From 0cb3c67eeaea5bdd80bfa5bbe12ad8a9bd589ecb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=94=D0=B0=D0=BD=D0=B8=D0=B8=D0=BB=20=D0=9F=D1=83=D1=82?= =?UTF-8?q?=D0=B8=D0=BD=D1=86=D0=B5=D0=B2?= Date: Sat, 2 Nov 2024 02:07:49 +0400 Subject: [PATCH] Lab2 --- lab_2/lab2.ipynb | 1451 ++++++++++++++++++++++++++++++++++++++++ lab_2/requirements.txt | Bin 0 -> 1426 bytes 2 files changed, 1451 insertions(+) create mode 100644 lab_2/lab2.ipynb create mode 100644 lab_2/requirements.txt diff --git a/lab_2/lab2.ipynb b/lab_2/lab2.ipynb new file mode 100644 index 0000000..e40576a --- /dev/null +++ b/lab_2/lab2.ipynb @@ -0,0 +1,1451 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Датасет №1 (Использование мобильных устройств и поведение пользователей)\n", + "Ссылка: https://www.kaggle.com/datasets/valakhorasani/mobile-device-usage-and-user-behavior-dataset\n", + "\n", + "Проблемная область: прогнозирование пользовательского поведения и сегментация пользователей для улучшения работы приложений, оптимизации потребления энергии, анализа пользовательского опыта или рекламы.\n", + "\n", + "Объекты наблюдения: пользователи мобильных устройств, чьи данные об использовании собираются и анализируются." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['User ID', 'Device Model', 'Operating System',\n", + " 'App Usage Time (min/day)', 'Screen On Time (hours/day)',\n", + " 'Battery Drain (mAh/day)', 'Number of Apps Installed',\n", + " 'Data Usage (MB/day)', 'Age', 'Gender', 'User Behavior Class'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "from imblearn.over_sampling import SMOTE\n", + "\n", + "df_mobiles = pd.read_csv(\"..//..//static//csv//user_behavior_data.csv\")\n", + "print(df_mobiles.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Атрибуты объектов:\n", + "* User ID — уникальный идентификатор пользователя.\n", + "* Device Model — модель устройства.\n", + "* Operating System — операционная система устройства.\n", + "* App Usage Time (min/day) — время использования приложений в минутах в день.\n", + "* Screen On Time (hours/day) — время включенного экрана в часах в день.\n", + "* Battery Drain (mAh/day) — потребление батареи в мАч в день.\n", + "* Number of Apps Installed — количество установленных приложений.\n", + "* Data Usage (MB/day) — объем данных в мегабайтах в день.\n", + "* Age — возраст пользователя.\n", + "* Gender — пол пользователя.\n", + "* User Behavior Class — класс поведения пользователя (категория для классификации).\n", + "\n", + "Связи между объектами:\n", + "Атрибуты, такие как модель устройства, ОС и время использования приложений, могут быть связаны с классом поведения, представляя зависимости между действиями пользователя и его характеристиками.\n", + "\n", + "Примеры бизнес-целей и целей технического проекта:\n", + "1. Оптимизация энергопотребления устройств:\n", + " - Бизнес-цель: Оптимизировать работу приложений для снижения расхода батареи, что увеличит время работы устройства и улучшит пользовательский опыт.\n", + " - Эффект: Повышение удовлетворенности клиентов и снижение вероятности перехода на конкурентные приложения.\n", + "\n", + "2. Сегментация пользователей для рекламы:\n", + " - Бизнес-цель: Создание таргетированной рекламы на основе поведения пользователей (классы поведения).\n", + " - Эффект: Увеличение конверсий и доходов от рекламных кампаний за счет более точной сегментации.\n", + "\n", + "3. Цель: Построение модели для прогнозирования расхода батареи.\n", + " - Вход: Модель устройства, ОС, время использования приложений, количество приложений, возраст.\n", + " - Целевой признак: Battery Drain (mAh/day)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проверка на пустые значения и дубликаты" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Пустые значения по столбцам:\n", + "User ID 0\n", + "Device Model 0\n", + "Operating System 0\n", + "App Usage Time (min/day) 0\n", + "Screen On Time (hours/day) 0\n", + "Battery Drain (mAh/day) 0\n", + "Number of Apps Installed 0\n", + "Data Usage (MB/day) 0\n", + "Age 0\n", + "Gender 0\n", + "User Behavior Class 0\n", + "dtype: int64\n", + "\n", + "Количество дубликатов: 0\n" + ] + } + ], + "source": [ + "null_values = df_mobiles.isnull().sum()\n", + "print(\"Пустые значения по столбцам:\")\n", + "print(null_values)\n", + "\n", + "duplicates = df_mobiles.duplicated().sum()\n", + "print(f\"\\nКоличество дубликатов: {duplicates}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Пустых значений и дубликатов нет, проверим на выбросы:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Количество выбросов в столбце 'App Usage Time (min/day)': 0\n", + "Количество выбросов в столбце 'Screen On Time (hours/day)': 0\n", + "Количество выбросов в столбце 'Battery Drain (mAh/day)': 0\n", + "Количество выбросов в столбце 'Number of Apps Installed': 0\n", + "Количество выбросов в столбце 'Data Usage (MB/day)': 0\n", + "Количество выбросов в столбце 'User Behavior Class': 0\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "columns_to_check = ['App Usage Time (min/day)', 'Screen On Time (hours/day)', 'Battery Drain (mAh/day)', 'Number of Apps Installed', 'Data Usage (MB/day)', 'User Behavior Class']\n", + "\n", + "def count_outliers(data, columns):\n", + " outliers_count = {}\n", + " for col in columns:\n", + " Q1 = data[col].quantile(0.25)\n", + " Q3 = data[col].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " \n", + " outliers = data[(data[col] < lower_bound) | (data[col] > upper_bound)]\n", + " outliers_count[col] = len(outliers)\n", + " \n", + " return outliers_count\n", + "\n", + "outliers_count = count_outliers(df_mobiles, columns_to_check)\n", + "\n", + "for col, count in outliers_count.items():\n", + " print(f\"Количество выбросов в столбце '{col}': {count}\")\n", + "\n", + "plt.figure(figsize=(15, 10))\n", + "for i, col in enumerate(columns_to_check, 1):\n", + " plt.subplot(2, 3, i)\n", + " sns.boxplot(x=df_mobiles[col])\n", + " plt.title(f'Box Plot of {col}')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Выбросов нет" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Разбиение набора данных на обучающую, контрольную и тестовую выборки" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 420\n", + "Размер контрольной выборки: 140\n", + "Размер тестовой выборки: 140\n" + ] + } + ], + "source": [ + "train_df, test_df = train_test_split(df_mobiles, test_size=0.2, random_state=42)\n", + "\n", + "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n", + "\n", + "print(\"Размер обучающей выборки:\", len(train_df))\n", + "print(\"Размер контрольной выборки:\", len(val_df))\n", + "print(\"Размер тестовой выборки:\", len(test_df))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение \"Класс поведения пользователя\" в обучающей выборке:\n", + "User Behavior Class\n", + "2 88\n", + "5 88\n", + "4 86\n", + "3 84\n", + "1 74\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Класс поведения пользователя\" в контрольной выборке:\n", + "User Behavior Class\n", + "1 35\n", + "2 29\n", + "4 26\n", + "5 25\n", + "3 25\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Класс поведения пользователя\" в тестовой выборке:\n", + "User Behavior Class\n", + "3 34\n", + "2 29\n", + "4 27\n", + "1 27\n", + "5 23\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "def check_balance(df, name):\n", + " counts = df['User Behavior Class'].value_counts()\n", + " print(f\"Распределение \\\"Класс поведения пользователя\\\" в {name}:\")\n", + " print(counts)\n", + " print()\n", + "\n", + "check_balance(train_df, \"обучающей выборке\")\n", + "check_balance(val_df, \"контрольной выборке\")\n", + "check_balance(test_df, \"тестовой выборке\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Оверсемплинг и андерсемплинг" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Оверсэмплинг:\n", + "Распределение \"Класс поведения пользователя\" в обучающей выборке:\n", + "User Behavior Class\n", + "1 88\n", + "2 88\n", + "5 88\n", + "4 88\n", + "3 88\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Класс поведения пользователя\" в контрольной выборке:\n", + "User Behavior Class\n", + "5 35\n", + "3 35\n", + "1 35\n", + "2 35\n", + "4 35\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Класс поведения пользователя\" в тестовой выборке:\n", + "User Behavior Class\n", + "4 34\n", + "1 34\n", + "2 34\n", + "3 34\n", + "5 34\n", + "Name: count, dtype: int64\n", + "\n", + "Андерсэмплинг:\n", + "Распределение \"Класс поведения пользователя\" в обучающей выборке:\n", + "User Behavior Class\n", + "1 74\n", + "2 74\n", + "3 74\n", + "4 74\n", + "5 74\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Класс поведения пользователя\" в контрольной выборке:\n", + "User Behavior Class\n", + "1 25\n", + "2 25\n", + "3 25\n", + "4 25\n", + "5 25\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Класс поведения пользователя\" в тестовой выборке:\n", + "User Behavior Class\n", + "1 23\n", + "2 23\n", + "3 23\n", + "4 23\n", + "5 23\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "from imblearn.over_sampling import RandomOverSampler\n", + "from imblearn.under_sampling import RandomUnderSampler\n", + "\n", + "def oversample(df, target_column):\n", + " X = df.drop(target_column, axis=1)\n", + " y = df[target_column]\n", + " \n", + " oversampler = RandomOverSampler(random_state=42)\n", + " x_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n", + " \n", + " resampled_df = pd.concat([x_resampled, y_resampled], axis=1) \n", + " return resampled_df\n", + "\n", + "def undersample(df, target_column):\n", + " X = df.drop(target_column, axis=1)\n", + " y = df[target_column]\n", + " \n", + " undersampler = RandomUnderSampler(random_state=42)\n", + " x_resampled, y_resampled = undersampler.fit_resample(X, y) # type: ignore\n", + " \n", + " resampled_df = pd.concat([x_resampled, y_resampled], axis=1)\n", + " return resampled_df\n", + "\n", + "train_df_oversampled = oversample(train_df, 'User Behavior Class')\n", + "val_df_oversampled = oversample(val_df, 'User Behavior Class')\n", + "test_df_oversampled = oversample(test_df, 'User Behavior Class')\n", + "\n", + "train_df_undersampled = undersample(train_df, 'User Behavior Class')\n", + "val_df_undersampled = undersample(val_df, 'User Behavior Class')\n", + "test_df_undersampled = undersample(test_df, 'User Behavior Class')\n", + "\n", + "print(\"Оверсэмплинг:\")\n", + "check_balance(train_df_oversampled, \"обучающей выборке\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке\")\n", + "\n", + "print(\"Андерсэмплинг:\")\n", + "check_balance(train_df_undersampled, \"обучающей выборке\")\n", + "check_balance(val_df_undersampled, \"контрольной выборке\")\n", + "check_balance(test_df_undersampled, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Датасет №2 (качество воды) \n", + "\n", + "Ссылка: https://www.kaggle.com/datasets/adityakadiwal/water-potability\n", + "\n", + "Проблемная область: качество питьевой воды и факторы, влияющие на ее безопасность для здоровья.\n", + "\n", + "Объекты наблюдения: водоемы, содержащие воду разного качества." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['ph', 'Hardness', 'Solids', 'Chloramines', 'Sulfate', 'Conductivity',\n", + " 'Organic_carbon', 'Trihalomethanes', 'Turbidity', 'Potability'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "# вывод всех столбцов\n", + "df = pd.read_csv(\"..//..//static//csv//water_potability.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Атрибуты: \n", + "* ph – параметр для оценки кислотно-щелочного баланса воды;\n", + "* Hardness – жесткость воды, определяемая содержанием кальция и магния;\n", + "* Solids – общее количество растворенных веществ, указывающее на минерализацию воды;\n", + "* Chloramines – концентрация хлора и хлораминов, использующихся для дезинфекции воды;\n", + "* Sulfate – содержание сульфатов, присутствующих в воде;\n", + "* Conductivity – электропроводность воды, измеряющая уровень ионов в растворе;\n", + "* Organic_carbon – уровень органического углерода, происходящего из разлагающихся органических веществ;\n", + "* Trihalomethanes – концентрация трихалометанов, образующихся при обработке хлором;\n", + "* Turbidity – мутность воды, указывающая на количество взвешенных твердых частиц;\n", + "* Potability – показатель пригодности воды для питья (1 – пригодна, 0 – непригодна).\n", + "\n", + "Примеры бизнес целей и целей технического проекта:\n", + "1. Совершенствование систем очистки воды.\n", + " * Бизнес-цель: разработка и внедрение инновационных технологий очистки воды, чтобы уменьшить расходы на водоочистные сооружения и повысить их эффективность.\n", + " * Цель технического проекта: проектирование и тестирование новых фильтров и процессов очистки на основе данных о загрязнении воды.\n", + "2. Интеграция данных для управления водными ресурсами.\n", + " * Бизнес-цель: объединение данных о водных ресурсах для комплексного анализа и управления, что позволит более эффективно распределять ресурсы.\n", + " * Цель технического проекта: разработка платформы для интеграции данных с различных датчиков и источников информации, использующей машинное обучение для прогнозирования изменений качества воды.\n", + "3. Повышение осведомленности о качестве воды.\n", + " * Бизнес-цель: информирование населения о состоянии водоемов и возможных рисках для здоровья.\n", + " * Цель технического проекта: разработка платформы для публичного доступа к данным о качестве воды.\n", + "\n", + "Входные данные и целевой признак могут быть следующими:\n", + "1. Входные данные:\n", + " * pH значение;\n", + " * Жесткость воды;\n", + " * Общее количество растворенных веществ;\n", + " * Концентрация хлора и хлораминов;\n", + " * Содержание сульфатов;\n", + " * Электропроводность;\n", + " * Уровень органического углерода;\n", + " * Концентрация трихалометанов;\n", + " * Мутность воды.\n", + "2. Целевой признак:\n", + " * Пригодность воды для питья.\n", + "\n", + "Актуальность: анализ качества питьевой воды и факторов, влияющих на ее безопасность, играет очень важную роль в охране здоровья населения. Так мониторинг факторов, влияющих на качество воды, позволит предотвращать вспышки заболеваний и способствовать повышению уровня жизни.\n", + "\n", + "### Проверяем на выбросы" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Количество выбросов в столбце 'Hardness': 83\n", + "Количество выбросов в столбце 'Solids': 47\n", + "Количество выбросов в столбце 'Organic_carbon': 25\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "columns_to_check = ['Hardness', 'Solids', 'Organic_carbon']\n", + "\n", + "def count_outliers(df, columns):\n", + " outliers_count = {}\n", + " for col in columns:\n", + " Q1 = df[col].quantile(0.25)\n", + " Q3 = df[col].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " \n", + " outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n", + " outliers_count[col] = len(outliers)\n", + " \n", + " return outliers_count\n", + "\n", + "outliers_count = count_outliers(df, columns_to_check)\n", + "\n", + "for col, count in outliers_count.items():\n", + " print(f\"Количество выбросов в столбце '{col}': {count}\")\n", + "\n", + "plt.figure(figsize=(15, 10))\n", + "for i, col in enumerate(columns_to_check, 1):\n", + " plt.subplot(2, 2, i)\n", + " sns.boxplot(x=df[col])\n", + " plt.title(f'Box Plot of {col}')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "В каждом из выбранных столбцов присутствуют выбросы. Очистим их." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Количество удаленных строк: 145\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Выбираем столбцы для очистки\n", + "columns_to_clean = ['Hardness', 'Solids', 'Organic_carbon']\n", + "\n", + "# Функция для удаления выбросов\n", + "def remove_outliers(df, columns):\n", + " for col in columns:\n", + " Q1 = df[col].quantile(0.25)\n", + " Q3 = df[col].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " \n", + " # Удаляем строки, содержащие выбросы\n", + " df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n", + " \n", + " return df\n", + "\n", + "# Удаляем выбросы\n", + "df_cleaned = remove_outliers(df, columns_to_clean)\n", + "\n", + "# Выводим количество удаленных строк\n", + "print(f\"Количество удаленных строк: {len(df) - len(df_cleaned)}\")\n", + "\n", + "# Создаем диаграммы размаха для очищенных данных\n", + "plt.figure(figsize=(15, 6))\n", + "\n", + "# Диаграмма размаха для Hardness\n", + "plt.subplot(1, 3, 1)\n", + "sns.boxplot(x=df_cleaned['Hardness'])\n", + "plt.title('Box Plot of Hardness (Cleaned)')\n", + "plt.xlabel('Hardness')\n", + "\n", + "# Диаграмма размаха для Solids\n", + "plt.subplot(1, 3, 2)\n", + "sns.boxplot(x=df_cleaned['Solids'])\n", + "plt.title('Box Plot of Solids (Cleaned)')\n", + "plt.xlabel('Solids')\n", + "\n", + "# Диаграмма размаха для Organic_carbon\n", + "plt.subplot(1, 3, 3)\n", + "sns.boxplot(x=df_cleaned['Organic_carbon'])\n", + "plt.title('Box Plot of Organic_carbon (Cleaned)')\n", + "plt.xlabel('Organic_carbon')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Сохраняем очищенный датасет\n", + "df_cleaned.to_csv(\"..//..//static//csv//water_potability_cleaned.csv\", index=False)\n", + "df = df_cleaned" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Теперь проверим на пустые значения" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ph 466\n", + "Hardness 0\n", + "Solids 0\n", + "Chloramines 0\n", + "Sulfate 746\n", + "Conductivity 0\n", + "Organic_carbon 0\n", + "Trihalomethanes 154\n", + "Turbidity 0\n", + "Potability 0\n", + "dtype: int64\n", + "\n", + "ph True\n", + "Hardness False\n", + "Solids False\n", + "Chloramines False\n", + "Sulfate True\n", + "Conductivity False\n", + "Organic_carbon False\n", + "Trihalomethanes True\n", + "Turbidity False\n", + "Potability False\n", + "dtype: bool\n", + "\n", + "ph процент пустых значений: %14.88\n", + "Sulfate процент пустых значений: %23.83\n", + "Trihalomethanes процент пустых значений: %4.92\n" + ] + } + ], + "source": [ + "# Количество пустых значений признаков\n", + "print(df.isnull().sum())\n", + "\n", + "print()\n", + "\n", + "# Есть ли пустые значения признаков\n", + "print(df.isnull().any())\n", + "\n", + "print()\n", + "\n", + "# Процент пустых значений признаков\n", + "for i in df.columns:\n", + " null_rate = df[i].isnull().sum() / len(df) * 100\n", + " if null_rate > 0:\n", + " print(f\"{i} процент пустых значений: %{null_rate:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "В трех столбцах встречается большое число пустых значений. Поэтому вместо удаления заменим их значения на медиану." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Количество пустых значений в каждом столбце после замены:\n", + "ph 0\n", + "Hardness 0\n", + "Solids 0\n", + "Chloramines 0\n", + "Sulfate 0\n", + "Conductivity 0\n", + "Organic_carbon 0\n", + "Trihalomethanes 0\n", + "Turbidity 0\n", + "Potability 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Замена значений\n", + "df[\"ph\"] = df[\"ph\"].fillna(df[\"ph\"].median())\n", + "df[\"Sulfate\"] = df[\"Sulfate\"].fillna(df[\"Sulfate\"].median())\n", + "df[\"Trihalomethanes\"] = df[\"Trihalomethanes\"].fillna(df[\"Trihalomethanes\"].median())\n", + "\n", + "# Проверка на пропущенные значения после замены\n", + "missing_values_after_drop = df.isnull().sum()\n", + "\n", + "# Вывод результатов после замены\n", + "print(\"\\nКоличество пустых значений в каждом столбце после замены:\")\n", + "print(missing_values_after_drop)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Выборки" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: (1878, 9)\n", + "Размер контрольной выборки: (626, 9)\n", + "Размер тестовой выборки: (627, 9)\n" + ] + } + ], + "source": [ + "X = df.drop('Potability', axis=1)\n", + "y = df['Potability']\n", + "\n", + "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42)\n", + "\n", + "X_val, X_test, y_val, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42)\n", + "\n", + "print(\"Размер обучающей выборки:\", X_train.shape)\n", + "print(\"Размер контрольной выборки:\", X_val.shape)\n", + "print(\"Размер тестовой выборки:\", X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение классов в обучающей выборке:\n", + "Potability\n", + "0 0.613951\n", + "1 0.386049\n", + "Name: proportion, dtype: float64\n", + "\n", + "Распределение классов в контрольной выборке:\n", + "Potability\n", + "0 0.616613\n", + "1 0.383387\n", + "Name: proportion, dtype: float64\n", + "\n", + "Распределение классов в тестовой выборке:\n", + "Potability\n", + "0 0.614035\n", + "1 0.385965\n", + "Name: proportion, dtype: float64\n" + ] + } + ], + "source": [ + "def analyze_balance(y_train, y_val, y_test):\n", + " print(\"Распределение классов в обучающей выборке:\")\n", + " print(y_train.value_counts(normalize=True))\n", + " \n", + " print(\"\\nРаспределение классов в контрольной выборке:\")\n", + " print(y_val.value_counts(normalize=True))\n", + " \n", + " print(\"\\nРаспределение классов в тестовой выборке:\")\n", + " print(y_test.value_counts(normalize=True))\n", + "\n", + "analyze_balance(y_train, y_val, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Используем метод оверсемплинг" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Оверсэмплинг:\n", + "Распределение \"Комбинированный расход топлива\" в обучающей выборке:\n", + "combination_mpg\n", + "21 32\n", + "22 32\n", + "25 32\n", + "19 32\n", + "29 32\n", + "23 32\n", + "28 32\n", + "18 32\n", + "27 32\n", + "20 32\n", + "16 32\n", + "30 32\n", + "32 32\n", + "31 32\n", + "24 32\n", + "26 32\n", + "17 32\n", + "36 32\n", + "34 32\n", + "33 32\n", + "14 32\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Комбинированный расход топлива\" в контрольной выборке:\n", + "combination_mpg\n", + "20 17\n", + "19 17\n", + "17 17\n", + "27 17\n", + "22 17\n", + "26 17\n", + "24 17\n", + "32 17\n", + "21 17\n", + "18 17\n", + "30 17\n", + "23 17\n", + "29 17\n", + "28 17\n", + "34 17\n", + "25 17\n", + "14 17\n", + "33 17\n", + "31 17\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Комбинированный расход топлива\" в тестовой выборке:\n", + "combination_mpg\n", + "28 14\n", + "32 14\n", + "30 14\n", + "23 14\n", + "20 14\n", + "26 14\n", + "21 14\n", + "18 14\n", + "27 14\n", + "25 14\n", + "22 14\n", + "19 14\n", + "29 14\n", + "24 14\n", + "31 14\n", + "17 14\n", + "Name: count, dtype: int64\n", + "\n", + "Андерсэмплинг:\n", + "Распределение \"Комбинированный расход топлива\" в обучающей выборке:\n", + "combination_mpg\n", + "14 1\n", + "16 1\n", + "17 1\n", + "18 1\n", + "19 1\n", + "20 1\n", + "21 1\n", + "22 1\n", + "23 1\n", + "24 1\n", + "25 1\n", + "26 1\n", + "27 1\n", + "28 1\n", + "29 1\n", + "30 1\n", + "31 1\n", + "32 1\n", + "33 1\n", + "34 1\n", + "36 1\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Комбинированный расход топлива\" в контрольной выборке:\n", + "combination_mpg\n", + "14 1\n", + "17 1\n", + "18 1\n", + "19 1\n", + "20 1\n", + "21 1\n", + "22 1\n", + "23 1\n", + "24 1\n", + "25 1\n", + "26 1\n", + "27 1\n", + "28 1\n", + "29 1\n", + "30 1\n", + "31 1\n", + "32 1\n", + "33 1\n", + "34 1\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Комбинированный расход топлива\" в тестовой выборке:\n", + "combination_mpg\n", + "17 1\n", + "18 1\n", + "19 1\n", + "20 1\n", + "21 1\n", + "22 1\n", + "23 1\n", + "24 1\n", + "25 1\n", + "26 1\n", + "27 1\n", + "28 1\n", + "29 1\n", + "30 1\n", + "31 1\n", + "32 1\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "smote = SMOTE(random_state=42)\n", + "\n", + "# Применение SMOTE для балансировки обучающей выборки\n", + "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n", + "\n", + "# Проверка сбалансированности после SMOTE\n", + "print(\"Сбалансированность обучающей выборки после SMOTE:\")\n", + "print(y_train_resampled.value_counts(normalize=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Датасет №3 (Экономика стран)\n", + "Ссылка: https://www.kaggle.com/datasets/pratik453609/economic-data-9-countries-19802020\n", + "\n", + "Проблемная область: экономический анализ и прогнозирование макроэкономических показателей.\n", + "\n", + "Объекты наблюдения: экономические индексы по странам за определённые годы." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['stock index', 'country', 'year', 'index price', 'log_indexprice',\n", + " 'inflationrate', 'oil prices', 'exchange_rate', 'gdppercent',\n", + " 'percapitaincome', 'unemploymentrate', 'manufacturingoutput',\n", + " 'tradebalance', 'USTreasury'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "df_countries = pd.read_csv(\"..//..//static//csv//economic_data.csv\")\n", + "print(df_countries.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Атрибуты объектов:\n", + "1. stock index — индекс акций.\n", + "2. country — страна.\n", + "3. year — год.\n", + "4. index price — цена индекса.\n", + "5. log_indexprice — логарифм цены индекса.\n", + "6. inflationrate — уровень инфляции.\n", + "7. oil prices — цены на нефть.\n", + "8. exchange_rate — валютный курс.\n", + "9. gdppercent — процент роста ВВП.\n", + "10. percapitaincome — доход на душу населения.\n", + "11. unemploymentrate — уровень безработицы.\n", + "12. manufacturingoutput — объём производства.\n", + "13. tradebalance — торговый баланс.\n", + "14. USTreasury — доходность казначейских облигаций США.\n", + "\n", + "Связи между объектами:\n", + "Некоторые атрибуты могут быть связаны друг с другом, например, уровень инфляции и процент роста ВВП могут коррелировать с ценами на нефть, уровнем безработицы и торговым балансом.\n", + "\n", + "Примеры бизнес-целей и эффект:\n", + "1. Прогнозирование экономического роста и планирование инвестиций:\n", + " - Бизнес-цель: Создать модель прогнозирования роста экономики для стран, чтобы принять стратегические инвестиционные решения.\n", + " - Эффект: Повышение точности экономических прогнозов и улучшение прибыльности инвестиционных стратегий.\n", + "\n", + "2. Анализ и оптимизация торговой политики:\n", + " - Бизнес-цель: Изучение влияния изменений торгового баланса и валютных курсов на экономику стран.\n", + " - Эффект: Улучшение торговых соглашений и политики, что приведёт к более устойчивому экономическому росту.\n", + "\n", + "Примеры целей технического проекта:\n", + "1. Цель: Построение модели для прогнозирования уровня инфляции.\n", + " - Вход: Уровень безработицы, ВВП, доход на душу населения, валютный курс, цены на нефть.\n", + " - Целевой признак: inflationrate.\n", + "\n", + "2. Цель: Построение модели для оценки экономического роста.\n", + " - Вход: Торговый баланс, доход на душу населения, валютный курс, инфляция.\n", + " - Целевой признак: gdppercent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проверка на пустые значения и дубликаты" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Пустые значения по столбцам:\n", + "stock index 0\n", + "country 0\n", + "year 0\n", + "index price 52\n", + "log_indexprice 0\n", + "inflationrate 43\n", + "oil prices 0\n", + "exchange_rate 2\n", + "gdppercent 19\n", + "percapitaincome 1\n", + "unemploymentrate 21\n", + "manufacturingoutput 91\n", + "tradebalance 4\n", + "USTreasury 0\n", + "dtype: int64\n", + "\n", + "Количество дубликатов: 0\n" + ] + } + ], + "source": [ + "null_values = df_countries.isnull().sum()\n", + "print(\"Пустые значения по столбцам:\")\n", + "print(null_values)\n", + "\n", + "duplicates = df_countries.duplicated().sum()\n", + "print(f\"\\nКоличество дубликатов: {duplicates}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Видим, что есть пустые данные, но нет дубликатов. Удаляем их" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "В наборе данных 'Countries' было удалено 150 строк с пустыми значениями.\n" + ] + } + ], + "source": [ + "def drop_missing_values(dataframe, name):\n", + " before_shape = dataframe.shape \n", + " cleaned_dataframe = dataframe.dropna() \n", + " after_shape = cleaned_dataframe.shape \n", + " print(f\"В наборе данных '{name}' было удалено {before_shape[0] - after_shape[0]} строк с пустыми значениями.\")\n", + " return cleaned_dataframe\n", + "\n", + "df_countries = drop_missing_values(df_countries, \"Countries\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проверка на выбросы:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Количество выбросов в столбце 'year': 0\n", + "Количество выбросов в столбце 'index price': 17\n", + "Количество выбросов в столбце 'log_indexprice': 1\n", + "Количество выбросов в столбце 'inflationrate': 35\n", + "Количество выбросов в столбце 'oil prices': 0\n", + "Количество выбросов в столбце 'exchange_rate': 53\n", + "Количество выбросов в столбце 'gdppercent': 13\n", + "Количество выбросов в столбце 'percapitaincome': 0\n", + "Количество выбросов в столбце 'unemploymentrate': 9\n", + "Количество выбросов в столбце 'manufacturingoutput': 29\n", + "Количество выбросов в столбце 'tradebalance': 47\n", + "Количество выбросов в столбце 'USTreasury': 9\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "columns_to_check = ['year', 'index price', 'log_indexprice',\n", + " 'inflationrate', 'oil prices', 'exchange_rate', 'gdppercent',\n", + " 'percapitaincome', 'unemploymentrate', 'manufacturingoutput',\n", + " 'tradebalance', 'USTreasury']\n", + "\n", + "outliers_count = count_outliers(df_countries, columns_to_check)\n", + "\n", + "for col, count in outliers_count.items():\n", + " print(f\"Количество выбросов в столбце '{col}': {count}\")\n", + "\n", + "plt.figure(figsize=(15, 10))\n", + "for i, col in enumerate(columns_to_check, 1):\n", + " plt.subplot(3, 4, i)\n", + " sns.boxplot(x=df_countries[col])\n", + " plt.title(f'Box Plot of {col}')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "В большинстве из выбранных столбцов присутствуют выбросы. Очистим их." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Количество удаленных строк: 136\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "columns_to_clean = ['index price', 'log_indexprice',\n", + " 'inflationrate', 'exchange_rate', 'gdppercent', 'unemploymentrate', 'manufacturingoutput',\n", + " 'tradebalance', 'USTreasury']\n", + "\n", + "df_countries_clean = remove_outliers(df_countries, columns_to_clean)\n", + "\n", + "print(f\"Количество удаленных строк: {len(df_countries) - len(df_countries_clean)}\")\n", + "\n", + "plt.figure(figsize=(15, 6))\n", + "\n", + "plt.figure(figsize=(15, 10))\n", + "for i, col in enumerate(columns_to_clean, 1):\n", + " plt.subplot(3, 3, i)\n", + " sns.boxplot(x=df_countries_clean[col])\n", + " plt.title(f'Box Plot of {col}')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "df_countries = df_countries_clean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Разбиение набора данных на обучающую, контрольную и тестовую выборки" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 49\n", + "Размер контрольной выборки: 17\n", + "Размер тестовой выборки: 17\n" + ] + } + ], + "source": [ + "train_df, test_df = train_test_split(df_countries, test_size=0.2, random_state=42)\n", + "\n", + "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n", + "\n", + "print(\"Размер обучающей выборки:\", len(train_df))\n", + "print(\"Размер контрольной выборки:\", len(val_df))\n", + "print(\"Размер тестовой выборки:\", len(test_df))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение \"Уровень инфляции\" в обучающей выборке:\n", + "inflationrate\n", + "0.02 25\n", + "0.03 11\n", + "0.01 9\n", + "0.04 4\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Уровень инфляции\" в контрольной выборке:\n", + "inflationrate\n", + "0.03 6\n", + "0.01 6\n", + "0.02 5\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Уровень инфляции\" в тестовой выборке:\n", + "inflationrate\n", + "0.02 6\n", + "0.03 6\n", + "0.01 4\n", + "0.04 1\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "def check_balance(df, name):\n", + " counts = df['inflationrate'].value_counts()\n", + " print(f\"Распределение \\\"Уровень инфляции\\\" в {name}:\")\n", + " print(counts)\n", + " print()\n", + "\n", + "check_balance(train_df, \"обучающей выборке\")\n", + "check_balance(val_df, \"контрольной выборке\")\n", + "check_balance(test_df, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Оверсемплинг и андерсемплинг" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Оверсэмплинг:\n", + "Распределение \"Уровень инфляции\" в обучающей выборке:\n", + "inflationrate\n", + "0.03 26\n", + "0.02 25\n", + "0.01 9\n", + "0.04 8\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Уровень инфляции\" в контрольной выборке:\n", + "inflationrate\n", + "0.03 11\n", + "0.01 6\n", + "0.02 5\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Уровень инфляции\" в тестовой выборке:\n", + "inflationrate\n", + "0.03 8\n", + "0.02 6\n", + "0.01 4\n", + "0.04 2\n", + "Name: count, dtype: int64\n", + "\n", + "Андерсэмплинг:\n", + "Распределение \"Уровень инфляции\" в обучающей выборке:\n", + "inflationrate\n", + "0.03 11\n", + "0.02 10\n", + "0.01 5\n", + "0.04 4\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Уровень инфляции\" в контрольной выборке:\n", + "inflationrate\n", + "0.03 6\n", + "0.01 4\n", + "0.02 2\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение \"Уровень инфляции\" в тестовой выборке:\n", + "inflationrate\n", + "0.03 6\n", + "0.02 5\n", + "0.01 2\n", + "0.04 1\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "def binning(target, bins):\n", + " return pd.qcut(target, q=bins, labels=False)\n", + "\n", + "train_df['inflationrate_binned'] = binning(train_df['inflationrate'], bins=2)\n", + "val_df['inflationrate_binned'] = binning(val_df['inflationrate'], bins=2)\n", + "test_df['inflationrate_binned'] = binning(test_df['inflationrate'], bins=2)\n", + "\n", + "train_df_oversampled = oversample(train_df, 'inflationrate_binned')\n", + "val_df_oversampled = oversample(val_df, 'inflationrate_binned')\n", + "test_df_oversampled = oversample(test_df, 'inflationrate_binned')\n", + "\n", + "train_df_undersampled = undersample(train_df, 'inflationrate_binned')\n", + "val_df_undersampled = undersample(val_df, 'inflationrate_binned')\n", + "test_df_undersampled = undersample(test_df, 'inflationrate_binned')\n", + "\n", + "print(\"Оверсэмплинг:\")\n", + "check_balance(train_df_oversampled, \"обучающей выборке\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке\")\n", + "\n", + "print(\"Андерсэмплинг:\")\n", + "check_balance(train_df_undersampled, \"обучающей выборке\")\n", + "check_balance(val_df_undersampled, \"контрольной выборке\")\n", + "check_balance(test_df_undersampled, \"тестовой выборке\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "aimenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lab_2/requirements.txt b/lab_2/requirements.txt new file mode 100644 index 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