diff --git a/lab_2/lab2.ipynb b/lab_2/lab2.ipynb
index 9a2e66e..940ee51 100644
--- a/lab_2/lab2.ipynb
+++ b/lab_2/lab2.ipynb
@@ -671,16 +671,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Размер обучающей выборки: (433, 8)\n",
-      "Размер контрольной выборки: (145, 8)\n",
-      "Размер тестовой выборки: (145, 8)\n"
+      "Размер обучающей выборки: 433\n",
+      "Размер контрольной выборки: 145\n",
+      "Размер тестовой выборки: 145\n"
      ]
     }
    ],
@@ -689,67 +689,606 @@
     "from sklearn.model_selection import train_test_split\n",
     "\n",
     "# Разделение на признаки (X) и целевую переменную (y)\n",
-    "X = df.drop('Insulin', axis=1)\n",
-    "y = df['Insulin']\n",
+    "X = df.drop('Outcome', axis=1)  # Признаки\n",
+    "y = df['Outcome']  # Целевая переменная\n",
     "\n",
-    "# Разбиение на обучающую и остальную выборку (контрольную + тестовую)\n",
-    "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42)\n",
+    "# Разбиение на обучающую и оставшуюся часть (контрольная + тестовая)\n",
+    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
     "\n",
-    "# Разбиение остатка на контрольную и тестовую выборки\n",
-    "X_val, X_test, y_val, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42)\n",
+    "# Разбиение оставшейся части на контрольную и тестовую выборки\n",
+    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
     "\n",
     "# Вывод размеров выборок\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": "markdown",
-   "metadata": {},
-   "source": [
-    "## НЛО"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Проблемная область: наблюдениями и отчетами о неопознанных летающих объектах (НЛО)\n",
-    "\n",
-    "Объект наблюдения: неопознанные летающие объекты (НЛО)\n",
-    "\n",
-    "Атрибуты: описание, город, штат, дата и время наблюдения, форма НЛО, продолжительность наблюдения, статистика, ссылка на отчет о наблюдении, текст отчета о наблюдении, дата публикации отчета, широта города, долгота города.\n",
-    "с\n",
-    "Пример бизнес-цели: \n",
-    "\n",
-    "    1. Повышение эффективности скрининга диабета. Цель технического проекта: Разработать и обучить модель машинного обучения с точностью предсказания не менее 85% для автоматизированного скрининга диабета на основе данных датасета \"Диабет у индейцев Пима\".\n",
-    "\n",
-    "    2. Снижение медицинских расходов. Цель технического проекта: Оптимизировать модель прогнозирования таким образом, чтобы минимизировать количество ложноотрицательных результатов (пациенты с диабетом, которые не были выявлены), что позволит снизить затраты на лечение осложнений.\n",
-    "\n",
-    "    3. Повышение качества жизни пациентов. Цель технического проекта: Разработать интерфейс для модели, который будет предоставлять пациентам персонализированные рекомендации по профилактике и лечению диабета на основе их индивидуальных рисков, определенных моделью."
+    "print(\"Размер обучающей выборки:\", X_train.shape[0])\n",
+    "print(\"Размер контрольной выборки:\", X_val.shape[0])\n",
+    "print(\"Размер тестовой выборки:\", X_test.shape[0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Index(['summary', 'city', 'state', 'date_time', 'shape', 'duration', 'stats',\n",
-      "       'report_link', 'text', 'posted', 'city_latitude', 'city_longitude'],\n",
+      "Сбалансированность обучающей выборки:\n",
+      "Outcome\n",
+      "0    0.658199\n",
+      "1    0.341801\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Сбалансированность контрольной выборки:\n",
+      "Outcome\n",
+      "0    0.655172\n",
+      "1    0.344828\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Сбалансированность тестовой выборки:\n",
+      "Outcome\n",
+      "0    0.662069\n",
+      "1    0.337931\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Разделение на признаки (X) и целевую переменную (y)\n",
+    "X = df.drop('Outcome', axis=1)  # Признаки\n",
+    "y = df['Outcome']  # Целевая переменная\n",
+    "\n",
+    "# Разбиение на обучающую и оставшуюся часть (контрольная + тестовая)\n",
+    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42, stratify=y)\n",
+    "\n",
+    "# Разбиение оставшейся части на контрольную и тестовую выборки\n",
+    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)\n",
+    "\n",
+    "# Функция для проверки сбалансированности выборок\n",
+    "def check_balance(y_train, y_val, y_test):\n",
+    "    print(\"Сбалансированность обучающей выборки:\")\n",
+    "    print(y_train.value_counts(normalize=True))\n",
+    "    \n",
+    "    print(\"\\nСбалансированность контрольной выборки:\")\n",
+    "    print(y_val.value_counts(normalize=True))\n",
+    "    \n",
+    "    print(\"\\nСбалансированность тестовой выборки:\")\n",
+    "    print(y_test.value_counts(normalize=True))\n",
+    "\n",
+    "# Проверка сбалансированности\n",
+    "check_balance(y_train, y_val, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выборки относительно сбалансированы, но не идеально. Сделаем приращение данных методом выборки с избытком (oversampling)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Сбалансированность обучающей выборки после SMOTE:\n",
+      "Outcome\n",
+      "0    0.5\n",
+      "1    0.5\n",
+      "Name: proportion, dtype: float64\n",
+      "Сбалансированность обучающей выборки:\n",
+      "Outcome\n",
+      "0    0.5\n",
+      "1    0.5\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Сбалансированность контрольной выборки:\n",
+      "Outcome\n",
+      "0    0.655172\n",
+      "1    0.344828\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Сбалансированность тестовой выборки:\n",
+      "Outcome\n",
+      "0    0.662069\n",
+      "1    0.337931\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "\n",
+    "# Предположим, что ваш датасет уже загружен в DataFrame df\n",
+    "# df = pd.read_csv('your_dataset.csv')\n",
+    "\n",
+    "# Разделение на признаки (X) и целевую переменную (y)\n",
+    "X = df.drop('Outcome', axis=1)  # Признаки\n",
+    "y = df['Outcome']  # Целевая переменная\n",
+    "\n",
+    "# Разбиение на обучающую и оставшуюся часть (контрольная + тестовая)\n",
+    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42, stratify=y)\n",
+    "\n",
+    "# Разбиение оставшейся части на контрольную и тестовую выборки\n",
+    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)\n",
+    "\n",
+    "# Применение SMOTE для балансировки обучающей выборки\n",
+    "smote = SMOTE(random_state=42)\n",
+    "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n",
+    "\n",
+    "# Функция для проверки сбалансированности выборок\n",
+    "def check_balance(y_train, y_val, y_test):\n",
+    "    print(\"Сбалансированность обучающей выборки:\")\n",
+    "    print(y_train.value_counts(normalize=True))\n",
+    "    \n",
+    "    print(\"\\nСбалансированность контрольной выборки:\")\n",
+    "    print(y_val.value_counts(normalize=True))\n",
+    "    \n",
+    "    print(\"\\nСбалансированность тестовой выборки:\")\n",
+    "    print(y_test.value_counts(normalize=True))\n",
+    "\n",
+    "# Проверка сбалансированности после SMOTE\n",
+    "print(\"Сбалансированность обучающей выборки после SMOTE:\")\n",
+    "print(y_train_resampled.value_counts(normalize=True))\n",
+    "\n",
+    "# Проверка сбалансированности контрольной и тестовой выборок\n",
+    "check_balance(y_train_resampled, y_val, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Покемоны"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проблемная область: серия игр \"Pokémon\"\n",
+    "\n",
+    "Объект наблюдения: покемоны - вымышленные существа, обладающие уникальными характеристиками и способностями\n",
+    "\n",
+    "Атрибуты: изображение, индекс, имя, тип 1, тип 2, показатель силы, очки здоровья, атака, защита, специальная атака, специальная защита, скорость\n",
+    "\n",
+    "Пример бизнес-цели: \n",
+    "\n",
+    "    1. Разработка рекомендательной системы для выбора покемона в игре. Цель технического проекта: Разработать алгоритм машинного обучения, который будет анализировать характеристики покемонов и предлагать игрокам наиболее подходящих покемонов для выбора в зависимости от их стиля игры, предпочтений и целей.\n",
+    "\n",
+    "    2. Анализ сильных и слабых сторон покемонов для оптимизации баланса игры. Цель технического проекта: Провести статистический анализ данных о покемонах для выявления дисбаланса в игре, такого как слишком сильные или слабые типы покемонов, несбалансированные характеристики и т.д.\n",
+    "\n",
+    "    3. Создание веб-приложения для поиска и сравнения покемонов. Цель технического проекта: Разработать веб-приложение, которое позволит пользователям искать покемонов по различным критериям (название, тип, характеристики) и сравнивать их между собой."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Image', 'Index', 'Name', 'Type 1', 'Type 2', 'Total', 'HP', 'Attack',\n",
+      "       'Defense', 'SP. Atk.', 'SP. Def', 'Speed'],\n",
       "      dtype='object')\n"
      ]
     }
    ],
    "source": [
     "import pandas as pd \n",
-    "df = pd.read_csv(\"..//static//csv//nuforc_reports.csv\")\n",
+    "df = pd.read_csv(\"..//static//csv//pokedex.csv\")\n",
     "print(df.columns)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверка на выбросы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество выбросов в столбце 'HP': 27\n",
+      "Количество выбросов в столбце 'Attack': 13\n",
+      "Количество выбросов в столбце 'Defense': 25\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "df = pd.read_csv(\"..//static//csv//pokedex.csv\")\n",
+    "\n",
+    "# Выбираем числовые столбцы\n",
+    "numeric_columns = ['HP', 'Attack', 'Defense']\n",
+    "\n",
+    "# Выбираем столбцы для анализа\n",
+    "columns_to_check = ['HP', 'Attack', 'Defense']\n",
+    "\n",
+    "# Функция для подсчета выбросов\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",
+    "        # Считаем количество выбросов\n",
+    "        outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
+    "        outliers_count[col] = len(outliers)\n",
+    "    \n",
+    "    return outliers_count\n",
+    "\n",
+    "# Подсчитываем выбросы\n",
+    "outliers_count = count_outliers(df, columns_to_check)\n",
+    "\n",
+    "# Выводим количество выбросов для каждого столбца\n",
+    "for col, count in outliers_count.items():\n",
+    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
+    "    \n",
+    "# Создаем гистограммы\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, col in enumerate(numeric_columns, 1):\n",
+    "    plt.subplot(2, 3, i)\n",
+    "    sns.histplot(df[col], kde=True)\n",
+    "    plt.title(f'Histogram of {col}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Во всех проверенных столбцах присутствуют выбросы. Удалим их."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество удаленных строк: 0\n",
+      "Количество выбросов в столбце 'HP': 0\n",
+      "Количество выбросов в столбце 'Attack': 0\n",
+      "Количество выбросов в столбце 'Defense': 0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "   \n",
+    "# Выбираем столбцы для очистки\n",
+    "columns_to_clean =  ['HP', 'Attack', 'Defense']\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",
+    "df = df_cleaned\n",
+    "\n",
+    "# Выбираем столбцы для анализа\n",
+    "columns_to_check = ['HP', 'Attack', 'Defense']\n",
+    "\n",
+    "# Функция для подсчета выбросов\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",
+    "        # Считаем количество выбросов\n",
+    "        outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
+    "        outliers_count[col] = len(outliers)\n",
+    "    \n",
+    "    return outliers_count\n",
+    "\n",
+    "# Подсчитываем выбросы\n",
+    "outliers_count = count_outliers(df, columns_to_check)\n",
+    "\n",
+    "# Выводим количество выбросов для каждого столбца\n",
+    "for col, count in outliers_count.items():\n",
+    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
+    " \n",
+    "\n",
+    "# Создаем гистограммы\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, col in enumerate(columns_to_clean, 1):\n",
+    "    plt.subplot(2, 3, i)\n",
+    "    sns.histplot(df[col], kde=True)\n",
+    "    plt.title(f'Histogram of {col}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверка на пропущенные значения"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество пропущенных значений в каждом столбце:\n",
+      "Image         0\n",
+      "Index         0\n",
+      "Name          0\n",
+      "Type 1        0\n",
+      "Type 2      526\n",
+      "Total         0\n",
+      "HP            0\n",
+      "Attack        0\n",
+      "Defense       0\n",
+      "SP. Atk.      0\n",
+      "SP. Def       0\n",
+      "Speed         0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Проверка на пропущенные значения\n",
+    "missing_values = df.isnull().sum()\n",
+    "\n",
+    "# Вывод результатов\n",
+    "print(\"Количество пропущенных значений в каждом столбце:\")\n",
+    "print(missing_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В датасете присутствуют пропущенные значения. Удалим их."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Количество пропущенных значений в каждом столбце после удаления:\n",
+      "Image       0\n",
+      "Index       0\n",
+      "Name        0\n",
+      "Type 1      0\n",
+      "Type 2      0\n",
+      "Total       0\n",
+      "HP          0\n",
+      "Attack      0\n",
+      "Defense     0\n",
+      "SP. Atk.    0\n",
+      "SP. Def     0\n",
+      "Speed       0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Удаление пропущенных значений в столбце 'Type 2'\n",
+    "df = df.dropna(subset=['Type 2'])\n",
+    "\n",
+    "# Проверка на пропущенные значения после удаления\n",
+    "missing_values_after_drop = df.isnull().sum()\n",
+    "\n",
+    "# Вывод результатов после удаления\n",
+    "print(\"\\nКоличество пропущенных значений в каждом столбце после удаления:\")\n",
+    "print(missing_values_after_drop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Пропущенные значения удалены. Сделаем разбиение для выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: (378, 11) (378,)\n",
+      "Размер контрольной выборки: (126, 11) (126,)\n",
+      "Размер тестовой выборки: (126, 11) (126,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Разделение на признаки (X) и целевую переменную (y)\n",
+    "# Предположим, что 'Total' - это целевая переменная\n",
+    "X = df.drop(columns=['Total'])\n",
+    "y = df['Total']\n",
+    "\n",
+    "# Разбиение на обучающую и остальную выборку (60% обучающая, 40% остальная)\n",
+    "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42)\n",
+    "\n",
+    "# Разбиение остальной выборки на контрольную и тестовую (50% контрольная, 50% тестовая)\n",
+    "X_valid, X_test, y_valid, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42)\n",
+    "\n",
+    "# Вывод размеров выборок\n",
+    "print(\"Размер обучающей выборки:\", X_train.shape, y_train.shape)\n",
+    "print(\"Размер контрольной выборки:\", X_valid.shape, y_valid.shape)\n",
+    "print(\"Размер тестовой выборки:\", X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Анализ сбалансированности выборки:\n",
+      "\n",
+      "Обучающая выборка:\n",
+      "count    378.000000\n",
+      "mean     462.941799\n",
+      "std      116.093563\n",
+      "min      185.000000\n",
+      "25%      377.750000\n",
+      "50%      485.000000\n",
+      "75%      535.000000\n",
+      "max      700.000000\n",
+      "Name: Total, dtype: float64\n",
+      "\n",
+      "Контрольная выборка:\n",
+      "count    126.000000\n",
+      "mean     455.753968\n",
+      "std      102.380599\n",
+      "min      198.000000\n",
+      "25%      402.000000\n",
+      "50%      478.000000\n",
+      "75%      520.000000\n",
+      "max      700.000000\n",
+      "Name: Total, dtype: float64\n",
+      "\n",
+      "Тестовая выборка:\n",
+      "count    126.000000\n",
+      "mean     462.547619\n",
+      "std      110.064680\n",
+      "min      205.000000\n",
+      "25%      362.500000\n",
+      "50%      485.000000\n",
+      "75%      530.000000\n",
+      "max      680.000000\n",
+      "Name: Total, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Разделение на признаки (X) и целевую переменную (y)\n",
+    "# Предположим, что 'Total' - это целевая переменная\n",
+    "X = df.drop(columns=['Total'])\n",
+    "y = df['Total']\n",
+    "\n",
+    "# Разбиение на обучающую и остальную выборку (60% обучающая, 40% остальная)\n",
+    "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42)\n",
+    "\n",
+    "# Разбиение остальной выборки на контрольную и тестовую (50% контрольная, 50% тестовая)\n",
+    "X_valid, X_test, y_valid, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42)\n",
+    "\n",
+    "# Функция для анализа сбалансированности выборки\n",
+    "def analyze_balance(y_train, y_valid, y_test):\n",
+    "    print(\"Анализ сбалансированности выборки:\")\n",
+    "    \n",
+    "    # Описание для обучающей выборки\n",
+    "    print(\"\\nОбучающая выборка:\")\n",
+    "    print(y_train.describe())\n",
+    "    \n",
+    "    # Описание для контрольной выборки\n",
+    "    print(\"\\nКонтрольная выборка:\")\n",
+    "    print(y_valid.describe())\n",
+    "    \n",
+    "    # Описание для тестовой выборки\n",
+    "    print(\"\\nТестовая выборка:\")\n",
+    "    print(y_test.describe())\n",
+    "\n",
+    "# Анализ сбалансированности выборки\n",
+    "analyze_balance(y_train, y_valid, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выборка сбалансирована"
+   ]
   }
  ],
  "metadata": {