diff --git a/mai/lab2.2.ipynb b/mai/lab2.2.ipynb
new file mode 100644
index 0000000..545d3fe
--- /dev/null
+++ b/mai/lab2.2.ipynb
@@ -0,0 +1,327 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n",
+      "0        8212\n",
+      "1        8215\n",
+      "2        8216\n",
+      "3        8217\n",
+      "4        8218\n",
+      "        ...  \n",
+      "8031    19860\n",
+      "8032    19863\n",
+      "8033    19864\n",
+      "8034    19865\n",
+      "8035    19866\n",
+      "Name: Date_numeric, Length: 8036, dtype: int64\n",
+      "Зашумленные столбцы: []\n",
+      "Смещение: Open             1.086680\n",
+      "High             1.086383\n",
+      "Low              1.087102\n",
+      "Close            1.086685\n",
+      "Adj Close        1.213587\n",
+      "Volume          13.602510\n",
+      "Date_numeric     0.000505\n",
+      "dtype: float64\n",
+      "Сильно смещенные столбцы: ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']\n",
+      "Данные за последние 25 лет\n",
+      "Выбросы в столбце 'Open':\n",
+      "Series([], Name: Open, dtype: float64)\n",
+      "\n",
+      "Выбросы в столбце 'High':\n",
+      "Series([], Name: High, dtype: float64)\n",
+      "\n",
+      "Выбросы в столбце 'Low':\n",
+      "Series([], Name: Low, dtype: float64)\n",
+      "\n",
+      "Выбросы в столбце 'Close':\n",
+      "Series([], Name: Close, dtype: float64)\n",
+      "\n",
+      "Выбросы в столбце 'Adj Close':\n",
+      "7321    114.799438\n",
+      "7322    117.926170\n",
+      "7323    118.010414\n",
+      "7324    117.982330\n",
+      "7325    114.593483\n",
+      "7326    114.565414\n",
+      "7327    113.676071\n",
+      "Name: Adj Close, dtype: float64\n",
+      "\n",
+      "Выбросы в столбце 'Volume':\n",
+      "0       224358400\n",
+      "1        58732800\n",
+      "2        34777600\n",
+      "33       48320000\n",
+      "103      46131200\n",
+      "          ...    \n",
+      "6444     51851700\n",
+      "6544     62091100\n",
+      "6550     33210100\n",
+      "6639     45573000\n",
+      "8019     66610700\n",
+      "Name: Volume, Length: 451, dtype: int64\n",
+      "\n",
+      "Выбросы в столбце 'Date_numeric':\n",
+      "Series([], Name: Date_numeric, dtype: int64)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Date' и 'Date_numeric'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Open' и 'High'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Open' и 'Low'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Open' и 'Close'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Open' и 'Adj Close'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'High' и 'Open'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'High' и 'Low'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'High' и 'Close'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'High' и 'Adj Close'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Low' и 'Open'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Low' и 'High'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Low' и 'Close'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Low' и 'Adj Close'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Close' и 'Open'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Close' и 'High'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Close' и 'Low'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Close' и 'Adj Close'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Adj Close' и 'Open'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Adj Close' и 'High'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Adj Close' и 'Low'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Adj Close' и 'Close'\n",
+      "Просачивание данных: Высокая корреляция (1.00) между столбцами 'Date_numeric' и 'Date'\n",
+      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n",
+      "       'Date_numeric', 'log_close', 'log_volume', 'Close_binned'],\n",
+      "      dtype='object')\n",
+      "Обучающая выборка:  (4821, 11)\n",
+      "Close\n",
+      "0.765625      14\n",
+      "0.750000      12\n",
+      "0.789063      11\n",
+      "0.882813      10\n",
+      "0.703125      10\n",
+      "              ..\n",
+      "91.949997      1\n",
+      "48.685001      1\n",
+      "103.870003     1\n",
+      "2.414063       1\n",
+      "4.280000       1\n",
+      "Name: count, Length: 3678, dtype: int64\n",
+      "Контрольная выборка:  (1607, 11)\n",
+      "Close\n",
+      "0.750000      6\n",
+      "0.796875      5\n",
+      "3.320313      4\n",
+      "0.835938      4\n",
+      "0.601563      4\n",
+      "             ..\n",
+      "0.414063      1\n",
+      "111.070000    1\n",
+      "11.790000     1\n",
+      "59.610001     1\n",
+      "2.472656      1\n",
+      "Name: count, Length: 1438, dtype: int64\n",
+      "Тестовая выборка:  (1607, 11)\n",
+      "Close\n",
+      "0.750000     6\n",
+      "0.765625     6\n",
+      "3.000000     4\n",
+      "0.601563     4\n",
+      "0.707031     4\n",
+      "            ..\n",
+      "98.599998    1\n",
+      "56.110001    1\n",
+      "0.621094     1\n",
+      "21.740000    1\n",
+      "98.000000    1\n",
+      "Name: count, Length: 1444, dtype: int64\n",
+      "Обучающая выборка после undersampling:  (4773, 11)\n",
+      "Close\n",
+      "0.765625     14\n",
+      "0.750000     12\n",
+      "0.789063     11\n",
+      "0.882813     10\n",
+      "0.703125     10\n",
+      "             ..\n",
+      "98.230003     1\n",
+      "27.315001     1\n",
+      "8.995000      1\n",
+      "81.930000     1\n",
+      "12.357500     1\n",
+      "Name: count, Length: 3641, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.feature_selection import mutual_info_regression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from imblearn.over_sampling import ADASYN\n",
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "df = pd.read_csv(\"data/Coffe.csv\")\n",
+    "print(df.columns)\n",
+    "\n",
+    "df['Date'] = pd.to_datetime(df['Date'])\n",
+    "df['Date_numeric'] = (df['Date'] - pd.Timestamp('1970-01-01')).dt.days\n",
+    "print(df['Date_numeric'])\n",
+    "\n",
+    "noisy_features = []\n",
+    "for col in df.columns:\n",
+    "  if df[col].isnull().sum() / len(df) > 0.1: \n",
+    "    noisy_features.append(col)\n",
+    "print(f\"Зашумленные столбцы: {noisy_features}\")\n",
+    " \n",
+    "skewness = df.select_dtypes(include=[np.number]).skew()\n",
+    "print(f\"Смещение: {skewness}\")\n",
+    "\n",
+    "skewed_features = skewness[abs(skewness) > 1].index.tolist()\n",
+    "print(f\"Сильно смещенные столбцы: {skewed_features}\")\n",
+    "\n",
+    "for col in df.select_dtypes(include=['number']).columns:\n",
+    "    if col == 'id':\n",
+    "        continue\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",
+    "    outliers = df[col][(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
+    "    print(f\"Выбросы в столбце '{col}':\\n{outliers}\\n\")\n",
+    "\n",
+    "numeric_cols = df.select_dtypes(include=['number']).columns\n",
+    "numeric_cols = [col for col in numeric_cols if col != 'id']\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "for i, col in enumerate(numeric_cols, 1):\n",
+    "    plt.subplot(len(numeric_cols) // 3 + 1, 3, i) \n",
+    "    sns.boxplot(data=df, x=col)\n",
+    "    plt.title(f'Boxplot for {col}')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "if len(df.columns) >= 2:\n",
+    "    for col1 in df.columns:\n",
+    "        for col2 in df.columns:\n",
+    "            if col1 != col2:\n",
+    "                correlation = df[col1].corr(df[col2])\n",
+    "                if abs(correlation) > 0.9:\n",
+    "                    print(f\"Просачивание данных: Высокая корреляция ({correlation:.2f}) между столбцами '{col1}' и '{col2}'\")\n",
+    "\n",
+    "df['log_close'] = np.log(df['Close'] + 1)\n",
+    "df['log_volume'] = np.log(df['Volume'] + 1)\n",
+    "\n",
+    "def split_stratified_into_train_val_test(\n",
+    "    df_input,\n",
+    "    stratify_colname=\"y\",\n",
+    "    frac_train=0.6,\n",
+    "    frac_val=0.15,\n",
+    "    frac_test=0.25,\n",
+    "    random_state=None,\n",
+    "):\n",
+    "    if frac_train + frac_val + frac_test != 1.0:\n",
+    "        raise ValueError(\n",
+    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
+    "            % (frac_train, frac_val, frac_test)\n",
+    "        )\n",
+    "\n",
+    "    if stratify_colname not in df_input.columns:\n",
+    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
+    "\n",
+    "    X = df_input \n",
+    "    y = df_input[\n",
+    "        [stratify_colname]\n",
+    "    ]  \n",
+    "\n",
+    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
+    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
+    "    )\n",
+    "\n",
+    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
+    "    df_val, df_test, y_val, y_test = train_test_split(\n",
+    "        df_temp,\n",
+    "        y_temp,\n",
+    "        stratify=y_temp,\n",
+    "        test_size=relative_frac_test,\n",
+    "        random_state=random_state,\n",
+    "    )\n",
+    "\n",
+    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
+    "\n",
+    "    return df_train, df_val, df_test\n",
+    "\n",
+    "bins = [df['Close'].min(), df['Close'].quantile(0.33), df['Close'].quantile(0.66), df['Close'].max()]\n",
+    "labels = ['Low', 'Medium', 'High']\n",
+    "df['Close_binned'] = pd.cut(df['Close'], bins=bins, labels=labels)\n",
+    "df = df.dropna()\n",
+    "# Now stratify using the binned values\n",
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "    df, stratify_colname=\"Close_binned\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "print(df_train.columns) \n",
+    "   \n",
+    "print(\"Обучающая выборка: \", df_train.shape)\n",
+    "print(df_train.Close.value_counts()) \n",
+    "\n",
+    "print(\"Контрольная выборка: \", df_val.shape)\n",
+    "print(df_val.Close.value_counts())\n",
+    "\n",
+    "print(\"Тестовая выборка: \", df_test.shape)\n",
+    "print(df_test.Close.value_counts())\n",
+    "\n",
+    "rus = RandomUnderSampler(random_state=42)\n",
+    "X_resampled, y_resampled = rus.fit_resample(df_train, df_train[\"Close_binned\"])\n",
+    "df_train_rus = pd.DataFrame(X_resampled)\n",
+    "print(\"Обучающая выборка после undersampling: \", df_train_rus.shape)\n",
+    "print(df_train_rus.Close.value_counts())\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/mai/lab2.ipynb b/mai/lab2.ipynb
index ff2d88b..b6a81d7 100644
--- a/mai/lab2.ipynb
+++ b/mai/lab2.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -42,9 +42,6 @@
       "dtype: float64\n",
       "Сильно смещенные столбцы: ['carat', 'price', 'y', 'z']\n",
       "Данные 2022 года, возможна неактуальность\n",
-      "Выбросы в столбце 'id':\n",
-      "Series([], Name: id, dtype: int64)\n",
-      "\n",
       "Выбросы в столбце 'carat':\n",
       "12246    2.06\n",
       "13002    2.14\n",
@@ -228,7 +225,23 @@
       "49557     0.00\n",
       "51506     0.00\n",
       "Name: z, dtype: float64\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Просачивание данных: Высокая корреляция (0.92) между столбцами 'carat' и 'price'\n",
       "Просачивание данных: Высокая корреляция (0.98) между столбцами 'carat' и 'x'\n",
       "Просачивание данных: Высокая корреляция (0.95) между столбцами 'carat' и 'y'\n",
@@ -243,116 +256,77 @@
       "Просачивание данных: Высокая корреляция (0.95) между столбцами 'z' и 'carat'\n",
       "Просачивание данных: Высокая корреляция (0.97) между столбцами 'z' и 'x'\n",
       "Просачивание данных: Высокая корреляция (0.95) между столбцами 'z' и 'y'\n",
-      "log_price    8.319386\n",
-      "id           8.135995\n",
-      "log_carat    1.963082\n",
-      "carat        1.961620\n",
-      "y            1.491815\n",
-      "x            1.481414\n",
-      "clarity      0.359837\n",
-      "color        0.288875\n",
-      "cut          0.104551\n",
-      "table        0.057094\n",
-      "depth        0.037126\n",
+      "Пример оценки информативности для целевой переменной 'price'\n",
+      "id           8.136888\n",
+      "carat        1.961910\n",
+      "log_carat    1.961630\n",
+      "y            1.494661\n",
+      "x            1.481508\n",
+      "z            1.431517\n",
+      "clarity      0.364394\n",
+      "color        0.286463\n",
+      "cut          0.104701\n",
+      "table        0.052611\n",
+      "depth        0.034731\n",
       "dtype: float64\n",
-      "Данные по массе достоверны\n",
-      "Уникальные значения 'cut': [4 3 1 2 0]\n",
-      "Уникальные значения 'clarity': [1 2 4 3 5 6 0 7]\n",
-      "carat\n",
-      "0.30    2604\n",
-      "0.31    2249\n",
-      "1.01    2242\n",
-      "2.00    2154\n",
-      "0.70    1982\n",
-      "        ... \n",
-      "1.95       3\n",
-      "1.85       3\n",
-      "1.94       3\n",
-      "1.99       3\n",
-      "1.92       2\n",
-      "Name: count, Length: 181, dtype: int64\n",
       "Index(['carat', 'price', 'cut'], dtype='object')\n",
       "Обучающая выборка:  (32365, 3)\n",
       "carat\n",
-      "0.30    1562\n",
-      "1.01    1355\n",
-      "0.31    1338\n",
-      "2.00    1269\n",
-      "0.70    1156\n",
+      "0.30    1503\n",
+      "1.01    1363\n",
+      "0.31    1326\n",
+      "0.70    1191\n",
+      "0.32    1136\n",
       "        ... \n",
-      "1.85       2\n",
-      "1.89       2\n",
-      "1.97       1\n",
-      "1.88       1\n",
+      "2.55       1\n",
+      "0.22       1\n",
       "1.92       1\n",
-      "Name: count, Length: 181, dtype: int64\n",
+      "2.74       1\n",
+      "3.67       1\n",
+      "Name: count, Length: 265, dtype: int64\n",
       "Контрольная выборка:  (10789, 3)\n",
       "carat\n",
-      "0.30    500\n",
-      "0.31    474\n",
-      "2.00    441\n",
-      "1.01    435\n",
-      "0.70    425\n",
-      "       ... \n",
-      "1.84      1\n",
-      "0.88      1\n",
-      "1.83      1\n",
-      "0.20      1\n",
-      "1.85      1\n",
-      "Name: count, Length: 173, dtype: int64\n",
-      "Тестовая выборка:  (10789, 3)\n",
-      "carat\n",
-      "0.30    542\n",
-      "1.01    452\n",
-      "2.00    444\n",
-      "0.31    437\n",
-      "0.70    401\n",
+      "0.30    555\n",
+      "0.31    456\n",
+      "0.70    438\n",
+      "1.01    417\n",
+      "0.32    380\n",
       "       ... \n",
       "1.68      1\n",
-      "1.98      1\n",
-      "1.87      1\n",
-      "1.48      1\n",
-      "1.99      1\n",
-      "Name: count, Length: 175, dtype: int64\n",
-      "Обучающая выборка:  (32365, 3)\n",
+      "2.57      1\n",
+      "2.53      1\n",
+      "1.93      1\n",
+      "2.66      1\n",
+      "Name: count, Length: 231, dtype: int64\n",
+      "Тестовая выборка:  (10789, 3)\n",
       "carat\n",
-      "0.30    1562\n",
-      "1.01    1355\n",
-      "0.31    1338\n",
-      "2.00    1269\n",
-      "0.70    1156\n",
-      "        ... \n",
-      "1.85       2\n",
-      "1.89       2\n",
-      "1.97       1\n",
-      "1.88       1\n",
-      "1.92       1\n",
-      "Name: count, Length: 181, dtype: int64\n"
+      "0.30    546\n",
+      "0.31    467\n",
+      "1.01    462\n",
+      "0.70    353\n",
+      "0.32    324\n",
+      "       ... \n",
+      "2.53      1\n",
+      "1.81      1\n",
+      "2.55      1\n",
+      "3.05      1\n",
+      "1.45      1\n",
+      "Name: count, Length: 232, dtype: int64\n",
+      "Обучающая выборка после oversampling:  (64069, 3)\n",
+      "carat\n",
+      "1.010000    1837\n",
+      "0.700000    1834\n",
+      "0.300000    1589\n",
+      "1.000000    1417\n",
+      "0.310000    1385\n",
+      "            ... \n",
+      "1.335296       1\n",
+      "1.183497       1\n",
+      "1.296552       1\n",
+      "2.003477       1\n",
+      "0.780485       1\n",
+      "Name: count, Length: 28027, dtype: int64\n"
      ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "Unknown label type: continuous. Maybe you are trying to fit a classifier, which expects discrete classes on a regression target with continuous values.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[26], line 157\u001b[0m\n\u001b[0;32m    154\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mОбучающая выборка: \u001b[39m\u001b[38;5;124m\"\u001b[39m, df_train\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m    155\u001b[0m \u001b[38;5;28mprint\u001b[39m(df_train\u001b[38;5;241m.\u001b[39mcarat\u001b[38;5;241m.\u001b[39mvalue_counts())\n\u001b[1;32m--> 157\u001b[0m X_resampled, y_resampled \u001b[38;5;241m=\u001b[39m \u001b[43mada\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_train\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcarat\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    158\u001b[0m df_train_adasyn \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(X_resampled)\n\u001b[0;32m    160\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mОбучающая выборка после oversampling: \u001b[39m\u001b[38;5;124m\"\u001b[39m, df_train_adasyn\u001b[38;5;241m.\u001b[39mshape)\n",
-      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\imblearn\\base.py:208\u001b[0m, in \u001b[0;36mBaseSampler.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    187\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Resample the dataset.\u001b[39;00m\n\u001b[0;32m    188\u001b[0m \n\u001b[0;32m    189\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    205\u001b[0m \u001b[38;5;124;03m    The corresponding label of `X_resampled`.\u001b[39;00m\n\u001b[0;32m    206\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m--> 208\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\imblearn\\base.py:104\u001b[0m, in \u001b[0;36mSamplerMixin.fit_resample\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m     83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_resample\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y):\n\u001b[0;32m     84\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Resample the dataset.\u001b[39;00m\n\u001b[0;32m     85\u001b[0m \n\u001b[0;32m     86\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    102\u001b[0m \u001b[38;5;124;03m        The corresponding label of `X_resampled`.\u001b[39;00m\n\u001b[0;32m    103\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 104\u001b[0m     \u001b[43mcheck_classification_targets\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    105\u001b[0m     arrays_transformer \u001b[38;5;241m=\u001b[39m ArraysTransformer(X, y)\n\u001b[0;32m    106\u001b[0m     X, y, binarize_y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_X_y(X, y)\n",
-      "File \u001b[1;32mc:\\Python312\\Lib\\site-packages\\sklearn\\utils\\multiclass.py:219\u001b[0m, in \u001b[0;36mcheck_classification_targets\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m    211\u001b[0m y_type \u001b[38;5;241m=\u001b[39m type_of_target(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[0;32m    213\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    214\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmultilabel-sequences\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    218\u001b[0m ]:\n\u001b[1;32m--> 219\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    220\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown label type: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Maybe you are trying to fit a \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    221\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclassifier, which expects discrete classes on a \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    222\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mregression target with continuous values.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    223\u001b[0m     )\n",
-      "\u001b[1;31mValueError\u001b[0m: Unknown label type: continuous. Maybe you are trying to fit a classifier, which expects discrete classes on a regression target with continuous values."
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 12 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -361,16 +335,17 @@
     "from sklearn.feature_selection import mutual_info_regression\n",
     "from sklearn.model_selection import train_test_split\n",
     "from imblearn.over_sampling import ADASYN\n",
+    "from imblearn.under_sampling import RandomUnderSampler\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
     "\n",
     "df = pd.read_csv(\"data/Diamonds.csv\")\n",
     "print(df.columns)\n",
     "\n",
-    "# Оценка зашумленности\n",
     "noisy_features = []\n",
     "for col in df.columns:\n",
     "  if df[col].isnull().sum() / len(df) > 0.1:  # Если более 10% пропусков\n",
     "    noisy_features.append(col)\n",
-    "      \n",
     "print(f\"Зашумленные столбцы: {noisy_features}\")\n",
     "\n",
     "cut_mapping = {'Fair': 0, 'Good': 1, 'Very Good': 2, 'Premium': 3, 'Ideal': 4}\n",
@@ -388,10 +363,9 @@
     "skewed_features = skewness[abs(skewness) > 1].index.tolist()\n",
     "print(f\"Сильно смещенные столбцы: {skewed_features}\")\n",
     "\n",
-    "# Оценка актуальности данных\n",
-    "print(f\"Данные 2022 года, возможна неактуальность\")\n",
-    "\n",
     "for col in df.select_dtypes(include=['number']).columns:\n",
+    "    if col == 'id':\n",
+    "        continue\n",
     "    Q1 = df[col].quantile(0.25)\n",
     "    Q3 = df[col].quantile(0.75)\n",
     "    IQR = Q3 - Q1\n",
@@ -400,6 +374,19 @@
     "    outliers = df[col][(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
     "    print(f\"Выбросы в столбце '{col}':\\n{outliers}\\n\")\n",
     "\n",
+    "numeric_cols = df.select_dtypes(include=['number']).columns\n",
+    "numeric_cols = [col for col in numeric_cols if col != 'id']\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "for i, col in enumerate(numeric_cols, 1):\n",
+    "    plt.subplot(len(numeric_cols) // 3 + 1, 3, i) \n",
+    "    sns.boxplot(data=df, x=col)\n",
+    "    plt.title(f'Boxplot for {col}')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
     "if len(df.columns) >= 2:\n",
     "    for col1 in df.columns:\n",
     "        for col2 in df.columns:\n",
@@ -408,43 +395,12 @@
     "                if abs(correlation) > 0.9:\n",
     "                    print(f\"Просачивание данных: Высокая корреляция ({correlation:.2f}) между столбцами '{col1}' и '{col2}'\")\n",
     "\n",
-    "df.hist(figsize=(10, 10))\n",
-    "\n",
-    "# решение смещения\n",
-    "df['log_price'] = np.log(df['price'] + 1)\n",
     "df['log_carat'] = np.log(df['carat'] + 1)\n",
     "\n",
-    "# решение выбросов\n",
-    "Q1 = df['carat'].quantile(0.25)\n",
-    "Q3 = df['carat'].quantile(0.75)\n",
-    "IQR = Q3 - Q1\n",
-    "lower_bound = Q1 - 1.5 * IQR\n",
-    "upper_bound = Q3 + 1.5 * IQR\n",
-    "df['carat'] = np.where(df['carat'] > upper_bound, upper_bound, df['carat'])\n",
-    "\n",
-    "df.drop(columns=['z'], inplace=True)  # Если z сильно коррелирует с y и x\n",
-    "\n",
-    "# Пример оценки информативности для целевой переменной 'price'\n",
-    "X = df.drop(columns=['price'])\n",
-    "y = df['price']\n",
-    "mi_scores = mutual_info_regression(X, y)\n",
-    "print(pd.Series(mi_scores, index=X.columns).sort_values(ascending=False))\n",
-    "\n",
-    "if df['carat'].max() > 5:\n",
-    "    print(\"Ошибка: Обнаружены значения массы, не соответствующие реальным бриллиантам.\")\n",
-    "else:\n",
-    "    print(\"Данные по массе достоверны\")\n",
-    "\n",
-    "print(\"Уникальные значения 'cut':\", df['cut'].unique())\n",
-    "print(\"Уникальные значения 'clarity':\", df['clarity'].unique())\n",
-    "\n",
     "df['cut'] = df['cut'].fillna('unknown')\n",
     "\n",
     "df['carat'] = df['carat'].fillna(df['carat'].mean())\n",
     "\n",
-    "\n",
-    "print(df.carat.value_counts())\n",
-    "\n",
     "def split_stratified_into_train_val_test(\n",
     "    df_input,\n",
     "    stratify_colname=\"y\",\n",
@@ -453,8 +409,6 @@
     "    frac_test=0.25,\n",
     "    random_state=None,\n",
     "):\n",
-    "\n",
-    "\n",
     "    if frac_train + frac_val + frac_test != 1.0:\n",
     "        raise ValueError(\n",
     "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
@@ -464,17 +418,15 @@
     "    if stratify_colname not in df_input.columns:\n",
     "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
     "\n",
-    "    X = df_input  # Contains all columns.\n",
+    "    X = df_input \n",
     "    y = df_input[\n",
     "        [stratify_colname]\n",
-    "    ]  # Dataframe of just the column on which to stratify.\n",
+    "    ]  \n",
     "\n",
-    "    # Split original dataframe into train and temp dataframes.\n",
     "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
     "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
     "    )\n",
     "\n",
-    "    # Split the temp dataframe into val and test dataframes.\n",
     "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
     "    df_val, df_test, y_val, y_test = train_test_split(\n",
     "        df_temp,\n",
@@ -494,10 +446,8 @@
     "    data, stratify_colname=\"cut\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
     ")\n",
     "   \n",
-    "\n",
     "print(df_train.columns) \n",
     "   \n",
-    "\n",
     "print(\"Обучающая выборка: \", df_train.shape)\n",
     "print(df_train.carat.value_counts()) \n",
     "\n",
@@ -509,16 +459,11 @@
     "\n",
     "ada = ADASYN()\n",
     "\n",
-    "print(\"Обучающая выборка: \", df_train.shape)\n",
-    "print(df_train.carat.value_counts())\n",
-    "\n",
-    "X_resampled, y_resampled = ada.fit_resample(df_train, df_train[\"carat\"])\n",
+    "X_resampled, y_resampled = ada.fit_resample(df_train, df_train[\"cut\"])\n",
     "df_train_adasyn = pd.DataFrame(X_resampled)\n",
     "\n",
     "print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
-    "print(df_train_adasyn.carat.value_counts())\n",
-    "\n",
-    "df_train_adasyn"
+    "print(df_train_adasyn.carat.value_counts())\n"
    ]
   },
   {