{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная работа №3\n",
    "\n",
    "*Вариант задания:* Товары Jio Mart (вариант - 23)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для выполнения лабораторной работы по датасету 'jio mart product items', приведу пример двух бизнес-целей:\n",
    "\n",
    "### Бизнес-цели:\n",
    "\n",
    "1. **Оптимизация ассортимента товаров в онлайн-магазине**\n",
    "   \n",
    "   **Формулировка:** Разработать модель, которая позволяет онлайн-магазину Jio Mart анализировать, какие товары наиболее востребованы, и автоматизировать оптимизацию ассортимента. Это поможет поддерживать в наличии наиболее популярные продукты и своевременно пополнять запасы.\n",
    "   \n",
    "   **Цель:** Увеличить объем продаж за счет оптимизации ассортимента и сокращения вероятности отсутствия популярных товаров на складе. Повысить клиентскую удовлетворенность за счет улучшения доступности товаров.\n",
    "   \n",
    "   **Ключевые показатели успеха (KPI):**  \n",
    "   - *Точность прогнозирования популярности товаров:* Модель должна иметь точность не менее 90% в прогнозировании популярных товаров.\n",
    "   - *Увеличение продаж:* Увеличение продаж наиболее популярных товаров на 15% за счет правильного планирования запасов.\n",
    "   - *Снижение потерь от неликвидов:* Снижение доли товаров, которые остаются нераспроданными, до уровня ниже 5%.\n",
    "\n",
    "2. **Оптимизация ценовой политики**\n",
    "   \n",
    "   **Формулировка:** Разработать модель для автоматической корректировки цен в зависимости от спроса и конкуренции, чтобы максимизировать доход. Модель должна учитывать такие факторы, как сезонные колебания спроса, конкуренция и изменения цен.\n",
    "   \n",
    "   **Цель:** Повысить доходность онлайн-магазина Jio Mart за счет гибкой и динамической ценовой стратегии.\n",
    "   \n",
    "   **Ключевые показатели успеха (KPI):**  \n",
    "   - *Рост среднего чека:* Увеличение среднего чека покупок на 10% за счет оптимизации цен.\n",
    "   - *Увеличение объема продаж:* Повышение объема продаж на 20% за счет корректировки цен в зависимости от спроса.\n",
    "   - *Конкурентоспособность цен:* Цены должны быть ниже или на уровне с ключевыми конкурентами для 80% ассортимента."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Технические цели проекта для каждой выделенной бизнес-цели\n",
    "\n",
    "1. **Создание модели для оптимизации ассортимента товаров в онлайн-магазине.**  \n",
    "   \n",
    "   - **Сбор и подготовка данных:**  \n",
    "     Необходимо собрать данные о продажах товаров, наличии на складе, временных трендах и сезонных изменениях спроса. Провести очистку данных от пропусков, дубликатов, аномальных значений (например, нулевые продажи при наличии товара). Преобразовать категориальные переменные (категории товаров, бренды, регионы) в числовую форму с помощью методов, таких как One-Hot-Encoding или Label Encoding. Выполнить временное сглаживание данных и стандартизацию числовых признаков для приведения их к одному масштабу. Разбить данные на обучающую и тестовую выборки.\n",
    "   \n",
    "   - **Разработка и обучение модели:**  \n",
    "     Провести эксперименты с различными алгоритмами машинного обучения, такими как регрессия, градиентный бустинг, нейронные сети, для прогнозирования спроса на товары. Обучить модель с использованием метрик оценки, таких как MAE (Mean Absolute Error) и MSE (Mean Squared Error). Оценить производительность моделей на тестовых данных, обеспечивая точность прогнозирования популярности товаров.\n",
    "   \n",
    "   - **Развёртывание модели:**  \n",
    "     Интеграция модели в систему управления запасами магазина для автоматической корректировки ассортимента. Создание API или интерфейса для отображения прогноза спроса и рекомендаций по пополнению запасов товаров. Модель должна предлагать автоматическое обновление ассортимента с учетом прогноза популярности и доступности товаров.\n",
    "\n",
    "2. **Создание модели для оптимизации ценовой политики.**  \n",
    "   \n",
    "   - **Сбор и подготовка данных:**  \n",
    "     Сбор данных о ценах товаров, продажах, спросе, а также информации о конкурентах и сезонных трендах. Очистка данных от пропусков и аномальных значений. Преобразование категориальных признаков (категории товаров, регионы продаж) в числовой формат. Нормализация числовых данных (например, цены, скидки, объем продаж). Разбиение данных на тренировочную и тестовую выборки для корректного обучения модели.\n",
    "   \n",
    "   - **Разработка и обучение модели:**  \n",
    "     Исследование и выбор подходящих моделей для прогнозирования динамических изменений цен с учетом спроса (например, случайные леса, градиентный бустинг, временные ряды). Обучение модели для прогнозирования изменения объема продаж в зависимости от цен и конкурентов. Оценка модели с использованием метрик MSE и RMSE для минимизации ошибки прогнозирования. Прогнозирование оптимальной цены для каждого товара, которая максимизирует продажи и прибыль.\n",
    "   \n",
    "   - **Развёртывание модели:**  \n",
    "     Создание системы, которая автоматически рекомендует изменение цен в зависимости от спроса и данных о конкурентах. Разработка API для интеграции в систему ценообразования магазина. Создание интерфейса для мониторинга изменения цен и влияния на продажи в реальном времени."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['category', 'sub_category', 'href', 'items', 'price'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//jio_mart_items.csv\")\n",
    "\n",
    "# Срез данных, первые 15000 строк\n",
    "df = df.iloc[:15000]\n",
    "\n",
    "# Вывод\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>sub_category</th>\n",
       "      <th>href</th>\n",
       "      <th>items</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Groceries</td>\n",
       "      <td>Fruits &amp; Vegetables</td>\n",
       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
       "      <td>Fresh Dates (Pack) (Approx 450 g - 500 g)</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Groceries</td>\n",
       "      <td>Fruits &amp; Vegetables</td>\n",
       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
       "      <td>Tender Coconut Cling Wrapped (1 pc) (Approx 90...</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Groceries</td>\n",
       "      <td>Fruits &amp; Vegetables</td>\n",
       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
       "      <td>Mosambi 1 kg</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Groceries</td>\n",
       "      <td>Fruits &amp; Vegetables</td>\n",
       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
       "      <td>Orange Imported 1 kg</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Groceries</td>\n",
       "      <td>Fruits &amp; Vegetables</td>\n",
       "      <td>https://www.jiomart.com/c/groceries/fruits-veg...</td>\n",
       "      <td>Banana Robusta 6 pcs (Box) (Approx 800 g - 110...</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    category         sub_category  \\\n",
       "0  Groceries  Fruits & Vegetables   \n",
       "1  Groceries  Fruits & Vegetables   \n",
       "2  Groceries  Fruits & Vegetables   \n",
       "3  Groceries  Fruits & Vegetables   \n",
       "4  Groceries  Fruits & Vegetables   \n",
       "\n",
       "                                                href  \\\n",
       "0  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
       "1  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
       "2  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
       "3  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
       "4  https://www.jiomart.com/c/groceries/fruits-veg...   \n",
       "\n",
       "                                               items  price  \n",
       "0          Fresh Dates (Pack) (Approx 450 g - 500 g)  109.0  \n",
       "1  Tender Coconut Cling Wrapped (1 pc) (Approx 90...   49.0  \n",
       "2                                       Mosambi 1 kg   69.0  \n",
       "3                               Orange Imported 1 kg  125.0  \n",
       "4  Banana Robusta 6 pcs (Box) (Approx 800 g - 110...   44.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Для наглядности\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>373.427633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>463.957949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>446.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price\n",
       "count  15000.000000\n",
       "mean     373.427633\n",
       "std      463.957949\n",
       "min        5.000000\n",
       "25%      123.000000\n",
       "50%      250.000000\n",
       "75%      446.000000\n",
       "max    14999.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Описание данных (основные статистические показатели)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category        0\n",
      "sub_category    0\n",
      "href            0\n",
      "items           0\n",
      "price           0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "category        False\n",
       "sub_category    False\n",
       "href            False\n",
       "items           False\n",
       "price           False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Процент пропущенных значений признаков\n",
    "for i in df.columns:\n",
    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
    "    if null_rate > 0:\n",
    "        print(f'{i} Процент пустых значений: %{null_rate:.2f}')\n",
    "\n",
    "# Проверка на пропущенные данные\n",
    "print(df.isnull().sum())\n",
    "\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Нет пропущенных данных."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Разбиваем на выборки (обучающую, тестовую, контрольную)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки:  12000\n",
      "Размер контрольной выборки:  3000\n",
      "Размер тестовой выборки:  3000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки (80% - обучение, 20% - тестовая)\n",
    "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение данных на обучающую и контрольную выборки (80% - обучение, 20% - контроль)\n",
    "train_data, val_data = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки: \", len(train_data))\n",
    "print(\"Размер контрольной выборки: \", len(val_data))\n",
    "print(\"Размер тестовой выборки: \", len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя цена в обучающей выборке:  373.7302916666667\n",
      "Средняя цена в контрольной выборке:  372.217\n",
      "Средняя цена в тестовой выборке:  372.217\n"
     ]
    }
   ],
   "source": [
    "# Оценка сбалансированности целевой переменной (цена)\n",
    "# Визуализация распределения цены в выборках (гистограмма)\n",
    "def plot_price_distribution(data, title):\n",
    "    sns.histplot(data['price'], kde=True)\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Цена')\n",
    "    plt.ylabel('Частота')\n",
    "    plt.show()\n",
    "\n",
    "plot_price_distribution(train_data, 'Распределение цены в обучающей выборке')\n",
    "plot_price_distribution(val_data, 'Распределение цены в контрольной выборке')\n",
    "plot_price_distribution(test_data, 'Распределение цены в тестовой выборке')\n",
    "\n",
    "# Оценка сбалансированности данных по целевой переменной (price)\n",
    "print(\"Средняя цена в обучающей выборке: \", train_data['price'].mean())\n",
    "print(\"Средняя цена в контрольной выборке: \", val_data['price'].mean())\n",
    "print(\"Средняя цена в тестовой выборке: \", test_data['price'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rqxznx8fHy6YPHjyo9qH2PpMnT4adnR0GDx6MrVu3qq3/xo0buS4z+/pz8uDBA/j4+CA5ORlPnjzJ8Y/NvXv3MGnSJPz5559q41USExM/YGv+f/9mhbbsTExM8rysrA9FZ2dnWbuuri7KlSsnzc9iZ2cHIyMjWVvFihUBvBkDUa9ePdm8jIwM/Pe//0WPHj1QvXr1d9YSHR2NlStXIigo6L3hPD8K81hbvXo1Vq9eDeDNa+fm5ob58+dLN2cUpNz2GQBUqlQJp06dAgDpj221atXytNzExMT3vleB/B93f//9N9zc3NTaK1euLM2vVq0aLC0t0bRpU2zevBnTpk0D8OZymY6Ojuzy8e3bt3H58uU8v3cdHR3V+kyYMAGHDh3CV199BScnJzRv3hzdu3dHgwYNpD6vXr3CzJkzsXbtWjx48EA27ien92728GlqagoAsLe3V2vP/lkAqH++A2/eY5s3b85pM98rez1Zn/tZ6846nrKPKzQ3N5f9jdAEBqIi4KuvvnrnB9mMGTPwww8/oF+/fpg2bRrMzc2hpaWFUaNGFfr/DvMiq4Y5c+agZs2aOfZ5+4MvNTUVDx8+RLNmzd67XJVKhX379kFbW/udywSA2NhYAICNjc07l2llZYWQkJAc52f/sHNzc8OPP/4oa1u6dCl27tyZ4/Nv3LiB4OBg/P777zmOD8nMzISLiwvmz5+f4/Ozf4jl5M6dO6hduzYWLFiAXr16Yd26dbIwmpGRgWbNmuHp06eYMGECKlWqBCMjIzx48AB9+vT54GMmq/9vv/2W42tb0AH8Y6xevRoxMTE4cODAe/t+//33qFChAnr37o2TJ08WeC2Feay1bdsW/v7+EEIgOjoaU6dORevWraUQUdQ9ffoUqamp732vAp/muOvatSv69u2LyMhI1KxZE5s3b0bTpk1haWkpq6dZs2YYP358jsvICupZ3j7Tk6Vy5cqIiorC7t27sX//fmzbtg3Lly/HpEmTpIHdw4cPx9q1azFq1Ci4u7vD1NQUKpUKXbt2zfG9m9NnY27tb4erwpJbPZ9i3R+r6HySUa62bt2Kr7/+WvofYZaEhATZG7Z8+fI4e/Ys0tLSCnSwZvYPWSEE7ty5I/0PPGuwtomJiWyQbm4uXbqEtLS09/5vtnz58hBCwNHRUe3DJifXr1+HSqXK8X+yby/z0KFDaNCgQY4fWNlZWlqqbdO7Bj4HBASgZs2aslPt2dd/6dIlNG3aNN+XMbMuV1pbW2Pnzp0YO3YsWrVqJf2BvXLlCm7duoV169bJBlCGhobma31Z+9fKyipP+/ddHBwcALy5g6dcuXJSe2pqKqKjo9WW/++//yI5OVl2lujWrVsAoDag9uXLl5gyZQqGDRsmrSc3Fy9exMaNG7Fjx45cP8A/VmEea6VLl5b1NTY2Ro8ePT74cmhevL3Psp+tiYqKkuZn7c+83KGUdbkq62xNTj72uHNwcEBUVJRa+82bN6X5Wdq1a4fBgwdLl81u3bqldpNJ+fLl8eLFi49+DxgZGaFLly7o0qULUlNT0aFDB0yfPh0BAQHQ19fH1q1b0bt3b8ybN096zuvXr9XuwiwoOYXoW7duvXPA+sfIet3v3LkjO4v25MmTHM9gfUocQ/QZ0NbWVkvXW7ZsUbvltWPHjnj8+LHsNtEsH5POs+5CyLJ161Y8fPgQLVu2BAC4urqifPnymDt3Ll68eKH2/Oy3XG7ZsgXa2to53mb8tg4dOkhfope9fiEEnjx5Ik2np6dj27Zt+Oqrr955Gr5z587IyMiQTo2/LT09/aM+dMLDw7Fz50789NNPuYadzp0748GDB/j555/V5r169QrJycnvXU/FihWluz+WLFmCzMxMjBw5Upqf9Qf+7ddMCIFFixZ90PZk8fb2homJCWbMmIG0tDS1+dn377t4eXlBV1cXixcvltW3evVqJCYmwsfHR9Y/PT0dK1eulKZTU1OxcuVKlCxZEq6urrK+ixYtQnJyco63eGf3n//8Bw0aNMA333yT59o/VGEea9llnTkojHBXp04dWFlZYcWKFbKvhti3bx9u3Lgh7bOSJUvCw8MDa9aswb1792TLyP7+3bhxI3R1dWXje7L72OOuVatWOHfuHMLDw6W25ORkrFq1CmXLlkWVKlWkdjMzM3h7e2Pz5s1Sbe3atZMtr3PnzggPD8/x7GNCQgLS09PfWQ8A2WcW8OZyZ5UqVSCEkLYxp8/7JUuWyIZKFKQdO3bI/pacO3cOZ8+elT7fC1rTpk2ho6ODoKAgWXtOf7c+NZ4h+gy0bt0aU6dORd++fVG/fn1cuXIFISEhsv9hA28GP//6668YM2YMzp07h0aNGiE5ORmHDh3CsGHD0LZt23yt39zcHA0bNkTfvn0RFxeHhQsXwsnJCQMHDgQAaGlp4ZdffkHLli1RtWpV9O3bF6VKlcKDBw9w9OhRmJiYYNeuXUhOTsayZcuwePFiVKxYUfZ9I1lB6vLlywgPD4e7uzvKly+PH3/8EQEBAYiJiUG7du1QvHhxREdHY/v27Rg0aBC+++47HDp0CD/88AMuX7783p9gaNy4MQYPHoyZM2ciMjISzZs3R7FixXD79m1s2bIFixYtwrfffpuv1+ngwYNo1qzZO/8H2atXL2zevBlDhgzB0aNH0aBBA2RkZODmzZvYvHkzDhw48EHjQGxsbDBnzhwMGDAAPXv2RKtWrVCpUiWUL18e3333HR48eAATExNs27Yt3//7MjExQVBQEHr16oXatWuja9euKFmyJO7du4c9e/agQYMGef4wK1myJAICAjBlyhS0aNEC33zzDaKiorB8+XLUrVsXPXv2lPW3s7PDrFmzEBMTg4oVK2LTpk2IjIzEqlWr1M6CHjx4ENOnT5eNq8vNwYMHc/2OrLw4cuSIbAxL1v+yr1y5gitXrsDFxaVQj7V79+5h//790iWz6dOnw8HBAbVq1Srwy2bFihXDrFmz0LdvXzRu3BjdunWTbrsvW7YsRo8eLfVdvHgxGjZsiNq1a2PQoEFwdHRETEwM9uzZI918MXnyZGzYsAH/+c9/3jkO6GOPu//85z/YsGEDWrZsiREjRsDc3Bzr1q1DdHQ0tm3bpvYty126dEHPnj2xfPlyeHt7q32lx7hx4/Dnn3+idevW6NOnD1xdXZGcnIwrV65g69atiImJkZ2xz0nz5s1hY2ODBg0awNraGjdu3MDSpUvh4+Mj3bjSunVr/PbbbzA1NUWVKlUQHh6OQ4cO5em4zg8nJyc0bNgQQ4cORUpKChYuXAgLC4tcLw1+LGtra4wcORLz5s3DN998gxYtWuDSpUvYt28fLC0tP+oGoI/2aW9qo7dl3aZ4/vz5d/Z7/fq1GDt2rLC1tRUGBgaiQYMGIjw8XHYLdpaXL1+K77//Xjg6OopixYoJGxsb8e2334q7d+8KIfJ32/2GDRtEQECAsLKyEgYGBsLHxyfHW7cvXrwoOnToICwsLISenp5wcHAQnTt3FocPH5at+32P7Lddbtu2TTRs2FAYGRkJIyMjUalSJeHn5yeioqKEEEIMHz5ceHh4iP3796vVlNPtokIIsWrVKuHq6ioMDAxE8eLFhYuLixg/frz4999/pT4feiu0SqUSERERsvac9lFqaqqYNWuWqFq1qtDT0xMlSpQQrq6uYsqUKSIxMVFtfe9bnhBCNGnSRJQpU0Y8f/5cCCHE9evXhZeXlzA2NhaWlpZi4MCB4tKlS2q3hmffhpxuu89y9OhR4e3tLUxNTYW+vr4oX7686NOnj7hw4YLU53233WdZunSpqFSpkihWrJiwtrYWQ4cOFc+ePVPb1qpVq4oLFy4Id3d3oa+vLxwcHMTSpUvV6gIgbG1tRXJy8ju3Ket4aNu2bY7LyOtt9x9y/BbGsZb1UKlUwsbGRnTo0EHcuHFDCFHwt91n2bRpk6hVq5bQ09MT5ubmokePHrLbtbNcvXpVtG/fXpiZmQl9fX3h7OwsfvjhByGEEBs2bBDVqlUTixYtUrsVP7d9kJfjLjd3794V3377rVTLV199JXbv3p1j36SkJGFgYCAAiN9//z3HPs+fPxcBAQHCyclJ6OrqCktLS1G/fn0xd+5c6Wsg3vUZu3LlSuHh4SF9RpYvX16MGzdO9r5/9uyZ6Nu3r7C0tBTGxsbC29tb3Lx5M9fb3LP//cjtqyGy7++365w3b56wt7cXenp6olGjRuLSpUs5LvNtea0np/2anp4ufvjhB2FjYyMMDAxEkyZNxI0bN4SFhYXs6wI+NZUQn8FIJ9KIY8eO4euvv8aWLVvy/T/Zt8XExMDR0RHR0dG5Xp8ODAxETEyM2jf+kvJ4enri8ePHeRqTUlRkfcsuj18q6rI+j+fMmYPvvvtO0+UgISEBJUqUwI8//piny96FgWOIiIiI6JN5+yeEsmR903f2nyb6lDiGiD6ZrDth3jXouXr16tJPkRB9blxcXDRdAlGRt2nTJgQHB6NVq1YwNjbGqVOnsGHDBjRv3lz2nUyfGgMRfTKWlpb4/fff39nn7S9CI/rcjB07VtMlEBV51atXh46ODmbPno2kpCRpoHX27+H61DiGiIiIiBSPY4iIiIhI8RiIiIiISPE4higPMjMz8e+//6J48eKa/dIoIiIiyjMhBJ4/fw47Ozu1L+PMjoEoD/799988/egmERERFT33799H6dKl39mHgSgPsr5S/f79++/8qnkiIiIqOpKSkmBvby/9HX8XBqI8yLpMZmJiwkBERET0mcnLcBcOqiYiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixdNoIAoKCkL16tVhYmICExMTuLu7Y9++fdL8169fw8/PDxYWFjA2NkbHjh0RFxcnW8a9e/fg4+MDQ0NDWFlZYdy4cUhPT5f1OXbsGGrXrg09PT04OTkhODj4U2weERERfSZ0NLny0qVL46effkKFChUghMC6devQtm1bXLx4EVWrVsXo0aOxZ88ebNmyBaampvD390eHDh0QFhYGAMjIyICPjw9sbGxw+vRpPHz4EL6+vihWrBhmzJgBAIiOjoaPjw+GDBmCkJAQHD58GAMGDICtrS28vb0Ldftcx/1aqMunvIuY41vo6+D+Ljq4v5WF+1tZCmt/azQQtWnTRjY9ffp0BAUF4cyZMyhdujRWr16N9evXo0mTJgCAtWvXonLlyjhz5gzq1auHgwcP4vr16zh06BCsra1Rs2ZNTJs2DRMmTEBgYCB0dXWxYsUKODo6Yt68eQCAypUr49SpU1iwYEGhByIiIiL6PBSZMUQZGRnYuHEjkpOT4e7ujoiICKSlpcHLy0vqU6lSJZQpUwbh4eEAgPDwcLi4uMDa2lrq4+3tjaSkJFy7dk3q8/YysvpkLSMnKSkpSEpKkj2IiIjoy6XxQHTlyhUYGxtDT08PQ4YMwfbt21GlShXExsZCV1cXZmZmsv7W1taIjY0FAMTGxsrCUNb8rHnv6pOUlIRXr17lWNPMmTNhamoqPezt7QtiU4mIiKiI0nggcnZ2RmRkJM6ePYuhQ4eid+/euH79ukZrCggIQGJiovS4f/++RushIiKiwqXRMUQAoKurCycnJwCAq6srzp8/j0WLFqFLly5ITU1FQkKC7CxRXFwcbGxsAAA2NjY4d+6cbHlZd6G93Sf7nWlxcXEwMTGBgYFBjjXp6elBT0+vQLaPiIiIij6NnyHKLjMzEykpKXB1dUWxYsVw+PBhaV5UVBTu3bsHd3d3AIC7uzuuXLmC+Ph4qU9oaChMTExQpUoVqc/by8jqk7UMIiIiIo2eIQoICEDLli1RpkwZPH/+HOvXr8exY8dw4MABmJqaon///hgzZgzMzc1hYmKC4cOHw93dHfXq1QMANG/eHFWqVEGvXr0we/ZsxMbGYuLEifDz85PO8AwZMgRLly7F+PHj0a9fPxw5cgSbN2/Gnj17NLnpREREVIRoNBDFx8fD19cXDx8+hKmpKapXr44DBw6gWbNmAIAFCxZAS0sLHTt2REpKCry9vbF8+XLp+dra2ti9ezeGDh0Kd3d3GBkZoXfv3pg6darUx9HREXv27MHo0aOxaNEilC5dGr/88gtvuSciIiKJRgPR6tWr3zlfX18fy5Ytw7Jly3Lt4+DggL17975zOZ6enrh48WK+aiQiIqIvX5EbQ0RERET0qTEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeJpNBDNnDkTdevWRfHixWFlZYV27dohKipK1sfT0xMqlUr2GDJkiKzPvXv34OPjA0NDQ1hZWWHcuHFIT0+X9Tl27Bhq164NPT09ODk5ITg4uLA3j4iIiD4TGg1Ex48fh5+fH86cOYPQ0FCkpaWhefPmSE5OlvUbOHAgHj58KD1mz54tzcvIyICPjw9SU1Nx+vRprFu3DsHBwZg0aZLUJzo6Gj4+Pvj6668RGRmJUaNGYcCAAThw4MAn21YiIiIqunQ0ufL9+/fLpoODg2FlZYWIiAh4eHhI7YaGhrCxsclxGQcPHsT169dx6NAhWFtbo2bNmpg2bRomTJiAwMBA6OrqYsWKFXB0dMS8efMAAJUrV8apU6ewYMECeHt7F94GEhER0WehSI0hSkxMBACYm5vL2kNCQmBpaYlq1aohICAAL1++lOaFh4fDxcUF1tbWUpu3tzeSkpJw7do1qY+Xl5dsmd7e3ggPD8+xjpSUFCQlJckeRERE9OXS6Bmit2VmZmLUqFFo0KABqlWrJrV3794dDg4OsLOzw+XLlzFhwgRERUXhjz/+AADExsbKwhAAaTo2NvadfZKSkvDq1SsYGBjI5s2cORNTpkwp8G0kIiKioqnIBCI/Pz9cvXoVp06dkrUPGjRI+reLiwtsbW3RtGlT3L17F+XLly+UWgICAjBmzBhpOikpCfb29oWyLiIiItK8InHJzN/fH7t378bRo0dRunTpd/Z1c3MDANy5cwcAYGNjg7i4OFmfrOmscUe59TExMVE7OwQAenp6MDExkT2IiIjoy6XRQCSEgL+/P7Zv344jR47A0dHxvc+JjIwEANja2gIA3N3dceXKFcTHx0t9QkNDYWJigipVqkh9Dh8+LFtOaGgo3N3dC2hLiIiI6HOm0UDk5+eH33//HevXr0fx4sURGxuL2NhYvHr1CgBw9+5dTJs2DREREYiJicGff/4JX19feHh4oHr16gCA5s2bo0qVKujVqxcuXbqEAwcOYOLEifDz84Oenh4AYMiQIfjf//6H8ePH4+bNm1i+fDk2b96M0aNHa2zbiYiIqOjQaCAKCgpCYmIiPD09YWtrKz02bdoEANDV1cWhQ4fQvHlzVKpUCWPHjkXHjh2xa9cuaRna2trYvXs3tLW14e7ujp49e8LX1xdTp06V+jg6OmLPnj0IDQ1FjRo1MG/ePPzyyy+85Z6IiIgAaHhQtRDinfPt7e1x/Pjx9y7HwcEBe/fufWcfT09PXLx48YPqIyIiImUoEoOqiYiIiDSJgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUT6OBaObMmahbty6KFy8OKysrtGvXDlFRUbI+r1+/hp+fHywsLGBsbIyOHTsiLi5O1ufevXvw8fGBoaEhrKysMG7cOKSnp8v6HDt2DLVr14aenh6cnJwQHBxc2JtHREREnwmNBqLjx4/Dz88PZ86cQWhoKNLS0tC8eXMkJydLfUaPHo1du3Zhy5YtOH78OP7991906NBBmp+RkQEfHx+kpqbi9OnTWLduHYKDgzFp0iSpT3R0NHx8fPD1118jMjISo0aNwoABA3DgwIFPur1ERERUNOlocuX79++XTQcHB8PKygoRERHw8PBAYmIiVq9ejfXr16NJkyYAgLVr16Jy5co4c+YM6tWrh4MHD+L69es4dOgQrK2tUbNmTUybNg0TJkxAYGAgdHV1sWLFCjg6OmLevHkAgMqVK+PUqVNYsGABvL29P/l2ExERUdFSpMYQJSYmAgDMzc0BABEREUhLS4OXl5fUp1KlSihTpgzCw8MBAOHh4XBxcYG1tbXUx9vbG0lJSbh27ZrU5+1lZPXJWkZ2KSkpSEpKkj2IiIjoy1VkAlFmZiZGjRqFBg0aoFq1agCA2NhY6OrqwszMTNbX2toasbGxUp+3w1DW/Kx57+qTlJSEV69eqdUyc+ZMmJqaSg97e/sC2UYiIiIqmopMIPLz88PVq1exceNGTZeCgIAAJCYmSo/79+9ruiQiIiIqRBodQ5TF398fu3fvxokTJ1C6dGmp3cbGBqmpqUhISJCdJYqLi4ONjY3U59y5c7LlZd2F9naf7HemxcXFwcTEBAYGBmr16OnpQU9Pr0C2jYiIiIo+jZ4hEkLA398f27dvx5EjR+Do6Cib7+rqimLFiuHw4cNSW1RUFO7duwd3d3cAgLu7O65cuYL4+HipT2hoKExMTFClShWpz9vLyOqTtQwiIiJSNo2eIfLz88P69euxc+dOFC9eXBrzY2pqCgMDA5iamqJ///4YM2YMzM3NYWJiguHDh8Pd3R316tUDADRv3hxVqlRBr169MHv2bMTGxmLixInw8/OTzvIMGTIES5cuxfjx49GvXz8cOXIEmzdvxp49ezS27URERFR0aPQMUVBQEBITE+Hp6QlbW1vpsWnTJqnPggUL0Lp1a3Ts2BEeHh6wsbHBH3/8Ic3X1tbG7t27oa2tDXd3d/Ts2RO+vr6YOnWq1MfR0RF79uxBaGgoatSogXnz5uGXX37hLfdEREQEQMNniIQQ7+2jr6+PZcuWYdmyZbn2cXBwwN69e9+5HE9PT1y8ePGDayQiIqIvX5G5y4yIiIhIUxiIiIiISPEYiIiIiEjxGIiIiIhI8RiIiIiISPEYiIiIiEjxGIiIiIhI8RiIiIiISPEYiIiIiEjxGIiIiIhI8RiIiIiISPEYiIiIiEjxGIiIiIhI8RiIiIiISPEYiIiIiEjxGIiIiIhI8XTy+8Tk5GQcP34c9+7dQ2pqqmzeiBEjProwIiIiok8lX4Ho4sWLaNWqFV6+fInk5GSYm5vj8ePHMDQ0hJWVFQMRERERfVbydcls9OjRaNOmDZ49ewYDAwOcOXMGf//9N1xdXTF37tyCrpGIiIioUOUrEEVGRmLs2LHQ0tKCtrY2UlJSYG9vj9mzZ+O///1vQddIREREVKjyFYiKFSsGLa03T7WyssK9e/cAAKamprh//37BVUdERET0CeRrDFGtWrVw/vx5VKhQAY0bN8akSZPw+PFj/Pbbb6hWrVpB10hERERUqPJ1hmjGjBmwtbUFAEyfPh0lSpTA0KFD8ejRI6xatapACyQiIiIqbPk6Q1SnTh3p31ZWVti/f3+BFURERET0qeXrDFGTJk2QkJBQwKUQERERaUa+AtGxY8fUvoyRiIiI6HOV75/uUKlUBVkHERERkcbk+6c72rdvD11d3RznHTlyJN8FEREREX1q+Q5E7u7uMDY2LshaiIiIiDQiX4FIpVJh3LhxsLKyKuh6iIiIiD65fI0hEkIUdB1EREREGpOvQDR58mReLiMiIqIvRr4umU2ePBkA8OjRI0RFRQEAnJ2dUbJkyYKrjIiIiOgTydcZopcvX6Jfv36ws7ODh4cHPDw8YGdnh/79++Ply5cFXSMRERFRocpXIBo9ejSOHz+OP//8EwkJCUhISMDOnTtx/PhxjB07tqBrJCIiIipU+bpktm3bNmzduhWenp5SW6tWrWBgYIDOnTsjKCiooOojIiIiKnT5vmRmbW2t1m5lZcVLZkRERPTZyVcgcnd3x+TJk/H69Wup7dWrV5gyZQrc3d0LrDgiIiKiTyFfl8wWLlyIFi1aoHTp0qhRowYA4NKlS9DX18eBAwcKtEAiIiKiwpavQOTi4oLbt28jJCQEN2/eBAB069YNPXr0gIGBQYEWSERERFTY8hWITpw4gfr162PgwIEFXQ8RERHRJ5evMURff/01nj59WtC1EBEREWkEf8uMiIiIFC9fl8wAIDw8HCVKlMhxnoeHR74LIiIiIvrU8h2I2rdvn2O7SqVCRkZGvgsiIiIi+tTydckMAGJjY5GZman2YBgiIiKiz02+ApFKpSroOoiIiIg0RqODqk+cOIE2bdrAzs4OKpUKO3bskM3v06cPVCqV7NGiRQtZn6dPn6JHjx4wMTGBmZkZ+vfvjxcvXsj6XL58GY0aNYK+vj7s7e0xe/bsAqmfiIiIvgz5CkSZmZmwsrL66JUnJyejRo0aWLZsWa59WrRogYcPH0qPDRs2yOb36NED165dQ2hoKHbv3o0TJ05g0KBB0vykpCQ0b94cDg4OiIiIwJw5cxAYGIhVq1Z9dP1ERET0ZcjXoOqZM2fC2toa/fr1k7WvWbMGjx49woQJE/K0nJYtW6Jly5bv7KOnpwcbG5sc5924cQP79+/H+fPnUadOHQDAkiVL0KpVK8ydOxd2dnYICQlBamoq1qxZA11dXVStWhWRkZGYP3++LDgRERGRcuXrDNHKlStRqVIltfaqVatixYoVH13U244dOwYrKys4Oztj6NChePLkiTQvPDwcZmZmUhgCAC8vL2hpaeHs2bNSHw8PD+jq6kp9vL29ERUVhWfPnuW4zpSUFCQlJckeRERE9OXKVyCKjY2Fra2tWnvJkiXx8OHDjy4qS4sWLfDrr7/i8OHDmDVrFo4fP46WLVtKd7LFxsaqXbrT0dGBubk5YmNjpT7W1tayPlnTWX2ymzlzJkxNTaWHvb19gW0TERERFT35umRmb2+PsLAwODo6ytrDwsJgZ2dXIIUBQNeuXaV/u7i4oHr16ihfvjyOHTuGpk2bFth6sgsICMCYMWOk6aSkJIYiIiKiL1i+AtHAgQMxatQopKWloUmTJgCAw4cPY/z48Rg7dmyBFvi2cuXKwdLSEnfu3EHTpk1hY2OD+Ph4WZ/09HQ8ffpUGndkY2ODuLg4WZ+s6dzGJunp6UFPT68QtoCIiIiKonwFonHjxuHJkycYNmwYUlNTAQD6+vqYMGECAgICCrTAt/3zzz948uSJdLnO3d0dCQkJiIiIgKurKwDgyJEjyMzMhJubm9Tn+++/R1paGooVKwYACA0NhbOzc64/PUJERETKku8vZpw1axYePXqEM2fO4NKlS3j69CkmTZr0Qct58eIFIiMjERkZCQCIjo5GZGQk7t27hxcvXmDcuHE4c+YMYmJicPjwYbRt2xZOTk7w9vYGAFSuXBktWrTAwIEDce7cOYSFhcHf3x9du3aVLt11794durq66N+/P65du4ZNmzZh0aJFsktiREREpGz5/i0zADA2NkbdunXz/fwLFy7g66+/lqazQkrv3r0RFBSEy5cvY926dUhISICdnR2aN2+OadOmyS5nhYSEwN/fH02bNoWWlhY6duyIxYsXS/NNTU1x8OBB+Pn5wdXVFZaWlpg0aRJvuSciIiJJvgPRhQsXsHnzZty7d0+6bJbljz/+yNMyPD093/mt1wcOHHjvMszNzbF+/fp39qlevTpOnjyZp5qIiIhIefJ1yWzjxo2oX78+bty4ge3btyMtLQ3Xrl3DkSNHYGpqWtA1EhERERWqfAWiGTNmYMGCBdi1axd0dXWxaNEi3Lx5E507d0aZMmUKukYiIiKiQpWvQHT37l34+PgAAHR1dZGcnAyVSoXRo0fzN8KIiIjos5OvQFSiRAk8f/4cAFCqVClcvXoVAJCQkICXL18WXHVEREREn0C+BlV7eHggNDQULi4u6NSpE0aOHIkjR44gNDS0UL9BmoiIiKgw5CsQLV26FK9fvwYAfP/99yhWrBhOnz6Njh07YuLEiQVaIBEREVFh+6BAlPWr7zo6OjA2Npamhw0bhmHDhhV8dURERESfwAcFIjMzM6hUqvf2y/o1eiIiIqLPwQcFoqNHj8qmhRBo1aoVfvnlF5QqVapACyMiIiL6VD4oEDVu3FitTVtbG/Xq1UO5cuUKrCgiIiKiTylft90TERERfUk+KhDdv38fL1++hIWFRUHVQ0RERPTJfdAls7d/Rf7x48fYsGEDmjRpwt8vIyIios/aBwWiBQsWAABUKhUsLS3Rpk0bfu8QERERffY+KBBFR0cXVh1EREREGsNB1URERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeBoNRCdOnECbNm1gZ2cHlUqFHTt2yOYLITBp0iTY2trCwMAAXl5euH37tqzP06dP0aNHD5iYmMDMzAz9+/fHixcvZH0uX76MRo0aQV9fH/b29pg9e3ZhbxoRERF9RjQaiJKTk1GjRg0sW7Ysx/mzZ8/G4sWLsWLFCpw9exZGRkbw9vbG69evpT49evTAtWvXEBoait27d+PEiRMYNGiQND8pKQnNmzeHg4MDIiIiMGfOHAQGBmLVqlWFvn1ERET0edDR5MpbtmyJli1b5jhPCIGFCxdi4sSJaNu2LQDg119/hbW1NXbs2IGuXbvixo0b2L9/P86fP486deoAAJYsWYJWrVph7ty5sLOzQ0hICFJTU7FmzRro6uqiatWqiIyMxPz582XBiYiIiJSryI4hio6ORmxsLLy8vKQ2U1NTuLm5ITw8HAAQHh4OMzMzKQwBgJeXF7S0tHD27Fmpj4eHB3R1daU+3t7eiIqKwrNnz3Jcd0pKCpKSkmQPIiIi+nIV2UAUGxsLALC2tpa1W1tbS/NiY2NhZWUlm6+jowNzc3NZn5yW8fY6sps5cyZMTU2lh729/cdvEBERERVZRTYQaVJAQAASExOlx/379zVdEhERERWiIhuIbGxsAABxcXGy9ri4OGmejY0N4uPjZfPT09Px9OlTWZ+clvH2OrLT09ODiYmJ7EFERERfriIbiBwdHWFjY4PDhw9LbUlJSTh79izc3d0BAO7u7khISEBERITU58iRI8jMzISbm5vU58SJE0hLS5P6hIaGwtnZGSVKlPhEW0NERERFmUYD0YsXLxAZGYnIyEgAbwZSR0ZG4t69e1CpVBg1ahR+/PFH/Pnnn7hy5Qp8fX1hZ2eHdu3aAQAqV66MFi1aYODAgTh37hzCwsLg7++Prl27ws7ODgDQvXt36Orqon///rh27Ro2bdqERYsWYcyYMRraaiIiIipqNHrb/YULF/D1119L01khpXfv3ggODsb48eORnJyMQYMGISEhAQ0bNsT+/fuhr68vPSckJAT+/v5o2rQptLS00LFjRyxevFiab2pqioMHD8LPzw+urq6wtLTEpEmTeMs9ERERSTQaiDw9PSGEyHW+SqXC1KlTMXXq1Fz7mJubY/369e9cT/Xq1XHy5Ml810lERERftiI7hoiIiIjoU2EgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsUr0oEoMDAQKpVK9qhUqZI0//Xr1/Dz84OFhQWMjY3RsWNHxMXFyZZx7949+Pj4wNDQEFZWVhg3bhzS09M/9aYQERFREaaj6QLep2rVqjh06JA0raPz/yWPHj0ae/bswZYtW2Bqagp/f3906NABYWFhAICMjAz4+PjAxsYGp0+fxsOHD+Hr64tixYphxowZn3xbiIiIqGgq8oFIR0cHNjY2au2JiYlYvXo11q9fjyZNmgAA1q5di8qVK+PMmTOoV68eDh48iOvXr+PQoUOwtrZGzZo1MW3aNEyYMAGBgYHQ1dX91JtDRERERVCRvmQGALdv34adnR3KlSuHHj164N69ewCAiIgIpKWlwcvLS+pbqVIllClTBuHh4QCA8PBwuLi4wNraWurj7e2NpKQkXLt2Ldd1pqSkICkpSfYgIiKiL1eRDkRubm4IDg7G/v37ERQUhOjoaDRq1AjPnz9HbGwsdHV1YWZmJnuOtbU1YmNjAQCxsbGyMJQ1P2tebmbOnAlTU1PpYW9vX7AbRkREREVKkb5k1rJlS+nf1atXh5ubGxwcHLB582YYGBgU2noDAgIwZswYaTopKYmhiIiI6AtWpM8QZWdmZoaKFSvizp07sLGxQWpqKhISEmR94uLipDFHNjY2anedZU3nNC4pi56eHkxMTGQPIiIi+nJ9VoHoxYsXuHv3LmxtbeHq6opixYrh8OHD0vyoqCjcu3cP7u7uAAB3d3dcuXIF8fHxUp/Q0FCYmJigSpUqn7x+IiIiKpqK9CWz7777Dm3atIGDgwP+/fdfTJ48Gdra2ujWrRtMTU3Rv39/jBkzBubm5jAxMcHw4cPh7u6OevXqAQCaN2+OKlWqoFevXpg9ezZiY2MxceJE+Pn5QU9PT8NbR0REREVFkQ5E//zzD7p164YnT56gZMmSaNiwIc6cOYOSJUsCABYsWAAtLS107NgRKSkp8Pb2xvLly6Xna2trY/fu3Rg6dCjc3d1hZGSE3r17Y+rUqZraJCIiIiqCinQg2rhx4zvn6+vrY9myZVi2bFmufRwcHLB3796CLo2IiIi+IJ/VGCIiIiKiwsBARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIrHQERERESKx0BEREREisdARERERIqnqEC0bNkylC1bFvr6+nBzc8O5c+c0XRIREREVAYoJRJs2bcKYMWMwefJk/PXXX6hRowa8vb0RHx+v6dKIiIhIwxQTiObPn4+BAweib9++qFKlClasWAFDQ0OsWbNG06URERGRhikiEKWmpiIiIgJeXl5Sm5aWFry8vBAeHq7ByoiIiKgo0NF0AZ/C48ePkZGRAWtra1m7tbU1bt68qdY/JSUFKSkp0nRiYiIAICkp6YPWm5HyKh/VUmH40H2XH9zfRQf3t7JwfyvLh+zvrL5CiPf2VUQg+lAzZ87ElClT1Nrt7e01UA0VBNMlQzRdAn1C3N/Kwv2tLPnZ38+fP4epqek7+ygiEFlaWkJbWxtxcXGy9ri4ONjY2Kj1DwgIwJgxY6TpzMxMPH36FBYWFlCpVIVeb1GRlJQEe3t73L9/HyYmJpouhwoZ97eycH8ri1L3txACz58/h52d3Xv7KiIQ6erqwtXVFYcPH0a7du0AvAk5hw8fhr+/v1p/PT096OnpydrMzMw+QaVFk4mJiaLeQErH/a0s3N/KosT9/b4zQ1kUEYgAYMyYMejduzfq1KmDr776CgsXLkRycjL69u2r6dKIiIhIwxQTiLp06YJHjx5h0qRJiI2NRc2aNbF//361gdZERESkPIoJRADg7++f4yUyypmenh4mT56sdvmQvkzc38rC/a0s3N/vpxJ5uReNiIiI6AumiC9mJCIiInoXBiIiIiJSPAYiIiIiUjwGIoXy9PTEqFGjNF0GFSHZj4myZcti4cKFGquHPsz73tMqlQo7duzI8/KOHTsGlUqFhISEj66Niq73HRf5OQ4CAwNRs2bNj67tU1PUXWZElHfnz5+HkZGRpsugAvLw4UOUKFFC02XQZ6Z+/fp4+PBhnr/c8HPGQEREOSpZsqSmS6AClNPPFNHnLS0tDcWKFSvUdejq6irm2OElM8KzZ8/g6+uLEiVKwNDQEC1btsTt27cBvPkdmJIlS2Lr1q1S/5o1a8LW1laaPnXqFPT09PDy5ctPXrsSeHp6Yvjw4Rg1ahRKlCgBa2tr/Pzzz9I3rRcvXhxOTk7Yt2+f9JyrV6+iZcuWMDY2hrW1NXr16oXHjx9L85OTk+Hr6wtjY2PY2tpi3rx5aut9+5JZTEwMVCoVIiMjpfkJCQlQqVQ4duwYgP8/tX7gwAHUqlULBgYGaNKkCeLj47Fv3z5UrlwZJiYm6N69O4+VQpKZmYnx48fD3NwcNjY2CAwMlOZlvzRy+vRp1KxZE/r6+qhTpw527Nihto8BICIiAnXq1IGhoSHq16+PqKioT7MxX5hVq1bBzs4OmZmZsva2bduiX79+AICdO3eidu3a0NfXR7ly5TBlyhSkp6dLfVUqFYKCgvDNN9/AyMgIP/74I5ycnDB37lzZMiMjI6FSqXDnzp081fb48WO0b98ehoaGqFChAv78809pXk6XzH7++WfY29vD0NAQ7du3x/z583P8eavffvsNZcuWhampKbp27Yrnz5/nqR5NYSAi9OnTBxcuXMCff/6J8PBwCCHQqlUrpKWlQaVSwcPDQ/qj9+zZM9y4cQOvXr3CzZs3AQDHjx9H3bp1YWhoqMGt+LKtW7cOlpaWOHfuHIYPH46hQ4eiU6dOqF+/Pv766y80b94cvXr1wsuXL5GQkIAmTZqgVq1auHDhAvbv34+4uDh07txZWt64ceNw/Phx7Ny5EwcPHsSxY8fw119/FUitgYGBWLp0KU6fPo379++jc+fOWLhwIdavX489e/bg4MGDWLJkSYGsi+TWrVsHIyMjnD17FrNnz8bUqVMRGhqq1i8pKQlt2rSBi4sL/vrrL0ybNg0TJkzIcZnff/895s2bhwsXLkBHR0f6400fplOnTnjy5AmOHj0qtT19+hT79+9Hjx49cPLkSfj6+mLkyJG4fv06Vq5cieDgYEyfPl22nMDAQLRv3x5XrlxB//790a9fP6xdu1bWZ+3atfDw8ICTk1OeapsyZQo6d+6My5cvo1WrVujRoweePn2aY9+wsDAMGTIEI0eORGRkJJo1a6ZWIwDcvXsXO3bswO7du7F7924cP34cP/30U57q0RhBitS4cWMxcuRIcevWLQFAhIWFSfMeP34sDAwMxObNm4UQQixevFhUrVpVCCHEjh07hJubm2jbtq0ICgoSQgjh5eUl/vvf/376jVCIxo0bi4YNG0rT6enpwsjISPTq1Utqe/jwoQAgwsPDxbRp00Tz5s1ly7h//74AIKKiosTz58+Frq6utH+FEOLJkyfCwMBAjBw5UmpzcHAQCxYsEEIIER0dLQCIixcvSvOfPXsmAIijR48KIYQ4evSoACAOHTok9Zk5c6YAIO7evSu1DR48WHh7e3/MS0I5yH6cCCFE3bp1xYQJE4QQQgAQ27dvF0IIERQUJCwsLMSrV6+kvj///LNsH+e0P/fs2SMAyJ5Hede2bVvRr18/aXrlypXCzs5OZGRkiKZNm4oZM2bI+v/222/C1tZWmgYgRo0aJevz4MEDoa2tLc6ePSuEECI1NVVYWlqK4ODgPNUEQEycOFGafvHihQAg9u3bJ4T4/+Pg2bNnQgghunTpInx8fGTL6NGjhzA1NZWmJ0+eLAwNDUVSUpLUNm7cOOHm5panmjSFZ4gU7saNG9DR0YGbm5vUZmFhAWdnZ9y4cQMA0LhxY1y/fh2PHj3C8ePH4enpCU9PTxw7dgxpaWk4ffo0PD09NbQFylC9enXp39ra2rCwsICLi4vUlvWbfPHx8bh06RKOHj0KY2Nj6VGpUiUAb/7XdvfuXaSmpsr2ubm5OZydnQu8VmtraxgaGqJcuXKytvj4+AJZF8m9/doDgK2tbY6vdVRUFKpXrw59fX2p7auvvnrvMrMulXP/5U+PHj2wbds2pKSkAABCQkLQtWtXaGlp4dKlS5g6darsfTtw4EA8fPhQdom5Tp06smXa2dnBx8cHa9asAQDs2rULKSkp6NSpU57rensfGxkZwcTEJNd9HBUVpXas5HTslC1bFsWLF5emczsWixIOqqb3cnFxgbm5OY4fP47jx49j+vTpsLGxwaxZs3D+/HmkpaWhfv36mi7zi5Z94KRKpZK1qVQqAG/GkLx48QJt2rTBrFmz1JZja2ub53EFb9PSevN/J/HWL/2kpaW9t9bsdWa1ZR9HQQWjMF7r3I4z+nBt2rSBEAJ79uxB3bp1cfLkSSxYsAAA8OLFC0yZMgUdOnRQe97bwTWnOz8HDBiAXr16YcGCBVi7di26dOnyQUMYCvu4KahlFjYGIoWrXLky0tPTcfbsWSnUPHnyBFFRUahSpQqANwdyo0aNsHPnTly7dg0NGzaEoaEhUlJSsHLlStSpU4e3ZxchtWvXxrZt21C2bFno6Ki/xcuXL49ixYrh7NmzKFOmDIA3Y8Nu3bqFxo0b57jMrDvOHj58iFq1agGA2uBb+nw4Ozvj999/R0pKivRjn+fPn9dwVV8+fX19dOjQASEhIbhz5w6cnZ1Ru3ZtAG/et1FRUXke9/O2Vq1awcjICEFBQdi/fz9OnDhR0KVLnJ2d1Y6VL+XY4SUzhatQoQLatm2LgQMH4tSpU7h06RJ69uyJUqVKoW3btlI/T09PbNiwATVr1oSxsTG0tLTg4eGBkJCQXP+Ikmb4+fnh6dOn6NatG86fP4+7d+/iwIED6Nu3LzIyMmBsbIz+/ftj3LhxOHLkCK5evYo+ffpIZ4FyYmBggHr16uGnn37CjRs3cPz4cUycOPETbhUVpO7duyMzMxODBg3CjRs3cODAAelOpayzQFQ4evTogT179mDNmjXo0aOH1D5p0iT8+uuvmDJlCq5du4YbN25g48aNeXqfaWtro0+fPggICECFChXg7u5eaPUPHz4ce/fuxfz583H79m2sXLkS+/bt+yKOGwYiwtq1a+Hq6orWrVvD3d0dQgjs3btXdsqzcePGyMjIkI0V8vT0VGsjzbOzs0NYWBgyMjLQvHlzuLi4YNSoUTAzM5NCz5w5c9CoUSO0adMGXl5eaNiwIVxdXd+53DVr1iA9PR2urq4YNWoUfvzxx0+xOVQITExMsGvXLkRGRqJmzZr4/vvvMWnSJADyyzNU8Jo0aQJzc3NERUWhe/fuUru3tzd2796NgwcPom7duqhXrx4WLFgABweHPC23f//+SE1NRd++fQurdABAgwYNsGLFCsyfPx81atTA/v37MXr06C/iuFGJtwcFEBGRIoWEhKBv375ITEyEgYGBpsuhD3Ty5Ek0bdoU9+/fl26y+FQGDhyImzdv4uTJk590vQWNY4iIiBTo119/Rbly5VCqVClcunQJEyZMQOfOnRmGPjMpKSl49OgRAgMD0alTp08ShubOnYtmzZrByMgI+/btw7p167B8+fJCX29h4yUzIiIFio2NRc+ePVG5cmWMHj0anTp1wqpVqzRdFn2gDRs2wMHBAQkJCZg9e7ZsXkhIiOw2/rcfVatWzfc6z507h2bNmsHFxQUrVqzA4sWLMWDAgI/dFI3jJTMiIqIv0PPnzxEXF5fjvGLFiuV5fJJSMBARERGR4vGSGRERESkeAxEREREpHgMRERERKR4DERERESkeAxER5apPnz5o166drO3Ro0eoVq0a3NzckJiYqJnCiIgKGAMREeXZo0eP0KRJExgYGODgwYMwNTXVdElERAWCgYiI8uTx48do2rQp9PT0EBoaKgtD8+fPh4uLC4yMjGBvb49hw4bhxYsXAIBjx45BpVLl+shy6tQpNGrUCAYGBrC3t8eIESOQnJwszS9btqzac7/77jtpflBQEMqXLw9dXV04Ozvjt99+k9WvUqkQFBSEli1bwsDAAOXKlcPWrVul+TExMVCpVIiMjJTafvjhB6hUKixcuFBqu3nzJpo1awZTU1OpDjMzs1xft6ztT0hIUKtnx44d0nRKSgq+++47lCpVCkZGRnBzc8OxY8ek+cHBwWrryV5zbusCgISEBKhUKtkyiej/MRAR0Xs9efIEXl5e0NHRQWhoqNofZi0tLSxevBjXrl3DunXrcOTIEYwfPx4AUL9+fTx8+BAPHz7Etm3bAECafvjwIQDg7t27aNGiBTp27IjLly9j06ZNOHXqFPz9/WXrmTp1quy5kydPBgBs374dI0eOxNixY3H16lUMHjwYffv2xdGjR2XP/+GHH9CxY0dcunQJPXr0QNeuXXHjxo0ct/mff/7BwoUL1X7Kol+/fkhLS0NYWBgePnwoC0sfw9/fH+Hh4di4cSMuX76MTp06oUWLFrh9+3aBLJ+I3kMQEeWid+/ewsPDQ9SsWVMUK1ZM1KtXT6Snp7/3eVu2bBEWFhZq7UePHhU5fez0799fDBo0SNZ28uRJoaWlJV69eiWEEMLBwUEsWLAgx/XVr19fDBw4UNbWqVMn0apVK2kagBgyZIisj5ubmxg6dKgQQojo6GgBQFy8eFEIIYSvr6/o37+/2noNDAxESEiINL127VphamqaY11vb/OzZ89k7QDE9u3bhRBC/P3330JbW1s8ePBA1qdp06YiICAg1/Vkrzm3dQkhxLNnzwQAcfTo0VxrJVIyniEionc6ceIEMjMzERkZiTt37qj9XhIAHDp0CE2bNkWpUqVQvHhx9OrVC0+ePMHLly/ztI5Lly4hODhY9ltL3t7eyMzMRHR09Huff+PGDTRo0EDW1qBBA7WzP+7u7mrTOZ0h+uuvv7B9+3ZMmzZNbZ6joyO2b9+e523LiytXriAjIwMVK1aUvQbHjx/H3bt3pX6JiYl5+j2q0qVLo3jx4nB0dMTAgQM5+J0oD/hr90T0TuXKlcPhw4dhaWmJ5cuXo2fPnvDx8UH16tUBvBnH0rp1awwdOhTTp0+Hubk5Tp06hf79+yM1NRWGhobvXceLFy8wePBgjBgxQm1emTJlCnyb3mfs2LH47rvvYGtrqzZv9erV6N27N4oXLw4DAwOkp6dDX1//o9b34sULaGtrIyIiAtra2rJ5xsbG0r+LFy+Ov/76S5p+8OABPD091ZZ38uRJFC9eHDExMRgwYAC+//57/Pjjjx9VI9GXjoGIiN7JxcUFlpaWAIBOnTrhjz/+gK+vL86dOwddXV1EREQgMzMT8+bNg5bWm5POmzdv/qB11K5dG9evX4eTk1O+aqxcuTLCwsLQu3dvqS0sLAxVqlSR9Ttz5gx8fX1l07Vq1ZL1+fPPP3Hr1i3s2bMnx3XVq1cP33zzDU6cOIHff/8d27dvx4wZM/JVd5ZatWohIyMD8fHxaNSoUa79tLS0ZK+Rjk7OH+GOjo4wMzODk5MTOnXqhPDw8I+qj0gJGIiI6IMsW7YM1apVw5QpUzB9+nQ4OTkhLS0NS5YsQZs2bRAWFoYVK1Z80DInTJiAevXqwd/fHwMGDICRkRGuX7+O0NBQLF269L3PHzduHDp37oxatWrBy8sLu3btwh9//IFDhw7J+m3ZsgV16tRBw4YNERISgnPnzmH16tWyPrNnz8aSJUtyPbO1bds2BAcHIyIiAmXKlIGVlVWetjElJQWvX7+WtaWlpSEzMxMVK1ZEjx494Ovri3nz5qFWrVp49OgRDh8+jOrVq8PHxydP68i+rpiYGOzbtw8NGzb8oOcTKRHHEBHRBzE3N8fPP/+MWbNm4ezZs6hRowbmz5+PWbNmoVq1aggJCcHMmTM/aJnVq1fH8ePHcevWLTRq1Ai1atXCpEmTYGdnl6fnt2vXDosWLcLcuXNRtWpVrFy5EmvXrlW7nDRlyhRs3LgR1atXx6+//ooNGzaonUVycnKSnWl6261btzBgwACsX7/+gy/l2djYwMDAQHoAQOfOnXHixAkAwNq1a+Hr64uxY8fC2dkZ7dq1w/nz5/N1yTBrXY0aNUKNGjU+eH8QKZFKCCE0XQQRUWFTqVTYvn272jdva1K7du0watSoHMcBEdGnxTNEREQaoqurK427IiLN4hgiIiIN+dDB50RUeBiIiEgRODqAiN6F52qJiIhI8RiIiIiISPEYiIiIiEjxGIiIiIhI8RiIiIiISPEYiIiIiEjxGIiIiIhI8RiIiIiISPEYiIiIiEjx/g/vV9EfCmi0ggAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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//POPFBZyy/vaFLV2LS0tVKtWTanNzs4OEydOxKJFixASEoI2bdrg008/Rf/+/aWwBADh4eGYMWMGIiIiVIJTcnKyUt+825gzz8bGJt/2vL87NDQ0ULNmTaW23O/Hoirsa547xOXIOy6xNDEQlaGPP/74jb/I5s6di2+//RZDhgzB7NmzYWZmBg0NDYwfP77U/zssjJwafvjhB6UUn1vuX3zp6emIjY1Fx44d37pchUKBAwcO5HuHQt5fpnFxcQAAS0vLNy7T3NwcISEh+c7P+wfJ2dkZ3333nVLbihUrsGvXrnyff/PmTQQHB+PXX3/Nd3xIdnY2HB0dsWjRonyfn/cXU37u3LmDpk2bYvHixRgwYAA2bNigFEazsrLQsWNHPHnyBFOnTkW9evVgaGiIhw8fYtCgQUU+ZnL6b9y4Md/XtqQD+LtYu3Yt7t27h4MHD7617zfffIM6depg4MCBOHXqVInXUprHWvfu3eHn5wchBGJiYjBr1ix88skn0h/i8u7JkydIT09/63sVKP5xV9BZ8dz/UOZW0F1Q4i0DhvNT1NpzXwXIbeHChRg0aBB27dqFQ4cO4csvv0RgYCDOnj2LatWq4e7du+jQoQPq1auHRYsWwcbGBjo6Oti/fz8WL16s8l4vaBtLctuLQl3rLary8xuO8Pvvv6Ndu3bSf4Q5kpKSULlyZWm6Vq1aOHfuHDIyMkp0sGbeX7JCCNy5c0f6DzxnsLaJiYnSIN2CXL58GRkZGW/9b7ZWrVoQQsDOzk76L+RNbty4AYVCke9/srmXefjwYbRq1SrfgZB5Va5cWWWb3jTw2d/fH40bN8YXX3xR4PovX76MDh06FPsyZs7lSgsLC+zatQuTJk1C165dpT+wV69exV9//YUNGzbAx8dHel5YWFix1pezf83NzQu1f9/E1tYWABAdHa30n2Z6ejpiYmJUlv/ff/+p3IL8119/AYDKgNoXL15g5syZGDNmjLSegly6dAmbN2/Gzp07S+124NI81qpVq6bU18jICN7e3kW+HFoYufdZ3jMe0dHR0vyc/Xnt2rW3LvPGjRsAAAcHhwL7vOtxl3NGOikpSenmi7xnIYvC1tY239AZHR2tNF2S7xlHR0c4Ojpi2rRpOHPmDFq1aoVVq1bhu+++w549e5CWlobdu3crnW0p6cuJObKzs/H3338r/T4u6P1YUmxtbXHnzh2V9vzaSgvHEJUjmpqaKol527ZtKre89urVC4mJiVixYoXKMt4lcf/yyy949uyZNP37778jNjYWXbp0AQA4OTmhVq1aWLBgAZ4/f67y/EePHqnUrqmpme9txrn17NlT+hC9vPULIfD48WNpOjMzE9u3b8fHH3/8xtPwXl5eyMrKwuzZs1XmZWZmqtz6XRQRERHYtWsXvv/++wLDjpeXFx4+fIiffvpJZd7Lly+Rmpr61vXUrVsXFhYWAIDly5cjOzsb48aNk+bn/IHP/ZoJIbB06dIibU8ODw8PmJiYYO7cucjIyFCZn3f/vom7uzt0dHSwbNkypfrWrl2L5ORkeHp6KvXPzMzE6tWrpen09HSsXr0aVapUgZOTk1LfpUuXIjU1Nd9bvPP63//+h1atWuHTTz8tdO1FVZrHWl45ZwJKI9w1a9YM5ubmWLVqldJHQxw4cAA3b96U9lmVKlXg6uqKdevW4f79+0rLyPv+3bx5M3R0dNC6desC1/uux11OKDl58qTUlpqaig0bNrzxeW/StWtXnD17FufPn1eqI+9ZwJJ4z6SkpCAzM1OpzdHRERoaGtJ+yO+9npycjPXr1xd+o4oo998XIQRWrFgBbW1tdOjQoVTW5+HhgYiICERFRUltT548KfDMa2ngGaJy5JNPPsGsWbMwePBgtGzZElevXkVISIjKtVwfHx/88ssvmDhxIs6fP482bdogNTUVhw8fxpgxY9C9e/dird/MzAytW7fG4MGDER8fjyVLlqB27doYPnw4gNfXlX/++Wd06dIFDRo0wODBg1G1alU8fPgQx44dg4mJCfbs2YPU1FSsXLkSy5YtQ926dZU+byQnSF25cgURERFwcXFBrVq18N1338Hf3x/37t1Djx49YGxsjJiYGOzYsQMjRozAV199hcOHD+Pbb7/FlStX3voVDG3btsXIkSMRGBiIqKgodOrUCdra2rh9+za2bduGpUuX4vPPPy/W63To0CF07Njxjf8RDhgwAFu3bsWoUaNw7NgxtGrVCllZWbh16xa2bt2KgwcPFmkciKWlJX744QcMGzYM/fv3R9euXVGvXj3UqlULX331FR4+fAgTExNs375dZTxAYZmYmCAoKAgDBgxA06ZN0adPH1SpUgX379/Hvn370KpVq3xDeH6qVKkCf39/zJw5E507d8ann36K6Oho/Pjjj2jevDn69++v1N/a2hrz5s3DvXv3ULduXWzZsgVRUVFYs2aNylnQQ4cOYc6cOUrj6gpy6NChAj8jqzCOHj2qNA4k56zB1atXcfXqVTg6OpbqsXb//n2EhoZKl8zmzJkDW1tbNGnSpMQvm2lra2PevHkYPHgw2rZti759+0q33deoUQMTJkyQ+i5btgytW7dG06ZNMWLECNjZ2eHevXvYt2+fdPPFjBkz8Ntvv+F///vfG8cBvetx16lTJ1SvXh1Dhw7F5MmToampiXXr1knLKI4pU6Zg48aN6Ny5M8aNGyfddm9ra4srV66UWO3A62PMz88PvXv3Rt26dZGZmYmNGzdCU1MTvXr1krZRR0cH3bp1w8iRI/H8+XP89NNPMDc3R2xsbLG28U309PQQGhqKgQMHwtnZGQcOHMC+ffvw9ddfF3msZWFNmTIFv/76Kzp27IixY8dKt91Xr14dT548eacbhgqt1O9jI+lWxAsXLryx36tXr8SkSZOElZWV0NfXF61atRIRERFKt2DnePHihfjmm2+EnZ2d0NbWFpaWluLzzz8Xd+/eFUIU77b73377Tfj7+wtzc3Ohr68vPD098711+9KlS6Jnz56iUqVKQldXV9ja2govLy9x5MgRpXW/7TFw4ECl5W7fvl20bt1aGBoaCkNDQ1GvXj3h6+sroqOjhRCvb8t0dXUVoaGhKjUVdOvtmjVrhJOTk9DX1xfGxsbC0dFRTJkyRfz3339Sn6LeCq1QKERkZKRSe377KD09XcybN080aNBA6OrqiooVKwonJycxc+ZMkZycrLK+ty1PCCHat28vqlevLp49eyaEEOLGjRvC3d1dGBkZicqVK4vhw4eLy5cvq9wanncb8rvtPsexY8eEh4eHMDU1FXp6eqJWrVpi0KBB4uLFi1Kft912n2PFihWiXr16QltbW1hYWIjRo0eLp0+fqmxrgwYNxMWLF4WLi4vQ09MTtra2YsWKFSp1ARBWVlYiNTX1jduUczx0794932UU9rb7ohy/pXGs5TwUCoWwtLQUPXv2FDdv3hRClPxt9zm2bNkimjRpInR1dYWZmZnw9vaWPhokt2vXronPPvtMVKhQQejp6Ql7e3vx7bffCiGE+O2330TDhg3F0qVLVW7FL2gfFOa4K0hkZKRwdnYWOjo6onr16mLRokUF3gKe3+uf3/vtypUrom3btkJPT09UrVpVzJ49W6xduzbf1/xd3jN///23GDJkiKhVq5bQ09MTZmZmol27duLw4cNK/Xbv3i0aNWok9PT0RI0aNcS8efOkj2ApzDYin49xyO/vRE6dd+/eFZ06dRIGBgbCwsJCzJgxQ+VjYfK+7971Nb906ZJo06aN0NXVFdWqVROBgYFi2bJlAoCIi4tTWUZJUwhRzkY1UZk7fvw42rVrh23bthX7P9nc7t27Bzs7O8TExBR4vTkgIAD37t1T+cRfkh83NzckJiYWakxKeTFo0CAA4PFLH5xBgwbh999/z3dYhDqMHz8eq1evxvPnz0v9a0E4hoiIiIjULvdXDgHA48ePsXHjRrRu3bpMviONY4ioxOXcCfOmQc+NGjWSvoqE6H3j6Oio7hKIPjguLi5wc3ODg4MD4uPjsXbtWqSkpODbb78tk/UzEFGJq1y5Mn799dc39sn9PVpE75tJkyapuwSiD07Xrl3x+++/Y82aNVAoFGjatCnWrl0LV1fXMlk/xxARERGR7HEMEREREckeAxERERHJHscQFUJ2djb+++8/GBsbl82HQxEREdE7E0Lg2bNnsLa2zvd75HJjICqE//77r1BfxklERETlz4MHD1CtWrU39mEgKgRjY2MAr1/QN30EPREREZUfKSkpsLGxkf6OvwkDUSHkXCYzMTFhICIiInrPFGa4CwdVExERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsqTUQBQUFoVGjRjAxMYGJiQlcXFxw4MABaf6rV6/g6+uLSpUqwcjICL169UJ8fLzSMu7fvw9PT08YGBjA3NwckydPRmZmplKf48ePo2nTptDV1UXt2rURHBxcFptHRERE7wktda68WrVq+P7771GnTh0IIbBhwwZ0794dly5dQoMGDTBhwgTs27cP27Ztg6mpKfz8/NCzZ0+Eh4cDALKysuDp6QlLS0ucOXMGsbGx8PHxgba2NubOnQsAiImJgaenJ0aNGoWQkBAcOXIEw4YNg5WVFTw8PEp1+5wm/1Kqy6fCi/zBp9TXwf1dfnB/ywv3t7yU1v5WayDq1q2b0vScOXMQFBSEs2fPolq1ali7di02bdqE9u3bAwDWr18PBwcHnD17Fi1atMChQ4dw48YNHD58GBYWFmjcuDFmz56NqVOnIiAgADo6Oli1ahXs7OywcOFCAICDgwNOnz6NxYsXl3ogIiIiovdDuRlDlJWVhc2bNyM1NRUuLi6IjIxERkYG3N3dpT716tVD9erVERERAQCIiIiAo6MjLCwspD4eHh5ISUnB9evXpT65l5HTJ2cZ+UlLS0NKSorSg4iIiD5cag9EV69ehZGREXR1dTFq1Cjs2LED9evXR1xcHHR0dFChQgWl/hYWFoiLiwMAxMXFKYWhnPk5897UJyUlBS9fvsy3psDAQJiamkoPGxubkthUIiIiKqfUHojs7e0RFRWFc+fOYfTo0Rg4cCBu3Lih1pr8/f2RnJwsPR48eKDWeoiIiKh0qXUMEQDo6Oigdu3aAAAnJydcuHABS5cuxRdffIH09HQkJSUpnSWKj4+HpaUlAMDS0hLnz59XWl7OXWi5++S9My0+Ph4mJibQ19fPtyZdXV3o6uqWyPYRERFR+af2M0R5ZWdnIy0tDU5OTtDW1saRI0ekedHR0bh//z5cXFwAAC4uLrh69SoSEhKkPmFhYTAxMUH9+vWlPrmXkdMnZxlEREREaj1D5O/vjy5duqB69ep49uwZNm3ahOPHj+PgwYMwNTXF0KFDMXHiRJiZmcHExARjx46Fi4sLWrRoAQDo1KkT6tevjwEDBmD+/PmIi4vDtGnT4OvrK53hGTVqFFasWIEpU6ZgyJAhOHr0KLZu3Yp9+/apc9OJiIioHFFrIEpISICPjw9iY2NhamqKRo0a4eDBg+jYsSMAYPHixdDQ0ECvXr2QlpYGDw8P/Pjjj9LzNTU1sXfvXowePRouLi4wNDTEwIEDMWvWLKmPnZ0d9u3bhwkTJmDp0qWoVq0afv75Z95yT0RERBK1BqK1a9e+cb6enh5WrlyJlStXFtjH1tYW+/fvf+Ny3NzccOnSpWLVSERERB++cjeGiIiIiKisMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7Kk1EAUGBqJ58+YwNjaGubk5evTogejoaKU+bm5uUCgUSo9Ro0Yp9bl//z48PT1hYGAAc3NzTJ48GZmZmUp9jh8/jqZNm0JXVxe1a9dGcHBwaW8eERERvSfUGohOnDgBX19fnD17FmFhYcjIyECnTp2Qmpqq1G/48OGIjY2VHvPnz5fmZWVlwdPTE+np6Thz5gw2bNiA4OBgTJ8+XeoTExMDT09PtGvXDlFRURg/fjyGDRuGgwcPltm2EhERUfmlpc6Vh4aGKk0HBwfD3NwckZGRcHV1ldoNDAxgaWmZ7zIOHTqEGzdu4PDhw7CwsEDjxo0xe/ZsTJ06FQEBAdDR0cGqVatgZ2eHhQsXAgAcHBxw+vRpLF68GB4eHqW3gURERPReKFdjiJKTkwEAZmZmSu0hISGoXLkyGjZsCH9/f7x48UKaFxERAUdHR1hYWEhtHh4eSElJwfXr16U+7u7uSsv08PBAREREvnWkpaUhJSVF6UFEREQfLrWeIcotOzsb48ePR6tWrdCwYUOpvV+/frC1tYW1tTWuXLmCqVOnIjo6Gn/88QcAIC4uTikMAZCm4+Li3tgnJSUFL1++hL6+vtK8wMBAzJw5s8S3kYiIiMqnchOIfH19ce3aNZw+fVqpfcSIEdLPjo6OsLKyQocOHXD37l3UqlWrVGrx9/fHxIkTpemUlBTY2NiUyrqIiIhI/crFJTM/Pz/s3bsXx44dQ7Vq1d7Y19nZGQBw584dAIClpSXi4+OV+uRM54w7KqiPiYmJytkhANDV1YWJiYnSg4iIiD5cag1EQgj4+flhx44dOHr0KOzs7N76nKioKACAlZUVAMDFxQVXr15FQkKC1CcsLAwmJiaoX7++1OfIkSNKywkLC4OLi0sJbQkRERG9z9QaiHx9ffHrr79i06ZNMDY2RlxcHOLi4vDy5UsAwN27dzF79mxERkbi3r172L17N3x8fODq6opGjRoBADp16oT69etjwIABuHz5Mg4ePIhp06bB19cXurq6AIBRo0bh77//xpQpU3Dr1i38+OOP2Lp1KyZMmKC2bSciIqLyQ62BKCgoCMnJyXBzc4OVlZX02LJlCwBAR0cHhw8fRqdOnVCvXj1MmjQJvXr1wp49e6RlaGpqYu/evdDU1ISLiwv69+8PHx8fzJo1S+pjZ2eHffv2ISwsDB999BEWLlyIn3/+mbfcExEREQA1D6oWQrxxvo2NDU6cOPHW5dja2mL//v1v7OPm5oZLly4VqT4iIiKSh3IxqJqIiIhInRiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPbUGogCAwPRvHlzGBsbw9zcHD169EB0dLRSn1evXsHX1xeVKlWCkZERevXqhfj4eKU+9+/fh6enJwwMDGBubo7JkycjMzNTqc/x48fRtGlT6Orqonbt2ggODi7tzSMiIqL3hFoD0YkTJ+Dr64uzZ88iLCwMGRkZ6NSpE1JTU6U+EyZMwJ49e7Bt2zacOHEC//33H3r27CnNz8rKgqenJ9LT03HmzBls2LABwcHBmD59utQnJiYGnp6eaNeuHaKiojB+/HgMGzYMBw8eLNPtJSIiovJJS50rDw0NVZoODg6Gubk5IiMj4erqiuTkZKxduxabNm1C+/btAQDr16+Hg4MDzp49ixYtWuDQoUO4ceMGDh8+DAsLCzRu3BizZ8/G1KlTERAQAB0dHaxatQp2dnZYuHAhAMDBwQGnT5/G4sWL4eHhUebbTUREROVLuRpDlJycDAAwMzMDAERGRiIjIwPu7u5Sn3r16qF69eqIiIgAAERERMDR0REWFhZSHw8PD6SkpOD69etSn9zLyOmTs4y80tLSkJKSovQgIiKiD1e5CUTZ2dkYP348WrVqhYYNGwIA4uLioKOjgwoVKij1tbCwQFxcnNQndxjKmZ8z7019UlJS8PLlS5VaAgMDYWpqKj1sbGxKZBuJiIiofCo3gcjX1xfXrl3D5s2b1V0K/P39kZycLD0ePHig7pKIiIioFKl1DFEOPz8/7N27FydPnkS1atWkdktLS6SnpyMpKUnpLFF8fDwsLS2lPufPn1daXs5daLn75L0zLT4+HiYmJtDX11epR1dXF7q6uiWybURERFT+qfUMkRACfn5+2LFjB44ePQo7Ozul+U5OTtDW1saRI0ektujoaNy/fx8uLi4AABcXF1y9ehUJCQlSn7CwMJiYmKB+/fpSn9zLyOmTswwiIiKSN7WeIfL19cWmTZuwa9cuGBsbS2N+TE1Noa+vD1NTUwwdOhQTJ06EmZkZTExMMHbsWLi4uKBFixYAgE6dOqF+/foYMGAA5s+fj7i4OEybNg2+vr7SWZ5Ro0ZhxYoVmDJlCoYMGYKjR49i69at2Ldvn9q2nYiIiMoPtZ4hCgoKQnJyMtzc3GBlZSU9tmzZIvVZvHgxPvnkE/Tq1Quurq6wtLTEH3/8Ic3X1NTE3r17oampCRcXF/Tv3x8+Pj6YNWuW1MfOzg779u1DWFgYPvroIyxcuBA///wzb7knIiIiAGo+QySEeGsfPT09rFy5EitXriywj62tLfbv3//G5bi5ueHSpUtFrpGIiIg+fOXmLjMiIiIidWEgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2WMgIiIiItljICIiIiLZYyAiIiIi2dMq7hNTU1Nx4sQJ3L9/H+np6Urzvvzyy3cujIiIiKisFCsQXbp0CV27dsWLFy+QmpoKMzMzJCYmwsDAAObm5gxERERE9F4p1iWzCRMmoFu3bnj69Cn09fVx9uxZ/PPPP3BycsKCBQtKukYiIiKiUlWsQBQVFYVJkyZBQ0MDmpqaSEtLg42NDebPn4+vv/66pGskIiIiKlXFCkTa2trQ0Hj9VHNzc9y/fx8AYGpqigcPHpRcdURERERloFhjiJo0aYILFy6gTp06aNu2LaZPn47ExERs3LgRDRs2LOkaiYiIiEpVsc4QzZ07F1ZWVgCAOXPmoGLFihg9ejQePXqENWvWlGiBRERERKWtWGeImjVrJv1sbm6O0NDQEiuIiIiIqKwV6wxR+/btkZSUVMKlEBEREalHsQLR8ePHVT6MkYiIiOh9Veyv7lAoFCVZBxEREZHaFPurOz777DPo6OjkO+/o0aPFLoiIiIiorBU7ELm4uMDIyKgkayEiIiJSi2IFIoVCgcmTJ8Pc3Lyk6yEiIiIqc8UaQySEKOk6iIiIiNSmWIFoxowZvFxGREREH4xiXTKbMWMGAODRo0eIjo4GANjb26NKlSolVxkRERFRGSnWGaIXL15gyJAhsLa2hqurK1xdXWFtbY2hQ4fixYsXJV0jERERUakqViCaMGECTpw4gd27dyMpKQlJSUnYtWsXTpw4gUmTJpV0jURERESlqliXzLZv347ff/8dbm5uUlvXrl2hr68PLy8vBAUFlVR9RERERKWu2JfMLCwsVNrNzc15yYyIiIjeO8UKRC4uLpgxYwZevXoltb18+RIzZ86Ei4tLiRVHREREVBaKdclsyZIl6Ny5M6pVq4aPPvoIAHD58mXo6enh4MGDJVogERERUWkrViBydHTE7du3ERISglu3bgEA+vbtC29vb+jr65dogURERESlrViB6OTJk2jZsiWGDx9e0vUQERERlblijSFq164dnjx5UtK1EBEREakFv8uMiIiIZK9Yl8wAICIiAhUrVsx3nqura7ELIiIiIiprxQ5En332Wb7tCoUCWVlZxS6IiIiIqKwV65IZAMTFxSE7O1vlwTBERERE75tiBSKFQlHSdRARERGpjVoHVZ88eRLdunWDtbU1FAoFdu7cqTR/0KBBUCgUSo/OnTsr9Xny5Am8vb1hYmKCChUqYOjQoXj+/LlSnytXrqBNmzbQ09ODjY0N5s+fXyL1ExER0YehWIEoOzsb5ubm77zy1NRUfPTRR1i5cmWBfTp37ozY2Fjp8dtvvynN9/b2xvXr1xEWFoa9e/fi5MmTGDFihDQ/JSUFnTp1gq2tLSIjI/HDDz8gICAAa9aseef6iYiI6MNQrEHVgYGBsLCwwJAhQ5Ta161bh0ePHmHq1KmFWk6XLl3QpUuXN/bR1dWFpaVlvvNu3ryJ0NBQXLhwAc2aNQMALF++HF27dsWCBQtgbW2NkJAQpKenY926ddDR0UGDBg0QFRWFRYsWKQUnIiIikq9inSFavXo16tWrp9LeoEEDrFq16p2Lyu348eMwNzeHvb09Ro8ejcePH0vzIiIiUKFCBSkMAYC7uzs0NDRw7tw5qY+rqyt0dHSkPh4eHoiOjsbTp0/zXWdaWhpSUlKUHkRERPThKlYgiouLg5WVlUp7lSpVEBsb+85F5ejcuTN++eUXHDlyBPPmzcOJEyfQpUsX6U62uLg4lUt3WlpaMDMzQ1xcnNTHwsJCqU/OdE6fvAIDA2Fqaio9bGxsSmybiIiIqPwp1iUzGxsbhIeHw87OTqk9PDwc1tbWJVIYAPTp00f62dHREY0aNUKtWrVw/PhxdOjQocTWk5e/vz8mTpwoTaekpDAUERERfcCKFYiGDx+O8ePHIyMjA+3btwcAHDlyBFOmTMGkSZNKtMDcatasicqVK+POnTvo0KEDLC0tkZCQoNQnMzMTT548kcYdWVpaIj4+XqlPznRBY5N0dXWhq6tbCltARERE5VGxAtHkyZPx+PFjjBkzBunp6QAAPT09TJ06Ff7+/iVaYG7//vsvHj9+LF2uc3FxQVJSEiIjI+Hk5AQAOHr0KLKzs+Hs7Cz1+eabb5CRkQFtbW0AQFhYGOzt7Qv86hEiIiKSl2J/MOO8efPw6NEjnD17FpcvX8aTJ08wffr0Ii3n+fPniIqKQlRUFAAgJiYGUVFRuH//Pp4/f47Jkyfj7NmzuHfvHo4cOYLu3bujdu3a8PDwAAA4ODigc+fOGD58OM6fP4/w8HD4+fmhT58+0qW7fv36QUdHB0OHDsX169exZcsWLF26VOmSGBEREclbsb/LDACMjIzQvHnzYj//4sWLaNeunTSdE1IGDhyIoKAgXLlyBRs2bEBSUhKsra3RqVMnzJ49W+lyVkhICPz8/NChQwdoaGigV69eWLZsmTTf1NQUhw4dgq+vL5ycnFC5cmVMnz6dt9wTERGRpNiB6OLFi9i6dSvu378vXTbL8ccffxRqGW5ubm/81OuDBw++dRlmZmbYtGnTG/s0atQIp06dKlRNREREJD/FumS2efNmtGzZEjdv3sSOHTuQkZGB69ev4+jRozA1NS3pGomIiIhKVbEC0dy5c7F48WLs2bMHOjo6WLp0KW7dugUvLy9Ur169pGskIiIiKlXFCkR3796Fp6cnAEBHRwepqalQKBSYMGECvyOMiIiI3jvFCkQVK1bEs2fPAABVq1bFtWvXAABJSUl48eJFyVVHREREVAaKNaja1dUVYWFhcHR0RO/evTFu3DgcPXoUYWFhpfoJ0kRERESloViBaMWKFXj16hUA4JtvvoG2tjbOnDmDXr16Ydq0aSVaIBEREVFpK1IgyvnWdy0tLRgZGUnTY8aMwZgxY0q+OiIiIqIyUKRAVKFCBSgUirf2y/k2eiIiIqL3QZEC0bFjx5SmhRDo2rUrfv75Z1StWrVECyMiIiIqK0UKRG3btlVp09TURIsWLVCzZs0SK4qIiIioLBXrtnsiIiKiD8k7BaIHDx7gxYsXqFSpUknVQ0RERFTminTJLPe3yCcmJuK3335D+/bt+f1lRERE9F4rUiBavHgxAEChUKBy5cro1q0bP3eIiIiI3ntFCkQxMTGlVQcRERGR2nBQNREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJnloD0cmTJ9GtWzdYW1tDoVBg586dSvOFEJg+fTqsrKygr68Pd3d33L59W6nPkydP4O3tDRMTE1SoUAFDhw7F8+fPlfpcuXIFbdq0gZ6eHmxsbDB//vzS3jQiIiJ6j6g1EKWmpuKjjz7CypUr850/f/58LFu2DKtWrcK5c+dgaGgIDw8PvHr1Surj7e2N69evIywsDHv37sXJkycxYsQIaX5KSgo6deoEW1tbREZG4ocffkBAQADWrFlT6ttHRERE7wctda68S5cu6NKlS77zhBBYsmQJpk2bhu7duwMAfvnlF1hYWGDnzp3o06cPbt68idDQUFy4cAHNmjUDACxfvhxdu3bFggULYG1tjZCQEKSnp2PdunXQ0dFBgwYNEBUVhUWLFikFJyIiIpKvcjuGKCYmBnFxcXB3d5faTE1N4ezsjIiICABAREQEKlSoIIUhAHB3d4eGhgbOnTsn9XF1dYWOjo7Ux8PDA9HR0Xj69Gm+605LS0NKSorSg4iIiD5c5TYQxcXFAQAsLCyU2i0sLKR5cXFxMDc3V5qvpaUFMzMzpT75LSP3OvIKDAyEqamp9LCxsXn3DSIiIqJyq9wGInXy9/dHcnKy9Hjw4IG6SyIiIqJSVG4DkaWlJQAgPj5eqT0+Pl6aZ2lpiYSEBKX5mZmZePLkiVKf/JaRex156erqwsTEROlBREREH65yG4js7OxgaWmJI0eOSG0pKSk4d+4cXFxcAAAuLi5ISkpCZGSk1Ofo0aPIzs6Gs7Oz1OfkyZPIyMiQ+oSFhcHe3h4VK1Yso60hIiKi8kytgej58+eIiopCVFQUgNcDqaOionD//n0oFAqMHz8e3333HXbv3o2rV6/Cx8cH1tbW6NGjBwDAwcEBnTt3xvDhw3H+/HmEh4fDz88Pffr0gbW1NQCgX79+0NHRwdChQ3H9+nVs2bIFS5cuxcSJE9W01URERFTeqPW2+4sXL6Jdu3bSdE5IGThwIIKDgzFlyhSkpqZixIgRSEpKQuvWrREaGgo9PT3pOSEhIfDz80OHDh2goaGBXr16YdmyZdJ8U1NTHDp0CL6+vnByckLlypUxffp03nJPREREErUGIjc3NwghCpyvUCgwa9YszJo1q8A+ZmZm2LRp0xvX06hRI5w6darYdRIREdGHrdyOISIiIiIqKwxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHsMRERERCR7DEREREQkewxEREREJHvlOhAFBARAoVAoPerVqyfNf/XqFXx9fVGpUiUYGRmhV69eiI+PV1rG/fv34enpCQMDA5ibm2Py5MnIzMws600hIiKickxL3QW8TYMGDXD48GFpWkvr/0ueMGEC9u3bh23btsHU1BR+fn7o2bMnwsPDAQBZWVnw9PSEpaUlzpw5g9jYWPj4+EBbWxtz584t820hIiKi8qncByItLS1YWlqqtCcnJ2Pt2rXYtGkT2rdvDwBYv349HBwccPbsWbRo0QKHDh3CjRs3cPjwYVhYWKBx48aYPXs2pk6dioCAAOjo6JT15hAREVE5VK4vmQHA7du3YW1tjZo1a8Lb2xv3798HAERGRiIjIwPu7u5S33r16qF69eqIiIgAAERERMDR0REWFhZSHw8PD6SkpOD69esFrjMtLQ0pKSlKDyIiIvpwletA5OzsjODgYISGhiIoKAgxMTFo06YNnj17hri4OOjo6KBChQpKz7GwsEBcXBwAIC4uTikM5czPmVeQwMBAmJqaSg8bG5uS3TAiIiIqV8r1JbMuXbpIPzdq1AjOzs6wtbXF1q1boa+vX2rr9ff3x8SJE6XplJQUhiIiIqIPWLk+Q5RXhQoVULduXdy5cweWlpZIT09HUlKSUp/4+HhpzJGlpaXKXWc50/mNS8qhq6sLExMTpQcRERF9uN6rQPT8+XPcvXsXVlZWcHJygra2No4cOSLNj46Oxv379+Hi4gIAcHFxwdWrV5GQkCD1CQsLg4mJCerXr1/m9RMREVH5VK4vmX311Vfo1q0bbG1t8d9//2HGjBnQ1NRE3759YWpqiqFDh2LixIkwMzODiYkJxo4dCxcXF7Ro0QIA0KlTJ9SvXx8DBgzA/PnzERcXh2nTpsHX1xe6urpq3joiIiIqL8p1IPr333/Rt29fPH78GFWqVEHr1q1x9uxZVKlSBQCwePFiaGhooFevXkhLS4OHhwd+/PFH6fmamprYu3cvRo8eDRcXFxgaGmLgwIGYNWuWujaJiIiIyqFyHYg2b978xvl6enpYuXIlVq5cWWAfW1tb7N+/v6RLIyIiog/IezWGiIiIiKg0MBARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7MkqEK1cuRI1atSAnp4enJ2dcf78eXWXREREROWAbALRli1bMHHiRMyYMQN//vknPvroI3h4eCAhIUHdpREREZGaySYQLVq0CMOHD8fgwYNRv359rFq1CgYGBli3bp26SyMiIiI1k0UgSk9PR2RkJNzd3aU2DQ0NuLu7IyIiQo2VERERUXmgpe4CykJiYiKysrJgYWGh1G5hYYFbt26p9E9LS0NaWpo0nZycDABISUkp0nqz0l4Wo1oqDUXdd8XB/V1+cH/LC/e3vBRlf+f0FUK8ta8sAlFRBQYGYubMmSrtNjY2aqiGSoLp8lHqLoHKEPe3vHB/y0tx9vezZ89gamr6xj6yCESVK1eGpqYm4uPjldrj4+NhaWmp0t/f3x8TJ06UprOzs/HkyRNUqlQJCoWi1OstL1JSUmBjY4MHDx7AxMRE3eVQKeP+lhfub3mR6/4WQuDZs2ewtrZ+a19ZBCIdHR04OTnhyJEj6NGjB4DXIefIkSPw8/NT6a+rqwtdXV2ltgoVKpRBpeWTiYmJrN5Acsf9LS/c3/Iix/39tjNDOWQRiABg4sSJGDhwIJo1a4aPP/4YS5YsQWpqKgYPHqzu0oiIiEjNZBOIvvjiCzx69AjTp09HXFwcGjdujNDQUJWB1kRERCQ/sglEAODn55fvJTLKn66uLmbMmKFy+ZA+TNzf8sL9LS/c32+nEIW5F42IiIjoAyaLD2YkIiIiehMGIiIiIpI9BiIiIiKSPQYimXJzc8P48ePVXQaVI3mPiRo1amDJkiVqq4eK5m3vaYVCgZ07dxZ6ecePH4dCoUBSUtI710bl19uOi+IcBwEBAWjcuPE711bWZHWXGREV3oULF2BoaKjuMqiExMbGomLFiuoug94zLVu2RGxsbKE/3PB9xkBERPmqUqWKukugEpTf1xTR+y0jIwPa2tqlug4dHR3ZHDu8ZEZ4+vQpfHx8ULFiRRgYGKBLly64ffs2gNffA1OlShX8/vvvUv/GjRvDyspKmj59+jR0dXXx4sWLMq9dDtzc3DB27FiMHz8eFStWhIWFBX766Sfpk9aNjY1Ru3ZtHDhwQHrOtWvX0KVLFxgZGcHCwgIDBgxAYmKiND81NRU+Pj4wMjKClZUVFi5cqLLe3JfM7t27B4VCgaioKGl+UlISFAoFjh8/DuD/T60fPHgQTZo0gb6+Ptq3b4+EhAQcOHAADg4OMDExQb9+/XislJLs7GxMmTIFZmZmsLS0REBAgDQv76WRM2fOoHHjxtDT00OzZs2wc+dOlX0MAJGRkWjWrBkMDAzQsmVLREdHl83GfGDWrFkDa2trZGdnK7V3794dQ4YMAQDs2rULTZs2hZ6eHmrWrImZM2ciMzNT6qtQKBAUFIRPP/0UhoaG+O6771C7dm0sWLBAaZlRUVFQKBS4c+dOoWpLTEzEZ599BgMDA9SpUwe7d++W5uV3yeynn36CjY0NDAwM8Nlnn2HRokX5fr3Vxo0bUaNGDZiamqJPnz549uxZoepRFwYiwqBBg3Dx4kXs3r0bEREREEKga9euyMjIgEKhgKurq/RH7+nTp7h58yZevnyJW7duAQBOnDiB5s2bw8DAQI1b8WHbsGEDKleujPPnz2Ps2LEYPXo0evfujZYtW+LPP/9Ep06dMGDAALx48QJJSUlo3749mjRpgosXLyI0NBTx8fHw8vKSljd58mScOHECu3btwqFDh3D8+HH8+eefJVJrQEAAVqxYgTNnzuDBgwfw8vLCkiVLsGnTJuzbtw+HDh3C8uXLS2RdpGzDhg0wNDTEuXPnMH/+fMyaNQthYWEq/VJSUtCtWzc4Ojrizz//xOzZszF16tR8l/nNN99g4cKFuHjxIrS0tKQ/3lQ0vXv3xuPHj3Hs2DGp7cmTJwgNDYW3tzdOnToFHx8fjBs3Djdu3MDq1asRHByMOXPmKC0nICAAn332Ga5evYqhQ4diyJAhWL9+vVKf9evXw9XVFbVr1y5UbTNnzoSXlxeuXLmCrl27wtvbG0+ePMm3b3h4OEaNGoVx48YhKioKHTt2VKkRAO7evYudO3di79692Lt3L06cOIHvv/++UPWojSBZatu2rRg3bpz466+/BAARHh4uzUtMTBT6+vpi69atQgghli1bJho0aCCEEGLnzp3C2dlZdO/eXQQFBQkhhHB3dxdff/112W+ETLRt21a0bt1ams7MzBSGhoZiwIABUltsbKwAICIiIsTs2bNFp06dlJbx4MEDAUBER0eLZ8+eCR0dHWn/CiHE48ePhb6+vhg3bpzUZmtrKxYvXiyEECImJkYAEJcuXZLmP336VAAQx44dE0IIcezYMQFAHD58WOoTGBgoAIi7d+9KbSNHjhQeHh7v8pJQPvIeJ0II0bx5czF16lQhhBAAxI4dO4QQQgQFBYlKlSqJly9fSn1/+uknpX2c3/7ct2+fAKD0PCq87t27iyFDhkjTq1evFtbW1iIrK0t06NBBzJ07V6n/xo0bhZWVlTQNQIwfP16pz8OHD4WmpqY4d+6cEEKI9PR0UblyZREcHFyomgCIadOmSdPPnz8XAMSBAweEEP9/HDx9+lQIIcQXX3whPD09lZbh7e0tTE1NpekZM2YIAwMDkZKSIrVNnjxZODs7F6omdeEZIpm7efMmtLS04OzsLLVVqlQJ9vb2uHnzJgCgbdu2uHHjBh49eoQTJ07Azc0Nbm5uOH78ODIyMnDmzBm4ubmpaQvkoVGjRtLPmpqaqFSpEhwdHaW2nO/kS0hIwOXLl3Hs2DEYGRlJj3r16gF4/V/b3bt3kZ6errTPzczMYG9vX+K1WlhYwMDAADVr1lRqS0hIKJF1kbLcrz0AWFlZ5ftaR0dHo1GjRtDT05PaPv7447cuM+dSOfdf8Xh7e2P79u1IS0sDAISEhKBPnz7Q0NDA5cuXMWvWLKX37fDhwxEbG6t0iblZs2ZKy7S2toanpyfWrVsHANizZw/S0tLQu3fvQteVex8bGhrCxMSkwH0cHR2tcqzkd+zUqFEDxsbG0nRBx2J5wkHV9FaOjo4wMzPDiRMncOLECcyZMweWlpaYN28eLly4gIyMDLRs2VLdZX7Q8g6cVCgUSm0KhQLA6zEkz58/R7du3TBv3jyV5VhZWRV6XEFuGhqv/3cSub7pJyMj46215q0zpy3vOAoqGaXxWhd0nFHRdevWDUII7Nu3D82bN8epU6ewePFiAMDz588xc+ZM9OzZU+V5uYNrfnd+Dhs2DAMGDMDixYuxfv16fPHFF0UawlDax01JLbO0MRDJnIODAzIzM3Hu3Dkp1Dx+/BjR0dGoX78+gNcHcps2bbBr1y5cv34drVu3hoGBAdLS0rB69Wo0a9aMt2eXI02bNsX27dtRo0YNaGmpvsVr1aoFbW1tnDt3DtWrVwfwemzYX3/9hbZt2+a7zJw7zmJjY9GkSRMAUBl8S+8Pe3t7/Prrr0hLS5O+7PPChQtqrurDp6enh549eyIkJAR37tyBvb09mjZtCuD1+zY6OrrQ435y69q1KwwNDREUFITQ0FCcPHmypEuX2NvbqxwrH8qxw0tmMlenTh10794dw4cPx+nTp3H58mX0798fVatWRffu3aV+bm5u+O2339C4cWMYGRlBQ0MDrq6uCAkJKfCPKKmHr68vnjx5gr59++LChQu4e/cuDh48iMGDByMrKwtGRkYYOnQoJk+ejKNHj+LatWsYNGiQdBYoP/r6+mjRogW+//573Lx5EydOnMC0adPKcKuoJPXr1w/Z2dkYMWIEbt68iYMHD0p3KuWcBaLS4e3tjX379mHdunXw9vaW2qdPn45ffvkFM2fOxPXr13Hz5k1s3ry5UO8zTU1NDBo0CP7+/qhTpw5cXFxKrf6xY8di//79WLRoEW7fvo3Vq1fjwIEDH8Rxw0BEWL9+PZycnPDJJ5/AxcUFQgjs379f6ZRn27ZtkZWVpTRWyM3NTaWN1M/a2hrh4eHIyspCp06d4OjoiPHjx6NChQpS6Pnhhx/Qpk0bdOvWDe7u7mjdujWcnJzeuNx169YhMzMTTk5OGD9+PL777ruy2BwqBSYmJtizZw+ioqLQuHFjfPPNN5g+fToA5cszVPLat28PMzMzREdHo1+/flK7h4cH9u7di0OHDqF58+Zo0aIFFi9eDFtb20Itd+jQoUhPT8fgwYNLq3QAQKtWrbBq1SosWrQIH330EUJDQzFhwoQP4rhRiNyDAoiISJZCQkIwePBgJCcnQ19fX93lUBGdOnUKHTp0wIMHD6SbLMrK8OHDcevWLZw6dapM11vSOIaIiEiGfvnlF9SsWRNVq1bF5cuXMXXqVHh5eTEMvWfS0tLw6NEjBAQEoHfv3mUShhYsWICOHTvC0NAQBw4cwIYNG/Djjz+W+npLGy+ZERHJUFxcHPr37w8HBwdMmDABvXv3xpo1a9RdFhXRb7/9BltbWyQlJWH+/PlK80JCQpRu48/9aNCgQbHXef78eXTs2BGOjo5YtWoVli1bhmHDhr3rpqgdL5kRERF9gJ49e4b4+Ph852lraxd6fJJcMBARERGR7PGSGREREckeAxERERHJHgMRERERyR4DEREREckeAxERFcmgQYPQo0cPpbZHjx6hYcOGcHZ2RnJysnoKIyJ6BwxERPROHj16hPbt20NfXx+HDh2CqampuksiIioyBiIiKrbExER06NABurq6CAsLUwpDixYtgqOjIwwNDWFjY4MxY8bg+fPnAIDjx49DoVAU+Mhx+vRptGnTBvr6+rCxscGXX36J1NRUaX6NGjVUnvvVV19J84OCglCrVi3o6OjA3t4eGzduVKpfoVAgKCgIXbp0gb6+PmrWrInff/9dmn/v3j0oFApERUVJbd9++y0UCgWWLFkitd26dQsdO3aEqampVEeFChUKfN1ytj8pKUmlnp07d0rTaWlp+Oqrr1C1alUYGhrC2dkZx48fl+YHBwerrCe/mono7RiIiKhYHj9+DHd3d2hpaSEsLEzlD7OGhgaWLVuG69evY8OGDTh69CimTJkCAGjZsiViY2MRGxuL7du3A4A0HRsbCwC4e/cuOnfujF69euHKlSvYsmULTp8+DT8/P6X1zJo1S+m5M2bMAADs2LED48aNw6RJk3Dt2jWMHDkSgwcPxrFjx5Se/+2336JXr164fPkyvL290adPH9y8eTPfbf7333+xZMkSla+3GDJkCDIyMhAeHo7Y2FilsPQu/Pz8EBERgc2bN+PKlSvo3bs3OnfujNu3b5fI8okoF0FEVAQDBw4Urq6uonHjxkJbW1u0aNFCZGZmvvV527ZtE5UqVVJpP3bsmMjvV9HQoUPFiBEjlNpOnTolNDQ0xMuXL4UQQtja2orFixfnu76WLVuK4cOHK7X17t1bdO3aVZoGIEaNGqXUx9nZWYwePVoIIURMTIwAIC5duiSEEMLHx0cMHTpUZb36+voiJCREml6/fr0wNTXNt67c2/z06VOldgBix44dQggh/vnnH6GpqSkePnyo1KdDhw7C39+/wPXkrZmICodniIioyE6ePIns7GxERUXhzp07Kt+hBACHDx9Ghw4dULVqVRgbG2PAgAF4/PgxXrx4Uah1XL58GcHBwUrfv+Th4YHs7GzExMS89fk3b95Eq1atlNpatWqlcvbHxcVFZTq/M0R//vknduzYgdmzZ6vMs7Ozw44dOwq9bYVx9epVZGVloW7dukqvwYkTJ3D37l2pX3Jycol9RxWRnPHb7omoyGrWrIkjR46gcuXK+PHHH9G/f394enqiUaNGAF6PY/nkk08wevRozJkzB2ZmZjh9+jSGDh2K9PR0GBgYvHUdz58/x8iRI/Hll1+qzKtevXqJb9PbTJo0CV999RWsrKxU5q1duxYDBw6EsbEx9PX1kZmZCT09vXda3/Pnz6GpqYnIyEhoamoqzTMyMpJ+NjY2xp9//ilNP3z4EG5ubu+0biI5YiAioiJzdHRE5cqVAQC9e/fGH3/8AR8fH5w/fx46OjqIjIxEdnY2Fi5cCA2N1yeit27dWqR1NG3aFDdu3EDt2rWLVaODgwPCw8MxcOBAqS08PBz169dX6nf27Fn4+PgoTTdp0kSpz+7du/HXX39h3759+a6rRYsW+PTTT3Hy5En8+uuv2LFjB+bOnVusunM0adIEWVlZSEhIQJs2bQrsp6GhofQaaWnx1zpRcfCdQ0TvbOXKlWjYsCFmzpyJOXPmoHbt2sjIyMDy5cvRrVs3hIeHY9WqVUVa5tSpU9GiRQv4+flh2LBhMDQ0xI0bNxAWFoYVK1a89fmTJ0+Gl5cXmjRpAnd3d+zZswd//PEHDh8+rNRv27ZtaNasGVq3bo2QkBCcP38ea9euVeozf/58LF++vMAzW9u3b0dwcDAiIyNRvXp1mJubF2ob09LS8OrVK6W2jIwMZGdno27duvD29oaPjw8WLlyIJk2a4NGjRzhy5AgaNWoET0/PQq2DiAqHY4iI6J2ZmZnhp59+wrx583Du3Dl89NFHWLRoEebNm4eGDRsiJCQEgYGBRVpmo0aNcOLECfz1119o06YNmjRpgunTp8Pa2rpQz+/RoweWLl2KBQsWoEGDBli9ejXWr1+vcjlp5syZ2Lx5Mxo1aoRffvkFv/32m8pZpNq1ayudacrtr7/+wrBhw7Bp06YiX8qztLSEvr6+9AAALy8vnDx5EgCwfv16+Pj4YNKkSbC3t0ePHj1w4cIFtVwyJPrQKYQQQt1FEBGpg0KhwI4dO1Q+eVudevTogfHjx3McEFEZ4xkiIqJyREdHRxp3RURlh2OIiIjKkaIOPieiksFARESyxREDRJSD52WJiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2GIiIiIhI9hiIiIiISPYYiIiIiEj2/g9XK2w5HseaMQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки после oversampling и undersampling:  12108\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Преобразование целевой переменной (цены) в категориальные диапазоны с использованием квантилей\n",
    "train_data['price_category'] = pd.qcut(train_data['price'], q=4, labels=['low', 'medium', 'high', 'very_high'])\n",
    "\n",
    "# Визуализация распределения цен после преобразования в категории\n",
    "sns.countplot(x=train_data['price_category'])\n",
    "plt.title('Распределение категорий цены в обучающей выборке')\n",
    "plt.xlabel('Категория цены')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Балансировка категорий с помощью RandomOverSampler (увеличение меньшинств)\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_train = train_data.drop(columns=['price', 'price_category'])\n",
    "y_train = train_data['price_category']\n",
    "\n",
    "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "# Визуализация распределения цен после oversampling\n",
    "sns.countplot(x=y_resampled)\n",
    "plt.title('Распределение категорий цены после oversampling')\n",
    "plt.xlabel('Категория цены')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Применение RandomUnderSampler для уменьшения большего класса\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "X_resampled, y_resampled = rus.fit_resample(X_resampled, y_resampled)\n",
    "\n",
    "# Визуализация распределения цен после undersampling\n",
    "sns.countplot(x=y_resampled)\n",
    "plt.title('Распределение категорий цены после undersampling')\n",
    "plt.xlabel('Категория цен')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Печать размеров выборки после балансировки\n",
    "print(\"Размер обучающей выборки после oversampling и undersampling: \", len(X_resampled))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Конструирование признаков\n",
    "\n",
    "**Процесс конструирования признаков для решения двух задач:**\n",
    "\n",
    "**Задача 1:** Оптимизация ассортимента товаров в онлайн-магазине.  \n",
    "**Цель технического проекта:** Разработка модели для прогнозирования спроса на товары.\n",
    "\n",
    "**Задача 2:** Оптимизация ценовой политики.  \n",
    "**Цель технического проекта:** Разработка модели для прогнозирования оптимальной цены товаров.\n",
    "\n",
    "**Унитарное кодирование**  \n",
    "Унитарное кодирование категориальных признаков (one-hot encoding). Преобразование категориальных признаков в бинарные векторы.\n",
    "\n",
    "**Дискретизация числовых признаков**  \n",
    "Процесс преобразования непрерывных числовых значений в дискретные категории или интервалы (бины)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Столбцы train_data_encoded: ['href', 'items', 'price', 'price_category', 'category_Groceries', 'sub_category_Dairy & Bakery', 'sub_category_Fruits & Vegetables', 'sub_category_Premium Fruits', 'sub_category_Snacks & Branded Foods', 'sub_category_Staples']\n",
      "Столбцы val_data_encoded: ['href', 'items', 'price', 'category_Groceries', 'sub_category_Dairy & Bakery', 'sub_category_Fruits & Vegetables', 'sub_category_Premium Fruits', 'sub_category_Snacks & Branded Foods', 'sub_category_Staples']\n",
      "Столбцы test_data_encoded: ['href', 'items', 'price', 'category_Groceries', 'sub_category_Dairy & Bakery', 'sub_category_Fruits & Vegetables', 'sub_category_Premium Fruits', 'sub_category_Snacks & Branded Foods', 'sub_category_Staples']\n"
     ]
    }
   ],
   "source": [
    "# Конструирование признаков\n",
    "# Унитарное кодирование категориальных признаков (применение one-hot encoding)\n",
    "\n",
    "# Пример категориальных признаков\n",
    "categorical_features = ['category', 'sub_category']\n",
    "\n",
    "# Применение one-hot encoding\n",
    "train_data_encoded = pd.get_dummies(train_data, columns=categorical_features)\n",
    "val_data_encoded = pd.get_dummies(val_data, columns=categorical_features)\n",
    "test_data_encoded = pd.get_dummies(test_data, columns=categorical_features)\n",
    "df_encoded = pd.get_dummies(df, columns=categorical_features)\n",
    "\n",
    "print(\"Столбцы train_data_encoded:\", train_data_encoded.columns.tolist())\n",
    "print(\"Столбцы val_data_encoded:\", val_data_encoded.columns.tolist())\n",
    "print(\"Столбцы test_data_encoded:\", test_data_encoded.columns.tolist())\n",
    "\n",
    "# Дискретизация числовых признаков (цены). Например, можно разделить цену на категории\n",
    "# Пример дискретизации признака 'price'\n",
    "train_data_encoded['price_category'] = pd.cut(train_data_encoded['price'], bins=5, labels=False)\n",
    "val_data_encoded['price_category'] = pd.cut(val_data_encoded['price'], bins=5, labels=False)\n",
    "test_data_encoded['price_category'] = pd.cut(test_data_encoded['price'], bins=5, labels=False)\n",
    "\n",
    "# Пример дискретизации признака 'price' на 5 категорий\n",
    "df_encoded['price_category'] = pd.cut(df_encoded['price'], bins=5, labels=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ручной синтез\n",
    "Создание новых признаков на основе экспертных знаний и логики предметной области. К примеру, для данных о продаже домов можно создать признак цена за единицу товара."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Преобразуем столбцы 'price' и 'items' в числовой формат\n",
    "train_data_encoded['price'] = pd.to_numeric(train_data_encoded['price'], errors='coerce')\n",
    "train_data_encoded['items'] = pd.to_numeric(train_data_encoded['items'], errors='coerce')\n",
    "\n",
    "val_data_encoded['price'] = pd.to_numeric(val_data_encoded['price'], errors='coerce')\n",
    "val_data_encoded['items'] = pd.to_numeric(val_data_encoded['items'], errors='coerce')\n",
    "\n",
    "test_data_encoded['price'] = pd.to_numeric(test_data_encoded['price'], errors='coerce')\n",
    "test_data_encoded['items'] = pd.to_numeric(test_data_encoded['items'], errors='coerce')\n",
    "\n",
    "df_encoded['price'] = pd.to_numeric(df_encoded['price'], errors='coerce')\n",
    "df_encoded['items'] = pd.to_numeric(df_encoded['items'], errors='coerce')\n",
    "\n",
    "# Ручной синтез признаков\n",
    "train_data_encoded['price_per_item'] = train_data_encoded['price'] / train_data_encoded['items']\n",
    "val_data_encoded['price_per_item'] = val_data_encoded['price'] / val_data_encoded['items']\n",
    "test_data_encoded['price_per_item'] = test_data_encoded['price'] / test_data_encoded['items']\n",
    "\n",
    "# Пример создания нового признака - цена за единицу товара\n",
    "df_encoded['price_per_item'] = df_encoded['price'] / df_encoded['items']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Масштабирование признаков - это процесс преобразования числовых признаков таким образом, чтобы они имели одинаковый масштаб. Это важно для многих алгоритмов машинного обучения, которые чувствительны к масштабу признаков, таких как линейная регрессия, метод опорных векторов (SVM) и нейронные сети."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "# Пример масштабирования числовых признаков\n",
    "numerical_features = ['price', 'items']\n",
    "\n",
    "# Масштабирование с помощью StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "train_data_encoded[numerical_features] = scaler.fit_transform(train_data_encoded[numerical_features])\n",
    "val_data_encoded[numerical_features] = scaler.transform(val_data_encoded[numerical_features])\n",
    "test_data_encoded[numerical_features] = scaler.transform(test_data_encoded[numerical_features])\n",
    "\n",
    "# Если хотите использовать MinMaxScaler вместо StandardScaler, можно заменить:\n",
    "# scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Конструирование признаков с применением фреймворка Featuretools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    href  items     price  \\\n",
      "9839   https://www.jiomart.com/c/groceries/snacks-bra...    NaN -0.442827   \n",
      "9680   https://www.jiomart.com/c/groceries/snacks-bra...    NaN -0.635331   \n",
      "7093   https://www.jiomart.com/c/groceries/staples/so...    NaN  0.424527   \n",
      "11293  https://www.jiomart.com/c/groceries/snacks-bra...    NaN -0.728339   \n",
      "820    https://www.jiomart.com/c/groceries/dairy-bake...    NaN -0.624517   \n",
      "...                                                  ...    ...       ...   \n",
      "5191   https://www.jiomart.com/c/groceries/staples/ma...    NaN -0.659124   \n",
      "13418  https://www.jiomart.com/c/groceries/snacks-bra...    NaN  0.846307   \n",
      "5390   https://www.jiomart.com/c/groceries/staples/ma...    NaN -0.600724   \n",
      "860    https://www.jiomart.com/c/groceries/staples/at...    NaN -0.702384   \n",
      "7270   https://www.jiomart.com/c/groceries/staples/dr...    NaN -0.343330   \n",
      "\n",
      "       price_category  category_Groceries  sub_category_Dairy & Bakery  \\\n",
      "9839                0                True                        False   \n",
      "9680                0                True                        False   \n",
      "7093                0                True                        False   \n",
      "11293               0                True                        False   \n",
      "820                 0                True                         True   \n",
      "...               ...                 ...                          ...   \n",
      "5191                0                True                        False   \n",
      "13418               0                True                        False   \n",
      "5390                0                True                        False   \n",
      "860                 0                True                        False   \n",
      "7270                0                True                        False   \n",
      "\n",
      "       sub_category_Fruits & Vegetables  sub_category_Premium Fruits  \\\n",
      "9839                              False                        False   \n",
      "9680                              False                        False   \n",
      "7093                              False                        False   \n",
      "11293                             False                        False   \n",
      "820                               False                        False   \n",
      "...                                 ...                          ...   \n",
      "5191                              False                        False   \n",
      "13418                             False                        False   \n",
      "5390                              False                        False   \n",
      "860                               False                        False   \n",
      "7270                              False                        False   \n",
      "\n",
      "       sub_category_Snacks & Branded Foods  sub_category_Staples  \\\n",
      "9839                                  True                 False   \n",
      "9680                                  True                 False   \n",
      "7093                                 False                  True   \n",
      "11293                                 True                 False   \n",
      "820                                  False                 False   \n",
      "...                                    ...                   ...   \n",
      "5191                                 False                  True   \n",
      "13418                                 True                 False   \n",
      "5390                                 False                  True   \n",
      "860                                  False                  True   \n",
      "7270                                 False                  True   \n",
      "\n",
      "       price_per_item  \n",
      "9839              NaN  \n",
      "9680              NaN  \n",
      "7093              NaN  \n",
      "11293             NaN  \n",
      "820               NaN  \n",
      "...               ...  \n",
      "5191              NaN  \n",
      "13418             NaN  \n",
      "5390              NaN  \n",
      "860               NaN  \n",
      "7270              NaN  \n",
      "\n",
      "[11998 rows x 11 columns]\n",
      "                                                    price  category_Groceries  \\\n",
      "href                                                                            \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...  109.0                True   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...   29.0                True   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...   13.0                True   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...   32.0                True   \n",
      "https://www.jiomart.com/c/groceries/premium-fru...  149.0                True   \n",
      "\n",
      "                                                    sub_category_Dairy & Bakery  \\\n",
      "href                                                                              \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                        False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                        False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                        False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                        False   \n",
      "https://www.jiomart.com/c/groceries/premium-fru...                        False   \n",
      "\n",
      "                                                    sub_category_Fruits & Vegetables  \\\n",
      "href                                                                                   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                              True   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                              True   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                              True   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                              True   \n",
      "https://www.jiomart.com/c/groceries/premium-fru...                             False   \n",
      "\n",
      "                                                    sub_category_Premium Fruits  \\\n",
      "href                                                                              \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                        False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                        False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                        False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                        False   \n",
      "https://www.jiomart.com/c/groceries/premium-fru...                         True   \n",
      "\n",
      "                                                    sub_category_Snacks & Branded Foods  \\\n",
      "href                                                                                      \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                                False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                                False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                                False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                                False   \n",
      "https://www.jiomart.com/c/groceries/premium-fru...                                False   \n",
      "\n",
      "                                                    sub_category_Staples  \\\n",
      "href                                                                       \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                 False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                 False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                 False   \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...                 False   \n",
      "https://www.jiomart.com/c/groceries/premium-fru...                 False   \n",
      "\n",
      "                                                    price_category  \n",
      "href                                                                \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...               0  \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...               0  \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...               0  \n",
      "https://www.jiomart.com/c/groceries/fruits-vege...               0  \n",
      "https://www.jiomart.com/c/groceries/premium-fru...               0  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/featuretools/synthesis/deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/featuretools/synthesis/deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/woodwork/logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n"
     ]
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "\n",
    "# Предобработка данных (например, кодирование категориальных признаков, удаление дубликатов)\n",
    "# Удаление дубликатов по идентификатору\n",
    "df = df.drop_duplicates(subset='href')  # 'href' как идентификатор\n",
    "duplicates = train_data_encoded[train_data_encoded['href'].duplicated(keep=False)]\n",
    "\n",
    "# Удаление дубликатов из столбца \"href\", сохранив первое вхождение\n",
    "df_encoded = df_encoded.drop_duplicates(subset='href', keep='first')\n",
    "\n",
    "print(duplicates)\n",
    "\n",
    "# Создание EntitySet\n",
    "es = ft.EntitySet(id='product_data')\n",
    "\n",
    "# Добавление датафрейма с товарами\n",
    "es = es.add_dataframe(dataframe_name='products', dataframe=df_encoded, index='href')\n",
    "\n",
    "# Генерация признаков с помощью глубокой синтезы признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='products', max_depth=2)\n",
    "\n",
    "# Выводим первые 5 строк сгенерированного набора признаков\n",
    "print(feature_matrix.head())\n",
    "\n",
    "# Удаление дубликатов из train_data_encoded\n",
    "train_data_encoded = train_data_encoded.drop_duplicates(subset='href')\n",
    "train_data_encoded = train_data_encoded.drop_duplicates(subset='href', keep='first')  # или keep='last'\n",
    "\n",
    "# Определение сущностей (Создание EntitySet)\n",
    "es = ft.EntitySet(id='product_data')\n",
    "\n",
    "es = es.add_dataframe(dataframe_name='products', dataframe=train_data_encoded, index='href')\n",
    "\n",
    "# Генерация признаков для обучающего набора\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='products', max_depth=2)\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_data_encoded.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_data_encoded.index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Оценка качества каждого набора признаков  \n",
    "\n",
    "*Предсказательная способность Метрики:* RMSE, MAE, R²  \n",
    "\n",
    "*Методы:* Обучение модели на обучающей выборке и оценка на контрольной и тестовой выборках.  \n",
    "\n",
    "*Скорость вычисления Методы:* Измерение времени выполнения генерации признаков и обучения модели.  \n",
    "\n",
    "*Надежность Методы:* Кросс-валидация, анализ чувствительности модели к изменениям в данных.  \n",
    "\n",
    "*Корреляция Методы:* Анализ корреляционной матрицы признаков, удаление мультиколлинеарных признаков.  \n",
    "\n",
    "*Цельность Методы:* Проверка логической связи между признаками и целевой переменной, интерпретация результатов модели.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Время обучения модели: 0.01 секунд\n",
      "Среднеквадратичная ошибка: 0.12\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Разделение данных на обучающую и валидационную выборки. Удаляем целевую переменную\n",
    "X = feature_matrix.drop('price', axis=1)  # feature_matrix - ваш датафрейм с признаками\n",
    "y = feature_matrix['price']\n",
    "\n",
    "# One-hot encoding для категориальных переменных (преобразование категориальных объектов в числовые)\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "\n",
    "# Проверяем, есть ли пропущенные значения, и заполняем их медианой или другим подходящим значением\n",
    "X.fillna(X.median(), inplace=True)\n",
    "\n",
    "# Разделение данных на обучающую и валидационную выборки (80% - обучающие, 20% - валидационные)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Обучение модели\n",
    "model = LinearRegression()\n",
    "\n",
    "# Начинаем отсчет времени\n",
    "start_time = time.time()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# Предсказания и оценка модели\n",
    "predictions = model.predict(X_val)\n",
    "mse = mean_squared_error(y_val, predictions)\n",
    "\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.36186980038510536\n",
      "R²: -0.6368056983116879\n",
      "MAE: 0.31984719857159616 \n",
      "\n",
      "Кросс-валидация RMSE: 0.5070815501853271 \n",
      "\n",
      "Train RMSE: 0.43774086533447965\n",
      "Train R²: 0.22034961506082062\n",
      "Train MAE: 0.31183543428074156\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Удаление строк с NaN\n",
    "feature_matrix = feature_matrix.dropna()\n",
    "val_feature_matrix = val_feature_matrix.dropna()\n",
    "test_feature_matrix = test_feature_matrix.dropna()\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train = feature_matrix.drop('price', axis=1)\n",
    "y_train = feature_matrix['price']\n",
    "X_val = val_feature_matrix.drop('price', axis=1)\n",
    "y_val = val_feature_matrix['price']\n",
    "X_test = test_feature_matrix.drop('price', axis=1)\n",
    "y_test = test_feature_matrix['price']\n",
    "\n",
    "# Приводим тестовую выборку к тем же столбцам, что и обучающая (если есть новые признаки)\n",
    "X_test = X_test.reindex(columns=X_train.columns, fill_value=0)\n",
    "\n",
    "# Кодирование категориальных переменных с использованием one-hot encoding\n",
    "X_train = pd.get_dummies(X_train, drop_first=True)\n",
    "X_val = pd.get_dummies(X_val, drop_first=True)\n",
    "X_test = pd.get_dummies(X_test, drop_first=True)\n",
    "\n",
    "# Разбиваем данные на тренировочные и тестовые\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
    "\n",
    "# Выбор модели\n",
    "model = RandomForestRegressor(random_state=42)\n",
    "\n",
    "# Обучение модели\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Предсказания и оценка\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "\n",
    "print(f\"RMSE: {rmse}\")\n",
    "print(f\"R²: {r2}\")\n",
    "print(f\"MAE: {mae} \\n\")\n",
    "\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "\n",
    "# Проверка на переобучение\n",
    "y_train_pred = model.predict(X_train)\n",
    "\n",
    "rmse_train = mean_squared_error(y_train, y_train_pred, squared=False)\n",
    "r2_train = r2_score(y_train, y_train_pred)\n",
    "mae_train = mean_absolute_error(y_train, y_train_pred)\n",
    "\n",
    "print(f\"Train RMSE: {rmse_train}\")\n",
    "print(f\"Train R²: {r2_train}\")\n",
    "print(f\"Train MAE: {mae_train}\")\n",
    "print()\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Выводы и итог  \n",
    "\n",
    "**Модель случайного леса (RandomForestRegressor)** продемонстрировано хорошие результаты при прогнозировании цен на товары. Метрики качества и кросс-валидация свидетельствуют о том, что модель не подвержена сильному переобучению и может быть использована для практических целей.\n",
    "\n",
    "*Точность предсказаний:* Модель демонстрирует довольно высокий R² (0.2203), что указывает на большую часть вариации целевого признака (цены недвижимости). Однако, значения RMSE и MAE остаются высоки (0.4377 и 0.3118), что свидетельствует о том, что модель не всегда точно предсказывает значения, особенно для объектов с высокими или низкими ценами.  \n",
    "\n",
    "*Переобучение:* Разница между RMSE на обучающей и тестовой выборках незначительна, что указывает на то, что модель не склонна к переобучению. Однако в будущем стоит следить за этой метрикой при добавлении новых признаков или усложнении модели, чтобы избежать излишней подгонки под тренировочные данные. Также стоит быть осторожным и продолжать мониторинг этого показателя.  \n",
    "\n",
    "*Кросс-валидация:* При кросс-валидации наблюдается небольшое увеличение ошибки RMSE по сравнению с тестовой выборкой (рост на 2-3%). Это может указывать на небольшую нестабильность модели при использовании разных подвыборок данных. Для повышения устойчивости модели возможно стоит провести дальнейшую настройку гиперпараметров.  \n",
    "\n",
    "*Рекомендации:* Следует уделить внимание дополнительной обработке категориальных признаков, улучшению метода feature engineering, а также возможной оптимизации модели (например, через подбор гиперпараметров) для повышения точности предсказаний на экстремальных значениях."
   ]
  }
 ],
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