From a492e2a6df3503e618346fcffb003862aca9f897 Mon Sep 17 00:00:00 2001 From: yulia Date: Thu, 2 Nov 2023 20:02:38 +0400 Subject: [PATCH] podkorytova_yulia_lab_3 --- podkorytova_yulia_lab_3/README.md | 45 + podkorytova_yulia_lab_3/dataset.csv | 1205 ++++++++++++++++++++++++ podkorytova_yulia_lab_3/lr3.py | 79 ++ podkorytova_yulia_lab_3/result1.JPG | Bin 0 -> 23792 bytes podkorytova_yulia_lab_3/result2.JPG | Bin 0 -> 23611 bytes podkorytova_yulia_lab_3/titanic.csv | 1310 +++++++++++++++++++++++++++ 6 files changed, 2639 insertions(+) create mode 100644 podkorytova_yulia_lab_3/README.md create mode 100644 podkorytova_yulia_lab_3/dataset.csv create mode 100644 podkorytova_yulia_lab_3/lr3.py create mode 100644 podkorytova_yulia_lab_3/result1.JPG create mode 100644 podkorytova_yulia_lab_3/result2.JPG create mode 100644 podkorytova_yulia_lab_3/titanic.csv diff --git a/podkorytova_yulia_lab_3/README.md b/podkorytova_yulia_lab_3/README.md new file mode 100644 index 0000000..0965c75 --- /dev/null +++ b/podkorytova_yulia_lab_3/README.md @@ -0,0 +1,45 @@ +# 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 20.** +Pclass, Parch, Fare + +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 лабораторной работы необходимо открыть файл `lr3.py`, нажать на ПКМ и в выпадающем списке выбрать опцию "Run". +*** +### 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: +В первой части лабораторной работе загружается выборка из файла `titanic.csv` с помощью пакета *Pandas*, пустые значения убираются из выборки. +Далее в выборку отбираются 3 признака *(Pclass, Parch, Fare)* и определяется целевая переменная *(Survived)*. +После обучается решающее дерево классификации с параметром *random_state=241* и остальными параметрами по умолчанию. +Результатом первой части лабораторной работы являются определение двух наиболее важных признаков у выживших пассажиров. + +Во второй части лабораторной работе загружается выборка из файла `dataset.csv` с помощью пакета *Pandas*, тип устройства и уровень гибкости приводятся в числовому виду. +Далее в выборку отбираются 2 признака *(Age и Device)* и определяется целевая переменная *(Flexibility Level)*. +После данные разделяются на обучающие и тестовые выборки, создается и обучается дерево регрессии с параметрами по умолчанию. +Результатом второй части лабораторной работы являются определение зависимости уровня гибкости от возраста и типа устройства и оценка точности модели. +*** +### Пример выходных данных: +***Часть 1:*** +выводятся первые 5 записей таблицы со столбцами по варианту, важности признаков и 2 наиболее важных признака из трех. +![](result1.JPG) + +***Часть 2:*** +выводятся первые 5 записей таблицы со столбцами по варианту, важности признаков, 2 наиболее важных признака из трех и средняя квадратичная ошибка. +![](result2.JPG) +*** +**Вывод**: результаты первой части лабораторной работы показали, что у выживших пассажиров наиболее важными признаками являются *Fare* и *Parch*, причем *Fare* оказался самым важным признаком. + +По результатам второй части лабораторной можно сказать, что уровень гибкости праткически одинаково зависит как от типа устройства, с которого человек работает, так и от возраста учащегося. +*Device* оказался более важным признаком, чем *Age*, но стоит сделать замечание, тип устройства и уровень гибкости были преобразованы к числовому виду, характер данных был искажен, +так как ранее объекты столбцов не могли быть математически сравнимы между собой, а после преобразований эта характеристика у них появилась. +Посчитанная среднеквадратичная ошибка находится ближе к 0, чем к 1, это говорит о высоком качестве модели. \ No newline at end of file diff --git a/podkorytova_yulia_lab_3/dataset.csv b/podkorytova_yulia_lab_3/dataset.csv new file mode 100644 index 0000000..2a89d7b --- /dev/null +++ b/podkorytova_yulia_lab_3/dataset.csv @@ -0,0 +1,1205 @@ +Education Level,Institution Type,Gender,Age,Device,IT Student,Location,Financial Condition,Internet Type,Network Type,Flexibility Level +University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,3G,Low +University,Private,Male,23,Computer,Yes,Town,Mid,Wifi,4G,Moderate +University,Private,Female,23,Mobile,No,Town,Mid,Mobile Data,4G,Moderate 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@@ -0,0 +1,79 @@ +import pandas as pd +from sklearn.metrics import mean_squared_error +from sklearn.model_selection import train_test_split +from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor +import numpy as np + +# 1 часть лабораторной работы +# Вариант 20. Pclass, Parch, Fare +def part_one(): + data = pd.read_csv('titanic.csv', index_col='Passengerid') + # выгрузка непустых данных + data = data.loc[(np.isnan(data['Pclass']) == False) & (np.isnan(data['Fare']) == False) & (np.isnan(data['Parch']) == False) & (np.isnan(data['Survived']) == False)] + # отбор нужных столбцов + corr = data[['Pclass', 'Parch', 'Fare']] + # респечатка первых 5 строк данных + print(corr.head()) + # определение целевой переменной + y = data['Survived'] + # 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) + top_importances = importances.argsort()[-2:][::-1] + print("Наиболее важные признаки:", corr.columns[top_importances][0], "и", corr.columns[top_importances][1]) + +# функция для приведения типа мобильного устройства к числу +def device_to_bool(device): + if device == "Computer": + return 0 + elif device == "Mobile": + return 1 + elif device == "Tab": + return 2 + +# функция для приведения уровня гибкости к числу +def flexibility_level_to_bool(flexibility_level): + if flexibility_level == "Low": + return 0 + elif flexibility_level == "Moderate": + return 1 + elif flexibility_level == "High": + return 2 + +# 2 часть лабораторной работы +# Вариант 20. Зависимость уровня гибкости от возраста и устройства, с которого человек работает +def part_two(): + data = pd.read_csv('dataset.csv') + # приведение типа мобильного устройства к числу + data['Device'] = data['Device'].apply(device_to_bool) + # приведение уровня гибкости к числу + data['Flexibility Level'] = data['Flexibility Level'].apply(flexibility_level_to_bool) + # отбор нужных столбцов + X = data[['Age', 'Device']] + # респечатка первых 5 строк данных + print(X.head()) + # определение целевой переменной + y = data['Flexibility Level'] + # 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) + # создание и обучение дерева регрессии + tree_reg = DecisionTreeRegressor() + tree_reg.fit(X_train, y_train) + # получение и распечатка важностей признаков + importances = tree_reg.feature_importances_ + print(importances) + top_importances = importances.argsort()[-2:][::-1] + print("Наиболее важные 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