diff --git a/lab_4/lab4.ipynb b/lab_4/lab4.ipynb
index 302dde8..bc73db7 100644
--- a/lab_4/lab4.ipynb
+++ b/lab_4/lab4.ipynb
@@ -1,39 +1,50 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Вариант: Список людей.\n",
+    "ссылка на датасет: https://www.kaggle.com/datasets/imoore/age-dataset"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: '../data/AgeDataset-V1.csv'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[3], line 107\u001b[0m\n\u001b[0;32m    102\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(df_input) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(df_train) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlen\u001b[39m(df_val) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlen\u001b[39m(df_test)\n\u001b[0;32m    104\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m df_train, df_val, df_test\n\u001b[1;32m--> 107\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../data/AgeDataset-V1.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnrows\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    108\u001b[0m df\u001b[38;5;241m.\u001b[39minfo()\n",
-      "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m   1014\u001b[0m     dialect,\n\u001b[0;32m   1015\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m   1023\u001b[0m )\n\u001b[0;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
-      "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
-      "File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m    874\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    875\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    876\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    878\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
-      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../data/AgeDataset-V1.csv'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Id                 1000 non-null   object \n",
+      " 1   Name               1000 non-null   object \n",
+      " 2   Short description  1000 non-null   object \n",
+      " 3   Gender             995 non-null    object \n",
+      " 4   Country            962 non-null    object \n",
+      " 5   Occupation         998 non-null    object \n",
+      " 6   Birth year         1000 non-null   int64  \n",
+      " 7   Death year         999 non-null    float64\n",
+      " 8   Manner of death    372 non-null    object \n",
+      " 9   Age of death       999 non-null    float64\n",
+      "dtypes: float64(2), int64(1), object(7)\n",
+      "memory usage: 78.3+ KB\n"
      ]
     }
    ],
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn import metrics\n",
     "from imblearn.over_sampling import RandomOverSampler\n",
     "from imblearn.under_sampling import RandomUnderSampler\n",
-    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.metrics import ConfusionMatrixDisplay\n",
-    "from sklearn.compose import ColumnTransformer\n",
     "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.impute import SimpleImputer\n",
     "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
     "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, RandomForestClassifier, GradientBoostingClassifier\n",
     "from sklearn.model_selection import train_test_split, GridSearchCV\n",
@@ -42,9 +53,7 @@
     "    matthews_corrcoef, cohen_kappa_score, confusion_matrix\n",
     ")\n",
     "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
-    "import numpy as np\n",
-    "import featuretools as ft\n",
-    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "from sklearn.metrics import accuracy_score\n",
     "\n",
     "# Функция для применения oversampling\n",
     "def apply_oversampling(X, y):\n",
@@ -66,34 +75,6 @@
     "    frac_test=0.25,\n",
     "    random_state=None,\n",
     "):\n",
-    "    \"\"\"\n",
-    "    Splits a Pandas dataframe into three subsets (train, val, and test)\n",
-    "    following fractional ratios provided by the user, where each subset is\n",
-    "    stratified by the values in a specific column (that is, each subset has\n",
-    "    the same relative frequency of the values in the column). It performs this\n",
-    "    splitting by running train_test_split() twice.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    df_input : Pandas dataframe\n",
-    "        Input dataframe to be split.\n",
-    "    stratify_colname : str\n",
-    "        The name of the column that will be used for stratification. Usually\n",
-    "        this column would be for the label.\n",
-    "    frac_train : float\n",
-    "    frac_val   : float\n",
-    "    frac_test  : float\n",
-    "        The ratios with which the dataframe will be split into train, val, and\n",
-    "        test data. The values should be expressed as float fractions and should\n",
-    "        sum to 1.0.\n",
-    "    random_state : int, None, or RandomStateInstance\n",
-    "        Value to be passed to train_test_split().\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    df_train, df_val, df_test :\n",
-    "        Dataframes containing the three splits.\n",
-    "    \"\"\"\n",
     "\n",
     "    if frac_train + frac_val + frac_test != 1.0:\n",
     "        raise ValueError(\n",
@@ -129,9 +110,525 @@
     "    return df_train, df_val, df_test\n",
     "\n",
     "\n",
-    "df = pd.read_csv(\"../static/csv/AgeDataset-V1.csv\", nrows=10000)\n",
+    "df = pd.read_csv(\"../static/csv/AgeDataset-V1.csv\", nrows=1000)\n",
     "df.info()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Как бизнес-цели выделим следующие 2 варианта: 1) GameDev. Создание игры про конкретного персонажа, живущего в конкретном временном промежутке в конкретной стране. 2) Классификация людей по возрастным группам, что может быть полезно для рекламных целей\n",
+    "\n",
+    "\n",
+    "Выполним подготовку данных\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.fillna({\"Gender\": \"NaN\", \"Country\": \"NaN\", \"Occupation\" : \"NaN\", \"Manner of death\" : \"NaN\"}, inplace=True)\n",
+    "df = df.dropna()\n",
+    "df['Country'] = df['Country'].str.split('; ')\n",
+    "df = df.explode('Country')\n",
+    "data = df.copy()\n",
+    "\n",
+    "\n",
+    "value_counts = data[\"Country\"].value_counts()\n",
+    "rare = value_counts[value_counts < 100].index\n",
+    "data = data[~data[\"Country\"].isin(rare)]\n",
+    "\n",
+    "data.drop(data[~data['Gender'].isin(['Male', 'Female'])].index, inplace=True)\n",
+    "\n",
+    "data1 = pd.get_dummies(data, columns=['Gender', 'Country', 'Occupation'], drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Определить достижимый уровень качества модели для каждой задачи. На основе имеющихся данных уровень качества моделей не будет высоким, поскольку все таки длительность жизни лишь примерная и точно ее угадать невозможно.\n",
+    "\n",
+    "Выберем ориентиры для наших 2х задач: 1)Регрессии - средний возраст человека 2)Классификации - аиболее часто встречающаяся возрастная группа\n",
+    "\n",
+    "Построим конвейер."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Id', 'Name', 'Short description', 'Gender', 'Country', 'Occupation',\n",
+      "       'Birth year', 'Death year', 'Manner of death', 'Age of death'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters for Linear Regression: {}\n",
+      "Best parameters for Random Forest Regressor: {'model__max_depth': None, 'model__n_estimators': 200}\n",
+      "Best parameters for Gradient Boosting Regressor: {'model__learning_rate': 0.1, 'model__max_depth': 5, 'model__n_estimators': 300}\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_reg = data1.drop(['Id', 'Name', 'Age of death', 'Short description', 'Manner of death'], axis=1)\n",
+    "y_reg = data1['Age of death']\n",
+    "\n",
+    "# Разделение данных\n",
+    "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Выбор моделей для регрессии\n",
+    "models_reg = {\n",
+    "    'Linear Regression': LinearRegression(),\n",
+    "    'Random Forest Regressor': RandomForestRegressor(random_state=42),\n",
+    "    'Gradient Boosting Regressor': GradientBoostingRegressor(random_state=42)\n",
+    "}\n",
+    "\n",
+    "# Создание конвейера для регрессии\n",
+    "pipelines_reg = {}\n",
+    "for name, model in models_reg.items():\n",
+    "    pipelines_reg[name] = Pipeline([\n",
+    "        ('scaler', StandardScaler()),\n",
+    "        ('model', model)\n",
+    "    ])\n",
+    "\n",
+    "# Определение сетки гиперпараметров для регрессии\n",
+    "param_grids_reg = {\n",
+    "    'Linear Regression': {},\n",
+    "    'Random Forest Regressor': {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__max_depth': [None, 10, 20, 30]\n",
+    "    },\n",
+    "    'Gradient Boosting Regressor': {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__learning_rate': [0.01, 0.1, 0.2],\n",
+    "        'model__max_depth': [3, 5, 7]\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Настройка гиперпараметров для регрессии\n",
+    "best_models_reg = {}\n",
+    "for name, pipeline in pipelines_reg.items():\n",
+    "    grid_search = GridSearchCV(pipeline, param_grids_reg[name], cv=5, scoring='neg_mean_squared_error')\n",
+    "    grid_search.fit(X_train_reg, y_train_reg)\n",
+    "    best_models_reg[name] = grid_search.best_estimator_\n",
+    "    print(f'Best parameters for {name}: {grid_search.best_params_}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: Linear Regression\n",
+      "Model: Random Forest Regressor\n",
+      "Model: Gradient Boosting Regressor\n",
+      "{'Linear Regression': {'pipeline': Pipeline(steps=[('scaler', StandardScaler()), ('model', LinearRegression())]), 'preds_train': array([29., 53., 82., 55., 62., 42., 21., 75., 52., 32., 36., 73., 37.,\n",
+      "       46., 72., 27., 71., 83., 55., 22., 65., 47., 57., 78., 91., 60.,\n",
+      "       72., 67., 61., 88., 60., 88., 91., 52., 84., 93., 44., 58., 90.,\n",
+      "       94., 59., 92., 84., 64., 51., 93., 79., 81., 72., 78., 74., 27.,\n",
+      "       92., 46., 72., 78., 96., 95., 77., 49., 87., 90., 88., 88., 65.,\n",
+      "       42., 74., 87., 41., 77., 92., 69., 94., 49., 85., 79., 73., 43.,\n",
+      "       55., 73., 51., 79., 52., 63., 60., 68., 70., 39., 59., 79., 27.,\n",
+      "       57., 78., 80., 19., 69., 68., 39., 60., 67., 68., 87., 81., 62.,\n",
+      "       73., 23., 69., 67., 47., 45., 90., 82., 87., 70., 77., 67., 66.,\n",
+      "       84., 64., 54., 46., 75., 84., 36., 72., 42., 52., 48., 55., 89.,\n",
+      "       64., 80., 28., 52., 81., 63., 74., 68., 53., 66., 36., 80., 75.,\n",
+      "       78., 34., 68., 71., 78., 83., 93., 68., 63., 79., 51., 68., 65.,\n",
+      "       87., 66., 52., 63., 88., 83., 85., 39., 78., 80., 65., 71., 76.,\n",
+      "       84., 51., 86., 65., 66., 80., 56., 67., 23., 49., 89., 69., 92.,\n",
+      "       63., 83., 62., 82., 34., 73., 59., 79., 60., 44., 53., 75., 71.,\n",
+      "       64., 60., 62., 85., 95., 90., 56., 94., 99., 51., 59., 88., 21.,\n",
+      "       65., 97., 67., 42., 93., 54., 56., 56., 68., 95., 55., 69., 65.,\n",
+      "       59., 56., 72., 28., 75., 56., 65., 74., 75., 80., 82.]), 'preds_test': array([56.        , 22.        , 87.        , 88.        , 25.        ,\n",
+      "       54.        , 87.        , 85.        , 60.        , 88.        ,\n",
+      "       42.        , 72.        , 69.        , 82.        , 81.        ,\n",
+      "       48.        , 94.        , 56.        , 65.        , 86.        ,\n",
+      "       74.        , 62.        , 99.        , 66.        , 74.        ,\n",
+      "       59.        , 60.        , 64.        , 64.        , 62.        ,\n",
+      "       71.        , 72.        , 77.        , 85.        , 81.        ,\n",
+      "       81.        , 55.84443686, 40.        , 69.        , 66.        ,\n",
+      "       95.        , 40.        , 81.        , 75.        , 91.        ,\n",
+      "       82.        , 76.        , 66.        , 54.        , 59.        ,\n",
+      "       80.        , 45.        , 44.        , 92.        , 67.        ,\n",
+      "       86.        , 89.        , 89.        , 53.        ]), 'MSE_train': np.float64(7.202572085669638e-26), 'MSE_test': np.float64(0.0004101676300062632), 'R2_train': 1.0, 'R2_test': 0.9999986319859576, 'MAE_train': np.float64(2.193188159818137e-13), 'MAE_test': np.float64(0.0026366633706367913)}, 'Random Forest Regressor': {'pipeline': Pipeline(steps=[('scaler', StandardScaler()),\n",
+      "                ('model',\n",
+      "                 RandomForestRegressor(n_estimators=200, random_state=42))]), 'preds_train': array([32.415, 54.05 , 80.21 , 53.42 , 61.825, 48.38 , 31.21 , 75.14 ,\n",
+      "       53.66 , 44.475, 37.86 , 69.925, 40.7  , 49.45 , 70.985, 31.605,\n",
+      "       71.845, 77.67 , 55.85 , 23.27 , 64.665, 51.965, 58.55 , 67.885,\n",
+      "       91.005, 60.08 , 72.905, 68.86 , 63.055, 87.615, 58.84 , 87.08 ,\n",
+      "       90.725, 52.17 , 80.965, 91.69 , 48.03 , 58.71 , 89.26 , 92.89 ,\n",
+      "       59.015, 91.75 , 82.57 , 63.895, 55.675, 92.27 , 79.905, 78.265,\n",
+      "       72.795, 76.885, 73.87 , 33.285, 91.75 , 47.07 , 72.47 , 76.92 ,\n",
+      "       94.385, 90.31 , 74.365, 50.7  , 85.73 , 83.985, 87.175, 86.815,\n",
+      "       62.32 , 46.985, 67.69 , 88.02 , 41.14 , 76.32 , 87.4  , 66.825,\n",
+      "       92.305, 51.86 , 83.51 , 80.005, 70.49 , 44.39 , 56.58 , 73.695,\n",
+      "       52.235, 79.01 , 50.495, 65.565, 62.43 , 66.77 , 69.69 , 40.495,\n",
+      "       63.64 , 79.755, 28.875, 59.86 , 79.155, 81.925, 33.975, 69.73 ,\n",
+      "       71.19 , 47.59 , 54.73 , 67.71 , 69.41 , 84.08 , 80.425, 60.615,\n",
+      "       73.29 , 24.925, 68.7  , 66.365, 46.64 , 50.88 , 89.61 , 78.34 ,\n",
+      "       85.715, 67.64 , 79.165, 64.275, 65.885, 81.285, 64.4  , 57.835,\n",
+      "       49.395, 75.095, 84.47 , 40.765, 62.5  , 44.53 , 56.76 , 47.27 ,\n",
+      "       53.965, 87.91 , 64.765, 71.42 , 28.32 , 55.25 , 80.665, 60.975,\n",
+      "       73.21 , 67.89 , 54.7  , 67.895, 44.685, 79.79 , 75.025, 76.505,\n",
+      "       38.565, 65.68 , 72.485, 77.19 , 84.71 , 91.46 , 67.54 , 61.705,\n",
+      "       77.92 , 53.51 , 69.705, 66.27 , 87.135, 65.785, 57.23 , 65.945,\n",
+      "       88.88 , 81.18 , 82.655, 40.31 , 77.985, 80.435, 62.81 , 73.05 ,\n",
+      "       75.88 , 82.215, 58.5  , 81.83 , 61.155, 65.455, 76.965, 57.17 ,\n",
+      "       66.44 , 34.205, 48.505, 87.855, 64.585, 91.78 , 63.915, 77.265,\n",
+      "       64.48 , 81.42 , 40.195, 71.515, 57.38 , 74.945, 57.52 , 47.795,\n",
+      "       51.94 , 74.025, 68.09 , 64.38 , 62.535, 59.58 , 80.365, 93.635,\n",
+      "       89.98 , 56.94 , 92.79 , 97.075, 53.34 , 58.425, 87.805, 24.685,\n",
+      "       64.205, 94.825, 65.02 , 43.075, 91.215, 56.39 , 56.38 , 57.195,\n",
+      "       68.27 , 90.75 , 56.04 , 68.77 , 65.135, 58.49 , 55.54 , 73.21 ,\n",
+      "       40.095, 75.28 , 55.98 , 63.535, 74.15 , 74.775, 76.405, 78.46 ]), 'preds_test': array([56.47 , 42.345, 87.025, 86.56 , 40.42 , 49.335, 86.02 , 81.75 ,\n",
+      "       62.32 , 90.2  , 63.72 , 74.3  , 67.43 , 58.9  , 83.06 , 46.655,\n",
+      "       92.365, 32.505, 71.02 , 89.43 , 63.06 , 63.645, 92.385, 53.625,\n",
+      "       71.25 , 68.73 , 66.38 , 70.14 , 62.755, 65.02 , 72.21 , 73.205,\n",
+      "       74.06 , 87.985, 83.44 , 78.265, 53.98 , 52.355, 61.145, 69.6  ,\n",
+      "       89.645, 55.83 , 77.695, 59.03 , 89.61 , 83.235, 70.58 , 71.92 ,\n",
+      "       69.175, 58.48 , 75.345, 59.55 , 52.395, 85.715, 65.425, 87.95 ,\n",
+      "       83.12 , 87.76 , 55.63 ]), 'MSE_train': np.float64(10.585386853448275), 'MSE_test': np.float64(73.41657415254235), 'R2_train': 0.9680762587778711, 'R2_test': 0.7551369317321679, 'MAE_train': np.float64(2.189698275862069), 'MAE_test': np.float64(6.021271186440678)}, 'Gradient Boosting Regressor': {'pipeline': Pipeline(steps=[('scaler', StandardScaler()),\n",
+      "                ('model',\n",
+      "                 GradientBoostingRegressor(max_depth=5, n_estimators=300,\n",
+      "                                           random_state=42))]), 'preds_train': array([28.72956041, 53.12127389, 82.08536004, 55.09719521, 61.75388192,\n",
+      "       41.97235916, 21.14883789, 74.54323397, 52.25364062, 32.10924489,\n",
+      "       36.08384782, 72.70845527, 37.11384401, 46.04284373, 72.05464788,\n",
+      "       27.19660712, 71.03059415, 82.78900252, 54.82581543, 22.08471572,\n",
+      "       64.95177947, 46.91864608, 57.0614107 , 77.77389579, 90.91849183,\n",
+      "       60.00063443, 72.32250587, 67.19480682, 61.21037107, 87.9493728 ,\n",
+      "       59.76167757, 87.9242189 , 91.11672076, 51.981465  , 83.97286576,\n",
+      "       92.99512162, 44.2265744 , 57.97309623, 89.8580269 , 93.93278779,\n",
+      "       58.9790766 , 92.10213846, 83.92831871, 64.01048318, 50.85907853,\n",
+      "       93.03022066, 79.35509757, 80.97385883, 72.26475898, 78.0822317 ,\n",
+      "       73.74605417, 26.94997048, 91.93353737, 46.11073777, 71.86943063,\n",
+      "       78.22666513, 95.97811062, 94.90309836, 76.79483994, 49.04234743,\n",
+      "       87.10113854, 90.00164369, 88.15604432, 88.06107202, 64.86758165,\n",
+      "       42.0662194 , 73.86206285, 87.06076311, 40.77837315, 76.93677631,\n",
+      "       91.80841172, 68.86009114, 93.99977552, 49.01611104, 85.1215977 ,\n",
+      "       79.20236795, 72.81006079, 42.88133804, 55.07471142, 73.08367579,\n",
+      "       51.10101262, 79.26235085, 51.93996986, 63.1400842 , 60.16031868,\n",
+      "       67.74505892, 69.92474149, 39.10249238, 59.19318532, 79.00162184,\n",
+      "       27.13287068, 57.14727171, 78.13131855, 80.04141944, 19.21578015,\n",
+      "       69.08228133, 68.34019354, 39.23243336, 59.69048347, 66.68312364,\n",
+      "       67.90455008, 86.87414348, 80.96263263, 62.01895029, 72.84596009,\n",
+      "       22.9688195 , 69.07247695, 67.07765118, 46.8756752 , 45.26635382,\n",
+      "       89.94753457, 82.01807007, 86.98465799, 70.20029179, 77.11751998,\n",
+      "       66.85572827, 65.88490085, 83.94892282, 64.05271048, 54.31302531,\n",
+      "       46.21858535, 74.71421415, 84.18057805, 35.93101794, 71.74544023,\n",
+      "       41.87496037, 51.97305771, 47.95325267, 54.99051089, 88.93282021,\n",
+      "       63.9305233 , 79.93395234, 27.90266723, 51.99225237, 80.96691129,\n",
+      "       62.96957561, 74.2341931 , 68.04553945, 53.25614425, 66.06230994,\n",
+      "       36.07177583, 79.73194835, 74.81165201, 77.92313651, 34.03449993,\n",
+      "       67.979729  , 71.21728118, 77.76422978, 83.48678921, 92.88231457,\n",
+      "       68.07190528, 63.0157021 , 78.64975048, 51.06214065, 68.08719803,\n",
+      "       65.07261616, 87.04733437, 66.02343628, 52.0736837 , 63.22318294,\n",
+      "       88.04851864, 82.82539568, 84.97439652, 39.24799156, 78.25738675,\n",
+      "       79.93756933, 64.4750149 , 71.56468737, 76.18401232, 84.06330088,\n",
+      "       51.02264414, 85.98802018, 64.92800866, 66.16320124, 79.94939849,\n",
+      "       56.07374628, 66.98345294, 23.02540478, 49.17449175, 88.88588133,\n",
+      "       68.9329792 , 92.03345878, 63.07777892, 82.73557105, 61.78437332,\n",
+      "       81.8909867 , 34.21616731, 72.87348414, 58.98687689, 78.8140383 ,\n",
+      "       59.9574234 , 44.19210735, 52.71369582, 75.20218936, 70.59615384,\n",
+      "       63.54886587, 60.49279846, 61.78645898, 84.87971032, 94.81801802,\n",
+      "       89.90842136, 55.66192951, 93.90927911, 98.9415322 , 51.01506961,\n",
+      "       58.55722323, 87.77450912, 20.9321725 , 64.89912387, 97.0158939 ,\n",
+      "       67.06399678, 41.91876756, 92.91632536, 54.03711532, 56.10247109,\n",
+      "       55.84819722, 67.98653048, 95.00209989, 54.94376476, 69.02145146,\n",
+      "       65.17895584, 59.02771118, 55.92396986, 72.8440164 , 28.29625663,\n",
+      "       75.01157336, 56.0700562 , 64.97176071, 74.08213306, 74.93307889,\n",
+      "       79.9003625 , 81.82188841]), 'preds_test': array([66.503057  , 38.21021974, 89.04970956, 87.89189709, 36.73554665,\n",
+      "       45.73843308, 87.79291212, 82.13464953, 60.57314255, 91.22929864,\n",
+      "       62.23036581, 71.58664491, 66.17112665, 62.51658214, 81.60921548,\n",
+      "       38.83018398, 91.30064235, 24.90175483, 69.16155336, 87.4365223 ,\n",
+      "       71.56622022, 63.57230002, 93.97558163, 49.45397887, 68.85601209,\n",
+      "       68.60673528, 63.94743518, 68.42632232, 65.30704897, 66.74142159,\n",
+      "       68.75949485, 74.90532442, 73.25421167, 89.6482385 , 82.66649342,\n",
+      "       78.86658868, 56.09338908, 61.2786305 , 60.68340277, 71.36372731,\n",
+      "       90.85782508, 52.24020316, 83.95183498, 62.00353481, 89.95327108,\n",
+      "       86.00387125, 71.50207355, 76.51105405, 67.41310326, 58.59170399,\n",
+      "       76.96828297, 62.60133656, 46.93230456, 87.0082761 , 68.74473539,\n",
+      "       88.07943744, 83.14111532, 87.20969454, 53.47940315]), 'MSE_train': np.float64(0.03170555378857528), 'MSE_test': np.float64(78.66069437097792), 'R2_train': 0.999904381397821, 'R2_test': 0.7376464483927592, 'MAE_train': np.float64(0.13311180849830512), 'MAE_test': np.float64(6.141062895444746)}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Обучение моделей и оценка качества\n",
+    "results_reg = {}\n",
+    "\n",
+    "for model_name in best_models_reg.keys():\n",
+    "    print(f\"Model: {model_name}\")\n",
+    "    model_pipeline = best_models_reg[model_name]\n",
+    "\n",
+    "    y_train_predict = model_pipeline.predict(X_train_reg)\n",
+    "    y_test_predict = model_pipeline.predict(X_test_reg)\n",
+    "\n",
+    "    results_reg[model_name] = {\n",
+    "        \"pipeline\": model_pipeline,\n",
+    "        \"preds_train\": y_train_predict,\n",
+    "        \"preds_test\": y_test_predict,\n",
+    "        \"MSE_train\": mean_squared_error(y_train_reg, y_train_predict),\n",
+    "        \"MSE_test\": mean_squared_error(y_test_reg, y_test_predict),\n",
+    "        \"R2_train\": r2_score(y_train_reg, y_train_predict),\n",
+    "        \"R2_test\": r2_score(y_test_reg, y_test_predict),\n",
+    "        \"MAE_train\": mean_absolute_error(y_train_reg, y_train_predict),\n",
+    "        \"MAE_test\": mean_absolute_error(y_test_reg, y_test_predict),\n",
+    "    }\n",
+    "\n",
+    "# Теперь результаты каждой модели находятся в results_reg\n",
+    "print(results_reg)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data2 = data.drop(['Short description', 'Manner of death', 'Gender', 'Country', 'Occupation'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Birth year', 'Death year'], dtype='object')\n",
+      "Best parameters for Logistic Regression: {'model__C': 10, 'model__solver': 'lbfgs'}\n",
+      "Best parameters for Random Forest Classifier: {'model__max_depth': 30, 'model__n_estimators': 200}\n",
+      "Best parameters for Gradient Boosting Classifier: {'model__learning_rate': 0.1, 'model__max_depth': 7, 'model__n_estimators': 200}\n",
+      "Model: Logistic Regression\n",
+      "Model: Random Forest Classifier\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: Gradient Boosting Classifier\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Создание возрастных групп\n",
+    "bins = [0, 18, 30, 50, 70, 100]\n",
+    "labels = ['0-18', '19-30', '31-50', '51-70', '71+']\n",
+    "data['Age Group'] = pd.cut(data['Age of death'], bins=bins, labels=labels)\n",
+    "data2['Age Group'] = pd.cut(data2['Age of death'], bins=bins, labels=labels)\n",
+    "\n",
+    "# Выбор признаков и целевой переменной для классификации\n",
+    "X_class = data2.drop(['Id', 'Name', 'Age of death', 'Age Group'], axis=1)\n",
+    "y_class = data['Age Group']  \n",
+    "print(X_class.columns)\n",
+    "# Разделение данных\n",
+    "X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class, y_class, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Выбор моделей для классификации\n",
+    "models_class = {\n",
+    "    'Logistic Regression': LogisticRegression(random_state=42, max_iter=5000, solver='liblinear'),\n",
+    "    'Random Forest Classifier': RandomForestClassifier(random_state=42),\n",
+    "    'Gradient Boosting Classifier': GradientBoostingClassifier(random_state=42)\n",
+    "}\n",
+    "\n",
+    "# Создание конвейера для классификации\n",
+    "pipelines_class = {}\n",
+    "for name, model in models_class.items():\n",
+    "    pipelines_class[name] = Pipeline([\n",
+    "        ('scaler', StandardScaler()),\n",
+    "        ('model', model)\n",
+    "    ])\n",
+    "\n",
+    "# Определение сетки гиперпараметров для классификации\n",
+    "\n",
+    "param_grids_class = {\n",
+    "    'Logistic Regression': {\n",
+    "        'model__C': [0.1, 1, 10],\n",
+    "        'model__solver': ['lbfgs', 'liblinear']\n",
+    "    },\n",
+    "    'Random Forest Classifier': {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__max_depth': [None, 10, 20, 30]\n",
+    "    },\n",
+    "    'Gradient Boosting Classifier': {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__learning_rate': [0.01, 0.1, 0.2],\n",
+    "        'model__max_depth': [3, 5, 7]\n",
+    "    }\n",
+    "}\n",
+    "# Убрал определение параметров поскольку уже был предподсчет данных, но вылетела ошибка. Сохранил лучшие параметры\n",
+    "\n",
+    "param_grids_class = {\n",
+    "    'Logistic Regression': {\n",
+    "        'model__C': [10],\n",
+    "        'model__solver': ['lbfgs']\n",
+    "    },\n",
+    "    'Random Forest Classifier': {\n",
+    "        'model__n_estimators': [200],\n",
+    "        'model__max_depth': [ 30]\n",
+    "    },\n",
+    "    'Gradient Boosting Classifier': {\n",
+    "        'model__n_estimators': [200],\n",
+    "        'model__learning_rate': [0.1],\n",
+    "        'model__max_depth': [7]\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Настройка гиперпараметров для классификации\n",
+    "best_models_class = {}\n",
+    "for name, pipeline in pipelines_class.items():\n",
+    "    grid_search = GridSearchCV(pipeline, param_grids_class[name], cv=5, scoring='accuracy', n_jobs=-1)\n",
+    "    grid_search.fit(X_train_class, y_train_class)\n",
+    "    best_models_class[name] = {\"model\": grid_search.best_estimator_}\n",
+    "    print(f'Best parameters for {name}: {grid_search.best_params_}')\n",
+    "\n",
+    "# Обучение моделей и оценка качества\n",
+    "for model_name in best_models_class.keys():\n",
+    "    print(f\"Model: {model_name}\")\n",
+    "    model = best_models_class[model_name][\"model\"]\n",
+    "\n",
+    "    model_pipeline = Pipeline([(\"scaler\", StandardScaler()), (\"model\", model)])\n",
+    "    model_pipeline = model_pipeline.fit(X_train_class, y_train_class)\n",
+    "\n",
+    "    y_train_predict = model_pipeline.predict(X_train_class)\n",
+    "    y_test_probs = model_pipeline.predict_proba(X_test_class)\n",
+    "    y_test_predict = model_pipeline.predict(X_test_class)\n",
+    "\n",
+    "    best_models_class[model_name][\"pipeline\"] = model_pipeline\n",
+    "    best_models_class[model_name][\"probs\"] = y_test_probs\n",
+    "    best_models_class[model_name][\"preds\"] = y_test_predict\n",
+    "\n",
+    "    best_models_class[model_name][\"Precision_train\"] = precision_score(y_train_class, y_train_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"Precision_test\"] = precision_score(y_test_class, y_test_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"Recall_train\"] = recall_score(y_train_class, y_train_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"Recall_test\"] = recall_score(y_test_class, y_test_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"Accuracy_train\"] = accuracy_score(y_train_class, y_train_predict)\n",
+    "    best_models_class[model_name][\"Accuracy_test\"] = accuracy_score(y_test_class, y_test_predict)\n",
+    "    best_models_class[model_name][\"ROC_AUC_test\"] = roc_auc_score(y_test_class, y_test_probs, multi_class='ovr')\n",
+    "    best_models_class[model_name][\"F1_train\"] = f1_score(y_train_class, y_train_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"F1_test\"] = f1_score(y_test_class, y_test_predict, average='weighted')\n",
+    "    best_models_class[model_name][\"MCC_test\"] = matthews_corrcoef(y_test_class, y_test_predict)\n",
+    "    best_models_class[model_name][\"Cohen_kappa_test\"] = cohen_kappa_score(y_test_class, y_test_predict)\n",
+    "    best_models_class[model_name][\"Confusion_matrix\"] = confusion_matrix(y_test_class, y_test_predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_models = len(best_models_class)\n",
+    "fig, ax = plt.subplots(num_models, 1, figsize=(12, 10), sharex=False, sharey=False)\n",
+    "\n",
+    "for index, key in enumerate(best_models_class.keys()):\n",
+    "    c_matrix = best_models_class[key][\"Confusion_matrix\"]\n",
+    "    \n",
+    "    # Получаем метки классов из матрицы ошибок\n",
+    "    num_classes = c_matrix.shape[0]\n",
+    "    actual_labels = [\"0-18\", \"19-30\", \"31-50\", \"51-70\", \"71+\"][:num_classes]\n",
+    "    \n",
+    "    disp = ConfusionMatrixDisplay(\n",
+    "        confusion_matrix=c_matrix, display_labels=actual_labels\n",
+    "    ).plot(ax=ax.flat[index])\n",
+    "    disp.ax_.set_title(key)\n",
+    "\n",
+    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, ax = plt.subplots(3, 2, figsize=(12, 10), sharex=False, sharey=False)\n",
+    "ax = ax.flatten()\n",
+    "\n",
+    "for index, (name, model) in enumerate(best_models_reg.items()):\n",
+    "    y_pred_reg = model.predict(X_test_reg)\n",
+    "\n",
+    "    # График фактических значений против предсказанных значений\n",
+    "    ax[index * 2].scatter(y_test_reg, y_pred_reg, alpha=0.5)\n",
+    "    ax[index * 2].plot([min(y_test_reg), max(y_test_reg)], [min(y_test_reg), max(y_test_reg)], color='red', linestyle='--')\n",
+    "    ax[index * 2].set_xlabel('Actual Values')\n",
+    "    ax[index * 2].set_ylabel('Predicted Values')\n",
+    "    ax[index * 2].set_title(f'{name}: Actual vs Predicted')\n",
+    "\n",
+    "    # График остатков\n",
+    "    residuals = y_test_reg - y_pred_reg\n",
+    "    ax[index * 2 + 1].scatter(y_pred_reg, residuals, alpha=0.5)\n",
+    "    ax[index * 2 + 1].axhline(y=0, color='red', linestyle='--')\n",
+    "    ax[index * 2 + 1].set_xlabel('Predicted Values')\n",
+    "    ax[index * 2 + 1].set_ylabel('Residuals')\n",
+    "    ax[index * 2 + 1].set_title(f'{name}: Residuals vs Predicted')\n",
+    "\n",
+    "\n",
+    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {