{
  "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": "iVBORw0KGgoAAAANSUhEUgAAAq4AAAHWCAYAAAC2Zgs3AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAAc3ZJREFUeJzt3QeYE1XbBuBn+7KUpfelI11EigJ+ojQrKHZAwN7wU7Gi/hZEwd57g88uqNgbAqKg9CIK0qVJb8uysDX/9ZzshEm2JWx2J8k+N9ewyWQyOZkzSd5555wzUS6XywURERERkRAX7XQBRERERET8ocBVRERERMKCAlcRERERCQsKXEVEREQkLChwFREREZGwoMBVRERERMKCAlcRERERCQsKXEVEREQkLChwFZGA5ebmYteuXVi3bp3TRRERkXJEgauI+GXbtm245ZZb0LhxY8THx6NWrVpo27YtUlNTHSvTmWeeiauvvtpz/+eff0ZUVJT564Tdu3ejYsWK+Pbbbx15/Uj2zz//mLqdOHGi00UREQcpcC3H+APAH4IFCxbke+yyyy4zj7Vv396RskloWbNmDbp27YqPPvoI1157Lb7++mtMnToV06ZNM4GaE2bPno0ff/wRd911F0JFjRo1cNVVV+G+++5DOGvSpIn5/FsT67hbt2545513UJ6D5oKmE088EaHogw8+wLPPPlvse4mOjkb16tVxxhln4Pfff3ekrCKBiA1oaSk3Qcp7773ndDEkhDBYZZZ1zpw5aNCgAULBE088gT59+qBFixYIJddddx2ef/55TJ8+Hb1790a4Ou6443DbbbeZ21u3bsWbb76JESNGICMjwyvLXZ4MHjzYZPnteOYhVAPXP//805wlKeq95OTkYNWqVXj55Zdx6qmnYv78+ejQoUOZl1fEXwpcJZ9HHnkEcXFxIRcQiDMWLlxogjBmN0MlaN2xYwe++eYbvPrqqwg1bdq0MWcqeEYjnANX1vWll17qdRamWbNmeOaZZ8pt4Hr88cd7bZNgOXz4sDkwZPbTqffyn//8x2RdX3nlFRPElqWDBw86duYmXOpLjtBWFy9r16412VZm2OrWrZuv/R6/2Bo2bIiEhATUq1cPQ4cOxYYNGwo8DfX555/n+7BXq1bNPPbkk096PbZlyxZcccUVqFOnjll3u3bt8Pbbb3stY7Vf/OSTT/KVu1KlSuaH1W7fvn0m25CSkmLWyUD8scceMx2LfMvrWx5i8HHKKacU237yrLPOMvMffPDBgN9TYbKzszF27Fg0b97cPJenbu+55x6T7bLj/LPPPttr3o033mjKY/fTTz+ZeTzFb+F7s78/YrbFqj8Ls6yJiYlm3+B7YHm4b3Af2bNnj9fzC1onD4T4Bc8MUKCvXRgGrdxGffv2hT8mT56Mzp07o0KFCqhZs6b5wWb9+Lbhvfzyy73273POOcfsIxY2qznttNPMOriupk2bmjr21a9fP3z11VdwuVx+lc9qmuM7+e7T9v3Qd+K+YMeOcxdeeCHq169vtr+13NE2/2FmsXXr1mY/sPv111/N6zRq1MhsN37eRo0ahUOHDuV7j/yccrufe+655jbXefvtt5usn+9nl8snJyejatWqJtPLeQXhQRWDLgY+XJZ1tmLFCq9l+Nnke2dmkXXP9fK12aSDdbRp0ybzvCpVqph9+6mnnjqqbWRtc556T0pKMs0IuK8WVH9sdvN///d/5gCBy1ptxefOnYvTTz/dlJHze/XqZZrF2B04cMB8t7HOuc1r165t9rlFixaZx/nZ4uvyu7mw/cMXtyH51q8/36PW78OwYcPMNrTqbOnSpfnaJVv7AV+HGd/KlSub3xHiOtm8gd8z/M7hdye/Z/bu3ev1Wv58Drl9+Znn+lkmZpGfe+65oNeXlD1lXMXLww8/jNjYWNNukKeS7DIzM82XwM0332za8vGL54UXXsAff/yBZcuWeS3LL50JEyaYHyjLZ599ZoJXX9u3bzdfGPxyYNDFH5TvvvsOV155pflyKOxUV1HS09PNFz5/JPnFxx/V3377DXfffbc57VlQ26+j8csvvxTYEaek74ntJP/3v//hggsuMKdr+WM2fvx484M8ZcoUlJaC2ovyB4n1dv3115sMIk+Fs+5feuklUy5O/EErCPcBftkzEBgyZEjAr10Y1iX3QXYUKw5/NBmQso0utyHrhj9gDAYWL15sfmTp/PPPx19//YX//ve/5keeWV224924caPnfv/+/U1djh492jyPQS33a1/8wWRmkuvzN1DkNuTpePs+UBQeyDC7S6+//ropp4WB4MCBA03gwn3tmGOOMfsiDyKOFg8UNm/ebA4+fQ8K+Hnj/sE6mTdvnvle4LJ8zI7lYsBxwgknmINFHlBx3+ABGp9PDCQZRM6aNcvsa3yP3OcZCPni83kwzUwwg1MGy3ztnj17miDON1i7+OKLzfoeffRRE6Dw+45By2uvvWb2bQZk77//vgmmub+cfPLJXs/n++RoGnYMMHmGivtVjx49zDI33XST2Rb8DLMeeLA9aNAgr+fxwJRZO74WD0h5m0E43w/3nwceeMAccPAzxLLxAIHtjInbhevkdws7SPIzyu3F7wdmUu+9917s37/f1AH3Q2KwWBTrAM1ev/5+jzLgHDBggKl71iMPcL744osC68zal7gfnHTSSWY/YCBIfA3r88ptuH79erz44ovmc8rPK7ezP59Dfm75+8WmRKxT4rbhOvj7RcGoL3GIS8qtCRMmMB3kmj9/vrm/du1aV2xsrOumm24y93v16uVq165dket4/PHHzTp27dpl7q9fv97cHzx4sFnXtm3bPMv26dPHNWTIEPP4E0884Zl/5ZVXuurVq+dZh+WSSy5xJScnu9LT0839GTNmmOdOnjw5XzkqVqzoGjFihOf+2LFjzbxVq1Z5LTd69GhXTEyMa+PGjV7ltZfHwvfObWCxXp9/LSeccILrjDPOMPMfeOCBgN9TQZYsWWLWd9VVV3nNv/3228386dOne+Y1btzYddZZZ3ktN3LkSLOc3dSpU828r776yjOP783+/r799luzzOmnn+71fL4v3mf9ZWdn59t/XnjhhQLX+c0335h94Lbbbsv3Hv197cKcdNJJrs6dO+eb71tHmZmZrtq1a7vat2/vOnTokGe5r7/+2ix3//33m/t79+4tdD+wTJkyxevzUpTffvvNLPvxxx+7/MHPRaVKlYrcp33rcubMmZ55XI77gmXlypVmmfHjx3s915/PNHFd/fv3d+3cudNMy5Ytcw0bNsysk/uXXUH7Ml83KirKtWHDBq8y8vkPPfSQ17KdOnXyqsvPP//cLMfvFgv3u//85z9mPvc7y3HHHWfqd/fu3Z55S5cudUVHR7uGDx+ebx++5pprvNbZsGFDU85HH33UM5/7QoUKFby2vfU9UdBk7Wu33HKLuf/rr796nnfgwAFX06ZNXU2aNHHl5OR47aPNmjXz2na5ubmuli1buk477TRz2759uY5+/fp55vE7xLcefPF7wb5P+L6XMWPGmLrldzTL3LVr13zfr/5+j3766afmuc8++6xnGb7f3r1756szaz/gOuxYBs5///33veZ///33XvP9+RzefPPNripVqnh9X/kqaX2Jc9RUQPJlW3kUWxSepuJRL3ugfvjhh+a0DrMWdjzq5/x3333X3GfmZ8aMGflOfTK78umnn5qjdd5mNsOaeETOrIF1+sv++vblfDMgxEwPT30xe2BfjqeWmfVhprSgTIp98j196YtH+Dy9zexNSd+TnZXBvfXWW73mWx1lfE9lBQPLySwKs47MhhWE5YmJifHc52lBnsorqDzMvFx00UVmfexEVdLX9sUMk2/mryA8pch99YYbbjBnAezNO5gVssrO043MoPC0oO9pSYuVmWVzi6ysrCJf1ypbQftmQZjRtpevKDzzQYVlua3PCDGLdLTYpplZLU48zcrPMjNhvvXJbWdvq8j3zEwW65WZMl/MFtrxc2ofD5j7P7+HrAwscb9jJtyOGb8lS5aY7xT798+xxx5rTpsXdCbEnsXmOrt06WLKyTMh9npu1apVgWMUX3PNNSabZ586duzoKTczoswiWpjl5HOYEVy+fLnXupiNtG87vpfVq1ebMxPcv63vDG5TZg75nWWdnmcZeabj33//xdFiRpd1y6YRrANmJJn95lmeQL9Hv//+e5MNtbd9ZrZ45MiRhb6+vX6t12L2mnVnfy1mn7kd+fthvffiPodchtuN9VOYktaXOCdiAld+gBgosD1XQe0r/fHDDz+Y07s8Hc4PNH9I7e3bIhm/pPnDxA8t2/YVhV9ODFj448QfGKv9pC/+yPE0F/H0D5dv2bKl1zI7d+40bah4qtP6kbQmPp8YeNixLZPvsvySsuMPAL9MfZez2kT6rtP6ErdPf//9d6HbgF/aPFXLtln8oSzpe7JjkM8vfd/OcfyB4ReyvU1xsPD0KE9rjxs3Lt9jVt0y0LPjDz/r0/czwtOKDAxZJ/wBLq7NalGvXRR/2o9a24qBiC++H+txBoE8pcjmHNy3eYr48ccfN+1eLTxlyu+EMWPGmLZ1PJ3N/du33bG9bP601yX+QPNH2x9WW8+iTv3y/TLYYCDC06PcJ/kaxQXcdjyI4A8/P0c8nct9j0G97ylSNlGwgker3Sq3FfEgzY7BuW8vfJbTfrDAOuF3kO/7863DouqWzQGsoM+Op7rtuM1ZJtan7/yCDmC4v/M7xD5ZByksT2FlsZfXwnaZvt9ZVoDk+73BJiTcz6ztyX2TIwaw3SmDLzaTCPRiIFYQzrbYVptk34N1f79HrTqzTvlbCuvgy98NtiX3fS2+P7bX9X29tLQ0z2v58znkgSqbx1h9MvibwfdhV9L6EudETBtXfkHxyJc76HnnnRfw89mWhh8AZpX4Q8oPED/MXFdR2bFIwbZvVtvW4rDNIgMwtnPkF+gll1xiglc+346dIO68807TuYdth/g8X1YGgcsW1h7KNzC8//77PR0JLDxo8V0vj9z5+gXhl5rvlzgb6dsV1XP6rbfeMgEbD3aC8Z4K4m/QU1LM4LGTCrNOvtuFAs0ycDg1ZtzZto5ZWdZ9YduhuNcuDDOJhWVGjxbbgnI/4kEv65XlYptYtjvs1KmTp2Mg92f+2HMZft8wOOQ8e6Bllc03ICoM9yXfg7rCWMG0b+dJO5bl448/NuWzZ5SIZ0L8wbJbAQrPFDDQZ0dAtg+2zgYw0OHnjJ30+N3BZdhJigcvDGZ9O/DYM/ZOKOj1CyuTvx3rjpbv58raVsxocyiyglj7GM9m8DuQbX+ZGedzeODFs0AM1vxhBeHEeuV24Nk2DonFTPTRfI/6iweKvj3y+VoMWvn7WxDrgMefzyHXwww2H+PBKCcGt8OHDzffR0dD2dbQETGBKz+sRX1geTTGBus8tc2MBTtM8INu9WzmkD/8EubpcusDxUbYDGaZpeBpkEjFoJ0Di/PUDTPWxeG2szqc8BQis1M8cvfd/gwu2NCdDe55tMwvW99Tp/wyYoab297fHuJ8Td9lfX982NmDR+n+rtP+JW4pbHgWNivg0T6P6gvqHHQ078mO6+SXODMQ1tG/1ZmA+64/HZICwaFvWD++oyL4ZhpWrlxpOsFYrDIyqLNj5oWn4Zi5ZAcNNnFg7+GCxrss7rULwwCJzTGKY20rlt13aCrO892W3G9YXk58bwwg+INoH9eYZ2U48WCPIyUw684ex/bT0PxMkb3+CsNsKLOWvp0hC8NTmNyWxTUDYMDBA0uWj8OGse74voprAlMYZtGZ7WJmnJ9pfj7YKZM99RkMMCiwFHWKtjisE17Ygp9f+8EA68t3uYLmE8+WMPAuyyGWWJ7CymI9XhTue8Qe8P58b/Bzxu8gTvwM8WCR+6T1PRzogS9/H9944w2TYLCyk/5+j/K98VQ+vxvtWVcexPqLr8UECDvW+RMkFvc55JkBHohy4ncVtxM74fGAlJngktaXOCdimgoUh70v2SaTOzZ7wTO7xiFHrNMzbEdj9eDkFzszrjx1zg9sJAetxB8i62g7UFYgWtDpUuKRsLW9Czq1ydflaR8GITz1VdCP+tFgkMz6LigjyuCPvVqPFjNOzPDzi74gJX1P1gDnviMfPP30054AIljYFpJf/Dy7UFgGj+3rmCHhoPr2DBozIwymfYfjYhaGQSuxhzefY/XkDfS1C9O9e3eT1Szu9CgzR8y+MHCz76PMwLBNn7Ut+YPrO+IFf0h5AGI9j6/nm4WzMmO++z8PhHm62Z/sptXzngfJxeE240GBP+PDcogn/lizxzTPKNhPax8tZlXZ/IMBjv2A0b5deNt32KFAcP/n55PjiVr4ncx9yTdw4/Zn0GwfKoufOWYhfS8UUNr4emzbbb/6FL8n2GSIoxuw939R+BvEfY7NMhgsFva9Yf0+2XEfZ9LBvh8yaPddrihsCsIDEn5nMlsZyPcoM/JM8Fj7BfFzz5FH/MXX4ntj731ffB2rjv35HHIfteNvu3WWy1qmpPUlzomYjGtRmM1gQMq/VkaR2VQeVXI+Azdmlfhlxw8PP7z8APHHsTxcc5xfUgzsi8u28kuJbYl5ZM+sADM/nMcfEAY3BeHBAb9wi2qPx85NPFpnmzqenucXBk89sokGj8B9xwr1xx133IEvv/zSBFU8ZckfBX4pMUPE00w8NevvaVxf3E8YcBWV8SrJe2KTF55a5xcov6yZ5eIXLH+gObwYT+XZcfva229ZwyLZ51k/RL5YHm6Hwk4FEtsuMgvDTAV/oBhgMWDkMDUsa1HDNjEg5WlMLsOmE/Zgwp/XLgwDTqt9NYOywvCgk2dW2LSF25FZTWs4LP44MWgmZg25D/Pzz7riunkalsuyKQxx+zNDzGFyGGAwiOT+z8+Cb5DEjCMzPcVlvfjDzm3LDCqb3tjH0OSPNbcz18Xs6aRJk0ymnz/cxR1kMmhgMw227/PtPFgSzObxbAsPotjxhplvbgt+n7J5ALcFD9hK0oyD241ZN75Hfk5ZHzwFXlAQxn2LZeJ3NZubWMNh8aAh0Cx+SbG8PKPH8vBggZ8b7jPMvnObFDdYPR9nW1Y+nwc83Gc5Zii3K79LuG15apz7HeuVnaj4+eN3Kz8H7ChqH3+W33lsLsJmHRzai8v5NqnyxQNMHjBzn2GSx9/vUX4vsa0tM/rMsnK/4POs7zl/sr/8fPK3l81z+H3FIa/4+WVyiQd3/MzyPfvzOeT3DV+bB3jcVmyvyv2CAa51FqSk9SUOckUgvi0OmeE79A2H9bBPHKrnoosuMsts3brVDEVyxx13uBYtWmSGmuHQMRwCyD40SSSxhjNKSEhwbd68Od/jvkPncJtwSJqqVaua53DIkKuvvtoMr2Ipanipoh7fvn27Gd4lJSXFFRcX56pbt67Z9q+//rpnmUCGw7KGNrn77rtdLVq0cMXHx7tq1qzp6tGjh+vJJ580wyQd7XBYHObq4MGDXsv6Dofl73sqTFZWlhmuhkOz8LlcB9/L4cOHvZbjcDeFDdNT0OQ7HBbnPfPMM17rtIYO8vXSSy+5WrdubcpTp04d17XXXus1DJG1Tvs2s3BYnEaNGpk6OZrXLsjAgQPN9rQraMgy4rBUHHaJ+2316tVdQ4cO9drnOWwZ64rvj/sShxviUGeTJk3yLMPvBQ7zxvfB9XAYprPPPtu1YMECr9dasWKFKcNPP/1U7Hvwp86s7Tlo0CAz9NrcuXPzrcd3OKxx48aZMnJoqKMdDst3mDXLxIkTvYY4Wr58uatv375mOC9+xvidwNctaBgkbltfBdU59ysOv8UhjVgXvL148eJ86yRu5549e5ohrLj8gAEDTJkKeg0O/+S73Qoqk+92Ku57zcIhBS+44ALzHZmYmOjq1q2b+f2xK+p7jPg+zzvvPFeNGjVMHbIu+Ds1bdo083hGRob5nerYsaOrcuXKpvy8/fLLL3utJy0tzQyzxrLw9az9o7j3ctlll5mhrtasWeP39yhx2/L1WCbWGdcze/Zs81offfRRsdvcwu9HDo/G+uS6OnTo4Lrzzjtd//77r9+fw08++cQM58bHWGYuy+8r/s4Hu76k7EXxP0QYHt0xW2INfs+jTrZ/Yc9l37aQPAplVojZJGaoeNRq4eDN7LXJUwlsSyMioYMDsrONOtuk+duxqSywkxfPTLC5QHGZJj7ObJrvVcQsHI2Dk+/V2kTCATs6MjPKiyMwiy4SDOWiqQA7j/DUPxuw+/ZGt7CNm++pASvI9e0ZKyLO42eZpxPZAcnets5JbFvH0708rV9Wo0KIhAI207B3qrLaJfMUPpuXiQRLxASubMxu78HIdipsJ8N2K+wswowre72yDRADWbYLZM9VNthmezlOHL7noYceMu3g2G6G43SyZ6Fvr2kRCQ3sZBVK2O65oI41heH3ktWRrSBsw8f2rSKhjheIYPDK9sbsAMV2ybw8LPuQaCgpCaaIaSrAU2m+nVaInVx4qo09HjnUFYd9YmN3Nijn6X92duDwSsTG6MzesKMGh/TgB5AdO3wHXhcREZEjOCQVE0NMIHGEDg45xSEW2fFXJJgiJnAVERERkcim8R5EREREJCwocBURERGRsBDWnbPY2//ff/81V7dRD14RERGR0MNWqez0zgsdlfTiDmEduDJo5TirIiIiIhLaeClqXs2s3AauzLRaG4JjxQULRyDgZT2tS85J5FDdRi7VbWRSvUYu1W35qdvU1FSTaLTitnIbuFrNAxi0Bjtw5XBYXKc+TJFFdRu5VLeRSfUauVS35a9uo4LQrFOds0REREQkLChwFREREZGwoMBVRERERMJCWLdxFRERkfCWnZ2NnJwcp4shJRATE4PY2NgyGZpUgauIiIg40oGnevXqWL9+vcZijwBJSUmoV68e4uPjS/V1FLiKiIhImV9AaOPGjahWrZoZlD4hIUHBaxhfXCAzMxM7d+40ByEtW7Ys1ddT4CoiIiJlioEOg9datWqZIZNKejUlcVaFChXMsFcbNmwwdcumA6VFe4qIiIg4QlnWyBFdRgcfClxFREREJCwocBURERGRsKDAVURERMQhTZo0wbPPPut0McKGAlcRERERP9rjFjU9+OCDR7Xe+fPn45prrilR2U455RRThkcffTTfY2eddVa+8rH3/5AhQ8yIDomJiWjYsCHOOecc/P33355lCnufH330EZykUQUCtX8zUKUBa9TpkoiIiEgZ2bp1q+f2xx9/jPvvvx8rV670zKtUqZLXEFG8qAIH5S8OR1YIhpSUFEycOBGjR4/2zNuyZQumTZtmxle1j5/br18/tGrVCp999pl5bPPmzfjuu++wb98+r3VOmDABp59+ute8qlWrwknKuPrL5QImDQeeaQ9sXuB0aURERCIGA730zGxHJr62P+rWreuZkpOTTfbRus9MZeXKlU3w17lzZzMu7axZs7B27VqTyaxTp44JbLt27YqffvqpyKYCXO+bb76JQYMGmUH9OS7ql19+WWz5zj77bOzatQuzZ8/2zPvf//6H/v37o3bt2p55f/31lynXyy+/jBNPPBGNGzdGz5498fDDD5v7vkGq/X1zYobWScq4+osZ1ngeTbmAea8DKV2dLpGIiEhEOJSVg7b3/+DIay9/6DQkxQcnHGK288knn0SzZs3MxRU2bdqEM888E4888ogJZt955x0MGDDAZGobNWpU6HrGjBmDxx9/HE888QReeOEFDB061IyRWr169UKfwytWcTlmSRmIEjOwXI+9mQAzvBy66pNPPsEtt9xSqmOulgZlXAPR7Wr337+mAGk7nC6NiIiIhJCHHnrInIZv3ry5CTI7duyIa6+9Fu3btzeZ07Fjx5rHisugXnbZZRg8eDBatGiBcePGIS0tDfPmzSv29a+44gpMmjQJBw8exC+//IL9+/ebTKxdgwYN8Pzzz5umDgyue/fubcq1bt26fOtjGZgptk+84pmTlHENRP1OQMOuwOb5wMKJQK87nS6RiIhI2KsQF2Myn069drB06dLF6z4DTmY7v/nmG9NGNjs7G4cOHSo2+Dv22GM9tytWrGiuLrZjR/EJMwbKDJCZTZ0xYwaGDRtWYDvbkSNHYvjw4fj5558xZ84cTJ482QTIDKgZeFueeeYZ9O3b1+u57NDlJAWugep2rTtwXfA2cNIoICbO6RKJiIiENbbrDNbpeicxyLS7/fbbMXXqVNN8gNlTXhr1ggsuMJdFLQovn+q7fXiJXH8w6/rSSy9h+fLlRWZp2SaXzRY4sX3raaedZv7aA1e2aWW5Q4maCgSq7TlAxdrAga3Aiq+cLo2IiIiEKHaU4ml/drTq0KGDCQT/+eefUn3NIUOGYNmyZaZ5Qtu2bf16DgPj1q1bmyYGoU6Ba6Bi44Eul7tvz3vD6dKIiIhIiOJpew45tWTJEixdutQElf5mTo9WtWrVTLMEDoNVEJaFIx2wOQGzsmvWrMFbb72Ft99+28y34/BY27Zt85qcDm7DPy/vhM6XA78+BWz8Ddi2DKjbwekSiYiISIh5+umnzan7Hj16oGbNmrjrrruQmppa6q9btYixVnmxAQ7BxZELmP1lttW6P2rUKK9lL788L1FnM378eK+xYsuaAtejUaUe0GaAe3QBDo018AWnSyQiIiJlhKf/OdmvXFXQeLAMCKdPn56vY5Sdb9OBgtbje2EAX+xkVRRmWS0MoJ977jkUx9/xbcuamgqUpJMW/TEZSN/jdGlEREREIp4C16PV6ESgTgcg+xCw5H2nSyMiIiIS8RS4luRKWtYFCdhJKzfH6RKJiIiIRDQFriXR4UIgsSqwbwOweqrTpRERERGJaApcSyI+CTh+mPs2O2mJiIiISKlR4FpSXa5kuwFg7TRg12qnSyMiIiISsRS4llT1psAxp7tvz3/T6dKIiIiIRCwFrsFgddJa/D6QccDp0oiIiIhEJAWuwdDsVKBGSyDzALD0I6dLIyIiIhKRFLgGQ3S099BYIXq1CREREZFwpsA1WDoOBuIrAbtWAutnOl0aERERCaKoqKgipwcffLBE6/7888/9LsOcOXO85mdkZKBGjRrmMfvlX2fOnInevXujevXqSEpKQsuWLTFixAhkZmaax7lsYe9n27ZtCEUKXIMlsYo7eLWyriIiIhIxtm7d6pmeffZZVKlSxWve7bffXiblSElJwYQJE7zmTZkyBZUqVfKat3z5cpx++uno0qULfvnlFyxbtgwvvPAC4uPjkZPjfdGklStXer0XTrVr10YoUuAaTFZzgZXfAvs2Ol0aERGR8MAmdpkHnZn8bN5Xt25dz5ScnGyykvZ5H330Edq0aYPExES0bt0aL7/8sue5zHDeeOONqFevnnm8cePGGD9+vHmsSZMm5u+gQYPMOq37hRkxYoR5rUOHDnnmvf3222a+3Y8//mjK9fjjj6N9+/Zo3ry5CWTfeOMNVKhQwWtZBqn298Ipms0gQ1Cs0wWIKLVaAU17uZsKzH8L6DfG6RKJiIiEvqx0YFx9Z177nn+B+IolWsX777+P+++/Hy+++CI6deqExYsX4+qrr0bFihVNQPn888/jyy+/xKRJk9CoUSNs2rTJTDR//nwTODKLysAyJiamyNfq3LmzCW4//fRTXHrppdi4caPJqL700ksYO3asZzkGn8yc8rGTTz4ZkUKBa7CdcK07cF30DnDKaCDO+6hGREREIssDDzyAp556Cuedd56537RpU3Oq/rXXXjOBK4NLti896aSTTFaVGVdLrVq1zN+qVauaYNMfV1xxhcmyMnCdOHEizjzzTM96LBdeeCF++OEH9OrVy6z3xBNPRJ8+fTB8+HDTzMGuYcOGXvdZvr/++guhSIFrsPFiBMkpwP5NwJ+fAZ2GOl0iERGR0BaX5M58OvXaJXDw4EGsXbsWV155pcmyWrKzs02TArrsssvQr18/tGrVymRVzz77bPTv3/+oX/PSSy/F6NGjsW7dOhO4MqPri5lbZnEffvhhTJ8+HXPnzsW4cePw2GOPYd68eabZguXXX39F5cqVPffj4uIQqhS4Blt0DND1SuCnB4F5rwHHDWE3QKdLJSIiErr4O1nC0/VOSUtLM3/ZdvSEE07wesw67X/88cdj/fr1+O677/DTTz/hoosuQt++ffHJJ58c1WvWqFHDBL8Mlg8fPowzzjgDBw4UfAGkBg0aYNiwYWZiU4JjjjkGr776KsaMOdKckRliZnzDQWi2vA13x48AYhOBrUuBzfOdLo2IiIiUkjp16qB+/fom+9miRQuviQGhhafnL774YhPgfvzxx6aN6p49ezwZTt+e/v40F/j555/Nqf/i2sVaqlWrZjKtzBKHK2VcS0NSdaD9BcCS94C5rwEp3ZwukYiIiJQSZi9vuukm0zSATQE4ruqCBQuwd+9e3HrrrXj66adNwMiOW+ytP3nyZNPu1MpysrPVtGnT0LNnTyQkJJgAszinn346du7cma+9qoXta5csWWJGK+CIAszMvvPOO6btKofFstuxY4d53DerG4pNBpRxLe2hsZZ/DhwIzUF8RUREpOSuuuoqvPnmm6ZNaYcOHUyHKLY9tTKubD/KYak4pmrXrl3xzz//4Ntvv/UMOcWOXVOnTjVjtDK49UdUVBRq1qxpxmUtSLdu3Uwzhuuuuw7t2rUzZeKFC3ihA962Y9tbBtb2aeHChQhFyriWlvrHASknAJvmAgv/B5xyl9MlEhERkSBgZytOdkOGDDFTQdhpy95xy9eAAQPMVBxXEWPOMntrf5wB8Lvvvlvk+k455ZQi1xmKlHEtTd2ucf9d8DaQ7b68moiIiIgcHQWupanNQKBSHSBtG/D3V06XRkRERCSsKXAtTbHxQOfL3bfnveF0aURERETCmgLX0tb5MiA6Ftj4O7D1D6dLIyIiIhK2FLiWtir1gLbnuG/Pe93p0oiIiISMcOsYJM7XpQLXsuyktWwykO4ebFhERKS8ssYHzcxUx+VIkZ6ebv6W9tivGg6rLHBYrLodgG3LgMXvAj1vdrpEIiIijuGVnjhwPgfQT0xMRKVKlcy4pBKemdb09HRzEQMOycW6zc3NLbXXU+BaFvhh7HYt8OWNwPw3ge43AtH+XZ5NREQkEtWuXRurVq0yV4ratWuX08WREmLQyquBlTYFrmWlwwXA1PuAfRuBVT8Arc90ukQiIiKOYYb1wIED6NGjh9NFkRJi8wBmWsuCAteyElcBOH44MPs5dyctBa4iIiIm4CntdpESOdQ5qyx1uZLHmMC6GcDOVU6XRkRERCSsKHAtS9UaA63OcN+erwsSiIiIiARCgatTQ2Mt+QA4nOp0aURERETChgLXstbsFKBGSyAzDfjjY6dLIyIiIhI2FLg6MjRWXtaVnbR01RARERERvyhwdcJxg4H4ysCuVcC6n50ujYiIiEhYUODqhITK7uCV5qmTloiIiIg/FLg6pevV7r+rvgP2bnC6NCIiIiIhT4GrU2odAzQ7FXDlAgvecro0IiIiIiFPgauTrE5ai94Bsg45XRoRERGRkKbA1UnHnAZUbQQc2gss+8Tp0oiIiIiENAWuToqOAbpe5b497zUNjSUiIiJSBAWuTus0DIhNBLYtAzbNdbo0IiIiIiFLgavTkqoDHS48ckECERERESmQAtdQ0C1vaKzlXwAHtjldGhEREZGQ5GjgmpOTg/vuuw9NmzZFhQoV0Lx5c4wdOxau8tbWs15HIOVEIDcbWDDB6dKIiIiIhCRHA9fHHnsMr7zyCl588UWsWLHC3H/88cfxwgsvoNw5IW9orIUTgOxMp0sjIiIiEnJinXzx3377Deeccw7OOussc79Jkyb48MMPMW/evAKXz8jIMJMlNTXV/M3KyjJTsFjrCuY6i9XidMRWqoOotO3I/nMKXO3OK7vXLkccqVspE6rbyKR6jVyq2/JTt1lBrOMol4Pn5ceNG4fXX38dP/74I4455hgsXboU/fv3x9NPP42hQ4fmW/7BBx/EmDFj8s3/4IMPkJSUhHDXausUtN42BbsrtsSsY+5zujgiIiIiJZaeno4hQ4Zg//79qFKlSvgGrrm5ubjnnntM84CYmBjT5vWRRx7B3Xff7XfGNSUlBbt27SrxhrDjkcHUqVPRr18/xMXFocykbUfsC8chKjcLWVdMc7d9laByrG6l1KluI5PqNXKpbstP3aampqJmzZpBCVwdbSowadIkvP/++yZj2q5dOyxZsgS33HIL6tevjxEjRuRbPiEhwUy+uFFKY6cvrfUWqlpDoO05wJ+fIG7RBODcl8rutcuZMq9bKTOq28ikeo1cqtvIr9u4INavo52z7rjjDowePRqXXHIJOnTogGHDhmHUqFEYP348yq1ueZ20lk0G0vc4XRoRERGRkBHtdJuH6GjvIrDJAJsQlFsp3dxNBHIygEXvOF0aERERkZDhaOA6YMAA06b1m2++wT///IMpU6aYjlmDBg1CuRUVdSTrOv8tIDfH6RKJiIiIhARHA1eO13rBBRfghhtuQJs2bXD77bfj2muvNRchKNfanw9UqA7s3wis+t7p0oiIiIiEBEcD18qVK+PZZ5/Fhg0bcOjQIaxduxYPP/ww4uPjUa7FVQCOH+6+Pe91p0sjIiIiEhIcDVylCF2vBKKigXU/AztXOl0aEREREccpcA1VVRsBrc503573htOlEREREXGcAtdQ1u1q99+lHwKH3Ze3FRERESmvFLiGsqa9gJqtgMw0d/AqIiIiUo4pcA35obHysq5zXwNyspwukYiIiIhjFLiGuo6XABWqAXvWAjMfd7o0IiIiIo5R4BrqEioDZz/jvv3rk8DGuU6XSERERMQRClzDQbtBQMfBgCsXmHINkHHA6RKJiIiIlDkFruHijMeA5EbA3n+A70c7XRoRERGRMqfANVwkJgPnvcYeW8Di94DlXzpdIhEREZEypcA1nDTuAZw0yn37q5uA1K1Ol0hERESkzChwDTen3A3U6wgc2gt8MRJwuZwukYiIiEiZUOAabmLjgfPeAGITgbXTdDlYERERKTcUuIajWq2A/g+7b0+9D9jxt9MlEhERESl1ClzDVdergBZ9gezDwGdXAdmZTpdIREREpFQpcA3ny8Ge8zKQVAPYtgyY8YjTJRIREREpVQpcw1nlOsCA5923Zz8H/DPL6RKJiIiIlBoFruGuzdlAp2EAXMCU64DD+50ukYiIiEipUOAaCU5/FKjWFNi/Cfj2DqdLIyIiIlIqFLhGgoRK7iGyomKAPz4Gln3idIlEREREgk6Ba6RI6QqcfLv79je3Avs3O10iERERkaBS4BpJTr4DaNDZ3c718+uB3FynSyQiIiISNApcI0lMnLvJQFwSsP4XYM7LTpdIREREJGgUuEaaGs2B08e7b08bA2z70+kSiYiIiASFAtdIdPwIoNWZQE4m8NnVQNZhp0skIiIiUmIKXCP1qlq8MEHFWsCO5cD0sU6XSERERKTEFLhGqkq1gHNect/+/UVg3c9Ol0hERESkRBS4RrJjTgO6XOm+PeV6IH2P0yUSEREROWoKXCNd/4eBGi2AA/8CX48CXC6nSyQiIiJyVBS4Rrr4JPcQWdGxwPLP3VfWEhEREQlDClzLgwbHA6eMdt/+9g5g7wanSyQiIiISMAWu5cVJtwIpJwIZqcCU64DcHKdLJCIiIhIQBa7lRXQMcN5rQHwlYONvwOznnC6RiIiISEAUuJYn1ZoAZzzuvj3jEeDfJU6XSERERMRvClzLm+OGAG0GArnZ7qtqZaY7XSIRERERvyhwLZdX1XoOqFQX2LUK+OkBp0skIiIi4hcFruVRUnXg3Jfdt+e9Dqye6nSJRERERIqlwLW8atEHOOE69+0vRgIHdzldIhEREZEiKXAtz/o+CNRqDaRtB766WVfVEhERkZCmwLU8i6uQd1WtOODvr4HF7zldIhEREZFCKXAt7+odC/S5z337u7uAPeucLpGIiIhIgRS4CtD9RqDxSUDWQeCza4CcbKdLJCIiIlKywDU7OxsPPfQQNm/eHMjTJByuqjXoVSAhGdg8H/j1KadLJCIiIlKywDU2NhZPPPGECWAlwlRNAc7KC1hnPgZsXuh0iURERERK1lSgd+/emDlzZqBPk3Bw7IVA+wsAVw7w2VVARprTJRIRERHxiEWAzjjjDIwePRrLli1D586dUbFiRa/HBw4cGOgqJZSc9SSw8Xd3J60f73VfZUtEREQkHAPXG264wfx9+umn8z0WFRWFnJyc4JRMnFGhmru96/8GAgsnAiknAscNdrpUIiIiIoE3FcjNzS10UtAaIZqeDPS40X378+uAj4YC+9UhT0RERJyl4bCkYH0eAE4aBUTHui9O8NIJwO8va6gsERERCa/AlZ2zBgwYgBYtWpiJ7Vp//fXX4JdOnBMT574k7LW/ACknAJlpwA93A2/2BrYscrp0IiIiUg4FHLi+99576Nu3L5KSknDTTTeZqUKFCujTpw8++OCD0imlOKdOO+Dy74GznwUSk4GtS4E3+wDf3gkcTnW6dCIiIlKOBBy4PvLII3j88cfx8ccfewJX3n700UcxduzY0imlOCs6GuhyOXDjAqDDhYArF5j3GvBSN2D5F4DL5XQJRUREpBwIOHBdt26daSbgi80F1q9fH6xySSiqVBs4/01g2BSgejPgwFZg0nDgg4uBvRucLp2IiIhEuIAD15SUFEybNi3f/J9++sk8JuVA897A9b8BJ98BRMcBq38AXj4RmP0ckJPldOlEREQkQgU8juttt91mmgcsWbIEPXr0MPNmz56NiRMn4rnnNFh9uRFXAej9f+6mA1+PAjbMBqbeD/wxyd0eNqWr0yUUERGR8h64Xn/99ahbty6eeuopTJo0ycxr06aNaed6zjnnlEYZJZTVagVc9g2w5H3gx/uA7X8Cb/Vzt4nlkFoVqjpdQhERESmPgWt2djbGjRuHK664ArNmzSq9Ukl4iYoCOl0KHHMGMPU+dxC74G1gxdfA6eOB9ue7lxEpbewomJsNZGYBOZnupiu8X7EWEBPwcbqIOPU55meXHYHNlHPkdq41r6DHeNuVf75fj/G2yz0UZGwiEJuQ9zc+769tXnSM01uoXAvomzw2NtaMKDB8+PDSK5GEr4o1gHNfBjoOdjcf2L0a+PRKdyB71lPuDl1S/hzeD+xeA+xeC6Tvzgso84LKfH+t25l+3PaeF5uTiYE5mYhaUsAoF2yLXaM5ULMlULMVUPMYoNYxQI2WQEIlJ7aKSPmVmQ6kbkHUng1otHsmon9Z5u7su3+T+yqNqVuA7MMIWbwwDwPYmHifINc32PWZH5NQwLIJQFyS+3LrnJKqu/8mVnWP6CP5BJyC4HitvABBkyZNAn2qlBdN/wNcPxuY9Szw61PA2unAy93dnbl63OT+UEtkyc4E9v6TF6Cudv/dxdtrgIM7yqQIheb0o6KB3Cxg59/uCV95P16loTugZbMXe2DLUTR0pkAkMMyI8jPPANQKRD1T3n0ewOYFIJ14Y+PRvFCU+7PN7Cf/msm6HVXIfC4fXfh8rpNnaBg0Z2d4/+V8z3vkWZ20YG2xQt5etDt49QSz1W1/q+UFuvZ5eQEvg+AI/94KOHA944wzMHr0aCxbtgydO3dGxYoV8w2LJWKOIk+5y91M4JtRwPpfgOljgWWTgbOfARq7O/ZJGOFptAPb8gemvM/h0Hj6rTCV6gI1WriDQe4bPB3HbIWZArld+ONZrmhMm/EL+vQ/A3GJSe75zIyw3Mzg7FrlnnauBHatBnatBA7uBFKZ4dkMrJvhXWZecMOena2ZN1VrolOFZSkrHZUO/4soHgCn/esOfvZx2ghkpAKV6wFVU4DkFKBqo7y/Ke75qqfgy0jLH4haWVJzf4v7QLE4cRXhSm6IHRkJqNniOMRUa+Q+iEzOm9g/wiuwtAeiecFpWeLlznMYxFpTAcEtz/54zbff9n3M9jfrIHBoL5C+Fzi0xx0Us+kCb3Pas9b/csYk+ASzVQsOcK3b/F4Os89JwIHrDTfcYP4+/fTT+R6LiopCTk4RP15S/tRsAQz/0j3awA/3uDNeE84AOg0D+j3k/uBIaOEV0fhFaQ9MrVP9RWUZ4iq665tfhDwFz7+8X705kFil9MudlYWMuGT3F3Vc3JH5/IFjIMOpRR/v56TvyQtiGdSuBHbmBbf7NribOGye557sGBCb9+bb7KAFEO99IC/F4EHF4X3uQNQKSM3fjZ77cem7YGptRSHr2LG84Pk8aKlSH0huZAtsbQFulQZAXGIpvrkwwmY3/Cyk73JnQz3THiBtuzsYtQJV1ldxGFhW5ra3AtEG7u3uud/QZBPZb2bOt9/izDPPRIz9MxuK2EaeU1l8xhnMMpA1wWxe8Gr9PWTNK+Cx3Lzgms0uOPnjnq1AfBIiOnDN5WkAkUAwcOh4MdCyH/DTg8Ci/wGL3wVWfgv0fwToeEnEn9oIyR8qZkkLOrWftq3w5zEDUq2xd2BqBaqV64ZfPfLAqdEJ7sku67A7eLdnZ01wuwbIPuQOlgoKmBgk2bOztdsCtduUTeAeyqeNTUC6sYAAdROQeaDY1WRFJyK2RlNEcd+zB6DMiqf+mz/oZfaPP+K8zamw66NUqnMkoDLr9Alyuf5w3OYMLgsMRPOC0YP2+XuAjP2BvQa3C7cRg39PMGoLTJntVmfIo8ezUvw+5RTIQWBmmk8wW1Dwu/fI7ax099CWYSagPSsrKwsVKlQwY7i2b9++9EolkYlBwsDnj3Te2rkC+Pw6YOkHwFnPuIMgKT38gedIDyu+BDb+7t1my1fF2vkDU/7lafLy0EaZmbg67dyTb1DAAIzBrAlqVx2ZGATwMU5rfvJ+Hn/UrSCWf+u0dQe2/IEKZ9weB/51t2/2BI4bjtxmpo4ZoOIk1Sz4dH9yCrIq1sO302fjzLPOQpy/WTn2EmezFntA6xsw8/Qss4mctiwoeD0JyQVkaxnQVrUdpOX99b1f0LyAlvGZZ2Wn8wWiu4GDttsMSniaOVDMkprTxzXyprzbFWt6B6YMVsvrgVgoi4oCEiq7Jx7gRbCAAld+aTRq1EjNAaRkGncHrv0F+P0FYObj7vavr3QH/nMbcNKo8P8xDyUMKFZ8BSz/Mv8pbzbiZ0/7gk7ta/zdgrEDB4N3TjyDYMfgwWpywMB2xwr3xMDOCpx4lTl79prbnMEsA2QrqA21NrTsAc79yDOtd//ds94dpLJdnz+njQsKAJnhZDBU1KnKrKzAM/ncfub0dAOg0Yn5H2cQyMyTrUmCbxMFEwAyE7md058IOwy6reCzoGDUa34NdxY1lPY7kUIEnMu/9957cc899+Ddd99F9epqnyhHiVk7BqrtzgO+vd2dofp5vLvz1mnjgKqNvRvhezXKL2jyc5nygO00mVXltHWp92MpJwBtBgKtzgCqNdVwK8EeDq5id/eBmR0DJBPELge2L8+7/Ze7Da0JclcCyz8/snxsBaB267wMrS1LW1pNMRjEpe3IH5Ra95mRLArbklqZUs/pdtspd7YzZUe6UMLtaIK46kD944rphOQT0PIvHzNcR7ah/X5B8wJappB5DC59A07ud77zmDktD2dGpFwKOHB98cUXsWbNGtSvXx+NGzfON6rAokWLglk+iXTVmwJDPwH+mgJ8P9rdxvKDi0rt5WIRhQGIQtRS3x6qzArVARp2dU8p3YBabcKjnRZ/2JgRWp4XrJohn/LwfTXuCbQ9B2h9NlClnpMlLZ/Yg5ejaNhH0jAjNGzNH8yy+QHb0P672D35rscezDJLW6u1f9lxdvZg8OUblFpZVLZ1Ky57V52Z5qbujDA/tybz3NR96jgcPieB4vi+5gCitdMlERGbgL9tzj333ECfIlJ89qP9eUDz3sCMR4DlX9iummJd6aSQyZ698Oel4DKTaZvna/cBd+C89MMjveQbHO8OYhty6urOboQCbpcti4AVX7ibAuxZ5z3YfrNe7sxq67PcpwUl9PZ5ZiI5tejr3TaTgeUOWzDLv9wvmbndMNs92XEIIZOVzQtmmQH1BKYb3OtjZ6WiPis8wOF62DbOHpRaQSqDZhGRcAxcH3jggdIpiQgzR2c+4Z78ZQLbYoJbWwCclZWB6dN+Qu9TT0VcTLT3Mgz+Ns8HNs0Dtix0jxH5z6/uycL2nyaQzcvKMvtVVu3CGNRsmpuXWf3KPfaohVdgad4HaDsQOOZ0tVENV9yX2M6YE+vSPsoB289aoxkwmGWm1hqDltOaqUWvmwdiXtnSvOCU93lKX6eWRSSSAtd58+aZCw7ExBT8I52RkYEvvvgCF11Ueqd5RfIxbVvZ7s/PtppZWTgcV82d6fLtocyOSlaHGwaJPG3LDk2b5rv/MnDgEEmcyiory0GvGTizCcDf33i3N4yvBLTsD7QZ4P6rS5dG9igH9Y51T3ZsJ+vbfpYXgijolD4z7+WlnbeIRCy/A9fu3btj69atqF27trlfpUoVMyxWs2bu68/v27cPgwcPDjhw3bJlC+666y589913SE9PR4sWLTBhwgR06dIl0PciEtzMF4cs4tT5Mvc8jnvHTCwzsmZg+oXuMSiDnZVle8R1P7szqyu/cZ8itnfOaHWmuxlA81PDcgw+CSLuD+w1X1DPeRGR8hy4urx6O+a/X9i8ouzduxc9e/bEqaeeagLXWrVqYfXq1ahWTe2pJASxBzIzsoFmZZkZZVbWysgWlJXlkEMcWYGZ1VU/uJspeF63prutKk8dNzlZp3RFRKTcCmpXUF7yNRCPPfYYUlJSTIbV0rRp02AWSSQ0srIcq5aTb1aWnWm4LINWe89uXnmGTQCYWW3UPTJ7bYuIiATI0V/DL7/8EqeddhouvPBCzJw5Ew0aNMANN9yAq6++utB2tJwsqampnit6cQoWa13BXKeEhlKv27jKQJNT3JOVld21ClFb5iN68wLzN4qXWLWysjau5EbIbX02XK0HwNWgs7unt1kHR0HQvlgcfW4jk+o1cqluy0/dZgWxjqNcfp7fj46OxvTp0z0XHejRowcmTZqEhg0bmvu7du1Cv379ArqqVmJiovl76623muB1/vz5uPnmm/Hqq69ixIgR+ZZ/8MEHMWbMmHzzP/jgAyQlFXHlFZEQEpedhmoH16J6+hpUObQJBxIb4N+qXbG/Ai+6oM4zIiISWdLT0zFkyBDs37/f9JEqs8CVTQEKWtyaz7+BBK7x8fGmE9Zvv/3mmXfTTTeZAPb333/3K+PKpgYMmku6Iex4ZDB16lQTiPt9bWwJC6rbyKW6jUyq18ilui0/dZuamoqaNWsGJXD1u6nA+vXrEWz16tVD27Ztvea1adMGn376aYHLJyQkmMkXN0pp7PSltV5xnuo2cqluI5PqNXKpbiO/buOCWL9+B668vGuwcUSBlStXes1btWpVqbyWiIiIiIQ3P0dtLx2jRo3CnDlzMG7cOKxZs8a0VX399dcxcuRIJ4slIiIiIiHI0cC1a9eumDJlCj788EO0b98eY8eOxbPPPouhQ4c6WSwRERERCUGODw559tlnm0lEREREJGQzriIiIiIi/lLgKiIiIiKR01SgU6dOfl/OddGiRSUtk4iIiIjI0QWu5557ruf24cOH8fLLL5vxV7t3727mcWSAv/76y1yuVURERETEscD1gQce8Ny+6qqrzNWtOAKA7zKbNm0KfglFRERERI6mjevkyZMxfPjwfPMvvfTSQq94JSIiIiJS5oFrhQoVMHv27HzzOS8xMbHEBRIRERERCco4rrfccguuv/560wmrW7duZt7cuXPx9ttv47777gt0dSIiIiIipRO4jh49Gs2aNcNzzz2H9957z8xr06YNJkyYgIsuuijQ1YmIiIiIlN6VsxigKkgVERERkZC/AMG+ffvw5ptv4p577sGePXvMPDYd2LJlS7DLJyIiIiJydBnXP/74A3379kVycjL++ecfMzxW9erV8dlnn2Hjxo145513Al2liIiIiEjwM6633norLrvsMqxevdprFIEzzzwTv/zyS6CrExEREREpncB1/vz5uPbaa/PNb9CgAbZt2xbo6kRERERESidwTUhIQGpqar75q1atQq1atQJdnYiIiIhI6QSuAwcOxEMPPYSsrCxzPyoqyrRtveuuu3D++ecHujoRERERkdIJXJ966imkpaWhdu3aOHToEHr16oUWLVqgcuXKeOSRRwJdnYiIiIhI6YwqwNEEpk6dai7xunTpUhPEHn/88WakARERERGRkAhc2TygQoUKWLJkCXr27GkmEREREZGQayoQFxeHRo0aIScnp/RKJCIiIiISjDau9957r9cVs0REREREQrKN64svvog1a9agfv36aNy4MSpWrOj1OC/9KiIiIiLieOB67rnnBr0QIiIiIiJBD1wfeOCBQJ8iIiIiIlL2bVxFRERERMIi48oRBZ555hlMmjTJXDErMzPT63F12hIRERGRkMi4jhkzBk8//TQuvvhi7N+/H7feeivOO+88REdH48EHHyyVQoqIiIiIBBy4vv/++3jjjTdw2223ITY2FoMHD8abb76J+++/H3PmzCmdUoqIiIhIuRdw4Lpt2zZ06NDB3K5UqZLJutLZZ5+Nb775JvglFBERERE5msC1YcOG2Lp1q7ndvHlz/Pjjj+b2/PnzkZCQEPwSioiIiIgcTeA6aNAgTJs2zdz+73//i/vuuw8tW7bE8OHDccUVV5RGGUVEREREAh9V4NFHH/XcZgetRo0a4ffffzfB64ABA4JdPhERERGRowtcfXXv3t1MIiIiIiIhFbi+8847RT7OJgMiIiIiIo4HrjfffLPX/aysLKSnpyM+Ph5JSUkKXEVEREQkNDpn7d2712tKS0vDypUrcdJJJ+HDDz8snVKKiIiISLkXcOBaEHbMYqct32ysiIiIiEhIBa7Eq2j9+++/wVqdiIiIiEjJ2rh++eWXXvddLpe5IMGLL76Inj17Bro6EREREZHSCVzPPfdcr/tRUVGoVasWevfujaeeeirQ1YmIiIiIlE7gmpubG+hTRERERERCp42riIiIiEhIZVxvvfVWv5d9+umnA129iIiIiEhwAtfFixebiRceaNWqlZm3atUqxMTE4Pjjj/dq+yoiIiIi4ljgOmDAAFSuXBn/+9//UK1aNTOPFyK4/PLL8Z///Ae33XZb0AonIiIiInLUbVw5csD48eM9QSvx9sMPP6xRBUREREQkdALX1NRU7Ny5M998zjtw4ECwyiUiIiIiUrLAddCgQaZZwGeffYbNmzeb6dNPP8WVV16J8847L9DViYiIiIiUThvXV199FbfffjuGDBliOmiZlcTGmsD1iSeeCHR1IiIiIiKlE7gmJSXh5ZdfNkHq2rVrzbzmzZujYsWKga5KRERERKT0L0DAQPXYY49FcnIyNmzYoCtqiYiIiEhoBK5vv/12vgsKXHPNNWjWrBk6dOiA9u3bY9OmTaVRRhERERER/wPX119/3WsIrO+//x4TJkzAO++8g/nz56Nq1aoYM2ZMaZVTRERERMo5v9u4rl69Gl26dPHc/+KLL3DOOedg6NCh5v64cePMaAMiIiIiIo5mXA8dOoQqVap47v/22284+eSTPffZZGDbtm3BL6GIiIiISCCBa+PGjbFw4UJze9euXfjrr7/Qs2dPz+MMWtlRS0RERETE0aYCI0aMwMiRI03AOn36dLRu3RqdO3f2ysCyg5aIiIiIiKOB65133on09HRzxay6deti8uTJXo/Pnj0bgwcPLo0yioiIiIj4H7hGR0fjoYceMlNBfANZEREREZGQuACBiIiIiEhZUuAqIiIiImFBgauIiIiIhAUFriIiIiISFhS4ioiIiEhkjSpgycnJwcSJEzFt2jTs2LEDubm5Xo9zjFcREREREccD15tvvtkErmeddZa54EBUVFTQCyUiIiIiUuLA9aOPPsKkSZNw5plnBvpUEREREZGya+MaHx+PFi1aHP0rioiIiIiUReB622234bnnnoPL5Tqa1xMRERERKZumArNmzcKMGTPw3XffoV27doiLi/N6/LPPPju6koiIiIiIBDPjWrVqVQwaNAi9evVCzZo1kZyc7DUdrUcffdR09LrllluOeh0iIiIiErkCzrhOmDAh6IWYP38+XnvtNRx77LFBX7eIiIiIlNPANdjS0tIwdOhQvPHGG3j44YeLXDYjI8NMltTUVPM3KyvLTMFirSuY65TQoLqNXKrbyKR6jVyq2/JTt1lBrOMo11H0svrkk0/MkFgbN25EZmam12OLFi0KaF0jRoxA9erV8cwzz+CUU07Bcccdh2effbbAZR988EGMGTMm3/wPPvgASUlJAb4LERERESlt6enpGDJkCPbv348qVaqUbcb1+eefx7333ovLLrsMX3zxBS6//HKsXbvWnO4fOXJkwGPCMtDlc/1x991349Zbb/XKuKakpKB///4l3hB2PDKYOnUq+vXrl6/zmYQ31W3kUt1GJtVr5FLdlp+6Tc07Qx4MAQeuL7/8Ml5//XUMHjzYXEHrzjvvRLNmzXD//fdjz549fq9n06ZN5ipcfGOJiYl+PSchIcFMvrhRSmOnL631ivNUt5FLdRuZVK+RS3Ub+XUbF8T6DXhUATYP6NGjh7ldoUIFHDhwwNweNmwYPvzwQ7/Xs3DhQuzYsQPHH388YmNjzTRz5kyT0eXtnJycQIsmIiIiIhEs4Ixr3bp1TWa1cePGaNSoEebMmYOOHTti/fr1AV2UoE+fPli2bJnXPDY7aN26Ne666y7ExMQEWjQRERERiWABB669e/fGl19+iU6dOplAc9SoUaaz1oIFC3Deeef5vZ7KlSujffv2XvMqVqyIGjVq5JsvIiIiIhJw4Mr2rbm5ueY2O2Mx0Pztt98wcOBAXHvttaVRRhERERGRwAPX6OhoM1kuueQSMwXDzz//HJT1iIiIiEjkCbhzFv3666+49NJL0b17d2zZssXMe/fddzFr1qxgl09ERERE5OgC108//RSnnXaaGVFg8eLFnitZcVDZcePGBbo6EREREZHSCVx5WdZXX33VXKLVPi5Xz549A75qloiIiIhIqQWuK1euxMknn5xvfnJyMvbt2xfo6kRERERESidw5Tiua9asyTef7Vt5BS0RERERkZAIXK+++mpzqda5c+ciKioK//77L95//33cfvvtuP7660ulkCIiIiIiAQ+HNXr0aDOOK698lZ6ebpoNJCQkmMD1v//9b+mUUkRERETKvYADV2ZZ7733Xtxxxx2myUBaWhratm2LSpUqlU4JRURERESOJnC1xMfHm4C1PFm57QCmLt+GISc0RvWK8U4XR0RERKRc8TtwveKKK/xa7u2330akuuXjJVixNRV1qiTiwi4pThdHREREpFzxO3CdOHEiGjdujE6dOsHlcqE86te2jglcf1qxXYGriIiISKgGrhwx4MMPP8T69etx+eWXm0u+Vq9eHeVJ/7Z18Py01fhl1S4czspBYlyM00USERERKTf8Hg7rpZdewtatW3HnnXfiq6++QkpKCi666CL88MMP5SYD265+FdRPTsShrBzMXrPL6eKIiIiIlCsBjePKYa8GDx6MqVOnYvny5WjXrh1uuOEGNGnSxIwuEOk4okLftnXM7anLtztdHBEREZFyJfqonxgdbQI5ZltzcnJQntq50k8rdiA3t3xkmkVERETCLnDNyMgw7Vz79euHY445BsuWLcOLL76IjRs3lptxXE9oWgOVE2KxKy0Dizftc7o4IiIiIuWG352z2CTgo48+Mm1bOTQWA9iaNWuivImPjUavVrXw9R9bTXOBzo2rOV0kERERkXLB78D11VdfRaNGjdCsWTPMnDnTTAX57LPPUB6aC7gD120YfUZrp4sjIiIiUi74HbgOHz7ctGkV4JRWtREbHYW1Ow9i3c40NKtVPppJiIiIiITNBQjELblCHE5sVgOz1uwyFyO4RoGriIiISOiOKlDeWaMLaFgsERERkbKhwPUoWeO5LtywF7vTMpwujoiIiEjEU+B6lBpUrWCupMWhXKf9vcPp4oiIiIhEPAWuJdC3jZoLiIiIiJQVBa5BaOf66+qdOJRZfq4eJiIiIuIEBa4lwKYCbDJwOCsXs9fscro4IiIiIhFNgWsJcFzbvm1qm9tqLiAiIiJSuhS4llC/tnXN32l/b0cOe2qJiIiISKlQ4FpCJzSrjsqJsdiVloklm/Y6XRwRERGRiKXAtYTiYqJxait3c4Ef1VxAREREpNQocA0CXUVLREREpPQpcA2CXq1qIS4mCut2HsTanWlOF0dEREQkIilwDYIqiXE4sVkNc1tZVxEREZHSocA1yM0FflLgKiIiIlIqFLgG+fKvCzfuxa60DKeLIyIiIhJxFLgGSf2qFdC+QRW4XMD0FTucLo6IiIhIxFHgGkT92rgvRqBhsURERESCT4FrKbRznbVmJw5l5jhdHBEREZGIosA1iNrUq4wGVSvgcFYufl290+niiIiIiEQUBa5BFBUVpYsRiIiIiJQSBa5BZgWu0//egZxcl9PFEREREYkYClyDrFvT6qiSGIvdBzOxeONep4sjIiIiEjEUuAZZXEw0Tm1d29xWcwERERGR4FHgWgrUzlVEREQk+BS4loJex9RCXEwU1u06iDU70pwujoiIiEhEUOBaCionxuHEZjXMbWVdRURERIJDgWsp6e9pLrDN6aKIiIiIRAQFrqWkb17gunjTPuw8kOF0cURERETCngLXUlIvuQI6NEiGy8UxXdVcQERERKSkFLiWIo0uICIiIhI8ClzLIHD9dfUupGdmO10cERERkbCmwLUUta5bGQ2rVUBGdq4JXkVERETk6ClwLUVRUVFqLiAiIiISJApcS1m/Nu7AdfrfO5CT63K6OCIiIiJhS4FrKevatDqqJMZiz8FMLNyw1+niiIiIiIQtBa6lLC4mGr1b1za3f1qh5gIiIiIiR0uBaxno17aup52riwO7ioiIiEjAFLiWgV6taiE+Jhrrdx3E2p1pThdHREREJCwpcC0DlRJi0b15DXP7R40uICIiInJUFLiWEQ2LJSIiIlIyClzLSN+8YbGWbNqHHQcOO10cERERkbCjwLWM1E1OxLENk8G+WdNW7HC6OCIiIiJhR4GrAxcjUHMBERERkcApcC1D/dq5A9dZa3YhPTPb6eKIiIiIhBUFrmWoVZ3KSKleAZnZufhl1S6niyMiIiISVhS4lqGoqCj0a3PkYgQiIiIi4j8Frg4NizX97+3Izsl1ujgiIiIiYUOBaxnr2qQakivEYW96FhZu2Ot0cURERETChgLXMhYbE43erWub22ouICIiIhImgev48ePRtWtXVK5cGbVr18a5556LlStXotxcRWvFdrg4sKuIiIiIhHbgOnPmTIwcORJz5szB1KlTkZWVhf79++PgwYOIZCcfUwvxMdHYsDsda3akOV0cERERkbAQ6+SLf//99173J06caDKvCxcuxMknn+xYuUpbpYRY9GhRAz+v3Ikfl29HyzqVnS6SiIiISMhzNHD1tX//fvO3evXqBT6ekZFhJktqaqr5y0wtp2Cx1hXMdfrq3aqmO3D9axuuOalxqb2OlH3dijNUt5FJ9Rq5VLflp26zgljHUa4QaWSZm5uLgQMHYt++fZg1a1aByzz44IMYM2ZMvvkffPABkpKSEE72ZwL3L3QfNzzUORvJ8U6XSERERCT40tPTMWTIEJOgrFKlSmQErtdffz2+++47E7Q2bNjQ74xrSkoKdu3aVeINYccjA7a57devH+Li4lBazn9tDv7YnIqxA9vikq4Fv2cJrrKqWyl7qtvIpHqNXKrb8lO3qampqFmzZlAC15BoKnDjjTfi66+/xi+//FJo0EoJCQlm8sWNUho7fWmt19K/bV0TuE5fuRPDejQttdeRsq9bcY7qNjKpXiOX6jby6zYuiPXr6KgCTPYyaJ0yZQqmT5+Opk3LV/DWr6378q+z1+7GwYxsp4sjIiIiEtIcDVw5FNZ7771n2qhyLNdt27aZ6dChQygPjqlTCY2qJyEzOxe/rNrpdHFEREREQpqjgesrr7xi2juccsopqFevnmf6+OOPUR5ERUV5XYxAREREREK0jWuI9AtzFAPXt2atx/S/dyA7J9dcElZERERE8lOU5LAujauhalIc9qVnYcGGvU4XR0RERCRkKXB1GDOsvVvXNrenLldzAREREZHCKHANAf3a5LVzXb5dzSdERERECqHANQScfEwtxMdGY+OedKzanuZ0cURERERCkgLXEFAxIRY9m9cwt6cu3+Z0cURERERCkgLXELsYwdQVO5wuioiIiEhIUuAaIvq2cXfQWrppH7anHna6OCIiIiIhR4FriKhdJRHHpVQ1t3/SxQhERERE8lHgGkI8V9HSsFgiIiIi+ShwDSH98wLX39bsRlpGttPFEREREQkpClxDSIvaldC4RhIyc3Lxy6qdThdHREREJKQocA0hUVFRXhcjEBEREZEjFLiGaDvX6X/vQFZOrtPFEREREQkZClxDTOfG1VAtKQ77D2VhwT97nS6OiIiISMhQ4BpiYmOi0bu1mguIiIiI+FLgGsrDYq3YBpfL5XRxREREREKCAtcQdPIxNREfG41New5h5fYDThdHREREJCQocA1BSfGxOKlFTXN76l9qLiAiIiJCClxDvrmAAlcRERERUuAaovq0qY2oKOCPzfuxbf9hp4sjIiIi4jgFriGqduVEHJdS1dz+SVlXEREREQWuYdFcQMNiiYiIiChwDWX98wLX39fuRlpGttPFEREREXGUAtcQ1rxWJTStWRGZObmYuXKn08URERERcZQC1xAWFRVlay6wzeniiIiIiDhKgWuI69vGHbh+s2wrrnlnAT6atxHbUzXKgIiIiJQ/sU4XQIrWuXE1dGyYjKWb9+PH5dvNRB0aJOPU1rXRp3Vtczs6OsrpooqIiIiUKgWuIS4mOgpTbuiJv/5NxbS/t2PG3ztMELtsi3t6ftpq1KyUgFNb1ULv1rVxUsuaqJwY53SxRURERIJOgWsYYDa1Q8NkM93S9xjsOHAYP6/ciekrduDX1TuxKy0DkxduNlNcTBS6Na2O3q3rmGxsk5oVnS6+iIiISFAocA3TixNc1CXFTJnZuZi3fg+m/70D0//ejn92p2P2mt1mGvv1cjSrWdFkYjl1aVId8bFq1iwiIiLhSYFrmGMgyuYBnO4f0BbrdqblBbE7TEC7btdBrJu1Hm/OWo/KCbH4zzE1TTb2lFa1TBMDERERkXChwDXCNKtVyUxX/acZUg9nYdbqXZi2Ygd+XrkDuw9m4ttl28wUFQV0bFjVNCdgJ6929auY4bdEREREQpUC1whWJTEOZ3aoZ6bcXBeWbt5nOndN+3uH6ey1ZNM+Mz01dRXqVknEqa3ZwasOeraogaR47RoiIiISWhSdlKMOXp0aVTPTrf1bYdv+w5ix0t2kgFnZbamH8eG8TWZi84PuzWqYdrHdm9dAi1qVNNyWiIiIOE6BazlVNzkRg7s1MtPhrBzMZQevFdtNNnbz3kOYuWqnmYhtYzumVEWnRu7puJRqqF4x3um3ICIiIuWMAldBYlwMeh1Ty0wPDnRhzQ53By8OucXmBQcysjFrzS4zWZrUSMrL4FZFp5RqaF2vMuJiNGKBiIiIlB4FruKFHbRa1qlspmt7NUd2Ti5WbU/D4k17sXjjPizeuBdrdx40w25xmrJ4i3leQmy0uYKXOyvrDmjrJVdw+u2IiIhIBFHgKkWKjYlG2/pVzDT0hMZm3v70LCzZ7A5i2bmLAe3+Q1lYsGGvmYD1Zjl2+LKaFzCYZWDL7K6IiIjI0VDgKgFLTorzNC0gl8uF9bsOujOyeZnZv7cdMB2+vvtzm5koNjoKbepVORLMplRD4xpJGoZLRERE/KLAVUqMgac1fuz5nRuaeemZ2Vi2eT8Wm4zsXizauA87D2Rg2Zb9Znrn9w1muWpJce6mBabzVzUcm5JshvESERER8aXAVUoFx4E9oVkNM1lZ2X/3HzZBrNVW9s8tqdibnuW50hcx+dq4ehIqJsSaZgWJcdFIjOXfGCTwNueZ+3m38/6yja37r/0x7+fzdgxy4XI5vHEkJHEfzXUBObn86zL7Cf+aKdd222Vb1jzms6xnGff+HJV3cMfb0fybt5/zNnGoOfu8wpYvch15j/FeDNensxgiEqEUuEqZ4A9pg6oVzHT2sfXNvIzsHKzYeuBIMLtpLzbtOWQ6fZWuWNy14CevgNceAHMcWwbAvJ3vflw04mPcQXSRy+Y97p6818u/DC4KYwVFWTm5JojK5pR3OyvXhZwczsvNm+/Km5/3uNdz+NiR5az18C9j94rxMeYAo1JCLComxJiDBTPFu2+H2igR3C5pGdmmPbU1pdpu703LwLJ10Zg66Q8cyMjxPM4ryGXlFBCM8nbukduRxL2v5e2TMdxvY8xfaz7/Hrntfsy9b9ue57PPeuYVsD5OVgDOINoTSEcVfN++HO/w42AtcyQI5+PuOtLBpjO47Xlw5q4r9/e4BPcg2f19nYvM7Fxk5uQiK9uFzJwcZGTnmu8tM9/czjXzMs0y7r9e8/NuW+vx+ms95pnvXi9/D34cdXLY1asCV3EMfxCPS+G4sFVxeU/3PDYnWLszDYeycpCRlYPDWblmnFl+MA9b97NzPLfNMua+9fiRZTJ8nsuAzcJ5nJwSFxPl+eFnqdyBpRV8hsavNMtmBbEV423Brbltv28Pen2Xc99mcMzghgHIgcPu4JMBpT0I9Z3sQak7AM0226do0cB2d5vq0nIkGHP/kMfk3fYEXtHuebxtBcostQnAzI+W+4fLzMsLps3Ef7blSxKsWT9WBxApYnHLnB+9tjO3Pe/HRrsPBK2Jbenz3482f6Ntj9uXOzIvOt8y+Z8TbV4ftjpy15i54WHdZF17L2tbxuf5BdW55/mwBzruTH+2LfCx5tuXOfKXQYr7IM0zP8e2Ls63HfRafwti1YFVD9wu9oOOI3V05Lbns2KW9T5DcDAtBq+s+w3RZtt7P9dz9sF+1sG6zX+2dZH9cfuZCbN03vrsB1FmCZ9lrTMuvtvLa3IVMC9vvv15ub51ZXteqMjOdZnfo3CiwFVCSq3KCWYqDfxiPnAoA998/yNO6nUqsl3ReYFtXhCcF+zyqDTDup8X4LqnHBMMmNue5XLyP5YXNFjP522+jv27isFpVk4ODmbm+F1++4+smWLcP9hxnB/j/tH1/MD63Gf21PPcvC+p9MwcHMzIxsEMliPbc5vvyx78sDlHMPD1rUCtpAF1coU4r6lKYiwqJ8Rgx+YN6HxsG1SrmHDksQpx5v1HF/gjy8DEdtsWEBW4bBlnnazMjF+Bbl5zBv4QWZkVa7/MLGS/tP5a+zwzPV7z7ft03v5+ZN3er2Ey2O6CHcloe4Jw7yYWR8vzGu6tY80NxqaWgOsgWMFXFLYeSgvSusIbA0h+V1lnNnjbOtPhNT/2yNkRkwTxWSYh77nWfOsx6wyM/TGruVE4UeAq5QYDPWb+KsfBNFmIiyvbTmAMnO0Bg/WDz0DICiit7BG/jKwMEOebrF4ZXXaXZWLnOgbV7mDWN7i1P5b313rM/py82wyQyZ5lYNOMfMFnvmA073aS9/zChlTLysrCt9+ux5k9Gpd53ZYWd0bX3EIkMYG4LcD1CsLt810uZGZlYerUn9C7Tx/ExMZ6LefJjvlkvuxZRivLZeb53i8kE5k/M5nrzqbl3bezasb++28d3HjVmpUVtM31ZArtixWxXP4ssO1g1XbQymDE+i4x8813y5HvlPyZZttjecu61+H+y/qxAlZ7HXH72OvL/Zht2VxbXXmee6SZTlZ2Nn6fMxddu3VDdHRMvno9cuYh74DNfvBmy0Z7H9AdOdCz9jPPmQ5rPbZ1HzkodN/23cZmO3htU9aD7W9eJtk3y+/9vPzLmOflfbfzPgNKXVrdPwpcRcowcOZUsXQSykHjPhKPR9Wk4KyPP1DpWe4glj/ADD7ZTETKL09HMz8C8qysKFSKA2pWSoiYAxI5crC5e4ULPZvXUN2K3xS4ikipYhaBmW5OIiIiJRFa3YZFRERERAqhwFVEREREwoICVxEREREJCwpcRURERCQsKHAVERERkbCgwFVEREREwoICVxEREREJCwpcRURERCQsKHAVERERkbCgwFVEREREwoICVxEREREJCwpcRURERCQsKHAVERERkbCgwFVEREREwkIswpjL5TJ/U1NTg7rerKwspKenm/XGxcUFdd3iLNVt5FLdRibVa+RS3Zafuk3Ni9OsuK3cBq4HDhwwf1NSUpwuioiIiIgUE7clJyejJKJcwQh/HZKbm4t///0XlStXRlRUVNDWyyMDBsObNm1ClSpVgrZecZ7qNnKpbiOT6jVyqW7LT926XC4TtNavXx/R0dHlN+PKN9+wYcNSWz83tj5MkUl1G7lUt5FJ9Rq5VLflo26TS5hptahzloiIiIiEBQWuIiIiIhIWFLgWICEhAQ888ID5K5FFdRu5VLeRSfUauVS3kSuhFOs2rDtniYiIiEj5oYyriIiIiIQFBa4iIiIiEhYUuIqIiIhIWFDgKiIiIiJhQYFrAV566SU0adIEiYmJOOGEEzBv3jyniyQBGD9+PLp27WquqFa7dm2ce+65WLlypdcyhw8fxsiRI1GjRg1UqlQJ559/PrZv3+5YmeXoPProo+aqebfccotnnuo2fG3ZsgWXXnqpqbsKFSqgQ4cOWLBggedx9iW+//77Ua9ePfN43759sXr1akfLLEXLycnBfffdh6ZNm5o6a968OcaOHet1zXrVa3j45ZdfMGDAAHP1K37vfv75516P+1OPe/bswdChQ81FCapWrYorr7wSaWlpAZVDgauPjz/+GLfeeqsZxmHRokXo2LEjTjvtNOzYscPpoomfZs6caQKXOXPmYOrUqcjKykL//v1x8OBBzzKjRo3CV199hcmTJ5vleeng8847z9FyS2Dmz5+P1157Dccee6zXfNVteNq7dy969uyJuLg4fPfdd1i+fDmeeuopVKtWzbPM448/jueffx6vvvoq5s6di4oVK5rvZx6sSGh67LHH8Morr+DFF1/EihUrzH3W4wsvvOBZRvUaHg4ePGhiIib3CuJPPTJo/euvv8xv89dff22C4WuuuSawgnA4LDmiW7durpEjR3ru5+TkuOrXr+8aP368o+WSo7djxw4e2rtmzpxp7u/bt88VFxfnmjx5smeZFStWmGV+//13B0sq/jpw4ICrZcuWrqlTp7p69erluvnmm8181W34uuuuu1wnnXRSoY/n5ua66tat63riiSc881jfCQkJrg8//LCMSimBOuuss1xXXHGF17zzzjvPNXToUHNb9RqeALimTJniue9PPS5fvtw8b/78+Z5lvvvuO1dUVJRry5Ytfr+2Mq42mZmZWLhwoUlvW6Kjo83933//3dGyydHbv3+/+Vu9enXzl3XMLKy9nlu3bo1GjRqpnsMEM+pnnXWWVx2S6jZ8ffnll+jSpQsuvPBC08SnU6dOeOONNzyPr1+/Htu2bfOqW177nM25VLehq0ePHpg2bRpWrVpl7i9duhSzZs3CGWecYe6rXiPDej/qkX/ZPICfcwuXZ5zFDK2/YoNc9rC2a9cu0x6nTp06XvN5/++//3asXHL0cnNzTftHnoJs3769mccPV3x8vPkA+dYzH5PQ9tFHH5lmPGwq4Et1G77WrVtnTimzqdY999xj6vemm24y9TlixAhP/RX0/ay6DV2jR49GamqqOYCMiYkxv7GPPPKIOWVMqtfIsM2PeuRfHpTaxcbGmqRSIHWtwFUiPjP3559/miN8CX+bNm3CzTffbNpHsfOkRNZBJjMx48aNM/eZceVnl+3lGLhKeJo0aRLef/99fPDBB2jXrh2WLFlikgns4KN6laOhpgI2NWvWNEeEvj2Qeb9u3bqOlUuOzo033mgaf8+YMQMNGzb0zGddslnIvn37vJZXPYc+NgVgR8njjz/eHKlzYgcsdgjgbR7dq27DE3sit23b1mtemzZtsHHjRnPbqj99P4eXO+64w2RdL7nkEjNKxLBhw0wHSo7+QqrXyFDXj3rkX9+O7tnZ2WakgUDqWoGrDU9Jde7c2bTHsWcBeL979+6Olk38x3bjDFqnTJmC6dOnm2FY7FjH7Llsr2cOl8UfSNVzaOvTpw+WLVtmsjbWxCwdTztat1W34YnNeXyHrWO7yMaNG5vb/Bzzx81etzwFzbZxqtvQlZ6ebtow2jFBxN9WUr1GhqZ+1CP/MqnABISFv9HcF9gW1m9B62IWIT766CPTC27ixImmB9w111zjqlq1qmvbtm1OF038dP3117uSk5NdP//8s2vr1q2eKT093bPMdddd52rUqJFr+vTprgULFri6d+9uJgk/9lEFSHUbnubNm+eKjY11PfLII67Vq1e73n//fVdSUpLrvffe8yzz6KOPmu/jL774wvXHH3+4zjnnHFfTpk1dhw4dcrTsUrgRI0a4GjRo4Pr6669d69evd3322WeumjVruu68807PMqrX8BnNZfHixWZi+Pj000+b2xs2bPC7Hk8//XRXp06dXHPnznXNmjXLjA4zePDggMqhwLUAL7zwgvnhi4+PN8NjzZkzx+kiSQD4gSpomjBhgmcZfpBuuOEGV7Vq1cyP46BBg0xwK+EfuKpuw9dXX33lat++vUketG7d2vX66697Pc4hd+677z5XnTp1zDJ9+vRxrVy50rHySvFSU1PN55O/qYmJia5mzZq57r33XldGRoZnGdVreJgxY0aBv608OPG3Hnfv3m0C1UqVKrmqVKniuvzyy01AHIgo/hfchLGIiIiISPCpjauIiIiIhAUFriIiIiISFhS4ioiIiEhYUOAqIiIiImFBgauIiIiIhAUFriIiIiISFhS4ioiIiEhYUOAqIiIiImFBgauIiBTovvvuwzXXXINQ1qRJEzz77LOIFJmZmeY9LViwwOmiiIQkBa4iIeSyyy7Dueee6zVv586daN++PU444QTs37/fsbJJ+bJt2zY899xzuPfeexEKJk6ciKpVq+abP3/+/DIJrssqQI6Pj8ftt9+Ou+66q9RfSyQcKXAVCWEMWnv37o0KFSrgxx9/RHJystNFknLizTffRI8ePdC4cWOEslq1aiEpKQnhlFEtztChQzFr1iz89ddfZVImkXCiwFUkRO3atQt9+vRBQkICpk6d6hW0bty4Eeeccw4qVaqEKlWq4KKLLsL27du9nv/PP/8gKioq37Rv3z7z+IMPPojjjjvO6we1RYsWXssUlAHm459//rnn/qZNm8zrMxtWvXp1Uy6+tt3bb7+Ndu3amfdSr1493HjjjZ4sVkFl5MQMm/V61sT32q9fP6xdu9az7r1792L48OGoVq2aCWDOOOMMrF69utjty/fv+5q+75XBw3/+8x9z4JCSkoKbbroJBw8e9DzO8o8dOxaDBw9GxYoV0aBBA7z00kte6+C2vOqqq0yAxfLzQGTp0qUFliM2Ntas86mnnio202j3559/mvfN/aFOnToYNmyY2X8sp5xyiuc1+F5Y799//32R6/zoo48wYMAAr3lcD7fBnXfeaeq6bt26pvz+Km5b8Papp56KypUrm8c7d+5sTpn//PPPuPzyy80ZB+t9WK/rmwnlY6+99hrOPvtssz+0adMGv//+O9asWWPKz3piQG7fh3ib+y23Hbdh165d8dNPP3m97w0bNmDUqFGe17d8+umnnn3bt+7s+wj3Ub4nZof5WeNngJ+FxMREc3Awfvx4z3O4L/fs2dPUgYh4U+AqEoJ2796Nvn37mkCGQas9cMnNzTU/snv27MHMmTPN4+vWrcPFF1/stQ6Xy2X+8gd469at5ge2KC+++GK+4Lc4WVlZOO2000yg8euvv2L27Nnmh//000/3ZJZeeeUVjBw50vxgL1u2DF9++aUJkK3TvCwbp4YNG5oAxLpvfz8TJkww83755Rfs2LED99xzj+cxBtcMbrheBih832eeeaYpW3EYcFivx+DbjsEM38f555+PP/74Ax9//LEJZK2g2/LEE0+gY8eOWLx4MUaPHo2bb77Z1InlwgsvNGX+7rvvsHDhQhx//PHmgIT151sOBvx8Pk8Vr1ixwu9gkAFgp06dzHZgQMp69H0/V199tXkNBrlsejJixIhC18myLV++HF26dMn32P/+9z8T/M2dOxePP/44HnroIa/3W5TitgUzjdwPuF/wcW7PuLg4E2hy32DgZ9UXt1FhrEBxyZIlaN26NYYMGYJrr70Wd999t9lG3Efs9ZiWlmb2mWnTppl6ZL0zaOcBIn322WemXHyv1usTy8jtfMkll5h9m8E02wVbB12WJ5980rOP8PHnn3/e7K+TJk3CypUr8f7775sA165bt27mMyUiPlwiEjJGjBjhOvnkk13HHXecKy4uznXiiSe6srOzvZb58ccfXTExMa6NGzd65v3111+MUl3z5s3zzFu5cqWZ9+eff5r7M2bMMPf37t1r7j/wwAOujh07mtu7d+92VatWzTV27FivZa677jpX//79vV6fj0+ZMsXcfvfdd12tWrVy5ebmeh7PyMhwVahQwfXDDz+Y+/Xr13fde++9xb73xo0buyZMmJBvvv319u3b5+rZs6fr6quvNvdXrVplHp89e7Zn+V27dpnXnzRpUpGvN3r0aFeXLl28tv0555zjuX/llVe6rrnmGq/n/Prrr67o6GjXoUOHPGU+/fTTvZa5+OKLXWeccYZn+SpVqrgOHz7stUzz5s1dr732Wr56oE8++cTU74YNG8x9bpPk5ORC3wfrzLeONm3aZLYL9wHq1auX6+abbza3s7KyXKNGjXK1bNmy0HUuXrzYPN++j1nrOemkk7zmde3a1XXXXXe5iuPPtqhcubJr4sSJBT6/sO3AOnjmmWc891nu//u///Pc//333828t956yzPvww8/dCUmJhZZ3nbt2rleeOGFQl+HhgwZ4urXr5/XvDvuuMPVtm1br+ede+65Xsv897//dfXu3dvrc+PrueeeczVp0qTIMoqUR8q4ioQYZhWZVWW2iKc3mdWyYyaOp605Wdq2bWuysvYsXWpqqvnL7FhxmEniKdqTTjrJaz4zc3PmzMH69esLfB5P7bKMzLgy08qJp5APHz5sMpbMrv37778mq1YSPBXPdfMU6oEDBzynVfl+mZVmxzVLjRo10KpVq2IzlsxqM4NXGL43Zs6s98WJ2WXWjX17dO/e3et5vG+9NtfBbB7LZF8Pn28/Vc1sHefztDGzd8zINWrUyPM4T5FbzUJatmxpso1WRpmvMWPGDK/1M8tI9td4+eWXzWNsKvDuu++azGlhDh06ZP6yPL6OPfZYr/s83c16Lo4/2+LWW281TQl4tuHRRx/1Kn8g7GXk6X/q0KGD1zzuo9ZnhOXiNmWzAn6OWC7WoZVxLQyX4Sl9O95nU5WcnBzPPN/MNc8S8PPN/ZRNL9h+3RfrKT09PeD3LhLpYp0ugIh4a9asmTllWbNmTRNsXHrppTjrrLPyBQzFYcAYHR1t2iEWhT+y7IjDH9LNmzd7PXbFFVdgypQppkwFBcD8wWc7RJ7q9MV2jHz9YHjmmWdMMMPT4uzlzh/+r776qkTrZPOKpk2bFvo43xtPLzOw8GUPKovCdTCwYxtNX/bmHwxgeOqYwQ4PFNi0gqfRTzzxRPM4DwwWLVpkTnHzFD5P87NeGWzxNXha+7HHHsv3GnxtC0/Dc9sxYGPQytP2XFdBwTv3Pav9MOvRjqfu7djek8F8MLYFT7XztP4333xjmhM88MADpp3noEGDil1/YWW02qMWNM8qN7cjmzvwlD6bsTBovOCCC/zqSOUP388O65YBO98jm/KwuQH3708++cSzDJtP+G57EVHgKhJymBmyAgcGF2xfx/Z68+bNM0PlMCvEDlGcrKwrAxAGdcy8WthOkJm3grJmdhx2h1ku/mD7Bq78AecPK9tMMtNJzPjZf4DZ9rN27dqFZi/Zdo+BODO6R4tBmtUu9r///S8GDhxoMo7cFtnZ2aa9JdtBWplUthu0bwtfDN64PdmJqTB8b9yu1usWhoGm732Wy1oHh5WyOl0VhvVqvQ6D2BdeeAFff/21J3DlAYD1OLc/O6jxQMN6DbZf5vr5OoVh5z5rHQwIGaRxGzBg8tW8eXNTn3z/xxxzDILB323B1+PEjlDMtLN9MwNXbiN7FjOY2DabB0NWgMwg27eDYUGvz3rmc33XxfLHxMQU+ZrcvmzHzYlBMtvVMljlGQtiW2S2WxYRb2oqIBLi2Eudp2LHjBlj7jPQYHDLDBqzcAw+GNj26tXLnJJkloingp9++mnTE7soPM3PDNj9999f5HI8tcqgxzeIYxkYZLOzGDuSMIvE9TFLaQXBzKKxpzVPfzO7yzIzMAsEg3IGPQxI33rrLZMBZgaNQRxfmx2P2HGKp6OZoWbvfs4vCIMS6/2yaQTXy4mnxzMyMjxj5TKg/+2330wnHgaJLPsXX3yRr3MWAxU251i1apWpq8mTJ5sOVlZdsekARyvg6WAGQ1wnM5/2AeYZfLMMzJJzxAYOg2Sd7rcH2ywjOwTxvbIZBzE7y4CHQR4PVnh6/YcffjB1bw+0eNqZr8He8dw3GEAWFpQzUGbZ+TrBUty24HvjtuX+wzJyu/L9WAcBDHZZdzwI4ogJwTyNzv2IB4isZ+5DzPr6ZpH5+mzGs2XLFs+IDbfddpspDzuDsf6ZyWYnx6I6jhG3/4cffoi///7bPI/7DA/O7Fl4fp769+8ftPcoEjGcbmQrIq5COwhZvv76a9NhZ86cOeY+O+4MHDjQVbFiRdOh5cILL3Rt27bNPLZgwQJXs2bNXOPHj3fl5OR41lFQ5yzef/LJJwtdpiD2zlK0detW1/Dhw101a9Z0JSQkmNdm56n9+/d7lnn11VdNJy52OKtXr57pnBJI5yxr4ntlByF2HrLs2bPHNWzYMNNxh52yTjvtNNNpqzDW+y5sYh1Y2NmNnW8qVapktvWxxx7reuSRR7zKPGbMGLP9k5KSXHXr1jWdauxSU1PN+2UnNb7/lJQU19ChQz0dn+zlYccvPn7fffd5Ou5wm1iPR0VFmde4/vrrTSc4C9/voEGDXFWrVjXboHXr1q5bbrnFsw5uM2sd8fHxpuPRxx9/7CrKt99+62rQoIHXPmTv5GXh/mrfZkUpalvw/VxyySVmHsvIZW688UZPRzirs2CNGjXM++B2K6xzln3/XL9+vZln32d893Muc+qpp5ptx9d/8cUX871XdvJi/XMft/90sjMdO2Px/TRq1Mj1xBNPeL3ngjp1vf7666YDJvcpdljr06ePa9GiRZ7Hf/vtN1OX6enpfm1XkfIkiv85HTyLiJQVa/zPgsYfZbaTk+9wRoVhFu6WW24xU6ThTwM7vVmn7KXssPkAh8+yD/smIm5qKiAi5YrVm70gbA+sq5Md6cD0+uuvm2YMUnbY1IdNgXjAICL5KeMqInKUIjnjGiiOLMFRGArCK0Pp8qUiEgwKXEVEpMQ46kRhV15jRzoGryIiJaXAVURERETCgtq4ioiIiEhYUOAqIiIiImFBgauIiIiIhAUFriIiIiISFhS4ioiIiEhYUOAqIiIiImFBgauIiIiIIBz8Pwvswd20Ss9/AAAAAElFTkSuQmCC",
            "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
}