создание и обучение дерева решений +clf = DecisionTreeClassifier(random_state=241) +clf.fit(X, y) + +# Получение и распечатка важностей признаков +importances = clf.feature_importances_ + +# Связываем важности с именами признаков +feature_importance = dict(zip(X.columns, importances)) + +# Выводим важности признаков +print("Важности признаков:") +for feature, importance in feature_importance.items(): + print(f"{feature}: {importance}") diff --git a/verina_daria_lab_3/result1.png b/verina_daria_lab_3/result1.png new file mode 100644 index 0000000000000000000000000000000000000000..b9dec9688e02d99f2628be248e89c0cb66ab56fe GIT binary patch literal 31710 zcmbrlc|6qX|Nmcy6WYd7*~%mb6%vj;BXrsnm9jIIP{tU`v5uK35wg|0ELoDANX(3- zvCmAhm0dBzSjG~{82b!n{k@#`_xty)#~5DM&0MbK`FuRD>;6c(VRPxgUWL88 zcI`TFRFEIL)V4(o$SG?!Paegi?b3ees^Gx%Y~Jif72|V$VJl8)y|pg=!}L+Hj!* zj3ceGm3iQ)V$+MtTOX=>6>CNU8$G+QH3~H}f5xwk{d>V|^B?`5N03Xz<%;z)jRM5n zy{-9q%v?yqu?Pi&k-Oif=mHir0mH4u?EBxH0lLKlPl8MrKXjc<)~Xo}84hYW@c4j% zqj|YOz>0Zj!_*31$ie^8JWi;>E2L|@`K$9Rp$>8AfBr8^@u`9Y@#bHfR^(K7#(TI( zm6HV=vXKC`>at0$T0#BAtK~nc-0aB8I?OcJJRn?L6=HdP93kHuo#~ZkuR4eB?1+zn zzKf~sUI3nx@5DH4YUS8(u4?U>fg*@3^m7)=W!H0=a82n~q3Xj|vxRd~|9S<)j%XxMl zSn!98ipcw z#%#H1{jr!*q{nc~dXG9-6Lt%RTyM(B^mqC^w+$i|+c9FsfU`+xfbZ0y;Febd?pB`J2ut&?UjVnQxhD zDtN(U1Pql5tebi~HJ6KUpF=$*;}UoUYpBAtw%7R(M8nVGsdhy~p6NudZO_d{_amp5 zsVi&k_Y2t?E)(4J+0??-Gatf7#v*4@6Gj6Y;dPvaGR#n@4Sk}|p^XN0Z@pLA5qJ$p z#5G$RlhAG(v-@ts5PzW-s7o=7ZvzpSgL{TBk^VYf&%r?hDCj@Pa1gg8@tEjn`#)|3%F>66% z!5uwF72IN)>5?32YdYDp0L6Y;yA%_71HIm3m{9U6+CPPUs~c)|e6_5VP?NHP>elHf zujpHE79^>^GU{!<7DcTY&EA$wR}+XTRB+qu%26e$VxqYAFCg zwmf2v=Pe)2jW>vE>N6L{)UEfLc0~DXcI)*W?SnGrr<1jn@TX#+&y1IXNm2rk(d2_K zky!r?Ot0~Lck1$+*9FsT2LFdHxHF2{8{&0Ul%bRBi@z5Y_@7Cz#$ zwbZ!W4!a;>80Jm_Mr(#JV#I-Ehb%|oNt^}kPT6EZ1hCSk62NFVyh_}4s`a{6UDIM! 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