1338 lines
286 KiB
Plaintext
1338 lines
286 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Выгрузка в датафрейм первый набор (игры в Steam)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"https://www.kaggle.com/datasets/wajihulhassan369/steam-games-dataset. Набор представляет собой данные об экшенах, доступных в Steam. Эта информация полезна для изучения игровых паттернов, моделирования цен и исследования корреляции между игровыми тегами и методами ценообразования. Этот набор позволяет провести предварительный анализ данных, построить модели машинного обучения или исследовать игровую индустрию. В наборе пресдтавлена дата, различные теги, рейтинг отзывов. Так можно понять, какие теги популярнее, что в играх людям нравится больше, изменилось ли качество игр со временем и т.д. Для бизнеса такой набор данных может быть полезен для прогнозирования, в разработку каки игр целесообразнее вкладываться. Так компания не потеряет деньги.\n",
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"Пример цели: Разработка игры на пк в нужную фазу рынка\n",
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"Входные данные: год выпуска, сумма продаж\n",
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"Целевой признак: продаваемость игр в текущей фазе рынка в сравнении с предыдущими."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Index(['Name', 'Price', 'Release_date', 'Review_no', 'Review_type', 'Tags',\n",
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" 'Description'],\n",
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" dtype='object')\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"df = pd.read_csv(\".//static//csv//steam_cleaned.csv\")\n",
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"print(df.columns)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Преобразуем дату выпуска в формат datetime\n",
|
||
"df['Release_date'] = pd.to_datetime(df['Release_date'])\n",
|
||
"\n",
|
||
"# Визуализация данных\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"plt.scatter(df['Release_date'], df['Review_no'])\n",
|
||
"plt.xlabel('Release Date')\n",
|
||
"plt.ylabel('Review Number')\n",
|
||
"plt.title('Scatter Plot of Review Number vs Release Date')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"При проверке на шум можно заметить выброс в 2014 году. количество обзоров там запредельное. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Все выбросы удалены путём определения порогов квантилями. Зашумленность не очень высокая. Покрытие данных высокое и подошло бы для поставленной задачи по актуальности."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Выбросы:\n",
|
||
" Name Price Release_date Review_no \\\n",
|
||
"18 GUNDAM BREAKER 4 59.99 2024-08-29 1846.0 \n",
|
||
"22 LOCKDOWN Protocol 5.49 2024-07-22 2192.0 \n",
|
||
"34 CarX Street 19.99 2024-08-29 4166.0 \n",
|
||
"45 Harry Potter: Quidditch Champions 25.99 2024-09-03 1216.0 \n",
|
||
"61 SMITE 2 18.00 2024-08-27 1633.0 \n",
|
||
"... ... ... ... ... \n",
|
||
"7695 Dude Simulator 2 2.99 2018-07-28 1734.0 \n",
|
||
"7717 Golfing Over It with Alva Majo 2.39 2018-03-28 1367.0 \n",
|
||
"7740 Dungeon Siege II 4.99 2005-08-16 2274.0 \n",
|
||
"7765 Phantom Doctrine 12.99 2018-08-14 3538.0 \n",
|
||
"7768 NECROPOLIS: BRUTAL EDITION 19.99 2016-07-12 3668.0 \n",
|
||
"\n",
|
||
" Review_type Tags \\\n",
|
||
"18 Very Positive Action,Robots,Hack and Slash,RPG,Mechs,Action ... \n",
|
||
"22 Very Positive Multiplayer,Social Deduction,Conversation,Acti... \n",
|
||
"34 Mixed Racing,Open World,Automobile Sim,PvP,Multiplay... \n",
|
||
"45 Mostly Positive Action,Sports,Flight,Arcade,Third Person,Magic... \n",
|
||
"61 Mixed Action,MOBA,Third Person,Strategy,Adventure,Ca... \n",
|
||
"... ... ... \n",
|
||
"7695 Mixed Life Sim,Indie,Simulation,Racing,Action,Advent... \n",
|
||
"7717 Mostly Positive Difficult,Physics,Golf,Platformer,Precision Pl... \n",
|
||
"7740 Mostly Positive RPG,Fantasy,Action RPG,Hack and Slash,Singlepl... \n",
|
||
"7765 Mostly Positive Turn-Based Tactics,Strategy,Cold War,Stealth,R... \n",
|
||
"7768 Mixed Souls-like,Action Roguelike,Co-op,Adventure,Ro... \n",
|
||
"\n",
|
||
" Description \n",
|
||
"18 Create your own ultimate Gundam in the newest ... \n",
|
||
"22 A first person social deduction game, combinin... \n",
|
||
"34 Conquer mountain roads, highways, and city str... \n",
|
||
"45 Your next chapter takes flight! Immerse yourse... \n",
|
||
"61 Become a god and wage war in SMITE 2, the Unre... \n",
|
||
"... ... \n",
|
||
"7695 Dude Simulator 2 is an open world sandbox game... \n",
|
||
"7717 The higher you climb, the bigger the fall. \n",
|
||
"7740 NaN \n",
|
||
"7765 The year is 1983. The world teeters on the ver... \n",
|
||
"7768 NECROPOLIS: BRUTAL EDITION is a major update f... \n",
|
||
"\n",
|
||
"[1049 rows x 7 columns]\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# Преобразуем дату выпуска в формат datetime\n",
|
||
"df['Release_date'] = pd.to_datetime(df['Release_date'])\n",
|
||
"\n",
|
||
"# Статистический анализ для определения выбросов\n",
|
||
"Q1 = df['Review_no'].quantile(0.25)\n",
|
||
"Q3 = df['Review_no'].quantile(0.75)\n",
|
||
"IQR = Q3 - Q1\n",
|
||
"\n",
|
||
"# Определение порога для выбросов\n",
|
||
"threshold = 1.5 * IQR\n",
|
||
"outliers = (df['Review_no'] < (Q1 - threshold)) | (df['Review_no'] > (Q3 + threshold))\n",
|
||
"\n",
|
||
"# Вывод выбросов\n",
|
||
"print(\"Выбросы:\")\n",
|
||
"print(df[outliers])\n",
|
||
"\n",
|
||
"# Обработка выбросов\n",
|
||
"# В данном случае мы заменим выбросы на медианное значение\n",
|
||
"median_review_no = df['Review_no'].median()\n",
|
||
"df.loc[outliers, 'Review_no'] = median_review_no\n",
|
||
"\n",
|
||
"# Визуализация данных после обработки\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"plt.scatter(df['Release_date'], df['Review_no'])\n",
|
||
"plt.xlabel('Release Date')\n",
|
||
"plt.ylabel('Review Number')\n",
|
||
"plt.title('Scatter Plot of Review Number vs Release Date (After Handling Outliers)')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Очистим от строк с пустыми значениями наш датасет"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Количество удаленных строк: 515\n",
|
||
"\n",
|
||
"DataFrame после удаления строк с пропущенными значениями:\n",
|
||
" Name Price Release_date \\\n",
|
||
"0 Black Myth: Wukong 59.99 2024-08-20 \n",
|
||
"2 Counter-Strike 2 0.00 2012-08-21 \n",
|
||
"4 Grand Theft Auto V 10.48 2015-04-14 \n",
|
||
"5 Red Dead Redemption 2 17.99 2019-12-05 \n",
|
||
"6 PUBG: BATTLEGROUNDS 0.00 2017-12-21 \n",
|
||
"... ... ... ... \n",
|
||
"7807 Monster Hunter World: Iceborne - MHW:I Monster... 2.99 2020-02-06 \n",
|
||
"7808 Gene Shift Auto: Deluxe Edition 8.99 2022-11-28 \n",
|
||
"7809 Run Ralph Run 0.45 2021-03-03 \n",
|
||
"7810 Quadroids 6.19 2024-02-22 \n",
|
||
"7811 Divekick 4.99 2013-08-20 \n",
|
||
"\n",
|
||
" Review_no Review_type \\\n",
|
||
"0 270.0 Overwhelmingly Positive \n",
|
||
"2 270.0 Very Positive \n",
|
||
"4 270.0 Very Positive \n",
|
||
"5 270.0 Very Positive \n",
|
||
"6 270.0 Mixed \n",
|
||
"... ... ... \n",
|
||
"7807 39.0 Positive \n",
|
||
"7808 16.0 Positive \n",
|
||
"7809 26.0 Mostly Positive \n",
|
||
"7810 15.0 Positive \n",
|
||
"7811 1118.0 Very Positive \n",
|
||
"\n",
|
||
" Tags \\\n",
|
||
"0 Mythology,Action RPG,Action,Souls-like,RPG,Com... \n",
|
||
"2 FPS,Shooter,Multiplayer,Competitive,Action,Tea... \n",
|
||
"4 Open World,Action,Multiplayer,Crime,Automobile... \n",
|
||
"5 Open World,Story Rich,Western,Adventure,Multip... \n",
|
||
"6 Survival,Shooter,Battle Royale,Multiplayer,FPS... \n",
|
||
"... ... \n",
|
||
"7807 Action \n",
|
||
"7808 Indie,Action,Free to Play,Battle Royale,Roguel... \n",
|
||
"7809 Adventure,Action,Puzzle,Arcade,Platformer,Shoo... \n",
|
||
"7810 Precision Platformer,Puzzle Platformer,2D Plat... \n",
|
||
"7811 Fighting,Indie,2D Fighter,Parody ,Local Multip... \n",
|
||
"\n",
|
||
" Description \n",
|
||
"0 Black Myth: Wukong is an action RPG rooted in ... \n",
|
||
"2 For over two decades, Counter-Strike has offer... \n",
|
||
"4 Grand Theft Auto V for PC offers players the o... \n",
|
||
"5 Winner of over 175 Game of the Year Awards and... \n",
|
||
"6 Play PUBG: BATTLEGROUNDS for free.\\n\\nLand on ... \n",
|
||
"... ... \n",
|
||
"7807 A monster figure you can use to decorate your ... \n",
|
||
"7808 Gene Shift Auto is a roguelike-inspired battle... \n",
|
||
"7809 Ralph is a smart dinosaur, and a great shooter. \n",
|
||
"7810 Quadroids is a single-player puzzle platformer... \n",
|
||
"7811 Divekick is the world’s first two-button fight... \n",
|
||
"\n",
|
||
"[7297 rows x 7 columns]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Удаление строк с пропущенными значениями\n",
|
||
"df_dropna = df.dropna()\n",
|
||
"\n",
|
||
"# Вывод количества удаленных строк\n",
|
||
"num_deleted_rows = len(df) - len(df_dropna)\n",
|
||
"print(f\"\\nКоличество удаленных строк: {num_deleted_rows}\")\n",
|
||
"\n",
|
||
"print(\"\\nDataFrame после удаления строк с пропущенными значениями:\")\n",
|
||
"print(df_dropna)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Теперь создадим выборки."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Размер обучающей выборки: 4687\n",
|
||
"Размер контрольной выборки: 1562\n",
|
||
"Размер тестовой выборки: 1563\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"df = pd.read_csv(\".//static//csv//steam_cleaned.csv\")\n",
|
||
"\n",
|
||
"train_df, temp_df = train_test_split(df, test_size=0.4, random_state=42)\n",
|
||
"\n",
|
||
"# Разделение остатка на контрольную и тестовую выборки\n",
|
||
"val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
|
||
"\n",
|
||
"# Проверка размеров выборок\n",
|
||
"print(\"Размер обучающей выборки:\", len(train_df))\n",
|
||
"print(\"Размер контрольной выборки:\", len(val_df))\n",
|
||
"print(\"Размер тестовой выборки:\", len(test_df))\n",
|
||
"\n",
|
||
"# Сохранение выборок в файлы\n",
|
||
"train_df.to_csv(\".//static//csv//train_data.csv\", index=False)\n",
|
||
"val_df.to_csv(\".//static//csv//val_data.csv\", index=False)\n",
|
||
"test_df.to_csv(\".//static//csv//test_data.csv\", index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Проанализируем сбалансированность выборок"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распределение Review_type в обучающей выборке:\n",
|
||
"Review_type\n",
|
||
"Very Positive 2117\n",
|
||
"Mostly Positive 810\n",
|
||
"Mixed 797\n",
|
||
"Positive 710\n",
|
||
"Overwhelmingly Positive 209\n",
|
||
"Mostly Negative 15\n",
|
||
"Very Negative 2\n",
|
||
"Overwhelmingly Negative 1\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент положительных отзывов: 17.28%\n",
|
||
"Процент отрицательных отзывов: 4.46%\n",
|
||
"\n",
|
||
"Распределение Review_type в контрольной выборке:\n",
|
||
"Review_type\n",
|
||
"Very Positive 708\n",
|
||
"Mostly Positive 290\n",
|
||
"Mixed 241\n",
|
||
"Positive 224\n",
|
||
"Overwhelmingly Positive 78\n",
|
||
"Mostly Negative 6\n",
|
||
"Very Negative 2\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент положительных отзывов: 18.57%\n",
|
||
"Процент отрицательных отзывов: 4.99%\n",
|
||
"\n",
|
||
"Распределение Review_type в тестовой выборке:\n",
|
||
"Review_type\n",
|
||
"Very Positive 713\n",
|
||
"Mostly Positive 276\n",
|
||
"Mixed 253\n",
|
||
"Positive 240\n",
|
||
"Overwhelmingly Positive 67\n",
|
||
"Mostly Negative 5\n",
|
||
"Very Negative 1\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Процент положительных отзывов: 17.66%\n",
|
||
"Процент отрицательных отзывов: 4.29%\n",
|
||
"\n",
|
||
"Необходима аугментация данных для балансировки классов.\n",
|
||
"Необходима аугментация данных для балансировки классов.\n",
|
||
"Необходима аугментация данных для балансировки классов.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"train_df = pd.read_csv(\".//static//csv//train_data.csv\")\n",
|
||
"val_df = pd.read_csv(\".//static//csv//val_data.csv\")\n",
|
||
"test_df = pd.read_csv(\".//static//csv//test_data.csv\")\n",
|
||
"\n",
|
||
"# Оценка сбалансированности\n",
|
||
"def check_balance(df, name):\n",
|
||
" counts = df['Review_type'].value_counts()\n",
|
||
" print(f\"Распределение Review_type в {name}:\")\n",
|
||
" print(counts)\n",
|
||
" print(f\"Процент положительных отзывов: {counts['Mostly Positive'] / len(df) * 100:.2f}%\")\n",
|
||
" print(f\"Процент отрицательных отзывов: {counts['Overwhelmingly Positive'] / len(df) * 100:.2f}%\")\n",
|
||
" print()\n",
|
||
"\n",
|
||
"# Определение необходимости аугментации данных\n",
|
||
"def need_augmentation(df):\n",
|
||
" counts = df['Review_type'].value_counts()\n",
|
||
" ratio = counts['Mostly Positive'] / counts['Overwhelmingly Positive']\n",
|
||
" if ratio > 1.5 or ratio < 0.67:\n",
|
||
" print(\"Необходима аугментация данных для балансировки классов.\")\n",
|
||
" else:\n",
|
||
" print(\"Аугментация данных не требуется.\")\n",
|
||
" \n",
|
||
"check_balance(train_df, \"обучающей выборке\")\n",
|
||
"check_balance(val_df, \"контрольной выборке\")\n",
|
||
"check_balance(test_df, \"тестовой выборке\")\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"need_augmentation(train_df)\n",
|
||
"need_augmentation(val_df)\n",
|
||
"need_augmentation(test_df)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"По результатам анализа требуется приращение, соотношения отзывов вне допустимого диапазона"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Оверсэмплинг:\n",
|
||
"Распределение Review_type в обучающей выборке:\n",
|
||
"Review_type\n",
|
||
"Mostly Positive 2117\n",
|
||
"Mixed 2117\n",
|
||
"Very Positive 2117\n",
|
||
"Positive 2117\n",
|
||
"Overwhelmingly Positive 2117\n",
|
||
"Mostly Negative 2117\n",
|
||
"Very Negative 2117\n",
|
||
"Overwhelmingly Negative 2117\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Отсутствуют один или оба класса (Positive/Negative).\n",
|
||
"\n",
|
||
"Распределение Review_type в контрольной выборке:\n",
|
||
"Review_type\n",
|
||
"Very Negative 708\n",
|
||
"Mostly Positive 708\n",
|
||
"Mixed 708\n",
|
||
"Overwhelmingly Positive 708\n",
|
||
"Overwhelmingly Negative 708\n",
|
||
"Positive 708\n",
|
||
"Mostly Negative 708\n",
|
||
"Very Positive 708\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Отсутствуют один или оба класса (Positive/Negative).\n",
|
||
"\n",
|
||
"Распределение Review_type в тестовой выборке:\n",
|
||
"Review_type\n",
|
||
"Very Negative 713\n",
|
||
"Mostly Positive 713\n",
|
||
"Overwhelmingly Positive 713\n",
|
||
"Mixed 713\n",
|
||
"Overwhelmingly Negative 713\n",
|
||
"Very Positive 713\n",
|
||
"Mostly Negative 713\n",
|
||
"Positive 713\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Отсутствуют один или оба класса (Positive/Negative).\n",
|
||
"\n",
|
||
"Андерсэмплинг:\n",
|
||
"Распределение Review_type в обучающей выборке:\n",
|
||
"Review_type\n",
|
||
"Mixed 1\n",
|
||
"Mostly Negative 1\n",
|
||
"Mostly Positive 1\n",
|
||
"Overwhelmingly Negative 1\n",
|
||
"Overwhelmingly Positive 1\n",
|
||
"Positive 1\n",
|
||
"Very Negative 1\n",
|
||
"Very Positive 1\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Отсутствуют один или оба класса (Positive/Negative).\n",
|
||
"\n",
|
||
"Распределение Review_type в контрольной выборке:\n",
|
||
"Review_type\n",
|
||
"Mixed 2\n",
|
||
"Mostly Negative 2\n",
|
||
"Mostly Positive 2\n",
|
||
"Overwhelmingly Negative 2\n",
|
||
"Overwhelmingly Positive 2\n",
|
||
"Positive 2\n",
|
||
"Very Negative 2\n",
|
||
"Very Positive 2\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Отсутствуют один или оба класса (Positive/Negative).\n",
|
||
"\n",
|
||
"Распределение Review_type в тестовой выборке:\n",
|
||
"Review_type\n",
|
||
"Mixed 1\n",
|
||
"Mostly Negative 1\n",
|
||
"Mostly Positive 1\n",
|
||
"Overwhelmingly Negative 1\n",
|
||
"Overwhelmingly Positive 1\n",
|
||
"Positive 1\n",
|
||
"Very Negative 1\n",
|
||
"Very Positive 1\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Отсутствуют один или оба класса (Positive/Negative).\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"from imblearn.over_sampling import RandomOverSampler\n",
|
||
"from imblearn.under_sampling import RandomUnderSampler\n",
|
||
"from sklearn.preprocessing import LabelEncoder\n",
|
||
"\n",
|
||
"# Загрузка данных\n",
|
||
"train_df = pd.read_csv(\".//static//csv//train_data.csv\")\n",
|
||
"val_df = pd.read_csv(\".//static//csv//val_data.csv\")\n",
|
||
"test_df = pd.read_csv(\".//static//csv//test_data.csv\")\n",
|
||
"\n",
|
||
"# Преобразование категориальных признаков в числовые\n",
|
||
"def encode(df):\n",
|
||
" label_encoders = {}\n",
|
||
" for column in df.select_dtypes(include=['object']).columns:\n",
|
||
" if column != 'Review_type': # Пропускаем целевую переменную\n",
|
||
" le = LabelEncoder()\n",
|
||
" df[column] = le.fit_transform(df[column])\n",
|
||
" label_encoders[column] = le\n",
|
||
" return label_encoders\n",
|
||
"\n",
|
||
"# Преобразование целевой переменной в числовые значения\n",
|
||
"def encode_target(df):\n",
|
||
" le = LabelEncoder()\n",
|
||
" df['Review_type'] = le.fit_transform(df['Review_type'])\n",
|
||
" return le\n",
|
||
"\n",
|
||
"# Применение кодирования\n",
|
||
"label_encoders = encode(train_df)\n",
|
||
"encode(val_df)\n",
|
||
"encode(test_df)\n",
|
||
"\n",
|
||
"# Кодирование целевой переменной\n",
|
||
"le_target = encode_target(train_df)\n",
|
||
"encode_target(val_df)\n",
|
||
"encode_target(test_df)\n",
|
||
"\n",
|
||
"# Проверка типов данных\n",
|
||
"def check_data_types(df):\n",
|
||
" for column in df.columns:\n",
|
||
" if df[column].dtype == 'object':\n",
|
||
" print(f\"Столбец '{column}' содержит строковые данные.\")\n",
|
||
"\n",
|
||
"check_data_types(train_df)\n",
|
||
"check_data_types(val_df)\n",
|
||
"check_data_types(test_df)\n",
|
||
"\n",
|
||
"# Функция для выполнения oversampling\n",
|
||
"def oversample(df):\n",
|
||
" if 'Review_type' not in df.columns:\n",
|
||
" print(\"Столбец 'Review_type' отсутствует.\")\n",
|
||
" return df\n",
|
||
" \n",
|
||
" X = df.drop('Review_type', axis=1)\n",
|
||
" y = df['Review_type']\n",
|
||
" \n",
|
||
" oversampler = RandomOverSampler(random_state=42)\n",
|
||
" X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
|
||
" \n",
|
||
" resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
|
||
" return resampled_df\n",
|
||
"\n",
|
||
"# Функция для выполнения undersampling\n",
|
||
"def undersample(df):\n",
|
||
" if 'Review_type' not in df.columns:\n",
|
||
" print(\"Столбец 'Review_type' отсутствует.\")\n",
|
||
" return df\n",
|
||
" \n",
|
||
" X = df.drop('Review_type', axis=1)\n",
|
||
" y = df['Review_type']\n",
|
||
" \n",
|
||
" undersampler = RandomUnderSampler(random_state=42)\n",
|
||
" X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
|
||
" \n",
|
||
" resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
|
||
" return resampled_df\n",
|
||
"\n",
|
||
"# Применение oversampling и undersampling к каждой выборке\n",
|
||
"train_df_oversampled = oversample(train_df)\n",
|
||
"val_df_oversampled = oversample(val_df)\n",
|
||
"test_df_oversampled = oversample(test_df)\n",
|
||
"\n",
|
||
"train_df_undersampled = undersample(train_df)\n",
|
||
"val_df_undersampled = undersample(val_df)\n",
|
||
"test_df_undersampled = undersample(test_df)\n",
|
||
"\n",
|
||
"# Обратное преобразование целевой переменной в строковые метки\n",
|
||
"def decode_target(df, le_target):\n",
|
||
" df['Review_type'] = le_target.inverse_transform(df['Review_type'])\n",
|
||
"\n",
|
||
"decode_target(train_df_oversampled, le_target)\n",
|
||
"decode_target(val_df_oversampled, le_target)\n",
|
||
"decode_target(test_df_oversampled, le_target)\n",
|
||
"\n",
|
||
"decode_target(train_df_undersampled, le_target)\n",
|
||
"decode_target(val_df_undersampled, le_target)\n",
|
||
"decode_target(test_df_undersampled, le_target)\n",
|
||
"\n",
|
||
"# Проверка результатов\n",
|
||
"def check_balance(df, name):\n",
|
||
" if 'Review_type' not in df.columns:\n",
|
||
" print(f\"Столбец 'Review_type' отсутствует в {name}.\")\n",
|
||
" return\n",
|
||
" \n",
|
||
" counts = df['Review_type'].value_counts()\n",
|
||
" print(f\"Распределение Review_type в {name}:\")\n",
|
||
" print(counts)\n",
|
||
" \n",
|
||
" if 'Positive' in counts and 'Negative' in counts:\n",
|
||
" print(f\"Процент положительных отзывов: {counts['Positive'] / len(df) * 100:.2f}%\")\n",
|
||
" print(f\"Процент отрицательных отзывов: {counts['Negative'] / len(df) * 100:.2f}%\")\n",
|
||
" else:\n",
|
||
" print(\"Отсутствуют один или оба класса (Positive/Negative).\")\n",
|
||
" print()\n",
|
||
"\n",
|
||
"# Проверка сбалансированности после oversampling\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",
|
||
"# Проверка сбалансированности после undersampling\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": [
|
||
"## 14,400 Classic Rock Tracks (with Spotify Data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"https://www.kaggle.com/datasets/thebumpkin/14400-classic-rock-tracks-with-spotify-data Этот набор данных, содержащий 1200 уникальных альбомов и 14 400 треков, представляет собой не просто коллекцию — это хроника эволюции классического рока. Каждый трек тщательно каталогизирован с 18 столбцами данных, включая ключевые метаданные, такие как название трека, исполнитель, альбом и год выпуска, наряду с функциями Spotify audio, которые позволяют получить представление о звуковом ландшафте этих неподвластных времени мелодий. Бизнес-цель может заключаться в улучшении стратегии маркетинга и продвижения музыкальных треков. Предположим как этот набор может быть полезен для бизнеса:\n",
|
||
"Персонализированные рекомендации: Создание алгоритмов, которые будут рекомендовать пользователям музыку на основе их предпочтений.\n",
|
||
"Цель технического проекта: Разработать и внедрить систему рекомендаций, которая будет предсказывать и рекомендовать пользователям музыкальные треки на основе их предпочтений и поведения.\n",
|
||
"Входные данные:\n",
|
||
"Данные о пользователях: Идентификатор пользователя, история прослушиваний, оценки треков, время прослушивания, частота прослушивания.\n",
|
||
"Данные о треках: Атрибуты треков (название, исполнитель, альбом, год, длительность, танцевальность, энергичность, акустичность и т.д.).\n",
|
||
"Данные о взаимодействии: Время и частота взаимодействия пользователя с определенными треками.\n",
|
||
"Целевой признак:\n",
|
||
"Рекомендации: Булева переменная, указывающая, должен ли конкретный трек быть рекомендован пользователю (1 - рекомендуется, 0 - не рекомендуется)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Index(['Track', 'Artist', 'Album', 'Year', 'Duration', 'Time_Signature',\n",
|
||
" 'Danceability', 'Energy', 'Key', 'Loudness', 'Mode', 'Speechiness',\n",
|
||
" 'Acousticness', 'Instrumentalness', 'Liveness', 'Valence', 'Tempo',\n",
|
||
" 'Popularity'],\n",
|
||
" dtype='object')\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"df = pd.read_csv(\".//static//csv//UltimateClassicRock.csv\")\n",
|
||
"print(df.columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Анализируем датафрейм при помощи \"ящика с усами\". Естьсмещение в сторону меньших значений, это можно исправить при помощи oversampling и undersampling."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# Box plot для столбца 'Popularity'\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"sns.boxplot(x=df['Popularity'])\n",
|
||
"plt.title('Box Plot для Popularity')\n",
|
||
"plt.xlabel('Popularity')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Решим проблему пустых значений при помощи удаления таких строк."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_cleaned = df.dropna()"
|
||
]
|
||
},
|
||
{
|
||
"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: 8650\n",
|
||
"Размер контрольной выборки: 2884\n",
|
||
"Размер тестовой выборки: 2884\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"# Разделение на обучающую и тестовую выборки\n",
|
||
"train_df, test_df = train_test_split(df_cleaned, test_size=0.2, random_state=42)\n",
|
||
"\n",
|
||
"# Разделение обучающей выборки на обучающую и контрольную\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": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Оценка сбалансированности выборок, по результатам видно что баланса тут мало"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распределение Popularity в обучающей выборке:\n",
|
||
"Popularity\n",
|
||
"23 258\n",
|
||
"15 250\n",
|
||
"26 246\n",
|
||
"21 245\n",
|
||
"14 245\n",
|
||
" ... \n",
|
||
"84 1\n",
|
||
"87 1\n",
|
||
"91 1\n",
|
||
"79 1\n",
|
||
"86 1\n",
|
||
"Name: count, Length: 88, dtype: int64\n",
|
||
"\n",
|
||
"Распределение Popularity в контрольной выборке:\n",
|
||
"Popularity\n",
|
||
"17 90\n",
|
||
"26 86\n",
|
||
"21 83\n",
|
||
"24 83\n",
|
||
"28 80\n",
|
||
" ..\n",
|
||
"85 1\n",
|
||
"83 1\n",
|
||
"84 1\n",
|
||
"80 1\n",
|
||
"77 1\n",
|
||
"Name: count, Length: 85, dtype: int64\n",
|
||
"\n",
|
||
"Распределение Popularity в тестовой выборке:\n",
|
||
"Popularity\n",
|
||
"22 86\n",
|
||
"21 85\n",
|
||
"12 84\n",
|
||
"20 82\n",
|
||
"26 81\n",
|
||
" ..\n",
|
||
"76 2\n",
|
||
"71 2\n",
|
||
"79 1\n",
|
||
"82 1\n",
|
||
"80 1\n",
|
||
"Name: count, Length: 80, dtype: int64\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def check_balance(df, name):\n",
|
||
" counts = df['Popularity'].value_counts()\n",
|
||
" print(f\"Распределение Popularity 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": [
|
||
"Выполним овер- и андер- слемпинг."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распределение Popularity в обучающей выборке после oversampling:\n",
|
||
"Popularity\n",
|
||
"44 258\n",
|
||
"20 258\n",
|
||
"30 258\n",
|
||
"27 258\n",
|
||
"8 258\n",
|
||
" ... \n",
|
||
"78 258\n",
|
||
"79 258\n",
|
||
"74 258\n",
|
||
"81 258\n",
|
||
"86 258\n",
|
||
"Name: count, Length: 88, dtype: int64\n",
|
||
"\n",
|
||
"Распределение Popularity в контрольной выборке после oversampling:\n",
|
||
"Popularity\n",
|
||
"21 90\n",
|
||
"11 90\n",
|
||
"28 90\n",
|
||
"23 90\n",
|
||
"37 90\n",
|
||
" ..\n",
|
||
"61 90\n",
|
||
"84 90\n",
|
||
"80 90\n",
|
||
"77 90\n",
|
||
"0 90\n",
|
||
"Name: count, Length: 85, dtype: int64\n",
|
||
"\n",
|
||
"Распределение Popularity в тестовой выборке после oversampling:\n",
|
||
"Popularity\n",
|
||
"14 86\n",
|
||
"47 86\n",
|
||
"27 86\n",
|
||
"13 86\n",
|
||
"66 86\n",
|
||
" ..\n",
|
||
"63 86\n",
|
||
"79 86\n",
|
||
"71 86\n",
|
||
"82 86\n",
|
||
"80 86\n",
|
||
"Name: count, Length: 80, dtype: int64\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from imblearn.over_sampling import RandomOverSampler\n",
|
||
"\n",
|
||
"def oversample(df):\n",
|
||
" X = df.drop('Popularity', axis=1)\n",
|
||
" y = df['Popularity']\n",
|
||
" \n",
|
||
" oversampler = RandomOverSampler(random_state=42)\n",
|
||
" X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
|
||
" \n",
|
||
" resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
|
||
" return resampled_df\n",
|
||
"\n",
|
||
"train_df_oversampled = oversample(train_df)\n",
|
||
"val_df_oversampled = oversample(val_df)\n",
|
||
"test_df_oversampled = oversample(test_df)\n",
|
||
"\n",
|
||
"check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
|
||
"check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
|
||
"check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распределение Popularity в обучающей выборке после undersampling:\n",
|
||
"Popularity\n",
|
||
"0 1\n",
|
||
"1 1\n",
|
||
"2 1\n",
|
||
"3 1\n",
|
||
"4 1\n",
|
||
" ..\n",
|
||
"84 1\n",
|
||
"85 1\n",
|
||
"86 1\n",
|
||
"87 1\n",
|
||
"91 1\n",
|
||
"Name: count, Length: 88, dtype: int64\n",
|
||
"\n",
|
||
"Распределение Popularity в контрольной выборке после undersampling:\n",
|
||
"Popularity\n",
|
||
"0 1\n",
|
||
"1 1\n",
|
||
"2 1\n",
|
||
"3 1\n",
|
||
"4 1\n",
|
||
" ..\n",
|
||
"82 1\n",
|
||
"83 1\n",
|
||
"84 1\n",
|
||
"85 1\n",
|
||
"87 1\n",
|
||
"Name: count, Length: 85, dtype: int64\n",
|
||
"\n",
|
||
"Распределение Popularity в тестовой выборке после undersampling:\n",
|
||
"Popularity\n",
|
||
"0 1\n",
|
||
"1 1\n",
|
||
"2 1\n",
|
||
"3 1\n",
|
||
"4 1\n",
|
||
" ..\n",
|
||
"76 1\n",
|
||
"77 1\n",
|
||
"79 1\n",
|
||
"80 1\n",
|
||
"82 1\n",
|
||
"Name: count, Length: 80, dtype: int64\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from imblearn.under_sampling import RandomUnderSampler\n",
|
||
"\n",
|
||
"def undersample(df):\n",
|
||
" X = df.drop('Popularity', axis=1)\n",
|
||
" y = df['Popularity']\n",
|
||
" \n",
|
||
" undersampler = RandomUnderSampler(random_state=42)\n",
|
||
" X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
|
||
" \n",
|
||
" resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
|
||
" return resampled_df\n",
|
||
"\n",
|
||
"train_df_undersampled = undersample(train_df)\n",
|
||
"val_df_undersampled = undersample(val_df)\n",
|
||
"test_df_undersampled = undersample(test_df)\n",
|
||
"\n",
|
||
"check_balance(train_df_undersampled, \"обучающей выборке после undersampling\")\n",
|
||
"check_balance(val_df_undersampled, \"контрольной выборке после undersampling\")\n",
|
||
"check_balance(test_df_undersampled, \"тестовой выборке после undersampling\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Police Shootings in the United States: 2015-2024"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"В этом наборе данных, составленном The Washington Post, регистрируется каждый человек, застреленный дежурным полицейским в Соединенных Штатах с 2015 по 2024 год. Он решает проблему занижения органами власти статистики реальных инцедентов. Это может быть использовано в журналисткой работе, например для прогнозирования или выявления закономерностей преступлений. Цель технического проекта установить закономерность в убийствах полицейскими определённых групп граждан. Входные данные: возраст, пол, штат, вооружённость. Целевой признак: общий портрет убитого гражданина."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Index(['date', 'name', 'age', 'gender', 'armed', 'race', 'city', 'state',\n",
|
||
" 'flee', 'body_camera', 'signs_of_mental_illness',\n",
|
||
" 'police_departments_involved'],\n",
|
||
" dtype='object')\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"df = pd.read_csv(\".//static//csv//2024-07-23-washington-post-police-shootings-export.csv\")\n",
|
||
"print(df.columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"При помощи ящика с усами и колонки возраста проверим набор на баланс. Он достаточно сбалансирован."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# Box plot для столбца 'age'\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"sns.boxplot(x=df['age'])\n",
|
||
"plt.title('Box Plot для age')\n",
|
||
"plt.xlabel('Age')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Теперь проверим на шум, здесь тоже особо проблем нет, однако смущает сочетание white и black, вероятно это мулаты."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"# Scatter plot для столбцов 'age' и 'race'\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"sns.scatterplot(x='age', y='race', data=df)\n",
|
||
"plt.title('Scatter Plot для age и race')\n",
|
||
"plt.xlabel('Age')\n",
|
||
"plt.ylabel('Race')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Удаление строк с пустыми значениями"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_cleaned = df.dropna()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Разбиение набора данных на обучающую, контрольную и тестовую выборки"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Размер обучающей выборки: 4770\n",
|
||
"Размер контрольной выборки: 1591\n",
|
||
"Размер тестовой выборки: 1591\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"# Разделение на обучающую и тестовую выборки\n",
|
||
"train_df, test_df = train_test_split(df_cleaned, test_size=0.2, random_state=42)\n",
|
||
"\n",
|
||
"# Разделение обучающей выборки на обучающую и контрольную\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": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Применение методов приращения данных (аугментации)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Распределение reace в обучающей выборке после oversampling:\n",
|
||
"race\n",
|
||
"Black 2187\n",
|
||
"White 2187\n",
|
||
"Hispanic 2187\n",
|
||
"Unknown 2187\n",
|
||
"Native American 2187\n",
|
||
"Asian 2187\n",
|
||
"White,Black,Native American 2187\n",
|
||
"Other 2187\n",
|
||
"White,Black 2187\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"Распределение reace в контрольной выборке после oversampling:\n",
|
||
"race\n",
|
||
"White 718\n",
|
||
"Black 718\n",
|
||
"Unknown 718\n",
|
||
"Hispanic 718\n",
|
||
"Asian 718\n",
|
||
"Native American 718\n",
|
||
"Other 718\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"Распределение reace в тестовой выборке после oversampling:\n",
|
||
"race\n",
|
||
"Unknown 750\n",
|
||
"White 750\n",
|
||
"Black 750\n",
|
||
"Hispanic 750\n",
|
||
"Asian 750\n",
|
||
"Native American 750\n",
|
||
"Black,Hispanic 750\n",
|
||
"Other 750\n",
|
||
"White,Black 750\n",
|
||
"Native American,Hispanic 750\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"Распределение reace в обучающей выборке после undersampling:\n",
|
||
"race\n",
|
||
"Asian 1\n",
|
||
"Black 1\n",
|
||
"Hispanic 1\n",
|
||
"Native American 1\n",
|
||
"Other 1\n",
|
||
"Unknown 1\n",
|
||
"White 1\n",
|
||
"White,Black 1\n",
|
||
"White,Black,Native American 1\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"Распределение reace в контрольной выборке после undersampling:\n",
|
||
"race\n",
|
||
"Asian 7\n",
|
||
"Black 7\n",
|
||
"Hispanic 7\n",
|
||
"Native American 7\n",
|
||
"Other 7\n",
|
||
"Unknown 7\n",
|
||
"White 7\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n",
|
||
"Распределение reace в тестовой выборке после undersampling:\n",
|
||
"race\n",
|
||
"Asian 1\n",
|
||
"Black 1\n",
|
||
"Black,Hispanic 1\n",
|
||
"Hispanic 1\n",
|
||
"Native American 1\n",
|
||
"Native American,Hispanic 1\n",
|
||
"Other 1\n",
|
||
"Unknown 1\n",
|
||
"White 1\n",
|
||
"White,Black 1\n",
|
||
"Name: count, dtype: int64\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from imblearn.over_sampling import RandomOverSampler\n",
|
||
"\n",
|
||
"def check_balance(df, name):\n",
|
||
" counts = df['race'].value_counts()\n",
|
||
" print(f\"Распределение reace в {name}:\")\n",
|
||
" print(counts)\n",
|
||
" print()\n",
|
||
"\n",
|
||
"def oversample(df):\n",
|
||
" X = df.drop('race', axis=1)\n",
|
||
" y = df['race']\n",
|
||
" \n",
|
||
" oversampler = RandomOverSampler(random_state=42)\n",
|
||
" X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
|
||
" \n",
|
||
" resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
|
||
" return resampled_df\n",
|
||
"\n",
|
||
"train_df_oversampled = oversample(train_df)\n",
|
||
"val_df_oversampled = oversample(val_df)\n",
|
||
"test_df_oversampled = oversample(test_df)\n",
|
||
"\n",
|
||
"check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
|
||
"check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
|
||
"check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")\n",
|
||
"\n",
|
||
"def undersample(df):\n",
|
||
" X = df.drop('race', axis=1)\n",
|
||
" y = df['race']\n",
|
||
" \n",
|
||
" undersampler = RandomUnderSampler(random_state=42)\n",
|
||
" X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
|
||
" \n",
|
||
" resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
|
||
" return resampled_df\n",
|
||
"\n",
|
||
"train_df_undersampled = undersample(train_df)\n",
|
||
"val_df_undersampled = undersample(val_df)\n",
|
||
"test_df_undersampled = undersample(test_df)\n",
|
||
"\n",
|
||
"check_balance(train_df_undersampled, \"обучающей выборке после undersampling\")\n",
|
||
"check_balance(val_df_undersampled, \"контрольной выборке после undersampling\")\n",
|
||
"check_balance(test_df_undersampled, \"тестовой выборке после undersampling\")"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|