правки
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Lab_3/lab3.ipynb
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Lab_3/lab3.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler, LabelEncoder\n",
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"from sklearn.impute import SimpleImputer\n",
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"from imblearn.over_sampling import SMOTE\n",
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"import featuretools as ft\n"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<>:1: SyntaxWarning: invalid escape sequence '\\j'\n",
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"<>:1: SyntaxWarning: invalid escape sequence '\\j'\n",
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"C:\\Users\\MaD\\AppData\\Local\\Temp\\ipykernel_6188\\750029597.py:1: SyntaxWarning: invalid escape sequence '\\j'\n",
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" df = pd.read_csv(\"../data\\jio_mart_items.csv\")\n"
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]
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},
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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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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 162313 entries, 0 to 162312\n",
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"Data columns (total 5 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 category 162313 non-null object \n",
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" 1 sub_category 162313 non-null object \n",
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" 2 href 162313 non-null object \n",
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" 3 items 162280 non-null object \n",
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" 4 price 162282 non-null float64\n",
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"dtypes: float64(1), object(4)\n",
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"memory usage: 6.2+ MB\n",
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"None\n",
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"Пропущенные значения:\n",
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" category 0\n",
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"sub_category 0\n",
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"href 0\n",
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"items 33\n",
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"price 31\n",
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"dtype: int64\n",
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" price\n",
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"count 1.622820e+05\n",
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"mean 1.991633e+03\n",
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"std 1.593479e+04\n",
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"min 5.000000e+00\n",
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"25% 2.840000e+02\n",
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"50% 4.990000e+02\n",
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"75% 9.990000e+02\n",
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"max 3.900000e+06\n"
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]
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}
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],
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"source": [
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"df = pd.read_csv(\"../data\\jio_mart_items.csv\")\n",
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"\n",
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"print(df.info())\n",
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"# print(df.head())\n",
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"\n",
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"print(\"Пропущенные значения:\\n\", df.isnull().sum())\n",
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"\n",
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"print(df.describe())\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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"Бизнес-цели:\n",
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"1. Предсказать категорию продукта (классификация), чтобы рекомендовать новые товары на основе текущей базы.\n",
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"2. Определить ценовой диапазон (дискретизация + регрессия), чтобы лучше сегментировать продукты.\n",
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"\n",
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"Технические цели:\n",
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"Для цели 1: Разработка модели классификации для предсказания категории продукта.\n",
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"Для цели 2: Разработка модели, предсказывающей ценовой диапазон продукта.\n"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Удаление бесполезных столбцов\n",
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"df = df.drop(columns=[\"Product_ID\", \"Unnamed: 0\"], errors=\"ignore\")\n",
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"\n",
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"# Обработка пропущенных значений\n",
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"imputer = SimpleImputer(strategy=\"most_frequent\") # Для категориальных данных\n",
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"df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)\n",
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"\n",
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"# Преобразование числовых столбцов\n",
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"numeric_cols = df.select_dtypes(include=[\"float64\", \"int64\"]).columns\n",
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"categorical_cols = df.select_dtypes(include=[\"object\"]).columns\n",
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"\n",
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"# Дискретизация ценового диапазона, разобъём его на 10 категорий\n",
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"df[\"Price_Range\"] = pd.qcut(df[\"price\"], q=10, labels=False)\n",
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"\n",
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"# Кодирование категорий\n",
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"encoder = LabelEncoder()\n",
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"for col in categorical_cols:\n",
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" df[col] = encoder.fit_transform(df[col])\n",
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"\n",
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"# Проверяем результат\n",
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"# print(df.head())\n"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# # Построем график распределение категорий чтобы убедится в верной дискретизации\n",
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"# sns.countplot(data=df, x=\"Price_Range\", palette=\"viridis\")\n",
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"# plt.title(\"Распределение значений Price_Range\")\n",
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"# plt.xlabel(\"Диапазон цен (Price_Range)\")\n",
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"# plt.ylabel(\"Количество товаров\")\n",
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"# plt.show()\n"
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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": 5,
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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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"Train shape (classification): (97387, 4)\n",
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"Validation shape (classification): (32463, 4)\n",
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"Test shape (classification): (32463, 4)\n"
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]
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}
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],
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"source": [
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"# Разделение данных на X и y для каждой задачи\n",
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"X = df.drop(columns=[\"category\", \"Price_Range\"]) # Признаки\n",
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"y_classification = df[\"category\"] # Для первой цели (категория продукта)\n",
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"y_regression = df[\"Price_Range\"] # Для второй цели (ценовой диапазон)\n",
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"\n",
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"# Разбиение данных\n",
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"X_train, X_temp, y_train_class, y_temp_class = train_test_split(X, y_classification, test_size=0.4, stratify=y_classification, random_state=42)\n",
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"X_val, X_test, y_val_class, y_test_class = train_test_split(X_temp, y_temp_class, test_size=0.5, stratify=y_temp_class, random_state=42)\n",
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"\n",
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"X_train_reg, X_temp_reg, y_train_reg, y_temp_reg = train_test_split(X, y_regression, test_size=0.4, stratify=y_regression, random_state=42)\n",
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"X_val_reg, X_test_reg, y_val_reg, y_test_reg = train_test_split(X_temp_reg, y_temp_reg, test_size=0.5, stratify=y_temp_reg, random_state=42)\n",
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"\n",
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"# Проверяем размеры выборок\n",
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"print(\"Train shape (classification):\", X_train.shape)\n",
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"print(\"Validation shape (classification):\", X_val.shape)\n",
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"print(\"Test shape (classification):\", X_test.shape)\n"
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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": 6,
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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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"Распределение классов (Classification):\n",
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" category\n",
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"4 36201\n",
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"3 27626\n",
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"2 15660\n",
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"1 11413\n",
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"0 6445\n",
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"5 42\n",
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"Name: count, dtype: int64\n",
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"Распределение классов после балансировки:\n",
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" category\n",
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"4 36201\n",
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"3 36201\n",
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"1 36201\n",
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"2 36201\n",
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"0 36201\n",
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"5 36201\n",
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"Name: count, dtype: int64\n"
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]
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}
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],
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"source": [
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"# Проверяем сбалансированность\n",
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"print(\"Распределение классов (Classification):\\n\", y_train_class.value_counts())\n",
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"\n",
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"# Применяем SMOTE для балансировки классов\n",
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"smote = SMOTE(random_state=42)\n",
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"X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train_class)\n",
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"\n",
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"# Проверяем результат\n",
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"print(\"Распределение классов после балансировки:\\n\", pd.Series(y_train_balanced).value_counts())\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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