diff --git a/lab_2/lab2.ipynb b/lab_2/lab2.ipynb
index ca778d3..cd3c03b 100644
--- a/lab_2/lab2.ipynb
+++ b/lab_2/lab2.ipynb
@@ -78,7 +78,9 @@
     "Диаметр, скорость, расстояние, орбитальные параметры астероидов.\n",
     "\n",
     "**Целевой признак:**\n",
-    "Признак \"hazardous\" – бинарная метка, указывающая на потенциальную опасность астероида."
+    "Признак \"hazardous\" – бинарная метка, указывающая на потенциальную опасность астероида.\n",
+    "\n",
+    "---"
    ]
   },
   {
@@ -95,6 +97,7 @@
    "outputs": [],
    "source": [
     "from typing import Any\n",
+    "from math import ceil\n",
     "\n",
     "import pandas as pd\n",
     "from pandas import DataFrame, Series\n",
@@ -139,24 +142,140 @@
       " 8   absolute_magnitude  90836 non-null  float64\n",
       " 9   hazardous           90836 non-null  bool   \n",
       "dtypes: bool(2), float64(5), int64(1), object(2)\n",
-      "memory usage: 5.7+ MB\n",
-      "\n",
-      "                       count          mean           std           min  \\\n",
-      "id                  90836.0  1.438288e+07  2.087202e+07  2.000433e+06   \n",
-      "est_diameter_min    90836.0  1.274321e-01  2.985112e-01  6.089126e-04   \n",
-      "est_diameter_max    90836.0  2.849469e-01  6.674914e-01  1.361570e-03   \n",
-      "relative_velocity   90836.0  4.806692e+04  2.529330e+04  2.033464e+02   \n",
-      "miss_distance       90836.0  3.706655e+07  2.235204e+07  6.745533e+03   \n",
-      "absolute_magnitude  90836.0  2.352710e+01  2.894086e+00  9.230000e+00   \n",
-      "\n",
-      "                             25%           50%           75%           max  \n",
-      "id                  3.448110e+06  3.748362e+06  3.884023e+06  5.427591e+07  \n",
-      "est_diameter_min    1.925551e-02  4.836765e-02  1.434019e-01  3.789265e+01  \n",
-      "est_diameter_max    4.305662e-02  1.081534e-01  3.206564e-01  8.473054e+01  \n",
-      "relative_velocity   2.861902e+04  4.419012e+04  6.292360e+04  2.369901e+05  \n",
-      "miss_distance       1.721082e+07  3.784658e+07  5.654900e+07  7.479865e+07  \n",
-      "absolute_magnitude  2.134000e+01  2.370000e+01  2.570000e+01  3.320000e+01  \n"
+      "memory usage: 5.7+ MB\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>est_diameter_min</th>\n",
+       "      <th>est_diameter_max</th>\n",
+       "      <th>relative_velocity</th>\n",
+       "      <th>miss_distance</th>\n",
+       "      <th>absolute_magnitude</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>9.083600e+04</td>\n",
+       "      <td>90836.000000</td>\n",
+       "      <td>90836.000000</td>\n",
+       "      <td>90836.000000</td>\n",
+       "      <td>9.083600e+04</td>\n",
+       "      <td>90836.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.438288e+07</td>\n",
+       "      <td>0.127432</td>\n",
+       "      <td>0.284947</td>\n",
+       "      <td>48066.918918</td>\n",
+       "      <td>3.706655e+07</td>\n",
+       "      <td>23.527103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.087202e+07</td>\n",
+       "      <td>0.298511</td>\n",
+       "      <td>0.667491</td>\n",
+       "      <td>25293.296961</td>\n",
+       "      <td>2.235204e+07</td>\n",
+       "      <td>2.894086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2.000433e+06</td>\n",
+       "      <td>0.000609</td>\n",
+       "      <td>0.001362</td>\n",
+       "      <td>203.346433</td>\n",
+       "      <td>6.745533e+03</td>\n",
+       "      <td>9.230000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.448110e+06</td>\n",
+       "      <td>0.019256</td>\n",
+       "      <td>0.043057</td>\n",
+       "      <td>28619.020645</td>\n",
+       "      <td>1.721082e+07</td>\n",
+       "      <td>21.340000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.748362e+06</td>\n",
+       "      <td>0.048368</td>\n",
+       "      <td>0.108153</td>\n",
+       "      <td>44190.117890</td>\n",
+       "      <td>3.784658e+07</td>\n",
+       "      <td>23.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.884023e+06</td>\n",
+       "      <td>0.143402</td>\n",
+       "      <td>0.320656</td>\n",
+       "      <td>62923.604633</td>\n",
+       "      <td>5.654900e+07</td>\n",
+       "      <td>25.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>5.427591e+07</td>\n",
+       "      <td>37.892650</td>\n",
+       "      <td>84.730541</td>\n",
+       "      <td>236990.128088</td>\n",
+       "      <td>7.479865e+07</td>\n",
+       "      <td>33.200000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 id  est_diameter_min  est_diameter_max  relative_velocity  \\\n",
+       "count  9.083600e+04      90836.000000      90836.000000       90836.000000   \n",
+       "mean   1.438288e+07          0.127432          0.284947       48066.918918   \n",
+       "std    2.087202e+07          0.298511          0.667491       25293.296961   \n",
+       "min    2.000433e+06          0.000609          0.001362         203.346433   \n",
+       "25%    3.448110e+06          0.019256          0.043057       28619.020645   \n",
+       "50%    3.748362e+06          0.048368          0.108153       44190.117890   \n",
+       "75%    3.884023e+06          0.143402          0.320656       62923.604633   \n",
+       "max    5.427591e+07         37.892650         84.730541      236990.128088   \n",
+       "\n",
+       "       miss_distance  absolute_magnitude  \n",
+       "count   9.083600e+04        90836.000000  \n",
+       "mean    3.706655e+07           23.527103  \n",
+       "std     2.235204e+07            2.894086  \n",
+       "min     6.745533e+03            9.230000  \n",
+       "25%     1.721082e+07           21.340000  \n",
+       "50%     3.784658e+07           23.700000  \n",
+       "75%     5.654900e+07           25.700000  \n",
+       "max     7.479865e+07           33.200000  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -164,7 +283,7 @@
     "df.info()\n",
     "\n",
     "# Статистическое описание числовых столбцов\n",
-    "print('\\n', df.describe().transpose())"
+    "df.describe()"
    ]
   },
   {
@@ -259,7 +378,7 @@
       "\tЕсть выбросы: Да\n",
       "\tКоличество выбросов: 8306\n",
       "\tМинимальное значение: 0.0006089126\n",
-      "\tМаксимальное значение: 0.32962154705\n",
+      "\tМаксимальное значение: 37.8926498379\n",
       "\t1-й квартиль (Q1): 0.0192555078\n",
       "\t3-й квартиль (Q3): 0.1434019235\n",
       "\n",
@@ -267,7 +386,7 @@
       "\tЕсть выбросы: Да\n",
       "\tКоличество выбросов: 8306\n",
       "\tМинимальное значение: 0.00136157\n",
-      "\tМаксимальное значение: 0.7370561859\n",
+      "\tМаксимальное значение: 84.7305408852\n",
       "\t1-й квартиль (Q1): 0.0430566244\n",
       "\t3-й квартиль (Q3): 0.320656449\n",
       "\n",
@@ -275,7 +394,7 @@
       "\tЕсть выбросы: Да\n",
       "\tКоличество выбросов: 1574\n",
       "\tМинимальное значение: 203.34643253\n",
-      "\tМаксимальное значение: 114380.48061454494\n",
+      "\tМаксимальное значение: 236990.1280878666\n",
       "\t1-й квартиль (Q1): 28619.02064490995\n",
       "\t3-й квартиль (Q3): 62923.60463276395\n",
       "\n",
@@ -290,12 +409,22 @@
       "Колонка absolute_magnitude:\n",
       "\tЕсть выбросы: Да\n",
       "\tКоличество выбросов: 101\n",
-      "\tМинимальное значение: 14.8\n",
-      "\tМаксимальное значение: 32.239999999999995\n",
+      "\tМинимальное значение: 9.23\n",
+      "\tМаксимальное значение: 33.2\n",
       "\t1-й квартиль (Q1): 21.34\n",
       "\t3-й квартиль (Q3): 25.7\n",
       "\n"
      ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -309,7 +438,7 @@
     "]\n",
     "\n",
     "# Проверка выбросов в DataFrame\n",
-    "def check_outliers(dataframe: DataFrame, columns: list[str]) -> DataFrame:\n",
+    "def check_outliers(dataframe: DataFrame, columns: list[str]) -> None:\n",
     "    for column in columns:\n",
     "        if not pd.api.types.is_numeric_dtype(dataframe[column]): # Проверяем, является ли колонка числовой\n",
     "            continue\n",
@@ -326,11 +455,6 @@
     "        outliers: DataFrame = dataframe[(dataframe[column] < lower_bound) | (dataframe[column] > upper_bound)]\n",
     "        outlier_count: int = outliers.shape[0]\n",
     "\n",
-    "        # Устраняем выбросы:\n",
-    "        # Заменяем значения ниже нижней границы на нижнюю границу\n",
-    "        # А значения выше верхней границы – на верхнюю\n",
-    "        dataframe[column] = dataframe[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
-    "\n",
     "        print(f\"Колонка {column}:\")\n",
     "        print(f\"\\tЕсть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
     "        print(f\"\\tКоличество выбросов: {outlier_count}\")\n",
@@ -338,12 +462,119 @@
     "        print(f\"\\tМаксимальное значение: {dataframe[column].max()}\")\n",
     "        print(f\"\\t1-й квартиль (Q1): {Q1}\")\n",
     "        print(f\"\\t3-й квартиль (Q3): {Q3}\\n\")\n",
+    "\n",
+    "# Визуализация выбросов\n",
+    "def visualize_outliers(dataframe: DataFrame, columns: list[str]) -> None:\n",
+    "    # Диаграммы размахов\n",
+    "    plt.figure(figsize=(15, 10))\n",
+    "    rows: int = ceil(len(columns) / 3)\n",
+    "    for index, column in enumerate(columns, 1):\n",
+    "        plt.subplot(rows, 3, index)\n",
+    "        plt.boxplot(dataframe[column], vert=True, patch_artist=True)\n",
+    "        plt.title(f\"Диаграмма размахов для \\\"{column}\\\"\")\n",
+    "        plt.xlabel(column)\n",
+    "            \n",
+    "    # Отображение графиков\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# Проверка выбросов\n",
+    "check_outliers(df, numeric_columns)\n",
+    "visualize_outliers(df, numeric_columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка est_diameter_min:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0.0006089126\n",
+      "\tМаксимальное значение: 0.32962154705\n",
+      "\t1-й квартиль (Q1): 0.0192555078\n",
+      "\t3-й квартиль (Q3): 0.1434019235\n",
+      "\n",
+      "Колонка est_diameter_max:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0.00136157\n",
+      "\tМаксимальное значение: 0.7370561859\n",
+      "\t1-й квартиль (Q1): 0.0430566244\n",
+      "\t3-й квартиль (Q3): 0.320656449\n",
+      "\n",
+      "Колонка relative_velocity:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 203.34643253\n",
+      "\tМаксимальное значение: 114380.48061454494\n",
+      "\t1-й квартиль (Q1): 28619.02064490995\n",
+      "\t3-й квартиль (Q3): 62923.60463276395\n",
+      "\n",
+      "Колонка miss_distance:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 6745.532515957\n",
+      "\tМаксимальное значение: 74798651.4521972\n",
+      "\t1-й квартиль (Q1): 17210820.23576468\n",
+      "\t3-й квартиль (Q3): 56548996.45139917\n",
+      "\n",
+      "Колонка absolute_magnitude:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 14.8\n",
+      "\tМаксимальное значение: 32.239999999999995\n",
+      "\t1-й квартиль (Q1): 21.34\n",
+      "\t3-й квартиль (Q3): 25.7\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Устранить выборсы в DataFrame\n",
+    "def remove_outliers(dataframe: DataFrame, columns: list[str]) -> DataFrame:\n",
+    "    for column in columns:\n",
+    "        if not pd.api.types.is_numeric_dtype(dataframe[column]): # Проверяем, является ли колонка числовой\n",
+    "            continue\n",
+    "        \n",
+    "        Q1: float = dataframe[column].quantile(0.25) # 1-й квартиль (25%)\n",
+    "        Q3: float = dataframe[column].quantile(0.75) # 3-й квартиль (75%)\n",
+    "        IQR: float = Q3 - Q1 # Вычисляем межквартильный размах\n",
+    "\n",
+    "        # Определяем границы для выбросов\n",
+    "        lower_bound: float = Q1 - 1.5 * IQR # Нижняя граница\n",
+    "        upper_bound: float = Q3 + 1.5 * IQR # Верхняя граница\n",
+    "\n",
+    "        # Устраняем выбросы:\n",
+    "        # Заменяем значения ниже нижней границы на нижнюю границу\n",
+    "        # А значения выше верхней границы – на верхнюю\n",
+    "        dataframe[column] = dataframe[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
     "    \n",
     "    return dataframe\n",
     "\n",
     "\n",
+    "# Устраняем выборсы\n",
+    "df: DataFrame = remove_outliers(df, numeric_columns)\n",
+    "\n",
     "# Проверка выбросов\n",
-    "df: DataFrame = check_outliers(df, numeric_columns)"
+    "check_outliers(df, numeric_columns)\n",
+    "visualize_outliers(df, numeric_columns)"
    ]
   },
   {
@@ -372,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -604,7 +835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -612,32 +843,32 @@
      "output_type": "stream",
      "text": [
       "После применения метода oversampling:\n",
-      "Обучающая выборка:  (100573, 6)\n",
+      "Обучающая выборка:  (100699, 6)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
-      "True     51376\n",
+      "True     51502\n",
       "False    49197\n",
       "Name: count, dtype: int64\n",
-      "Процент объектов класса \"True\": 51.08%\n",
-      "Процент объектов класса \"False\": 48.92%\n",
+      "Процент объектов класса \"True\": 51.14%\n",
+      "Процент объектов класса \"False\": 48.86%\n",
       "\n",
-      "Контрольная выборка:  (32787, 6)\n",
+      "Контрольная выборка:  (32759, 6)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
       "False    16399\n",
-      "True     16388\n",
+      "True     16360\n",
       "Name: count, dtype: int64\n",
-      "Процент объектов класса \"False\": 50.02%\n",
-      "Процент объектов класса \"True\": 49.98%\n",
+      "Процент объектов класса \"False\": 50.06%\n",
+      "Процент объектов класса \"True\": 49.94%\n",
       "\n",
-      "Тестовая выборка:  (32750, 6)\n",
+      "Тестовая выборка:  (33556, 6)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
+      "True     17156\n",
       "False    16400\n",
-      "True     16350\n",
       "Name: count, dtype: int64\n",
-      "Процент объектов класса \"False\": 50.08%\n",
-      "Процент объектов класса \"True\": 49.92%\n",
+      "Процент объектов класса \"True\": 51.13%\n",
+      "Процент объектов класса \"False\": 48.87%\n",
       "\n",
       "Для обучающей выборки аугментация данных не требуется\n",
       "Для контрольной выборки аугментация данных не требуется\n",
@@ -646,7 +877,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -690,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -759,7 +990,6 @@
     "df_val_undersampled: DataFrame = undersample(df_val, 'hazardous')\n",
     "df_test_undersampled: DataFrame = undersample(df_test, 'hazardous')\n",
     "\n",
-    "\n",
     "# Проверка сбалансированности\n",
     "print('После применения метода undersampling:')\n",
     "check_balance(df_train_undersampled, 'Обучающая выборка', 'hazardous')\n",