commit b1f6e19256d75ab0a39958c5144f1be70569446e
Author: Serafim <simasuh@gmail.com>
Date:   Mon Dec 15 22:14:13 2025 +0300

    init

diff --git a/AIS1.ipynb b/AIS1.ipynb
new file mode 100644
index 0000000..49a9c03
--- /dev/null
+++ b/AIS1.ipynb
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kU6mKXJpxPwQ"
+      },
+      "source": [
+        "# Лабораторная работа №1\n",
+        "## по дисциплине \"Системы искусственного интеллекта\"\n",
+        "\n",
+        "Лабораторная работа посвящена работе с табличными данными с помощью библиотеки pandas и визуализации с помощью matplotlib.\n",
+        "Для выполнения работы вам был предоставлен набор данных, содержащий информацию об атлетах, принявших участие в Летних Олимпийских играх 2024 в Париже.\n",
+        "Описание каждой колоноки в наборе данных приведено дальше.\n",
+        "\n",
+        "code - код спортсмена;\n",
+        "\n",
+        "name - полное имя спортсмена;\n",
+        "\n",
+        "name_short - сокращенное имя;\n",
+        "\n",
+        "name_tv - имя, отображаемое в эфире;\n",
+        "\n",
+        "gender - пол спортсмена;\n",
+        "\n",
+        "function - роль на олимпиаде;\n",
+        "\n",
+        "country_code - код страны;\n",
+        "\n",
+        "country - название страны;\n",
+        "\n",
+        "country_full - полное название страны;\n",
+        "\n",
+        "nationality - сокращенная национальность спортсмена;\n",
+        "\n",
+        "nationality_full - полная национальность;\n",
+        "\n",
+        "nationality_code - код национальности;\n",
+        "\n",
+        "height - рост;\n",
+        "\n",
+        "weight - вес;\n",
+        "\n",
+        "disciplines - дисциплины, в которых принимает участие спортсмен;\n",
+        "\n",
+        "events - состязания, в которых участвует спортсмен;\n",
+        "\n",
+        "birth_date - дата рождения спортсмена.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aMR4h5Ok15nq"
+      },
+      "source": [
+        "Импортируйте необходимые библиотеки.\n",
+        "\n",
+        "Загрузите набор данных из csv-файла. Выведите информацию обо всех колонках, количестве данных в наборе и их статистических показателях (среднее, медиана и т.д.)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 82,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 900
+        },
+        "id": "PA0A4lVR2IgW",
+        "outputId": "2cdac7bb-35d3-417b-bcde-877a6c53e2d9"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "df = pd.read_csv('./athletes new.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "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>code</th>\n",
+              "      <th>name</th>\n",
+              "      <th>name_short</th>\n",
+              "      <th>name_tv</th>\n",
+              "      <th>gender</th>\n",
+              "      <th>function</th>\n",
+              "      <th>country_code</th>\n",
+              "      <th>country</th>\n",
+              "      <th>country_full</th>\n",
+              "      <th>nationality</th>\n",
+              "      <th>nationality_full</th>\n",
+              "      <th>nationality_code</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>disciplines</th>\n",
+              "      <th>events</th>\n",
+              "      <th>birth_date</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1535420</td>\n",
+              "      <td>VALENCIA Alejandra</td>\n",
+              "      <td>VALENCIA A</td>\n",
+              "      <td>Alejandra VALENCIA</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Women's Individual\", \"Women's Team\", 'Mixed ...</td>\n",
+              "      <td>1994-10-17</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1535429</td>\n",
+              "      <td>RUIZ Angela</td>\n",
+              "      <td>RUIZ A</td>\n",
+              "      <td>Angela RUIZ</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Women's Individual\", \"Women's Team\"]</td>\n",
+              "      <td>2006-07-28</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1535430</td>\n",
+              "      <td>GRANDE Matias</td>\n",
+              "      <td>GRANDE M</td>\n",
+              "      <td>Matias GRANDE</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Men's Individual\", \"Men's Team\", 'Mixed Team']</td>\n",
+              "      <td>2004-04-26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1536460</td>\n",
+              "      <td>ROJAS Carlos</td>\n",
+              "      <td>ROJAS C</td>\n",
+              "      <td>Carlos ROJAS</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Men's Individual\", \"Men's Team\"]</td>\n",
+              "      <td>2000-01-14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1536467</td>\n",
+              "      <td>MARTINEZ WING Bruno</td>\n",
+              "      <td>MARTINEZ WING B</td>\n",
+              "      <td>Bruno MARTINEZ WING</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Men's Individual\", \"Men's Team\"]</td>\n",
+              "      <td>1998-03-08</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      code                 name       name_short              name_tv  gender  \\\n",
+              "0  1535420   VALENCIA Alejandra       VALENCIA A   Alejandra VALENCIA  Female   \n",
+              "1  1535429          RUIZ Angela           RUIZ A          Angela RUIZ  Female   \n",
+              "2  1535430        GRANDE Matias         GRANDE M        Matias GRANDE    Male   \n",
+              "3  1536460         ROJAS Carlos          ROJAS C         Carlos ROJAS    Male   \n",
+              "4  1536467  MARTINEZ WING Bruno  MARTINEZ WING B  Bruno MARTINEZ WING    Male   \n",
+              "\n",
+              "  function country_code country country_full nationality nationality_full  \\\n",
+              "0  Athlete          MEX  Mexico       Mexico      Mexico           Mexico   \n",
+              "1  Athlete          MEX  Mexico       Mexico      Mexico           Mexico   \n",
+              "2  Athlete          MEX  Mexico       Mexico      Mexico           Mexico   \n",
+              "3  Athlete          MEX  Mexico       Mexico      Mexico           Mexico   \n",
+              "4  Athlete          MEX  Mexico       Mexico      Mexico           Mexico   \n",
+              "\n",
+              "  nationality_code  height  weight  disciplines  \\\n",
+              "0              MEX       0     0.0  ['Archery']   \n",
+              "1              MEX       0     0.0  ['Archery']   \n",
+              "2              MEX       0     0.0  ['Archery']   \n",
+              "3              MEX       0     0.0  ['Archery']   \n",
+              "4              MEX       0     0.0  ['Archery']   \n",
+              "\n",
+              "                                              events  birth_date  \n",
+              "0  [\"Women's Individual\", \"Women's Team\", 'Mixed ...  1994-10-17  \n",
+              "1             [\"Women's Individual\", \"Women's Team\"]  2006-07-28  \n",
+              "2   [\"Men's Individual\", \"Men's Team\", 'Mixed Team']  2004-04-26  \n",
+              "3                 [\"Men's Individual\", \"Men's Team\"]  2000-01-14  \n",
+              "4                 [\"Men's Individual\", \"Men's Team\"]  1998-03-08  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display(df.head())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 11115 entries, 0 to 11114\n",
+            "Data columns (total 17 columns):\n",
+            " #   Column            Non-Null Count  Dtype  \n",
+            "---  ------            --------------  -----  \n",
+            " 0   code              11115 non-null  int64  \n",
+            " 1   name              11115 non-null  object \n",
+            " 2   name_short        11115 non-null  object \n",
+            " 3   name_tv           11115 non-null  object \n",
+            " 4   gender            11115 non-null  object \n",
+            " 5   function          11115 non-null  object \n",
+            " 6   country_code      11115 non-null  object \n",
+            " 7   country           11115 non-null  object \n",
+            " 8   country_full      11115 non-null  object \n",
+            " 9   nationality       11115 non-null  object \n",
+            " 10  nationality_full  11115 non-null  object \n",
+            " 11  nationality_code  11115 non-null  object \n",
+            " 12  height            11115 non-null  int64  \n",
+            " 13  weight            11099 non-null  float64\n",
+            " 14  disciplines       11115 non-null  object \n",
+            " 15  events            11115 non-null  object \n",
+            " 16  birth_date        11115 non-null  object \n",
+            "dtypes: float64(1), int64(2), object(14)\n",
+            "memory usage: 1.4+ MB\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {},
+      "outputs": [
+        {
+          "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>code</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1.111500e+04</td>\n",
+              "      <td>11115.000000</td>\n",
+              "      <td>11099.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>1.887426e+06</td>\n",
+              "      <td>81.835178</td>\n",
+              "      <td>2.213713</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>3.587687e+05</td>\n",
+              "      <td>89.504711</td>\n",
+              "      <td>13.114771</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.532872e+06</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1.888186e+06</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1.918842e+06</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.948950e+06</td>\n",
+              "      <td>177.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>9.460001e+06</td>\n",
+              "      <td>222.000000</td>\n",
+              "      <td>113.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "               code        height        weight\n",
+              "count  1.111500e+04  11115.000000  11099.000000\n",
+              "mean   1.887426e+06     81.835178      2.213713\n",
+              "std    3.587687e+05     89.504711     13.114771\n",
+              "min    1.532872e+06      0.000000      0.000000\n",
+              "25%    1.888186e+06      0.000000      0.000000\n",
+              "50%    1.918842e+06      0.000000      0.000000\n",
+              "75%    1.948950e+06    177.000000      0.000000\n",
+              "max    9.460001e+06    222.000000    113.000000"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display(df.describe())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uesSsuuD2bY7"
+      },
+      "source": [
+        "Проверьте наличие пропусков в данных и заполните их, выбрав стратегию работы с пропусками."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1p9b-wqB2pn7",
+        "outputId": "7cd5c2de-c6f0-4459-81b6-774221dd2d37"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "code                 0\n",
+              "name                 0\n",
+              "name_short           0\n",
+              "name_tv              0\n",
+              "gender               0\n",
+              "function             0\n",
+              "country_code         0\n",
+              "country              0\n",
+              "country_full         0\n",
+              "nationality          0\n",
+              "nationality_full     0\n",
+              "nationality_code     0\n",
+              "height               0\n",
+              "weight              16\n",
+              "disciplines          0\n",
+              "events               0\n",
+              "birth_date           0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display(df.isnull().sum())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 83,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "code                0\n",
+              "name                0\n",
+              "name_short          0\n",
+              "name_tv             0\n",
+              "gender              0\n",
+              "function            0\n",
+              "country_code        0\n",
+              "country             0\n",
+              "country_full        0\n",
+              "nationality         0\n",
+              "nationality_full    0\n",
+              "nationality_code    0\n",
+              "height              0\n",
+              "weight              0\n",
+              "disciplines         0\n",
+              "events              0\n",
+              "birth_date          0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "df['weight'].fillna(df['weight'].mean(), inplace=True)\n",
+        "\n",
+        "\n",
+        "display(df.isnull().sum())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 85,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "code                    0\n",
+              "name                    0\n",
+              "name_short              0\n",
+              "name_tv                 0\n",
+              "gender                  0\n",
+              "function                0\n",
+              "country_code            0\n",
+              "country                 0\n",
+              "country_full            0\n",
+              "nationality             0\n",
+              "nationality_full        0\n",
+              "nationality_code        0\n",
+              "height               6032\n",
+              "weight              10783\n",
+              "disciplines             0\n",
+              "events                  0\n",
+              "birth_date              0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display((df == 0).sum())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 86,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\Serafim\\AppData\\Local\\Temp\\ipykernel_6592\\1268530913.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
+            "  df.fillna(df.mean())\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>code</th>\n",
+              "      <th>name</th>\n",
+              "      <th>name_short</th>\n",
+              "      <th>name_tv</th>\n",
+              "      <th>gender</th>\n",
+              "      <th>function</th>\n",
+              "      <th>country_code</th>\n",
+              "      <th>country</th>\n",
+              "      <th>country_full</th>\n",
+              "      <th>nationality</th>\n",
+              "      <th>nationality_full</th>\n",
+              "      <th>nationality_code</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>disciplines</th>\n",
+              "      <th>events</th>\n",
+              "      <th>birth_date</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1535420</td>\n",
+              "      <td>VALENCIA Alejandra</td>\n",
+              "      <td>VALENCIA A</td>\n",
+              "      <td>Alejandra VALENCIA</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>178.949046</td>\n",
+              "      <td>74.112709</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Women's Individual\", \"Women's Team\", 'Mixed ...</td>\n",
+              "      <td>1994-10-17</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1535429</td>\n",
+              "      <td>RUIZ Angela</td>\n",
+              "      <td>RUIZ A</td>\n",
+              "      <td>Angela RUIZ</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>178.949046</td>\n",
+              "      <td>74.112709</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Women's Individual\", \"Women's Team\"]</td>\n",
+              "      <td>2006-07-28</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1535430</td>\n",
+              "      <td>GRANDE Matias</td>\n",
+              "      <td>GRANDE M</td>\n",
+              "      <td>Matias GRANDE</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>178.949046</td>\n",
+              "      <td>74.112709</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Men's Individual\", \"Men's Team\", 'Mixed Team']</td>\n",
+              "      <td>2004-04-26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1536460</td>\n",
+              "      <td>ROJAS Carlos</td>\n",
+              "      <td>ROJAS C</td>\n",
+              "      <td>Carlos ROJAS</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>178.949046</td>\n",
+              "      <td>74.112709</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Men's Individual\", \"Men's Team\"]</td>\n",
+              "      <td>2000-01-14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1536467</td>\n",
+              "      <td>MARTINEZ WING Bruno</td>\n",
+              "      <td>MARTINEZ WING B</td>\n",
+              "      <td>Bruno MARTINEZ WING</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>178.949046</td>\n",
+              "      <td>74.112709</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Men's Individual\", \"Men's Team\"]</td>\n",
+              "      <td>1998-03-08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11110</th>\n",
+              "      <td>1981775</td>\n",
+              "      <td>SULEYMANOV Sanan</td>\n",
+              "      <td>SULEYMANOV S</td>\n",
+              "      <td>Sanan SULEYMANOV</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>AZE</td>\n",
+              "      <td>Azerbaijan</td>\n",
+              "      <td>Azerbaijan</td>\n",
+              "      <td>Azerbaijan</td>\n",
+              "      <td>Azerbaijan</td>\n",
+              "      <td>AZE</td>\n",
+              "      <td>178.949046</td>\n",
+              "      <td>74.112709</td>\n",
+              "      <td>['Wrestling']</td>\n",
+              "      <td>[\"Men's Greco-Roman 77kg\"]</td>\n",
+              "      <td>1996-12-15</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11111</th>\n",
+              "      <td>1981785</td>\n",
+              "      <td>MAGOMEDOV Magomedkhan</td>\n",
+              "      <td>MAGOMEDOV M</td>\n",
+              "      <td>Magomedkhan MAGOMEDOV</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>AZE</td>\n",
+              "      <td>Azerbaijan</td>\n",
+              "      <td>Azerbaijan</td>\n",
+              "      <td>Azerbaijan</td>\n",
+              "      <td>Azerbaijan</td>\n",
+              "      <td>AZE</td>\n",
+              "      <td>178.949046</td>\n",
+              "      <td>74.112709</td>\n",
+              "      <td>['Wrestling']</td>\n",
+              "      <td>[\"Men's Freestyle 97kg\"]</td>\n",
+              "      <td>1998-01-27</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11112</th>\n",
+              "      <td>2008688</td>\n",
+              "      <td>SACULTAN Maxim</td>\n",
+              "      <td>SACULTAN M</td>\n",
+              "      <td>Maxim SACULTAN</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MDA</td>\n",
+              "      <td>Republic of Moldova</td>\n",
+              "      <td>Republic of Moldova</td>\n",
+              "      <td>Republic of Moldova</td>\n",
+              "      <td>Republic of Moldova</td>\n",
+              "      <td>MDA</td>\n",
+              "      <td>178.949046</td>\n",
+              "      <td>74.112709</td>\n",
+              "      <td>['Wrestling']</td>\n",
+              "      <td>[\"Men's Freestyle 65kg\"]</td>\n",
+              "      <td>1996-11-18</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11113</th>\n",
+              "      <td>3429662</td>\n",
+              "      <td>VALIEV Chermen</td>\n",
+              "      <td>VALIEV C</td>\n",
+              "      <td>Chermen VALIEV</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>ALB</td>\n",
+              "      <td>Albania</td>\n",
+              "      <td>Albania</td>\n",
+              "      <td>Albania</td>\n",
+              "      <td>Albania</td>\n",
+              "      <td>ALB</td>\n",
+              "      <td>178.949046</td>\n",
+              "      <td>74.112709</td>\n",
+              "      <td>['Wrestling']</td>\n",
+              "      <td>[\"Men's Freestyle 74kg\"]</td>\n",
+              "      <td>1998-12-10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11114</th>\n",
+              "      <td>9460001</td>\n",
+              "      <td>LIUZZI Emanuela</td>\n",
+              "      <td>LIUZZI E</td>\n",
+              "      <td>Emanuela LIUZZI</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>ITA</td>\n",
+              "      <td>Italy</td>\n",
+              "      <td>Italy</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>178.949046</td>\n",
+              "      <td>74.112709</td>\n",
+              "      <td>['Wrestling']</td>\n",
+              "      <td>[\"Women's Freestyle 50kg\"]</td>\n",
+              "      <td>2000-04-27</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>11115 rows × 17 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          code                   name       name_short                name_tv  \\\n",
+              "0      1535420     VALENCIA Alejandra       VALENCIA A     Alejandra VALENCIA   \n",
+              "1      1535429            RUIZ Angela           RUIZ A            Angela RUIZ   \n",
+              "2      1535430          GRANDE Matias         GRANDE M          Matias GRANDE   \n",
+              "3      1536460           ROJAS Carlos          ROJAS C           Carlos ROJAS   \n",
+              "4      1536467    MARTINEZ WING Bruno  MARTINEZ WING B    Bruno MARTINEZ WING   \n",
+              "...        ...                    ...              ...                    ...   \n",
+              "11110  1981775       SULEYMANOV Sanan     SULEYMANOV S       Sanan SULEYMANOV   \n",
+              "11111  1981785  MAGOMEDOV Magomedkhan      MAGOMEDOV M  Magomedkhan MAGOMEDOV   \n",
+              "11112  2008688         SACULTAN Maxim       SACULTAN M         Maxim SACULTAN   \n",
+              "11113  3429662         VALIEV Chermen         VALIEV C         Chermen VALIEV   \n",
+              "11114  9460001        LIUZZI Emanuela         LIUZZI E        Emanuela LIUZZI   \n",
+              "\n",
+              "       gender function country_code              country         country_full  \\\n",
+              "0      Female  Athlete          MEX               Mexico               Mexico   \n",
+              "1      Female  Athlete          MEX               Mexico               Mexico   \n",
+              "2        Male  Athlete          MEX               Mexico               Mexico   \n",
+              "3        Male  Athlete          MEX               Mexico               Mexico   \n",
+              "4        Male  Athlete          MEX               Mexico               Mexico   \n",
+              "...       ...      ...          ...                  ...                  ...   \n",
+              "11110    Male  Athlete          AZE           Azerbaijan           Azerbaijan   \n",
+              "11111    Male  Athlete          AZE           Azerbaijan           Azerbaijan   \n",
+              "11112    Male  Athlete          MDA  Republic of Moldova  Republic of Moldova   \n",
+              "11113    Male  Athlete          ALB              Albania              Albania   \n",
+              "11114  Female  Athlete          ITA                Italy                Italy   \n",
+              "\n",
+              "               nationality     nationality_full nationality_code      height  \\\n",
+              "0                   Mexico               Mexico              MEX  178.949046   \n",
+              "1                   Mexico               Mexico              MEX  178.949046   \n",
+              "2                   Mexico               Mexico              MEX  178.949046   \n",
+              "3                   Mexico               Mexico              MEX  178.949046   \n",
+              "4                   Mexico               Mexico              MEX  178.949046   \n",
+              "...                    ...                  ...              ...         ...   \n",
+              "11110           Azerbaijan           Azerbaijan              AZE  178.949046   \n",
+              "11111           Azerbaijan           Azerbaijan              AZE  178.949046   \n",
+              "11112  Republic of Moldova  Republic of Moldova              MDA  178.949046   \n",
+              "11113              Albania              Albania              ALB  178.949046   \n",
+              "11114                    0                    0                0  178.949046   \n",
+              "\n",
+              "          weight    disciplines  \\\n",
+              "0      74.112709    ['Archery']   \n",
+              "1      74.112709    ['Archery']   \n",
+              "2      74.112709    ['Archery']   \n",
+              "3      74.112709    ['Archery']   \n",
+              "4      74.112709    ['Archery']   \n",
+              "...          ...            ...   \n",
+              "11110  74.112709  ['Wrestling']   \n",
+              "11111  74.112709  ['Wrestling']   \n",
+              "11112  74.112709  ['Wrestling']   \n",
+              "11113  74.112709  ['Wrestling']   \n",
+              "11114  74.112709  ['Wrestling']   \n",
+              "\n",
+              "                                                  events  birth_date  \n",
+              "0      [\"Women's Individual\", \"Women's Team\", 'Mixed ...  1994-10-17  \n",
+              "1                 [\"Women's Individual\", \"Women's Team\"]  2006-07-28  \n",
+              "2       [\"Men's Individual\", \"Men's Team\", 'Mixed Team']  2004-04-26  \n",
+              "3                     [\"Men's Individual\", \"Men's Team\"]  2000-01-14  \n",
+              "4                     [\"Men's Individual\", \"Men's Team\"]  1998-03-08  \n",
+              "...                                                  ...         ...  \n",
+              "11110                         [\"Men's Greco-Roman 77kg\"]  1996-12-15  \n",
+              "11111                           [\"Men's Freestyle 97kg\"]  1998-01-27  \n",
+              "11112                           [\"Men's Freestyle 65kg\"]  1996-11-18  \n",
+              "11113                           [\"Men's Freestyle 74kg\"]  1998-12-10  \n",
+              "11114                         [\"Women's Freestyle 50kg\"]  2000-04-27  \n",
+              "\n",
+              "[11115 rows x 17 columns]"
+            ]
+          },
+          "execution_count": 86,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df = df.replace(0, np.nan)\n",
+        "\n",
+        "df.fillna(df.mean())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {},
+      "outputs": [
+        {
+          "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>height</th>\n",
+              "      <th>weight</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5083.000000</td>\n",
+              "      <td>332.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>178.949046</td>\n",
+              "      <td>74.112709</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>11.742262</td>\n",
+              "      <td>20.545441</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>140.000000</td>\n",
+              "      <td>2.213713</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>170.000000</td>\n",
+              "      <td>65.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>178.000000</td>\n",
+              "      <td>75.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>187.000000</td>\n",
+              "      <td>88.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>222.000000</td>\n",
+              "      <td>113.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            height      weight\n",
+              "count  5083.000000  332.000000\n",
+              "mean    178.949046   74.112709\n",
+              "std      11.742262   20.545441\n",
+              "min     140.000000    2.213713\n",
+              "25%     170.000000   65.000000\n",
+              "50%     178.000000   75.000000\n",
+              "75%     187.000000   88.000000\n",
+              "max     222.000000  113.000000"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display(df.describe())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gZZoTM-x4yTI"
+      },
+      "source": [
+        "Удалите колонки code, name_short, name_tv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 573
+        },
+        "id": "0sV9BdDP5eKg",
+        "outputId": "02c246ed-b231-4a2f-d3b4-a05fde29dec4"
+      },
+      "outputs": [
+        {
+          "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>name</th>\n",
+              "      <th>gender</th>\n",
+              "      <th>function</th>\n",
+              "      <th>country_code</th>\n",
+              "      <th>country</th>\n",
+              "      <th>country_full</th>\n",
+              "      <th>nationality</th>\n",
+              "      <th>nationality_full</th>\n",
+              "      <th>nationality_code</th>\n",
+              "      <th>height</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>disciplines</th>\n",
+              "      <th>events</th>\n",
+              "      <th>birth_date</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>VALENCIA Alejandra</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Women's Individual\", \"Women's Team\", 'Mixed ...</td>\n",
+              "      <td>1994-10-17</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>RUIZ Angela</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Women's Individual\", \"Women's Team\"]</td>\n",
+              "      <td>2006-07-28</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>GRANDE Matias</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Men's Individual\", \"Men's Team\", 'Mixed Team']</td>\n",
+              "      <td>2004-04-26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ROJAS Carlos</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Men's Individual\", \"Men's Team\"]</td>\n",
+              "      <td>2000-01-14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>MARTINEZ WING Bruno</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Athlete</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>MEX</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>['Archery']</td>\n",
+              "      <td>[\"Men's Individual\", \"Men's Team\"]</td>\n",
+              "      <td>1998-03-08</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                  name  gender function country_code country country_full  \\\n",
+              "0   VALENCIA Alejandra  Female  Athlete          MEX  Mexico       Mexico   \n",
+              "1          RUIZ Angela  Female  Athlete          MEX  Mexico       Mexico   \n",
+              "2        GRANDE Matias    Male  Athlete          MEX  Mexico       Mexico   \n",
+              "3         ROJAS Carlos    Male  Athlete          MEX  Mexico       Mexico   \n",
+              "4  MARTINEZ WING Bruno    Male  Athlete          MEX  Mexico       Mexico   \n",
+              "\n",
+              "  nationality nationality_full nationality_code  height  weight  disciplines  \\\n",
+              "0      Mexico           Mexico              MEX     NaN     NaN  ['Archery']   \n",
+              "1      Mexico           Mexico              MEX     NaN     NaN  ['Archery']   \n",
+              "2      Mexico           Mexico              MEX     NaN     NaN  ['Archery']   \n",
+              "3      Mexico           Mexico              MEX     NaN     NaN  ['Archery']   \n",
+              "4      Mexico           Mexico              MEX     NaN     NaN  ['Archery']   \n",
+              "\n",
+              "                                              events  birth_date  \n",
+              "0  [\"Women's Individual\", \"Women's Team\", 'Mixed ...  1994-10-17  \n",
+              "1             [\"Women's Individual\", \"Women's Team\"]  2006-07-28  \n",
+              "2   [\"Men's Individual\", \"Men's Team\", 'Mixed Team']  2004-04-26  \n",
+              "3                 [\"Men's Individual\", \"Men's Team\"]  2000-01-14  \n",
+              "4                 [\"Men's Individual\", \"Men's Team\"]  1998-03-08  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "df = df.drop(['code', 'name_short', 'name_tv'], axis=1)\n",
+        "display(df.head())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q6MxGujF5ecN"
+      },
+      "source": [
+        "Из какой страны было больше всего атлетов? Сколько их было?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tzcVTPBj5tpi",
+        "outputId": "1d9037dc-d87b-4fbb-ccbf-7f90fc4a1909"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "United States    620\n",
+              "France           600\n",
+              "Australia        476\n",
+              "Germany          457\n",
+              "Japan            432\n",
+              "                ... \n",
+              "Tuvalu             2\n",
+              "Belize             1\n",
+              "Somalia            1\n",
+              "Nauru              1\n",
+              "Liechtenstein      1\n",
+              "Name: country, Length: 206, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "countries_athletes = df['country'].value_counts()\n",
+        "display(countries_athletes)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MCnLpnXz5t4_"
+      },
+      "source": [
+        "Найдите 15 стран, из которых было больше всего спортсменов. Постройте гистограмму, где по горизонтальной оси будет отображаться код страны, а по вертикальной - количество спортсменов."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 550
+        },
+        "id": "lTAa0uOc6nJz",
+        "outputId": "acfcdd4a-6ae8-47ab-a8d6-1e38ca8c4ffb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "countries_athletes.head(15).plot(kind='bar')\n",
+        "plt.xlabel('Страна')\n",
+        "plt.ylabel('Количество атлетов')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uA-Ae_Wa67zb"
+      },
+      "source": [
+        "Сколько женщин и мужчин участвовало в Олимпиаде?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wBM2BwJR8lPH",
+        "outputId": "d7eaa205-d514-4a68-8476-7d6691b975ad"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Male      5655\n",
+              "Female    5460\n",
+              "Name: gender, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display(df['gender'].value_counts())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZrJDz_ZU8lcB"
+      },
+      "source": [
+        "Выведите количество женщин, которые участвовали только в одной соревновательной группе (колонка events)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 109,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EwCr00ny-CZn",
+        "outputId": "59772e20-94b0-445e-a4f2-755c94615499"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "4280"
+            ]
+          },
+          "execution_count": 109,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "female_athletes = df[df['gender'] == 'Female']\n",
+        "len(female_athletes[female_athletes['events'].apply(lambda events: len(eval(events)) == 1)])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_5__lGjSBvBW"
+      },
+      "source": [
+        "Выведите количество спортсменов, у которых национальность не совпадает со страной, за которую они выступают."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "khISD9_HB2LF",
+        "outputId": "4bd4b01e-4dbd-4e2b-c3ec-7984004c66d5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "203"
+            ]
+          },
+          "execution_count": 60,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(df[df['nationality'] != df['country']])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B9kM7gIK-Cla"
+      },
+      "source": [
+        "Какой процент мужчин моложе 23 участвовало в этой Олимпиаде?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 91,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YcEA3Ani-JHv",
+        "outputId": "1db7412c-2d7d-494a-96e8-31db4312b5a5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "12.18%\n"
+          ]
+        }
+      ],
+      "source": [
+        "from datetime import date\n",
+        "\n",
+        "df['birth_date'] = pd.to_datetime(df['birth_date'])\n",
+        "\n",
+        "today = pd.to_datetime(date.today())\n",
+        "\n",
+        "df['age'] = df['birth_date'].apply(\n",
+        "    lambda bd: today.year - bd.year - 1 if (today.month, today.day) < (bd.month, bd.day) else today.year - bd.year\n",
+        ")\n",
+        "\n",
+        "male_athletes = df[df['gender'] == 'Male']\n",
+        "young_male_athletes = male_athletes[male_athletes['age'] < 23]\n",
+        "print(f'{round(len(young_male_athletes) / len(male_athletes) * 100, 2)}%')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uK1liVxz-JVi"
+      },
+      "source": [
+        "Постройте график зависимости роста от веса у женщин (scatter plot)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 211
+        },
+        "id": "D37_Ncmj-e_2",
+        "outputId": "663e3b39-d1c1-4711-f5c2-dcc29ea97785"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Use the df DataFrame with filled weight values and filter for female athletes\n",
+        "female = df[df['gender'] == 'Female']\n",
+        "\n",
+        "plt.scatter(female['height'], female['weight'])\n",
+        "plt.xlabel('Рост (см)')\n",
+        "plt.ylabel('Вес (кг)')\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 101,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "raw = pd.read_csv('./athletes new.csv')\n",
+        "\n",
+        "female = raw[raw['gender'] == 'Female']\n",
+        "\n",
+        "plt.scatter(female['height'], female['weight'])\n",
+        "plt.xlabel('Рост (см)')\n",
+        "plt.ylabel('Вес (кг)')\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 108,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "filtered = raw[(raw['weight'] != 0) & (raw['height'] != 0)]\n",
+        "\n",
+        "female = filtered[filtered['gender'] == 'Female']\n",
+        "\n",
+        "plt.scatter(female['height'], female['weight'])\n",
+        "plt.xlabel('Рост (см)')\n",
+        "plt.ylabel('Вес (кг)')\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PE57eW19-fNM"
+      },
+      "source": [
+        "Постройте график распределения, где по оси X будет показан возраст, а по оси Y - процент спортсменов этого возраста, принявших участие в Олимпиаде для женщин и мужчин на одном графике."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 98,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "GPiewHIz_IUg",
+        "outputId": "df0ffc07-85ef-446e-e0cf-e26819daef45"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "male_athletes = df[df['gender'] == 'Male']\n",
+        "female_athletes = df[df['gender'] == 'Female']\n",
+        "\n",
+        "male_counts = male_athletes['age'].value_counts(normalize=True).sort_index() * 100\n",
+        "female_counts = female_athletes['age'].value_counts(normalize=True).sort_index() * 100\n",
+        "\n",
+        "plt.plot(male_counts.index, male_counts.values, label='Мужчины')\n",
+        "plt.plot(female_counts.index, female_counts.values, label='Женщины')\n",
+        "\n",
+        "plt.xlabel('Возраст')\n",
+        "plt.ylabel('Процент спортсменов')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9ocREq3J_IsU"
+      },
+      "source": [
+        "Постройте круговую диаграмму, чтобы отобразить долю спортсменов по пяти странам с наибольшим количеством участников."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 96,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 675
+        },
+        "id": "PkpF_i1V_dgH",
+        "outputId": "f4d544fc-8499-4d25-89b9-21959b92542c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "countries_counts = df['country'].value_counts().head(5)\n",
+        "\n",
+        "plt.pie(countries_counts, labels=countries_counts.index, autopct='%1.1f%%')\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "base",
+      "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": "3.9.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/AIS2.ipynb b/AIS2.ipynb
new file mode 100644
index 0000000..a8aaac0
--- /dev/null
+++ b/AIS2.ipynb
@@ -0,0 +1,2708 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CvmiakWDcuyf"
+      },
+      "source": [
+        "# Лабораторная работа №2\n",
+        "## по предмету \"Системы искусственного интеллекта\"\n",
+        "\n",
+        "В данной лабораторной работе вы будете работать с набором данных, который содержит информацию о технических характеристиках ноутбуков и их цену.\n",
+        "Целью работы является изучение теоретических основ методов машинного обучения.\n",
+        "\n",
+        "В наборе данных для лабораторной работы содержится абор характеристик  мобильных телефонов, включая мощность аккумулятора, характеристики камеры, поддержку сети, память, размеры экрана и другие атрибуты. Столбец «price_range» классифицирует телефоны по ценовым диапазонам (этот столбец необходимо предсказать)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YEZ0T1uwj34v"
+      },
+      "source": [
+        "### Задание 1\n",
+        "\n",
+        "Выгрузите данные из датасета. Изучите колонки, проверьте наличие пропусков. Постройте матрицу корреляции между признаками и целевой переменной. Сделайте выводы, что показывает эта матрица."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "91NHysjQj26f"
+      },
+      "outputs": [
+        {
+          "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>battery_power</th>\n",
+              "      <th>blue</th>\n",
+              "      <th>clock_speed</th>\n",
+              "      <th>dual_sim</th>\n",
+              "      <th>fc</th>\n",
+              "      <th>four_g</th>\n",
+              "      <th>int_memory</th>\n",
+              "      <th>m_dep</th>\n",
+              "      <th>mobile_wt</th>\n",
+              "      <th>n_cores</th>\n",
+              "      <th>...</th>\n",
+              "      <th>px_height</th>\n",
+              "      <th>px_width</th>\n",
+              "      <th>ram</th>\n",
+              "      <th>sc_h</th>\n",
+              "      <th>sc_w</th>\n",
+              "      <th>talk_time</th>\n",
+              "      <th>three_g</th>\n",
+              "      <th>touch_screen</th>\n",
+              "      <th>wifi</th>\n",
+              "      <th>price_range</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>842</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>188</td>\n",
+              "      <td>2</td>\n",
+              "      <td>...</td>\n",
+              "      <td>20</td>\n",
+              "      <td>756</td>\n",
+              "      <td>2549</td>\n",
+              "      <td>9</td>\n",
+              "      <td>7</td>\n",
+              "      <td>19</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1021</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>53</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>136</td>\n",
+              "      <td>3</td>\n",
+              "      <td>...</td>\n",
+              "      <td>905</td>\n",
+              "      <td>1988</td>\n",
+              "      <td>2631</td>\n",
+              "      <td>17</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>563</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>41</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>145</td>\n",
+              "      <td>5</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1263</td>\n",
+              "      <td>1716</td>\n",
+              "      <td>2603</td>\n",
+              "      <td>11</td>\n",
+              "      <td>2</td>\n",
+              "      <td>9</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>615</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>131</td>\n",
+              "      <td>6</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1216</td>\n",
+              "      <td>1786</td>\n",
+              "      <td>2769</td>\n",
+              "      <td>16</td>\n",
+              "      <td>8</td>\n",
+              "      <td>11</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1821</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13</td>\n",
+              "      <td>1</td>\n",
+              "      <td>44</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>141</td>\n",
+              "      <td>2</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1208</td>\n",
+              "      <td>1212</td>\n",
+              "      <td>1411</td>\n",
+              "      <td>8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>15</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 21 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
+              "0            842     0          2.2         0   1       0           7    0.6   \n",
+              "1           1021     1          0.5         1   0       1          53    0.7   \n",
+              "2            563     1          0.5         1   2       1          41    0.9   \n",
+              "3            615     1          2.5         0   0       0          10    0.8   \n",
+              "4           1821     1          1.2         0  13       1          44    0.6   \n",
+              "\n",
+              "   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
+              "0        188        2  ...         20       756  2549     9     7         19   \n",
+              "1        136        3  ...        905      1988  2631    17     3          7   \n",
+              "2        145        5  ...       1263      1716  2603    11     2          9   \n",
+              "3        131        6  ...       1216      1786  2769    16     8         11   \n",
+              "4        141        2  ...       1208      1212  1411     8     2         15   \n",
+              "\n",
+              "   three_g  touch_screen  wifi  price_range  \n",
+              "0        0             0     1            1  \n",
+              "1        1             1     0            2  \n",
+              "2        1             1     0            2  \n",
+              "3        1             0     0            2  \n",
+              "4        1             1     0            1  \n",
+              "\n",
+              "[5 rows x 21 columns]"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.read_csv('AIS2.csv')\n",
+        "\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "battery_power    0\n",
+              "blue             0\n",
+              "clock_speed      0\n",
+              "dual_sim         0\n",
+              "fc               0\n",
+              "four_g           0\n",
+              "int_memory       0\n",
+              "m_dep            0\n",
+              "mobile_wt        0\n",
+              "n_cores          0\n",
+              "pc               0\n",
+              "px_height        0\n",
+              "px_width         0\n",
+              "ram              0\n",
+              "sc_h             0\n",
+              "sc_w             0\n",
+              "talk_time        0\n",
+              "three_g          0\n",
+              "touch_screen     0\n",
+              "wifi             0\n",
+              "price_range      0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "battery_power    0\n",
+              "blue             0\n",
+              "clock_speed      0\n",
+              "dual_sim         0\n",
+              "fc               0\n",
+              "four_g           0\n",
+              "int_memory       0\n",
+              "m_dep            0\n",
+              "mobile_wt        0\n",
+              "n_cores          0\n",
+              "pc               0\n",
+              "px_height        0\n",
+              "px_width         0\n",
+              "ram              0\n",
+              "sc_h             0\n",
+              "sc_w             0\n",
+              "talk_time        0\n",
+              "three_g          0\n",
+              "touch_screen     0\n",
+              "wifi             0\n",
+              "price_range      0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "battery_power       0\n",
+              "blue             1010\n",
+              "clock_speed         0\n",
+              "dual_sim          981\n",
+              "fc                474\n",
+              "four_g            957\n",
+              "int_memory          0\n",
+              "m_dep               0\n",
+              "mobile_wt           0\n",
+              "n_cores             0\n",
+              "pc                101\n",
+              "px_height           2\n",
+              "px_width            0\n",
+              "ram                 0\n",
+              "sc_h                0\n",
+              "sc_w              180\n",
+              "talk_time           0\n",
+              "three_g           477\n",
+              "touch_screen      994\n",
+              "wifi              986\n",
+              "price_range       500\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display(df.isnull().sum())\n",
+        "display(df.isna().sum())\n",
+        "display(df.eq(0).sum())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "price_range      1.000000\n",
+              "ram              0.917046\n",
+              "battery_power    0.200723\n",
+              "px_width         0.165818\n",
+              "px_height        0.148858\n",
+              "int_memory       0.044435\n",
+              "sc_w             0.038711\n",
+              "pc               0.033599\n",
+              "three_g          0.023611\n",
+              "sc_h             0.022986\n",
+              "fc               0.021998\n",
+              "talk_time        0.021859\n",
+              "blue             0.020573\n",
+              "wifi             0.018785\n",
+              "dual_sim         0.017444\n",
+              "four_g           0.014772\n",
+              "n_cores          0.004399\n",
+              "m_dep            0.000853\n",
+              "clock_speed     -0.006606\n",
+              "mobile_wt       -0.030302\n",
+              "touch_screen    -0.030411\n",
+              "Name: price_range, dtype: float64"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.corr()['price_range'].sort_values(ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ch5WytHwlGpd"
+      },
+      "outputs": [
+        {
+          "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>battery_power</th>\n",
+              "      <th>blue</th>\n",
+              "      <th>clock_speed</th>\n",
+              "      <th>dual_sim</th>\n",
+              "      <th>fc</th>\n",
+              "      <th>four_g</th>\n",
+              "      <th>int_memory</th>\n",
+              "      <th>m_dep</th>\n",
+              "      <th>mobile_wt</th>\n",
+              "      <th>n_cores</th>\n",
+              "      <th>pc</th>\n",
+              "      <th>px_height</th>\n",
+              "      <th>px_width</th>\n",
+              "      <th>ram</th>\n",
+              "      <th>sc_h</th>\n",
+              "      <th>sc_w</th>\n",
+              "      <th>talk_time</th>\n",
+              "      <th>three_g</th>\n",
+              "      <th>touch_screen</th>\n",
+              "      <th>wifi</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>842</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>188</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>20</td>\n",
+              "      <td>756</td>\n",
+              "      <td>2549</td>\n",
+              "      <td>9</td>\n",
+              "      <td>7</td>\n",
+              "      <td>19</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1021</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>53</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>136</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>905</td>\n",
+              "      <td>1988</td>\n",
+              "      <td>2631</td>\n",
+              "      <td>17</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>563</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>41</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>145</td>\n",
+              "      <td>5</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1263</td>\n",
+              "      <td>1716</td>\n",
+              "      <td>2603</td>\n",
+              "      <td>11</td>\n",
+              "      <td>2</td>\n",
+              "      <td>9</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>615</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>131</td>\n",
+              "      <td>6</td>\n",
+              "      <td>9</td>\n",
+              "      <td>1216</td>\n",
+              "      <td>1786</td>\n",
+              "      <td>2769</td>\n",
+              "      <td>16</td>\n",
+              "      <td>8</td>\n",
+              "      <td>11</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1821</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13</td>\n",
+              "      <td>1</td>\n",
+              "      <td>44</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>141</td>\n",
+              "      <td>2</td>\n",
+              "      <td>14</td>\n",
+              "      <td>1208</td>\n",
+              "      <td>1212</td>\n",
+              "      <td>1411</td>\n",
+              "      <td>8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>15</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
+              "0            842     0          2.2         0   1       0           7    0.6   \n",
+              "1           1021     1          0.5         1   0       1          53    0.7   \n",
+              "2            563     1          0.5         1   2       1          41    0.9   \n",
+              "3            615     1          2.5         0   0       0          10    0.8   \n",
+              "4           1821     1          1.2         0  13       1          44    0.6   \n",
+              "\n",
+              "   mobile_wt  n_cores  pc  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
+              "0        188        2   2         20       756  2549     9     7         19   \n",
+              "1        136        3   6        905      1988  2631    17     3          7   \n",
+              "2        145        5   6       1263      1716  2603    11     2          9   \n",
+              "3        131        6   9       1216      1786  2769    16     8         11   \n",
+              "4        141        2  14       1208      1212  1411     8     2         15   \n",
+              "\n",
+              "   three_g  touch_screen  wifi  \n",
+              "0        0             0     1  \n",
+              "1        1             1     0  \n",
+              "2        1             1     0  \n",
+              "3        1             0     0  \n",
+              "4        1             1     0  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "array([[ 1.38919326,  1.011314  , -1.25918898, ..., -1.78870765,\n",
+              "        -1.00878862, -1.02532046],\n",
+              "       [ 0.07840603,  1.011314  , -1.25918898, ..., -1.78870765,\n",
+              "        -1.00878862,  0.97530483],\n",
+              "       [-1.02457347, -0.98881258,  1.18747125, ...,  0.55906285,\n",
+              "         0.99128795,  0.97530483],\n",
+              "       ...,\n",
+              "       [ 1.10374308, -0.98881258,  0.5758062 , ...,  0.55906285,\n",
+              "         0.99128795,  0.97530483],\n",
+              "       [-1.21867959,  1.011314  ,  1.43213728, ...,  0.55906285,\n",
+              "        -1.00878862, -1.02532046],\n",
+              "       [-0.39429947, -0.98881258,  1.6768033 , ...,  0.55906285,\n",
+              "        -1.00878862, -1.02532046]], shape=(1600, 20))"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "\n",
+        "X = df.drop('price_range', axis=1)\n",
+        "y = df['price_range']\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X, y, test_size=0.2, random_state=42, stratify=y\n",
+        ")\n",
+        "\n",
+        "display(X.head())\n",
+        "\n",
+        "scaler = StandardScaler() # С minmax хуже (x - mean) / std\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)\n",
+        "\n",
+        "display(X_train_scaled)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Задание 2\n",
+        "\n",
+        "Реализуйте с алгоритм логистической регрессии для многоклассовой классификации."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-7 {\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-7 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7 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-7 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content {\n",
+              "  display: none;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 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-7 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 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-7 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-7 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-7 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-7 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-7 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-7 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-7 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-7 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-7 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-7 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-7 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-7 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-7 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 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-7 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-7 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-7 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-7 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    font-family: monospace;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table td {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value pre {\n",
+              "    color:rgb(255, 94, 0) !important;\n",
+              "    background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".default td {\n",
+              "    color: black;\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=42)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('penalty',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">penalty&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;l2&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('dual',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">dual&nbsp;</td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('tol',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">tol&nbsp;</td>\n",
+              "            <td class=\"value\">0.0001</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('C',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">C&nbsp;</td>\n",
+              "            <td class=\"value\">1.0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('fit_intercept',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('intercept_scaling',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">intercept_scaling&nbsp;</td>\n",
+              "            <td class=\"value\">1</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('class_weight',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">class_weight&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('random_state',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">random_state&nbsp;</td>\n",
+              "            <td class=\"value\">42</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('solver',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">solver&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_iter',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_iter&nbsp;</td>\n",
+              "            <td class=\"value\">1000</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('multi_class',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">multi_class&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('verbose',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">verbose&nbsp;</td>\n",
+              "            <td class=\"value\">0</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('warm_start',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">warm_start&nbsp;</td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_jobs',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('l1_ratio',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "</script></body>"
+            ],
+            "text/plain": [
+              "LogisticRegression(max_iter=1000, random_state=42)"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "\n",
+        "model = LogisticRegression(\n",
+        "    max_iter=1000,\n",
+        "    random_state=42\n",
+        ")\n",
+        "\n",
+        "model.fit(X_train_scaled, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "nwDuPoSHlKDP"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Точность модели: 0.965\n",
+            "[[98  2  0  0]\n",
+            " [ 1 96  3  0]\n",
+            " [ 0  2 94  4]\n",
+            " [ 0  0  2 98]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+        "\n",
+        "y_pred = model.predict(X_test_scaled)\n",
+        "\n",
+        "print(\"Точность модели:\", accuracy_score(y_test, y_pred))\n",
+        "print(confusion_matrix(y_test, y_pred))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eWc9D163lKPB"
+      },
+      "source": [
+        "### Задание 3\n",
+        "\n",
+        "Реализуйте алгоритм Наивный Байес."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "LRuuj9PDli5A"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-5 {\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-5 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content {\n",
+              "  display: none;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-5 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 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-5 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-5 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-5 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-5 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    font-family: monospace;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table td {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value pre {\n",
+              "    color:rgb(255, 94, 0) !important;\n",
+              "    background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".default td {\n",
+              "    color: black;\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GaussianNB</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('priors',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">priors&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('var_smoothing',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">var_smoothing&nbsp;</td>\n",
+              "            <td class=\"value\">1e-09</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "</script></body>"
+            ],
+            "text/plain": [
+              "GaussianNB()"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.naive_bayes import GaussianNB\n",
+        "\n",
+        "model = GaussianNB()\n",
+        "\n",
+        "model.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "amg6aqULlovg"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Точность модели: 0.8100\n",
+            "[[90 10  0  0]\n",
+            " [ 7 69 24  0]\n",
+            " [ 0 18 73  9]\n",
+            " [ 0  0  8 92]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = model.predict(X_test)\n",
+        "\n",
+        "accuracy = accuracy_score(y_test, y_pred)\n",
+        "print(f\"Точность модели: {accuracy:.4f}\")\n",
+        "\n",
+        "print(confusion_matrix(y_test, y_pred))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RYFhErkHlmFV"
+      },
+      "source": [
+        "### Задание 4\n",
+        "\n",
+        "Реализуйте алгоритм kNN."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "fUQ70y9Plq9u"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-6 {\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-6 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content {\n",
+              "  display: none;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-6 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-6 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-6 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-6 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-6 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-6 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-6 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-6 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 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-6 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-6 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-6 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-6 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    font-family: monospace;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table td {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value pre {\n",
+              "    color:rgb(255, 94, 0) !important;\n",
+              "    background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".default td {\n",
+              "    color: black;\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=16)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_neighbors',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_neighbors&nbsp;</td>\n",
+              "            <td class=\"value\">16</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('weights',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">weights&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('algorithm',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">algorithm&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('leaf_size',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">leaf_size&nbsp;</td>\n",
+              "            <td class=\"value\">30</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('p',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">p&nbsp;</td>\n",
+              "            <td class=\"value\">2</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('metric',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">metric&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('metric_params',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">metric_params&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_jobs',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "</script></body>"
+            ],
+            "text/plain": [
+              "KNeighborsClassifier(n_neighbors=16)"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "\n",
+        "knn = KNeighborsClassifier(n_neighbors=16)\n",
+        "knn.fit(X_train_scaled, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Accuracy: 0.56\n",
+            "[[72 28  0  0]\n",
+            " [31 44 25  0]\n",
+            " [ 5 27 47 21]\n",
+            " [ 0  5 34 61]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = knn.predict(X_test_scaled)\n",
+        "\n",
+        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
+        "print(confusion_matrix(y_test, y_pred))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ssFzfn1Pl4AI"
+      },
+      "source": [
+        "### Задание 5\n",
+        "\n",
+        "Сделайте выводы о результатах обучения."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Логистическая регрессия хорошо подходит для линейно разделимых данных, что, вероятно, имело место в данном случае. Наивный Байес хуже справился с задачей из-за нарушения предположения о независимости признаков. Например, такие параметры, как оперативная память и размер экрана, могут быть коррелированы, что снижает качество прогноза. Метод KNN, вероятно, не подходит для данной задачи из-за большой разности в корреляции цены в зависимости от параметра."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/AIS3.ipynb b/AIS3.ipynb
new file mode 100644
index 0000000..88eaf53
--- /dev/null
+++ b/AIS3.ipynb
@@ -0,0 +1,2719 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CvmiakWDcuyf"
+      },
+      "source": [
+        "# Лабораторная работа №3\n",
+        "## по предмету \"Системы искусственного интеллекта\"\n",
+        "\n",
+        "Целью работы является изучение методов регуляризации.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YEZ0T1uwj34v"
+      },
+      "source": [
+        "### Задание 1\n",
+        "\n",
+        "Выгрузите данные из датасета. Изучите колонки, проверьте наличие пропусков. Постройте матрицу корреляции между признаками и целевой переменной. Сделайте выводы, что показывает эта матрица."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 128,
+      "metadata": {
+        "id": "91NHysjQj26f"
+      },
+      "outputs": [
+        {
+          "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>brand</th>\n",
+              "      <th>processor_brand</th>\n",
+              "      <th>processor_name</th>\n",
+              "      <th>processor_gnrtn</th>\n",
+              "      <th>ram_gb</th>\n",
+              "      <th>ram_type</th>\n",
+              "      <th>ssd</th>\n",
+              "      <th>hdd</th>\n",
+              "      <th>os</th>\n",
+              "      <th>os_bit</th>\n",
+              "      <th>graphic_card_gb</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>warranty</th>\n",
+              "      <th>Touchscreen</th>\n",
+              "      <th>msoffice</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>rating</th>\n",
+              "      <th>Number of Ratings</th>\n",
+              "      <th>Number of Reviews</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ASUS</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i3</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>1024 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>34649</td>\n",
+              "      <td>2 stars</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Lenovo</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i3</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>1024 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>38999</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>65</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Lenovo</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i3</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>1024 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>39999</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ASUS</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i5</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>8 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>512 GB</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>32-bit</td>\n",
+              "      <td>2 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>69990</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ASUS</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Celeron Dual</td>\n",
+              "      <td>Not Available</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>512 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>26990</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    brand processor_brand processor_name processor_gnrtn ram_gb ram_type  \\\n",
+              "0    ASUS           Intel        Core i3            10th   4 GB     DDR4   \n",
+              "1  Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
+              "2  Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
+              "3    ASUS           Intel        Core i5            10th   8 GB     DDR4   \n",
+              "4    ASUS           Intel   Celeron Dual   Not Available   4 GB     DDR4   \n",
+              "\n",
+              "      ssd      hdd       os  os_bit graphic_card_gb  weight     warranty  \\\n",
+              "0    0 GB  1024 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "1    0 GB  1024 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "2    0 GB  1024 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "3  512 GB     0 GB  Windows  32-bit            2 GB  Casual  No warranty   \n",
+              "4    0 GB   512 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "\n",
+              "  Touchscreen msoffice  Price   rating  Number of Ratings  Number of Reviews  \n",
+              "0          No       No  34649  2 stars                  3                  0  \n",
+              "1          No       No  38999  3 stars                 65                  5  \n",
+              "2          No       No  39999  3 stars                  8                  1  \n",
+              "3          No       No  69990  3 stars                  0                  0  \n",
+              "4          No       No  26990  3 stars                  0                  0  "
+            ]
+          },
+          "execution_count": 128,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.read_csv('AISP2.csv')\n",
+        "\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 118,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "brand                0\n",
+              "processor_brand      0\n",
+              "processor_name       0\n",
+              "processor_gnrtn      0\n",
+              "ram_gb               0\n",
+              "ram_type             0\n",
+              "ssd                  0\n",
+              "hdd                  0\n",
+              "os                   0\n",
+              "os_bit               0\n",
+              "graphic_card_gb      0\n",
+              "weight               0\n",
+              "warranty             0\n",
+              "Touchscreen          0\n",
+              "msoffice             0\n",
+              "Price                0\n",
+              "rating               0\n",
+              "Number of Ratings    0\n",
+              "Number of Reviews    0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "brand                0\n",
+              "processor_brand      0\n",
+              "processor_name       0\n",
+              "processor_gnrtn      0\n",
+              "ram_gb               0\n",
+              "ram_type             0\n",
+              "ssd                  0\n",
+              "hdd                  0\n",
+              "os                   0\n",
+              "os_bit               0\n",
+              "graphic_card_gb      0\n",
+              "weight               0\n",
+              "warranty             0\n",
+              "Touchscreen          0\n",
+              "msoffice             0\n",
+              "Price                0\n",
+              "rating               0\n",
+              "Number of Ratings    0\n",
+              "Number of Reviews    0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display(df.isnull().sum())\n",
+        "display(df.isna().sum())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 119,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Price                            1.000000\n",
+              "ssd                              0.628272\n",
+              "ram_gb                           0.518323\n",
+              "graphic_card_gb                  0.459986\n",
+              "processor_name_Core i7           0.377777\n",
+              "processor_name_Core i9           0.359096\n",
+              "brand_APPLE                      0.312112\n",
+              "os_Mac                           0.312112\n",
+              "processor_name_M1                0.274581\n",
+              "processor_brand_M1               0.274581\n",
+              "processor_name_Ryzen 9           0.253506\n",
+              "weight_Casual                    0.247878\n",
+              "processor_gnrtn_12th             0.219060\n",
+              "Touchscreen_Yes                  0.189126\n",
+              "ram_type_LPDDR3                  0.181314\n",
+              "ram_type_LPDDR4X                 0.173809\n",
+              "ram_type_DDR5                    0.168689\n",
+              "processor_gnrtn_10th             0.164034\n",
+              "os_DOS                           0.140780\n",
+              "brand_MSI                        0.123952\n",
+              "msoffice_No                      0.105752\n",
+              "warranty_3 years                 0.080610\n",
+              "processor_name_Ryzen 7           0.061872\n",
+              "ram_type_DDR3                    0.042006\n",
+              "processor_gnrtn_8th              0.040292\n",
+              "warranty_1 year                  0.033312\n",
+              "brand_ASUS                       0.032036\n",
+              "ram_type_LPDDR4                  0.028034\n",
+              "processor_gnrtn_9th              0.021192\n",
+              "os_bit_32-bit                    0.018458\n",
+              "processor_brand_AMD             -0.001583\n",
+              "weight_Gaming                   -0.012524\n",
+              "os_bit_64-bit                   -0.018458\n",
+              "processor_gnrtn_4th             -0.018769\n",
+              "processor_name_Core i5          -0.023218\n",
+              "brand_acer                      -0.024663\n",
+              "warranty_2 years                -0.029339\n",
+              "brand_HP                        -0.030649\n",
+              "rating                          -0.033528\n",
+              "brand_Avita                     -0.033819\n",
+              "brand_Lenovo                    -0.039079\n",
+              "warranty_No warranty            -0.045241\n",
+              "processor_gnrtn_7th             -0.045656\n",
+              "processor_gnrtn_11th            -0.085683\n",
+              "processor_brand_Intel           -0.103966\n",
+              "processor_gnrtn_Not Available   -0.105722\n",
+              "msoffice_Yes                    -0.105752\n",
+              "processor_name_Pentium Quad     -0.111755\n",
+              "processor_name_Ryzen 5          -0.114138\n",
+              "Number of Ratings               -0.140392\n",
+              "Number of Reviews               -0.148738\n",
+              "processor_name_Ryzen 3          -0.150211\n",
+              "brand_DELL                      -0.166272\n",
+              "Touchscreen_No                  -0.189126\n",
+              "processor_name_Celeron Dual     -0.200490\n",
+              "weight_ThinNlight               -0.250425\n",
+              "hdd                             -0.252699\n",
+              "ram_type_DDR4                   -0.270184\n",
+              "os_Windows                      -0.337929\n",
+              "processor_name_Core i3          -0.377232\n",
+              "Name: Price, dtype: float64"
+            ]
+          },
+          "execution_count": 119,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df['ram_gb'] = df['ram_gb'].str.replace(' GB', '').astype(float)\n",
+        "df['ssd'] = df['ssd'].str.replace(' GB', '').astype(float)\n",
+        "df['hdd'] = df['hdd'].str.replace(' GB', '').astype(float)\n",
+        "df['graphic_card_gb'] = df['graphic_card_gb'].str.replace(' GB', '').astype(float)\n",
+        "df['rating'] = df['rating'].str.replace(' stars', '').str.replace(' star', '').astype(float)\n",
+        "\n",
+        "df = pd.get_dummies(df, \n",
+        "    columns=['brand', 'processor_brand', 'processor_name', 'ram_type', \n",
+        "             'os', 'os_bit', 'Touchscreen', 'msoffice', 'warranty', 'processor_gnrtn', 'weight'])\n",
+        "\n",
+        "df.corr(numeric_only=True)['Price'].sort_values(ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 120,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "ram_gb                           float64\n",
+              "ssd                              float64\n",
+              "hdd                              float64\n",
+              "graphic_card_gb                  float64\n",
+              "Price                              int64\n",
+              "rating                           float64\n",
+              "Number of Ratings                  int64\n",
+              "Number of Reviews                  int64\n",
+              "brand_APPLE                         bool\n",
+              "brand_ASUS                          bool\n",
+              "brand_Avita                         bool\n",
+              "brand_DELL                          bool\n",
+              "brand_HP                            bool\n",
+              "brand_Lenovo                        bool\n",
+              "brand_MSI                           bool\n",
+              "brand_acer                          bool\n",
+              "processor_brand_AMD                 bool\n",
+              "processor_brand_Intel               bool\n",
+              "processor_brand_M1                  bool\n",
+              "processor_name_Celeron Dual         bool\n",
+              "processor_name_Core i3              bool\n",
+              "processor_name_Core i5              bool\n",
+              "processor_name_Core i7              bool\n",
+              "processor_name_Core i9              bool\n",
+              "processor_name_M1                   bool\n",
+              "processor_name_Pentium Quad         bool\n",
+              "processor_name_Ryzen 3              bool\n",
+              "processor_name_Ryzen 5              bool\n",
+              "processor_name_Ryzen 7              bool\n",
+              "processor_name_Ryzen 9              bool\n",
+              "ram_type_DDR3                       bool\n",
+              "ram_type_DDR4                       bool\n",
+              "ram_type_DDR5                       bool\n",
+              "ram_type_LPDDR3                     bool\n",
+              "ram_type_LPDDR4                     bool\n",
+              "ram_type_LPDDR4X                    bool\n",
+              "os_DOS                              bool\n",
+              "os_Mac                              bool\n",
+              "os_Windows                          bool\n",
+              "os_bit_32-bit                       bool\n",
+              "os_bit_64-bit                       bool\n",
+              "Touchscreen_No                      bool\n",
+              "Touchscreen_Yes                     bool\n",
+              "msoffice_No                         bool\n",
+              "msoffice_Yes                        bool\n",
+              "warranty_1 year                     bool\n",
+              "warranty_2 years                    bool\n",
+              "warranty_3 years                    bool\n",
+              "warranty_No warranty                bool\n",
+              "processor_gnrtn_10th                bool\n",
+              "processor_gnrtn_11th                bool\n",
+              "processor_gnrtn_12th                bool\n",
+              "processor_gnrtn_4th                 bool\n",
+              "processor_gnrtn_7th                 bool\n",
+              "processor_gnrtn_8th                 bool\n",
+              "processor_gnrtn_9th                 bool\n",
+              "processor_gnrtn_Not Available       bool\n",
+              "weight_Casual                       bool\n",
+              "weight_Gaming                       bool\n",
+              "weight_ThinNlight                   bool\n",
+              "dtype: object"
+            ]
+          },
+          "execution_count": 120,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.dtypes\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 121,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "y = df['Price']\n",
+        "\n",
+        "X = df.drop('Price', axis=1)\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hgqsngyck7xl"
+      },
+      "source": [
+        "### Задание 2\n",
+        "\n",
+        "Реализуйте алгоритм линеной регрессии  без использования регуляризации."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 122,
+      "metadata": {
+        "id": "Ch5WytHwlGpd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-21 {\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-21 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-21 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-21 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-21 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-21 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-21 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-21 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-21 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-21 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-21 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-21 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-21 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-21 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-21 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-21 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-21 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-21 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-21 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-21 div.sk-toggleable__content {\n",
+              "  display: none;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-21 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-21 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-21 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-21 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-21 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-21 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-21 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-21 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-21 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-21 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-21 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-21 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-21 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-21 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-21 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-21 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-21 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-21 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-21 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-21 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-21 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-21 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    font-family: monospace;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table td {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value pre {\n",
+              "    color:rgb(255, 94, 0) !important;\n",
+              "    background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".default td {\n",
+              "    color: black;\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" checked><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('fit_intercept',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('copy_X',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">copy_X&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('tol',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">tol&nbsp;</td>\n",
+              "            <td class=\"value\">1e-06</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_jobs',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('positive',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">positive&nbsp;</td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "</script></body>"
+            ],
+            "text/plain": [
+              "LinearRegression()"
+            ]
+          },
+          "execution_count": 122,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "model = LinearRegression()\n",
+        "model.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 123,
+      "metadata": {
+        "id": "nwDuPoSHlKDP"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MSE: 638671150.10\n",
+            "RMSE: 25271.94\n",
+            "R²: 0.6723\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "from sklearn.metrics import mean_squared_error, r2_score\n",
+        "\n",
+        "y_pred = model.predict(X_test)\n",
+        "\n",
+        "mse = mean_squared_error(y_test, y_pred)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(f'MSE: {mse:.2f}')\n",
+        "print(f\"RMSE: {np.sqrt(mse):.2f}\") # рублей\n",
+        "print(f'R²: {r2:.4f}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eWc9D163lKPB"
+      },
+      "source": [
+        "### Задание 3\n",
+        "\n",
+        "Реализуйте алгоритм линейной регрессии с L1-регуляризацией."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 124,
+      "metadata": {
+        "id": "LRuuj9PDli5A"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-22 {\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-22 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-22 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-22 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-22 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-22 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-22 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-22 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-22 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-22 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-22 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-22 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-22 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-22 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-22 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-22 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-22 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-22 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-22 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-22 div.sk-toggleable__content {\n",
+              "  display: none;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-22 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-22 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-22 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-22 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-22 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-22 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-22 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-22 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-22 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-22 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-22 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-22 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-22 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-22 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-22 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-22 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-22 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-22 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-22 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-22 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-22 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-22 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-22 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    font-family: monospace;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table td {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value pre {\n",
+              "    color:rgb(255, 94, 0) !important;\n",
+              "    background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".default td {\n",
+              "    color: black;\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-22\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;lasso&#x27;, Lasso(alpha=100, random_state=42))])</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-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('steps',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">steps&nbsp;</td>\n",
+              "            <td class=\"value\">[(&#x27;standardscaler&#x27;, ...), (&#x27;lasso&#x27;, ...)]</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('transform_input',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">transform_input&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('memory',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">memory&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('verbose',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">verbose&nbsp;</td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </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-57\" type=\"checkbox\" ><label for=\"sk-estimator-id-57\" 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/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"standardscaler__\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('copy',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">copy&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('with_mean',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">with_mean&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('with_std',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">with_std&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </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-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" 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/1.7/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"lasso__\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('alpha',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">alpha&nbsp;</td>\n",
+              "            <td class=\"value\">100</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('fit_intercept',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('precompute',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">precompute&nbsp;</td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('copy_X',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">copy_X&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_iter',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_iter&nbsp;</td>\n",
+              "            <td class=\"value\">1000</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('tol',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">tol&nbsp;</td>\n",
+              "            <td class=\"value\">0.0001</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('warm_start',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">warm_start&nbsp;</td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('positive',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">positive&nbsp;</td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('random_state',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">random_state&nbsp;</td>\n",
+              "            <td class=\"value\">42</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('selection',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">selection&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;cyclic&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "</script></body>"
+            ],
+            "text/plain": [
+              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
+              "                ('lasso', Lasso(alpha=100, random_state=42))])"
+            ]
+          },
+          "execution_count": 124,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import Lasso\n",
+        "from sklearn.pipeline import make_pipeline\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "\n",
+        "lasso = make_pipeline(StandardScaler(), Lasso(alpha=100, random_state=42))\n",
+        "lasso.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 125,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MSE: 608702817.47\n",
+            "RMSE: 24671.90\n",
+            "R²: 0.6877\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = lasso.predict(X_test)\n",
+        "mse = mean_squared_error(y_test, y_pred)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(f\"MSE: {mse:.2f}\")\n",
+        "print(f\"RMSE: {np.sqrt(mse):.2f}\")\n",
+        "print(f\"R²: {r2:.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RYFhErkHlmFV"
+      },
+      "source": [
+        "### Задание 4\n",
+        "\n",
+        "Реализуйте алгоритм линейной регрессии с L2-регуляризацией."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 126,
+      "metadata": {
+        "id": "fUQ70y9Plq9u"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-23 {\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-23 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-23 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-23 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-23 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-23 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-23 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-23 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-23 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-23 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-23 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-23 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-23 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-23 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-23 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-23 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-23 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-23 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-23 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-23 div.sk-toggleable__content {\n",
+              "  display: none;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-23 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-23 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-23 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-23 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-23 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-23 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-23 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-23 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-23 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-23 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-23 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-23 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-23 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-23 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-23 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-23 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-23 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-23 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-23 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-23 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-23 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-23 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    font-family: monospace;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table td {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value pre {\n",
+              "    color:rgb(255, 94, 0) !important;\n",
+              "    background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".default td {\n",
+              "    color: black;\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, Ridge(alpha=100, random_state=42))])</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-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('steps',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">steps&nbsp;</td>\n",
+              "            <td class=\"value\">[(&#x27;standardscaler&#x27;, ...), (&#x27;ridge&#x27;, ...)]</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('transform_input',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">transform_input&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('memory',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">memory&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('verbose',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">verbose&nbsp;</td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </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-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" 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/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"standardscaler__\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('copy',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">copy&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('with_mean',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">with_mean&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('with_std',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">with_std&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </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-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" 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/1.7/modules/generated/sklearn.linear_model.Ridge.html\">?<span>Documentation for Ridge</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"ridge__\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('alpha',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">alpha&nbsp;</td>\n",
+              "            <td class=\"value\">100</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('fit_intercept',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('copy_X',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">copy_X&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_iter',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_iter&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('tol',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">tol&nbsp;</td>\n",
+              "            <td class=\"value\">0.0001</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('solver',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">solver&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('positive',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">positive&nbsp;</td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('random_state',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">random_state&nbsp;</td>\n",
+              "            <td class=\"value\">42</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "</script></body>"
+            ],
+            "text/plain": [
+              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
+              "                ('ridge', Ridge(alpha=100, random_state=42))])"
+            ]
+          },
+          "execution_count": 126,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import Ridge\n",
+        "\n",
+        "ridge = make_pipeline(StandardScaler(), Ridge(alpha=100, random_state=42))\n",
+        "ridge.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 127,
+      "metadata": {
+        "id": "78PA6hmwl-1p"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MSE: 576791993.95\n",
+            "RMSE: 24016.49\n",
+            "R²: 0.7040\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_pred = ridge.predict(X_test)\n",
+        "\n",
+        "mse = mean_squared_error(y_test, y_pred)\n",
+        "rmse = np.sqrt(mse)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(f\"MSE: {mse:.2f}\")\n",
+        "print(f\"RMSE: {rmse:.2f}\")\n",
+        "print(f\"R²: {r2:.4f}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ssFzfn1Pl4AI"
+      },
+      "source": [
+        "### Задание 5\n",
+        "\n",
+        "Сделайте выводы о результатах обучения."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.12.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/AIS4.ipynb b/AIS4.ipynb
new file mode 100644
index 0000000..2bf1a2a
--- /dev/null
+++ b/AIS4.ipynb
@@ -0,0 +1,22573 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CvmiakWDcuyf"
+      },
+      "source": [
+        "# Лабораторная работа №4\n",
+        "## по предмету \"Системы искусственного интеллекта\"\n",
+        "\n",
+        "Целью работы является введение и знакомство с нейронными сетями.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YEZ0T1uwj34v"
+      },
+      "source": [
+        "### Задание 1\n",
+        "\n",
+        "Загрузите набор данных MNIST.\n",
+        "Проверьте баланс классов, нормализуйте данные."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "91NHysjQj26f"
+      },
+      "outputs": [],
+      "source": [
+        "from PIL import Image\n",
+        "import os\n",
+        "import numpy as np\n",
+        "\n",
+        "def load_dataset(path):\n",
+        "    if not os.path.isdir(path):\n",
+        "        raise Exception('Invalid path')\n",
+        "    print(f'Loading data from {path}')\n",
+        "    \n",
+        "    x = []\n",
+        "    y = []\n",
+        "\n",
+        "    for dir in os.listdir(path):\n",
+        "        for image in os.listdir(os.path.join(path, dir)):\n",
+        "            image = os.path.join(path, dir, image)\n",
+        "            print(f'{image}')\n",
+        "            img = Image.open(image).convert(\"L\")\n",
+        "            x.append(list(img.getdata()))\n",
+        "            y.append(dir)\n",
+        "\n",
+        "    return [x, np.array(y).astype(int)]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loading data from ./MNIST Dataset JPG format/MNIST - JPG - training\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\58076.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\59887.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\54451.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\10529.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\52030.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\14984.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\55159.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\59668.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\10810.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\39927.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\25046.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\18382.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\45625.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\23944.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\40528.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\53154.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\55780.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\3355.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\14442.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\3000.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\51574.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\49925.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\43693.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\13362.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\53166.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\54509.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\13032.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\25664.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\14735.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\58680.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\55600.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\58369.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\24643.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\38137.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\36481.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\44641.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\51012.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\29012.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\20254.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\21646.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\17551.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\30099.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\6345.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\35694.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\26612.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\26349.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\14281.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\52236.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\31573.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\49143.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\1683.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\7056.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - training\\9\\31131.jpg\n",
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+            "./MNIST Dataset JPG format/MNIST - JPG - training\\6\\42409.jpg\n"
+          ]
+        },
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13644\\248954580.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0mx_train_raw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'./MNIST Dataset JPG format/MNIST - JPG - training'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13644\\1429168712.py\u001b[0m in \u001b[0;36mload_dataset\u001b[1;34m(path)\u001b[0m\n\u001b[0;32m     15\u001b[0m             \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{image}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m             \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"L\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetdata\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m             \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(fp, mode, formats)\u001b[0m\n\u001b[0;32m   3090\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3091\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3092\u001b[1;33m         \u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3093\u001b[0m         \u001b[0mexclusive_fp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3094\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+            "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ],
+      "source": [
+        "[x_train_raw, y_train] = load_dataset('./MNIST Dataset JPG format/MNIST - JPG - training')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 85,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loading data from ./MNIST Dataset JPG format/MNIST - JPG - testing\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\10.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1001.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1009.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\101.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1034.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1047.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1061.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1084.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1094.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1121.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1148.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1154.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1176.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1188.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1191.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1195.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1197.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1203.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1218.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1220.jpg\n",
+            "./MNIST Dataset JPG format/MNIST - JPG - testing\\0\\1223.jpg\n",
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+      ],
+      "source": [
+        "[x_test_raw, y_test] = load_dataset('./MNIST Dataset JPG format/MNIST - JPG - testing')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 86,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from collections import Counter\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "train_counts = Counter(y_train)\n",
+        "test_counts = Counter(y_test)\n",
+        "\n",
+        "classes = sorted(set(train_counts.keys()) | set(test_counts.keys()))\n",
+        "\n",
+        "train_vals = [train_counts.get(c, 0) for c in classes]\n",
+        "test_vals = [test_counts.get(c, 0) for c in classes]\n",
+        "\n",
+        "x = range(len(classes))\n",
+        "\n",
+        "plt.figure(figsize=(10,5))\n",
+        "plt.bar([i - 0.2 for i in x], train_vals, width=0.4, label=\"train\")\n",
+        "plt.bar([i + 0.2 for i in x], test_vals, width=0.4, label=\"test\")\n",
+        "\n",
+        "plt.xticks(x, classes, rotation=45)\n",
+        "plt.ylabel(\"Количество образцов\")\n",
+        "plt.title(\"Баланс классов в train и test\")\n",
+        "plt.legend()\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 87,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "x_train = np.array(x_train_raw, dtype=np.float32) / 255.0\n",
+        "x_test = np.array(x_test_raw, dtype=np.float32) / 255.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hgqsngyck7xl"
+      },
+      "source": [
+        "### Задание 2\n",
+        "\n",
+        "Реализуйте однослойный перцептрон с применением алгоритма градиентного спуска для обучения."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "class LogisticRegression:\n",
+        "    def __init__(self, input_size=784, output_size=10):\n",
+        "        self.W = np.random.randn(input_size, output_size) * 0.01\n",
+        "        self.b = np.zeros((1, output_size))\n",
+        "    \n",
+        "    def forward(self, X):\n",
+        "        Z = np.dot(X, self.W) + self.b\n",
+        "        return self.softmax(Z)\n",
+        "    \n",
+        "    def softmax(self, Z):\n",
+        "        exp_Z = np.exp(Z - np.max(Z, axis=1, keepdims=True))\n",
+        "        return exp_Z / np.sum(exp_Z, axis=1, keepdims=True)\n",
+        "    \n",
+        "    def compute_loss(self, y_true, y_pred):\n",
+        "        m = y_true.shape[0]\n",
+        "        loss = -np.sum(y_true * np.log(y_pred + 1e-8)) / m\n",
+        "        return loss\n",
+        "    \n",
+        "    def train(self, X, Y, lr=0.1, epochs=10, batch_size=32):\n",
+        "        X = np.array(X)\n",
+        "        Y = np.array(Y).astype(int)\n",
+        "        \n",
+        "        for epoch in range(epochs):\n",
+        "            total_loss = 0\n",
+        "            num_batches = 0\n",
+        "            \n",
+        "            # Перемешиваем данные каждый эпох\n",
+        "            indices = np.random.permutation(len(X))\n",
+        "            X_shuffled = X[indices]\n",
+        "            Y_shuffled = Y[indices]\n",
+        "            \n",
+        "            for i in range(0, len(X), batch_size):\n",
+        "                end_idx = min(i + batch_size, len(X))\n",
+        "                x_batch = X_shuffled[i:end_idx]\n",
+        "                y_batch = Y_shuffled[i:end_idx]\n",
+        "                \n",
+        "                y_true = np.zeros((len(x_batch), 10))\n",
+        "                y_true[np.arange(len(x_batch)), y_batch] = 1\n",
+        "                \n",
+        "                y_pred = self.forward(x_batch)\n",
+        "                \n",
+        "                loss = self.compute_loss(y_true, y_pred)\n",
+        "                total_loss += loss\n",
+        "                num_batches += 1\n",
+        "                \n",
+        "                dZ = y_pred - y_true\n",
+        "                dW = np.dot(x_batch.T, dZ) / x_batch.shape[0]\n",
+        "                db = np.sum(dZ, axis=0, keepdims=True) / x_batch.shape[0]\n",
+        "                \n",
+        "                self.W -= lr * dW\n",
+        "                self.b -= lr * db\n",
+        "            \n",
+        "            avg_loss = total_loss / num_batches\n",
+        "            print(f\"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}\")\n",
+        "    \n",
+        "    def predict(self, X):\n",
+        "        if X.ndim == 1:\n",
+        "            X = X.reshape(1, -1)\n",
+        "        y_pred = self.forward(X)\n",
+        "        return np.argmax(y_pred, axis=1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 89,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/10, Loss: 0.4154\n",
+            "Epoch 2/10, Loss: 0.3138\n",
+            "Epoch 3/10, Loss: 0.2968\n",
+            "Epoch 4/10, Loss: 0.2870\n",
+            "Epoch 5/10, Loss: 0.2811\n",
+            "Epoch 6/10, Loss: 0.2769\n",
+            "Epoch 7/10, Loss: 0.2732\n",
+            "Epoch 8/10, Loss: 0.2706\n",
+            "Epoch 9/10, Loss: 0.2680\n",
+            "Epoch 10/10, Loss: 0.2659\n"
+          ]
+        }
+      ],
+      "source": [
+        "lr = LogisticRegression()\n",
+        "\n",
+        "lr.train(x_train, y_train)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 90,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0.9259\n"
+          ]
+        }
+      ],
+      "source": [
+        "pred = lr.predict(x_test)\n",
+        "\n",
+        "ctr = 0\n",
+        "\n",
+        "for i, label in enumerate(y_test):\n",
+        "    label = int(label)\n",
+        "    if label == pred[i]:\n",
+        "        ctr += 1\n",
+        "\n",
+        "print(ctr/len(y_test))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "og_HzsA2lHyX"
+      },
+      "source": [
+        "### Задание 3\n",
+        "\n",
+        "Изучите документацию библиотеки PyTorch, посмотрите, как создаются сверточные, полносвязные слои и слои пуллинга. Напишите свою нейронную сеть, поэксперементируйте со слоями. Обучите ее на основе набора данных MNIST."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 102,
+      "metadata": {
+        "id": "nwDuPoSHlKDP"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "from torch.utils.data import DataLoader, TensorDataset\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "class SingleLayerPerceptron(nn.Module):\n",
+        "    def __init__(self, input_size, output_size):\n",
+        "        super(SingleLayerPerceptron, self).__init__()\n",
+        "        self.linear = nn.Linear(input_size, output_size)\n",
+        "    \n",
+        "    def forward(self, x):\n",
+        "        return self.linear(x)\n",
+        "\n",
+        "def train_and_evaluate(model, x_train, y_train, x_test, y_test, epochs=20, lr=0.01):\n",
+        "    train_dataset = TensorDataset(x_train, y_train)\n",
+        "    test_dataset = TensorDataset(x_test, y_test)\n",
+        "    \n",
+        "    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
+        "    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
+        "    \n",
+        "    # Оптимизатор и функция потерь\n",
+        "    optimizer = optim.SGD(model.parameters(), lr=lr)\n",
+        "    criterion = nn.CrossEntropyLoss()\n",
+        "    \n",
+        "    # Обучение\n",
+        "    train_losses = []\n",
+        "    for epoch in range(epochs):\n",
+        "        model.train()\n",
+        "        epoch_loss = 0\n",
+        "        for batch_x, batch_y in train_loader:\n",
+        "            optimizer.zero_grad()\n",
+        "            outputs = model(batch_x)\n",
+        "            loss = criterion(outputs, batch_y)\n",
+        "            loss.backward()\n",
+        "            optimizer.step()\n",
+        "            epoch_loss += loss.item()\n",
+        "        \n",
+        "        train_losses.append(epoch_loss / len(train_loader))\n",
+        "        \n",
+        "        print(f'Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss/len(train_loader):.4f}')\n",
+        "    \n",
+        "    # Предсказания\n",
+        "    def get_predictions(loader):\n",
+        "        model.eval()\n",
+        "        all_predictions = []\n",
+        "        all_labels = []\n",
+        "        with torch.no_grad():\n",
+        "            for batch_x, batch_y in loader:\n",
+        "                outputs = model(batch_x)\n",
+        "                _, predicted = torch.max(outputs.data, 1)\n",
+        "                all_predictions.extend(predicted.cpu().numpy())\n",
+        "                all_labels.extend(batch_y.cpu().numpy())\n",
+        "        return all_predictions, all_labels\n",
+        "    \n",
+        "    test_predictions, test_labels = get_predictions(test_loader)\n",
+        "    \n",
+        "    test_accuracy = accuracy_score(test_labels, test_predictions)\n",
+        "    \n",
+        "    print(f'Test Accuracy: {test_accuracy:.4f}')\n",
+        "    \n",
+        "    return train_losses, test_accuracy\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 103,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Однослойный персептрон:\n",
+            "Epoch [1/10], Loss: 0.9915\n",
+            "Epoch [2/10], Loss: 0.5592\n",
+            "Epoch [3/10], Loss: 0.4770\n",
+            "Epoch [4/10], Loss: 0.4371\n",
+            "Epoch [5/10], Loss: 0.4126\n",
+            "Epoch [6/10], Loss: 0.3950\n",
+            "Epoch [7/10], Loss: 0.3821\n",
+            "Epoch [8/10], Loss: 0.3719\n",
+            "Epoch [9/10], Loss: 0.3637\n",
+            "Epoch [10/10], Loss: 0.3565\n",
+            "Test Accuracy: 0.9093\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'Loss')"
+            ]
+          },
+          "execution_count": 103,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x_train_tensor = torch.tensor(x_train, dtype=torch.float32)\n",
+        "y_train_tensor = torch.tensor(y_train, dtype=torch.long)\n",
+        "x_test_tensor = torch.tensor(x_test, dtype=torch.float32)\n",
+        "y_test_tensor = torch.tensor(y_test, dtype=torch.long)\n",
+        "\n",
+        "model = SingleLayerPerceptron(784, 10)\n",
+        "\n",
+        "print(\"Однослойный персептрон:\")\n",
+        "\n",
+        "# Обучение и оценка\n",
+        "train_losses, test_acc = train_and_evaluate(\n",
+        "    model, x_train_tensor, y_train_tensor, x_test_tensor, y_test_tensor,\n",
+        "    epochs=10, lr=0.01\n",
+        ")\n",
+        "\n",
+        "plt.figure(figsize=(10, 4))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.plot(train_losses)\n",
+        "plt.title('Функция потерь во время обучения')\n",
+        "plt.xlabel('Эпоха')\n",
+        "plt.ylabel('Loss')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 106,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "class SimplePerceptron(nn.Module):\n",
+        "    def __init__(self, input_size, output_size):\n",
+        "        super(SimplePerceptron, self).__init__()\n",
+        "        \n",
+        "        # Полносвязный слой (классический персептрон)\n",
+        "        self.fc = nn.Linear(input_size, output_size)\n",
+        "    \n",
+        "    def forward(self, x):\n",
+        "        x = x.view(x.size(0), -1)\n",
+        "        \n",
+        "        x = self.fc(x)\n",
+        "        return x"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Персептрон со скрытым слоем:\n",
+            "Epoch [1/10], Loss: 0.9927\n",
+            "Epoch [2/10], Loss: 0.5579\n",
+            "Epoch [3/10], Loss: 0.4761\n",
+            "Epoch [4/10], Loss: 0.4363\n",
+            "Epoch [5/10], Loss: 0.4117\n",
+            "Epoch [6/10], Loss: 0.3945\n",
+            "Epoch [7/10], Loss: 0.3815\n",
+            "Epoch [8/10], Loss: 0.3714\n",
+            "Epoch [9/10], Loss: 0.3630\n",
+            "Epoch [10/10], Loss: 0.3563\n",
+            "Test Accuracy: 0.9091\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'Loss')"
+            ]
+          },
+          "execution_count": 107,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
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",
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model = SimplePerceptron(784, 10)\n",
+        "\n",
+        "print(\"Персептрон со скрытым слоем:\")\n",
+        "\n",
+        "# Обучение и оценка\n",
+        "train_losses, test_acc = train_and_evaluate(\n",
+        "    model, x_train_tensor, y_train_tensor, x_test_tensor, y_test_tensor,\n",
+        "    epochs=10, lr=0.01\n",
+        ")\n",
+        "\n",
+        "plt.figure(figsize=(10, 4))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.plot(train_losses)\n",
+        "plt.title('Функция потерь во время обучения')\n",
+        "plt.xlabel('Эпоха')\n",
+        "plt.ylabel('Loss')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.optim as optim\n",
+        "from torch.utils.data import DataLoader, TensorDataset\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "class CNNPerceptron(nn.Module):\n",
+        "    def __init__(self, input_size, output_size):\n",
+        "        super(CNNPerceptron, self).__init__()\n",
+        "        \n",
+        "        # Сверточный слой\n",
+        "        self.conv1 = nn.Conv2d(\n",
+        "            in_channels=1,\n",
+        "            out_channels=16,\n",
+        "            kernel_size=3,\n",
+        "            padding=1\n",
+        "        )\n",
+        "        \n",
+        "        # Пулинг слой (уменьшает размерность)\n",
+        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
+        "        \n",
+        "        conv_output_size = 16 * 14 * 14\n",
+        "        \n",
+        "        # Полносвязный слой (классический персептрон)\n",
+        "        self.fc = nn.Linear(conv_output_size, output_size)\n",
+        "    \n",
+        "    def forward(self, x):\n",
+        "        x = x.view(-1, 1, 28, 28)\n",
+        "        \n",
+        "        # Применяем сверточный слой + ReLU + пулинг\n",
+        "        x = self.pool(torch.relu(self.conv1(x)))\n",
+        "        \n",
+        "        x = x.view(x.size(0), -1)\n",
+        "        \n",
+        "        x = self.fc(x)\n",
+        "        return x"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 105,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Сверточный персептрон:\n",
+            "Epoch [1/10], Loss: 2.0494\n",
+            "Epoch [2/10], Loss: 1.2180\n",
+            "Epoch [3/10], Loss: 0.6953\n",
+            "Epoch [4/10], Loss: 0.5274\n",
+            "Epoch [5/10], Loss: 0.4553\n",
+            "Epoch [6/10], Loss: 0.4159\n",
+            "Epoch [7/10], Loss: 0.3909\n",
+            "Epoch [8/10], Loss: 0.3731\n",
+            "Epoch [9/10], Loss: 0.3600\n",
+            "Epoch [10/10], Loss: 0.3496\n",
+            "Test Accuracy: 0.9112\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model = CNNPerceptron(784, 10)\n",
+        "\n",
+        "print(\"Сверточный персептрон:\")\n",
+        "\n",
+        "# Обучение и оценка\n",
+        "train_losses, train_acc = train_and_evaluate(\n",
+        "    model, x_train_tensor, y_train_tensor, x_test_tensor, y_test_tensor,\n",
+        "    epochs=10, lr=0.001\n",
+        ")\n",
+        "\n",
+        "# Визуализация процесса обучения\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.plot(train_losses)\n",
+        "plt.title('Функция потерь во время обучения')\n",
+        "plt.xlabel('Эпоха')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.grid(True)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "base",
+      "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": "3.9.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/AISP1.ipynb b/AISP1.ipynb
new file mode 100644
index 0000000..79d66fa
--- /dev/null
+++ b/AISP1.ipynb
@@ -0,0 +1,2207 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Практическая работа №1 \n",
+    "## по предмету \"Системы искусственного интеллекта\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В данной практической работе Вы будете работать с базой данных, посвященной баскетболу, которая включает в себя информацию об игроках, играх и командах. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Задание 1\n",
+    "Вам необходимо запустить в docker базу данных PostgreSQL и выгрузить туда все данные из файла nba.sqlite, приложенного к лабороторной работе.\n",
+    "Выгрузить данные можно с помощью утилиты pgloader (https://pgloader.readthedocs.io/en/latest/ref/sqlite.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Задание 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Collecting psycopg2-binary\n",
+      "  Downloading psycopg2_binary-2.9.10-cp39-cp39-win_amd64.whl (1.2 MB)\n",
+      "     ---------------------------------------- 1.2/1.2 MB 3.0 MB/s eta 0:00:00\n",
+      "Installing collected packages: psycopg2-binary\n",
+      "Successfully installed psycopg2-binary-2.9.10\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install psycopg2-binary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Загрузите данные из таблицы . Посмотрите, какие есть типы игр в сезоне (season_type) и количество этих игр."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\sql.py:762: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import psycopg2\n",
+    "import pandas as pd\n",
+    "\n",
+    "conn = psycopg2.connect(\n",
+    "    dbname='nba',\n",
+    "    user='admin',\n",
+    "    password='admin',\n",
+    "    host='localhost',\n",
+    "    port=5432\n",
+    ")\n",
+    "\n",
+    "df = pd.read_sql('SELECT * from game', conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>season_id</th>\n",
+       "      <th>team_id_home</th>\n",
+       "      <th>team_abbreviation_home</th>\n",
+       "      <th>team_name_home</th>\n",
+       "      <th>game_id</th>\n",
+       "      <th>game_date</th>\n",
+       "      <th>matchup_home</th>\n",
+       "      <th>wl_home</th>\n",
+       "      <th>min</th>\n",
+       "      <th>fgm_home</th>\n",
+       "      <th>...</th>\n",
+       "      <th>reb_away</th>\n",
+       "      <th>ast_away</th>\n",
+       "      <th>stl_away</th>\n",
+       "      <th>blk_away</th>\n",
+       "      <th>tov_away</th>\n",
+       "      <th>pf_away</th>\n",
+       "      <th>pts_away</th>\n",
+       "      <th>plus_minus_away</th>\n",
+       "      <th>video_available_away</th>\n",
+       "      <th>season_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610035</td>\n",
+       "      <td>HUS</td>\n",
+       "      <td>Toronto Huskies</td>\n",
+       "      <td>0024600001</td>\n",
+       "      <td>1946-11-01</td>\n",
+       "      <td>HUS vs. NYK</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610034</td>\n",
+       "      <td>BOM</td>\n",
+       "      <td>St. Louis Bombers</td>\n",
+       "      <td>0024600003</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>BOM vs. PIT</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>-5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610032</td>\n",
+       "      <td>PRO</td>\n",
+       "      <td>Providence Steamrollers</td>\n",
+       "      <td>0024600002</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>PRO vs. BOS</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>-6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610025</td>\n",
+       "      <td>CHS</td>\n",
+       "      <td>Chicago Stags</td>\n",
+       "      <td>0024600004</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>CHS vs. NYK</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>-16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610028</td>\n",
+       "      <td>DEF</td>\n",
+       "      <td>Detroit Falcons</td>\n",
+       "      <td>0024600005</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>DEF vs. WAS</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 55 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  season_id team_id_home team_abbreviation_home           team_name_home  \\\n",
+       "0     21946   1610610035                    HUS          Toronto Huskies   \n",
+       "1     21946   1610610034                    BOM        St. Louis Bombers   \n",
+       "2     21946   1610610032                    PRO  Providence Steamrollers   \n",
+       "3     21946   1610610025                    CHS            Chicago Stags   \n",
+       "4     21946   1610610028                    DEF          Detroit Falcons   \n",
+       "\n",
+       "      game_id  game_date matchup_home wl_home  min  fgm_home  ...  reb_away  \\\n",
+       "0  0024600001 1946-11-01  HUS vs. NYK       L    0      25.0  ...       NaN   \n",
+       "1  0024600003 1946-11-02  BOM vs. PIT       W    0      20.0  ...       NaN   \n",
+       "2  0024600002 1946-11-02  PRO vs. BOS       W    0      21.0  ...       NaN   \n",
+       "3  0024600004 1946-11-02  CHS vs. NYK       W    0      21.0  ...       NaN   \n",
+       "4  0024600005 1946-11-02  DEF vs. WAS       L    0      10.0  ...       NaN   \n",
+       "\n",
+       "   ast_away  stl_away  blk_away  tov_away  pf_away  pts_away  plus_minus_away  \\\n",
+       "0       NaN       NaN       NaN       NaN      NaN      68.0                2   \n",
+       "1       NaN       NaN       NaN       NaN     25.0      51.0               -5   \n",
+       "2       NaN       NaN       NaN       NaN      NaN      53.0               -6   \n",
+       "3       NaN       NaN       NaN       NaN     22.0      47.0              -16   \n",
+       "4       NaN       NaN       NaN       NaN      NaN      50.0               17   \n",
+       "\n",
+       "   video_available_away     season_type  \n",
+       "0                     0  Regular Season  \n",
+       "1                     0  Regular Season  \n",
+       "2                     0  Regular Season  \n",
+       "3                     0  Regular Season  \n",
+       "4                     0  Regular Season  \n",
+       "\n",
+       "[5 rows x 55 columns]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Regular Season    60192\n",
+       "Playoffs           3842\n",
+       "Pre Season         1536\n",
+       "All Star             65\n",
+       "All-Star             63\n",
+       "Name: season_type, dtype: int64"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['season_type'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Regular Season    60192\n",
+       "Playoffs           3842\n",
+       "Pre Season         1536\n",
+       "All Star            128\n",
+       "Name: season_type, dtype: int64"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.replace('All-Star', 'All Star', inplace=True)\n",
+    "df['season_type'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Нам понадобятся только регулярные игры сезона и play-off. Отделите эти игры в новый датафрейм."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Regular Season    60192\n",
+       "Playoffs           3842\n",
+       "Name: season_type, dtype: int64"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df[(df['season_type'] == 'Regular Season') | (df['season_type'] == 'Playoffs')]\n",
+    "df['season_type'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В датах игр выделите только год, убрав день и месяц, в которых была проведена игра"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>season_id</th>\n",
+       "      <th>team_id_home</th>\n",
+       "      <th>team_abbreviation_home</th>\n",
+       "      <th>team_name_home</th>\n",
+       "      <th>game_id</th>\n",
+       "      <th>game_date</th>\n",
+       "      <th>matchup_home</th>\n",
+       "      <th>wl_home</th>\n",
+       "      <th>min</th>\n",
+       "      <th>fgm_home</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stl_away</th>\n",
+       "      <th>blk_away</th>\n",
+       "      <th>tov_away</th>\n",
+       "      <th>pf_away</th>\n",
+       "      <th>pts_away</th>\n",
+       "      <th>plus_minus_away</th>\n",
+       "      <th>video_available_away</th>\n",
+       "      <th>season_type</th>\n",
+       "      <th>game_date_pd</th>\n",
+       "      <th>game_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610035</td>\n",
+       "      <td>HUS</td>\n",
+       "      <td>Toronto Huskies</td>\n",
+       "      <td>0024600001</td>\n",
+       "      <td>1946-11-01</td>\n",
+       "      <td>HUS vs. NYK</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-01</td>\n",
+       "      <td>1946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610034</td>\n",
+       "      <td>BOM</td>\n",
+       "      <td>St. Louis Bombers</td>\n",
+       "      <td>0024600003</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>BOM vs. PIT</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>-5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610032</td>\n",
+       "      <td>PRO</td>\n",
+       "      <td>Providence Steamrollers</td>\n",
+       "      <td>0024600002</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>PRO vs. BOS</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>-6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610025</td>\n",
+       "      <td>CHS</td>\n",
+       "      <td>Chicago Stags</td>\n",
+       "      <td>0024600004</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>CHS vs. NYK</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>-16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610028</td>\n",
+       "      <td>DEF</td>\n",
+       "      <td>Detroit Falcons</td>\n",
+       "      <td>0024600005</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>DEF vs. WAS</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 57 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  season_id team_id_home team_abbreviation_home           team_name_home  \\\n",
+       "0     21946   1610610035                    HUS          Toronto Huskies   \n",
+       "1     21946   1610610034                    BOM        St. Louis Bombers   \n",
+       "2     21946   1610610032                    PRO  Providence Steamrollers   \n",
+       "3     21946   1610610025                    CHS            Chicago Stags   \n",
+       "4     21946   1610610028                    DEF          Detroit Falcons   \n",
+       "\n",
+       "      game_id  game_date matchup_home wl_home  min  fgm_home  ...  stl_away  \\\n",
+       "0  0024600001 1946-11-01  HUS vs. NYK       L    0      25.0  ...       NaN   \n",
+       "1  0024600003 1946-11-02  BOM vs. PIT       W    0      20.0  ...       NaN   \n",
+       "2  0024600002 1946-11-02  PRO vs. BOS       W    0      21.0  ...       NaN   \n",
+       "3  0024600004 1946-11-02  CHS vs. NYK       W    0      21.0  ...       NaN   \n",
+       "4  0024600005 1946-11-02  DEF vs. WAS       L    0      10.0  ...       NaN   \n",
+       "\n",
+       "   blk_away  tov_away  pf_away  pts_away  plus_minus_away  \\\n",
+       "0       NaN       NaN      NaN      68.0                2   \n",
+       "1       NaN       NaN     25.0      51.0               -5   \n",
+       "2       NaN       NaN      NaN      53.0               -6   \n",
+       "3       NaN       NaN     22.0      47.0              -16   \n",
+       "4       NaN       NaN      NaN      50.0               17   \n",
+       "\n",
+       "   video_available_away     season_type  game_date_pd  game_year  \n",
+       "0                     0  Regular Season    1946-11-01       1946  \n",
+       "1                     0  Regular Season    1946-11-02       1946  \n",
+       "2                     0  Regular Season    1946-11-02       1946  \n",
+       "3                     0  Regular Season    1946-11-02       1946  \n",
+       "4                     0  Regular Season    1946-11-02       1946  \n",
+       "\n",
+       "[5 rows x 57 columns]"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['game_date_pd'] = pd.to_datetime(df['game_date'])\n",
+    "df['game_year'] = df['game_date_pd'].apply(lambda date: date.year)\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Нас интересует общий счет, поэтому уберите столбцы pts_home и pts_away, заменив их стобцом с их суммой"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>season_id</th>\n",
+       "      <th>team_id_home</th>\n",
+       "      <th>team_abbreviation_home</th>\n",
+       "      <th>team_name_home</th>\n",
+       "      <th>game_id</th>\n",
+       "      <th>game_date</th>\n",
+       "      <th>matchup_home</th>\n",
+       "      <th>wl_home</th>\n",
+       "      <th>min</th>\n",
+       "      <th>fgm_home</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stl_away</th>\n",
+       "      <th>blk_away</th>\n",
+       "      <th>tov_away</th>\n",
+       "      <th>pf_away</th>\n",
+       "      <th>plus_minus_away</th>\n",
+       "      <th>video_available_away</th>\n",
+       "      <th>season_type</th>\n",
+       "      <th>game_date_pd</th>\n",
+       "      <th>game_year</th>\n",
+       "      <th>pts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610035</td>\n",
+       "      <td>HUS</td>\n",
+       "      <td>Toronto Huskies</td>\n",
+       "      <td>0024600001</td>\n",
+       "      <td>1946-11-01</td>\n",
+       "      <td>HUS vs. NYK</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-01</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>134.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610034</td>\n",
+       "      <td>BOM</td>\n",
+       "      <td>St. Louis Bombers</td>\n",
+       "      <td>0024600003</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>BOM vs. PIT</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>-5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>107.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610032</td>\n",
+       "      <td>PRO</td>\n",
+       "      <td>Providence Steamrollers</td>\n",
+       "      <td>0024600002</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>PRO vs. BOS</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>112.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610025</td>\n",
+       "      <td>CHS</td>\n",
+       "      <td>Chicago Stags</td>\n",
+       "      <td>0024600004</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>CHS vs. NYK</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>-16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>110.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610028</td>\n",
+       "      <td>DEF</td>\n",
+       "      <td>Detroit Falcons</td>\n",
+       "      <td>0024600005</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>DEF vs. WAS</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>83.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 56 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  season_id team_id_home team_abbreviation_home           team_name_home  \\\n",
+       "0     21946   1610610035                    HUS          Toronto Huskies   \n",
+       "1     21946   1610610034                    BOM        St. Louis Bombers   \n",
+       "2     21946   1610610032                    PRO  Providence Steamrollers   \n",
+       "3     21946   1610610025                    CHS            Chicago Stags   \n",
+       "4     21946   1610610028                    DEF          Detroit Falcons   \n",
+       "\n",
+       "      game_id  game_date matchup_home wl_home  min  fgm_home  ...  stl_away  \\\n",
+       "0  0024600001 1946-11-01  HUS vs. NYK       L    0      25.0  ...       NaN   \n",
+       "1  0024600003 1946-11-02  BOM vs. PIT       W    0      20.0  ...       NaN   \n",
+       "2  0024600002 1946-11-02  PRO vs. BOS       W    0      21.0  ...       NaN   \n",
+       "3  0024600004 1946-11-02  CHS vs. NYK       W    0      21.0  ...       NaN   \n",
+       "4  0024600005 1946-11-02  DEF vs. WAS       L    0      10.0  ...       NaN   \n",
+       "\n",
+       "   blk_away  tov_away  pf_away  plus_minus_away  video_available_away  \\\n",
+       "0       NaN       NaN      NaN                2                     0   \n",
+       "1       NaN       NaN     25.0               -5                     0   \n",
+       "2       NaN       NaN      NaN               -6                     0   \n",
+       "3       NaN       NaN     22.0              -16                     0   \n",
+       "4       NaN       NaN      NaN               17                     0   \n",
+       "\n",
+       "      season_type  game_date_pd  game_year    pts  \n",
+       "0  Regular Season    1946-11-01       1946  134.0  \n",
+       "1  Regular Season    1946-11-02       1946  107.0  \n",
+       "2  Regular Season    1946-11-02       1946  112.0  \n",
+       "3  Regular Season    1946-11-02       1946  110.0  \n",
+       "4  Regular Season    1946-11-02       1946   83.0  \n",
+       "\n",
+       "[5 rows x 56 columns]"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['pts'] = df['pts_home'] + df['pts_away']\n",
+    "df.drop(columns=['pts_home', 'pts_away'], inplace=True)\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Постройте график, где по оси Х будет год игр, а по оси Y - среднее количество очков за игру в этом году."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "avg_points = df.groupby('game_year')['pts'].mean().reset_index()\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(avg_points['game_year'], avg_points['pts'])\n",
+    "plt.xlabel('Год')\n",
+    "plt.ylabel('Среднее количество очков')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Задание 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выполните запрос к таблице draft_history, получив датафрейм."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\sql.py:762: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "draft_history = pd.read_sql('SELECT * from draft_history', conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>person_id</th>\n",
+       "      <th>player_name</th>\n",
+       "      <th>season</th>\n",
+       "      <th>round_number</th>\n",
+       "      <th>round_pick</th>\n",
+       "      <th>overall_pick</th>\n",
+       "      <th>draft_type</th>\n",
+       "      <th>team_id</th>\n",
+       "      <th>team_city</th>\n",
+       "      <th>team_name</th>\n",
+       "      <th>team_abbreviation</th>\n",
+       "      <th>organization</th>\n",
+       "      <th>organization_type</th>\n",
+       "      <th>player_profile_flag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>79299</td>\n",
+       "      <td>Clifton McNeeley</td>\n",
+       "      <td>1947</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Draft</td>\n",
+       "      <td>1610610031</td>\n",
+       "      <td>Pittsburgh</td>\n",
+       "      <td>Ironmen</td>\n",
+       "      <td>PIT</td>\n",
+       "      <td>Texas-El Paso</td>\n",
+       "      <td>College/University</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>78109</td>\n",
+       "      <td>Glen Selbo</td>\n",
+       "      <td>1947</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Draft</td>\n",
+       "      <td>1610610035</td>\n",
+       "      <td>Toronto</td>\n",
+       "      <td>Huskies</td>\n",
+       "      <td>HUS</td>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>College/University</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>76649</td>\n",
+       "      <td>Eddie Ehlers</td>\n",
+       "      <td>1947</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Draft</td>\n",
+       "      <td>1610612738</td>\n",
+       "      <td>Boston</td>\n",
+       "      <td>Celtics</td>\n",
+       "      <td>BOS</td>\n",
+       "      <td>Purdue</td>\n",
+       "      <td>College/University</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>79302</td>\n",
+       "      <td>Walt Dropo</td>\n",
+       "      <td>1947</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Draft</td>\n",
+       "      <td>1610610032</td>\n",
+       "      <td>Providence</td>\n",
+       "      <td>Steamrollers</td>\n",
+       "      <td>PRO</td>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>College/University</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>77048</td>\n",
+       "      <td>Dick Holub</td>\n",
+       "      <td>1947</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Draft</td>\n",
+       "      <td>1610612752</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>Knicks</td>\n",
+       "      <td>NYK</td>\n",
+       "      <td>Long Island-Brooklyn</td>\n",
+       "      <td>College/University</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  person_id       player_name season  round_number  round_pick  overall_pick  \\\n",
+       "0     79299  Clifton McNeeley   1947             1           1             1   \n",
+       "1     78109        Glen Selbo   1947             1           2             2   \n",
+       "2     76649      Eddie Ehlers   1947             1           3             3   \n",
+       "3     79302        Walt Dropo   1947             1           4             4   \n",
+       "4     77048        Dick Holub   1947             1           5             5   \n",
+       "\n",
+       "  draft_type     team_id   team_city     team_name team_abbreviation  \\\n",
+       "0      Draft  1610610031  Pittsburgh       Ironmen               PIT   \n",
+       "1      Draft  1610610035     Toronto       Huskies               HUS   \n",
+       "2      Draft  1610612738      Boston       Celtics               BOS   \n",
+       "3      Draft  1610610032  Providence  Steamrollers               PRO   \n",
+       "4      Draft  1610612752    New York        Knicks               NYK   \n",
+       "\n",
+       "           organization   organization_type player_profile_flag  \n",
+       "0         Texas-El Paso  College/University                   0  \n",
+       "1             Wisconsin  College/University                   1  \n",
+       "2                Purdue  College/University                   1  \n",
+       "3           Connecticut  College/University                   0  \n",
+       "4  Long Island-Brooklyn  College/University                   1  "
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "draft_history.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Постройте график, где по оси X будет год, а по оси Y - количество выбранных игроков в этот год (каждая строка в таблице - выбранный игрок)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ow+7Ec18eBAD8z5yRiFHAuS7de9o5iI6IiEhuLNqJiGRU4i3aQ7WfHTix086iXYn+sb4ENVYb8pJjsPT0PLnTAdC9p73T4UaXwyVzNkRERNGNRTsRkUxEUcRRq6doD9V+dqDn8vjB5oRQeDW02fCXtUcBAA8tGAejThn/LVtMOmjg+bPCYXRERETyUsZPB0REUai8qRNWhwC9VsDE3NBNdc9O9BTt7XYXCzCFeWHNYbTZnCjKScDFE7PlTsdHEASYvcv0m7hEnoiISFYs2omIZLLNe9TbhOx4mPShO6LSpNci3WIEAJRzGJ1ilDa0461NpQCAhxeeErKZBkMV61khz6KdiIhIZizaiYhksrW0GYBnCF2ocV+78jzz+QE4XCLmjE3DrNGpcqfTi9Rp5+oMIiIiebFoJyKSSXFpEwBgen5iyL9XXrI0QZ6ddiXYWd6Mj3dVQRCAXy48Re50+hSr8+xpZ6ediIhIXizaiYhksHpfDQ7XtQMApoShaGenXTlEUcSTn+4HAFw+JRenZsXLnFHfYtlpJyIiUgQFnAZLRBQ9RFHE/319GH9Y7TmXe3KyG8mxhpB/31ye1a4YXx+oxaaSRhh0GjywYKzc6fRLKtqb2tlpJyIikhOLdiKiMOmwO/Gzd3fhk11VAICfzMzDFJSE5XufeOwbycflFvH0fw8AAG6aPQI53sn+SmTWS8vj2WknIiKSE5fHExGFQWVzJ6586Xt8sqsKeq2A315ehF9fdCq0YfpXWOq0lzd28qx2Gf1nWwUO1LQiIUaPn84ZLXc6A4rlkW9ERESKwE47EVGIbS5pxP+sKEZDux0psQb85SfTMGNEMhyO8HUwsxNNEASg0+FCY7sdKXHGsH1v8ui0u/DsF55tEXfPG40Es17mjAbGop2IiEgZ2GknIgqhlZvKsPTljWhot2N8djw+vOcszBiRHPY8jDotMiwmANzXLpc3Npah2tqFnMQY/OTMArnTGZQ0PZ6D6IiIiOTFop2IKAQcLjcefX8PfrVqN5xuERdNzMK7d86SdQ8zJ8jLp80B/GWdZ37BQxeMhUmvlTmjwUkLAdhpJyIikheXxxMRBVmbA7jxtWJsPtYEQQAeWjAOP507CoIgyJpXblIMtpY2cRidDL6o1KDN5sRpWfFYMilH7nT8Ii2Pb+l0wOUWodXI++eXiIgoWrFoJyIKov1VrfjDbi0abU2IM+rw/DWTcf5pGXKnBYDHvsmlvKkD66s9Be/Di06BJkKKX6loF0XA2ulAUhiOJiQiIqLeWLQTEQVJXasN172yGe02AfnJMXjlhhkYk2GROy0fHvsmjz99dQQuUcDsUSk4e0ya3On4TacBYg1atNtdaOqws2gnIiKSCYt2IqIg2V7WhHabCylGEf+54wykJZjlTqkH37Fv7LSHTX2bDZ/sqQYAPHC+so9460uiWe8t2jmMjoiISC4cREdEFCS1rTYAQJZZRKICj/PKS+7utPOs9vD419ZyOFwiCuJETMxNkDudgEl/jps5jI6IiEg2LNqJiIKkzlu0xyuvXgcAZCXEQBCALocbDe0swkLN7RaxclMZAGB2hlvmbIYmMcazJJ6ddiIiIvmwaCciCpK6Nm/RrtCtvwadBpnxPKs9XNYeqkNFUyfiTTpMSYnMlQ3stBMREcmPRTsRUZBInXaLXrkFGofRhc9bGz1d9sunZMOg/GPZ+5TsLdp5VjsREZF8WLQTEQWJb3m8QjvtwPCOfXO5RdR2gvvh/VDZ3Ik1P9QAAK6bkSdzNkOX6CvauTyeiIhILizaiYiCpHtPu3KLWqnTXt4YeKf996sP4YkdOqzeXxvstFTn7c1lcIvAmSNTMDItVu50hizR7PkEisvjiYiI5MOinYgoCERR9O1ptyh0EB0A5A2x095pd+HtLRUAgE0lTUHPS00cLjfe3lIOAPjxGQUyZzM8iTHeTns7O+1ERERyYdFORBQE1i4n7E7PhHAlF+1D3dP+xb5qtNmcAIBjDe1Bz0tNVu+rQV2rDalxRsw/LUPudIYliXvaiYiIZMeinYgoCOpauwAAFpNO0UPHTtzTHsje9H9vrfD9vqSeQ+wG8tamUgDAtTPyYNBF9n+z3dPj2WknIiKSS2T/NEFEpBC13v3saXEKnkIHIDPBBI0A2Jxu1Lf51z2tbO7Ed0fqe3xtc7pClWJEO1rXhu8ON0AQgGtPj9wBdJJEdtqJiIhkx6KdiCgIpCF0aRajzJkMrOdZ7f51zN8rroAoAqePSIJRK8ItDm2QXTRYuclzzNu549J9qxoiWWKM50Mom9ONTjs/qCEiIpIDi3YioiCQivbUOGUX7UD3EvlyP4bRiaKId7d5lsZfMTUb6Z56H0fruK/9ZF0OF/5d7HmtfnRGvszZBEecUQudRgDAbjsREZFcWLQTEQWBNDle6cvjASA32f9hdFuONaG0oQOxBi0uHJ+BNJNnH3xJPYv2k32yqwotnQ7kJMZgzth0udMJCkEQfMe+sWgnIiKSB4t2IqIgiMROuz/Hvr1b7Dm6bFFRFswGHdI99T6L9j6s8A6gWzozH1pvd1oNkjiMjoiISFYs2omIgqB7T3sEdNp9x74NXLR32J34ZFcVAOCq6Z6halKn/SiL9h72Hm/B9rJm6DQCrp4e+QPoTpTETjsREZGsWLQTEQWBr2iPiE67f8vj/7u7Gu12FwpSzJgxIgkAkB7D5fF9ecs7gO6CCZmKH0YYqO4J8uy0ExERyYFFOxFREETS8vg87/L4ykHOav+3d2n8lVNzIQie5d5p3kF0da02tHaxiAOANpsTH2yvBAD8eGaBzNkEn9Rpb25np52IiEgOLNqJiIbJ4XKj0bt0OBKWx/tzVnt5Ywc2Hm2EIACXT8v13R6jA1K9w/aO1fPYNwBYtb0S7XYXRqXF4oyRyXKnE3SJsey0ExERyYlFOxHRMDW22yGKgFYj+LqSSqbXapCVMPC+9ne9R5fNHpWKnMSYHveNSPF06o/Wt4Uwy8ggiiLe2ugZQPejmQW+FQlq4uu0c087ERGRLFi0ExENk7Q0PiXWEDFTw3372pt7F+1ut4j/eM9mv/KELrukMDUWAPe1A8D28hb8UN0Kk16DK6b2fq3UIMm3p51FOxERkRxYtBMRDVP35Hjl72eX5J6wr/1kG0saUNHUCYtRhwvGZ/a6X+q0s2gHVm727Pu/eGI2ErzFrdp0n9PO5fFERERyYNFORDRMkVm0S532rl73SUvjL5qUhRiDttf9hSnstANAmwP4794aAMCPzlDfADoJl8cTERHJi0U7EdEw1bVFznFvEqlorzxpeXybzYn/7q4G0PfSeAAYkerttNe1Dzh9Xu021wmwO92YkBOPSbkJcqcTMkk88o2IiEhWLNqJiIYpEjvtecl9L4//dFcVOh0ujEyNxdT8pD4fm59shiAArTZnv9Pn1c7tFvFdjee/ULUOoJNIy+OtXQ643NH7IQ0REZFcWLQTEQ1TbatniXkkFe0nDqI7sQ6Tzma/Ylpuv4WoUafxPT5al8hvONqI+i4BcUYdLpmULXc6IZXo7bSLItDSyW47ERFRuOnkToCIKNJFYqc9M94ErUaAwyWi1VuHHatvx5ZjTdAIGHQSemFqHMobO1FS34bTC9VxNvnTnx/EJzu1+PPRDYN2zqUPai6dnIVYo7r/K9VrNbAYdWi1OdHUYUdyrPKPNSQiIlITdf+kQUQUBlLRnm4xyZyJ/3RaDbISTKho6kSjJ33fALqzxqQhM2Hg5zIyNRbrDtbhqEo67S0dDvx9/TEAAqo6/Dt/XiOIWHp6XkjzUorEWD1abU4OoyMiIpKBrEX7b3/7W7z33nv44YcfEBMTg1mzZuHpp5/GuHHjfDGiKGL58uX429/+hqamJsycORP/93//h/Hjx/tibDYbHnroIfzzn/9EZ2cnzjvvPPz5z39Gbq46z8wlImWJxE474FkiX9HUiYYuAa4Tzma/qp8BdCfyndVep46i/Ui9p1C36EW88KPp0GkH/u/R6XLi0I5NGJMeF470ZJdkNqC8sRNN7VweT0REFG6yFu1r167FXXfdhRkzZsDpdOKRRx7BggULsG/fPsTGen4g/N3vfodnn30Wr732GsaOHYvf/OY3mD9/Pg4cOACLxQIAuO+++/DRRx/h7bffRkpKCh588EFcdNFFKC4uhlbb+7giIqJgabc50W53AZCK9sgZ1OU5q70RjTbg+6ONqGrpQrxJh/mnZQz6WF/RrpJO+1Hvhw+ZMSJmj0qBXj/wmesOhwMtB8KRmTJ0n9XOTjsREVG4yVq0f/bZZz2+fvXVV5Geno7i4mKcc845EEURzz//PB555BFcfvnlAIDXX38dGRkZWLlyJe644w60tLTglVdewZtvvonzzz8fALBixQrk5eXhyy+/xAUXXBD250VE0aPee9xbjF6LWIMWTqdT5oz8l5fkmSDfaBPw3vZKAMAlk7Nh0g/+YadUtJc2dMDlFqHVRPb09CN1nk57RozMiSiUdOxbM499IyIiCjtF7WlvaWkBACQne4YalZSUoLq6GgsWLPDFGI1GzJkzBxs2bMAdd9yB4uJiOByOHjHZ2dmYMGECNmzY0GfRbrPZYLPZfF9brVYAns6Jw6HcH0ik3PzJMVSxSslDzbFKyUPNscG8dlWTp0ObGmeA0+lUxPPzNzYr3tM9rWwXsG1fLQDg0klZ/T7uxOumxepg0Glgd7pRWm/1fQAQ6pxDFXu4phUAkB4jRkS+4c4j3uT5caGhravX45XwWqjhNY7WWKXkoeZYpeSh5lil5KHm2FBfWy7+5ieIoqiItZyiKGLJkiVoamrCt99+CwDYsGEDZs+ejcrKSmRndx+pc/vtt6O0tBSff/45Vq5ciZtuuqlHEQ4ACxYsQGFhIf7617/2+l7Lli3D8uXLe92+cuVKmM29f/AkIurPjgYBrx7UotAi4r4JLrnTCchhK/DC3u7PbjNjRPxykgv+Hjn+2x1aVHcKuPNUF05NVMR/JUOmpucSCv8tF/BZhRaz0t24ZpRb7nSIiIhUoaOjA0uXLkVLSwvi4+P7jVNMp/3uu+/Grl27sH79+l73nXz0jiiKgx7HM1DMww8/jAceeMD3tdVqRV5eHhYsWDDgiyU3h8OB1atXY/78+X7ttwxFrFLyUHOsUvJQc2wwr92wsQw4+APG5GVg0aLJinh+/sYeb+7EC3u/9X19/TljsfisQr+v+3HzDlTvr0XayPFYdEa+4p6fv7Eut4iHNn8JQES6SVR8vnLk0bCxDJ9V/ABLaiYWLZosSw5yxColDzXHKiUPNccqJQ81xyolDzXHhvracpFWfA9GEUX7Pffcgw8//BDr1q3rMfE9MzMTAFBdXY2srCzf7bW1tcjIyPDF2O12NDU1ISkpqUfMrFmz+vx+RqMRRmPvKc96vV7Rb6okkDxDFauUPNQcq5Q81BwbjGs3dnj2sGcmxPS4TwnPb7DYnGQtdBoBTu+e9Cun5ft1bem6o9ItWL2/FmWNnQM+TumvxfGGdjhcIow6DZKMys9XjjxSvMcZtnQ5+32sEl6LSH6Noz1WKXmoOVYpeag5Vil5qDk21NcON39z04Q4jwGJooi7774b7733HtasWYPCwp4dnsLCQmRmZmL16tW+2+x2O9auXesryKdNmwa9Xt8jpqqqCnv27Om3aCciChbfcW9xkXXcG+A5q106j/3s0SlIjw/snPmR3mF0kX5WuzSErjDFjAifpxcySd7p8RxER0REFH6ydtrvuusurFy5Eh988AEsFguqq6sBAAkJCYiJiYEgCLjvvvvw5JNPYsyYMRgzZgyefPJJmM1mLF261Bd7yy234MEHH0RKSgqSk5Px0EMPoaioyDdNnogoVOraIvOMdsnEnHhUNHVi6el5AT+2MM1TtEf6sW/ScW+eifjNsuaiVEk88o2IiEg2shbtL730EgBg7ty5PW5/9dVXceONNwIAfv7zn6OzsxM//elP0dTUhJkzZ+KLL77wndEOAM899xx0Oh2uvvpqdHZ24rzzzsNrr73GM9qJKOR8nfYILdofv+Q0nCZUYt64tIAfKx37VtnciS6Hy6+j4pToyIlFO2vSPiV6j3xr6nD4NVeGiIiIgkfWot2fwfWCIGDZsmVYtmxZvzEmkwkvvPACXnjhhSBmR0Q0uEgv2hNi9MiLG9pjU2INsJh0aO1yoqyxA2MzLIM/SIGOepfHj0w1A8dlTkahkmI9nXa7041OhwtmgyJG4hAREUUFWfe0ExFFMrdbRH2EL48fDkEQuve110XuEnlpT760coB6izVoodd6uutN3NdOREQUVizaiYiGqLnTAafbs2IoJTb6inagu9CN1H3t1i6Hb7UEi/b+CYKARGlfezv3EBAREYUTi3YioiGqbe0CACSZ9TDoovOf08JUz9r6kvo2mTMZGmmFQLrFCIuJS74HkuTd184J8kREROEVnT9lEhEFgdShTbcEdlSamkT6BHnffvY0dtkHk8gJ8kRERLJg0U5ENESRPoQuGEZG+PJ4qdM+Mm2I0/iiSHennUU7ERFROLFoJyIaIhbtwAhv0V7fZkdLZ+Qtmz7imxzPTvtgus9qj7z3mYiIKJKxaCciGiIW7UCcUYd07/M/FoHddqnTPiqdnfbBcHk8ERGRPFi0ExENUZ103Ftc9BbtQOROkHe5RZQ0eIv2VBbtg+EgOiIiInmwaCciGiJ22j2kIW5HI6xoP97cCbvTDYNOg5ykGLnTUbwkdtqJiIhkwaKdiGiIWLR7RGqnXdrPPiLFDK1GkDkb5Uv0dtq5p52IiCi8WLQTEQ1RLYt2AJF7VvsRaXI8l8b7JSnW02nn9HgiIqLwYtFORDQENqfLNy09PeqLdm+nva4doijKnI3/pDPaR6Vzcrw/pD3tTe0s2omIiMKJRTsR0RDUt3kKF71WQEKMXuZs5JWfbIZGANrtLt+WgUhwlJ32gEjT461dTjhdbpmzISIiih4s2omIhsC3nz3OCEGI7v3QBp0GeclmAJE1jO6odzm/NEiPBpZ4wodT0ioTIiIiCj0W7UREQ8AhdD1F2jC6NpsTNVbPezgyjZ12f+i0GlhMOgAcRkdERBROLNqJiIaARXtPkVa0S/vZU+MMUb+9IRDSsW8cRkdERBQ+LNqJiIaARXtPI71Fu7RPXOl8+9nZZQ9IEo99IyIiCjsW7UREQ1DX1gXAs6edIu/YN9/keO5nD4g0jK6JnXYiIqKwYdFORDQEtVZ22k9U6C1+yxo7ImKy+JF6To4fCqnTzuXxRERE4cOinYhoCOrapKLdJHMmypAVb4JRp4HDJaKyuVPudAZ1pJaT44eiu9PO5fFEREThwqKdiGgIuKe9J41G8A2jU/qxb263iGMN3NM+FBxER0REFH4s2omIAiSKoq9oT2fR7uObIK/wYXTHWzrR5XBDrxWQlxQjdzoRJSnWO4iunZ12IiKicGHRTkQUoFabEzanZ992KgfR+UTKsW/S5PiClFjotPxvMBAcREdERBR+/GmFiChAUpfdYtQhxqCVORvliJyi3bufPZX72QPVPYiOnXYiIqJwYdFORBQg7mfvmzTUTSqKleoIz2gfsiR22omIiMKORTsRUYCkoj2VRXsP0lntx1u60Gl3yZxN/47Wc3L8UCWe0GkXRVHmbIiIiKIDi3YiogDVstPepySzHgkxnqJOms6uRNKe9lHstAdM6rTbXW50KPiDGSIiIjVh0U5EFCBOju+bIAiK39feYXeiqqULADCKnfaAmQ1aGLzD+7hEnoiIKDxYtBMRBYh72vsnLTlXatEuddmTYw2+SejkP0EQeiyRJyIiotBj0U5EFKC6Nm/RzuPeepEmsh9V6FntRzg5ftg4jI6IiCi8WLQTEQWInfb+ScPoSuqVOUH+qG9yPIv2oZI67U3stBMREYUFi3YiogCxaO+f0ve0H63nELrhkjrtzey0ExERhQWLdiKiALjcIhrbWbT3Z0SqGYCnC6vE5dPSGfI8o33okmK9nfZ2dtqJiIjCgUU7EVEAGtptcIuARgBSYlm0n8xs0CErwQQAONbQIXM2PbndIpfHB0Ei97QTERGFFYt2IqIA1Fo9XfaUOCO0GkHmbJRJWiJ/rF5ZRXu1tQudDhd0GgH5yWa504lYSb7p8SzaiYiIwoFFOxFRADg5fnC+fe0NytrXLnXZ85PN0Gv5399QdXfauTyeiIgoHPhTCxFRADiEbnBK7bQfred+9mDgIDoiIqLwYtFORBQAFu2Dk/aLlyhsT7vUaR/F/ezDksQj34iIiMKKRTsRUQBYtA9OOqu9tKEdblHmZE5wxDc5nkX7cHAQHRERUXixaCciCgD3tA8uNykGOo2ATocbVgXVdd2T47k8fjikTntrlxNOl1vmbIiIiNSPRTsRUQDYaR+cXqvxTWev7VLGhP1OuwuVzZ0AgJGp7LQPR0KM3vf75k4ukSciIgo1Fu1ERAGoZ9HuF2kYXW2nzIl4SWfGJ5r1SI41yJxNZNNpNYg36QBwGB0REVE4DLtodzgc2L59O5qamoKRDxGRotV6i/Z0Fu0DkvaNr6/W+Drcciqp9y6NT42FICij+x/JkmJ57BsREVG4BFy0FxcX48wzz8TixYtx+PBhTJw4EdOmTUNubi6++OKLUORIRKQIHXYn2mxOAOy0D+a60/ORHKtHVaeAy/+yEZtLGmXN50g997MHk28YXTs77URERKEWcNF+7733wmKxIC4uDgsWLMCcOXNQXl6OO++8E4888kgociQiUoT6Nk+BYtJrEGfUyZyNso1Mi8OqO89AbqyIxnYHlr68ESs3lcmWj6/TzsnxQSENo2tmp52IiCjkAv6pc+fOnSguLkZBQQHi4uJw9913IycnB3fffTf+8pe/hCJHIiJFkIr2NIuRS6z9kJ0Yg/8d78I3Hbn4ZE81frVqN/ZXWfHri0+DXhvekSol9Z497SNT2WkPhiQe+0ZERBQ2Af/U1NHRgeTkZJhMJsTExMBs9kwINpvN6OrqCuha69atw8UXX4zs7GwIgoD333+/x/2CIPT565lnnvHFzJ07t9f91157baBPi4hoUL7J8TzuzW8GLfDc1UX42QXjIAjAmxtL8ZNXNqExjMuqRbG70z46nZ32YEj0dtq5p52IiCj0hrS+8+WXX0ZcXBycTidee+01pKamorW1NeDrtLe3Y9KkSbjppptwxRVX9Lq/qqqqx9f//e9/ccstt/SKve222/D444/7vo6JiQk4FyKiwdS3cXL8UAiCgLvmjca4DAvue2cHNh5txCUvrsfL10/HqVnxIf/+LXag3e6CViMgP5lFezBInXZOjyciIgq9gIv2/Px8vPzyywCAzMxMvPnmmz3uC8TChQuxcOHCfu/PzMzs8fUHH3yAefPmYeTIkT1uN5vNvWKJiIKt7oTl8RS480/LwKqfzsKtb2xFaUMHLv/zBjx79SQsLMoK6feVzorPS4qBQceTToMhyddpZ9FOREQUagEX7ceOHQtBGoOrqanBJ598gtdff73XfW+99RZWrFiBjIwMLFy4EI899hgsFku/17LZbLDZbL6vrVYrAM/xdQ6Hcpf6Sbn5k2OoYpWSh5pjlZKHmmOHeu1aq+fosmSzvt/HKeH5KSG2v/gRySa8e/tM/O+/dmLDkUb8z1vbcM+8kbjjrPyQ5SydFT8ixTzgY5TwukXKvxUWoxYA0NhuV8RrocbXOFpilZKHmmOVkoeaY5WSh5pjQ31tufibnyCKojjUbyI9NBgDmQRBwKpVq3DppZf2ef/vfvc7PPXUUzh+/DhMJpPv9pdffhmFhYXIzMzEnj178PDDD2P06NFYvXp1v99r2bJlWL58ea/bV65c6dujT0R0sr/9oMHeJg2uGenCrIwh/9NJAFwi8EGpBmurPJ3vicluXDHCjcQQLGJ4r0SDtdUazM1y47IR7uB/gyh0oFnAn/drkRkj4uHJLrnTISIiikgdHR1YunQpWlpaEB/f/5bBIRXtb7zxBp555hkcOnQIADB27Fj87Gc/w09+8pMhJzxY0X7KKadg/vz5eOGFFwa8TnFxMaZPn47i4mJMnTq1z5i+Ou15eXmor68f8MWSm8PhwOrVqzF//nzo9XpZYpWSh5pjlZKHmmOHeu2XS5Ow53gr/vKjyTjvlPSw5hxpsf7Gv7utEr/+cB8cLs9/RWlxBhTlJGBCTjyKcuJRlB2PlJMG/wWa8xV/+gr7mzX4zZLTcM303KA8PyXEypnH3uNWXPrSRqTFGbD2gdmyvxZqfI2jJVYpeag5Vil5qDlWKXmoOTbU15aL1WpFamrqoEV7wMvjn332WTz66KO4++67MXv2bIiiiO+++w533nkn6uvrcf/99w8r8b58++23OHDgAN55551BY6dOnQq9Xo9Dhw71W7QbjUYYjb3bOXq9XtFvqiSQPEMVq5Q81ByrlDzUHBtovHTkW1Zi7KCPUcLzU0LsYPHXzRyBsZkJWPbhHuytbEFdmx1rDtRhzYE6X0xOYgyKchJQlJuASbmJOCXDHFAeNZ2e1WBjMuL9ilfC66b0fyvSEjzvQXOnAzqdTpYcwhmrlDzUHKuUPNQcq5Q81ByrlDzUHBvqa4ebv7kFXLS/8MILeOmll3D99df7bluyZAnGjx+PZcuWhaRof+WVVzBt2jRMmjRp0Ni9e/fC4XAgKyu0g42IKLq4RaChnYPoQmFaQRLeu/MMvP/Rp8ifNAv7qtuwu6IFOyuacbS+HZXNnahs7sRne6t9jzk1UYPTZrZjTGbigNfucrjQ5F1YNTKNZ7QHizSIzuES0W7n8ngiIqJQCrhor6qqwqxZs3rdPmvWrF5HtA2mra0Nhw8f9n1dUlKCHTt2IDk52TeJ3mq14t///jf+8Ic/9Hr8kSNH8NZbb2HRokVITU3Fvn378OCDD2LKlCmYPXt2gM+MiKh/nU74lnCnxBlkzkadDFpgan4iZo5K893W2uXA3uNWXxG/u7IFpQ0d2N+sweIXv8f/zBmF/5k7Cia9ts9rljZ0QIQAi0mHVL5vQROj18Kg08DudKOZZ7UTERGFVMBn34wePRr/+te/et3+zjvvYMyYMQFda+vWrZgyZQqmTJkCAHjggQcwZcoU/PrXv/bFvP322xBFEdddd12vxxsMBnz11Ve44IILMG7cONx7771YsGABvvzyS2i1ff8AR0Q0FFZvXZJo1sOo478v4WIx6XHGyBTcds5IvLh0Ktb+bB5W3zcbpyS4YXe68cevDuGC59dh7cG6Ph9/tL4dAFCYag7K0FTyEATB121n0U5ERBRaAXfaly9fjmuuuQbr1q3D7NmzIQgC1q9fj6+++qrPYn4gc+fOxWBz8G6//Xbcfvvtfd6Xl5eHtWvXBvQ9iYiGwurwFHxpcVwaL7cRKbG481Q3NAWT8cR/D6C0oQM3/GMzFhdl4dGLTkNmQvcJIyX1HQCAUamxcqWrWklmA2qsNjR18qx2IiKiUAq4037FFVdg06ZNSE1Nxfvvv4/33nsPqamp2Lx5My677LJQ5EhEJLtWb13C/ezKIAjAwgmZ+PKBObh5diE0AvDJ7iqc94dv8Mr6EjhdnqPdujvtLNqDLZGddiIiorAIuNMOANOmTcOKFSuCnQsRkWJJy+PTWbQrisWkx68vPg1XTMvB/3t/D7aXNeP/+3gf3i2uwG8unYASFu0hk2T2zAho7nAgReZciIiI1CzgTntpaWmftzscDvzyl78cdkJEREpktXuXx7NoV6Tx2Qn4z52z8NvLi5AQo8f+KiuueGkD9la1AgBGppplzlB9Ek8o2omIiCh0Ai7azzrrLBw4cKDHbVu3bsXkyZPx8ccfBy0xIiIlkTrtLNqVS6MRcN3p+Vjz4BxcNS0XAOByixAgoiCZRXuwSYPomjpZtBMREYVSwEX7zTffjLPPPhvbt2+Hw+HAww8/jLPPPhuXXHIJtm3bFoociYhk18qiPWKkxBnxzFWT8O87z8S0/ETMyhBh7OdIOBq67uXxHERHREQUSkOaHp+YmIh58+YhJycHgiBg3bp1mDFjRijyIyJSBN/y+DjTIJGkFDNGJOPt207Hp59+KncqqiQNomvqcAAcGUBERBQyQxpEd//99yM+Ph533nkn3nnnHRbsRKR67LQT9ZTEPe1ERERhEXDR/qc//cn3+3POOQdLly7Fww8/jKSkJADAvffeG7zsiIgUwO50o93JQXREJ0qKlY584/J4IiKiUAq4aH/uued6fJ2VlYXXXnsNACAIAot2IlKdhnZPUaLTCEiM0cucDZEySNPjOYiOiIgotAIu2ktKSkKRBxGRYtW12gAAqXEGaDSCzNkQKYO0PL7d5oLTLXMyREREKhbw9PgTiaIIURSDlQsRkSLVtXmKdi6NJ+qWEKOH4P0Mq8Mpby5ERERqNqSi/Y033kBRURFiYmIQExODiRMn4s033wx2bkREilDf5lkenxpnkDkTIuXQagTEmzzbRdpZtBMREYVMwMvjn332WTz66KO4++67MXv2bIiiiO+++w533nkn6uvrcf/994ciTyIi2UjL49Pi2GknOlGSWY+WTgeLdiIiohAKuGh/4YUX8NJLL+H666/33bZkyRKMHz8ey5YtY9FORKrT3Wln0U50okSzAWjoQLuDsx6IiIhCJeDl8VVVVZg1a1av22fNmoWqqqqgJEVEpCTde9q5PJ7oRElmz/J47mknIiIKnYCL9tGjR+Nf//pXr9vfeecdjBkzJihJEREpCTvtRH3zTZBn0U5ERBQyAS+PX758Oa655hqsW7cOs2fPhiAIWL9+Pb766qs+i3kiokgn7WlP5/R4oh4SfUU7l8cTERGFSsCd9iuuuAKbNm1Camoq3n//fbz33ntITU3F5s2bcdlll4UiRyIi2Yii6Fsez+nxRD1Jy+PbHTInQkREpGIBd9oBYNq0aVixYkWwcyEiUpw2mxNdDjcAFu1EJ0uM9fyd4J52IiKi0BlS0e5yubBq1Srs378fgiDg1FNPxZIlS6DTDelyRESKJS2NN2pFmA38N47oRL5OO5fHExERhUzAP4Hu2bMHS5YsQXV1NcaNGwcAOHjwINLS0vDhhx+iqKgo6EkSEclFKtrj9TInQqRAJw6ic7tFuN3igPFutwhx4BAiIiI6ScBF+6233orx48dj69atSEpKAgA0NTXhxhtvxO23347vv/8+6EkSEclF2s/Oop2ot0Rvp72mU8C4x1b79ZgYrRb/qt2KSflJmJiTgKLcBOQkxkAQ2K0nIiLqS8BF+86dO3sU7ACQlJSEJ554AjNmzAhqckREcqu1eop2i4HtQaKTjUqLQ26iCRXNXX4/ptMlYMPRRmw42ui7LSXWgKLcBG8Rn4iJuQlIjtGGImUiIqKIE3DRPm7cONTU1GD8+PE9bq+trcXo0aODlhgRkRLUcnk8Ub9Mei1W33cWVn38Gc6ffz70uoH/otgdDrz76ZdIKJyIvVVt2FXRjAPVrWhot+ObA3X45kCdLzbDYkRhjAYXuEXwrx8REUWzgIv2J598Evfeey+WLVuGM844AwCwceNGPP7443j66adhtVp9sfHx8cHLlIhIBrWtng5iPDvtRH3SaTWI1Xv2t+v1A5fXDoeA3Fhg0fRcX2yXw4Ufqluxq6IZuypasLuiBYdqW1HTakNNqwb7q1oxZURKOJ4KERGRIgVctF900UUAgKuvvtq3/0z0TpW5+OKLfV8LggCXyxWsPImIZCENokvgaW9EIWHSazE5LxGT8xJ9t3XYnVj8x29R0tABaxcPgSciougWcNG+Zs0aDoshoqhRY/V22rk+lyhszAYdkmINKGnoQGsXD4EnIqLoFnDRPnfu3BCkQUSkTL497VweTxRWcUbPILo2G4t2IiKKbppAH3D66aejqakpFLkQESmKzelCc4dnaW4CO+1EYWUxev7StbJoJyKiKBdw0b5161Y4HNxfRkTqJ+1n12sFmANel0REwxFn8nbauTyeiIiiXMBFOxFRtJCWxqfFGcFRHkThFWf0fFLG5fFERBTtAi7aBUHgIDoiigq1Vm/RbjHKnAlR9LGYvMvj2WknIqIoF/CCT1EUceONN8Jo7PuH2Pfee2/YSRERKUGd94z2dBbtRGHHQXREREQeARftN9xwQyjyICJSHN/yeAsPaScKN4vJ8yMKO+1ERBTtAi7aX3311VDkQUSkOL7l8XFGoFPmZIiiDPe0ExEReXAQHRFRP2q5PJ5INuy0ExEReQTcaZ86deqA92/btm3IyRARKYm0PD493oiOWpmTIYoyPKediIjII+Cifffu3TCbzbj11lsRHx8fipyIiBThxCPfSmXOhSja+M5pZ9FORERRLuCifc+ePfjZz36GN998E4899hjuvPNOaLXaUORGRCQbp8uN+jZvp93Cop0o3CzePe3tNhdcbhFaDY+bJSKi6BTwnvZx48bhww8/xDvvvIN//OMfmDBhAj766KNQ5EZEJJuGdjtEEdAIQHIsp8cThZs0iA4A2u3sthMRUfQa8iC6efPmobi4GA8//DB++tOf4txzz8X27duDmRsRkWykyfGpcUZ2+IhkYNRroRVEABxGR0RE0S3g5fEPPPBAr9sWLVqElStX4vTTT4fD4QhKYkREcvJNjo/n5HgiuZi0QLsTaO1yAIiROx0iIiJZBFy099dNnz59+rCTISJSCt/keItJ5kyIoleMt2hvY6ediIiiWMBF+9dffx2KPIiIFEVaHs8z2onkY9IBsHF5PBERRbch72knIlIz3/J4Fu1EsjFpvXvaeewbERFFMRbtRER98J3RHs/l8URyifGeKOvZ005ERBSdZC3a161bh4svvhjZ2dkQBAHvv/9+j/tvvPFGCILQ49cZZ5zRI8Zms+Gee+5BamoqYmNjcckll6CioiKMz4KI1Kh7Tzs77URyMXmLdu5pJyKiaCZr0d7e3o5JkybhxRdf7DfmwgsvRFVVle/Xp59+2uP+++67D6tWrcLbb7+N9evXo62tDRdddBFcLleo0yciFauzcnk8kdxM3sk73NNORETRLOBBdMG0cOFCLFy4cMAYo9GIzMzMPu9raWnBK6+8gjfffBPnn38+AGDFihXIy8vDl19+iQsuuKDPx9lsNthsNt/XVqsVAOBwOBR9ZJ2Umz85hipWKXmoOVYpeag5drB4t1tEXZvn34gUs04ROUdarFLyUHOsUvIIZay0PL6lwzbgY/gaR26sUvJQc6xS8lBzrFLyUHNsqK8tF3/zE0RRFAO9+JEjR/D8889j//79EAQBp556Kv73f/8Xo0aNCjhRXyKCgFWrVuHSSy/13XbjjTfi/fffh8FgQGJiIubMmYMnnngC6enpAIA1a9bgvPPOQ2NjI5KSknyPmzRpEi699FIsX768z++1bNmyPu9buXIlzGbzkJ8DEalDmwN4ZKvnM80/zHRCx+kfRLL4qlLAh2VazEhz48ej3XKnQ0REFFQdHR1YunQpWlpaEB8f329cwJ32zz//HJdccgkmT56M2bNnQxRFbNiwAePHj8dHH32E+fPnDyvxEy1cuBBXXXUVCgoKUFJSgkcffRTnnnsuiouLYTQaUV1dDYPB0KNgB4CMjAxUV1f3e92HH34YDzzwgO9rq9WKvLw8LFiwYMAXS24OhwOrV6/G/PnzodfrZYlVSh5qjlVKHmqOHSz+h+pWYOv3SDLrcclFCxSRc6TFKiUPNccqJY9Qxn634ksAQEJKBhYtmhL2HEJ5bcYqKw81xyolDzXHKiUPNceG+tpykVZ8Dybgov2Xv/wl7r//fjz11FO9bv/FL34R1KL9mmuu8f1+woQJmD59OgoKCvDJJ5/g8ssv7/dxoihCEIR+7zcajTAae+9T1ev1in5TJYHkGapYpeSh5lil5KHm2P7iGzs9MzEy4k097lNCzpEWq5Q81ByrlDxCESsNomu3u/2K52scubFKyUPNsUrJQ82xSslDzbGhvna4+ZtbwIs+9+/fj1tuuaXX7TfffDP27dsX6OUCkpWVhYKCAhw6dAgAkJmZCbvdjqamph5xtbW1yMjICGkuRKRetd4hdGkcQkckK6lob7Upe08iERFRKAVctKelpWHHjh29bt+xY4dvr3moNDQ0oLy8HFlZWQCAadOmQa/XY/Xq1b6Yqqoq7NmzB7NmzQppLkSkXt3HvfGMdiI5mbSesTucHk9ERNEs4OXxt912G26//XYcPXoUs2bNgiAIWL9+PZ5++mk8+OCDAV2rra0Nhw8f9n1dUlKCHTt2IDk5GcnJyVi2bBmuuOIKZGVl4dixY/jVr36F1NRUXHbZZQCAhIQE3HLLLXjwwQeRkpKC5ORkPPTQQygqKvJNkyciClSdVLTHs9NOJKcYntNOREQUeNH+6KOPwmKx4A9/+AMefvhhAEB2djaWLVuGe++9N6Brbd26FfPmzfN9LQ2Hu+GGG/DSSy9h9+7deOONN9Dc3IysrCzMmzcP77zzDiwWi+8xzz33HHQ6Ha6++mp0dnbivPPOw2uvvQatVhvoUyMiAgDUtvKMdiIl4DntREREQyjaBUHA/fffj/vvvx+tra0A0KOIDsTcuXMx0Ilzn3/++aDXMJlMeOGFF/DCCy8MKQciopPVWrk8nkgJpD3tdpcbNqcLRh0/kCciougzrNOHLRYLTCYTtm/f3msYHBFRpKrl8ngiRTCdUKOz205ERNEq4KK9uLgYZ555JhYvXozDhw9j4sSJmDZtGnJzc/HFF1+EIkciorARRZHL44kUQiMAsQZP5c6inYiIolXARfu9994Li8WCuLg4LFiwAHPmzEF5eTnuvPNOPPLII6HIkYgobFptTnQ53AC4PJ5ICeK8G9s5jI6IiKJVwHvad+7cieLiYhQUFCAuLg533303cnJycPfdd+Mvf/lLKHIkIgobaT+7xaRDjIH7Z4nkZjHqUAMbWrt4VjsREUWngDvtHR0dSE5OhslkQkxMDMxmMwDAbDajq6sr6AkSEYVTrZVL44mUROq0t9rYaSciougUcKcdAF5++WXExcXB6XTitddeQ2pqqm+SPBFRJPMNoePSeCJFsBi9RTuXxxMRUZQKuGjPz8/Hyy+/DADIzMzEm2++2eM+IqJI5htCx8nxRIoQZ5T2tHN5PBERRaeAi/Zjx46FIA0iImXoPqOdRTuRElhM7LQTEVF0C3hP++OPP46Ojo5Q5EJEJDsujydSFgv3tBMRUZQLuGhfvnw52traQpELEZHsuDyeSFliuaediIiiXMBFuyiKociDiEgRpE57GpfHEylC9/J47mknIqLoNKTp8b///e8RFxfX532//vWvh5UQEZGc6qxcHk+kJL5BdFweT0REUWpIRft3330Hg8HQ63ZBEFi0E1HE6rS7fPtmuTyeSBl45BsREUW7IRXtq1atQnp6erBzISKSlbSfPUav9RUKRCSvOC6PJyKiKBfwnnYiIrXyTY6PN0IQBJmzISKgu9Pexk47ERFFqYCL9jlz5vS5NJ6IKNLxjHYi5eE57UREFO0CXv/59ddfhyIPIiLZ1Vi9x71xCB2RYvgG0dmdcLtFaDRcBUNERNEl4E77lVdeiaeeeqrX7c888wyuuuqqoCRFRCQHHvdGpDxSp10UgXY7u+1ERBR9Ai7a165di8WLF/e6/cILL8S6deuCkhQRkRykQXScHE+kHEadBjpvd51L5ImIKBoFXLS3tbX1uaddr9fDarUGJSkiIjnUtfKMdiKlEQTB123nWe1ERBSNAi7aJ0yYgHfeeafX7W+//TZOO+20oCRFRCQHDqIjUiaLSQ+Ax74REVF0CngQ3aOPPoorrrgCR44cwbnnngsA+Oqrr/DPf/4T//73v4OeIBFRuHB5PJEyScPouDyeiIiiUcBF+yWXXIL3338fTz75JN59913ExMRg4sSJ+PLLLzFnzpxQ5EhEFHJ2pxtNHZ4uHpfHEykLj30jIqJoFnDRDgCLFy/ucxgdEVGkqmvzLI3XawUkmfUyZ0NEJ2LRTkRE0SzgPe0A0NzcjL///e/41a9+hcbGRgDAtm3bUFlZGdTkiIjCpfaEM9oFgedAEymJtKe9zcY97UREFH0C7rTv2rUL559/PhISEnDs2DHceuutSE5OxqpVq1BaWoo33ngjFHkSEYUUz2gnUi7uaSciomgWcKf9gQcewI033ohDhw7BZOre97lw4UKe005EEau2lZPjiZSKy+OJiCiaBVy0b9myBXfccUev23NyclBdXR2UpIiIws23PJ6T44kUp/vINxbtREQUfQIu2k0mE6xWa6/bDxw4gLS0tKAkRUQUbt1ntHNyPJHSxHk77dzTTkRE0Sjgon3JkiV4/PHH4XB4/uMUBAFlZWX45S9/iSuuuCLoCRIRhYPvjHYujydSnHgujycioigWcNH++9//HnV1dUhPT0dnZyfmzJmD0aNHw2Kx4IknnghFjkREIefb087l8USKw0F0REQUzQKeHh8fH4/169djzZo12LZtG9xuN6ZOnYrzzz8/FPkREYVF9yA6Lo8nUpruI99YtBMRUfQJuGiXnHvuuTj33HODmQsRkSxcbhENbZweT6RU3dPjuaediIiiT8BF+5/+9KcB77/33nuHnAwRkRwa2mxwi4BGAFLiWLQTKQ2XxxMRUTQLuGh/7rnnenxdXl6OrKws6HQ6CILAop2IIo60ND4lzgitRpA5GyI6Wbx3ebzN6Ybd6YZBF/BIHiIioogVcNFeUlLS42uLxYK1a9di5MiRQUuKiCicODmeSNlijVrf71u7HFwRQ0REUWXYH1ULArtSRBTZpDPaM+I5hI5IiXRaDcwGT+HOYXRERBRthlW0b9myBe3t7UhOTg5WPkREYdc9OZ7dOyKl4r52IiKKVgEvj58yZQoEQUBnZycOHz6Ma6+9FomJiSFIjYgoPLg8nkj5LCYdalttLNqJiCjqBFy0X3rppQCAmJgYjB8/HosXLw52TkREYVXjXR6fxuXxRIolndXOY9+IiCjaBFy0P/bYY6HIg4hINlweT6R83We1s9NORETRJeCifdeuXQPeP3HixCEnQ0Qkhzorl8cTKZ1UtHMQHRERRZuAi/bJkyf7JsaLogjAM0FeFEUIggCXyxXcDImIQkgURdS1eTvtXB5PpFjdg+i4PJ6IiKJLwEX77NmzsXPnTvzyl7/E0qVLeeQbEUW0pg4HHC7PB5BpPPuZSLF8e9rZaScioigT8JFv3377LV577TW89tpruPrqq1FeXo6CggLfLyKiSFLn3c+eZNbDoBvWKZhEFELc005ERNFqSD+hXn755di3bx+WLl2KSy+9FJdffjkOHz4c8HXWrVuHiy++GNnZ2RAEAe+//77vPofDgV/84hcoKipCbGwssrOzcf311+P48eM9rjF37lwIgtDj17XXXjuUp0VEUahWWhpv4dJ4IiWTlse3sWgnIqIoM+S2kk6nw3333YfDhw+jsLAQU6dOxX333RfQNdrb2zFp0iS8+OKLve7r6OjAtm3b8Oijj2Lbtm147733cPDgQVxyySW9Ym+77TZUVVX5fv31r38d6tMioigjddrT47k0nkjJ4nnkGxERRamA97QnJSX1uY/dZrPhhRdewPPPP+/3tRYuXIiFCxf2eV9CQgJWr17d47YXXngBp59+OsrKypCfn++73Ww2IzMz0+/vS0QkqWu1A2CnnUjp4rg8noiIolTARftzzz0n2/C5lpYWCIKAxMTEHre/9dZbWLFiBTIyMrBw4UI89thjsFgs/V7HZrPBZrP5vrZarQA8S/IdDuV+gi/l5k+OoYpVSh5qjlVKHmqOPTGuuqUTAJAaq+/3sUrIOdJilZKHmmOVkke4YmN0np89Wrt6/1/N1zhyY5WSh5pjlZKHmmOVkoeaY0N9bbn4m58gSue2yUwQBKxatQqXXnppn/d3dXXhrLPOwimnnIIVK1b4bn/55ZdRWFiIzMxM7NmzBw8//DBGjx7dq0t/omXLlmH58uW9bl+5ciXMZvOwnwsRRY5XD2qwo0GDy0e4MCdLEf8cElEfSluBZ/fokGQQsWwaj5clIqLI19HRgaVLl6KlpQXx8fH9xgVctO/atWvA+ydOnBjI5boTGaBodzgcuOqqq1BWVoZvvvlmwCdUXFyM6dOno7i4GFOnTu0zpq9Oe15eHurr6we8ttwcDgdWr16N+fPnQ6/XyxKrlDzUHKuUPNQce2L86xUp2Fbegj9dMxELJ/S9zUYJOUdarFLyUHOsUvIIV+yRunZc+KfvEG/SofiRc8OSQzifX7TGKiUPNccqJQ81xyolDzXHhvracrFarUhNTR20aA94efzkyZMhCAL6qvUFQYDLFdxPvx0OB66++mqUlJRgzZo1gxbVU6dOhV6vx6FDh/ot2o1GI4zG3kOn9Hq9ot9USSB5hipWKXmoOVYpeag5FgDq2jx72rOSYgd9nBJyjrRYpeSh5lil5BHq2OQ4z9yJNpsTOp2uz616fI0jN1Ypeag5Vil5qDlWKXmoOTbU1w43f3MLuGgHgE2bNiEtLW0oDw2IVLAfOnQIX3/9NVJSUgZ9zN69e+FwOJCVlRXy/IgosokiUOc78o3T44mUTBpE5xaBdrvLdwQcERGR2g3pf7z8/Hykp6cP+5u3tbX1ON+9pKQEO3bsQHJyMrKzs3HllVdi27Zt+Pjjj+FyuVBdXQ0ASE5OhsFgwJEjR/DWW29h0aJFSE1Nxb59+/Dggw9iypQpmD179rDzIyJ163IBXQ43AE6PJ1K6GL0WWo0Al1tEW5eTRTsREUWNIf2P9/nnnyM1NRWxsbHIzs7GqFGjhjRRfuvWrZg3b57v6wceeAAAcMMNN2DZsmX48MMPAXiW5J/o66+/xty5c2EwGPDVV1/hj3/8I9ra2pCXl4fFixfjscceg1arHcpTowhh7XTAxjlENExW78BOi1GHGAP/zSBSMkEQEGfUoaXTgdYuBzIT+EEbERFFhyEV7TfccIPv94IgID4+HjfccAOeeeaZgPYMzJ07t8+98ZLBZuTl5eVh7dq1fn8/UoemdjsW/PE7mEQtLr2I075p6Kx2z4eNafFcGk8UCSwmb9Fu41ntREQUPQIu2t1uz1JSh8MBq9WK48ePY/PmzXjkkUcQExOD3/72t0FPkuhEX/1Qi4Z2OwAB5U2dGJVhkDslilAtnhl03M9OFCEsJj2ATrR2sWgnIqLooRnqA/V6PVJSUlBUVIRbbrkFf/vb33qcn07qtK2sCQv/9B32NgW+HSJYvtxX4/v99vIW2fKgyCctj+d+dqLIYPHuY2/tcsicCRERUfgMuWg/2cUXX4ydO3cG63KkUJ/tqcbhunZsqZOnaO9yuLDuUJ3v6x3lzbLkQeogLY/P4PJ4oohg8U6Qb2OnnYiIosiQ9rS7XC68//772L9/PwRBwKmnnoolS5YgOTk52PmRwlS1dAEA6rrkKdq/P9KADnv3BLrtLNppGNhpJ4os0rFvXB5PRETRJOCi/fDhw1i8eDEqKiowbtw4iKKIgwcPIi8vD5988glGjRoVijxJIWq8RXt91+CDAkPhC+/S+HPHpWHNgTr8UN2GDrsTZgOP/qHAWaU97ey0E0UEqdPOQXRERBRNAl4ef++992LkyJEoLy/Htm3bsH37dpSVlaGwsBD33ntvKHIkBam2eor2LpeAxnZ7WL+32y3iq/2eov3HM/OQYBDhcovYVcF97TQ0Vod3ejwH0RFFBM8gOu5pJyKi6BJw0b527Vr87ne/67EUPiUlBU899RSPX1M5URR9RTsAHGvoCOv331XZgtpWG+KMOpxemIzCOE+nv7i0Kax5kHp0T4/n8niiSBBn5J52IiKKPgEX7UajEa2trb1ub2trg8HAo7fUrKnDAbvT7fs63EW7NDV+ztg0GHUajLB4ivbtZSzaKXCddhe6XJ5OO5fHE0WGeO5pJyKiKBRw0X7RRRfh9ttvx6ZNmyCKIkRRxMaNG3HnnXfikksuCUWOpBDVLV09vi4Nd9HuXRp//mnpAOAr2reVNcuyv54iW22bDQBg0mt8x0gRkbL5BtHZuDyeiIiiR8BF+5/+9CeMGjUKZ555JkwmE0wmE2bPno3Ro0fjj3/8YyhyJIWosfYs2sPZaS9v7MAP1a3QagTMG+cp2vNiAb3Ws7c+3B8gUOSra/UU7WlxRgiCPKchEFFgLEbPnnYujyciomgScHspMTERH3zwAQ4fPoz9+/dDFEWcdtppGD16dCjyIwWRjnsz6DSwO91hLdpXe5fGzxiRhESzAQ6HAzoNMD47HjvKW7CtrAkjUmPDlg9FPqloT+cQOqKIwSPfiIgoGvndaT95H/vo0aNx8cUX45JLLvEV7Js3bw5udqQo0hC6ybkJAIDSxo6wLUuXivbzT83ocfuUvEQAwDbua6cA1UqddhbtRBGDR74REVE08rtonz9/fp8D6ADA6XTiV7/6Fc4+++ygJUbKI53RPr0gCQJEdNhdvm5lKLV0OLD5WCMAYP5pJxftng8QtpU2hzwPUpe6Vs/oeBbtRJEjnke+ERFRFPK7aO/o6MD555+PlpaeZ2Lv2rUL06ZNw5tvvokPP/ww6AmSckid9tykGCR765yS+vaQf99vDtbC5RYxNiMOBSk9l8BP9nbaf6i2op2dFwqANIgug0U7UcSQjnzrcrjhcLkHiSYiIlIHv4v2NWvWoKury1e4u91uPPHEE5gxYwaKioqwe/duXHDBBaHMlWQmTY/PjDcizeRZFh+OAXBfeJfGn9xlB4CsBBOyEkxwi8DOiuaQ50Lq4RtEZ+FRlUSRQtrTDnAYHRERRQ+/i/bU1FSsWbMGDocD8+bNw6xZs/DHP/4R//znP7FixQokJiaGME1SAqnTnhlvQprJc1tJQ2g77XanG2sP1AHovZ9dMjU/CQCwvaw5pLmQutRxTztRxNFrNTDpPT+6cBgdERFFi4COfEtJScFXX30FURSxY8cOrFu3DpdffnmociMF6XK40NLp2UOYEW9Eaoyn034sxMvjNx5tQJvNiTSLEZNyE/uMmZLvuX1bKYfRkf+kQXTpcSzaiSKJRdrXzrPaiYgoSgR8TntKSgrWrFmD8ePHY+nSpWhqYqEUDaSl8TF6LSwmXXenPcRF+5f7panx6dBo+j5Le2qBt9Ne3hy2afYU2exON5o6PD/ws9NOFFksPPaNiIiijN/ntJ/cUbdYLFi3bh1OP/10FBUV+W5/7733gpcdKYZ0RntmggmCIPTY0y6KIgSh74J6OERRxJf9HPV2ovHZ8TBoNWhst+NYQwcKeV47DaLOO4ROK4hIMutlzoaIAmExsmgnIqLo4nfRnpCQ0OvrwsLCoCdEwbHmQB1eO6jBWfMcSNEPvyipOWE/OwCkGAGtRkCnw4Uaqw2ZCaZhf4+T7T1uxfGWLsTotZg9OrXfOKNOi6LcBBSXNmFbaROLdhpUrffPs0WPkHzgREShIy2Pb+PyeCIiihJ+F+2vvvpqKPOgIPvzN0exs0GD1ftrce3MEcO+nm8Inbc412qAnEQTyho7UVLfHpKiXVoaf/aYVJj02gFjp+Yneor2siZcMS036LmQukj72RM4OJ4o4sSx005ERFHG76KdIot0FFt5U2dQriftac+I7y7OR6SYUdbYiWMN7ThzVEpQvs+JVktL4/s46u1kngnyJdjGCfJRq8bahdWVAo6sOQKNduBxHXsqrQCAeD1nIBBFGu5pJyKiaMOiXYVaOhxo9k56rwhy0Z51Qke9ICUWONQQkgnyx5s7sfe4FYIAnHdK+qDx0jC6A9VWtNmcvk4MRY//98E+fFOmBcqO+P2YJM6gI4o4cSzaiYgoyrCyUaHSxu4iOmhFu7XvTjsAHAvBWe1feZfGT8tPQoofR3JlxJuQkxiDyuZO7CpvxqwB9sCT+hyubcU3B+shQMTV03Oh0w68nQIAjFoB+V3+F/hEpAzc005ERNGGRbsKHfMujQeCV7TXnLSnHTihaK/v6PMxw/GFd2n8fD+Wxkum5CeisrkT28qaWLRHmVfWHwMATEgS8Zsl46H3Y/iiw+HAp5+yaCeKNPHstBMRUZQJ+Jx2Ur7SE5ar17Ta0OVwDet6LrfoG9yVGX/i8vjuTrvbHby9wa1dTmw82gDAv/3sEs++dnBfe5RpaLPhvW0VAIB52W6ZsyGiUOMgOiIiijYs2lXoxE47AFQ2D6/bXt9mg8stQqsRkGbpXqqek2CCTiPA5nT7ls8Hw7eH6uFwiRiZGotRaXF+P07a1769rAmiyAFj0eKtTWWwOd0oyonHSIvc2RBRqPmWx7NoJyKiKMGiXYVKT9pjXt44vOXr0hC6tDgjtJruM611Wg3ykqUl8sHb1/7VD3UAAlsaDwCnZcXDqNOgqcOBkhAMxyPl6XK48Mb3xwAAN80qAI9cJ1I/aRCdtYt72omIKDqwaFehUm+RnmDwdJuHe+ybbwhdH2exS/vaS4I0jM7lBr456CnaA1kaDwAGnQZFOQkAuEQ+Wny48zjq2+zISjDhwvGB/XkhosgkHfnWZmOnnYiIogOLdpVptzlR591/Pi7BU7RXBKnTnhnfe4r7iNRYAMHrtB9tFWDtciI51uDbox4IaYn8trKmoORDyiWKIl75tgQAcOOsEdAPcjY7EakDB9EREVG04U+5KlPq3c+eZNYjJ1bqtA+zaLdKZ7TH9Lqv0Fu0lwRpgvzuJs/65nNPSe+xFN9fU/MTAQDbSlm0q936w/U4UNOKWIMW156eL3c6RBQmcUbpyDcn55cQEVFUYNGuMtJ+9oIUM1K8jfHyxuEtj69p6X1Gu2REirfTHoTl8aIoYk+jp1A//9ShLXWWuvMHa1q5dFLl/u7tsl89Iw8JMYMf8UZE6iAtj3e5RXQO83QUIiKiSMCiXWWkyfH5SWYkG4PTaa+Slscn9F4eL3Xayxo64BrmsW+HatvQYBNg0GlwztihnbOeHm9CTmIM3CKws7x5WPlEu067C0E8yS+oDta0Yu3BOmgE4KZZhXKnQ0RhZDZoIS3E4hJ5IiKKBizaVaa70x6DFG9jvLnDgdZhTNmtsfbfac9OjIFBq4Hd5UZVy/A6+l/u9wygmzUyGWaDbsjX8e1r5xL5IStr6MB5z32L3+7Q4vgwjwwMhX+s93TZLxifiXzvMEQiig6CIPCsdiIiiios2lVGWqZekGyGSevZ2w4MfYm8KIoD7mnXagTkJXtuPzbMfe1fHagFAJx3SvqwruPb185hdEPidLlx/792oK7NjtouAT95datvGKES1LfZ8N72SgDArWezy04UjaSz2ofzgTQREVGkYNGuMmXS8nhv9zE3yVNQD3WJfKvNiQ67Z89gZh+dduCEYXTD2Ndea+3CrgorAODcU9KGfB2ge1/79vJmDikagr+sPYLi0ibEGrVIMYooa+zEdS9vRK1VGYX7io2lsDvdmJyXOKQTBogo8lk4QZ6IiKIIi3YV6XK4cNzbES1I9hbtid6ifYjHvkkd1niTDjEGbZ8xBSnDP/bt20P1AIC8WBHplt575wNxalY8jDoNmjscOBqko+iixa6KZjz/5SEAwGOLT8Xd413ISTShpL4d17280XecoFy6HC68+X0pAE+XXRACP2GAiCIfz2onIqJowqJdRaTC3GLS+ZbFS532iqahLY+Xiva+lsZLgnFW+4YjDQC6z5YfDoNOg4m5CQCAYu5r91un3YX73tkBp1vEoqJMXDo5C8lG4I2bpiMrwYQjde340d83oqFNvsL9gx2VaGi3IycxBheOz5QtDyKSV/eedi6PJyIi9WPRriLS5PiCFLOvA+lbHj/UTrs0hC6h76XxAFCYMrzl8aIo4vsjnk77mCAU7cAJS+S5r91vT366H0fr2pFuMeKJS4t8f4byk834521nICPeiIM1bfjR3zehqd0e9vxEUfQd83bT7BHQafnPF1G06t7Tzk47ERGpH3/qVZHuyfGxvtukIXFD3dMundGeGd//kvURqZ6l+OWNHXC63AF/j9KGDhxv6YJeK2CkJThF+5R8aYJ8c1Cup3ZfH6jFmxs9y85/f9UkJMUaetw/IjUWK287A2kWI36obsWPX9mElo7wdrjWHarHodo2xBl1uHpGXli/NxEpSxz3tBMRURRh0a4i0uT4ESccgZXn67R3DmkoW5VVKtr777RnJ8TAoNPA4RJxvDnwYWXS0vjJeYnoZ9t8wKYWJAIADta2cvnkIBrb7fj5u7sAADfOGoFzxvY9CHBUWhxW3joTKbEG7D1uxfX/2ARrGF/bv397FABwzYw8xHu7bEQUnbinnYiIogmLdhUp9S2P7+60ZyXEQBCATocLDUNY0uzrtA+wp12jEXyD74ayRH6Dd2n8mYXJAT+2P+kWE3KTYiCKwE7vVHrqTRRF/PI/u1DXasOY9Dj8cuEpA8aPybBg5W1nIMmsx86KFtz4j81h+aH5QHUrvj1UD43g+WCBiKJbPI98IyKiKMKiXUWkon3ECUW7UafxdcmHsq9d2tOemTDwRHdpGF1pgEW7Zz+7p9N+xsjgFe1Az6PfqG//3lqBL/bVQK8V8Nw1k2HSD77UYVymBStunYmEGD22lTXjplc3oz3Ehfur3onxCydkIS/ZPEg0Eald9yA6dtqJiEj9WLSrhN3pRkWTVLT3LGrykrx7zocwQV6aHp8xwPJ44ISz2gOcIH+wpg0N7XaY9BpM8k58D5ap+YkAgB0s2vtU1tCB5R/tBQDcP38sJuT4//qPz07AiltmwmLSYcuxJtzx1nbYXaHJ02oHPtxZBQC45ezC0HwTIoooXB5PRETRhEW7SlQ2d8ItAjF6LdJOOuc8N3loE+Rtzu4l9QPtaQe6u/uBHvsmLY2fMSIZBl1w/zhOLfB02neUt8AdnPl2quF0uXH/v3ag3e7C6SOSccc5owK+RlFuAt68ZSbijDpsKmnCKwc0Q5qbMJj11Z55CVPzE32rJ4goukmddis77UREFAVYtKvEMd/k+O7j3iRSp70iwAnytVbPedwGrQbJJ00TP5k0QV46ds5f0hC6WaNSA3qcP07NiodJr4G1y4naoR1Tr1p/+/YYikubEGfU4Q9XT4JWIwz+oD5MzkvE6zfPgFGnwQ8tGuyvbg1qnl0OF9bXeHK79eyRQb02EUUu6ci3Nu5pJyKiKCBr0b5u3TpcfPHFyM7OhiAIeP/993vcL4oili1bhuzsbMTExGDu3LnYu3dvjxibzYZ77rkHqampiI2NxSWXXIKKioowPgtlKK3vLtpPJu0BLm8MrHKt8Z3Rbuz1QcDJpE57IMe+udwiNh6VivaUgHLzh16rwcTcRADAsbahFaVqVNYGvPD1EQDA8kvGD3uP+LSCZMwa5ZlHsP5ww7DzO9H7O6rQ7hSQm2jCgtMygnptIopcFh75RkREUUTWor29vR2TJk3Ciy++2Of9v/vd7/Dss8/ixRdfxJYtW5CZmYn58+ejtbW7m3ffffdh1apVePvtt7F+/Xq0tbXhoosugssVog22CnWsjyF0Et+xbwF22qtaBj/uTZIZb4JRp4HTLaLCz73ze4+3oLXLCYtJh/HZ8QHl5i9pOfWxVhbtANBpd+HNQ1o43SIWFWXi8qk5QbnuWaM9KyW+C3LR/u9izwdw159ZAJ2WC4OIyINFOxERRROdnN984cKFWLhwYZ/3iaKI559/Ho888gguv/xyAMDrr7+OjIwMrFy5EnfccQdaWlrwyiuv4M0338T5558PAFixYgXy8vLw5Zdf4oILLgjbc5FbWWPv494kUif1eHMnXG7R76XQvk67H0W7RiNgREosDtS0oqSh3TdNfiDS0viZhSnQaTVwuIP/QYs0jK6ERTsA4OnPD6K2S0C6xYgnLi0adAWFv87yrpTYWtaMTrsLMYbBp9APpq7Vhl2VnuP6FhdlDvt6RKQe0vL4TofL79VdREREkUrWon0gJSUlqK6uxoIFC3y3GY1GzJkzBxs2bMAdd9yB4uJiOByOHjHZ2dmYMGECNmzY0G/RbrPZYLPZfF9brZ7CwOFwwOFQ7v44Kbe+cjxW3wYAyE009HgeDocDyTE66LUCHC4RFQ2tyE7seeZ6f9c97u3MZ1gMPe7rLz4/OQYHalpxpMaKs0YmDZrzd4fqAAAzCxN75TwYf2MnZscBAGo6gXUHanDOuIGXWIcih0BjQ3XtvceteGtzOQDgiUtOQZxBGPAxgeSQm6BHokFEs92N7w/X4uwx/c8o8Pe6X++v9lw7VkSSSTNovBJeY8YqLw81xyolDzlijZruoZdNbV2I1aPf2OHkEGg8YwOPVUoeao5VSh5qjlVKHmqODfW15eJvfoIYinHPQyAIAlatWoVLL70UALBhwwbMnj0blZWVyM7O9sXdfvvtKC0txeeff46VK1fipptu6lGAA8CCBQtQWFiIv/71r31+r2XLlmH58uW9bl+5ciXM5sg7A9otAg9t0sIlCnhsqhPJfRyp/v9t16K+S8A9pzkx2s+TvV47qMH2Bg0uLXBhXvbgf0w+LNXgq+ManJ3pxpWFA3c+nG7g4S1a2N0CfjHRiexY/3IaCul56DUi7jzF5ffzV5sPSjVYc1yDSclu3Dwu+J2pfx7RYGOtBnOz3LhsxPCvL71vC3LcWJzPThoR9fTQRi0cooBfT3EiZfAFYURERIrT0dGBpUuXoqWlBfHx/W8XVmynXXLy8l1RFAdd0jtYzMMPP4wHHnjA97XVakVeXh4WLFgw4IslN4fDgdWrV2P+/PnQ6/W+2yuaOuHa+C30WgHXLVkIrUboFfuv2mLUH2lAzrhJWHTSPub+rvtG5WagoRnzZk7BohOWJ/cX37a1Al99sA+IS8OiRdMGjN1a2gT7pi1IjtXj5ivmQ9NHzkN5LfoyZ54N1/35a+xv1uCVw0b84/qpmFbQ99FhocohkNhQXFsURTzz3HoAnZiaKobk+W2v/xIba4EqdzwWLZo1rOs6XW48uv0bAE6cluSOiNeYscrMQ82xSslDrtjHd32DhnY7pp95NkanmvgaR2isUvJQc6xS8lBzrFLyUHNsqK8tF2nF92AUW7RnZnqKxOrqamRlZflur62tRUZGhi/GbrejqakJSUlJPWJmzeq/aDAajTAae7ej9Xq9ot9Uycl5Vra0AADyk80wGQ19xuanxOK7Iw04brX3+xxPvm5Nq2cFQ25KbJ+POTl+VIbnA4+yps5e8SfHbj7myfnMUakw9pOzP/yJjQVw81g3VjWkYsORRtz65na8ccvpA575HewchhIbzGvvqWxBRVMnTHoNTk10hiTnsQkiBAE4UNOGpk4X0geZhTDQdXdWNsLa5URijB4FcaHJdyjxjA08Vil5qDlWKXmEOzY+Ro+Gdjs6naLvfr7GkRurlDzUHKuUPNQcq5Q81Bwb6muHm7+5KXYcc2FhITIzM7F69WrfbXa7HWvXrvUV5NOmTYNer+8RU1VVhT179gxYtKuNdEZ7X5PjJXnJnn3sFY3+TZAXRdF3Trs/g+gAoNA7fK6iqROOQQYDbThSDyA0R731xaAF/rJ0Cs4cmYI2mxM3vLIZuyqaw/K9leC/e6oAAOeMSYVx+DPi+hSrByZ4TwH49lD9sK719YFaAMBZo1MwxCPkiUjlpAnybTZOkCciInWTtWhva2vDjh07sGPHDgCe4XM7duxAWVkZBEHAfffdhyeffBKrVq3Cnj17cOONN8JsNmPp0qUAgISEBNxyyy148MEH8dVXX2H79u348Y9/jKKiIt80+Wgw0OR4SV6S96x2P499a2y3w+4tvNMt/hXt6RYjYvRauNwiygf4cKDT7sL2smYAwKxR/Q8sC7YYgxav3Dgdp49IRqvNiR//fRP2VLaE7fvLRRRF/He3Z6jbheNDe9a5NEV+/eFhFu0/1AEA5o4N358PIooscUYe+0ZERNFB1qJ969atmDJlCqZMmQIAeOCBBzBlyhT8+te/BgD8/Oc/x3333Yef/vSnmD59OiorK/HFF1/AYrH4rvHcc8/h0ksvxdVXX43Zs2fDbDbjo48+glYbonaiAh2r93baU/sfoicd+1be6N8Z6tIZ7alxBhh0/v0xEQQBBSme7yN1//uytbQRdpcbWQkmjEgJ7+A/s0GHf9w0A1PzE2HtcuLHr2zC/ir/9pJEqoM1bTha3w6DVoO5Y9NC+r1mj/YU7d8eqofbPbQZl9UtXdhXZYUgAGcNMIWeiKKb76x2dtqJiEjlZC3a586dC1EUe/167bXXAHiKwGXLlqGqqgpdXV1Yu3YtJkyY0OMaJpMJL7zwAhoaGtDR0YGPPvoIeXl5Mjwb+ZQ2+NNp9yyPr2ntgs05+Hno0hntmQmBjeSVlsiX1PffaZfOZz9zVErQzgkPRJxRh9duPh2T8hLR3OHAj/6+CQdrWsOeR7h8utu7NH5squ+H3FCZnJcIs0GL+jYbfqge2mu69qBnafzE3ESkxBoGiSaiaBVn9OwDbO1S9nE+REREw6XYPe3kH7dbRGmjp6tdkNx/1zo51gCzQQtRBCqbBu+2V0tFu5/72SUjvEW71P3vi1S0h3Np/MniTXq8cfPpmJATj8Z2O5a+vAmHa9tkyyeUpP3sCydkDRI5fEadBmeMlJbI1w3pGt8c8Dxu3rjQrgogosjm29PO5fFERKRyLNojXG2rDV0ON7QaATnebnpfBEE4YV+7H0W7d3m8v0PoJIXebn9/y+OtXQ7s9g6AOzNMQ+j6kxCjx4pbZuLUrHjUt9mw9OWNAy7rj0SHa9twsKYNOo2A808N7X52yVmjPR/GDGUYncPl9j1u3rj0oOZFROoSb+KediIiig4s2iOcVGTmJsVArx347ZQmyA80JE4iFe1D7bSX9NNp33y0EW4RGJFiRk5i/x8yhEui2YC3bp2JcRkW1Lba8JN/bEV9l9xZBc9n3i777NGpSDCH57iLc7zD4zaVNKLLMfhWjBNtPdaENpsTKbEGFOUkhCI9IlKJOF/RzuXxRESkbizaI1yZH/vZJbkBTJCvHuKedmkY3vHmzj73znfvZ1fOgLHkWAPeum0mRqfHodpqwwt7tVh/uEHutILiU+/U+IUTMsP2PUelxSErwQS7043NJY0BPfYb71Fvc8amQcOz3ohoABaT54NIHvlGRERqx6I9wnWf0T74FHZpgnyFHxPkhzqILi3OiFiDFm6x70n14T6f3V+pcUasvHUmRqaa0WwXcNPrxbh75Tbf6xCJShvasa/KCq1GwILx4SvaBUHwLZEP9Og3aT/73FO4NJ6IBiYd+Wbl8ngiIlI5Fu0Rzp/J8RJpgrw/nfaqIS6PFwSh32F0DSdMFJeGlSlJerwJ794xE+dkuqERgI93VeG8P6zFP9aXwOk9sz6S/HePp8t+xshkJId5CvvZ3qPl1h30fxhdZXMnDtS0QiMA5/CoNyIaBAfRERFRtGDRHuGG0mkfbE97h93pG+yTEWCnHThhgvxJQ902HvUslR6XYUGaxRjwdcPBYtLjikI33rvzDEzKS0SbzYnHP96HJf/3HbaXNcmdXkCkov3CMEyNP9lZo1MhCMAP1a2obfVvtYK0NH5qfhISzTzqjYgG1n1OO/e0ExGRurFoj2CiKJ7Qafe/aG/qcAy4B1AaQhdr0MJiDPxcb+kDhJOH0UlL4+WeGu+P8dnxeO9/ZuGJyyYg3qTD3uNWXP7SBjz83m40d9jlTm9QFU0d2FneDEEALhgfnqnxJ0qONWB8djwA4Ds/l8h//YP3qDcujSciP0h72jk9noiI1I5FewRraLejzeaEIHQPmRtInFGHJO8E8YG67dIQuowEEwQh8GFgI/o59u173/nsyi/aAUCrEfCjmQVY89BcXDE1F6II/HNzGc77w1q8W1wBURTlTrFfn3m77DMKkpFuCXy1RDCcPcazRP7bg4MX7Tany/ehzpyxPJ+diAZ34vJ4Jf97TERENFws2iNYqbcozk6IgUmv9esx/iyRH+pxb5JC35727u9R1dKFo/Xt0AjATAXuZx9IapwRf7h6Et65/QyMSY9DQ7sdD/17J5a+sgVVg48HkIVUtC8sCt8AupOd7d2X/u3h+kF/oN5S0oQOuwvpFqOvQ09ENBBpEJ3TLaLLEXlzR4iIiPzFoj2CBbI0XpLnO/at/wnyQz3uTSLtaT/e0gmb95zuTd6jvybkJCAhJjznhQfbzJEp+OTes/HLhacgRq/F1tJmPLNL69urrxTVLV3YWurZf39hGI96O9m0giTE6LWoa7XhQE3rgLFfe/ezzx2XNqTVHUQUfWINOkj/XPDYNyIiUjMW7RHsWACT4yW5yd4J8gN02muG2WlPiTXAYtRBFIEy77Fv33sL20jYzz4Qg06DO+eMwuoHzsFZo1PgEgX87D+70dKhnEFIn+/1dNmn5CciKyFGtjyMOi1mjkwGMPgSealonzeO+9mJyD8ajYA4g3cYHfe1ExGRirFoj2ClAUyOl0id9ooBjn3zHfc2xE57j2PfGjogit2T42eNUsdRXrlJZvzfdZOQZhJRbbXh0Q/2yJ2Sz3/3VAEAFskwNf5k0r72dYf6P/qtrKEDR+vaodMImM2j3ogoAN0T5Fm0ExGRerFoj2DHhrI83renvf/l8TXSILohdtqBE459a2xHgw043tIFnUbAjBFJQ76m0pgNOvxktAtajYAPdx7HBzsq5U4J9W02bPZuRZBzabxE2te+uaQRXd6tEif75qCnyz6tIAnxpsjcOkFE8pAmyHN5PBERqRmL9ggmddoDWR6fl+RdHt/U0e9wMGlPe9YQO+0AUOj9IKG0oRMHWzybDqfkJ8JsCPwIOSUrsAB3zRkJAPh/7+9BZXP/H4aEwxd7a+AWgaKcBN8HNHIakx6HjHgjbE43th7r+5z7r3/wLo3nUW9EFKA4E5fHExGR+rFoj1AtHQ40e/dRB9Jpz0mKgSAAHXYXGtt7nzfudLlR12oDMPQ97UB3p720oR2HvEX7mSpZGn+y/5lTiMl5iWjtcuKhf+2E2y3f0UPS0ng5p8afSBCE7qPf+lgi3+VwYYP3KEDuZyeiQPmOfWOnnYiIVIxFe4QqbfR02dMtxoC610adFhnec7v7miBf12aDW/ScUZ4SZxxyflL3v6ShAwetnqI9Us5nD5ROq8Fz10xGjF6L74824JX1JbLk0dRh9xXACxWwn10iLZFfd6j3MLrvjzbA5nQjO8GEsRlx4U6NiCKcdOwbO+1ERKRmLNojlLSffUQAS+MleQNMkJfOaE+3GKHVDP3oLems9hqrDW0OAUadBlPyE4d8PaUrTI3Fry8+DQDwzOcHsL/KGvYcvvqhDi63iFMyLb7XXwlmj/YU7furrL5VHJJvvEvj54xL51FvRBQw7mknIqJowKI9QpXWS/vZA9+33H1We++ivWaYZ7RLksx6xJu6VwBMK0iEUacd1jWV7toZeTj/1AzYXW7c/86Ofgev+aO/eQMD+WxvDQBgUZFyuuwAkBpnxPjseADAd4e7u+2iKOLrA54l8/PGpcmSGxFFNun/mTZ22omISMVYtEeooUyOl+QOMEG+ephntEsEQejR7T2zMHlY14sEgiDgqSuKkBpnwA/Vrfj95wcCvkabzYnffLwPU5/4Gm8f0aCpo/fcgb50OHHC0nhl7Gc/0Vm+JfLd+9qPNXSgrLEDeq3g68YTEQXCtzyenXYiIlIxFu0RaiiT4yXSBPm+zmqvCsJxb5IRJxTtZ4xUf9EOeLrKT18xEQDw9/Ul2HC49z7uvoiiiE93V+H8P6zF39eXoM3mxPe1Glzwx+/wr63lgw6329skwOESMTo9DmMyLMN+HsF2jncY3fpD9b5VBN8c9Lw2MwtTEGtU16kCRBQeFnbaiYgoCrBoj1DD29Muddr7WB7fEpzl8UB3bkatiAne5dHR4LxTM3Dd6fkAgAf/vRMt3in//SltaMeNr27BT9/ahmprF/KTzVh28anIihHR1OHAz9/dhav/+j1+qO5/n/zOBs9+cCV22QHPGewmvQa1rTYcqm0DAKz1Fu1zuTSeiIYozrunnZ12IiJSMxbtEajN5kR9m2egV/5Q9rR7i/bK5k64TurgBuOMdsm0giQAwIQkETptdP1Re/SiU1GYGouqli48+sGePmNsThf++OUhzH9uHdYerINBq8G9543BF/efgx+dnoefTXThFxeMhdmgxdbSJiz+03o8+el+tJ/0w2mbzYn9zVLRrqz97BKTXovTCz2nB6w/3ACbC9h8rBEAMJdHvRHREPHINyIiigbRVUmphLQXPTnWgIQYfcCPz4w3Qa/1LKeWBs9JpD3twVgef87YNLx7x0xcPdI97GtFGrNBh+eumQytRsCHO4/jw51VPe5ff6geFz7/LZ778iDsTjfOHpOKz+8/Bw/MHwuT3jOwT6sBbj1rBL58YA4uHJ8Jl1vE39YdxfnPrsVne6p8y8zXHqyHUxRQkGzGqVnKWxovOce7r/27Iw041OL585eXHINRacqZdE9EkcXCI9+IiCgKsGiPQKWNQx9CB3jOYM9O7H3smyiKvk77cAfRSSblJsCk7qHx/Zqcl4h7zh0NAFj28X402jzT+e/553b8+JVNKKlvR7rFiBeum4I3bj6932PashNj8JefTMOrN85AXnIMqlq6cOeKbbj5tS0oa+jwTY2/YLyyj00727uvffOxJuxs9OQ5j0e9EdEw+I58Y9FOREQqxulPEah0GPvZJXlJZpQ2dKC8qRNT8zz7za1dTnQ5PF3xYOxpJ+DueaPxzYE67Chvxl/3a/H7vd+h3eaCRgBumDUCD8wf6/uhczDzTknHFyPn4M/fHMZf1h7B1wfqsOG5tZA2OFw4PiN0TyQIxmbEId1iRG2rDVvrPIU697MT0XBweTwREUUDdtojUJm3O56fPLROOwDkJffutEtL5RPNet8SbRoenVaD566ZjBi9BtWdAtptLkzOS8SHd5+Fxy4e73fBLokxaPHggnH47L5zMGtUCmxON+xON5KNyh/2JwiC7+g3NwQYdBqcOZJHvRHR0MV5i/Z2uwuDHLJBREQUsVi0RyBpefyI1KEX7blJ3gnyJxz7Vm31DLcL1tJ48ihMjcXvrihCXqyIxy85Fe/9zyxMyEkY1jVHpcXhrVtn4o/XTsa0/ERcnO+OiGXm0tFvAHBGYRJiDPxwiIiGTuq0A0CXS8ZEiIiIQojL4yOQtDx+KGe0S6QJ8hXeoXZAd6c9GEPoqKcLx2fAXerCohl50GiCU1wLgoAlk3OwaHw6Pv3006BcM9Rmj+7urM8Zy6XxRDQ8Rp0WBq0GdpebRTsREakWO+0Rxu7q7ogPb0+7d3l8H532YBz3RtSXNIsRc8emwqwVMf9UHvVGRMMndds7ua2diIhUikV7hGn01NWwmHRIMgd+3JtE6rRXW7tgc3qGz9V4i3Z22imU/rx0MpZPc/HDISIKCqloZ6ediIjUikV7hKnr8iytHpESO6w9zCmxBsTotRBFoKrFs0Ted9wbiykKIb1WA25lJ6JgifMV7cqf60FERDQULNojTL2nrkb+EM9olwiC0D1BvslTtNdwEB0REUUYi9Gz6oyddiIiUisW7RGmu9M+vKId8JzVDgAVvqKdnXYiIooscdzTTkREKseiPcJInfbhTI6X+CbIN3XC4QaaOhwA2GknIqLIIe1pt7HTTkREKsWiPcLUn7CnfbhyvRPkK5o60WL33GbQaZA4jAF3RERE4WQxejvt3NNOREQqxaI9gtidbt/0+KAsjz+h0y4V7ZnxpmENuCMiIgoni8m7p53L44mISKVYtEeQ4y2dECEgRq9BmsU47OtJe9rLmzrRYvcU6tzPTkREkYRHvhERkdqxaI8gpQ0dAICCZHNQuuHS9PimDgdqOr1FO/ezExFRBPENomPRTkREKsWiPYKUNnqmvEvL2ofLYtL79q8fbfXcxk47ERFFEt/yeBbtRESkUizaI4iv0x6E/ewSaYn8sVZPpz2DnXYiIoog0iC6Lg6iIyIilWLRHkFKG7uXxweLtETe7vb8sJPFTjsREUUQC89pJyIilWPRHkHKpaI9JSZo15Q67RJ22omIKJJweTwREamdTu4EyH/jMizoaG8Paqc996RrcU87ERFFkrgTpseLoihzNkRERMHHTnsE+dO1k/DwZBeyE4PZae++liAA6UE4So6IiChcpOXxLlGA3emWORsiIqLgY9Ee5U6cRJ8aa4Beyz8SREQUOWIN3YsGW23c2E5EROqj+AptxIgREASh16+77roLAHDjjTf2uu+MM86QOevIkXNC15772YmIKNJoNQJijVoAQGsXi3YiIlIfxe9p37JlC1yu7ukye/bswfz583HVVVf5brvwwgvx6quv+r42GAxhzTGSmfRaZFiMqGm1ISOeS+OJiCjyxBl1aLe50MZOOxERqZDii/a0tLQeXz/11FMYNWoU5syZ47vNaDQiMzMz3KmpRm5SDGpabchkp52IiCKQxahDDWzstBMRkSopvmg/kd1ux4oVK/DAAw9AEATf7d988w3S09ORmJiIOXPm4IknnkB6enq/17HZbLDZbL6vrVYrAMDhcMDhcITuCQyTlJs/OQYSW5Acg+KyZmTFG4J+bcYGHquUPNQcq5Q81ByrlDzUHKuUPJQQG+8dRlfT0jloPF9jZcUqJQ81xyolDzXHKiUPNceG+tpy8Tc/QYyg81H+9a9/YenSpSgrK0N2djYA4J133kFcXBwKCgpQUlKCRx99FE6nE8XFxTAa+17uvWzZMixfvrzX7StXroTZHLzj1CJFQxewpU7A2ZkiYvVyZ0NERBSYd0s0+LZag3My3biikBPkiYgoMnR0dGDp0qVoaWlBfHx8v3ERVbRfcMEFMBgM+Oijj/qNqaqqQkFBAd5++21cfvnlfcb01WnPy8tDfX39gC+W3BwOB1avXo358+dDrx+4ug5VrFLyUHOsUvJQc6xS8lBzrFLyUHOsUvJQQuyHOyrw4H/24bSsOHzw01lBu24oc2assvJQc6xS8lBzrFLyUHNsqK8tF6vVitTU1EGL9ohZHl9aWoovv/wS77333oBxWVlZKCgowKFDh/qNMRqNfXbh9Xq9ot9USSB5hipWKXmoOVYpeag5Vil5qDlWKXmoOVYpecgZO6MwFQDwQ3UbbG4BccbBf7zha6ysWKXkoeZYpeSh5lil5KHm2FBfO9z8zU3xR75JXn31VaSnp2Px4sUDxjU0NKC8vBxZWVlhyoyIiIjklJVgQrJRhFsEtpc1yZ0OERFRUEVE0e52u/Hqq6/ihhtugE7X/el5W1sbHnroIXz//fc4duwYvvnmG1x88cVITU3FZZddJmPGREREFE6FFs9uv63HWLQTEZG6RETR/uWXX6KsrAw333xzj9u1Wi12796NJUuWYOzYsbjhhhswduxYfP/997BYLDJlS0REROE2UiraSxtlzoSIiCi4ImJP+4IFC9DXvLyYmBh8/vnnMmRERERESjIy3vNzwvayZjhcbui1EdGXICIiGhT/RyMiIqKIlxnjOa+9w+7C/iqr3OkQEREFDYt2IiIiingaAZiSnwiA+9qJiEhdWLQTERGRKkyXinbuayciIhVh0U5ERESqMLUgEQCw5VhTn7NwiIiIIhGLdiIiIlKFiTkJ0GsF1LXaUN7YKXc6REREQcGinYiIiFTBpNeiKCcBALDlGJfIExGROrBoJyIiItWYMSIZAPe1ExGRerBoJyIiItWYVpAEwLOvnYiISA1YtBMREZFqSEX74do2NLXbZc6GiIho+Fi0ExERkWqkxBkxKi0WAFBcym47ERFFPhbtREREpCrSvvYt3NdOREQqwKKdiIiIVEVaIl/Mfe1ERKQCLNqJiIhIVaRO+66KFnQ5XDJnQ0RENDws2omIiEhVClLMSI0zwu5yY3dli9zpEBERDQuLdiIiIlIVQRAwY4R09Bv3tRMRUWRj0U5ERESqw33tRESkFizaiYiISHWkfe1bS5vgdosyZ0NERDR0LNqJiIhIdU7LjkeMXouWTgcO17XJnQ4REdGQsWgnIiIi1dFrNZiSnwiA+9qJiCiysWgnIiIiVZrOfe1ERKQCLNqJiIhIlaZ797VvKWWnnYiIIheLdiIiIlKlKfmJ0AhAeWMnaqxdcqdDREQ0JCzaiYiISJUsJj1OzYoHAGzlEnkiIopQLNqJiIhItaR97RxGR0REkYpFOxEREanWdN957SzaiYgoMunkToCIiIgoVKaP8HTa9x23os3mhJHtCopwDW02bC9twDdVAmo2lEKjGfwPtdvtxr7j/sUzNvBYpeQRibFHagTMaLUhO1k/YGy0Y9FOREREqpWVEIPcpBhUNHViR1kzZo5IkDslIr+1dDqwp7IFOyuasbuiBbsqWlDZ3Om9V4tVxw4EcDUt3i/1N56xgccqJY/Ii/3P79fhggmZ+NHMfJw5MgWCIPj52OjBop2IiIhUbXpBEiqaOrHlWCOLdlKsdpsTh63AP747hr1VbdhV0YxjDR19xo5MNSPe3Yb83BxoNIMXOG63iMrjlcjJHjyesYHHKiWPSIzdefQ4StuAT3ZV4ZNdVRiZFosfzSzAlVNzkWBm913Cop2IiIhUbfqIZLy/47h3X3uh3OkQocvhwt7jVuyuaMauyhbsrmjB4bo2iKIO2HuwR2x+shlFuQmYlJuAopxETMiJh0kLfPrpp1i0qAh6/eCFjcPhwKeflvsVz9jAY5WSR6TGFk45G+8UV+L97ZU4WteO/+/jffjdZz/g4knZ+NHMfEzOS4z67juLdiIiIlK1Gd5hdNvLmuF0uWXOhqKN0w3srmzBvup2zxL3yhYcrGmFyy32ik00iJgxKgOT85NQlJOAibkJSDQbesU5HI5wpE4UFqdmWfDEZUV4eNGpeH97JVZsLMUP1a14t7gC7xZXYHx2PH40swCLxqfJnapsWLQTERGRqo1Jj0O8SQdrlxM/VLfJnY4itNuc+PUHe7B6txa/2f0NMFgXSxThdmjxacsOTMpPwsScRBTlJAxp+Wp1Sxd2VTRjd6Vnj/aBais6Ov3Pw2bzPzYWWmwVf8DkvCRMyktAYWoctH4sdR4qp8uNQ7Vt2F3h2Ye+q6IZ+45r4dq0qVdsapzR0z3P9RTnp2bEYvO6r7Bo0WS/OrtEahNn1OHHZxTgRzPzsa2sGW9tKsXHu6qw97gVv1q1G49/rIEB/v/9H2XWYFE4Eg8DFu1ERESkahqNgGkFSfj6QB22ljUhXe6EZFbe2IHb3tiKH6pbAQiAw+7nIwV8vq8Wn++r9d0yIsWMotxETMzxFJ8TchJ6TOivb7P5BqjtrmzGrooW1Lba+rx2IHn4G1sHAcc2luHNjWUAgFiDFuNzEjAxJwET8zx5F6SY/fy+PbncIkprW7HL+/x2VTRjX5UVXY6TV3MISIzR+76fVKRnxpt6LPll95zIQxA8/2ZPK0jCo4tPw3+2VeCtTWUoqW9HVwB//7N7L1KJWCzaiYiISPWmj0jG1wfqUFzajIXxcmcjn++PNOCnbxWjqcOB1DgDLsvtxMXnngWdbuAfCZ1OJ774Zj3Meadgb5Wnk1zW2IFjDZ5fH+08DsDT/BqZGgujQ4On963D8ZauXtfSCMDYDItn+XdeIsalmbF10wacddbZfuWxfv23fsXaHQ78Z/V30KYVYm9VK/ZUWtFud2FzSSM2lzT64uJNOkzIjoejVYNv/rMbwmDHVLnc2FOixa+K16Dd7up1v8Wow4ScBEzMS8D4zDjUH9yGH182HwaDiioIojBJijXg1rNH4ubZhThQ1Yyv167z+9+Krd9/G6YsQ49FOxEREametK99W1kzLhwvczIyEEURKzaWYvlH++B0iyjKScD/XTcJ279bg1OzLH4NjCpJELHorEJfbFO7HbsrW7zL3D1Hkh1v6cKRunYAGgBdviJ+Yq5nOf2kvASclpWAGIO2x7Wr9sD/PGL9jy1NE7Fo0SnQ6/VwuUUcrm3rsTR/X5UV1i4nNhxtBKDBlroqf15OAAIAF2L0WkzIiUdRTiImejvoI1JifVOzHQ4HPi1D1A/RIhoujUbA6PQ4HAzg73+JKUzJhQGLdiIiIlK9ibkJ0GsF1Lba0NDX6mwVszvdeOzDvfjnZs8S8SWTs/H0FROhhRvbh3HdpFgDzhmbhnPGdg+Hqmu1YXtpA774bisunTcTk/KTYTEpY3+2ViNgXKYF4zItuGp6HgDPa3OwphU7yxqxecdunHLKqdBqB+60u1xulB/ejx8tPAvjshKhGySeiGi4WLQTERGR6pn0WhTlJGBbWTOOtkZP17O+zYafrtiGzccaIQjALy48BXecMxKCIMDRa+/18KVZjJg3Lg2dR0TMLExW/EA1g06DCTkJGJduRlztLiw6a4R/x1RZ92FshoUFOxGFBYt2IiIiigozRiR7inZrdBTteypbcMebxahs7oTFqMOfrpuCeadE+xg+IqLIw48HiYiIKCpM9+5rL4mCTvunu6tx5V82oLK5EyNTY7Hqrtks2ImIIhQ77URERBQVphUkAQCqOwU0ddiRnqDspdtD4XaL+KRMgy++3wUAmDM2DX+6bgoSYtT3XImIogWLdiIiIooKybEGjEyNxdH6dlz5100wGwb/MUgURVhbtfjz0Q2DTgBXQmy7zYnyJs9CyjvOGYmfX3gKtBr1rywgIlIzFu1EREQUNeaNS8XR+naUNXYG8CgBVR1tEROrE0Q8dXkRrpxR4Oe1iYhIyVi0ExERUdR44PwxiG0+gqkzZkKnHfzHIKfLic2bNuP0macPGq+U2NJdm7BkcvaAcUREFDlYtBMREVHUMOg0GBUPzB6V4tdxZA6HAy0HRL/ilRM7YAgREUUYTo8nIiIiIiIiUigW7UREREREREQKxaKdiIiIiIiISKEUXbQvW7YMgiD0+JWZmem7XxRFLFu2DNnZ2YiJicHcuXOxd+9eGTMmIiIiIiIiCh5FF+0AMH78eFRVVfl+7d6923ff7373Ozz77LN48cUXsWXLFmRmZmL+/PlobW2VMWMiIiIiIiKi4FD89HidTtejuy4RRRHPP/88HnnkEVx++eUAgNdffx0ZGRlYuXIl7rjjjn6vabPZYLPZfF9brVYAnomrDocjyM8geKTc/MkxVLFKyUPNsUrJQ82xSslDzbFKyUPNsUrJQ82xSslDzbFKyUPNsUrJQ82xSslDzbGhvrZc/M1PEEVRDHEuQ7Zs2TI888wzSEhIgNFoxMyZM/Hkk09i5MiROHr0KEaNGoVt27ZhypQpvscsWbIEiYmJeP311we87vLly3vdvnLlSpjN5pA8FyIiIiIiIiJJR0cHli5dipaWFsTHx/cbp+ii/b///S86OjowduxY1NTU4De/+Q1++OEH7N27FwcOHMDs2bNRWVmJ7Oxs32Nuv/12lJaW4vPPP+/3un112vPy8lBfXz/giyU3h8OB1atXY/78+X6d0xqKWKXkoeZYpeSh5lil5KHmWKXkoeZYpeSh5lil5KHmWKXkoeZYpeSh5lil5KHm2FBfWy5WqxWpqamDFu2KXh6/cOFC3++Liopw5plnYtSoUXj99ddxxhlnAAAEQejxGFEUe912MqPRCKPR2Ot2vV6v6DdVEkieoYpVSh5qjlVKHmqOVUoeao5VSh5qjlVKHmqOVUoeao5VSh5qjlVKHmqOVUoeao4N9bXDzd/cFD+I7kSxsbEoKirCoUOHfPvcq6ure8TU1tYiIyNDjvSIiIiIiIiIgiqiinabzYb9+/cjKysLhYWFyMzMxOrVq3332+12rF27FrNmzZIxSyIiIiIiIqLgUPTy+IceeggXX3wx8vPzUVtbi9/85jewWq244YYbIAgC7rvvPjz55JMYM2YMxowZgyeffBJmsxlLly6VO3UiIiIiIiKiYVN00V5RUYHrrrsO9fX1SEtLwxlnnIGNGzeioKAAAPDzn/8cnZ2d+OlPf4qmpibMnDkTX3zxBSwWi8yZExEREREREQ2foov2t99+e8D7BUHAsmXLsGzZsvAkRERERERERBRGEbWnnYiIiIiIiCiasGgnIiIiIiIiUigW7UREREREREQKpeg97eEiiiIAwGq1ypzJwBwOBzo6OmC1WqHX62WJVUoeao5VSh5qjlVKHmqOVUoeao5VSh5qjlVKHmqOVUoeao5VSh5qjlVKHmqODfW15SLVn1I92h8W7QBaW1sBAHl5eTJnQkRERERERNGktbUVCQkJ/d4viIOV9VHA7Xbj+PHjsFgsEARB7nT6ZbVakZeXh/LycsTHx8sSq5Q81ByrlDzUHKuUPNQcq5Q81ByrlDzUHKuUPNQcq5Q81ByrlDzUHKuUPNQcG+pry0UURbS2tiI7OxsaTf8719lpB6DRaJCbmyt3Gn6Lj4/3+w9fqGKVkoeaY5WSh5pjlZKHmmOVkoeaY5WSh5pjlZKHmmOVkoeaY5WSh5pjlZKHmmNDfW05DNRhl3AQHREREREREZFCsWgnIiIiIiIiUigW7RHEaDTiscceg9FolC1WKXmoOVYpeag5Vil5qDlWKXmoOVYpeag5Vil5qDlWKXmoOVYpeag5Vil5qDk21NdWOg6iIyIiIiIiIlIodtqJiIiIiIiIFIpFOxEREREREZFCsWgnIiIiIiIiUigW7UREREREREQKxaKdiIiIiIiISKFYtBMREREREREplE7uBIiIiIiIguHQoUPYsGEDqqurIQgCMjIyMGvWLIwZM8bva7S3t6O4uBjnnHPOkHJwuVzQarW+rzdt2gSbzYYzzzwTer1+wMfedNNNeOKJJ5CdnT1gXFNTEw4fPoysrCzk5uYOGNvc3Ix///vfKCsrQ0FBAa666iokJCQAAIqLizFt2jQ/nxlQW1uLvXv3Ytq0aYiPj0dNTQ1ef/11uN1uLF68GEVFRT3ijx49ivXr16OqqgparRaFhYWYP38+4uPje1070t87IPjvH9+7wAz3/VM0kRRr69atAcXX1NSIa9asEVtaWkRRFMXq6mrx6aefFn/729+Ku3bt6hV/5MgR8fXXXxefeuop8ZlnnhHfffdd32NPdvDgQfG1114Tn3rqKfHpp58WX3vtNfHgwYMB5dfW1iauXbs2oMecyOl09vh648aN4tq1a0W73e7X42+88UaxsrJy0LjGxkZx8+bNYnl5+YBxTU1N4t/+9jfx//2//ye+/PLLYnNzs+8+vne9Def9i9b3ThSV8f6p9b0TRfX/3VPSeyeKkfN3L9LfO1FU9t+9ULx3zc3N4iWXXCIKgiAmJiaKY8eOFceMGSMmJiaKGo1GXLJkyYD/1p5ox44dokajEUVRFO12u/izn/1MHDVqlDhjxgzxH//4R4/Y6upqX+zx48fF2bNni1qtVjznnHPExsZGcfHixaIgCKIgCOLYsWPF48ePi6Ioijt37uzzl16vF1etWuX7WhRF8eGHHxbb29t9+dx2222iRqMRBUEQNRqNeNlll4mdnZ2+nK644grxP//5jyiKorh3714xNTVVTEtLE2fOnClmZGSImZmZ4r59+0RRFEVBEMSRI0eKTzzxhFhRUTHg6/L111+LsbGxoiAIYlZWlrhz504xNzdXHDNmjDhu3DjRaDSKn3/+uSiKnj//V155pe+5azQaMTMzU9RqtWJcXJz44osvRux7F8r3j++dxvd1KN+/SMSiXcHU/JeR/5BG7nsXyveP7123SPu7F2nvXSjfP753LB7C9d6F8v2LtPfuJz/5iVhUVCRu3Lix1/fcuHGjOHHiRPH6668P+L177LHHxIyMDPGZZ54RH3nkETEhIUG8/fbbe7x3giD4cpg1a5b44Ycfitdcc404a9Ys8eyzzxYrKirEsrIy8eyzzxbvuusu32smvfYn/zrxPRFFUdRoNGJNTY0oiqL4xBNPiGlpaeJ//vMfsbKyUvzoo4/EnJwc8fHHH/fllJqa6vuQaeHCheLSpUtFm80miqLnfb/lllvEBQsW+PK47bbbxIyMDFGn04mLFy/+/9u716iorvt94N8zM9wV8BK5RAewogZTlZgWlWZBUBMoCZqVpMY2raRpNDYmNdUmbZcx1ra01jap0faFMUraRpulLq0kbZeCEkPBKBISEiEg42UJjNa7XIoXnv8L/xwZAeP4c2Cf7fNZa17MzMP24WzOOHsu52Dz5s2dXjQCgOTkZDz33HM4f/48li1bhsGDB5u/DwAsWLAAEydOBADMmjULycnJKC8vR1VVFR599FG89NJLaGpqwltvvYXg4GC88847lpw7X84f5+7qc01fzp8VcdGuMJ13Rj6QWnfufDl/nLt3zJ+12r5ntbnz5fxx7rh46Km58+X8WW3uwsLCupy3diUlJQgLCwMA9OvX77qX0NBQc5sNGzYMeXl55jgHDhxAfHw8srOz0dbW5vGCS1RUFEpKSgAAJ0+ehGEYyM/PN392x44dGDp0KABgzJgxyMzMRGVlJQ4dOoRDhw7h4MGDcDgc2L59u3lb+/Ztn7exY8firbfe8vjd3n33Xdx1113m9aCgIBw4cMDsVFZW5pH/4osvzG3RPvbFixexceNGfPOb34TdbkdERAReeuklVFVVmT8XGhpqjnvx4kU4HA58/PHH5v3V1dXmuAMHDvT4BMapU6cQGBhovmi0cuVKjB07FoD15g7w3fxx7q4+1/Tl/FkRF+0K03ln5AOpdecOsN6TGKvNHWC9fc9qcwdYb9+z2twB1tv3rDZ3gPX2PV/O3UcffdTNzF150aV93ODgYMyfPx+5ubldXn7xi1+Y8xEUFISDBw96jFVXV4cRI0bgO9/5Durq6sxsYGAgjhw5YuZCQkJQU1NjXj98+DCCgoIAAK2trfjRj36EhIQEj23rcDjw+eefe/x7hmHg+PHjAIABAwagoqLC4/6DBw8iODjYvJ6UlIRVq1YBABITE7F582aP/LZt2xAZGWmO3f430e7o0aNYsmQJhg4dCpvNhvvuuw/Alfn47LPPAABNTU2w2Wzm3ypw5VMfAwcOBACEh4d7fKXkwoULcDgc5u9RXV2NwMBAANabO8B388e5u/pc05fzZ0VctCtM552RD6TWnTvAek9irDZ3gPX2PavNHWC9fc9qcwdYb9+z2twB1tv3fDV3Tz75JEaPHo29e/fiWnv37sXYsWPx3e9+FwAwceJE/PGPf+yUa9fxUxJxcXEeL5q0q6urw/DhwzF58mQz63Q6Pf5+Xn75ZZw8edJj3Pbfrd0///lPDB48GDk5Obh8+XK38/brX/8ay5cvR3R0NHbt2tWpb79+/czr7733Hvr374+1a9di7dq1iI2NxerVq/Gf//wHa9aswZAhQ/CTn/wEgOenL7qSn5+Pb3/72wCAqVOn4qGHHkJRURFmzZqFe++9F5mZmWhsbERTUxMee+wxpKenAwCmTJni8QmKZcuWISoqyrxeVlZmbgurzh1w6+ePc3f1uWZPzJ+VcNGuMJ13Rj6QWnfuAOs9ibHa3AHW3fesMneA9fY9q80dYL19z6pzB1hn3/PV3J0+fRrp6ekwDAP9+vXDiBEjMHLkSPTr1w82mw0ZGRk4ffo0gCtfDVi8eHG3fY8cOYLs7GwAwNNPP43vf//7XeaOHj2KYcOGmXOXlZV13b+JlStXIi0trdPtbrcbGRkZ+MY3vtHlvMXExCA2Nta8XPtvvP766xg/frzHbRs3bsTgwYM7fXUiMDAQ8+bNM7+60NULLt2prq7GsGHDYBgGRo0ahbq6OmRlZcHhcMDhcOCOO+7Avn37AAD79u1D//79ERkZCafTCX9/f6xfv95jW7R/1cTKcwfc+vnj3F3RU/NnFVy0K0znnZEPpFdZbe4Aaz6JsdLcAdbe96wwd4D19j0rzh1grX3PynMHWGPf8+XjJgDs378fa9asQU5ODnJycrBmzRpUVlbeULeuHDp0CP/+97+7vb++vh65ubk3NNaePXs6fbqho+XLl2PatGk3dCaGjkpKSjp9fQG4cgaCjz76CH//+9+xbt067Ny5E+fOnfPIFBYW4uLFi179eydOnPC4np+fj7y8vE6319fXY9WqVVixYkWnv8WuVFZWWnbugFs7f5cuXcKePXtu27kDen7+VGcAQG+fdo669sEHH0hycrI4HI4b/pmTJ0/KgAEDzOsFBQXS0tIiEyZM8Li9oaFB3nvvPWltbZW0tDRJSEi47rhVVVVSUlIibrdbREQiIyNlwoQJMnLkSC9/qysOHz4sVVVV8uCDD3Z5f0NDg2zbtk1mzpz5pWPt3btXgoKC5O677+4288Ybb8jOnTtlxYoVX3o+0452794tAQEBkpiYaN52+fJlKSsrE5fLJW1tbRIVFSXjxo2Tvn37mhmd506kZ+fvdp47EWvve1aYOxHfzV9lZaXs3r37tp87Eevte1aeO5Fbv+/t27dPDh48aIm5IyLSERftRERERGR5ACQ/P1+Ki4vF7XaLYRgSEREhycnJMmnSJDEM47bIXi8/ceJEmTx5spKdu3P69GnJy8uT733ve5bIqtKjJ7NtbW1is9k6Zdva2uTo0aPidDpvKuvrsa2Ei3YLSktLk7Vr10pMTEyvZMvLy6WmpkaioqIkOTn5ug+6KmRV6dHT2dbWVrHZbOLn5yciIrW1tbJmzRo5cuSIxMTEyNNPPy1xcXFeZ305NrNXt/GmTZskIyNDgoODu517Zm8+6+uxP/nkEykrK5PU1FSJi4uTzz//XP70pz9JW1ubPPLIIx7vuOqcVaVHe/b++++X2NjYL+28Y8cOKSoqkoaGBrHb7RIXFydZWVkSHx/faa5VyHaVHzp0qDz88MPKdr7V2bq6OnnooYekoqJC7r77bomIiBAAcvz4cfnss89kzJgxsnXrVrnzzju1zlpxW3yZTz75RO655x65fPmyJbKq9OiJ7Llz5+QHP/iB5OXlSWhoqDz77LOyaNEisdvtIiJy7NgxiY6O9jorIj4d25J66nP45L1//OMfXV7sdjtWrlxpXvdldsaMGeZ3aM6fP48HHngAhmHA398fhmHg3nvvNb/fp0JWlR4qZO+//35s2rQJAFBUVISAgACMHj0a06dPR2JiIoKDg1FcXOx11pdjM3t1GxuGgb59++KZZ5657imomL25rC/H3rhxI+x2OwYMGIC+ffsiPz8f4eHhmDx5Mh588EHY7Xbz3NI6Z1Xp4U322LFj+PrXvw7DMGC322Gz2TBu3DhERkbCbrebB15TJatKDxWyWVlZSEtLQ319fad9sr6+HmlpaZg6dar2WVV6eJM9e/bsdS8ffvihecwHFbKq9FAh+8ILL2D48OHYsGED3nzzTcTExCAzMxOtra0ArhxnwzAMr7O+HtuKuGhXmGEYnQ7+cu2lfafxVbbjEWEXLFiAuLg48wAxFRUVuOuuu/Diiy8qk1WlhwrZ8PBw81y4KSkpHtsIABYuXIjk5GSvs74cm9mr29gwDCxZsgSJiYnmAZtef/31TgeLYfbmsr4c+5577sGvfvUrAMD69esRHh6OJUuWmPf//ve/N88trXNWlR7eZKdPn45p06bh9OnTaG5uxnPPPWce6KygoAADBgwwD96mQlaVHipkQ0JCUF5eju6UlZUhJCRE+6wqPbzJtj/v7O7S1XPY3syq0kOFrNPpxM6dO83tcuLECSQlJeGBBx7A//73P7jd7pvK+npsK+KiXWHp6enIzMzsdETWro4I66us0eGIsKNGjcK7777rcf/777+P+Ph4ZbKq9FAhGxISYh65MyIiotN/ngcOHECfPn28zvpybGavbuOOc11aWoo5c+YgPDwcAQEBePzxx7Ft2zZm/w9ZX44dEhJinpO7ra0Nfn5++PTTT837a2trPf4udM2q0sObbGhoqHkOcQBobGyEn58fzp49CwD461//ihEjRiiTVaWHCtmBAwdix44d6E5BQYF5ejids6r08CYbGhqKpUuXorCwsMvLm2++aS64VMiq0kOFbHBwMFwul8fcnjt3DhMmTEBaWhpcLtdNZX09thVx0a641157DU6nE3l5eeZtXS2ufZU1DAPHjx8HcOUB+Nr7Dx06hMDAQGWyqvRQIZuWlobf/e53AK6cT/jtt9/2yG7cuBFOp9PrrC/HZvbqNu64SGzX0tKCv/zlL0hNTYXNZkNMTAyzN5n15diRkZEoLS0FAJw6dQqGYXi8A7Bnzx5ERkZqn1WlhzfZO+64w+Nxtbm5GTabzTxPem1tLQICApTJqtJDhezcuXMxZMgQbNiwAWfOnDF/5syZM9iwYQOcTideeOEF7bOq9PAmm5qaiqVLl6I75eXl5kebVciq0kOF7IgRI/D+++93ypw/fx4TJkzAmDFjzMWyN1lfj21FXLRbQHl5ORISEjBr1iw0NTV1uxD3RdYwDMyePRsvvvgiBg0ahIKCAo/7S0tLzVdKVciq0kOFbHFxMcLCwvDqq69ixYoVGDhwIBYuXIh33nkHixYtQnh4uPmg7E3Wl2Mze3Ubd/wqRFdqamrw85//nNmbzPpy7CeffBJJSUn429/+hocffhjp6ekYP348KisrUVVVhZSUFDz22GPaZ1Xp4U32kUcewaOPPorGxkZcuHAB8+bNw7Bhw8zfZ/fu3eYCX4WsKj1UyLa2tuLZZ5+Fv78/bDYbAgMDERgYCJvNBn9/f8yZM8f8fqvOWVV6eJNdtWoVli9fju643W4sXrxYmawqPVTIPv/88x6P+R2dO3cOSUlJ5mLZm6yvx7YiLtotorm5GbNnz0Z8fDzsdnu3C/FbnU1JSUFqaqp5Wb16tcf9S5YsQUpKijJZVXqokAWuLBTHjx/f6ZgFd955p8d3Ir3N+nJsZq/o6p3d7jDrfdaXY7vdbkyePBl9+vRBRkYGzp49i7lz55rfA4yPjzePb6BzVpUe3mRra2vxla98BQ6HA35+fggPD8f27dvN32ft2rX46U9/qkxWlR4qZNudPXsWBQUFWLduHdatW4cdO3aYH6e/ls5ZVXp425ms5dSpUx5fYbnW+fPnUVhY6HXW12NbEU/5ZjFbt26VnTt3ys9+9jMZNGhQr2Q7crlc4u/vL4MHD7ZEVpUePZ3973//Ky6XS9ra2iQqKkpiY2O7HcObrC/Hvt2zhw8fFqfTeUPnsWXW+6yvx+6Ky+WS5uZmGTlypDgcjts2q0qP7rLNzc1SVFQkFy5ckPHjx8vAgQO7HUOFrCo9VMgSEemKi3YiIiIisrympiZZt26dFBcXi9vtFsMwJCIiQpKTk2XGjBkSEhJyW2RV6aFzVpUeOmd9PbbVcNGuuJaWFlm/fr0UFRVJQ0OD2O12iYuLk2nTpsmkSZOY7SKrSg+ds6r00DmrSg+ds6r00DmrSg+ds6r06O3s/v37ZcqUKdLc3CwpKSkSEREhAOT48ePywQcfSEhIiGzbtk0SEhK0znJbcBvrkPXlNrasnvgMPt2cmpoaxMTEYMCAAYiKioJhGMjMzERSUhLsdjsef/xxXLx4kdkOWVV66JxVpYfOWVV66JxVpYfOWVV66JxVpYcK2dTUVDzxxBMeB2Rr19raihkzZiA1NVX7rCo9dM6q0kPnrK/HtiIu2hWWkZGB2bNn4/LlywCA3/zmN8jIyAAAVFdXIzY2Fq+++iqzHbKq9NA5q0oPnbOq9NA5q0oPnbOq9NA5q0oPFbJBQUHXPfBuRUUFgoKCtM+q0kPnrCo9dM76emwr4qJdYcHBwaiurjavt7a2ws/PDydOnAAAbNmyBbGxscx2yKrSQ+esKj10zqrSQ+esKj10zqrSQ+esKj1UyEZHR2PLli3ozubNmxEdHa19VpUeOmdV6aFz1tdjW9GXH+KVek14eLicP3/evN7c3CyXLl0Sf39/EREZPXq0NDQ0MNshq0oPnbOq9NA5q0oPnbOq9NA5q0oPnbOq9FAh+8wzz8jMmTNl4cKFMmXKFImIiBDDMMTtdsv27dslJydH5s2bp31WlR46Z1XpoXPW12NbUm+/akDdmzlzJlJSUlBZWQmXy4Xp06cjMTHRvL+wsBBDhgxhtkNWlR46Z1XpoXNWlR46Z1XpoXNWlR46Z1XpoUIWAH7729+a33232Wyw2WwwDANRUVFYunQpOtI5q0oPnbOq9NA56+uxrYaLdoUdO3YM48ePN//4YmNjUVZWZt6/YcMGvPHGG8x2yKrSQ+esKj10zqrSQ+esKj10zqrSQ+esKj1UyHbkcrlQXFyM4uJiuFyuTvffLllVeuicVaWHzllfj20VPOWbBdTU1Ehra6uMHDlSHI7rf6OBWbV66JxVpYfOWVV66JxVpYfOWVV66JxVpYcKWSIiLfX2qwZ0844cOYKnnnqKWS+yqvTQOatKD52zqvTQOatKD52zqvTQOatKj57KNjc348MPP+zyKNItLS14++23b4usKj10zqrSQ+esr8e2Gi7aLay8vBw2m41ZL7Kq9NA5q0oPnbOq9NA5q0oPnbOq9NA5q0qPnsh+8cUXiImJMT9Kn5KSgvr6ejPrdrtvi6wqPXTOqtJD56yvx7YifsZIYVu3br3u/S6Xi9lrsqr00DmrSg+ds6r00DmrSg+ds6r00DmrSg8Vsi+//LJ89atfldLSUjlz5oz8+Mc/luTkZCksLBSn0+nxczpnVemhc1aVHjpnfT22JfX2qwbUvfZXiwzD6PbS/qoRszZuN25jbbKq9NA5q0oPnbOq9NA5q0oPFbKDBg3Cp59+io5++MMfwul0ora21uOdNp2zqvTQOatKD52zvh7birhoV1h0dDQ2b97c7f0ff/yx+QfI7NUdUYUeOmdV6aFzVpUeOmdV6aFzVpUeOmdV6aFCtm/fvti/f3+nzNy5czF48GDs2rXrtsiq0kPnrCo9dM76emwrsvX2O/3UvXHjxklZWVm39xuGIfj/B/9n9upJEFTooXNWlR46Z1XpoXNWlR46Z1XpoXNWlR4qZEeOHCmlpaWdMitWrJCpU6dKVlaWeZvOWVV66JxVpYfOWV+PbUm39CUAuqV27dqFf/3rX93e39jYiMLCQmY7ZFXpoXNWlR46Z1XpoXNWlR46Z1XpoXNWlR4qZHNycpCRkdFtds6cOTAMQ/usKj10zqrSQ+esr8e2Ip6nnYiIiIiIiEhR/Hg8ERERERERkaK4aCciIiIiIiJSFBftRERERERERIriop2IiIiIiIhIUVy0ExERERERESmKi3YiIiIiIiIiRXHRTkRERB6ys7PFMIxOl+zs7N6uRkREdNtx9HYBIiIiUk96erqsXbvW47agoKBeakNERHT74jvtRERE1ElAQIBERkZ6XMLCwkREZNOmTTJq1CgJCAiQ2NhY+cMf/tDp53Nzczu9Uz927Nge/i2IiIisj4t2IiIiumH79u2Tb33rW/LEE09IRUWFLF68WF555RXJzc3tlA0NDZWGhgZpaGiQ+fPn93xZIiIiDfDj8URERHTDXnvtNZk0aZK88sorIiIyfPhw2b9/vyxbtszjO++tra3i7+8vkZGRIiLSp0+f3qhLRERkeXynnYiIiG5YZWWlJCcne9yWnJwsNTU1cvnyZfO2kydPSmhoaE/XIyIi0g4X7URERHTDAIhhGJ1uu5bL5ZLY2NgeakVERKQvLtqJiIjohiUkJEhRUZHHbcXFxTJ8+HCx2+3mbbt27ZL77ruvp+sRERFph99pJyIiohs2f/58+drXvia//OUvZfr06VJSUiIrV66UP//5zyIi0tLSIqtXr5ba2lpJT08Xt9stIiKNjY1y6dIlOXXqlPTv3783fwUiIiJLMdDVZ9qIiIjotpWdnS1nzpyRLVu2dHn/pk2bZNGiRVJTUyNRUVHy/PPPy4IFC0TkyqnennrqqW7HTklJkcLCQh+0JiIi0hMX7URERHTL5ObmSmFhYZengCsvL5d58+Zx0U5EROQFfqediIiIbpmgoCAJCwvr8j4/Pz9+NJ6IiMhLfKediIiIiIiISFF8p52IiIiIiIhIUVy0ExERERERESmKi3YiIiIiIiIiRXHRTkRERERERKQoLtqJiIiIiIiIFMVFOxEREREREZGiuGgnIiIiIiIiUhQX7URERERERESK+n9iKEuihubKBQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "players = draft_history.groupby('season').size().reset_index(name='num_players')\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(players['season'], players['num_players'])\n",
+    "plt.xlabel('Год')\n",
+    "plt.ylabel('Количество игроков')\n",
+    "plt.grid(True)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Задание 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Необходимо построить график, показывающий топ 10 школ, игроки из которых были выбраны. Для этого вам понадобятся таблицы common_player_info и draft_history."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\sql.py:762: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "common_player_info = pd.read_sql('SELECT * from common_player_info', conn)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>person_id</th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>last_name</th>\n",
+       "      <th>display_first_last</th>\n",
+       "      <th>display_last_comma_first</th>\n",
+       "      <th>display_fi_last</th>\n",
+       "      <th>player_slug</th>\n",
+       "      <th>birthdate</th>\n",
+       "      <th>school</th>\n",
+       "      <th>country</th>\n",
+       "      <th>...</th>\n",
+       "      <th>playercode</th>\n",
+       "      <th>from_year</th>\n",
+       "      <th>to_year</th>\n",
+       "      <th>dleague_flag</th>\n",
+       "      <th>nba_flag</th>\n",
+       "      <th>games_played_flag</th>\n",
+       "      <th>draft_year</th>\n",
+       "      <th>draft_round</th>\n",
+       "      <th>draft_number</th>\n",
+       "      <th>greatest_75_flag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>76001</td>\n",
+       "      <td>Alaa</td>\n",
+       "      <td>Abdelnaby</td>\n",
+       "      <td>Alaa Abdelnaby</td>\n",
+       "      <td>Abdelnaby, Alaa</td>\n",
+       "      <td>A. Abdelnaby</td>\n",
+       "      <td>alaa-abdelnaby</td>\n",
+       "      <td>1968-06-24</td>\n",
+       "      <td>Duke</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HISTADD_alaa_abdelnaby</td>\n",
+       "      <td>1990.0</td>\n",
+       "      <td>1994.0</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>76002</td>\n",
+       "      <td>Zaid</td>\n",
+       "      <td>Abdul-Aziz</td>\n",
+       "      <td>Zaid Abdul-Aziz</td>\n",
+       "      <td>Abdul-Aziz, Zaid</td>\n",
+       "      <td>Z. Abdul-Aziz</td>\n",
+       "      <td>zaid-abdul-aziz</td>\n",
+       "      <td>1946-04-07</td>\n",
+       "      <td>Iowa State</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HISTADD_zaid_abdul-aziz</td>\n",
+       "      <td>1968.0</td>\n",
+       "      <td>1977.0</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1968</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>76003</td>\n",
+       "      <td>Kareem</td>\n",
+       "      <td>Abdul-Jabbar</td>\n",
+       "      <td>Kareem Abdul-Jabbar</td>\n",
+       "      <td>Abdul-Jabbar, Kareem</td>\n",
+       "      <td>K. Abdul-Jabbar</td>\n",
+       "      <td>kareem-abdul-jabbar</td>\n",
+       "      <td>1947-04-16</td>\n",
+       "      <td>UCLA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HISTADD_kareem_abdul-jabbar</td>\n",
+       "      <td>1969.0</td>\n",
+       "      <td>1988.0</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1969</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>949</td>\n",
+       "      <td>Shareef</td>\n",
+       "      <td>Abdur-Rahim</td>\n",
+       "      <td>Shareef Abdur-Rahim</td>\n",
+       "      <td>Abdur-Rahim, Shareef</td>\n",
+       "      <td>S. Abdur-Rahim</td>\n",
+       "      <td>shareef-abdur-rahim</td>\n",
+       "      <td>1976-12-11</td>\n",
+       "      <td>California</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>...</td>\n",
+       "      <td>shareef_abdur-rahim</td>\n",
+       "      <td>1996.0</td>\n",
+       "      <td>2007.0</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>76006</td>\n",
+       "      <td>Forest</td>\n",
+       "      <td>Able</td>\n",
+       "      <td>Forest Able</td>\n",
+       "      <td>Able, Forest</td>\n",
+       "      <td>F. Able</td>\n",
+       "      <td>forest-able</td>\n",
+       "      <td>1932-07-27</td>\n",
+       "      <td>Western Kentucky</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HISTADD_frosty_able</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1956</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  person_id first_name     last_name   display_first_last  \\\n",
+       "0     76001       Alaa     Abdelnaby       Alaa Abdelnaby   \n",
+       "1     76002       Zaid    Abdul-Aziz      Zaid Abdul-Aziz   \n",
+       "2     76003     Kareem  Abdul-Jabbar  Kareem Abdul-Jabbar   \n",
+       "3       949    Shareef   Abdur-Rahim  Shareef Abdur-Rahim   \n",
+       "4     76006     Forest          Able          Forest Able   \n",
+       "\n",
+       "  display_last_comma_first  display_fi_last          player_slug  birthdate  \\\n",
+       "0          Abdelnaby, Alaa     A. Abdelnaby       alaa-abdelnaby 1968-06-24   \n",
+       "1         Abdul-Aziz, Zaid    Z. Abdul-Aziz      zaid-abdul-aziz 1946-04-07   \n",
+       "2     Abdul-Jabbar, Kareem  K. Abdul-Jabbar  kareem-abdul-jabbar 1947-04-16   \n",
+       "3     Abdur-Rahim, Shareef   S. Abdur-Rahim  shareef-abdur-rahim 1976-12-11   \n",
+       "4             Able, Forest          F. Able          forest-able 1932-07-27   \n",
+       "\n",
+       "             school country  ...                   playercode from_year  \\\n",
+       "0              Duke     USA  ...       HISTADD_alaa_abdelnaby    1990.0   \n",
+       "1        Iowa State     USA  ...      HISTADD_zaid_abdul-aziz    1968.0   \n",
+       "2              UCLA     USA  ...  HISTADD_kareem_abdul-jabbar    1969.0   \n",
+       "3        California     USA  ...          shareef_abdur-rahim    1996.0   \n",
+       "4  Western Kentucky     USA  ...          HISTADD_frosty_able    1956.0   \n",
+       "\n",
+       "  to_year  dleague_flag nba_flag games_played_flag draft_year draft_round  \\\n",
+       "0  1994.0             N        Y                 Y       1990           1   \n",
+       "1  1977.0             N        Y                 Y       1968           1   \n",
+       "2  1988.0             N        Y                 Y       1969           1   \n",
+       "3  2007.0             N        Y                 Y       1996           1   \n",
+       "4  1956.0             N        Y                 Y       1956        None   \n",
+       "\n",
+       "   draft_number greatest_75_flag  \n",
+       "0            25                N  \n",
+       "1             5                N  \n",
+       "2             1                Y  \n",
+       "3             3                N  \n",
+       "4          None                N  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "common_player_info.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "merged = pd.merge(draft_history, common_player_info[['person_id', 'school']], on='person_id', how='left')\n",
+    "\n",
+    "school_counts = merged.groupby('school').size().reset_index(name='num_players')\n",
+    "\n",
+    "school_counts = school_counts.sort_values(by='num_players').tail(10)\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.barh(school_counts['school'], school_counts['num_players'])\n",
+    "plt.xlabel(\"Количество игроков\")\n",
+    "plt.ylabel(\"Школа\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Задание 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Постройте график сравнения выигранных домашних и выездных игр в каждом году. Для этого используйте таблицу game."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>season_id</th>\n",
+       "      <th>team_id_home</th>\n",
+       "      <th>team_abbreviation_home</th>\n",
+       "      <th>team_name_home</th>\n",
+       "      <th>game_id</th>\n",
+       "      <th>game_date</th>\n",
+       "      <th>matchup_home</th>\n",
+       "      <th>wl_home</th>\n",
+       "      <th>min</th>\n",
+       "      <th>fgm_home</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stl_away</th>\n",
+       "      <th>blk_away</th>\n",
+       "      <th>tov_away</th>\n",
+       "      <th>pf_away</th>\n",
+       "      <th>plus_minus_away</th>\n",
+       "      <th>video_available_away</th>\n",
+       "      <th>season_type</th>\n",
+       "      <th>game_date_pd</th>\n",
+       "      <th>game_year</th>\n",
+       "      <th>pts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610035</td>\n",
+       "      <td>HUS</td>\n",
+       "      <td>Toronto Huskies</td>\n",
+       "      <td>0024600001</td>\n",
+       "      <td>1946-11-01</td>\n",
+       "      <td>HUS vs. NYK</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-01</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>134.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610034</td>\n",
+       "      <td>BOM</td>\n",
+       "      <td>St. Louis Bombers</td>\n",
+       "      <td>0024600003</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>BOM vs. PIT</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>-5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>107.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610032</td>\n",
+       "      <td>PRO</td>\n",
+       "      <td>Providence Steamrollers</td>\n",
+       "      <td>0024600002</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>PRO vs. BOS</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>112.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610025</td>\n",
+       "      <td>CHS</td>\n",
+       "      <td>Chicago Stags</td>\n",
+       "      <td>0024600004</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>CHS vs. NYK</td>\n",
+       "      <td>W</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>-16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>110.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>21946</td>\n",
+       "      <td>1610610028</td>\n",
+       "      <td>DEF</td>\n",
+       "      <td>Detroit Falcons</td>\n",
+       "      <td>0024600005</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>DEF vs. WAS</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Regular Season</td>\n",
+       "      <td>1946-11-02</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>83.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 56 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  season_id team_id_home team_abbreviation_home           team_name_home  \\\n",
+       "0     21946   1610610035                    HUS          Toronto Huskies   \n",
+       "1     21946   1610610034                    BOM        St. Louis Bombers   \n",
+       "2     21946   1610610032                    PRO  Providence Steamrollers   \n",
+       "3     21946   1610610025                    CHS            Chicago Stags   \n",
+       "4     21946   1610610028                    DEF          Detroit Falcons   \n",
+       "\n",
+       "      game_id  game_date matchup_home wl_home  min  fgm_home  ...  stl_away  \\\n",
+       "0  0024600001 1946-11-01  HUS vs. NYK       L    0      25.0  ...       NaN   \n",
+       "1  0024600003 1946-11-02  BOM vs. PIT       W    0      20.0  ...       NaN   \n",
+       "2  0024600002 1946-11-02  PRO vs. BOS       W    0      21.0  ...       NaN   \n",
+       "3  0024600004 1946-11-02  CHS vs. NYK       W    0      21.0  ...       NaN   \n",
+       "4  0024600005 1946-11-02  DEF vs. WAS       L    0      10.0  ...       NaN   \n",
+       "\n",
+       "   blk_away  tov_away  pf_away  plus_minus_away  video_available_away  \\\n",
+       "0       NaN       NaN      NaN                2                     0   \n",
+       "1       NaN       NaN     25.0               -5                     0   \n",
+       "2       NaN       NaN      NaN               -6                     0   \n",
+       "3       NaN       NaN     22.0              -16                     0   \n",
+       "4       NaN       NaN      NaN               17                     0   \n",
+       "\n",
+       "      season_type  game_date_pd  game_year    pts  \n",
+       "0  Regular Season    1946-11-01       1946  134.0  \n",
+       "1  Regular Season    1946-11-02       1946  107.0  \n",
+       "2  Regular Season    1946-11-02       1946  112.0  \n",
+       "3  Regular Season    1946-11-02       1946  110.0  \n",
+       "4  Regular Season    1946-11-02       1946   83.0  \n",
+       "\n",
+       "[5 rows x 56 columns]"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "df['wl_away'] = df['wl_home'].map({'W': 'L', 'L': 'W'})\n",
+    "\n",
+    "home_wins = df[df['wl_home']=='W'].groupby('game_year').size().reset_index(name='home_wins')\n",
+    "away_wins = df[df['wl_away']=='W'].groupby('game_year').size().reset_index(name='away_wins')\n",
+    "\n",
+    "wins = pd.merge(home_wins, away_wins, on='game_year', how='outer')\n",
+    "\n",
+    "# строим график\n",
+    "plt.figure(figsize=(12,6))\n",
+    "plt.plot(wins['game_year'], wins['home_wins'], label='Домашние победы')\n",
+    "plt.plot(wins['game_year'], wins['away_wins'], label='Выездные победы')\n",
+    "plt.xlabel(\"Год\")\n",
+    "plt.ylabel(\"Количество побед\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Задание 6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Какова средняя продолжительность карьеры активного игрока NBA? Используйте таблицы common_player_info и player. Активный игрок - это тот, у которого в колонке is_active стоит 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\sql.py:762: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "player = pd.read_sql('SELECT * from player', conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>person_id</th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>last_name</th>\n",
+       "      <th>display_first_last</th>\n",
+       "      <th>display_last_comma_first</th>\n",
+       "      <th>display_fi_last</th>\n",
+       "      <th>player_slug</th>\n",
+       "      <th>birthdate</th>\n",
+       "      <th>school</th>\n",
+       "      <th>country</th>\n",
+       "      <th>...</th>\n",
+       "      <th>playercode</th>\n",
+       "      <th>from_year</th>\n",
+       "      <th>to_year</th>\n",
+       "      <th>dleague_flag</th>\n",
+       "      <th>nba_flag</th>\n",
+       "      <th>games_played_flag</th>\n",
+       "      <th>draft_year</th>\n",
+       "      <th>draft_round</th>\n",
+       "      <th>draft_number</th>\n",
+       "      <th>greatest_75_flag</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>76001</td>\n",
+       "      <td>Alaa</td>\n",
+       "      <td>Abdelnaby</td>\n",
+       "      <td>Alaa Abdelnaby</td>\n",
+       "      <td>Abdelnaby, Alaa</td>\n",
+       "      <td>A. Abdelnaby</td>\n",
+       "      <td>alaa-abdelnaby</td>\n",
+       "      <td>1968-06-24</td>\n",
+       "      <td>Duke</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HISTADD_alaa_abdelnaby</td>\n",
+       "      <td>1990.0</td>\n",
+       "      <td>1994.0</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>76002</td>\n",
+       "      <td>Zaid</td>\n",
+       "      <td>Abdul-Aziz</td>\n",
+       "      <td>Zaid Abdul-Aziz</td>\n",
+       "      <td>Abdul-Aziz, Zaid</td>\n",
+       "      <td>Z. Abdul-Aziz</td>\n",
+       "      <td>zaid-abdul-aziz</td>\n",
+       "      <td>1946-04-07</td>\n",
+       "      <td>Iowa State</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HISTADD_zaid_abdul-aziz</td>\n",
+       "      <td>1968.0</td>\n",
+       "      <td>1977.0</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1968</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>76003</td>\n",
+       "      <td>Kareem</td>\n",
+       "      <td>Abdul-Jabbar</td>\n",
+       "      <td>Kareem Abdul-Jabbar</td>\n",
+       "      <td>Abdul-Jabbar, Kareem</td>\n",
+       "      <td>K. Abdul-Jabbar</td>\n",
+       "      <td>kareem-abdul-jabbar</td>\n",
+       "      <td>1947-04-16</td>\n",
+       "      <td>UCLA</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HISTADD_kareem_abdul-jabbar</td>\n",
+       "      <td>1969.0</td>\n",
+       "      <td>1988.0</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1969</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>949</td>\n",
+       "      <td>Shareef</td>\n",
+       "      <td>Abdur-Rahim</td>\n",
+       "      <td>Shareef Abdur-Rahim</td>\n",
+       "      <td>Abdur-Rahim, Shareef</td>\n",
+       "      <td>S. Abdur-Rahim</td>\n",
+       "      <td>shareef-abdur-rahim</td>\n",
+       "      <td>1976-12-11</td>\n",
+       "      <td>California</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>...</td>\n",
+       "      <td>shareef_abdur-rahim</td>\n",
+       "      <td>1996.0</td>\n",
+       "      <td>2007.0</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1996</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>76006</td>\n",
+       "      <td>Forest</td>\n",
+       "      <td>Able</td>\n",
+       "      <td>Forest Able</td>\n",
+       "      <td>Able, Forest</td>\n",
+       "      <td>F. Able</td>\n",
+       "      <td>forest-able</td>\n",
+       "      <td>1932-07-27</td>\n",
+       "      <td>Western Kentucky</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>...</td>\n",
+       "      <td>HISTADD_frosty_able</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>1956</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>N</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  person_id first_name     last_name   display_first_last  \\\n",
+       "0     76001       Alaa     Abdelnaby       Alaa Abdelnaby   \n",
+       "1     76002       Zaid    Abdul-Aziz      Zaid Abdul-Aziz   \n",
+       "2     76003     Kareem  Abdul-Jabbar  Kareem Abdul-Jabbar   \n",
+       "3       949    Shareef   Abdur-Rahim  Shareef Abdur-Rahim   \n",
+       "4     76006     Forest          Able          Forest Able   \n",
+       "\n",
+       "  display_last_comma_first  display_fi_last          player_slug  birthdate  \\\n",
+       "0          Abdelnaby, Alaa     A. Abdelnaby       alaa-abdelnaby 1968-06-24   \n",
+       "1         Abdul-Aziz, Zaid    Z. Abdul-Aziz      zaid-abdul-aziz 1946-04-07   \n",
+       "2     Abdul-Jabbar, Kareem  K. Abdul-Jabbar  kareem-abdul-jabbar 1947-04-16   \n",
+       "3     Abdur-Rahim, Shareef   S. Abdur-Rahim  shareef-abdur-rahim 1976-12-11   \n",
+       "4             Able, Forest          F. Able          forest-able 1932-07-27   \n",
+       "\n",
+       "             school country  ...                   playercode from_year  \\\n",
+       "0              Duke     USA  ...       HISTADD_alaa_abdelnaby    1990.0   \n",
+       "1        Iowa State     USA  ...      HISTADD_zaid_abdul-aziz    1968.0   \n",
+       "2              UCLA     USA  ...  HISTADD_kareem_abdul-jabbar    1969.0   \n",
+       "3        California     USA  ...          shareef_abdur-rahim    1996.0   \n",
+       "4  Western Kentucky     USA  ...          HISTADD_frosty_able    1956.0   \n",
+       "\n",
+       "  to_year  dleague_flag nba_flag games_played_flag draft_year draft_round  \\\n",
+       "0  1994.0             N        Y                 Y       1990           1   \n",
+       "1  1977.0             N        Y                 Y       1968           1   \n",
+       "2  1988.0             N        Y                 Y       1969           1   \n",
+       "3  2007.0             N        Y                 Y       1996           1   \n",
+       "4  1956.0             N        Y                 Y       1956        None   \n",
+       "\n",
+       "   draft_number greatest_75_flag  \n",
+       "0            25                N  \n",
+       "1             5                N  \n",
+       "2             1                Y  \n",
+       "3             3                N  \n",
+       "4          None                N  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "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>id</th>\n",
+       "      <th>full_name</th>\n",
+       "      <th>first_name</th>\n",
+       "      <th>last_name</th>\n",
+       "      <th>is_active</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>76001</td>\n",
+       "      <td>Alaa Abdelnaby</td>\n",
+       "      <td>Alaa</td>\n",
+       "      <td>Abdelnaby</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>76002</td>\n",
+       "      <td>Zaid Abdul-Aziz</td>\n",
+       "      <td>Zaid</td>\n",
+       "      <td>Abdul-Aziz</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>76003</td>\n",
+       "      <td>Kareem Abdul-Jabbar</td>\n",
+       "      <td>Kareem</td>\n",
+       "      <td>Abdul-Jabbar</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>51</td>\n",
+       "      <td>Mahmoud Abdul-Rauf</td>\n",
+       "      <td>Mahmoud</td>\n",
+       "      <td>Abdul-Rauf</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1505</td>\n",
+       "      <td>Tariq Abdul-Wahad</td>\n",
+       "      <td>Tariq</td>\n",
+       "      <td>Abdul-Wahad</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      id            full_name first_name     last_name  is_active\n",
+       "0  76001       Alaa Abdelnaby       Alaa     Abdelnaby          0\n",
+       "1  76002      Zaid Abdul-Aziz       Zaid    Abdul-Aziz          0\n",
+       "2  76003  Kareem Abdul-Jabbar     Kareem  Abdul-Jabbar          0\n",
+       "3     51   Mahmoud Abdul-Rauf    Mahmoud    Abdul-Rauf          0\n",
+       "4   1505    Tariq Abdul-Wahad      Tariq   Abdul-Wahad          0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(common_player_info.head())\n",
+    "display(player.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>person_id</th>\n",
+       "      <th>is_active</th>\n",
+       "      <th>from_year</th>\n",
+       "      <th>to_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>76001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1990.0</td>\n",
+       "      <td>1994.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>76002</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1968.0</td>\n",
+       "      <td>1977.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>76003</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1969.0</td>\n",
+       "      <td>1988.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>949</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1996.0</td>\n",
+       "      <td>2007.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>76006</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>1956.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  person_id  is_active  from_year  to_year\n",
+       "0     76001          0     1990.0   1994.0\n",
+       "1     76002          0     1968.0   1977.0\n",
+       "2     76003          0     1969.0   1988.0\n",
+       "3       949          0     1996.0   2007.0\n",
+       "4     76006          0     1956.0   1956.0"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "player.rename(columns={'id': 'person_id'}, inplace=True)\n",
+    "merged = pd.merge(player[['person_id', 'is_active']], common_player_info[['person_id', 'from_year', 'to_year']], on='person_id', how='inner')\n",
+    "merged.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.121428571428571"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged = merged[merged['is_active'] == 1]\n",
+    "merged['career_len'] = merged['to_year'] - merged['from_year']\n",
+    "merged['career_len'].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Задание 7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Каково соотношение позиций, на которых играют игроки? Используйте таблицу common_player_info."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Guard             1416\n",
+       "Forward           1306\n",
+       "Center             497\n",
+       "Guard-Forward      134\n",
+       "Forward-Center     112\n",
+       "Center-Forward      68\n",
+       "Forward-Guard       54\n",
+       "                    45\n",
+       "Name: position, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "position_counts = common_player_info['position'].value_counts()\n",
+    "display(position_counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "position_counts = position_counts[position_counts.index != '']\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "position_counts.plot(kind='bar')\n",
+    "plt.xlabel(\"Позиция\")\n",
+    "plt.ylabel(\"Количество игроков\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/AISP2.ipynb b/AISP2.ipynb
new file mode 100644
index 0000000..de89e99
--- /dev/null
+++ b/AISP2.ipynb
@@ -0,0 +1,1653 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wwHZs5pbv5lw"
+      },
+      "source": [
+        "# Практическая работа №2\n",
+        "## по предмету \"Системы искусственного интеллекта\"\n",
+        "\n",
+        "Целью практической работы является изучение моделей машинного обучения для задачи регрессии.\n",
+        "\n",
+        "Выполните предварительную обработку и анализ набора данных.\n",
+        "\n",
+        "Затем вам необходимо выбрать 3 модели машинного обучения, которые могут решать задачу регрессии, и обучить их на основе данного набора данных. Обязательным условием является построение графика изменения loss для каждой из выбранных моделей. В результате выполнения работы необходимо сделать вывод, какая из моделей лучше подошла для решения поставленной задачи."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "EP_MhQGkw5sW"
+      },
+      "outputs": [
+        {
+          "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>brand</th>\n",
+              "      <th>processor_brand</th>\n",
+              "      <th>processor_name</th>\n",
+              "      <th>processor_gnrtn</th>\n",
+              "      <th>ram_gb</th>\n",
+              "      <th>ram_type</th>\n",
+              "      <th>ssd</th>\n",
+              "      <th>hdd</th>\n",
+              "      <th>os</th>\n",
+              "      <th>os_bit</th>\n",
+              "      <th>graphic_card_gb</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>warranty</th>\n",
+              "      <th>Touchscreen</th>\n",
+              "      <th>msoffice</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>rating</th>\n",
+              "      <th>Number of Ratings</th>\n",
+              "      <th>Number of Reviews</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ASUS</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i3</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>1024 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>34649</td>\n",
+              "      <td>2 stars</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Lenovo</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i3</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>1024 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>38999</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>65</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Lenovo</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i3</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>1024 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>39999</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ASUS</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i5</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>8 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>512 GB</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>32-bit</td>\n",
+              "      <td>2 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>69990</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ASUS</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Celeron Dual</td>\n",
+              "      <td>Not Available</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>512 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>26990</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    brand processor_brand processor_name processor_gnrtn ram_gb ram_type  \\\n",
+              "0    ASUS           Intel        Core i3            10th   4 GB     DDR4   \n",
+              "1  Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
+              "2  Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
+              "3    ASUS           Intel        Core i5            10th   8 GB     DDR4   \n",
+              "4    ASUS           Intel   Celeron Dual   Not Available   4 GB     DDR4   \n",
+              "\n",
+              "      ssd      hdd       os  os_bit graphic_card_gb  weight     warranty  \\\n",
+              "0    0 GB  1024 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "1    0 GB  1024 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "2    0 GB  1024 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "3  512 GB     0 GB  Windows  32-bit            2 GB  Casual  No warranty   \n",
+              "4    0 GB   512 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "\n",
+              "  Touchscreen msoffice  Price   rating  Number of Ratings  Number of Reviews  \n",
+              "0          No       No  34649  2 stars                  3                  0  \n",
+              "1          No       No  38999  3 stars                 65                  5  \n",
+              "2          No       No  39999  3 stars                  8                  1  \n",
+              "3          No       No  69990  3 stars                  0                  0  \n",
+              "4          No       No  26990  3 stars                  0                  0  "
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.read_csv('AISP2.csv')\n",
+        "\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Price                            1.000000\n",
+              "ssd                              0.628272\n",
+              "ram_gb                           0.518323\n",
+              "graphic_card_gb                  0.459986\n",
+              "processor_name_Core i7           0.377777\n",
+              "processor_name_Core i9           0.359096\n",
+              "brand_APPLE                      0.312112\n",
+              "os_Mac                           0.312112\n",
+              "processor_name_M1                0.274581\n",
+              "processor_brand_M1               0.274581\n",
+              "processor_name_Ryzen 9           0.253506\n",
+              "weight_Casual                    0.247878\n",
+              "processor_gnrtn_12th             0.219060\n",
+              "Touchscreen_Yes                  0.189126\n",
+              "ram_type_LPDDR3                  0.181314\n",
+              "ram_type_LPDDR4X                 0.173809\n",
+              "ram_type_DDR5                    0.168689\n",
+              "processor_gnrtn_10th             0.164034\n",
+              "os_DOS                           0.140780\n",
+              "brand_MSI                        0.123952\n",
+              "msoffice_No                      0.105752\n",
+              "warranty_3 years                 0.080610\n",
+              "processor_name_Ryzen 7           0.061872\n",
+              "ram_type_DDR3                    0.042006\n",
+              "processor_gnrtn_8th              0.040292\n",
+              "warranty_1 year                  0.033312\n",
+              "brand_ASUS                       0.032036\n",
+              "ram_type_LPDDR4                  0.028034\n",
+              "processor_gnrtn_9th              0.021192\n",
+              "os_bit_32-bit                    0.018458\n",
+              "processor_brand_AMD             -0.001583\n",
+              "weight_Gaming                   -0.012524\n",
+              "os_bit_64-bit                   -0.018458\n",
+              "processor_gnrtn_4th             -0.018769\n",
+              "processor_name_Core i5          -0.023218\n",
+              "brand_acer                      -0.024663\n",
+              "warranty_2 years                -0.029339\n",
+              "brand_HP                        -0.030649\n",
+              "rating                          -0.033528\n",
+              "brand_Avita                     -0.033819\n",
+              "brand_Lenovo                    -0.039079\n",
+              "warranty_No warranty            -0.045241\n",
+              "processor_gnrtn_7th             -0.045656\n",
+              "processor_gnrtn_11th            -0.085683\n",
+              "processor_brand_Intel           -0.103966\n",
+              "processor_gnrtn_Not Available   -0.105722\n",
+              "msoffice_Yes                    -0.105752\n",
+              "processor_name_Pentium Quad     -0.111755\n",
+              "processor_name_Ryzen 5          -0.114138\n",
+              "Number of Ratings               -0.140392\n",
+              "Number of Reviews               -0.148738\n",
+              "processor_name_Ryzen 3          -0.150211\n",
+              "brand_DELL                      -0.166272\n",
+              "Touchscreen_No                  -0.189126\n",
+              "processor_name_Celeron Dual     -0.200490\n",
+              "weight_ThinNlight               -0.250425\n",
+              "hdd                             -0.252699\n",
+              "ram_type_DDR4                   -0.270184\n",
+              "os_Windows                      -0.337929\n",
+              "processor_name_Core i3          -0.377232\n",
+              "Name: Price, dtype: float64"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df['ram_gb'] = df['ram_gb'].str.replace(' GB', '').astype(float)\n",
+        "df['ssd'] = df['ssd'].str.replace(' GB', '').astype(float)\n",
+        "df['hdd'] = df['hdd'].str.replace(' GB', '').astype(float)\n",
+        "df['graphic_card_gb'] = df['graphic_card_gb'].str.replace(' GB', '').astype(float)\n",
+        "df['rating'] = df['rating'].str.replace(' stars', '').str.replace(' star', '').astype(float)\n",
+        "\n",
+        "df = pd.get_dummies(df, \n",
+        "    columns=['brand', 'processor_brand', 'processor_name', 'ram_type', \n",
+        "             'os', 'os_bit', 'Touchscreen', 'msoffice', 'warranty', 'processor_gnrtn', 'weight'])\n",
+        "\n",
+        "df.corr(numeric_only=True)['Price'].sort_values(ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "y = df['Price']\n",
+        "\n",
+        "X = df.drop('Price', axis=1)\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "scaler = StandardScaler()\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:1579: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MSE  = 793687226.89\n",
+            "RMSE = 28172.46\n",
+            "R²   = 0.5927\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.linear_model import SGDRegressor\n",
+        "from sklearn.metrics import mean_squared_error, r2_score\n",
+        "\n",
+        "sgd = SGDRegressor(\n",
+        "    loss='squared_error',\n",
+        "    max_iter=1,\n",
+        "    learning_rate='invscaling',\n",
+        "    eta0=0.01,\n",
+        "    warm_start=True,\n",
+        "    random_state=42\n",
+        ")\n",
+        "\n",
+        "train_losses = []\n",
+        "test_losses = []\n",
+        "\n",
+        "n_epochs = 50\n",
+        "for epoch in range(n_epochs):\n",
+        "    sgd.fit(X_train_scaled, y_train)\n",
+        "    y_train_pred = sgd.predict(X_train_scaled)\n",
+        "    y_test_pred = sgd.predict(X_test_scaled)\n",
+        "    \n",
+        "    train_mse = mean_squared_error(y_train, y_train_pred)\n",
+        "    test_mse = mean_squared_error(y_test, y_test_pred)\n",
+        "    \n",
+        "    train_losses.append(train_mse)\n",
+        "    test_losses.append(test_mse)\n",
+        "\n",
+        "plt.figure(figsize=(8,5))\n",
+        "plt.plot(range(n_epochs), train_losses, label='Train Loss (MSE)')\n",
+        "plt.plot(range(n_epochs), test_losses, label='Test Loss (MSE)')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Mean Squared Error')\n",
+        "plt.title('Изменение loss для линейной регрессии (SGDRegressor)')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "y_pred = sgd.predict(X_test_scaled)\n",
+        "\n",
+        "mse = mean_squared_error(y_test, y_pred)\n",
+        "rmse = np.sqrt(mse)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(f\"MSE  = {mse:.2f}\")\n",
+        "print(f\"RMSE = {rmse:.2f}\")\n",
+        "print(f\"R²   = {r2:.4f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Test  MSE:  633754519.6506\n",
+            "Test  RMSE: 25174.4815\n",
+            "Test  R²:   0.6748\n"
+          ]
+        }
+      ],
+      "source": [
+        "train_mse = []\n",
+        "test_mse = []\n",
+        "n_trees = range(1, 101, 5)  # шаг по 5 деревьев\n",
+        "\n",
+        "for n in n_trees:\n",
+        "    model = RandomForestRegressor(n_estimators=n, random_state=42, n_jobs=-1)\n",
+        "    model.fit(X_train, y_train)\n",
+        "    y_train_pred = model.predict(X_train)\n",
+        "    y_test_pred = model.predict(X_test)\n",
+        "    train_mse.append(mean_squared_error(y_train, y_train_pred))\n",
+        "    test_mse.append(mean_squared_error(y_test, y_test_pred))\n",
+        "\n",
+        "plt.figure(figsize=(8,5))\n",
+        "plt.plot(n_trees, train_mse, label='Train MSE')\n",
+        "plt.plot(n_trees, test_mse, label='Test MSE')\n",
+        "plt.xlabel('Количество деревьев (n_estimators)')\n",
+        "plt.ylabel('Mean Squared Error')\n",
+        "plt.title('Изменение ошибки (loss) для RandomForestRegressor')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "final_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
+        "final_model.fit(X_train, y_train)\n",
+        "y_test_pred = final_model.predict(X_test)\n",
+        "\n",
+        "test_mse = mean_squared_error(y_test, y_test_pred)\n",
+        "test_rmse = np.sqrt(test_mse)\n",
+        "test_r2 = r2_score(y_test, y_test_pred)\n",
+        "\n",
+        "print(f\"Test  MSE:  {test_mse:.4f}\")\n",
+        "print(f\"Test  RMSE: {test_rmse:.4f}\")\n",
+        "print(f\"Test  R²:   {test_r2:.4f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "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",
+              "  display: none;\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",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\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",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    font-family: monospace;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table td {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value pre {\n",
+              "    color:rgb(255, 94, 0) !important;\n",
+              "    background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".default td {\n",
+              "    color: black;\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=&#x27;rmse&#x27;, feature_types=None,\n",
+              "             feature_weights=None, gamma=None, grow_policy=None,\n",
+              "             importance_type=None, interaction_constraints=None,\n",
+              "             learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+              "             max_cat_to_onehot=None, max_delta_step=None, max_depth=6,\n",
+              "             max_leaves=None, min_child_weight=None, missing=nan,\n",
+              "             monotone_constraints=None, multi_strategy=None, n_estimators=200,\n",
+              "             n_jobs=None, num_parallel_tree=None, ...)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.1.0/python/python_api.html#xgboost.XGBRegressor\">?<span>Documentation for XGBRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('objective',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">objective&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;reg:squarederror&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('base_score',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">base_score&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('booster',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">booster&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('callbacks',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">callbacks&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('colsample_bylevel',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">colsample_bylevel&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('colsample_bynode',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">colsample_bynode&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('colsample_bytree',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">colsample_bytree&nbsp;</td>\n",
+              "            <td class=\"value\">0.8</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('device',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">device&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('early_stopping_rounds',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">early_stopping_rounds&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('enable_categorical',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">enable_categorical&nbsp;</td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('eval_metric',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">eval_metric&nbsp;</td>\n",
+              "            <td class=\"value\">&#x27;rmse&#x27;</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('feature_types',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">feature_types&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('feature_weights',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">feature_weights&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('gamma',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">gamma&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('grow_policy',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">grow_policy&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('importance_type',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">importance_type&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('interaction_constraints',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">interaction_constraints&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('learning_rate',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">learning_rate&nbsp;</td>\n",
+              "            <td class=\"value\">0.1</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_bin',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_bin&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_cat_threshold',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_cat_threshold&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_cat_to_onehot',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_cat_to_onehot&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_delta_step',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_delta_step&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_depth',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_depth&nbsp;</td>\n",
+              "            <td class=\"value\">6</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('max_leaves',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">max_leaves&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('min_child_weight',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">min_child_weight&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('missing',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">missing&nbsp;</td>\n",
+              "            <td class=\"value\">nan</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('monotone_constraints',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">monotone_constraints&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('multi_strategy',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">multi_strategy&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_estimators',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_estimators&nbsp;</td>\n",
+              "            <td class=\"value\">200</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_jobs',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('num_parallel_tree',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">num_parallel_tree&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('random_state',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">random_state&nbsp;</td>\n",
+              "            <td class=\"value\">42</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('reg_alpha',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">reg_alpha&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('reg_lambda',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">reg_lambda&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('sampling_method',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">sampling_method&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('scale_pos_weight',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">scale_pos_weight&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('subsample',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">subsample&nbsp;</td>\n",
+              "            <td class=\"value\">0.8</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('tree_method',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">tree_method&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('validate_parameters',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">validate_parameters&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"user-set\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('verbosity',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">verbosity&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "</script></body>"
+            ],
+            "text/plain": [
+              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=0.8, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric='rmse', feature_types=None,\n",
+              "             feature_weights=None, gamma=None, grow_policy=None,\n",
+              "             importance_type=None, interaction_constraints=None,\n",
+              "             learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
+              "             max_cat_to_onehot=None, max_delta_step=None, max_depth=6,\n",
+              "             max_leaves=None, min_child_weight=None, missing=nan,\n",
+              "             monotone_constraints=None, multi_strategy=None, n_estimators=200,\n",
+              "             n_jobs=None, num_parallel_tree=None, ...)"
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from xgboost import XGBRegressor\n",
+        "from sklearn.metrics import mean_squared_error, r2_score\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "# Модель XGBoost\n",
+        "xgb = XGBRegressor(\n",
+        "    n_estimators=200,\n",
+        "    learning_rate=0.1,\n",
+        "    max_depth=6,\n",
+        "    subsample=0.8,\n",
+        "    colsample_bytree=0.8,\n",
+        "    random_state=42,\n",
+        "    objective='reg:squarederror',\n",
+        "    eval_metric='rmse'\n",
+        ")\n",
+        "\n",
+        "# Обучение с отслеживанием loss на train и test\n",
+        "eval_set = [(X_train, y_train), (X_test, y_test)]\n",
+        "xgb.fit(\n",
+        "    X_train, y_train,\n",
+        "    eval_set=eval_set,\n",
+        "    verbose=False\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Test  MSE:  488966272.0000\n",
+            "Test  RMSE: 22112.5818\n",
+            "Test  R²:   0.7491\n"
+          ]
+        }
+      ],
+      "source": [
+        "# История изменения loss\n",
+        "results = xgb.evals_result()\n",
+        "\n",
+        "train_loss = results['validation_0']['rmse']\n",
+        "test_loss = results['validation_1']['rmse']\n",
+        "epochs = range(1, len(train_loss) + 1)\n",
+        "\n",
+        "# График изменения ошибки\n",
+        "plt.figure(figsize=(8,5))\n",
+        "plt.plot(epochs, train_loss, label='Train RMSE')\n",
+        "plt.plot(epochs, test_loss, label='Test RMSE')\n",
+        "plt.xlabel('Boosting iteration (эпоха)')\n",
+        "plt.ylabel('Root Mean Squared Error (RMSE)')\n",
+        "plt.title('Изменение ошибки (loss) при обучении XGBRegressor')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "y_test_pred = xgb.predict(X_test)\n",
+        "\n",
+        "test_mse = mean_squared_error(y_test, y_test_pred)\n",
+        "test_rmse = np.sqrt(test_mse)\n",
+        "test_r2 = r2_score(y_test, y_test_pred)\n",
+        "\n",
+        "print(f\"Test  MSE:  {test_mse:.4f}\")\n",
+        "print(f\"Test  RMSE: {test_rmse:.4f}\")\n",
+        "print(f\"Test  R²:   {test_r2:.4f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.12.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/AISP3.ipynb b/AISP3.ipynb
new file mode 100644
index 0000000..b202f03
--- /dev/null
+++ b/AISP3.ipynb
@@ -0,0 +1,1457 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wwHZs5pbv5lw"
+      },
+      "source": [
+        "# Практическая работа №3\n",
+        "## по предмету \"Системы искусственного интеллекта\"\n",
+        "\n",
+        "Целью практической работы является изучение методов регрессии.\n",
+        "\n",
+        "В данно работе вам необходимо:\n",
+        "1. используя библиотеку sklearn, обучить линейную регрессию без использования регуляризации\n",
+        "2. изучить работу класса Lasso для регуляризации, подобрать наилучший параметр для данного набора данных.\n",
+        "3. изучить работу класса Ridge для регуляризации, подобрать наилучший параметр альфа для данного набора данных."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "EP_MhQGkw5sW"
+      },
+      "outputs": [
+        {
+          "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>brand</th>\n",
+              "      <th>processor_brand</th>\n",
+              "      <th>processor_name</th>\n",
+              "      <th>processor_gnrtn</th>\n",
+              "      <th>ram_gb</th>\n",
+              "      <th>ram_type</th>\n",
+              "      <th>ssd</th>\n",
+              "      <th>hdd</th>\n",
+              "      <th>os</th>\n",
+              "      <th>os_bit</th>\n",
+              "      <th>graphic_card_gb</th>\n",
+              "      <th>weight</th>\n",
+              "      <th>warranty</th>\n",
+              "      <th>Touchscreen</th>\n",
+              "      <th>msoffice</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>rating</th>\n",
+              "      <th>Number of Ratings</th>\n",
+              "      <th>Number of Reviews</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ASUS</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i3</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>1024 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>34649</td>\n",
+              "      <td>2 stars</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Lenovo</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i3</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>1024 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>38999</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>65</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Lenovo</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i3</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>1024 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>39999</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ASUS</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Core i5</td>\n",
+              "      <td>10th</td>\n",
+              "      <td>8 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>512 GB</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>32-bit</td>\n",
+              "      <td>2 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>69990</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ASUS</td>\n",
+              "      <td>Intel</td>\n",
+              "      <td>Celeron Dual</td>\n",
+              "      <td>Not Available</td>\n",
+              "      <td>4 GB</td>\n",
+              "      <td>DDR4</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>512 GB</td>\n",
+              "      <td>Windows</td>\n",
+              "      <td>64-bit</td>\n",
+              "      <td>0 GB</td>\n",
+              "      <td>Casual</td>\n",
+              "      <td>No warranty</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>26990</td>\n",
+              "      <td>3 stars</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    brand processor_brand processor_name processor_gnrtn ram_gb ram_type  \\\n",
+              "0    ASUS           Intel        Core i3            10th   4 GB     DDR4   \n",
+              "1  Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
+              "2  Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
+              "3    ASUS           Intel        Core i5            10th   8 GB     DDR4   \n",
+              "4    ASUS           Intel   Celeron Dual   Not Available   4 GB     DDR4   \n",
+              "\n",
+              "      ssd      hdd       os  os_bit graphic_card_gb  weight     warranty  \\\n",
+              "0    0 GB  1024 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "1    0 GB  1024 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "2    0 GB  1024 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "3  512 GB     0 GB  Windows  32-bit            2 GB  Casual  No warranty   \n",
+              "4    0 GB   512 GB  Windows  64-bit            0 GB  Casual  No warranty   \n",
+              "\n",
+              "  Touchscreen msoffice  Price   rating  Number of Ratings  Number of Reviews  \n",
+              "0          No       No  34649  2 stars                  3                  0  \n",
+              "1          No       No  38999  3 stars                 65                  5  \n",
+              "2          No       No  39999  3 stars                  8                  1  \n",
+              "3          No       No  69990  3 stars                  0                  0  \n",
+              "4          No       No  26990  3 stars                  0                  0  "
+            ]
+          },
+          "execution_count": 1,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.read_csv('AISP2.csv')\n",
+        "\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Price                            1.000000\n",
+              "ssd                              0.628272\n",
+              "ram_gb                           0.518323\n",
+              "graphic_card_gb                  0.459986\n",
+              "processor_name_Core i7           0.377777\n",
+              "processor_name_Core i9           0.359096\n",
+              "brand_APPLE                      0.312112\n",
+              "os_Mac                           0.312112\n",
+              "processor_name_M1                0.274581\n",
+              "processor_brand_M1               0.274581\n",
+              "processor_name_Ryzen 9           0.253506\n",
+              "weight_Casual                    0.247878\n",
+              "processor_gnrtn_12th             0.219060\n",
+              "Touchscreen_Yes                  0.189126\n",
+              "ram_type_LPDDR3                  0.181314\n",
+              "ram_type_LPDDR4X                 0.173809\n",
+              "ram_type_DDR5                    0.168689\n",
+              "processor_gnrtn_10th             0.164034\n",
+              "os_DOS                           0.140780\n",
+              "brand_MSI                        0.123952\n",
+              "msoffice_No                      0.105752\n",
+              "warranty_3 years                 0.080610\n",
+              "processor_name_Ryzen 7           0.061872\n",
+              "ram_type_DDR3                    0.042006\n",
+              "processor_gnrtn_8th              0.040292\n",
+              "warranty_1 year                  0.033312\n",
+              "brand_ASUS                       0.032036\n",
+              "ram_type_LPDDR4                  0.028034\n",
+              "processor_gnrtn_9th              0.021192\n",
+              "os_bit_32-bit                    0.018458\n",
+              "processor_brand_AMD             -0.001583\n",
+              "weight_Gaming                   -0.012524\n",
+              "os_bit_64-bit                   -0.018458\n",
+              "processor_gnrtn_4th             -0.018769\n",
+              "processor_name_Core i5          -0.023218\n",
+              "brand_acer                      -0.024663\n",
+              "warranty_2 years                -0.029339\n",
+              "brand_HP                        -0.030649\n",
+              "rating                          -0.033528\n",
+              "brand_Avita                     -0.033819\n",
+              "brand_Lenovo                    -0.039079\n",
+              "warranty_No warranty            -0.045241\n",
+              "processor_gnrtn_7th             -0.045656\n",
+              "processor_gnrtn_11th            -0.085683\n",
+              "processor_brand_Intel           -0.103966\n",
+              "processor_gnrtn_Not Available   -0.105722\n",
+              "msoffice_Yes                    -0.105752\n",
+              "processor_name_Pentium Quad     -0.111755\n",
+              "processor_name_Ryzen 5          -0.114138\n",
+              "Number of Ratings               -0.140392\n",
+              "Number of Reviews               -0.148738\n",
+              "processor_name_Ryzen 3          -0.150211\n",
+              "brand_DELL                      -0.166272\n",
+              "Touchscreen_No                  -0.189126\n",
+              "processor_name_Celeron Dual     -0.200490\n",
+              "weight_ThinNlight               -0.250425\n",
+              "hdd                             -0.252699\n",
+              "ram_type_DDR4                   -0.270184\n",
+              "os_Windows                      -0.337929\n",
+              "processor_name_Core i3          -0.377232\n",
+              "Name: Price, dtype: float64"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df['ram_gb'] = df['ram_gb'].str.replace(' GB', '').astype(float)\n",
+        "df['ssd'] = df['ssd'].str.replace(' GB', '').astype(float)\n",
+        "df['hdd'] = df['hdd'].str.replace(' GB', '').astype(float)\n",
+        "df['graphic_card_gb'] = df['graphic_card_gb'].str.replace(' GB', '').astype(float)\n",
+        "df['rating'] = df['rating'].str.replace(' stars', '').str.replace(' star', '').astype(float)\n",
+        "\n",
+        "df = pd.get_dummies(df, \n",
+        "    columns=['brand', 'processor_brand', 'processor_name', 'ram_type', \n",
+        "             'os', 'os_bit', 'Touchscreen', 'msoffice', 'warranty', 'processor_gnrtn', 'weight'])\n",
+        "\n",
+        "df.corr(numeric_only=True)['Price'].sort_values(ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "y = df['Price']\n",
+        "\n",
+        "X = df.drop('Price', axis=1)\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "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",
+              "  display: none;\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",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  overflow: visible;\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",
+              "\n",
+              ".estimator-table summary {\n",
+              "    padding: .5rem;\n",
+              "    font-family: monospace;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "\n",
+              ".estimator-table details[open] {\n",
+              "    padding-left: 0.1rem;\n",
+              "    padding-right: 0.1rem;\n",
+              "    padding-bottom: 0.3rem;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+              "    background-color: #fff;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:nth-child(even) {\n",
+              "    background-color: #f6f6f6;\n",
+              "}\n",
+              "\n",
+              ".estimator-table .parameters-table tr:hover {\n",
+              "    background-color: #e0e0e0;\n",
+              "}\n",
+              "\n",
+              ".estimator-table table td {\n",
+              "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+              "}\n",
+              "\n",
+              ".user-set td {\n",
+              "    color:rgb(255, 94, 0);\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td.value pre {\n",
+              "    color:rgb(255, 94, 0) !important;\n",
+              "    background-color: transparent !important;\n",
+              "}\n",
+              "\n",
+              ".default td {\n",
+              "    color: black;\n",
+              "    text-align: left;\n",
+              "}\n",
+              "\n",
+              ".user-set td i,\n",
+              ".default td i {\n",
+              "    color: black;\n",
+              "}\n",
+              "\n",
+              ".copy-paste-icon {\n",
+              "    background-image: url(data:image/svg+xml;base64,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);\n",
+              "    background-repeat: no-repeat;\n",
+              "    background-size: 14px 14px;\n",
+              "    background-position: 0;\n",
+              "    display: inline-block;\n",
+              "    width: 14px;\n",
+              "    height: 14px;\n",
+              "    cursor: pointer;\n",
+              "}\n",
+              "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
+              "        <div class=\"estimator-table\">\n",
+              "            <details>\n",
+              "                <summary>Parameters</summary>\n",
+              "                <table class=\"parameters-table\">\n",
+              "                  <tbody>\n",
+              "                    \n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('fit_intercept',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('copy_X',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">copy_X&nbsp;</td>\n",
+              "            <td class=\"value\">True</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('tol',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">tol&nbsp;</td>\n",
+              "            <td class=\"value\">1e-06</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('n_jobs',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">n_jobs&nbsp;</td>\n",
+              "            <td class=\"value\">None</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "\n",
+              "        <tr class=\"default\">\n",
+              "            <td><i class=\"copy-paste-icon\"\n",
+              "                 onclick=\"copyToClipboard('positive',\n",
+              "                          this.parentElement.nextElementSibling)\"\n",
+              "            ></i></td>\n",
+              "            <td class=\"param\">positive&nbsp;</td>\n",
+              "            <td class=\"value\">False</td>\n",
+              "        </tr>\n",
+              "    \n",
+              "                  </tbody>\n",
+              "                </table>\n",
+              "            </details>\n",
+              "        </div>\n",
+              "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
+              "    // Get the parameter prefix from the closest toggleable content\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+              "\n",
+              "    const originalStyle = element.style;\n",
+              "    const computedStyle = window.getComputedStyle(element);\n",
+              "    const originalWidth = computedStyle.width;\n",
+              "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+              "\n",
+              "    navigator.clipboard.writeText(fullParamName)\n",
+              "        .then(() => {\n",
+              "            element.style.width = originalWidth;\n",
+              "            element.style.color = 'green';\n",
+              "            element.innerHTML = \"Copied!\";\n",
+              "\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        })\n",
+              "        .catch(err => {\n",
+              "            console.error('Failed to copy:', err);\n",
+              "            element.style.color = 'red';\n",
+              "            element.innerHTML = \"Failed!\";\n",
+              "            setTimeout(() => {\n",
+              "                element.innerHTML = originalHTML;\n",
+              "                element.style = originalStyle;\n",
+              "            }, 2000);\n",
+              "        });\n",
+              "    return false;\n",
+              "}\n",
+              "\n",
+              "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
+              "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+              "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+              "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
+              "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+              "\n",
+              "    element.setAttribute('title', fullParamName);\n",
+              "});\n",
+              "</script></body>"
+            ],
+            "text/plain": [
+              "LinearRegression()"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "model = LinearRegression()\n",
+        "model.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MSE: 638671150.10\n",
+            "RMSE: 25271.94\n",
+            "R²: 0.6723\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "from sklearn.metrics import mean_squared_error, r2_score\n",
+        "\n",
+        "y_pred = model.predict(X_test)\n",
+        "\n",
+        "mse = mean_squared_error(y_test, y_pred)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(f'MSE: {mse:.2f}')\n",
+        "print(f\"RMSE: {np.sqrt(mse):.2f}\") # рублей\n",
+        "print(f'R²: {r2:.4f}')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.145e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.426e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+11, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.723e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.081e+11, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.170e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.424e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+11, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.675e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.079e+11, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.187e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.422e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+11, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.606e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.076e+11, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.198e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.420e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+11, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.507e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.072e+11, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.206e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.416e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+11, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.366e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.066e+11, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.212e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.410e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+11, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.166e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+11, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.215e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.401e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.859e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.887e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.046e+11, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.218e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.389e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.648e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.505e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+11, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.220e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.371e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.352e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.994e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+11, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.221e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.345e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.943e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.336e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.699e+10, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.222e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.307e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.393e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.527e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+10, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.222e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.252e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.676e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.591e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.614e+10, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.223e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.174e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.780e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.589e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.818e+10, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.223e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.061e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.724e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.611e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.844e+10, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.223e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.899e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.565e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.750e+10, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.722e+10, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.223e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.672e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.247e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.049e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.527e+10, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.224e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.355e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.169e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.480e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.361e+10, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.224e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.923e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+10, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.958e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.333e+10, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.224e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.349e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.609e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.961e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.263e+10, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.181e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.612e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.909e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.964e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.558e+09, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.606e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.668e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.912e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.966e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.566e+09, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.606e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.625e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.916e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.979e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.574e+09, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.288e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.517e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.921e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.002e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.583e+09, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.293e+10, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.451e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.928e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.595e+09, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.498e+09, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.542e+10, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.252e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.382e+08, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.774e+09, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.802e+09, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.491e+09, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.398e+08, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.464e+08, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.515e+09, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.829e+09, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.993e+09, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.825e+08, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.574e+08, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.574e+09, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.505e+09, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+09, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.009e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.709e+08, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.918e+09, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.678e+09, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.289e+09, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.323e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.880e+08, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.941e+09, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.177e+08, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.148e+09, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.352e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.009e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.147e+08, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.406e+08, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+09, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.395e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.009e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+08, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.709e+08, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.354e+09, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.444e+09, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.025e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.461e+08, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.627e+08, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.325e+09, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+08, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.163e+09, tolerance: 9.526e+07\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.621e+08, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.929e+08, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.454e+09, tolerance: 1.135e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.903e+08, tolerance: 1.074e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.807e+08, tolerance: 1.139e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.186e+08, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.430e+08, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n",
+            "c:\\Users\\Serafim\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.654e+08, tolerance: 1.100e+08\n",
+            "  model = cd_fast.enet_coordinate_descent(\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Максимальный R²: 0.7321\n",
+            "Лучшее alpha: 494.1713361323828\n"
+          ]
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.linear_model import Lasso\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.pipeline import make_pipeline\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "import numpy as np\n",
+        "\n",
+        "# Сетка значений alpha\n",
+        "alphas = np.logspace(-4, 4, 50)\n",
+        "r2_scores = []\n",
+        "\n",
+        "for alpha in alphas:\n",
+        "    lasso = make_pipeline(StandardScaler(), Lasso(alpha=alpha, random_state=42))\n",
+        "    scores = cross_val_score(lasso, X_train, y_train, cv=5, scoring='r2')\n",
+        "    r2_scores.append(np.mean(scores))\n",
+        "\n",
+        "# Построим график\n",
+        "plt.figure(figsize=(8,5))\n",
+        "plt.semilogx(alphas, r2_scores, marker='o')\n",
+        "plt.xlabel(\"Alpha (log scale)\")\n",
+        "plt.ylabel(\"Mean R² (5-fold CV)\")\n",
+        "plt.title(\"Зависимость R² от alpha в Lasso\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "max_idx = np.argmax(r2_scores)\n",
+        "max_r2 = r2_scores[max_idx]\n",
+        "best_alpha = alphas[max_idx]\n",
+        "\n",
+        "print(f\"Максимальный R²: {max_r2:.4f}\")\n",
+        "print(f\"Лучшее alpha: {best_alpha}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Максимальный R²: 0.7344\n",
+            "Лучшее alpha: 51.79474679231202\n"
+          ]
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.linear_model import Ridge\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.pipeline import make_pipeline\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "import numpy as np\n",
+        "\n",
+        "# Сетка значений alpha\n",
+        "alphas = np.logspace(-4, 4, 50)\n",
+        "r2_scores = []\n",
+        "\n",
+        "for alpha in alphas:\n",
+        "    lasso = make_pipeline(StandardScaler(), Ridge(alpha=alpha, random_state=42))\n",
+        "    scores = cross_val_score(lasso, X_train, y_train, cv=5, scoring='r2')\n",
+        "    r2_scores.append(np.mean(scores))\n",
+        "\n",
+        "# Построим график\n",
+        "plt.figure(figsize=(8,5))\n",
+        "plt.semilogx(alphas, r2_scores, marker='o')\n",
+        "plt.xlabel(\"Alpha (log scale)\")\n",
+        "plt.ylabel(\"Mean R² (5-fold CV)\")\n",
+        "plt.title(\"Зависимость R² от alpha в Ridge\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "max_idx = np.argmax(r2_scores)\n",
+        "max_r2 = r2_scores[max_idx]\n",
+        "best_alpha = alphas[max_idx]\n",
+        "\n",
+        "print(f\"Максимальный R²: {max_r2:.4f}\")\n",
+        "print(f\"Лучшее alpha: {best_alpha}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.12.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}