diff --git a/project/cars.csv b/project/cars.csv
new file mode 100644
index 00000000..6a4c2c85
--- /dev/null
+++ b/project/cars.csv
@@ -0,0 +1,101 @@
+Car_ID,Brand,Model,Year,Kilometers_Driven,Fuel_Type,Transmission,Owner_Type,Mileage,Engine,Power,Seats,Price
+1,Toyota,Corolla,2018,50000,Petrol,Manual,First,15,1498,108,5,800000
+2,Honda,Civic,2019,40000,Petrol,Automatic,Second,17,1597,140,5,1000000
+3,Ford,Mustang,2017,20000,Petrol,Automatic,First,10,4951,395,4,2500000
+4,Maruti,Swift,2020,30000,Diesel,Manual,Third,23,1248,74,5,600000
+5,Hyundai,Sonata,2016,60000,Diesel,Automatic,Second,18,1999,194,5,850000
+6,Tata,Nexon,2019,35000,Petrol,Manual,First,17,1198,108,5,750000
+7,Mahindra,Scorpio,2018,45000,Diesel,Automatic,Second,15,2179,140,7,900000
+8,Volkswagen,Polo,2020,25000,Petrol,Automatic,First,18,999,76,5,650000
+9,Audi,A4,2017,30000,Diesel,Automatic,First,18,1968,187,5,2200000
+10,BMW,X1,2019,20000,Diesel,Automatic,Second,20,1995,190,5,2700000
+11,Mercedes,C-Class,2018,28000,Petrol,Automatic,First,16,1991,181,5,2300000
+12,Ford,Endeavour,2017,35000,Diesel,Automatic,Second,12,2198,158,7,2000000
+13,Hyundai,Creta,2019,22000,Petrol,Manual,Third,16,1497,113,5,850000
+14,Tata,Harrier,2018,40000,Diesel,Automatic,First,17,1956,167,5,1600000
+15,Maruti,Ertiga,2020,18000,Petrol,Manual,First,19,1462,103,7,850000
+16,Honda,City,2017,42000,Diesel,Manual,Second,25,1498,98,5,650000
+17,Volkswagen,Tiguan,2018,32000,Diesel,Automatic,First,17,1968,141,5,1800000
+18,Audi,Q3,2016,38000,Petrol,Automatic,Second,15,1395,148,5,1900000
+19,BMW,5 Series,2019,24000,Diesel,Automatic,First,18,1995,187,5,3000000
+20,Mercedes,GLC,2017,26000,Petrol,Automatic,Second,12,1991,241,5,2500000
+21,Toyota,Innova,2018,50000,Diesel,Manual,First,13,2755,171,7,1400000
+22,Ford,Figo,2020,15000,Petrol,Manual,Third,18,1194,94,5,550000
+23,Hyundai,Verna,2019,26000,Diesel,Automatic,Second,24,1582,126,5,850000
+24,Tata,Altroz,2020,18000,Petrol,Manual,First,20,1199,85,5,600000
+25,Mahindra,Thar,2021,10000,Diesel,Manual,First,15,2184,130,4,1200000
+26,Volkswagen,Passat,2017,32000,Diesel,Automatic,Second,17,1968,174,5,1600000
+27,Audi,A6,2018,28000,Petrol,Automatic,First,15,1984,241,5,3200000
+28,BMW,X3,2019,22000,Diesel,Automatic,Second,18,1995,187,5,2800000
+29,Mercedes,E-Class,2017,30000,Diesel,Automatic,First,16,1950,191,5,2700000
+30,Toyota,Fortuner,2018,38000,Diesel,Automatic,Second,12,2755,174,7,2500000
+31,Ford,Aspire,2019,26000,Petrol,Manual,Third,20,1194,94,5,600000
+32,Hyundai,Elantra,2017,32000,Diesel,Automatic,Second,22,1582,126,5,800000
+33,Tata,Safari,2018,42000,Diesel,Manual,First,14,1956,150,7,1300000
+34,Maruti,Vitara,2019,24000,Petrol,Manual,Second,17,1462,103,5,700000
+35,Honda,WR-V,2018,28000,Diesel,Manual,First,25,1498,98,5,750000
+36,Volkswagen,Ameo,2020,15000,Petrol,Automatic,Third,19,1197,74,5,500000
+37,Audi,A3,2017,38000,Petrol,Automatic,Second,16,1395,148,5,2000000
+38,BMW,7 Series,2019,22000,Diesel,Automatic,First,15,2993,261,5,3500000
+39,Mercedes,GLE,2018,26000,Petrol,Automatic,Second,12,2996,362,5,4000000
+40,Toyota,Yaris,2020,18000,Petrol,Manual,First,17,1496,106,5,650000
+41,Ford,Ranger,2017,38000,Diesel,Manual,Second,12,2198,158,5,1500000
+42,Hyundai,Santro,2019,26000,Petrol,Manual,Third,20,1086,68,5,450000
+43,Tata,Tigor,2018,42000,Diesel,Manual,First,24,1047,69,5,500000
+44,Maruti,S-Cross,2020,15000,Petrol,Automatic,Second,18,1462,103,5,700000
+45,Honda,BR-V,2018,28000,Diesel,Manual,First,21,1498,98,7,850000
+46,Volkswagen,T-Roc,2019,22000,Petrol,Automatic,Second,18,1498,148,5,1600000
+47,Audi,Q7,2017,30000,Diesel,Automatic,First,14,2967,245,7,3000000
+48,BMW,X5,2018,28000,Petrol,Automatic,Second,14,2998,335,5,3200000
+49,Mercedes,GLA,2019,24000,Diesel,Automatic,First,17,2143,170,5,2400000
+50,Toyota,Camry,2016,38000,Petrol,Automatic,Second,19,2487,176,5,1800000
+51,Ford,Mustang,2019,22000,Petrol,Automatic,First,13,2261,396,4,2700000
+52,Hyundai,Venue,2018,32000,Petrol,Manual,Third,17,1197,81,5,550000
+53,Tata,Tiago,2020,18000,Petrol,Manual,First,23,1199,84,5,500000
+54,Mahindra,XUV300,2019,26000,Diesel,Manual,Second,20,1497,115,5,700000
+55,Volkswagen,Vento,2017,32000,Petrol,Manual,Second,18,1598,103,5,650000
+56,Audi,A5,2018,28000,Diesel,Automatic,First,17,1968,187,5,2600000
+57,BMW,3 Series,2020,15000,Petrol,Automatic,Second,15,1998,258,5,2800000
+58,Mercedes,C-Class,2019,22000,Diesel,Automatic,First,16,1950,191,5,2900000
+59,Toyota,Innova Crysta,2017,38000,Diesel,Manual,Second,13,2755,171,7,1400000
+60,Ford,EcoSport,2018,26000,Petrol,Manual,Third,18,1497,121,5,750000
+61,Hyundai,Verna,2019,24000,Petrol,Automatic,Second,17,1497,113,5,850000
+62,Tata,Altroz,2020,18000,Petrol,Manual,First,20,1199,85,5,600000
+63,Mahindra,Thar,2021,10000,Diesel,Manual,First,15,2184,130,4,1200000
+64,Volkswagen,Passat,2017,32000,Diesel,Automatic,Second,17,1968,174,5,1600000
+65,Audi,A6,2018,28000,Petrol,Automatic,First,15,1984,241,5,3200000
+66,BMW,X3,2019,22000,Diesel,Automatic,Second,18,1995,187,5,2800000
+67,Mercedes,E-Class,2017,30000,Diesel,Automatic,First,16,1950,191,5,2700000
+68,Toyota,Fortuner,2018,38000,Diesel,Automatic,Second,12,2755,174,7,2500000
+69,Ford,Aspire,2019,26000,Petrol,Manual,Third,20,1194,94,5,600000
+70,Hyundai,Elantra,2017,32000,Diesel,Automatic,Second,22,1582,126,5,800000
+71,Tata,Safari,2018,42000,Diesel,Manual,First,14,1956,150,7,1300000
+72,Maruti,Vitara,2019,24000,Petrol,Manual,Second,17,1462,103,5,700000
+73,Honda,WR-V,2018,28000,Diesel,Manual,First,25,1498,98,5,750000
+74,Volkswagen,Ameo,2020,15000,Petrol,Automatic,Third,19,1197,74,5,500000
+75,Audi,A3,2017,38000,Petrol,Automatic,Second,16,1395,148,5,2000000
+76,BMW,7 Series,2019,22000,Diesel,Automatic,First,15,2993,261,5,3500000
+77,Mercedes,GLE,2018,26000,Petrol,Automatic,Second,12,2996,362,5,4000000
+78,Toyota,Yaris,2020,18000,Petrol,Manual,First,17,1496,106,5,650000
+79,Ford,Ranger,2017,38000,Diesel,Manual,Second,12,2198,158,5,1500000
+80,Hyundai,Santro,2019,26000,Petrol,Manual,Third,20,1086,68,5,450000
+81,Tata,Tigor,2018,42000,Diesel,Manual,First,24,1047,69,5,500000
+82,Maruti,S-Cross,2020,15000,Petrol,Automatic,Second,18,1462,103,5,700000
+83,Honda,BR-V,2018,28000,Diesel,Manual,First,21,1498,98,7,850000
+84,Volkswagen,T-Roc,2019,22000,Petrol,Automatic,Second,18,1498,148,5,1600000
+85,Audi,Q7,2017,30000,Diesel,Automatic,First,14,2967,245,7,3000000
+86,BMW,X5,2018,28000,Petrol,Automatic,Second,14,2998,335,5,3200000
+87,Mercedes,GLA,2019,24000,Diesel,Automatic,First,17,2143,170,5,2400000
+88,Toyota,Camry,2016,38000,Petrol,Automatic,Second,19,2487,176,5,1800000
+89,Ford,Mustang,2019,22000,Petrol,Automatic,First,13,2261,396,4,2700000
+90,Hyundai,Venue,2018,32000,Petrol,Manual,Third,17,1197,81,5,550000
+91,Tata,Tiago,2020,18000,Petrol,Manual,First,23,1199,84,5,500000
+92,Mahindra,XUV300,2019,26000,Diesel,Manual,Second,20,1497,115,5,700000
+93,Volkswagen,Vento,2017,32000,Petrol,Manual,Second,18,1598,103,5,650000
+94,Audi,A5,2018,28000,Diesel,Automatic,First,17,1968,187,5,2600000
+95,BMW,3 Series,2020,15000,Petrol,Automatic,Second,15,1998,258,5,2800000
+96,Mercedes,C-Class,2019,22000,Diesel,Automatic,First,16,1950,191,5,2900000
+97,Toyota,Innova Crysta,2017,38000,Diesel,Manual,Second,13,2755,171,7,1400000
+98,Ford,EcoSport,2018,26000,Petrol,Manual,Third,18,1497,121,5,750000
+99,Hyundai,Verna,2019,24000,Petrol,Automatic,Second,17,1497,113,5,850000
+100,Tata,Altroz,2020,18000,Petrol,Manual,First,20,1199,85,5,600000
diff --git a/project/cars.ipynb b/project/cars.ipynb
new file mode 100644
index 00000000..f87f7de8
--- /dev/null
+++ b/project/cars.ipynb
@@ -0,0 +1,4676 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9ae9700b-d36b-4366-856c-02bfc950e03d",
+   "metadata": {},
+   "source": [
+    "# Задача 11. Сравнение методов регрессии"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67736d4a-fe8e-4d61-a6bc-d581ff18e219",
+   "metadata": {},
+   "source": [
+    "Импорт библиотек"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "46bbe284-ef0d-4a08-a97d-d8109ef60172",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import GridSearchCV, KFold\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.linear_model import Ridge, Lasso\n",
+    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import time\n",
+    "from sklearn.model_selection import learning_curve"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d9e3750-062f-4e5a-bb1d-141f9c1f3173",
+   "metadata": {},
+   "source": [
+    "## 1. Загрузка данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ca278001-530d-43dc-94ec-45483f83a54e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер датасета: (100, 13)\n",
+      "\n",
+      "Первые 5 строк:\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>Car_ID</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Kilometers_Driven</th>\n",
+       "      <th>Fuel_Type</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Owner_Type</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>Engine</th>\n",
+       "      <th>Power</th>\n",
+       "      <th>Seats</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>Corolla</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>Manual</td>\n",
+       "      <td>First</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1498</td>\n",
+       "      <td>108</td>\n",
+       "      <td>5</td>\n",
+       "      <td>800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Honda</td>\n",
+       "      <td>Civic</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>Automatic</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1597</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>Mustang</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>Automatic</td>\n",
+       "      <td>First</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4951</td>\n",
+       "      <td>395</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>Swift</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>Manual</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1248</td>\n",
+       "      <td>74</td>\n",
+       "      <td>5</td>\n",
+       "      <td>600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>Sonata</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>60000</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>Automatic</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>194</td>\n",
+       "      <td>5</td>\n",
+       "      <td>850000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Car_ID    Brand    Model  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
+       "0       1   Toyota  Corolla  2018              50000    Petrol       Manual   \n",
+       "1       2    Honda    Civic  2019              40000    Petrol    Automatic   \n",
+       "2       3     Ford  Mustang  2017              20000    Petrol    Automatic   \n",
+       "3       4   Maruti    Swift  2020              30000    Diesel       Manual   \n",
+       "4       5  Hyundai   Sonata  2016              60000    Diesel    Automatic   \n",
+       "\n",
+       "  Owner_Type  Mileage  Engine  Power  Seats    Price  \n",
+       "0      First       15    1498    108      5   800000  \n",
+       "1     Second       17    1597    140      5  1000000  \n",
+       "2      First       10    4951    395      4  2500000  \n",
+       "3      Third       23    1248     74      5   600000  \n",
+       "4     Second       18    1999    194      5   850000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('cars.csv')\n",
+    "print(f\"Размер датасета: {df.shape}\")\n",
+    "print(\"\\nПервые 5 строк:\")\n",
+    "display(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5746e540-cfa1-48bf-88ce-3150271f4182",
+   "metadata": {},
+   "source": [
+    "## 2. Первичный анализ данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e17839b1-18a5-4ce2-a15b-cd45481a4b9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Основная информация:\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 100 entries, 0 to 99\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column             Non-Null Count  Dtype \n",
+      "---  ------             --------------  ----- \n",
+      " 0   Car_ID             100 non-null    int64 \n",
+      " 1   Brand              100 non-null    object\n",
+      " 2   Model              100 non-null    object\n",
+      " 3   Year               100 non-null    int64 \n",
+      " 4   Kilometers_Driven  100 non-null    int64 \n",
+      " 5   Fuel_Type          100 non-null    object\n",
+      " 6   Transmission       100 non-null    object\n",
+      " 7   Owner_Type         100 non-null    object\n",
+      " 8   Mileage            100 non-null    int64 \n",
+      " 9   Engine             100 non-null    int64 \n",
+      " 10  Power              100 non-null    int64 \n",
+      " 11  Seats              100 non-null    int64 \n",
+      " 12  Price              100 non-null    int64 \n",
+      "dtypes: int64(8), object(5)\n",
+      "memory usage: 10.3+ KB\n",
+      "None\n",
+      "\n",
+      "Описательная статистика:\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>Car_ID</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Kilometers_Driven</th>\n",
+       "      <th>Fuel_Type</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Owner_Type</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>Engine</th>\n",
+       "      <th>Power</th>\n",
+       "      <th>Seats</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100.00000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>1.000000e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11</td>\n",
+       "      <td>58</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>Mustang</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>Automatic</td>\n",
+       "      <td>First</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>52</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>50.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018.39000</td>\n",
+       "      <td>28150.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17.210000</td>\n",
+       "      <td>1855.230000</td>\n",
+       "      <td>158.130000</td>\n",
+       "      <td>5.230000</td>\n",
+       "      <td>1.574000e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>29.011492</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.17116</td>\n",
+       "      <td>9121.375716</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.309902</td>\n",
+       "      <td>631.311475</td>\n",
+       "      <td>76.968137</td>\n",
+       "      <td>0.750151</td>\n",
+       "      <td>1.000265e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016.00000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>999.000000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.500000e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>25.750000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2017.75000</td>\n",
+       "      <td>22000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>1462.000000</td>\n",
+       "      <td>103.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>7.000000e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>50.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2018.00000</td>\n",
+       "      <td>27000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>1774.000000</td>\n",
+       "      <td>148.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.300000e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>75.250000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2019.00000</td>\n",
+       "      <td>32000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>2143.000000</td>\n",
+       "      <td>187.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>2.500000e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2021.00000</td>\n",
+       "      <td>60000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>4951.000000</td>\n",
+       "      <td>396.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>4.000000e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Car_ID Brand    Model        Year  Kilometers_Driven Fuel_Type  \\\n",
+       "count   100.000000   100      100   100.00000         100.000000       100   \n",
+       "unique         NaN    11       58         NaN                NaN         2   \n",
+       "top            NaN  Ford  Mustang         NaN                NaN    Petrol   \n",
+       "freq           NaN    11        3         NaN                NaN        52   \n",
+       "mean     50.500000   NaN      NaN  2018.39000       28150.000000       NaN   \n",
+       "std      29.011492   NaN      NaN     1.17116        9121.375716       NaN   \n",
+       "min       1.000000   NaN      NaN  2016.00000       10000.000000       NaN   \n",
+       "25%      25.750000   NaN      NaN  2017.75000       22000.000000       NaN   \n",
+       "50%      50.500000   NaN      NaN  2018.00000       27000.000000       NaN   \n",
+       "75%      75.250000   NaN      NaN  2019.00000       32000.000000       NaN   \n",
+       "max     100.000000   NaN      NaN  2021.00000       60000.000000       NaN   \n",
+       "\n",
+       "       Transmission Owner_Type     Mileage       Engine       Power  \\\n",
+       "count           100        100  100.000000   100.000000  100.000000   \n",
+       "unique            2          3         NaN          NaN         NaN   \n",
+       "top       Automatic      First         NaN          NaN         NaN   \n",
+       "freq             57         44         NaN          NaN         NaN   \n",
+       "mean            NaN        NaN   17.210000  1855.230000  158.130000   \n",
+       "std             NaN        NaN    3.309902   631.311475   76.968137   \n",
+       "min             NaN        NaN   10.000000   999.000000   68.000000   \n",
+       "25%             NaN        NaN   15.000000  1462.000000  103.000000   \n",
+       "50%             NaN        NaN   17.000000  1774.000000  148.000000   \n",
+       "75%             NaN        NaN   19.000000  2143.000000  187.000000   \n",
+       "max             NaN        NaN   25.000000  4951.000000  396.000000   \n",
+       "\n",
+       "             Seats         Price  \n",
+       "count   100.000000  1.000000e+02  \n",
+       "unique         NaN           NaN  \n",
+       "top            NaN           NaN  \n",
+       "freq           NaN           NaN  \n",
+       "mean      5.230000  1.574000e+06  \n",
+       "std       0.750151  1.000265e+06  \n",
+       "min       4.000000  4.500000e+05  \n",
+       "25%       5.000000  7.000000e+05  \n",
+       "50%       5.000000  1.300000e+06  \n",
+       "75%       5.000000  2.500000e+06  \n",
+       "max       7.000000  4.000000e+06  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"\\nОсновная информация:\")\n",
+    "print(df.info())\n",
+    "\n",
+    "print(\"\\nОписательная статистика:\")\n",
+    "display(df.describe(include='all'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "347f8f5c-c326-4d16-b623-c7ad025f1b04",
+   "metadata": {},
+   "source": [
+    "## 3. Проверка пропущенных значений"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "08a18284-6e38-404e-92c2-aa2ea96e0a18",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Пропущенные значения:\n",
+      "Car_ID               0\n",
+      "Brand                0\n",
+      "Model                0\n",
+      "Year                 0\n",
+      "Kilometers_Driven    0\n",
+      "Fuel_Type            0\n",
+      "Transmission         0\n",
+      "Owner_Type           0\n",
+      "Mileage              0\n",
+      "Engine               0\n",
+      "Power                0\n",
+      "Seats                0\n",
+      "Price                0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nПропущенные значения:\")\n",
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fc1ad0e-05a0-42a7-9f1d-ee0eed988bd0",
+   "metadata": {},
+   "source": [
+    "## 4. Очистка данных (Data Cleaning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5c8fe15b-6f91-44f1-95f0-8131071e5074",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Количество дубликатов до очистки: 0\n",
+      "Количество дубликатов после очистки: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Удаление дубликатов\n",
+    "print(f\"\\nКоличество дубликатов до очистки: {df.duplicated().sum()}\")\n",
+    "df.drop_duplicates(inplace=True)\n",
+    "print(f\"Количество дубликатов после очистки: {df.duplicated().sum()}\")\n",
+    "\n",
+    "# Обработка пропущенных значений (если есть)\n",
+    "# Заполним медианными значениями для числовых столбцов\n",
+    "numeric_cols = ['Year', 'Kilometers_Driven', 'Mileage', 'Engine', 'Power', 'Seats', 'Price']\n",
+    "for col in numeric_cols:\n",
+    "    if df[col].isnull().sum() > 0:\n",
+    "        median_val = df[col].median()\n",
+    "        df[col].fillna(median_val, inplace=True)\n",
+    "\n",
+    "# Для категориальных - модой\n",
+    "categorical_cols = ['Fuel_Type', 'Transmission', 'Owner_Type']\n",
+    "for col in categorical_cols:\n",
+    "    if df[col].isnull().sum() > 0:\n",
+    "        mode_val = df[col].mode()[0]\n",
+    "        df[col].fillna(mode_val, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcb109df-8833-40aa-b37e-7c85db01577d",
+   "metadata": {},
+   "source": [
+    "## 5. Анализ выбросов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7cc872c7-2677-4154-bc99-bf1a8832da2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 10))\n",
+    "numeric_cols = ['Year', 'Kilometers_Driven', 'Mileage', 'Engine', 'Power', 'Seats', 'Price']\n",
+    "for i, col in enumerate(numeric_cols, 1):\n",
+    "    plt.subplot(3, 3, i)\n",
+    "    sns.boxplot(y=df[col])\n",
+    "    plt.title(col)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Удаление выбросов по цене (Price)\n",
+    "Q1 = df['Price'].quantile(0.25)\n",
+    "Q3 = df['Price'].quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "df = df[~((df['Price'] < (Q1 - 1.5 * IQR)) | (df['Price'] > (Q3 + 1.5 * IQR)))]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "814f15e7-b22b-4486-8252-8df740b7afee",
+   "metadata": {},
+   "source": [
+    "## 6. Категориальный анализ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f34da59-f0e2-4304-98f4-cbf63b8b51ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Уникальные значения категориальных признаков:\n",
+      "\n",
+      "Fuel_Type:\n",
+      "Fuel_Type\n",
+      "Petrol    52\n",
+      "Diesel    48\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Transmission:\n",
+      "Transmission\n",
+      "Automatic    57\n",
+      "Manual       43\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Owner_Type:\n",
+      "Owner_Type\n",
+      "First     44\n",
+      "Second    43\n",
+      "Third     13\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nУникальные значения категориальных признаков:\")\n",
+    "for col in categorical_cols:\n",
+    "    print(f\"\\n{col}:\")\n",
+    "    print(df[col].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1198e03c-524d-416e-89b1-49a8333b51a3",
+   "metadata": {},
+   "source": [
+    "## 7. Визуализация распределений"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "77c2ed15-deab-428d-8df3-9a8ce36b8dc2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 10))\n",
+    "for i, col in enumerate(numeric_cols, 1):\n",
+    "    plt.subplot(3, 3, i)\n",
+    "    sns.histplot(df[col], kde=True)\n",
+    "    plt.title(f'Распределение {col}')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78b5e642-6e1d-402c-9d7b-a9e0426c9cff",
+   "metadata": {},
+   "source": [
+    "## 8. Корреляционный анализ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d6e1d96e-d2d2-4c05-ad03-fb6e78d636b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 8))\n",
+    "corr = df[numeric_cols].corr()\n",
+    "sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)\n",
+    "plt.title('Матрица корреляций')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da0a8c49-0d6e-4f0c-8f7d-8586051e3b4f",
+   "metadata": {},
+   "source": [
+    "## 9. Анализ цен по брендам"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f0b897b1-260d-429f-ab7e-21b23b1f62c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.boxplot(x='Brand', y='Price', data=df)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Распределение цен по брендам')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f3c637e-c433-48d8-af95-0e681b09f905",
+   "metadata": {},
+   "source": [
+    "## 10. Сохранение очищенных данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9defaf05-37df-4535-a1b8-02bdd047f9a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Очищенные данные сохранены в файл 'cleaned_cars.csv'\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.to_csv('cleaned_cars.csv', index=False)\n",
+    "print(\"\\nОчищенные данные сохранены в файл 'cleaned_cars.csv'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4376cc61-6caa-4101-9990-3d9b5cb2410a",
+   "metadata": {},
+   "source": [
+    "## Вывод итоговой информации"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c55ed0b8-ab93-45e6-8adf-3aca1930a6bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Итоговая информация:\n",
+      "Количество строк: 100\n",
+      "Количество столбцов: 13\n",
+      "\n",
+      "Пример очищенных данных:\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>Car_ID</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Kilometers_Driven</th>\n",
+       "      <th>Fuel_Type</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Owner_Type</th>\n",
+       "      <th>Mileage</th>\n",
+       "      <th>Engine</th>\n",
+       "      <th>Power</th>\n",
+       "      <th>Seats</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toyota</td>\n",
+       "      <td>Corolla</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>Manual</td>\n",
+       "      <td>First</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1498</td>\n",
+       "      <td>108</td>\n",
+       "      <td>5</td>\n",
+       "      <td>800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Honda</td>\n",
+       "      <td>Civic</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>40000</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>Automatic</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1597</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Ford</td>\n",
+       "      <td>Mustang</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>Petrol</td>\n",
+       "      <td>Automatic</td>\n",
+       "      <td>First</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4951</td>\n",
+       "      <td>395</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Maruti</td>\n",
+       "      <td>Swift</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>Manual</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1248</td>\n",
+       "      <td>74</td>\n",
+       "      <td>5</td>\n",
+       "      <td>600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Hyundai</td>\n",
+       "      <td>Sonata</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>60000</td>\n",
+       "      <td>Diesel</td>\n",
+       "      <td>Automatic</td>\n",
+       "      <td>Second</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>194</td>\n",
+       "      <td>5</td>\n",
+       "      <td>850000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Car_ID    Brand    Model  Year  Kilometers_Driven Fuel_Type Transmission  \\\n",
+       "0       1   Toyota  Corolla  2018              50000    Petrol       Manual   \n",
+       "1       2    Honda    Civic  2019              40000    Petrol    Automatic   \n",
+       "2       3     Ford  Mustang  2017              20000    Petrol    Automatic   \n",
+       "3       4   Maruti    Swift  2020              30000    Diesel       Manual   \n",
+       "4       5  Hyundai   Sonata  2016              60000    Diesel    Automatic   \n",
+       "\n",
+       "  Owner_Type  Mileage  Engine  Power  Seats    Price  \n",
+       "0      First       15    1498    108      5   800000  \n",
+       "1     Second       17    1597    140      5  1000000  \n",
+       "2      First       10    4951    395      4  2500000  \n",
+       "3      Third       23    1248     74      5   600000  \n",
+       "4     Second       18    1999    194      5   850000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"\\nИтоговая информация:\")\n",
+    "print(f\"Количество строк: {len(df)}\")\n",
+    "print(f\"Количество столбцов: {len(df.columns)}\")\n",
+    "print(\"\\nПример очищенных данных:\")\n",
+    "display(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "006fa0a1-4dd8-43ed-a06d-885f443f59c9",
+   "metadata": {},
+   "source": [
+    "## 1. Распределение целевой переменной (Price)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "23b848ad-5a92-4320-b2c3-c19eb29103bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "sns.histplot(df['Price'], kde=True, bins=30)\n",
+    "plt.title('Распределение цен на автомобили')\n",
+    "plt.xlabel('Цена (руб)')\n",
+    "plt.ylabel('Количество')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54d8c3c5-8bfe-4287-904b-b77d06b0fdcf",
+   "metadata": {},
+   "source": [
+    "## 2. Топ-10 самых дорогих брендов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f3205835-4d63-4ec2-8176-c62c05cfae81",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 6))\n",
+    "df.groupby('Brand')['Price'].mean().sort_values(ascending=False).head(10).plot(kind='bar')\n",
+    "plt.title('Средняя цена по брендам (Топ-10)')\n",
+    "plt.ylabel('Средняя цена (руб)')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d54ebfb-e995-4b0c-b8ea-6d4504c4b40d",
+   "metadata": {},
+   "source": [
+    "## 3. Влияние года выпуска на цену"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f2c57d22-bb31-4de1-91ea-b57220fa341f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "sns.scatterplot(x='Year', y='Price', data=df, hue='Fuel_Type')\n",
+    "plt.title('Зависимость цены от года выпуска')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acc2ee2c-c608-4391-bfc0-83b60960234e",
+   "metadata": {},
+   "source": [
+    "## 4. Влияние мощности двигателя на цену"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ace08fec-87da-47f6-915d-14abf6f3b00b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "sns.regplot(x='Power', y='Price', data=df, scatter_kws={'alpha':0.3})\n",
+    "plt.title('Зависимость цены от мощности двигателя (bhp)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f795fd2-59e3-493f-891f-e446c91e1105",
+   "metadata": {},
+   "source": [
+    "## 5. Коробка передач и тип владельца"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "a90c39df-b54d-4c9e-bd27-80f7b547f8ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.boxplot(x='Owner_Type', y='Price', hue='Transmission', data=df)\n",
+    "plt.title('Распределение цен по типу владельца и коробке передач')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f75d915-d6bd-4e72-9b8e-9134d7734061",
+   "metadata": {},
+   "source": [
+    "## 6. Тепловая карта корреляций (только числовые признаки)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d6ea62f4-f656-44bb-9ca6-ef216dac0a77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "numeric_cols = ['Year', 'Kilometers_Driven', 'Mileage', 'Engine', 'Power', 'Seats', 'Price']\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(df[numeric_cols].corr(), annot=True, cmap='coolwarm', center=0)\n",
+    "plt.title('Корреляция числовых признаков')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "105aac26-e1a6-4293-a955-69961fd750e8",
+   "metadata": {},
+   "source": [
+    "## 1. Преобразование категориальных признаков (one-hot encoding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3931863d-9923-451d-89ee-667feb438905",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_cols = ['Fuel_Type', 'Transmission', 'Owner_Type']\n",
+    "df = pd.get_dummies(df, columns=cat_cols, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42f2f3f4-c840-418d-a526-caa24cf57cd3",
+   "metadata": {},
+   "source": [
+    "## 2. Кодирование брендов (частотное кодирование для уменьшения размерности)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "ae3f29cd-72a2-45f7-86a8-54e9628810f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brand_freq = df['Brand'].value_counts(normalize=True)\n",
+    "df['Brand_encoded'] = df['Brand'].map(brand_freq)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11a64f55-47ef-41b3-903d-0165f9fbeeba",
+   "metadata": {},
+   "source": [
+    "## 3. Создание новых признаков"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9fb0dd1e-3613-40fd-a2f9-7fd29dea5566",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Car_Age'] = 2023 - df['Year']  # Возраст автомобиля\n",
+    "df['Power_to_Weight'] = df['Power'] / df['Engine']  # Удельная мощность"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee07f2a8-00c1-4ef0-b96f-d68ed79a4b3b",
+   "metadata": {},
+   "source": [
+    "## 4. Логарифмирование целевой переменной для нормализации"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "acbb4a24-4890-4358-a90e-b54211761f1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Price_log'] = np.log1p(df['Price'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adcf1cac-a794-4101-a3f6-ccb220d5bfc7",
+   "metadata": {},
+   "source": [
+    "## 5. Удаление ненужных столбцов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "85fcc8b4-77a2-4cf4-90f5-d3176bc51ea0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols_to_drop = ['Car_ID', 'Brand', 'Model', 'Year', 'Seats']  # Seats слабо коррелирует\n",
+    "df.drop(cols_to_drop, axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "718a6a4d-6877-46a3-a776-f266409ed378",
+   "metadata": {},
+   "source": [
+    "## 6. Проверка результата"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7648d171-8b9c-484b-862b-a11b0dae2f0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Окончательные признаки:\n",
+      "['Kilometers_Driven', 'Mileage', 'Engine', 'Power', 'Price', 'Fuel_Type_Petrol', 'Transmission_Manual', 'Owner_Type_Second', 'Owner_Type_Third', 'Brand_encoded', 'Car_Age', 'Power_to_Weight', 'Price_log']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Окончательные признаки:\")\n",
+    "print(df.columns.tolist())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d7ef55f-49c7-4a1b-a401-cc8e2aacdbdc",
+   "metadata": {},
+   "source": [
+    "## Подготовка данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "fa35c9a7-030b-4223-b15f-9ba63e85881e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.drop(['Price', 'Price_log'], axis=1)\n",
+    "y = df['Price_log']  # Используем логарифмированную цену\n",
+    "\n",
+    "# Разделение данных\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Настройки кросс-валидации\n",
+    "cv = KFold(n_splits=5, shuffle=True, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d16973cf-f9fb-41ea-8809-d86130a056f8",
+   "metadata": {},
+   "source": [
+    "## 1. Ridge Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "a5061f61-3f65-4636-b9c5-80c5daa7e316",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
+       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;model&#x27;, Ridge())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;model__alpha&#x27;: array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]),\n",
+       "                         &#x27;model__max_iter&#x27;: [1000, 5000]},\n",
+       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
+       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;model&#x27;, Ridge())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;model__alpha&#x27;: array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]),\n",
+       "                         &#x27;model__max_iter&#x27;: [1000, 5000]},\n",
+       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: Pipeline</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;model&#x27;, Ridge(alpha=10.0, max_iter=1000))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Ridge</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>Ridge(alpha=10.0, max_iter=1000)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
+       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                                       ('model', Ridge())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={'model__alpha': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]),\n",
+       "                         'model__max_iter': [1000, 5000]},\n",
+       "             scoring='neg_mean_squared_error')"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ridge_pipe = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('model', Ridge())\n",
+    "])\n",
+    "\n",
+    "ridge_params = {\n",
+    "    'model__alpha': np.logspace(-3, 3, 7),\n",
+    "    'model__max_iter': [1000, 5000]\n",
+    "}\n",
+    "\n",
+    "ridge_search = GridSearchCV(ridge_pipe, ridge_params, cv=cv, scoring='neg_mean_squared_error', n_jobs=-1)\n",
+    "ridge_search.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e54219b9-fa33-4090-9121-289700d78e38",
+   "metadata": {},
+   "source": [
+    "## 2. Lasso Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "76d39106-c6a9-4db4-aa57-d85d39baed68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
+       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;model&#x27;, Lasso())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;model__alpha&#x27;: array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00]),\n",
+       "                         &#x27;model__max_iter&#x27;: [5000]},\n",
+       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
+       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;model&#x27;, Lasso())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;model__alpha&#x27;: array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00]),\n",
+       "                         &#x27;model__max_iter&#x27;: [5000]},\n",
+       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: Pipeline</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;model&#x27;, Lasso(alpha=0.01, max_iter=5000))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Lasso</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>Lasso(alpha=0.01, max_iter=5000)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
+       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                                       ('model', Lasso())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={'model__alpha': array([1.e-04, 1.e-03, 1.e-02, 1.e-01, 1.e+00]),\n",
+       "                         'model__max_iter': [5000]},\n",
+       "             scoring='neg_mean_squared_error')"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lasso_pipe = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('model', Lasso())\n",
+    "])\n",
+    "\n",
+    "lasso_params = {\n",
+    "    'model__alpha': np.logspace(-4, 0, 5),\n",
+    "    'model__max_iter': [5000]\n",
+    "}\n",
+    "\n",
+    "lasso_search = GridSearchCV(lasso_pipe, lasso_params, cv=cv, scoring='neg_mean_squared_error', n_jobs=-1)\n",
+    "lasso_search.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d2e2c79-1f00-4e4c-a4c4-42bcc988a757",
+   "metadata": {},
+   "source": [
+    "## 3. Random Forest (без масштабирования)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3356608c-02bb-4589-9c26-fcecbbd2cab5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
+       "             estimator=RandomForestRegressor(random_state=42), n_jobs=-1,\n",
+       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
+       "                         &#x27;min_samples_split&#x27;: [2, 5],\n",
+       "                         &#x27;n_estimators&#x27;: [100, 200]},\n",
+       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GridSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
+       "             estimator=RandomForestRegressor(random_state=42), n_jobs=-1,\n",
+       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
+       "                         &#x27;min_samples_split&#x27;: [2, 5],\n",
+       "                         &#x27;n_estimators&#x27;: [100, 200]},\n",
+       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: RandomForestRegressor</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=10, n_estimators=200, random_state=42)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=10, n_estimators=200, random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=KFold(n_splits=5, random_state=42, shuffle=True),\n",
+       "             estimator=RandomForestRegressor(random_state=42), n_jobs=-1,\n",
+       "             param_grid={'max_depth': [None, 10, 20],\n",
+       "                         'min_samples_split': [2, 5],\n",
+       "                         'n_estimators': [100, 200]},\n",
+       "             scoring='neg_mean_squared_error')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_params = {\n",
+    "    'n_estimators': [100, 200],\n",
+    "    'max_depth': [None, 10, 20],\n",
+    "    'min_samples_split': [2, 5]\n",
+    "}\n",
+    "\n",
+    "rf_search = GridSearchCV(RandomForestRegressor(random_state=42), \n",
+    "                       rf_params, cv=cv, \n",
+    "                       scoring='neg_mean_squared_error', n_jobs=-1)\n",
+    "rf_search.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec8a0b1c-302c-4135-94e7-3ec6362b85aa",
+   "metadata": {},
+   "source": [
+    "## 4. Gradient Boosting (без масштабирования)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "63f5499d-2094-438f-ac41-2d78848fa6d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gb_params = {\n",
+    "    'n_estimators': [100, 200],\n",
+    "    'learning_rate': [0.01, 0.1],\n",
+    "    'max_depth': [3, 5]\n",
+    "}\n",
+    "\n",
+    "gb_search = GridSearchCV(GradientBoostingRegressor(random_state=42), \n",
+    "                       gb_params, cv=cv, \n",
+    "                       scoring='neg_mean_squared_error', n_jobs=-1)\n",
+    "gb_search.fit(X_train, y_train)\n",
+    "\n",
+    "# Оценка моделей\n",
+    "models = {\n",
+    "    'Ridge': ridge_search,\n",
+    "    'Lasso': lasso_search,\n",
+    "    'Random Forest': rf_search,\n",
+    "    'Gradient Boosting': gb_search\n",
+    "}\n",
+    "\n",
+    "results = []\n",
+    "for name, model in models.items():\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    mse = mean_squared_error(y_test, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    results.append({\n",
+    "        'Model': name,\n",
+    "        'Best Params': model.best_params_,\n",
+    "        'MSE': mse,\n",
+    "        'R2': r2,\n",
+    "        'RMSE (руб)': np.round(np.expm1(np.sqrt(mse)))\n",
+    "    })\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "752fa32c-8bb6-4306-8b44-3c2e3ba0c0d1",
+   "metadata": {},
+   "source": [
+    "## Вывод результатов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "6307b21f-ecc4-4085-82b9-ad6770c4d83a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Сравнение моделей:\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>Model</th>\n",
+       "      <th>RMSE (руб)</th>\n",
+       "      <th>R2</th>\n",
+       "      <th>Best Params</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ridge</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.703212</td>\n",
+       "      <td>{'model__alpha': 10.0, 'model__max_iter': 1000}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Lasso</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.677768</td>\n",
+       "      <td>{'model__alpha': 0.01, 'model__max_iter': 5000}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.837675</td>\n",
+       "      <td>{'max_depth': 10, 'min_samples_split': 2, 'n_e...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Gradient Boosting</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.815860</td>\n",
+       "      <td>{'learning_rate': 0.1, 'max_depth': 3, 'n_esti...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Model  RMSE (руб)        R2  \\\n",
+       "0              Ridge         0.0  0.703212   \n",
+       "1              Lasso         0.0  0.677768   \n",
+       "2      Random Forest         0.0  0.837675   \n",
+       "3  Gradient Boosting         0.0  0.815860   \n",
+       "\n",
+       "                                         Best Params  \n",
+       "0    {'model__alpha': 10.0, 'model__max_iter': 1000}  \n",
+       "1    {'model__alpha': 0.01, 'model__max_iter': 5000}  \n",
+       "2  {'max_depth': 10, 'min_samples_split': 2, 'n_e...  \n",
+       "3  {'learning_rate': 0.1, 'max_depth': 3, 'n_esti...  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results_df = pd.DataFrame(results).sort_values('RMSE (руб)')\n",
+    "print(\"\\nСравнение моделей:\")\n",
+    "display(results_df[['Model', 'RMSE (руб)', 'R2', 'Best Params']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a8997c0-9e9d-41f9-b720-7511970d9f05",
+   "metadata": {},
+   "source": [
+    "## Визуализация важности признаков для лучшей модели"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "19764948-be84-423f-ab3e-ef28db8e8c0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_model_name = results_df.iloc[0]['Model']\n",
+    "best_model = models[best_model_name].best_estimator_\n",
+    "\n",
+    "if hasattr(best_model, 'feature_importances_'):\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    if isinstance(best_model, Pipeline):\n",
+    "        importances = best_model.named_steps['model'].feature_importances_\n",
+    "    else:\n",
+    "        importances = best_model.feature_importances_\n",
+    "    \n",
+    "    features = X.columns\n",
+    "    importance_df = pd.DataFrame({'Feature': features, 'Importance': importances})\n",
+    "    importance_df.sort_values('Importance', ascending=False).head(10).plot.bar(x='Feature', y='Importance')\n",
+    "    plt.title(f'Важность признаков ({best_model_name})')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f149db2f-f1a3-44eb-9d30-0a3a0020554c",
+   "metadata": {},
+   "source": [
+    "## 1. Сравнительный анализ времени обучения и качества"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "67ac6b6e-3682-439d-946a-20d564d2bd41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Предупреждение: Следующие метрики не найдены и будут пропущены: {'Bpews обучения (c)', 'RUSE (py6)'}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Список желаемых метрик\n",
+    "desired_metrics = ['RUSE (py6)', 'R2', 'Bpews обучения (c)']\n",
+    "\n",
+    "# Проверяем, какие метрики действительно существуют в DataFrame\n",
+    "available_metrics = [metric for metric in desired_metrics if metric in results_df.columns]\n",
+    "\n",
+    "# Если ни одна метрика не найдена, выводим сообщение\n",
+    "if not available_metrics:\n",
+    "    print(\"Ошибка: Ни одна из указанных метрик не найдена в DataFrame.\")\n",
+    "    print(\"Доступные колонки:\", results_df.columns.tolist())\n",
+    "else:\n",
+    "    # Создаем DataFrame для сравнения только с доступными метриками\n",
+    "    comparison = results_df[['Model'] + available_metrics].set_index('Model')\n",
+    "    \n",
+    "    # Если какая-то метрика отсутствует, выводим предупреждение\n",
+    "    missing_metrics = set(desired_metrics) - set(available_metrics)\n",
+    "    if missing_metrics:\n",
+    "        print(f\"Предупреждение: Следующие метрики не найдены и будут пропущены: {missing_metrics}\")\n",
+    "    \n",
+    "    # График сравнения метрик\n",
+    "    fig, axes = plt.subplots(1, len(available_metrics), figsize=(5*len(available_metrics), 5))\n",
+    "    \n",
+    "    if len(available_metrics) == 1:  # Если только одна метрика, axes будет не массивом\n",
+    "        axes = [axes]\n",
+    "    \n",
+    "    for i, metric in enumerate(available_metrics):\n",
+    "        comparison[metric].plot(kind='bar', ax=axes[i], title=metric)\n",
+    "        axes[i].set_ylabel('Значение')\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "ba79ee02-8e89-49dc-ab7f-5b922c97038a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Разделение на признаки и целевую переменную\n",
+    "X = data.drop('Price', axis=1)\n",
+    "y = data['Price']\n",
+    "\n",
+    "# Разделение на тренировочный и тестовый наборы\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Определение категориальных и числовых признаков\n",
+    "categorical_features = ['Brand', 'Model', 'Fuel_Type', 'Transmission', 'Owner_Type']\n",
+    "numeric_features = ['Year', 'Kilometers_Driven', 'Mileage', 'Engine', 'Power', 'Seats']\n",
+    "\n",
+    "# Создание преобразователя для категориальных и числовых признаков\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('num', StandardScaler(), numeric_features),\n",
+    "        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)\n",
+    "    ])\n",
+    "\n",
+    "# Функция для оценки модели\n",
+    "def evaluate_model(model, X_test, y_test):\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    mse = mean_squared_error(y_test, y_pred)\n",
+    "    mae = mean_absolute_error(y_test, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    return mse, mae, r2\n",
+    "\n",
+    "# Список для хранения результатов\n",
+    "results = []\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c0881ff-25da-44c1-b6fc-c5ffd578721f",
+   "metadata": {},
+   "source": [
+    "## 1. Линейная регрессия"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "af0252b3-4e5f-4b2e-8d20-14302abc95fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start_time = time.time()\n",
+    "lr_pipeline = Pipeline([\n",
+    "    ('preprocessor', preprocessor),\n",
+    "    ('regressor', LinearRegression())\n",
+    "])\n",
+    "\n",
+    "lr_pipeline.fit(X_train, y_train)\n",
+    "lr_time = time.time() - start_time\n",
+    "lr_mse, lr_mae, lr_r2 = evaluate_model(lr_pipeline, X_test, y_test)\n",
+    "results.append(('Linear Regression', lr_mse, lr_mae, lr_r2, lr_time))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66cbac98-e74e-457d-8830-c6d21ae46f0c",
+   "metadata": {},
+   "source": [
+    "## 2. Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "ed4a28e7-0f92-4381-a01e-c5986f1ffc8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start_time = time.time()\n",
+    "rf_pipeline = Pipeline([\n",
+    "    ('preprocessor', preprocessor),\n",
+    "    ('regressor', RandomForestRegressor(n_estimators=100, random_state=42))\n",
+    "])\n",
+    "\n",
+    "rf_pipeline.fit(X_train, y_train)\n",
+    "rf_time = time.time() - start_time\n",
+    "rf_mse, rf_mae, rf_r2 = evaluate_model(rf_pipeline, X_test, y_test)\n",
+    "results.append(('Random Forest', rf_mse, rf_mae, rf_r2, rf_time))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0172ad5f-d28b-414a-84a9-0c965ca47817",
+   "metadata": {},
+   "source": [
+    "## 3. Gradient Boosting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "5cbaa80d-c0e8-4306-a684-4071aaebbdcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start_time = time.time()\n",
+    "gb_pipeline = Pipeline([\n",
+    "    ('preprocessor', preprocessor),\n",
+    "    ('regressor', GradientBoostingRegressor(n_estimators=100, random_state=42))\n",
+    "])\n",
+    "\n",
+    "gb_pipeline.fit(X_train, y_train)\n",
+    "gb_time = time.time() - start_time\n",
+    "gb_mse, gb_mae, gb_r2 = evaluate_model(gb_pipeline, X_test, y_test)\n",
+    "results.append(('Gradient Boosting', gb_mse, gb_mae, gb_r2, gb_time))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47817db8-774c-4986-b2ee-6b83d3a612d9",
+   "metadata": {},
+   "source": [
+    "## 4. Нейронная сеть"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "e3d4f317-7fce-45d6-94af-9a5641f91984",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "start_time = time.time()\n",
+    "nn_pipeline = Pipeline([\n",
+    "    ('preprocessor', preprocessor),\n",
+    "    ('regressor', MLPRegressor(hidden_layer_sizes=(100, 50),\n",
+    "                              max_iter=500,\n",
+    "                              random_state=42,\n",
+    "                              early_stopping=True,\n",
+    "                              validation_fraction=0.2))\n",
+    "])\n",
+    "\n",
+    "nn_pipeline.fit(X_train, y_train)\n",
+    "nn_time = time.time() - start_time\n",
+    "nn_mse, nn_mae, nn_r2 = evaluate_model(nn_pipeline, X_test, y_test)\n",
+    "results.append(('Neural Network', nn_mse, nn_mae, nn_r2, nn_time))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6caf5c5f-7682-42b6-b21b-8e5552d5697d",
+   "metadata": {},
+   "source": [
+    "## Создаем DataFrame с результатами"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "b4c61664-91d9-40a8-ae9d-f34d56caf6b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Model, MSE, MAE, R2, Training Time]\n",
+      "Index: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = []\n",
+    "results_df = pd.DataFrame(results, columns=['Model', 'MSE', 'MAE', 'R2', 'Training Time'])\n",
+    "print(results_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b81ef13c-7a2b-444e-bc94-8673d98cee9a",
+   "metadata": {},
+   "source": [
+    "## Визуализация результатов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "8f31562d-0fa9-43f4-b3c8-9c0e50016d13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cmeMcv/jiC3Pb7t27DUmGg4OD8euvv5rbV61aleP7mX0eHTt2tIihX79+hiTjzz//NLfl9nMRGhpqVK9e3aItr89D2euu/szQsGFDi5/T3Nj6PXnuuecstn/88ceNihUrWj0GgPxjahcA15V9m+W3336rc+fO6dtvv83zltFFixbJw8NDrVq10smTJ81LYGCgypYtqx9++MHc9+p5s8+dO6eTJ0+qWbNmSktL0+7duy32W7ZsWYt5AJ2dndW4cWMdOHDAauwmk0mrVq3SO++8o/Lly2v+/Pnq37+/qlWrpq5du5pvD8zMzNSqVavUqVMn3Xnnnebt69atq9DQ0Hy/V9e6+hwvXbqkU6dOqWbNmvL09NTmzZtz9O/Tp48cHR3Nr9esWaOzZ8+qe/fuFu+no6OjgoKCzO/nsWPHtHXrVoWHh1s8GLRVq1aqV69evuP19/fXmjVrciyvvvpqjr4vvvhirvu46667crxnK1asUOPGjS1u+Sxbtqyef/55HTp0yHwrabbw8PB8zauePeXLtaNKcpP9Hj377LOqUKGCub1BgwZq1aqVVqxYkWOb3r17m792dHRUo0aNZBiGevXqZW739PRU7dq1c70Ww8LCLGJ78skndccdd1gc6+rzTE1N1cmTJ/XQQw/JMAxt2bIlxz779u173XP19PRUampqjttCr2br9yQiIsJihFOzZs0k6bo/gwAAoHAU5xxdujJaetWqVerevbu5rXPnzuYp5XITGRlp/tpkMikyMlIZGRnmKTsKIwcqSKdPn9b333+vLl26mN/bkydP6tSpUwoNDdW+fft09OjR6+7nscceyzVn/89//mPRz8XFRREREbnuo3PnzuYpaKQby5Xz+jxwrZSUlHzl69KVEdgZGRl69dVXLZ6R1KdPH7m7u+eYgqds2bLq1q2b+XXt2rXl6empunXrKigoyNye/XVu1+a1I8pfeuklScozZ09OTtbJkyfVvHlzHThwQMnJyRbb5/Z5KDeenp7asWOH9u3bl+v6gvieNGvWTKdOncpzqkwAtmFqFwDXValSJYWEhOiLL75QWlqaMjMz87wtb9++fUpOTpa3t3eu648fP27+eseOHRoyZIi+//77HH/Yr01GqlatajFPuSSVL19ef/3113Xjd3Fx0eDBgzV48GAdO3ZMP/74oyZOnKgvv/xSTk5O+vzzz3XixAlduHBBd999d47ta9eunWuSkh8XLlxQbGysZs2apaNHj1rc8njtOUpXkq6rZSdVjzzySK77d3d3l3RlLnZJecafW9E+N2XKlFFISEi++l4bq7X2w4cPWySy2erWrWteX79+/evu+1rZ53/u3Dl5enpa7Zv9HuV2a2XdunW1atWqHA9JuvqfKtKVh7G6urrKy8srR/upU6dy7Pfa74fJZFLNmjUt5shPSEjQsGHDtGzZshxzlF57jZQqVSpfU/X069dPX375pdq2bStfX1+1bt1aXbp0UZs2bcx9bP2eXPtelC9fXpJyxAwAAG6N4p6jL1y4UJcuXdJ9991n8dyVoKAgzZs3L0dx08HBQdWrV7doq1WrliSZc6vCyIEK0v79+2UYhoYOHaqhQ4fm2uf48ePy9fW1up+qVavmK2f39fXNc4rEa/PtG8mVbcnZz507l6++ecXh7Oys6tWrm9dny+0a9PDwkJ+fX442Kffc9dqcvUaNGnJwcLDI2X/55RfFxMQoPj4+x5zjycnJFoOZ8vu+jBw5Uo899phq1aql+vXrq02bNnrmmWfM0wAVxOeXq3P27M9OAG4chXQA+dKjRw/16dNHiYmJatu2bZ5Fy6ysLHl7e2vevHm5rs8e9XD27Fk1b95c7u7uGjlypGrUqCFXV1dt3rxZAwcOVFZWlsV2V4/SvpphZS6+3Nxxxx3q1q2bOnfurHvuuUdffvllrg8NtebaRC3btQ9gkq6MZpg1a5ZeffVVBQcHy8PDQyaTSd26dctxjpJyjMLO7vPZZ5+pcuXKOfpbm/O9sOU1Yjw/I8lvdN/Xyn5Y0bZt28wjpAtSbtddQV2L0pVrplWrVjp9+rQGDhyoOnXqqEyZMjp69KieffbZHNeIi4uLxcicvHh7e2vr1q1atWqVvvvuO3333XeaNWuWwsLCNGfOHJvjlAr2vAEAQMEozjl6dixNmjTJdf2BAwdyFM6vpzByoKuZTKZczy23zwG5yX7/BgwYkOeI5Zo1a954gNewllPf6px969atysjIyLOwf6PyugZv5tq89vPe33//rZYtW6pOnToaP368/Pz85OzsrBUrVuiDDz7I8XOR3/fl4Ycf1t9//62vv/5aq1ev1qeffqoPPvhAU6dOtbgz1hbk7EDhopAOIF8ef/xxvfDCC/r111+1cOHCPPvVqFFDa9euVZMmTawmEOvWrdOpU6e0dOlSiwc0Hjx4sEDjzouTk5MaNGigffv26eTJk6pUqZJKly6d6211e/bssXid/V/9a58af+3oCElavHixwsPD9f7775vbLl68mGPbvNSoUUPSlQ8F1kadZD9UNT/x20O1atVyjSP79uDs+G3VoUMHxcbG6vPPP79uIT37GHnF4eXlleOBqjfr2u+HYRjav3+/eZTJtm3btHfvXs2ZM0dhYWHmftZuR84vZ2dndejQQR06dFBWVpb69eunTz75REOHDlXNmjUL7XsCAABuneKaox88eFAbNmxQZGSkmjdvbrEuKytLzzzzjL744gsNGTLEov3AgQPmUeiStHfvXkmyeOBiYeZA5cuXz3VqkGs/B+Q18Cb7HwNOTk75vgv0VinMXLlDhw6Kj4/XkiVLLKbyuV4cV/8jJSMjQwcPHiyU923fvn0Wo8j379+vrKws83X1zTffKD09XcuWLbMY8X31lEg3qkKFCoqIiFBERITOnz+vhx9+WMOHD1fv3r3t8vkFgHXMkQ4gX8qWLaspU6Zo+PDh6tChQ579unTposzMTL399ts51l2+fNlcQM7+T/nV/xnPyMjQxx9/XKBx79u3TwkJCTnaz549q/j4eJUvX16VKlWSo6OjQkND9d///tei/65du7Rq1SqLbd3d3eXl5aWffvrJoj232B0dHXP89/+jjz7K96iV0NBQubu7a9SoUbp06VKO9SdOnJB0ZaR9QECA5syZY3HL7Zo1awp1nsf8ateunTZu3Kj4+HhzW2pqqqZNmyZ/f3+b5nG/WnBwsNq0aaNPP/1U//3vf3Osz8jI0IABAyRZvkdX/yNj+/btWr16tdq1a3dDMVgzd+5ci9tYFy9erGPHjqlt27aScv85MAxDEydOvKnjXjvNjIODg7l4n56eLqnwvicAAODWKa45evZo9DfffFNPPvmkxdKlSxc1b94819HzkyZNMn9tGIYmTZokJycntWzZUlLh50A1atTQ7t27zTm4JP3555/65ZdfLPq5ublJyjnwxtvbWy1atNAnn3yiY8eO5dj/1fu91QozV37xxRd1xx136PXXXzf/8+Nqx48f1zvvvCNJCgkJkbOzsz788EOL63DGjBlKTk5W+/btbziOvEyePNni9UcffSRJVnP25ORkzZo166aOe+31WrZsWdWsWdN8rdrj8wsA6xiRDiDfwsPDr9unefPmeuGFFxQbG6utW7eqdevWcnJy0r59+7Ro0SJNnDhRTz75pB566CGVL19e4eHhevnll2UymfTZZ58V+C1nf/75p3r06KG2bduqWbNmqlChgo4ePao5c+bo33//1YQJE8yJ0YgRI7Ry5Uo1a9ZM/fr10+XLl/XRRx/pnnvuyTHPY+/evTV69Gj17t1bjRo10k8//ZRrUvjoo4/qs88+k4eHh+rVq6f4+HitXbtWFStWzFf87u7umjJlip555hndf//96tatmypVqqSEhAQtX75cTZo0MX+giI2NVfv27dW0aVM999xzOn36tDn+8+fP5+t4ycnJ+vzzz3Ndd/WDpGw1aNAgzZ8/X23bttXLL7+sChUqaM6cOTp48KCWLFmSr+lK8jJ37ly1bt1aTzzxhDp06KCWLVuqTJky2rdvnxYsWKBjx45p3LhxkqSxY8eqbdu2Cg4OVq9evXThwgV99NFH8vDw0PDhw284hrxUqFBBTZs2VUREhJKSkjRhwgTVrFlTffr0kXTlNtcaNWpowIABOnr0qNzd3bVkyZKbnne8d+/eOn36tB555BFVrVpVhw8f1kcffaSAgADz/J+F+T0BAAC3TnHM0efNm6eAgIAc81hn69ixo1566SVt3rxZ999/vyTJ1dVVK1euVHh4uIKCgvTdd99p+fLleuutt8xT0xR2DvTcc89p/PjxCg0NVa9evXT8+HFNnTpV99xzj8V88qVLl1a9evW0cOFC1apVSxUqVFD9+vVVv359TZ48WU2bNtW9996rPn36qHr16kpKSlJ8fLz++ecf/fnnn9d9//bu3Ztrzu7j46NWrVpdd/u8FFauXL58eX311Vdq166dAgIC1LNnTwUGBkqSNm/erPnz5ys4OFjSlWmGoqOjNWLECLVp00YdO3bUnj179PHHH+uBBx64qc8keTl48KA6duyoNm3aKD4+Xp9//rl69Oihhg0bSpJat25tvtPhhRde0Pnz5zV9+nR5e3vn+g+R/KpXr55atGihwMBAVahQQX/88YcWL15s8VDdW/35BcB1GACQi1mzZhmSjN9//91qv2rVqhnt27fP0T5t2jQjMDDQKF26tFGuXDnj3nvvNd58803j33//Nff55ZdfjAcffNAoXbq0UaVKFePNN980Vq1aZUgyfvjhB3O/5s2bG/fcc0+OY4SHhxvVqlWzGl9SUpIxevRoo3nz5sYdd9xhlCpVyihfvrzxyCOPGIsXL87R/8cffzQCAwMNZ2dno3r16sbUqVONmJgY49pfl2lpaUavXr0MDw8Po1y5ckaXLl2M48ePG5KMmJgYc78zZ84YERERhpeXl1G2bFkjNDTU2L17t1GtWjUjPDzc3O967/cPP/xghIaGGh4eHoarq6tRo0YN49l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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Данные (пример)\n",
+    "results_df = pd.DataFrame({\n",
+    "    'Model': ['Linear Regression', 'Random Forest', 'Gradient Boosting', 'Neural Network'],\n",
+    "    'MSE': [0.8, 0.4, 0.2, 0.6],\n",
+    "    'MAE': [0.7, 0.3, 0.15, 0.5],\n",
+    "    'R2': [0.5, 0.8, 0.9, 0.7],\n",
+    "    'Training Time': [1, 10, 15, 20]\n",
+    "})\n",
+    "\n",
+    "# Создание фигуры\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "# График MSE\n",
+    "plt.subplot(2, 2, 1)\n",
+    "sns.barplot(x='Model', y='MSE', data=results_df)\n",
+    "plt.title('Mean Squared Error Comparison')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.ylabel('MSE (lower is better)')\n",
+    "\n",
+    "# График MAE\n",
+    "plt.subplot(2, 2, 2)\n",
+    "sns.barplot(x='Model', y='MAE', data=results_df)\n",
+    "plt.title('Mean Absolute Error Comparison')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.ylabel('MAE (lower is better)')\n",
+    "\n",
+    "# График R2\n",
+    "plt.subplot(2, 2, 3)\n",
+    "sns.barplot(x='Model', y='R2', data=results_df)\n",
+    "plt.title('R-squared Comparison')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.ylabel('R2 (higher is better)')\n",
+    "\n",
+    "# График времени обучения\n",
+    "plt.subplot(2, 2, 4)\n",
+    "sns.barplot(x='Model', y='Training Time', data=results_df)\n",
+    "plt.title('Training Time Comparison')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.ylabel('Time (seconds)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()  # Критически важно!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1e7c70b-d6f5-4440-812e-1f987fe4e3dc",
+   "metadata": {},
+   "source": [
+    "## 1. Реализация градиентного бустинга"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "726937e7-b336-4a71-8ef6-62442268b019",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "class GradientBoostingRegressorCustom:\n",
+    "    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3, min_samples_split=2):\n",
+    "        self.n_estimators = n_estimators\n",
+    "        self.learning_rate = learning_rate\n",
+    "        self.max_depth = max_depth\n",
+    "        self.min_samples_split = min_samples_split\n",
+    "        self.trees = []\n",
+    "        self.loss_history = []\n",
+    "        \n",
+    "    def fit(self, X, y):\n",
+    "        # Начальное предсказание - среднее значение y\n",
+    "        self.mean = np.mean(y)\n",
+    "        y_pred = np.full_like(y, self.mean)\n",
+    "        \n",
+    "        for _ in range(self.n_estimators):\n",
+    "            # Вычисляем антиградиент (остатки)\n",
+    "            residuals = y - y_pred\n",
+    "            \n",
+    "            # Обучаем дерево на остатках\n",
+    "            tree = DecisionTreeRegressor(\n",
+    "                max_depth=self.max_depth,\n",
+    "                min_samples_split=self.min_samples_split\n",
+    "            )\n",
+    "            tree.fit(X, residuals)\n",
+    "            \n",
+    "            # Делаем предсказание и обновляем общее предсказание\n",
+    "            update = tree.predict(X)\n",
+    "            y_pred += self.learning_rate * update\n",
+    "            \n",
+    "            # Сохраняем дерево и записываем ошибку\n",
+    "            self.trees.append(tree)\n",
+    "            self.loss_history.append(mean_squared_error(y, y_pred))\n",
+    "            \n",
+    "        return self\n",
+    "    \n",
+    "    def predict(self, X):\n",
+    "        # Начинаем со среднего значения\n",
+    "        y_pred = np.full(X.shape[0], self.mean)\n",
+    "        \n",
+    "        # Добавляем предсказания всех деревьев\n",
+    "        for tree in self.trees:\n",
+    "            y_pred += self.learning_rate * tree.predict(X)\n",
+    "            \n",
+    "        return y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96f291e4-5b7c-4ebc-a757-09fb5c7f40dd",
+   "metadata": {},
+   "source": [
+    "## 2. Подготовка данных и обучение моделей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "f5c8ad69-6246-4c80-81b7-82ca3de7cb79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "class GradientBoostingRegressorCustom:\n",
+    "    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3, min_samples_split=2):\n",
+    "        self.n_estimators = n_estimators\n",
+    "        self.learning_rate = float(learning_rate)  # Гарантируем float\n",
+    "        self.max_depth = max_depth\n",
+    "        self.min_samples_split = min_samples_split\n",
+    "        self.trees = []\n",
+    "        self.loss_history = []\n",
+    "        self.mean = 0.0\n",
+    "        \n",
+    "    def fit(self, X, y):\n",
+    "        # Гарантируем, что y является float numpy array\n",
+    "        y = np.array(y, dtype=np.float64)\n",
+    "        \n",
+    "        # Начальное предсказание - среднее значение y\n",
+    "        self.mean = float(np.mean(y))\n",
+    "        y_pred = np.full(y.shape[0], self.mean, dtype=np.float64)\n",
+    "        \n",
+    "        for _ in range(self.n_estimators):\n",
+    "            # Вычисляем остатки (антиградиент)\n",
+    "            residuals = y - y_pred\n",
+    "            \n",
+    "            # Обучаем дерево на остатках\n",
+    "            tree = DecisionTreeRegressor(\n",
+    "                max_depth=self.max_depth,\n",
+    "                min_samples_split=self.min_samples_split\n",
+    "            )\n",
+    "            tree.fit(X, residuals)\n",
+    "            \n",
+    "            # Получаем предсказания и гарантируем float64\n",
+    "            update = tree.predict(X).astype(np.float64)\n",
+    "            \n",
+    "            # Обновляем предсказания с контролем типов\n",
+    "            y_pred = y_pred.astype(np.float64) + (self.learning_rate * update)\n",
+    "            \n",
+    "            # Сохраняем дерево и ошибку\n",
+    "            self.trees.append(tree)\n",
+    "            self.loss_history.append(mean_squared_error(y, y_pred))\n",
+    "            \n",
+    "        return self\n",
+    "    \n",
+    "    def predict(self, X):\n",
+    "        # Инициализируем предсказания\n",
+    "        y_pred = np.full(X.shape[0], self.mean, dtype=np.float64)\n",
+    "        \n",
+    "        # Добавляем вклады каждого дерева\n",
+    "        for tree in self.trees:\n",
+    "            update = tree.predict(X).astype(np.float64)\n",
+    "            y_pred += self.learning_rate * update\n",
+    "            \n",
+    "        return y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5729748f-5ab8-44d0-bd61-53fbec4f35a2",
+   "metadata": {},
+   "source": [
+    "## 3. Оценка и сравнение моделей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "f089136e-c07b-4030-a46b-26a9081310bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "class GradientBoostingRegressorCustom:\n",
+    "    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3, min_samples_split=2):\n",
+    "        # Гарантируем правильные типы параметров\n",
+    "        self.n_estimators = int(n_estimators)\n",
+    "        self.learning_rate = float(learning_rate)\n",
+    "        self.max_depth = int(max_depth)\n",
+    "        self.min_samples_split = int(min_samples_split)\n",
+    "        self.trees = []\n",
+    "        self.loss_history = []\n",
+    "        self.mean = 0.0\n",
+    "        \n",
+    "    def _ensure_float_array(self, arr):\n",
+    "        \"\"\"Гарантирует, что массив имеет тип float64\"\"\"\n",
+    "        return np.array(arr, dtype=np.float64)\n",
+    "        \n",
+    "    def fit(self, X, y):\n",
+    "        # Преобразуем y в float64 numpy array\n",
+    "        y = self._ensure_float_array(y)\n",
+    "        \n",
+    "        # Начальное предсказание - среднее значение y\n",
+    "        self.mean = float(np.mean(y))\n",
+    "        y_pred = np.full(y.shape[0], self.mean, dtype=np.float64)\n",
+    "        \n",
+    "        for _ in range(self.n_estimators):\n",
+    "            # Вычисляем антиградиент (остатки)\n",
+    "            residuals = y - y_pred\n",
+    "            \n",
+    "            # Обучаем дерево на остатках\n",
+    "            tree = DecisionTreeRegressor(\n",
+    "                max_depth=self.max_depth,\n",
+    "                min_samples_split=self.min_samples_split\n",
+    "            )\n",
+    "            tree.fit(X, residuals)\n",
+    "            \n",
+    "            # Получаем предсказания (гарантируем float64)\n",
+    "            update = self._ensure_float_array(tree.predict(X))\n",
+    "            \n",
+    "            # Обновляем предсказания с контролем типов\n",
+    "            y_pred = y_pred + (self.learning_rate * update)\n",
+    "            \n",
+    "            # Сохраняем дерево и записываем ошибку\n",
+    "            self.trees.append(tree)\n",
+    "            self.loss_history.append(mean_squared_error(y, y_pred))\n",
+    "            \n",
+    "        return self\n",
+    "    \n",
+    "    def predict(self, X):\n",
+    "        # Инициализируем предсказания (гарантируем float64)\n",
+    "        y_pred = np.full(X.shape[0], self.mean, dtype=np.float64)\n",
+    "        \n",
+    "        # Добавляем вклады всех деревьев\n",
+    "        for tree in self.trees:\n",
+    "            update = self._ensure_float_array(tree.predict(X))\n",
+    "            y_pred += self.learning_rate * update\n",
+    "            \n",
+    "        return y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6ecf096-5ae1-4f9b-913a-db2e25bd4c39",
+   "metadata": {},
+   "source": [
+    "## 4. Визуализация результатов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "0286ffbc-a295-4e49-9207-f4c7c11cdfef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# 1. Проверка наличия необходимых данных\n",
+    "try:\n",
+    "    # График изменения loss во время обучения\n",
+    "    plt.figure(figsize=(12, 6))\n",
+    "    \n",
+    "    # Для кастомной модели\n",
+    "    if hasattr(custom_gb, 'loss_history'):\n",
+    "        plt.plot(custom_gb.loss_history, 'b-', linewidth=2, label='Custom GB')\n",
+    "    \n",
+    "    # Для sklearn модели (используем правильное имя переменной)\n",
+    "    if 'sklearn_gb' in locals() and hasattr(sklearn_gb, 'train_score_'):\n",
+    "        plt.plot(-np.array(sklearn_gb.train_score_), 'r-', linewidth=2, label='Sklearn GB')\n",
+    "    elif 'sklearn_gb' in locals():\n",
+    "        print(\"Предупреждение: sklearn_gb не имеет атрибута train_score_\")\n",
+    "    \n",
+    "    plt.xlabel('Iterations', fontsize=12)\n",
+    "    plt.ylabel('MSE', fontsize=12)\n",
+    "    plt.title('Training Loss Comparison', fontsize=14)\n",
+    "    plt.legend(fontsize=12)\n",
+    "    plt.grid(True, linestyle='--', alpha=0.7)\n",
+    "    plt.xticks(fontsize=10)\n",
+    "    plt.yticks(fontsize=10)\n",
+    "    plt.show()\n",
+    "\n",
+    "except NameError as e:\n",
+    "    print(f\"Ошибка: {str(e)}\")\n",
+    "    print(\"Проверьте, что все модели были корректно обучены\")\n",
+    "\n",
+    "# 2. Сравнение метрик (с проверкой наличия результатов)\n",
+    "try:\n",
+    "    if 'custom_results' in locals() and 'sklearn_results' in locals():\n",
+    "        metrics = ['MSE', 'MAE', 'R2']\n",
+    "        \n",
+    "        # Получаем метрики из результатов\n",
+    "        custom_metrics = [custom_results[0], custom_results[1], custom_results[2]]\n",
+    "        sklearn_metrics = [sklearn_results[0], sklearn_results[1], sklearn_results[2]]\n",
+    "        \n",
+    "        # Создаем график\n",
+    "        x = np.arange(len(metrics))\n",
+    "        width = 0.35\n",
+    "\n",
+    "        fig, ax = plt.subplots(figsize=(12, 6))\n",
+    "        rects1 = ax.bar(x - width/2, custom_metrics, width, label='Custom GB', color='blue', alpha=0.7)\n",
+    "        rects2 = ax.bar(x + width/2, sklearn_metrics, width, label='Sklearn GB', color='red', alpha=0.7)\n",
+    "\n",
+    "        ax.set_ylabel('Score', fontsize=12)\n",
+    "        ax.set_title('Metrics Comparison', fontsize=14)\n",
+    "        ax.set_xticks(x)\n",
+    "        ax.set_xticklabels(metrics, fontsize=11)\n",
+    "        ax.legend(fontsize=12)\n",
+    "        \n",
+    "        # Добавляем значения на столбцы\n",
+    "        def autolabel(rects):\n",
+    "            for rect in rects:\n",
+    "                height = rect.get_height()\n",
+    "                ax.annotate(f'{height:.2f}',\n",
+    "                            xy=(rect.get_x() + rect.get_width() / 2, height),\n",
+    "                            xytext=(0, 3),  # 3 points vertical offset\n",
+    "                            textcoords=\"offset points\",\n",
+    "                            ha='center', va='bottom', fontsize=10)\n",
+    "        \n",
+    "        autolabel(rects1)\n",
+    "        autolabel(rects2)\n",
+    "        \n",
+    "        plt.grid(True, linestyle='--', alpha=0.3)\n",
+    "        fig.tight_layout()\n",
+    "        plt.show()\n",
+    "    else:\n",
+    "        print(\"Ошибка: результаты сравнения не найдены. Сначала выполните обучение и оценку моделей.\")\n",
+    "        \n",
+    "except Exception as e:\n",
+    "    print(f\"Ошибка при построении графиков: {str(e)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dc53d1a-6683-425b-9d1d-b6c2fb95bccc",
+   "metadata": {},
+   "source": [
+    "## 5. Настройка гиперпараметров"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "968f6050-89c3-44a1-af12-2c39953606ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters for custom GB:\n",
+      "{'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 2, 'min_samples_split': 10}\n",
+      "Best MSE: 103015619266.55\n",
+      "\n",
+      "Best parameters for sklearn GB:\n",
+      "{'learning_rate': 0.2, 'max_depth': 2, 'min_samples_split': 2, 'n_estimators': 150}\n",
+      "Best MSE: 52526336145.38\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "# Для собственной реализации\n",
+    "param_grid = {\n",
+    "    'n_estimators': [50, 100, 150],\n",
+    "    'learning_rate': [0.01, 0.1, 0.2],\n",
+    "    'max_depth': [2, 3, 4],\n",
+    "    'min_samples_split': [2, 5, 10]\n",
+    "}\n",
+    "\n",
+    "# Поскольку наша реализация не совместима с GridSearchCV, сделаем ручной поиск\n",
+    "best_score = float('inf')\n",
+    "best_params = {}\n",
+    "\n",
+    "for n_est in param_grid['n_estimators']:\n",
+    "    for lr in param_grid['learning_rate']:\n",
+    "        for depth in param_grid['max_depth']:\n",
+    "            for min_split in param_grid['min_samples_split']:\n",
+    "                model = GradientBoostingRegressorCustom(\n",
+    "                    n_estimators=n_est,\n",
+    "                    learning_rate=lr,\n",
+    "                    max_depth=depth,\n",
+    "                    min_samples_split=min_split\n",
+    "                )\n",
+    "                model.fit(X_train, y_train)\n",
+    "                y_pred = model.predict(X_test)\n",
+    "                score = mean_squared_error(y_test, y_pred)\n",
+    "                \n",
+    "                if score < best_score:\n",
+    "                    best_score = score\n",
+    "                    best_params = {\n",
+    "                        'n_estimators': n_est,\n",
+    "                        'learning_rate': lr,\n",
+    "                        'max_depth': depth,\n",
+    "                        'min_samples_split': min_split\n",
+    "                    }\n",
+    "\n",
+    "print(\"Best parameters for custom GB:\")\n",
+    "print(best_params)\n",
+    "print(f\"Best MSE: {best_score:.2f}\")\n",
+    "\n",
+    "# Для sklearn реализации\n",
+    "sklearn_gb = GradientBoostingRegressor(random_state=42)\n",
+    "grid_search = GridSearchCV(\n",
+    "    sklearn_gb,\n",
+    "    param_grid,\n",
+    "    cv=5,\n",
+    "    scoring='neg_mean_squared_error',\n",
+    "    n_jobs=-1\n",
+    ")\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "\n",
+    "print(\"\\nBest parameters for sklearn GB:\")\n",
+    "print(grid_search.best_params_)\n",
+    "print(f\"Best MSE: {-grid_search.best_score_:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6cb2fb9-40a8-4194-9d66-e4aac29d7a62",
+   "metadata": {},
+   "source": [
+    "## 1. Использование AutoML фреймворков"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "b9190ca1-40ff-4eda-ae81-2525d71a749e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting autogluon\n",
+      "  Downloading autogluon-1.2-py3-none-any.whl.metadata (11 kB)\n",
+      "Collecting h2o\n",
+      "  Downloading h2o-3.46.0.7-py2.py3-none-any.whl.metadata (2.1 kB)\n",
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+      "Collecting confection<1.0.0,>=0.0.1 (from thinc<8.3.0,>=8.2.2->spacy<3.8->autogluon.tabular[all]==1.2->autogluon)\n",
+      "  Downloading confection-0.1.5-py3-none-any.whl.metadata (19 kB)\n",
+      "Collecting fs (from triad>=0.9.7->fugue>=0.9.0->autogluon.timeseries==1.2->autogluon.timeseries[all]==1.2->autogluon)\n",
+      "  Downloading fs-2.4.16-py2.py3-none-any.whl.metadata (6.3 kB)\n",
+      "Collecting shellingham>=1.3.0 (from typer<1.0.0,>=0.3.0->spacy<3.8->autogluon.tabular[all]==1.2->autogluon)\n",
+      "  Downloading shellingham-1.5.4-py2.py3-none-any.whl.metadata (3.5 kB)\n",
+      "Collecting markdown-it-py>=2.2.0 (from rich->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.2->autogluon)\n",
+      "  Downloading markdown_it_py-3.0.0-py3-none-any.whl.metadata (6.9 kB)\n",
+      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\user\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from rich->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.2->autogluon) (2.17.2)\n",
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+      "Collecting cloudpathlib<1.0.0,>=0.7.0 (from weasel<0.5.0,>=0.1.0->spacy<3.8->autogluon.tabular[all]==1.2->autogluon)\n",
+      "  Downloading cloudpathlib-0.21.0-py3-none-any.whl.metadata (14 kB)\n",
+      "Collecting wrapt (from smart-open->ray[default,tune]<2.40,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.2->autogluon)\n",
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+      "Collecting ordered-set (from model-index->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.2->autogluon)\n",
+      "  Downloading ordered_set-4.1.0-py3-none-any.whl.metadata (5.3 kB)\n",
+      "Collecting opencensus-context>=0.1.3 (from opencensus->ray[default,tune]<2.40,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.2->autogluon)\n",
+      "  Downloading opencensus_context-0.1.3-py2.py3-none-any.whl.metadata (3.3 kB)\n",
+      "Collecting google-api-core<3.0.0,>=1.0.0 (from opencensus->ray[default,tune]<2.40,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.2->autogluon)\n",
+      "  Downloading google_api_core-2.24.2-py3-none-any.whl.metadata (3.0 kB)\n",
+      "Collecting pycryptodome (from opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.2->autogluon)\n",
+      "  Downloading pycryptodome-3.22.0-cp37-abi3-win_amd64.whl.metadata (3.4 kB)\n",
+      "Collecting openxlab (from opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.2->autogluon)\n",
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+      "Collecting alembic>=1.5.0 (from optuna->mlforecast==0.13.4->autogluon.timeseries==1.2->autogluon.timeseries[all]==1.2->autogluon)\n",
+      "  Downloading alembic-1.15.2-py3-none-any.whl.metadata (7.3 kB)\n",
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+      "Collecting sqlalchemy>=1.4.2 (from optuna->mlforecast==0.13.4->autogluon.timeseries==1.2->autogluon.timeseries[all]==1.2->autogluon)\n",
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+      "Collecting Mako (from alembic>=1.5.0->optuna->mlforecast==0.13.4->autogluon.timeseries==1.2->autogluon.timeseries[all]==1.2->autogluon)\n",
+      "  Downloading mako-1.3.10-py3-none-any.whl.metadata (2.9 kB)\n",
+      "Collecting googleapis-common-protos<2.0.0,>=1.56.2 (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]<2.40,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.2->autogluon)\n",
+      "  Downloading googleapis_common_protos-1.70.0-py3-none-any.whl.metadata (9.3 kB)\n",
+      "Collecting proto-plus<2.0.0,>=1.22.3 (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]<2.40,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.2->autogluon)\n",
+      "  Downloading proto_plus-1.26.1-py3-none-any.whl.metadata (2.2 kB)\n",
+      "Collecting google-auth<3.0.0,>=2.14.1 (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]<2.40,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.2->autogluon)\n",
+      "  Downloading google_auth-2.39.0-py2.py3-none-any.whl.metadata (6.2 kB)\n",
+      "Collecting marisa-trie>=1.1.0 (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8->autogluon.tabular[all]==1.2->autogluon)\n",
+      "  Downloading marisa_trie-1.2.1-cp312-cp312-win_amd64.whl.metadata (9.3 kB)\n",
+      "Collecting mdurl~=0.1 (from markdown-it-py>=2.2.0->rich->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.2->autogluon)\n",
+      "  Downloading mdurl-0.1.2-py3-none-any.whl.metadata (1.6 kB)\n",
+      "Collecting greenlet>=1 (from sqlalchemy>=1.4.2->optuna->mlforecast==0.13.4->autogluon.timeseries==1.2->autogluon.timeseries[all]==1.2->autogluon)\n",
+      "  Downloading greenlet-3.2.1-cp312-cp312-win_amd64.whl.metadata (4.2 kB)\n",
+      "Requirement already satisfied: soupsieve>1.2 in c:\\users\\user\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from beautifulsoup4->gdown>=4.0.0->nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==1.2->autogluon) (2.5)\n",
+      "Collecting appdirs~=1.4.3 (from fs->triad>=0.9.7->fugue>=0.9.0->autogluon.timeseries==1.2->autogluon.timeseries[all]==1.2->autogluon)\n",
+      "  Downloading appdirs-1.4.4-py2.py3-none-any.whl.metadata (9.0 kB)\n",
+      "Collecting filelock (from ray<2.40,>=2.10.0->ray[default]<2.40,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.2->autogluon)\n",
+      "  Downloading filelock-3.14.0-py3-none-any.whl.metadata (2.8 kB)\n",
+      "Collecting oss2~=2.17.0 (from openxlab->opendatalab->openmim<0.4.0,>=0.3.7->autogluon.multimodal==1.2->autogluon)\n",
+      "  Downloading oss2-2.17.0.tar.gz (259 kB)\n",
+      "  Installing build dependencies: started\n",
+      "  Installing build dependencies: finished with status 'done'\n",
+      "  Getting requirements to build wheel: started\n",
+      "  Getting requirements to build wheel: finished with status 'done'\n",
+      "  Preparing metadata (pyproject.toml): started\n",
+      "  Preparing metadata (pyproject.toml): finished with status 'done'\n",
+      "Collecting packaging (from mlforecast==0.13.4->autogluon.timeseries==1.2->autogluon.timeseries[all]==1.2->autogluon)\n",
+      "  Downloading packaging-24.2-py3-none-any.whl.metadata (3.2 kB)\n",
+      "Collecting pytz>=2020.1 (from pandas<2.3.0,>=2.0.0->autogluon.core==1.2->autogluon.core[all]==1.2->autogluon)\n",
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+      "INFO: pip is looking at multiple versions of openxlab to determine which version is compatible with other requirements. This could take a while.\n",
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+      "INFO: pip is still looking at multiple versions of openxlab to determine which version is compatible with other requirements. This could take a while.\n",
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+      "INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.\n",
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+      "Collecting PySocks!=1.5.7,>=1.5.6 (from requests[socks]->gdown>=4.0.0->nlpaug<1.2.0,>=1.1.10->autogluon.multimodal==1.2->autogluon)\n",
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+      "Collecting cachetools<6.0,>=2.0.0 (from google-auth<3.0.0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]<2.40,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.2->autogluon)\n",
+      "  Downloading cachetools-5.5.2-py3-none-any.whl.metadata (5.4 kB)\n",
+      "Collecting pyasn1-modules>=0.2.1 (from google-auth<3.0.0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]<2.40,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.2->autogluon)\n",
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+      "Collecting rsa<5,>=3.1.4 (from google-auth<3.0.0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]<2.40,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.2->autogluon)\n",
+      "  Downloading rsa-4.9.1-py3-none-any.whl.metadata (5.6 kB)\n",
+      "Collecting pyasn1<0.7.0,>=0.6.1 (from pyasn1-modules>=0.2.1->google-auth<3.0.0,>=2.14.1->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default,tune]<2.40,>=2.10.0; extra == \"all\"->autogluon.core[all]==1.2->autogluon)\n",
+      "  Downloading pyasn1-0.6.1-py3-none-any.whl.metadata (8.4 kB)\n",
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR: Exception:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 438, in _error_catcher\n",
+      "    yield\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 561, in read\n",
+      "    data = self._fp_read(amt) if not fp_closed else b\"\"\n",
+      "           ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 527, in _fp_read\n",
+      "    return self._fp.read(amt) if amt is not None else self._fp.read()\n",
+      "           ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_vendor\\cachecontrol\\filewrapper.py\", line 102, in read\n",
+      "    self.__buf.write(data)\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\tempfile.py\", line 499, in func_wrapper\n",
+      "    return func(*args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^\n",
+      "OSError: [Errno 28] No space left on device\n",
+      "\n",
+      "During handling of the above exception, another exception occurred:\n",
+      "\n",
+      "Traceback (most recent call last):\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_internal\\cli\\base_command.py\", line 106, in _run_wrapper\n",
+      "    status = _inner_run()\n",
+      "             ^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_internal\\cli\\base_command.py\", line 97, in _inner_run\n",
+      "    return self.run(options, args)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_internal\\cli\\req_command.py\", line 67, in wrapper\n",
+      "    return func(self, options, args)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_internal\\commands\\install.py\", line 386, in run\n",
+      "    requirement_set = resolver.resolve(\n",
+      "                      ^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_internal\\resolution\\resolvelib\\resolver.py\", line 179, in resolve\n",
+      "    self.factory.preparer.prepare_linked_requirements_more(reqs)\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_internal\\operations\\prepare.py\", line 554, in prepare_linked_requirements_more\n",
+      "    self._complete_partial_requirements(\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_internal\\operations\\prepare.py\", line 469, in _complete_partial_requirements\n",
+      "    for link, (filepath, _) in batch_download:\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_internal\\network\\download.py\", line 184, in __call__\n",
+      "    for chunk in chunks:\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_internal\\cli\\progress_bars.py\", line 55, in _rich_progress_bar\n",
+      "    for chunk in iterable:\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_internal\\network\\utils.py\", line 65, in response_chunks\n",
+      "    for chunk in response.raw.stream(\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 622, in stream\n",
+      "    data = self.read(amt=amt, decode_content=decode_content)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 560, in read\n",
+      "    with self._error_catcher():\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\contextlib.py\", line 158, in __exit__\n",
+      "    self.gen.throw(value)\n",
+      "  File \"C:\\Users\\User\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pip\\_vendor\\urllib3\\response.py\", line 455, in _error_catcher\n",
+      "    raise ProtocolError(\"Connection broken: %r\" % e, e)\n",
+      "pip._vendor.urllib3.exceptions.ProtocolError: (\"Connection broken: OSError(28, 'No space left on device')\", OSError(28, 'No space left on device'))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Установка необходимых пакетов\n",
+    "!pip install autogluon h2o\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import time\n",
+    "\n",
+    "# Загрузка и подготовка данных\n",
+    "data = pd.read_csv('cars.csv')\n",
+    "data.drop('Car_ID', axis=1, inplace=True)\n",
+    "X = data.drop('Price', axis=1)\n",
+    "y = data['Price']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed2f2e64-3a67-44a8-8ce6-db33d80288d4",
+   "metadata": {},
+   "source": [
+    "## 1.1 AutoGluon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "02a69bcf-32d1-4fa9-9607-4805fd3dcba2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Ошибка в AutoGluon: No module named 'autogluon'\n",
+      "\n",
+      "Ошибка в H2O AutoML: No module named 'h2o'\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>MSE</th>\n",
+       "      <th>R2</th>\n",
+       "      <th>Time</th>\n",
+       "      <th>Model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1060466250000.0</td>\n",
+       "      <td>-0.3</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>Custom GB</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>980000000000.0</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>Sklearn GB</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               MSE    R2   Time       Model\n",
+       "0  1060466250000.0  -0.3  120.0   Custom GB\n",
+       "1   980000000000.0  0.45   60.0  Sklearn GB"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import time\n",
+    "\n",
+    "# Загрузка данных\n",
+    "data = pd.read_csv('cars.csv')\n",
+    "data.drop('Car_ID', axis=1, inplace=True)\n",
+    "\n",
+    "# Подготовка данных\n",
+    "X = data.drop('Price', axis=1)\n",
+    "y = data['Price']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Словарь для хранения результатов\n",
+    "results = {}\n",
+    "\n",
+    "# 1. Ваша кастомная модель (пример)\n",
+    "results['Custom GB'] = {\n",
+    "    'MSE': 1060466250000.00, \n",
+    "    'R2': -0.30,\n",
+    "    'Time': 120  # Примерное время\n",
+    "}\n",
+    "\n",
+    "# 2. Sklearn Gradient Boosting (пример)\n",
+    "results['Sklearn GB'] = {\n",
+    "    'MSE': 980000000000.00,\n",
+    "    'R2': 0.45,\n",
+    "    'Time': 60\n",
+    "}\n",
+    "\n",
+    "# 3. AutoGluon\n",
+    "try:\n",
+    "    from autogluon.tabular import TabularPredictor\n",
+    "    \n",
+    "    print(\"\\nЗапуск AutoGluon...\")\n",
+    "    start_time = time.time()\n",
+    "    \n",
+    "    # Преобразуем данные в один DataFrame\n",
+    "    train_data = pd.concat([X_train, y_train], axis=1)\n",
+    "    \n",
+    "    # Инициализация и обучение\n",
+    "    ag_predictor = TabularPredictor(\n",
+    "        label='Price', \n",
+    "        problem_type='regression',\n",
+    "        eval_metric='rmse'\n",
+    "    ).fit(\n",
+    "        train_data=train_data,\n",
+    "        time_limit=120,  # 2 минуты на обучение\n",
+    "        presets='medium_quality'  # Для более быстрого обучения\n",
+    "    )\n",
+    "    \n",
+    "    ag_time = time.time() - start_time\n",
+    "    \n",
+    "    # Предсказания и оценка\n",
+    "    ag_pred = ag_predictor.predict(X_test)\n",
+    "    results['AutoGluon'] = {\n",
+    "        'MSE': mean_squared_error(y_test, ag_pred),\n",
+    "        'R2': r2_score(y_test, ag_pred),\n",
+    "        'Time': ag_time\n",
+    "    }\n",
+    "    print(\"AutoGluon успешно завершен!\")\n",
+    "    \n",
+    "except Exception as e:\n",
+    "    print(f\"\\nОшибка в AutoGluon: {str(e)}\")\n",
+    "    results['AutoGluon'] = {\n",
+    "        'MSE': None,\n",
+    "        'R2': None,\n",
+    "        'Time': None\n",
+    "    }\n",
+    "\n",
+    "# 4. H2O AutoML\n",
+    "try:\n",
+    "    import h2o\n",
+    "    from h2o.automl import H2OAutoML\n",
+    "    \n",
+    "    print(\"\\nЗапуск H2O AutoML...\")\n",
+    "    h2o.init()\n",
+    "    \n",
+    "    # Преобразование данных\n",
+    "    train = pd.concat([X_train, y_train], axis=1)\n",
+    "    test = pd.concat([X_test, y_test], axis=1)\n",
+    "    h2o_train = h2o.H2OFrame(train)\n",
+    "    h2o_test = h2o.H2OFrame(test)\n",
+    "    \n",
+    "    # Определение признаков и цели\n",
+    "    x = h2o_train.columns[:-1]\n",
+    "    y_col = 'Price'\n",
+    "    \n",
+    "    # Обучение\n",
+    "    start_time = time.time()\n",
+    "    aml = H2OAutoML(\n",
+    "        max_runtime_secs=120,  # 2 минуты\n",
+    "        exclude_algos=[\"DeepLearning\"],  # Исключаем DL для скорости\n",
+    "        seed=42\n",
+    "    )\n",
+    "    aml.train(x=x, y=y_col, training_frame=h2o_train)\n",
+    "    h2o_time = time.time() - start_time\n",
+    "    \n",
+    "    # Предсказания\n",
+    "    h2o_pred = aml.predict(h2o_test).as_data_frame().values.flatten()\n",
+    "    results['H2O AutoML'] = {\n",
+    "        'MSE': mean_squared_error(y_test, h2o_pred),\n",
+    "        'R2': r2_score(y_test, h2o_pred),\n",
+    "        'Time': h2o_time\n",
+    "    }\n",
+    "    print(\"H2O AutoML успешно завершен!\")\n",
+    "    h2o.shutdown()\n",
+    "    \n",
+    "except Exception as e:\n",
+    "    print(f\"\\nОшибка в H2O AutoML: {str(e)}\")\n",
+    "    results['H2O AutoML'] = {\n",
+    "        'MSE': None,\n",
+    "        'R2': None,\n",
+    "        'Time': None\n",
+    "    }\n",
+    "    if 'h2o' in locals():\n",
+    "        h2o.shutdown()\n",
+    "\n",
+    "# 5. Визуализация результатов\n",
+    "comparison_df = pd.DataFrame(results).T\n",
+    "comparison_df['Model'] = comparison_df.index\n",
+    "comparison_df = comparison_df.reset_index(drop=True)\n",
+    "\n",
+    "# Удаляем строки с ошибками\n",
+    "comparison_df = comparison_df.dropna()\n",
+    "\n",
+    "if not comparison_df.empty:\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    import seaborn as sns\n",
+    "    \n",
+    "    plt.figure(figsize=(15, 5))\n",
+    "    \n",
+    "    # График MSE\n",
+    "    plt.subplot(1, 3, 1)\n",
+    "    sns.barplot(x='Model', y='MSE', data=comparison_df)\n",
+    "    plt.yscale('log')\n",
+    "    plt.title('MSE (log scale)')\n",
+    "    plt.xticks(rotation=45)\n",
+    "    \n",
+    "    # График R2\n",
+    "    plt.subplot(1, 3, 2)\n",
+    "    sns.barplot(x='Model', y='R2', data=comparison_df)\n",
+    "    plt.title('R-squared Score')\n",
+    "    plt.xticks(rotation=45)\n",
+    "    \n",
+    "    # График времени\n",
+    "    plt.subplot(1, 3, 3)\n",
+    "    sns.barplot(x='Model', y='Time', data=comparison_df)\n",
+    "    plt.title('Training Time (seconds)')\n",
+    "    plt.xticks(rotation=45)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    display(comparison_df)\n",
+    "else:\n",
+    "    print(\"Нет успешных результатов для сравнения\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbaf7763-6bfe-4aee-9709-d0e8a0abdc47",
+   "metadata": {},
+   "source": [
+    "## 1.2 H2O AutoML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "288b81b4-c4ba-4f05-8aae-8bca01909499",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h2o\n",
+    "from h2o.automl import H2OAutoML\n",
+    "\n",
+    "# Инициализация H2O\n",
+    "h2o.init()\n",
+    "\n",
+    "# Преобразование данных в H2OFrame\n",
+    "train = pd.concat([X_train, y_train], axis=1)\n",
+    "test = pd.concat([X_test, y_test], axis=1)\n",
+    "h2o_train = h2o.H2OFrame(train)\n",
+    "h2o_test = h2o.H2OFrame(test)\n",
+    "\n",
+    "# Определение признаков и целевой переменной\n",
+    "x = h2o_train.columns[:-1]\n",
+    "y = 'Price'\n",
+    "\n",
+    "# Обучение H2O AutoML\n",
+    "start_time = time.time()\n",
+    "aml = H2OAutoML(max_runtime_secs=300)  # 5 минут на обучение\n",
+    "aml.train(x=x, y=y, training_frame=h2o_train)\n",
+    "h2o_time = time.time() - start_time\n",
+    "\n",
+    "# Предсказания и оценка\n",
+    "h2o_pred = aml.predict(h2o_test).as_data_frame().values.flatten()\n",
+    "h2o_mse = mean_squared_error(y_test, h2o_pred)\n",
+    "h2o_r2 = r2_score(y_test, h2o_pred)\n",
+    "\n",
+    "# Остановка H2O\n",
+    "h2o.shutdown()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10210331-2d81-41f0-996b-33e3cc6a826e",
+   "metadata": {},
+   "source": [
+    "## 2. Сравнение всех моделей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4813a46d-7fb9-4145-9799-f6967c631148",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Результаты ваших моделей (примерные значения)\n",
+    "results = {\n",
+    "    'Custom GB': {'MSE': 1060466250000.00, 'R2': -0.30, 'Time': custom_time},\n",
+    "    'Sklearn GB': {'MSE': sklearn_results[0], 'R2': sklearn_results[2], 'Time': sklearn_time},\n",
+    "    'AutoGluon': {'MSE': ag_mse, 'R2': ag_r2, 'Time': ag_time},\n",
+    "    'H2O AutoML': {'MSE': h2o_mse, 'R2': h2o_r2, 'Time': h2o_time}\n",
+    "}\n",
+    "\n",
+    "# Создание DataFrame для сравнения\n",
+    "comparison_df = pd.DataFrame(results).T\n",
+    "comparison_df['Model'] = comparison_df.index\n",
+    "comparison_df.reset_index(drop=True, inplace=True)\n",
+    "\n",
+    "# Визуализация сравнения\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "# График MSE\n",
+    "plt.subplot(1, 3, 1)\n",
+    "sns.barplot(x='Model', y='MSE', data=comparison_df)\n",
+    "plt.yscale('log')  # Логарифмическая шкала из-за больших значений\n",
+    "plt.title('Mean Squared Error (log scale)')\n",
+    "plt.xticks(rotation=45)\n",
+    "\n",
+    "# График R2\n",
+    "plt.subplot(1, 3, 2)\n",
+    "sns.barplot(x='Model', y='R2', data=comparison_df)\n",
+    "plt.title('R-squared Score')\n",
+    "plt.xticks(rotation=45)\n",
+    "\n",
+    "# График времени обучения\n",
+    "plt.subplot(1, 3, 3)\n",
+    "sns.barplot(x='Model', y='Time', data=comparison_df)\n",
+    "plt.title('Training Time (seconds)')\n",
+    "plt.xticks(rotation=45)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c815d487-a39b-44f6-8f7f-2a3a538fb74c",
+   "metadata": {},
+   "source": [
+    "## 4. Дополнительные улучшения"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a7f1ffd-7bc8-4a0d-b35d-8a66acfce75b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Улучшение данных перед AutoML\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "# Кодирование категориальных признаков\n",
+    "for col in X_train.select_dtypes(include=['object']).columns:\n",
+    "    le = LabelEncoder()\n",
+    "    X_train[col] = le.fit_transform(X_train[col].astype(str))\n",
+    "    X_test[col] = le.transform(X_test[col].astype(str))\n",
+    "\n",
+    "# Масштабирование целевой переменной\n",
+    "y_train_log = np.log1p(y_train)\n",
+    "y_test_log = np.log1p(y_test)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/project/cleaned_cars.csv b/project/cleaned_cars.csv
new file mode 100644
index 00000000..6a4c2c85
--- /dev/null
+++ b/project/cleaned_cars.csv
@@ -0,0 +1,101 @@
+Car_ID,Brand,Model,Year,Kilometers_Driven,Fuel_Type,Transmission,Owner_Type,Mileage,Engine,Power,Seats,Price
+1,Toyota,Corolla,2018,50000,Petrol,Manual,First,15,1498,108,5,800000
+2,Honda,Civic,2019,40000,Petrol,Automatic,Second,17,1597,140,5,1000000
+3,Ford,Mustang,2017,20000,Petrol,Automatic,First,10,4951,395,4,2500000
+4,Maruti,Swift,2020,30000,Diesel,Manual,Third,23,1248,74,5,600000
+5,Hyundai,Sonata,2016,60000,Diesel,Automatic,Second,18,1999,194,5,850000
+6,Tata,Nexon,2019,35000,Petrol,Manual,First,17,1198,108,5,750000
+7,Mahindra,Scorpio,2018,45000,Diesel,Automatic,Second,15,2179,140,7,900000
+8,Volkswagen,Polo,2020,25000,Petrol,Automatic,First,18,999,76,5,650000
+9,Audi,A4,2017,30000,Diesel,Automatic,First,18,1968,187,5,2200000
+10,BMW,X1,2019,20000,Diesel,Automatic,Second,20,1995,190,5,2700000
+11,Mercedes,C-Class,2018,28000,Petrol,Automatic,First,16,1991,181,5,2300000
+12,Ford,Endeavour,2017,35000,Diesel,Automatic,Second,12,2198,158,7,2000000
+13,Hyundai,Creta,2019,22000,Petrol,Manual,Third,16,1497,113,5,850000
+14,Tata,Harrier,2018,40000,Diesel,Automatic,First,17,1956,167,5,1600000
+15,Maruti,Ertiga,2020,18000,Petrol,Manual,First,19,1462,103,7,850000
+16,Honda,City,2017,42000,Diesel,Manual,Second,25,1498,98,5,650000
+17,Volkswagen,Tiguan,2018,32000,Diesel,Automatic,First,17,1968,141,5,1800000
+18,Audi,Q3,2016,38000,Petrol,Automatic,Second,15,1395,148,5,1900000
+19,BMW,5 Series,2019,24000,Diesel,Automatic,First,18,1995,187,5,3000000
+20,Mercedes,GLC,2017,26000,Petrol,Automatic,Second,12,1991,241,5,2500000
+21,Toyota,Innova,2018,50000,Diesel,Manual,First,13,2755,171,7,1400000
+22,Ford,Figo,2020,15000,Petrol,Manual,Third,18,1194,94,5,550000
+23,Hyundai,Verna,2019,26000,Diesel,Automatic,Second,24,1582,126,5,850000
+24,Tata,Altroz,2020,18000,Petrol,Manual,First,20,1199,85,5,600000
+25,Mahindra,Thar,2021,10000,Diesel,Manual,First,15,2184,130,4,1200000
+26,Volkswagen,Passat,2017,32000,Diesel,Automatic,Second,17,1968,174,5,1600000
+27,Audi,A6,2018,28000,Petrol,Automatic,First,15,1984,241,5,3200000
+28,BMW,X3,2019,22000,Diesel,Automatic,Second,18,1995,187,5,2800000
+29,Mercedes,E-Class,2017,30000,Diesel,Automatic,First,16,1950,191,5,2700000
+30,Toyota,Fortuner,2018,38000,Diesel,Automatic,Second,12,2755,174,7,2500000
+31,Ford,Aspire,2019,26000,Petrol,Manual,Third,20,1194,94,5,600000
+32,Hyundai,Elantra,2017,32000,Diesel,Automatic,Second,22,1582,126,5,800000
+33,Tata,Safari,2018,42000,Diesel,Manual,First,14,1956,150,7,1300000
+34,Maruti,Vitara,2019,24000,Petrol,Manual,Second,17,1462,103,5,700000
+35,Honda,WR-V,2018,28000,Diesel,Manual,First,25,1498,98,5,750000
+36,Volkswagen,Ameo,2020,15000,Petrol,Automatic,Third,19,1197,74,5,500000
+37,Audi,A3,2017,38000,Petrol,Automatic,Second,16,1395,148,5,2000000
+38,BMW,7 Series,2019,22000,Diesel,Automatic,First,15,2993,261,5,3500000
+39,Mercedes,GLE,2018,26000,Petrol,Automatic,Second,12,2996,362,5,4000000
+40,Toyota,Yaris,2020,18000,Petrol,Manual,First,17,1496,106,5,650000
+41,Ford,Ranger,2017,38000,Diesel,Manual,Second,12,2198,158,5,1500000
+42,Hyundai,Santro,2019,26000,Petrol,Manual,Third,20,1086,68,5,450000
+43,Tata,Tigor,2018,42000,Diesel,Manual,First,24,1047,69,5,500000
+44,Maruti,S-Cross,2020,15000,Petrol,Automatic,Second,18,1462,103,5,700000
+45,Honda,BR-V,2018,28000,Diesel,Manual,First,21,1498,98,7,850000
+46,Volkswagen,T-Roc,2019,22000,Petrol,Automatic,Second,18,1498,148,5,1600000
+47,Audi,Q7,2017,30000,Diesel,Automatic,First,14,2967,245,7,3000000
+48,BMW,X5,2018,28000,Petrol,Automatic,Second,14,2998,335,5,3200000
+49,Mercedes,GLA,2019,24000,Diesel,Automatic,First,17,2143,170,5,2400000
+50,Toyota,Camry,2016,38000,Petrol,Automatic,Second,19,2487,176,5,1800000
+51,Ford,Mustang,2019,22000,Petrol,Automatic,First,13,2261,396,4,2700000
+52,Hyundai,Venue,2018,32000,Petrol,Manual,Third,17,1197,81,5,550000
+53,Tata,Tiago,2020,18000,Petrol,Manual,First,23,1199,84,5,500000
+54,Mahindra,XUV300,2019,26000,Diesel,Manual,Second,20,1497,115,5,700000
+55,Volkswagen,Vento,2017,32000,Petrol,Manual,Second,18,1598,103,5,650000
+56,Audi,A5,2018,28000,Diesel,Automatic,First,17,1968,187,5,2600000
+57,BMW,3 Series,2020,15000,Petrol,Automatic,Second,15,1998,258,5,2800000
+58,Mercedes,C-Class,2019,22000,Diesel,Automatic,First,16,1950,191,5,2900000
+59,Toyota,Innova Crysta,2017,38000,Diesel,Manual,Second,13,2755,171,7,1400000
+60,Ford,EcoSport,2018,26000,Petrol,Manual,Third,18,1497,121,5,750000
+61,Hyundai,Verna,2019,24000,Petrol,Automatic,Second,17,1497,113,5,850000
+62,Tata,Altroz,2020,18000,Petrol,Manual,First,20,1199,85,5,600000
+63,Mahindra,Thar,2021,10000,Diesel,Manual,First,15,2184,130,4,1200000
+64,Volkswagen,Passat,2017,32000,Diesel,Automatic,Second,17,1968,174,5,1600000
+65,Audi,A6,2018,28000,Petrol,Automatic,First,15,1984,241,5,3200000
+66,BMW,X3,2019,22000,Diesel,Automatic,Second,18,1995,187,5,2800000
+67,Mercedes,E-Class,2017,30000,Diesel,Automatic,First,16,1950,191,5,2700000
+68,Toyota,Fortuner,2018,38000,Diesel,Automatic,Second,12,2755,174,7,2500000
+69,Ford,Aspire,2019,26000,Petrol,Manual,Third,20,1194,94,5,600000
+70,Hyundai,Elantra,2017,32000,Diesel,Automatic,Second,22,1582,126,5,800000
+71,Tata,Safari,2018,42000,Diesel,Manual,First,14,1956,150,7,1300000
+72,Maruti,Vitara,2019,24000,Petrol,Manual,Second,17,1462,103,5,700000
+73,Honda,WR-V,2018,28000,Diesel,Manual,First,25,1498,98,5,750000
+74,Volkswagen,Ameo,2020,15000,Petrol,Automatic,Third,19,1197,74,5,500000
+75,Audi,A3,2017,38000,Petrol,Automatic,Second,16,1395,148,5,2000000
+76,BMW,7 Series,2019,22000,Diesel,Automatic,First,15,2993,261,5,3500000
+77,Mercedes,GLE,2018,26000,Petrol,Automatic,Second,12,2996,362,5,4000000
+78,Toyota,Yaris,2020,18000,Petrol,Manual,First,17,1496,106,5,650000
+79,Ford,Ranger,2017,38000,Diesel,Manual,Second,12,2198,158,5,1500000
+80,Hyundai,Santro,2019,26000,Petrol,Manual,Third,20,1086,68,5,450000
+81,Tata,Tigor,2018,42000,Diesel,Manual,First,24,1047,69,5,500000
+82,Maruti,S-Cross,2020,15000,Petrol,Automatic,Second,18,1462,103,5,700000
+83,Honda,BR-V,2018,28000,Diesel,Manual,First,21,1498,98,7,850000
+84,Volkswagen,T-Roc,2019,22000,Petrol,Automatic,Second,18,1498,148,5,1600000
+85,Audi,Q7,2017,30000,Diesel,Automatic,First,14,2967,245,7,3000000
+86,BMW,X5,2018,28000,Petrol,Automatic,Second,14,2998,335,5,3200000
+87,Mercedes,GLA,2019,24000,Diesel,Automatic,First,17,2143,170,5,2400000
+88,Toyota,Camry,2016,38000,Petrol,Automatic,Second,19,2487,176,5,1800000
+89,Ford,Mustang,2019,22000,Petrol,Automatic,First,13,2261,396,4,2700000
+90,Hyundai,Venue,2018,32000,Petrol,Manual,Third,17,1197,81,5,550000
+91,Tata,Tiago,2020,18000,Petrol,Manual,First,23,1199,84,5,500000
+92,Mahindra,XUV300,2019,26000,Diesel,Manual,Second,20,1497,115,5,700000
+93,Volkswagen,Vento,2017,32000,Petrol,Manual,Second,18,1598,103,5,650000
+94,Audi,A5,2018,28000,Diesel,Automatic,First,17,1968,187,5,2600000
+95,BMW,3 Series,2020,15000,Petrol,Automatic,Second,15,1998,258,5,2800000
+96,Mercedes,C-Class,2019,22000,Diesel,Automatic,First,16,1950,191,5,2900000
+97,Toyota,Innova Crysta,2017,38000,Diesel,Manual,Second,13,2755,171,7,1400000
+98,Ford,EcoSport,2018,26000,Petrol,Manual,Third,18,1497,121,5,750000
+99,Hyundai,Verna,2019,24000,Petrol,Automatic,Second,17,1497,113,5,850000
+100,Tata,Altroz,2020,18000,Petrol,Manual,First,20,1199,85,5,600000