{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
       "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
       "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
       "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd;\n",
    "df = pd.read_csv(\"data/house_data.csv\", sep=\",\", nrows=10000)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Определение бизнес целей:\n",
    "1. Прогнозирование цены недвижимости\n",
    "2. Оценка влияния факторов на цену недвижимости\n",
    "### Определение целей технического проекта:\n",
    "1. Построить модель, которая будет прогнозировать стоимость недвижимости на основе данных характеристик.\n",
    "2. Провести анализ данных для выявления факторов, которые больше всего влияют на цену\n",
    "### Проверка данных на пропуски"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id               0\n",
      "date             0\n",
      "price            0\n",
      "bedrooms         0\n",
      "bathrooms        0\n",
      "sqft_living      0\n",
      "sqft_lot         0\n",
      "floors           0\n",
      "waterfront       0\n",
      "view             0\n",
      "condition        0\n",
      "grade            0\n",
      "sqft_above       0\n",
      "sqft_basement    0\n",
      "yr_built         0\n",
      "yr_renovated     0\n",
      "zipcode          0\n",
      "lat              0\n",
      "long             0\n",
      "sqft_living15    0\n",
      "sqft_lot15       0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id               False\n",
       "date             False\n",
       "price            False\n",
       "bedrooms         False\n",
       "bathrooms        False\n",
       "sqft_living      False\n",
       "sqft_lot         False\n",
       "floors           False\n",
       "waterfront       False\n",
       "view             False\n",
       "condition        False\n",
       "grade            False\n",
       "sqft_above       False\n",
       "sqft_basement    False\n",
       "yr_built         False\n",
       "yr_renovated     False\n",
       "zipcode          False\n",
       "lat              False\n",
       "long             False\n",
       "sqft_living15    False\n",
       "sqft_lot15       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "print(df.isnull().sum())\n",
    "\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пустых значений нет, номинальных значений тоже."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Проверка выбросов, и их усреднение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонка price:\n",
      "  Есть выбросы: Да\n",
      "  Количество выбросов: 499\n",
      "  Минимальное значение: 75000.0\n",
      "  Максимальное значение: 1127312.5\n",
      "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
      "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
      "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
      "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
      "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
      "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
      "\n",
      "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
      "0      5650     1.0           0     0  ...      7        1180              0   \n",
      "1      7242     2.0           0     0  ...      7        2170            400   \n",
      "2     10000     1.0           0     0  ...      6         770              0   \n",
      "3      5000     1.0           0     0  ...      7        1050            910   \n",
      "4      8080     1.0           0     0  ...      8        1680              0   \n",
      "\n",
      "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
      "0      1955             0    98178  47.5112 -122.257           1340   \n",
      "1      1951          1991    98125  47.7210 -122.319           1690   \n",
      "2      1933             0    98028  47.7379 -122.233           2720   \n",
      "3      1965             0    98136  47.5208 -122.393           1360   \n",
      "4      1987             0    98074  47.6168 -122.045           1800   \n",
      "\n",
      "   sqft_lot15  \n",
      "0        5650  \n",
      "1        7639  \n",
      "2        8062  \n",
      "3        5000  \n",
      "4        7503  \n",
      "\n",
      "[5 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "numeric_columns = ['price']\n",
    "for column in numeric_columns:\n",
    "    if pd.api.types.is_numeric_dtype(df[column]):  # Проверяем, является ли колонка числовой\n",
    "        q1 = df[column].quantile(0.25)  # Находим 1-й квартиль (Q1)\n",
    "        q3 = df[column].quantile(0.75)  # Находим 3-й квартиль (Q3)\n",
    "        iqr = q3 - q1  # Вычисляем межквартильный размах (IQR)\n",
    "\n",
    "        # Определяем границы для выбросов\n",
    "        lower_bound = q1 - 1.5 * iqr  # Нижняя граница\n",
    "        upper_bound = q3 + 1.5 * iqr  # Верхняя граница\n",
    "\n",
    "        # Подсчитываем количество выбросов\n",
    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "        outlier_count = outliers.shape[0]\n",
    "\n",
    "        # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n",
    "        df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
    "\n",
    "        print(f\"Колонка {column}:\")\n",
    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
    "        print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Создание выборок\n",
    "Так как мы будет предсказывать цену, то и целевым признаком будет параметр цены. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 6400\n",
      "Размер контрольной выборки: 1600\n",
      "Размер тестовой выборки: 2000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#Разделение данных на обучающую и тестовую выборки\n",
    "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n",
    "#Разделение обучающей выборки на обучающую и контрольную\n",
    "train_data, val_data = train_test_split(train_data, 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": "markdown",
   "metadata": {},
   "source": [
    "### Визуализвация цен в выборках "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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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d5/Y/zmq1Gunp6Xe9nn37diUSCf7++2/IZLJ7bhOA/r+o2/+4VLdNT09PrFu3rtrH7/wj06lTJ3z44YcGy7744gts3bq12ufHxcVhzZo1+PHHH6ttD6LT6RAeHo5PP/202uf7+/vftfaa0ul0ePTRR/Hmm29W+3jlH+RKU6ZMwciRIw2WTZ48udrnvv/+++jRowfKy8sRExOD+fPnIy8vr9ozEfcyYMAAzJo1CwBw48YNLF68GH369MHJkyf17cHu5syZM4iKikK/fv0wa9YsjBs3rkqj2poICgqq0rZk48aN+Pbbb2u9zbqWm5sLtVp9z/d4pdvfu2lpaVi8eDGGDx+OCxcuGLQDqsnvvSb++ecf7Nq1C9HR0TV+7qxZszBgwABotVpcuHAB8+fPhyAIWL16tcF6lf8oqlQq7Nu3T9/Y+ssvv6yyzfu9n+70MGfD7+bUqVPYunUrpk+fjilTpmDPnj0Gj9f0M2vJGHYaoF9//RV9+vTBqlWrDJbn5eXBw8NDfz8kJATHjh1DeXl5nTSyrVR55qaSIAhISEhARESEfr9Axenb2//TupszZ86gvLz8ngGvcruCICA4OPiBPuQXL16ERCKp9r/k27e5a9cudOvW7YH++Hl4eFR5TfdqRDx79my0bdsWTz/99F33f+bMGfTr188of0zv3FdRUdED/U6AirMQd65b3X/IABAeHq5fNyoqCsnJyVi7du09L5dUx8fHx2CfoaGh6Nq1K7Zs2XLfYRfCw8OxceNG2NraYuPGjZgyZQrOnj2rvzwaGBgIALh8+TKaNGmif55arUZSUlK1r/XOZbGxsTV6PXe687MDAFeuXNGHjdtrvNOlS5fg4eFh8Du4ePEigH/PqNzLne/dpk2bolu3bjhw4IBB2HmQ33tgYCDOnj0LnU5ncHbn0qVLBq+jkiAIePvttzF8+HB07tz5vrXeqWXLlvqaBg4cCJVKhXfeeQcfffSRwbQOt/+jOGTIEJw5cwbbt2+vdpvBwcH4559/UFhYaHBZ/sqVK9DpdAa/E51Oh/j4eIPjnJmZiby8vCqvNSEhAYIgGHyer1y5AgBVGpd/9913GDp0KGQyGR577DGsWrUKL7zwgv7xmn5mLRnb7DRAMpkMgiAYLNu4cWOV4deffPJJKJVKfPHFF1W2cefza6KyR0mlX3/9Fenp6YiKigIAREZGIiQkBEuXLtX3VLlddnZ2ldorP+z3MmLECMhkMsybN69K/YIgICcnR39fo9Hgt99+Q8eOHe95in/UqFHQarXV9urQaDQP1QYlOjoaW7duxaJFi+4aZEaNGoXU1NRqe6eUlpaiuLi41vuvbl/R0dHYsWNHlcfy8vJqHEzupfJL8GEDXGlpKQA8UDf89u3bw97eHlKpFN999x2uXbuG+fPn6x/v378/rK2t8fnnnxu8f1atWoX8/HwMGTLkoWp9EFu2bDH4nB4/fhzHjh3Tf3Z8fHzQtm1brF271uC9d/78efzzzz9VekyuX78e1tbW6N69e41rqTwDW91Z0vsZPHgwMjIy8Msvv+iXaTQarFixAg4ODujVq1eVOs+ePVttj8TaqHxf3N79vDo6ne6ur2/w4MHQarVV/j5WnmWtfD9UHvM7ez7duV6ltLQ0gx5aBQUF+OGHH9C2bdsqZ+Aqe4kOGTIEo0ePxqxZswxGy6/Pz6yp45mdBuixxx7D/PnzMXHiRHTt2hXnzp3DunXrDP5bBSoaEv/www+YOXMmjh8/jh49eqC4uBi7du3Cyy+/jCeeeKJW+3dzc0P37t0xceJEZGZmYtmyZWjatKn+VHfll01UVBRatWqFiRMnws/PD6mpqdi7dy+cnJzwxx9/oLi4GCtXrsTnn3+O5s2bG4wLUhmSzp49i+joaHTp0gUhISH48MMP9V1Dhw0bBkdHRyQlJWHz5s2YMmUK3njjDezatQvvvfcezp49iz/++OOer6VXr16YOnUqFi5ciNjYWAwYMABWVlaIj4/Hxo0bsXz5cjz11FO1Ok7//PMPHn300Xv+V/bss89iw4YNePHFF7F3715069YNWq0Wly5dwoYNG7Bjx477nvEqKiqq8t9r5ZmB/fv3w8rKCn5+fpg1axZ+//13PPbYY5gwYQIiIyNRXFyMc+fO4ddff8W1a9cMzgzWRGxsLBwcHKDRaBATE4MffvgBTzzxRI2/SK9evYoff/wRQEXD7i+++AJOTk41aqQMVIwL89Zbb2HRokUYPXo0IiIi0KhRI8yePRvz5s3DoEGDMHToUFy+fBlffvklHnnkEYwbN65G+6iNpk2bonv37njppZegUqmwbNkyuLu7G1ymWLJkCaKiotClSxe88MIL+q7nzs7O+rGb4uPjMWfOHPz88894++23q7Q7qU5lI1igoiH04sWL4ezsjD59+tT4dUyZMgXffPMNJkyYgJiYGAQFBeHXX3/F4cOHsWzZMoMzJUDFZ2Hy5Mn3PMt6L9HR0ZDL5frLWCtWrEC7du2qnCmJjo6GUqnUX8bavXs33njjjWq3OXjwYPTv3x/vvvsukpKS0LZtW+zZswe//fYbXnzxRf04S23atMH48ePx7bffIi8vD7169cLx48exdu1aDBs2rMrxa968OV544QWcOHECXl5e+P7775GZmVnlktudli9fjhYtWuCVV17Rt+Ey5mfW7IjUC4yM4EG6TwpCRdfz119/XfDx8RFsbW2Fbt26CdHR0VW6VgpCRXfvd999VwgODhasrKwEb29v4amnnhISExMFQahdN+Off/5ZmD17tuDp6SnY2toKQ4YMEa5fv17l+adPnxZGjBghuLu7CwqFQggMDBRGjRol7N6922Df97vd2T31t99+E7p37y7Y29sL9vb2QlhYmDBt2jTh8uXLgiAIwiuvvCL07NlT2L59e5Wa7ux6Xunbb78VIiMjBVtbW8HR0VEIDw8X3nzzTSEtLU2/Tk27nkskEiEmJsZgeXW/I7VaLSxevFho1aqVoFAoBFdXVyEyMlKYN2+ekJ+fX2V/d27vfsfv9m67hYWFwuzZs4WmTZsK1tbWgoeHh9C1a1dh6dKlglqtFgShdu+JyptcLhcCAwOFV199Vbh586YgCDXren77tjw8PIQBAwYI0dHRD/TcO98nZWVlQlhYmPDII48IGo1Gv/yLL74QwsLCBCsrK8HLy0t46aWX9LVWMlbX8yVLlgiffPKJ4O/vLygUCqFHjx7CmTNnqqy/a9cuoVu3boKtra3g5OQkPP7448LFixf1j//8889C69atheXLlxsM73C3Ou52bI8ePVptjXe68/cuCIKQmZkpTJw4UfDw8BCsra2F8PDwKl3Eb+9yn5qaavBYdb+zux23yptUKhUaN24sjB8/3qALf+XfzsqbtbW10LRpU+H9998XVCqVIAjVf/aKioqEGTNmCL6+voKVlZXQtGlTYdGiRVWG9igvLxfmzZun/zvq7+8vzJ49WygrK6vymoYMGSLs2LFDiIiIEBQKhRAWFlZlaIbbu57fbu3atQIA4ffff9cve5DPbEMgEYSHuB5BVAP79u1Dnz59sHHjxlqf7bjdtWvXEBwcjKSkpLuOsDt37lxcu3YNa9aseej9NURBQUGYO3fufUeqJeOqfK8vWbLkrmcayPwFBQWhdevW+PPPP8UuxeKwzQ4RERFZNLbZIbPl4OCAsWPH3rMBcUREhEFvC6qZXr16GQxiR0Rkjhh2yGx5eHjoG6PezYgRI+qpGst058SMRETmiG12iIiIyKKxzQ4RERFZNIYdIiIismhss4OKUTLT0tLg6Oho9CH3iYiIqG4IgoDCwkL4+vpWmVj2dgw7qBieuy4nTCQiIqL6k5KSgsaNG9/1cYYdQD80eUpKygMNm05ERETiKygogL+/f5UpRu7EsAPoL105OTkx7BAREZmZ+zVBYQNlIiIismgMO0RERGTRGHaIiIjIojHsEBERkUVj2CEiIiKLxrBDREREFo1hh4iIiCwaww4RERFZNIYdIiIismgMO0RERGTRGHaIiIjIojHsEBERkUVj2CEiIiKLxrBDREREFk0udgFElZKTk6FUKo26Dw8PDwQEBBh1H0REZFoYdsgkJCcnI6xFC5SWlBh1P7Z2drgUF8fAQ0TUgDDskElQKpUoLSnB2LeWwCsgxCj7yExOxLrFs6BUKhl2iIgaEIYdMileASFo3KyV2GUQEZEFYQNlIiIismgMO0RERGTRRL2MdeDAASxZsgQxMTFIT0/H5s2bMWzYMP3jEomk2ud9/PHHmDVrFgAgKCgI169fN3h84cKFePvtt41WN5m3uLg4o26fPb6IiEyLqGGnuLgYbdq0wfPPP48RI0ZUeTw9Pd3g/t9//40XXngBTz75pMHy+fPnY/Lkyfr7jo6OximYzFpBbjYAYNy4cUbdD3t8ERGZFlHDTlRUFKKiou76uLe3t8H9rVu3ok+fPmjSpInBckdHxyrrEt2ptKgAADBk6rsIjYg0yj7Y44uIyPSYTW+szMxM/PXXX1i7dm2VxxYtWoQPPvgAAQEBeOaZZzBjxgzI5Wbz0qieufsGsscXEVEDYjaJYO3atXB0dKxyuevVV19F+/bt4ebmhiNHjmD27NlIT0/Hp59+etdtqVQqqFQq/f2CggKj1U1ERETiMpuw8/3332Ps2LGwsbExWD5z5kz9zxEREbC2tsbUqVOxcOFCKBSKare1cOFCzJs3z6j1EhERkWkwi67nBw8exOXLlzFp0qT7rtupUydoNBpcu3btruvMnj0b+fn5+ltKSkodVktERESmxCzO7KxatQqRkZFo06bNfdeNjY2FVCqFp6fnXddRKBR3PetDRERElkXUsFNUVISEhAT9/aSkJMTGxsLNzU3fk6WgoAAbN27EJ598UuX50dHROHbsGPr06QNHR0dER0djxowZGDduHFxdXevtdRAREZHpEjXsnDx5En369NHfr2x/M378eKxZswYAsH79egiCgDFjxlR5vkKhwPr16zF37lyoVCoEBwdjxowZBu14iIiIqGETNez07t0bgiDcc50pU6ZgypQp1T7Wvn17HD161BilERERkYUwiwbKRERERLXFsENEREQWzSx6YxE9rFK1Fko4wqnzSMSrnZFxIQMyiQRymQTOtlZwsbOCt5MN7Kz5kSAisjT8y04WS6XR4lJ6IS6kFyC7UAWgMVx7jUeaBkBGYbXPcXewRpC7PcK8HeHhwOEJiIgsAcMOWZxyrQ4nr93E6ZSbKNf+2wDeDmXIOncILdt1QmBwMHQ6AWqtDnkl5cgpViO3WI2coopbzPWbaOSgQBt/Z4R6O0Iu5RVfIiJzxbBDFuVqdhH2XclGYZkGAOBqZ4U2jV3Q1NMBlw5tw7ptyxDc6Vu0Dag6DlOJWoOU3FLEZxUiSVmM7CIVdsVl4UhiDiIDXBHR2BlyGUMPEZG5Ydghi6DTCTicqMSp5DwAgKONHD2aeaBpIwdIJJIH2oadtRyh3o4I9XZEWbkWF9IKEJuShyKVBgcTlDiVchOdm7ijlY/TA2+TiIjEx7BDZk9VrsWfZ9NxI68UANDO3wVdQtxh9RBnYWysZIgMdEVbfxdcyijAsaRcFJZpsDsuCxfTCtAn1BONHNmmh4jIHDDskFkrLddi8+lUZBeqYCWT4NEWXmjm5Vhn25dJJWjlW9Fu52xKPo4m5SA9vww/n0hG28Yu6NzEHdZyXtoiIjJlDDtktkrUGmw6nYqcIjVsrWQY3s7PaGdb5FIp2ge6opmXAw7GKxGfVYTTKXm4klWI/i28EORub5T9EhHRw2PYIbOk0erwx5l05BSpYWctw4h2fnCvh67ijjZWGBzug2s5xdh3ORv5peXYGpuGNo2d0b2ph369uLg4o9fi4eGhnzCXiIjujmGHzI4gCNh5MRMZBWVQyKV4sn1juNlb12sNQe72GNfJFocSlDhzIx9nbuQj5WYpmuvyAADjxo0zeg22dna4FBfHwENEdB8MO2R2jifl4kpWEaQSYEi4T70HnUpymRS9Qz0R5GGPnRczkVusxjE0guMjw9GjfUuERUQabd+ZyYlYt3gWlEolww4R0X0w7JBZuXGzBEeTcgEAfcM84e9mJ3JFlWd5ArH7UiYSs4vh1vcFZMpK0SM4DAq5TOzyiIgaPHYjIbNRVq7FjguZAICWPk5o5essckX/srWWYUi4D0KQDkFTDqXWFj8fT7k1TQUREYmJYYfMgiAI2BWXiSKVBi52VujVvJHYJVUhkUjggzxkrHsTCokG+aXl+OVkCi6mFYhdGhFRg8awQ2YhIbsIidnFkEqAqFbeJj22jTojHpE22Qh0t4NWJ2BnXCb2Xs6CVifc/8lERFTnTPcbg+gWlUaL/ZezAQAdgtzg6WQjckX3ZyUR8EQbX3QOdgMAnL2Rjy2xqSgt14pcGRFRw8OwQybvSGIOitVauNha4ZHAqhN4miqJRIJOTdzxWIQPrGQS3LhZil9OpCCniO14iIjqE8MOmbTMgjKcvZEPoKL3lTnOOh7SyAGjOvjDyUaO/NJybDh5A0nKYrHLIiJqMMzvm4MaDEEQcCheCQAI9XI0iW7mteXhoMDTj/jDz8UWaq0Of5xJw5kbeWKXRUTUIDDskMm6llOCG3mlkEkl6BriLnY5D83OWo7h7fzQytcJAoB9l7NxMD4bgsCGy0RExsSwQyZJpxNwKKHirE5bfxc42VqJXFHdkEkl6BfmiS5NKsLbqeQ8bDufAY1WJ3JlRESWi2GHTNLFjALkFqthI5eaVaPkByGRSNAx2A0DW3lBKgESsoqw6TR7ahERGQvDDpkcrU7AiVtTQjwS7AaFlWVOuRDm7YTh7fygkEuRnl+GX0/eQGFZudhlERFZHIYdMjmXMwpRUKaBrZUM4X6mMyWEMTR2tcPIyMZwUMiRW6LGxpgbuFmsFrssIiKLwrBDJkUnACeuVZzViQx0hZUZdjWvKXcHBUZ2aAwXOysUlmmwMeYGMgvKxC6LiMhiWP43CZmVGyVS5JWWw8ZKavFndW7nZGOFkZGN4emoQGm5FptOpSIlt0TssoiILALDDpmUywUVb8l2/q4mPf+VMdhZyzGivR8au1aMxbP1TBqu5XDwQSKih9Wwvk3IpNkEtUVBuRRWMgnaNG44Z3Vup5DL8EQbXzTxsIdWJ+DPM+m4qiwSuywiIrPGsEMmw6nDMABASx8ni+2B9SDkMikGh/sgpJE9tIKAv86mIzGbgYeIqLYYdsgk3Cgoh21IBwAC2vq7iF2O6GRSCaJa+6CZpwN0ArDtXDriMwvFLouIyCwx7JBJ+PNKRWNcH1sBLnbWIldjGmRSCQa18kaolyN0AvD3hQwGHiKiWpCLXQBRfkk59l2vCDvNHDmK8O2kUgkGtPKCVArEpRdi+4UMyGVSWMbkGURE9YNndkh0m07fgFoLqLOS4KHgpJh3kkok6N/CC829Ki5p/XUuHVllErHLIiIyGww7JCpBEPDTsWQAQGHsdkj4HV4tqUSCAS299b20jmTLofALE7ssIiKzwLBDooq5fhPxWUVQyCQovrBX7HJMmkwqQVS4NwLc7KAVJPAcOQ+JNzmXFhHR/TDskKgqz+p0D7CBoOaIwfcjl0rxWIQPPBQ6SBX2mL8/B1fZLZ2I6J5EDTsHDhzA448/Dl9fX0gkEmzZssXg8QkTJkAikRjcBg0aZLBObm4uxo4dCycnJ7i4uOCFF15AURH/+JuDvBI1/jyXDgB4tImdyNWYDyuZFF0baaBKv4JCtYDxq48jq5BzaRER3Y2oYae4uBht2rTBypUr77rOoEGDkJ6err/9/PPPBo+PHTsWFy5cwM6dO/Hnn3/iwIEDmDJlirFLpzrw+5k0qDU6hHk7opkb+xfVhJUUyPp1HrwdZEjJLcXza06gSKURuywiIpMkatfzqKgoREVF3XMdhUIBb2/vah+Li4vD9u3bceLECXTo0AEAsGLFCgwePBhLly6Fr69vnddMdee3U6kAgFEd/CGR3BS5GvOjK8nH+z3d8N7+fJxPLcBLP8Zg1fhHGtycYkRE92Py4+zs27cPnp6ecHV1Rd++ffHhhx/C3d0dABAdHQ0XFxd90AGA/v37QyqV4tixYxg+fHi121SpVFCpVPr7BQUFxn0RFiA5ORlKpbLOtnejQIMzKXmQSoAgqRJxcVfqbNsNibeDHKsnPoLR3x7FwXgl3v7tLD4Z1QYSdmsjItIz6bAzaNAgjBgxAsHBwUhMTMQ777yDqKgoREdHQyaTISMjA56engbPkcvlcHNzQ0ZGxl23u3DhQsybN8/Y5VuM5ORkhLVogdKSumtA7NLzOTh3GYWi+OPo122+fjnbW9VcRGMXrBzbHpPWnsSm06nwc7XF6wNCxS6LiMhkmHTYGT16tP7n8PBwREREICQkBPv27UO/fv1qvd3Zs2dj5syZ+vsFBQXw9/d/qFotmVKpRGlJCca+tQReASEPvT1BAP5Os0KpFujTuR0a992EuOP78ffa5SgrY0Pb2ugT6omFI8Lx5q9nsWJPApp6OuCJtn5il0VEZBJMOuzcqUmTJvDw8EBCQgL69esHb29vZGVlGayj0WiQm5t713Y+QEU7IIVCYexyLY5XQAgaN2v10NtJyS1BaUoqFHIpOrQOg1wmRWZyYh1U2LCN6uCPq9nF+Hp/Imb9ehYBbnZoF+AqdllERKIzq5aMN27cQE5ODnx8fAAAXbp0QV5eHmJiYvTr7NmzBzqdDp06dRKrTLqPuIyKNlLNPB0gl5nVW9DkvTkwFI+29IJao8PkH2KQllcqdklERKIT9ZumqKgIsbGxiI2NBQAkJSUhNjYWycnJKCoqwqxZs3D06FFcu3YNu3fvxhNPPIGmTZti4MCBAIAWLVpg0KBBmDx5Mo4fP47Dhw9j+vTpGD16NHtimSiNTofE7GIAQJi3k8jVWB6pVIJlT7dFmLcjlEUqvLD2JIrZJZ2IGjhRw87JkyfRrl07tGvXDgAwc+ZMtGvXDu+//z5kMhnOnj2LoUOHonnz5njhhRcQGRmJgwcPGlyCWrduHcLCwtCvXz8MHjwY3bt3x7fffivWS6L7SM4pgVqjg71CBl8XG7HLsUj2Cjm+G98BHg7WiEsvwIxfYqHTcYJVImq4RG2z07t3bwjC3f8I79ix477bcHNzw08//VSXZZERXcms6G3VzNOR3aONqLGrHb55tgPGfHsU/1zMxMq9CXilXzOxyyIiEgUbTFC90Wh1uKqsCDvNvRxErsbyRQa64sNhrQEAn+66gv1XskWuiIhIHAw7VG+ScopRrhXgaCOHtxMvYdWHUY/4Y0zHAAgC8J/1p5GSy8lWiajhYdihehOvv4TlwEtY9Wju0JZo09gZeSXleGldDMrKtWKXRERUrxh2qF5otDokKSt6YTXzchS5moZFIZfhy3GRcLO3xvnUAry35fw928oREVkahh2qF9dzS6DRVVzC8nLkgI71zc/FFivGtINUAmyMuYGfj6eIXRIRUb1h2KF6kZhdcQkrxIOXsMTSrakH3hhYMWfWvD8u4FIGJ8AlooaBYYeMTqcTkHRrIMEQT3uRq2nYXuwZgt6hjaDS6DBt3SmUqDngIBFZPoYdMrrUvFKUaXSwsZLC19lW7HIaNKlUgk9GtoGXkwKJ2cWYs/WC2CURERkdww4ZXeUlrCYeDpBKeQlLbO4OCix7+t/2O1tOp4pdEhGRUTHskFEJgqCfCyukES9hmYouIe549daIyu9uPoertwIpEZElEnW6CLJ8WYUqFKk0sJJJEOBmJ3Y5dJtX+jbD0as5OHo1F9N/Oo3N07pCIZcZrJOcnAylUmnUOjw8PBAQEGDUfRBRw8awQ0ZVObZOgJsd5DKeSDQlMqkEy0e3Q9Tyg7iYXoBP/rmCdwa30D+enJyMsBYtUFpi3FGXbe3scCkujoGHiIyGYYeMqjLsBHvwEpYp8nKyweInIzD5h5P438Gr6B3aCF1DPAAASqUSpSUlGPvWEngFhBhl/5nJiVi3eBaUSiXDDhEZDcMOGU2xSoOsQhUAIMidYcdUPdrSC2M6+uPn4yl4Y8MZ/P1aTzjbWukf9woIQeNmrUSskIjo4fC6AhlNUk7FWR0vJwXsFczVpuy/Q1oiyN0OaflleH/rebHLISKqUww7ZDTXeAnLbNgr5Pjs6baQSSXYGpuGrbHsjk5EloNhh4xCo9Xhek5Fw1aGHfPQLsAVr/RtCgD475bzyC7m7OhEZBkYdsgobuSVQqMTYK+QoZEDJ/40F9P7NEVbfxcUlmnwxYk8ABwEkojMH8MOGUXlWZ0gd3tO/GlG5DIpPnu6LWyspDiXpYZD20Fil0RE9NAYdsgoknMrwk4gBxI0O8Ee9nhrUBgAwLXP8yjmXKFEZOYYdqjOFZaVI7dYDQkAf4YdszS+SxBaNrKG1NoWMTlyCIIgdklERLXGsEN1rvKsjpeTDWysZPdZm0yRVCrB9EecoVOXIVslxdkb+WKXRERUaww7VOeSb7XXCXDnWR1z5u0gR97+NQCAQwlK5JeWi1sQEVEtMexQndIJAtvrWJDCU3/BQ6GDRidg58VMXs4iIrPEsEN1KrtQhTKNDtYyKbycbMQuhx6agEh3DeRSCVLzSnEulZeziMj8MOxQnbp+66yOv5stZFJ2ObcEDnKga4g7AOBwQg6Kytg9i4jMCycsojqlb6/DS1j1Ii4url623cbfBZczC5FZoMK+K1l4LMLXaPslIqprDDtUZ9QaHdLzSwEw7BhbQW42AGDcuHFG31dRURGkEgn6hXlh/YlkJGYXIyGrCE09HYy+byKiusCwQ3XmRl4JdALgbGsFFztrscuxaKVFBQCAIVPfRWhEpFH2EXd8P/5euxxlZWUAgEaOCkQGuuLEtZvYdzkL/m62UMg5tAARmT6GHaozvIRV/9x9A9G4WSujbDszObHKso5BbojPLEJeaTkOJ+Sgb5inUfZNRFSX2ECZ6kxll3OGHcsll0nRr0VFwDmXmo+0vFKRKyIiuj+GHaoTBaXluFlSDokE8He1FbscMqLGrnZo5esEANgdlwWNTidyRURE98awQ3Wi8qyOt5MNFJwiwuJ1b+oBO2sZckvUOHntptjlEBHdE8MO1QlewmpYbKxk6NW8EQDg5LWbyC1Wi1wREdHdMezQQxMEASkMOw1OM08HBHvYQysI2B3HqSSIyHQx7NBDUxapUabRwUom4RQRDYhEIkGf0EawkkmQll+Gi+kFYpdERFQthh16aDduVpzV8XXhFBENjaONFToH/zuVRFm5VuSKiIiqYtihh5Z6q/txYxf2wmqI2vi7wN3eGqXlWhxOVIpdDhFRFaKGnQMHDuDxxx+Hr68vJBIJtmzZon+svLwcb731FsLDw2Fvbw9fX18899xzSEtLM9hGUFAQJBKJwW3RokX1/EoaLkEQcOPmrbDjyvY6DZFMKkGf0Iqxd86nFiAjv0zkioiIDIkadoqLi9GmTRusXLmyymMlJSU4deoU3nvvPZw6dQqbNm3C5cuXMXTo0Crrzp8/H+np6frbK6+8Uh/lEyra66hutddp5KgQuxwSiZ+rLVp4OwIA9l7Ogo6NlYnIhIg6XURUVBSioqKqfczZ2Rk7d+40WPbFF1+gY8eOSE5ORkBAgH65o6MjvL29jVorVY/tdahSt6YeuKosRlahCudu5KONv4vYJRERATCzNjv5+fmQSCRwcXExWL5o0SK4u7ujXbt2WLJkCTQazT23o1KpUFBQYHCj2mF7Hapkr5CjS0hFY+UjV3NQrLr355CIqL6YTdgpKyvDW2+9hTFjxsDJyUm//NVXX8X69euxd+9eTJ06FQsWLMCbb755z20tXLgQzs7O+pu/v7+xy7dIbK9Ddwr3c4anowJqjQ6HEthYmYhMg1mEnfLycowaNQqCIOCrr74yeGzmzJno3bs3IiIi8OKLL+KTTz7BihUroFKp7rq92bNnIz8/X39LSUkx9kuwSGyvQ3eSSv5trHwpo1B/mZOISEwmH3Yqg87169exc+dOg7M61enUqRM0Gg2uXbt213UUCgWcnJwMblRzbK9D1fF2tkFrv4rP1L7L2dDq2FiZiMRl0mGnMujEx8dj165dcHd3v+9zYmNjIZVK4enpWQ8VNmxsr0N30y3EA7ZWMuQUqxGbkid2OUTUwInaG6uoqAgJCQn6+0lJSYiNjYWbmxt8fHzw1FNP4dSpU/jzzz+h1WqRkZEBAHBzc4O1tTWio6Nx7Ngx9OnTB46OjoiOjsaMGTMwbtw4uLq6ivWyGgS216F7sbGSoVtTd+yKy8KxpByEejvCQSHqnxsiasBE/etz8uRJ9OnTR39/5syZAIDx48dj7ty5+P333wEAbdu2NXje3r170bt3bygUCqxfvx5z586FSqVCcHAwZsyYod8OGQ/b69D9tPRxqhhksKAMhxOUGNiKw0MQkThEDTu9e/e+50zJ95tFuX379jh69Ghdl0UPgO116H4kEgl6hTbCLydScCmjEOF+zvDlJU8iEoFJt9kh08X2OvQgvJ1s0Mr3VmPlK9kcWZmIRMGwQzXG9jpUE11D3GEtlyK7UIULqRzAk4jqH8MO1Rjb61BN2FnL0aVJ5cjKSpSVa0WuiIgaGoYdqjG216GaCvdzhru9NcrKdYi+miN2OUTUwDDsUI2l5ZcBAPzYXocekEwqQa/mjQAA527kI7vw7iOcExHVNYYdqhFBEJB2q3GyrzPDDj04fzc7NPV0gABg/5Xs+/a2JCKqKww7VCP5peUoUWshk0jg5cT2OlQzPZp6QC6VIDWvFPFZRWKXQ0QNBMMO1UjlJSxPJwXkMr59qGacbK3QIbBidPOD8UpodCIXREQNAr+tqEbSKy9hsb0O1VJkoCucbOQoUmlwuUAmdjlE1AAw7FCNpOVVnNnxdbYRuRIyV3KZFD1vNVa+UiCF3IXTSBCRcTHs0ANTaYHcEjUAwIdndughNPGwR4CbHXSQwLXvJLHLISILx7BDDyxHVTGmjpudNWytePmBak8iqeiKLoEAu2adcSq9TOySiMiCMezQA8tRVbxdfF14CYsenpu9NZo6VrRQ/j62AGq2ViYiI2HYoQemvHVmh42Tqa60cNZCW3QTaYVarD6cJHY5RGShGHbogUjk1ripZtihumUlBW7uXwMA+Hx3PLIKeDmLiOoeww49EGvvZhAggb21DE42crHLIQtSfH4PmrlZoVitxaK/L4ldDhFZIIYdeiCKxi0BVPTCkkg4+SfVJQGT2jtBIgE2nU7FyWu5YhdERBaGYYceSGXY4fg6ZAzN3KzxdAd/AMD7Wy9Aq+O8WURUdxh26L50ggAbvxYA2F6HjGfWwFA42chxMb0APx27LnY5RGRBGHbovlLyNZDaOEAmEdDIgZN/knG4Oygwa2AoAGDJjsvIKVKJXBERWQqGHbqvOGXFqMnuCgFSKdvrkPE80ykQLX2cUFCmwcfbL4tdDhFZCIYduq9/ww4HfSPjkkklmP9EKwDALydTEJuSJ25BRGQRGHbovi4pywEAHgo2GiXj6xDkhhHt/QAA7289z8bKRPTQGHbonjLyy5BdooWg08LNml86VD9mR7WAo0KOszfyseFkitjlEJGZY9ihezqVfBMAUJ59DXK+W6ieNHJUYMajzQEAH2+/hLwStcgVEZE549cX3dOp6xVhR5XGxqJUv57rEohQL0fcLCnHkh18/xFR7THs0D1VntlRpXIYf6pfcpkU8241Vv7peDLOp+aLXBERmSuGHborlUaL82kFFT+nMexQ/evcxB1D2/hCEID3tp6Hjo2ViagWaj2jY3FxMfbv34/k5GSo1YbX01999dWHLozEdzGtAGqNDk4KKa7fTBO7HGqg3h3SArvjMnE6OQ+/nbqBkbemlSAielC1CjunT5/G4MGDUVJSguLiYri5uUGpVMLOzg6enp4MOxbiVHIeAKC5mxXOiVsKNWBeTjZ4tV8zLPz7Ehb9fQkDWnnD2dZK7LKIyIzU6jLWjBkz8Pjjj+PmzZuwtbXF0aNHcf36dURGRmLp0qV1XSOJpLK9TnN3a5EroYZuYrdghDSyR06xGp/tvCJ2OURkZmoVdmJjY/H6669DKpVCJpNBpVLB398fH3/8Md555526rpFEcvpWT6xQD/4XTeKylksxb2hrAMAP0ddwIY2NlYnowdUq7FhZWUEqrXiqp6cnkpOTAQDOzs5ISeEAYJYgI78MafllkEqApq4MOyS+7s08MCTcBzoBeGczR1YmogdXqzY77dq1w4kTJ9CsWTP06tUL77//PpRKJf7v//4PrVu3rusaSQSVl7DCvJ1ga8VOe2Qa3n+8JfZfycaZlDz8dOw6nu0SJHZJRGYvOTkZSqXSqPvw8PBAQECAUfdxL7UKOwsWLEBhYSEA4KOPPsJzzz2Hl156Cc2aNcP3339fpwWSOCoHE2wf6AKgXNRaiCp5Odlg1sBQzPn9Aj7efhkDW3nD08lG7LKIzFZycjLCWrRAaUmJUfdja2eHS3FxogWeWoWdDh066H/29PTE9u3b66wgMg2nb8023c7fFUCWqLUQ3W5c50D8duoGzt7Ix/w/L+KLZ9qLXRKR2VIqlSgtKcHYt5bAKyDEKPvITE7EusWzoFQqzSvs9O3bF5s2bYKLi0sdl0OmQK3R4dyt0WrbB7riZjLDDpkOmVSCBcPDMfSLQ/jzbDqeisxC71BPscsiMmteASFo3KyV2GUYTa3Czr59+6oMJEiW40JaPtQaHdzsrRHkboebyWJXRJYuLi6uxs8Z0swef1wpxlsbTmHZwEZQyCV3XVfs9gJEJK5aj6Askdz9D8uDOnDgAJYsWYKYmBikp6dj8+bNGDZsmP5xQRAwZ84c/O9//0NeXh66deuGr776Cs2aNdOvk5ubi1deeQV//PEHpFIpnnzySSxfvhwODg4PXV9DVTmYYDt/lzr5PRPdTUFuNgBg3LhxNX6uxMoGvpO+QiYaYfCsFcg7sPau64rdXoCIxFXrsDN8+HBYW1c/2NyePXseaBvFxcVo06YNnn/+eYwYMaLK4x9//DE+//xzrF27FsHBwXjvvfcwcOBAXLx4ETY2FY0Sx44di/T0dOzcuRPl5eWYOHEipkyZgp9++qm2L63Bq+yJ1T7QVeRKyNKVFlXMvTZk6rsIjYis8fPTSiSIVgIuXZ7CiOHD4GJdtTu6KbQXICJx1TrsdOnS5aHPnkRFRSEqKqraxwRBwLJly/Df//4XTzzxBADghx9+gJeXF7Zs2YLRo0cjLi4O27dvx4kTJ/SNplesWIHBgwdj6dKl8PX1faj6GqrKwQTbBbiIWwg1GO6+gbVqL9AYQPa5dCRkFeFssQOebuEPmZRnI4nIUK3CjkQiwaxZs+DpabxGgUlJScjIyED//v31y5ydndGpUydER0dj9OjRiI6OhouLi0HvsP79+0MqleLYsWMYPnx4tdtWqVRQqVT6+wUFBUZ7Hebm9sEE2zR2Ebscovvq3bwRUnJLkF2owqnkm3gkyE3skojIxNRqtDhBMP7IpRkZGQAALy8vg+VeXl76xzIyMqoELrlcDjc3N/061Vm4cCGcnZ31N39/zqJc6fbBBO0VtT7xR1Rv7BVy9GreCABwLCkXN4vZeYKIDNUq7MyZM8esGwDPnj0b+fn5+hunuPjX6eTbBxMkMg9h3o4IdLeDVidgZ1xmvfxDRkTmo9Zhx87ODtnZ2Th06BAOHTqE7OzsOi3M29sbAJCZmWmwPDMzU/+Yt7c3srIMx4DRaDTIzc3Vr1MdhUIBJycngxtVqOyJ1T6AjZPJfEgkEvQN9YSVTIL0/DKcvcGJQonoX7UKOyUlJXj++efh6+uLnj17omfPnvD19cULL7yAkjoacjo4OBje3t7YvXu3fllBQQGOHTuGLl26AKhoJJ2Xl4eYmBj9Onv27IFOp0OnTp3qpI6G5PbBBNsx7JCZcbK1QrcQDwDA4UQlCko5zQkRVahV2JkxYwb279+P33//HXl5ecjLy8PWrVuxf/9+vP766w+8naKiIsTGxiI2NhZARaPk2NhYJCcnQyKR4LXXXsOHH36I33//HefOncNzzz0HX19f/Vg8LVq0wKBBgzB58mQcP34chw8fxvTp0zF69Gj2xKqFOwcTJDI3EY2d4etsg3KtgH8u8nIWEVWoVQvU3377Db/++it69+6tXzZ48GDY2tpi1KhR+Oqrrx5oOydPnkSfPn3092fOnAkAGD9+PNasWYM333wTxcXFmDJlCvLy8tC9e3ds375dP8YOAKxbtw7Tp09Hv3799IMKfv7557V5WQ0eBxMkcyeRSPBoSy/8dDwZqXmliE3JQyOxiyIi0dUq7JSUlFTpJQVUTApak8tYvXv3vud/XhKJBPPnz8f8+fPvuo6bmxsHEKwjHEyQLIGLnTW6N/XA3svZOJyYg75V/1QRUQNTq8tYXbp0wZw5c1BWVqZfVlpainnz5unb05D54WCCZCnC/ZwR6FbRO+tkjhyQysQuiYhEVKszO8uWLcOgQYPQuHFjtGnTBgBw5swZ2NjYYMeOHXVaINUPDiZIlkQikaB/Cy/8eOw6bqoB584jxS6JiERUq7ATHh6O+Ph4rFu3DpcuXQIAjBkzBmPHjoWtrW2dFkj14zQHEyQL42AjR59QT2y/kAHnrqORmFuO9mIXRUSiqNW32oEDB9C1a1dMnjy5rushkZziYIJkgZp7OeBckhapJXIsO3YTQ3pqYGfNME/U0NSqzU6fPn2Qm5tb17WQiDiYIFkiiUSCdq5aaApzkFqoxQd/XhS7JCISgcnOjUX15/bBBBl2yNIoZEDOn59AAuDn4yn462y62CURUT2r9fnc6OhouLpW/8XYs2fPWhdE9S8uvQBqjQ6udlYI5GCCZIHKks9iRAt7/BZXjLc3nUUbf2c0duV7naihqHXYGT58eLXLJRIJtFptrQui+lfZOLldgCsHEySL9XQrR1wttsLp5Dz8Z30sfpnSGXJZrU5uE5GZqfUnPSMjAzqdrsqNQcf83D5yMpGlkksl+Hx0Ozgq5Ii5fhOf744XuyQiqie1Cjv879+ynE7598wOkSXzd7PDghHhAIAVexNwJEEpckVEVB/YQLmByy5UISW3FBIJ0MbfWexyiIzu8Ta+eLqDPwQBeHX9aWTkl93/SURk1moVdnQ6HTw9Peu6FhJBbEoeAKC5pyMcbazELYaonsx7ohVa+DhBWaTG9J9OoVyrE7skIjKiWoWdhQsX4vvvv6+y/Pvvv8fixYsfuiiqP6eSOR8WNTw2VjJ8NbY9HBVynLx+E4v+viR2SURkRLUKO9988w3CwsKqLG/VqhW+/vrrhy6K6s9phh1qoII87LF0VMXcfqsOJWHbOY6/Q2SpahV2MjIy4OPjU2V5o0aNkJ7OPxjmQqPV4eyNisEE2TiZGqKBrbwxtVcTAMCbv57F1ewikSsiImOoVdjx9/fH4cOHqyw/fPgwfH19H7ooqh9XMotQotbCUSFH00YOYpdDJIpZA0LRKdgNRSoNXvwxBkUqjdglEVEdq1XYmTx5Ml577TWsXr0a169fx/Xr1/H9999jxowZnBzUjFR2OW8b4AKplMMJUMMkl0mx4pl28HRU4EpmEWb8Egudjj1OiSxJrUZQnjVrFnJycvDyyy9DrVYDAGxsbPDWW29h9uzZdVogGc+p63kAOJggkaejDb55NhJPf3sUOy9mYtnueMx8tLnYZRFRHan1oIKLFy9GdnY2jh49ijNnziA3Nxfvv/9+XddHRsTBBIn+1S7AFQuHVww4+PnuePzNBstEFqPWc2MBgIODAx555JG6qoXqUV6JGleziwEAbXlmhwgA8GRkY1xML8CqQ0mYueEMgjzs0cLHSeyyiOgh1TrsnDx5Ehs2bEBycrL+UlalTZs2PXRhZFyVgwkGe9jD1d5a3GKITMjsqDBcySzEwXglJv9wElundYO7g0LssojoIdTqMtb69evRtWtXxMXFYfPmzSgvL8eFCxewZ88eODtzygFzoJ/8k+PrEBmQy6RYMaYdAt3tcONmKab+XwzKyjnBMZE5q1XYWbBgAT777DP88ccfsLa2xvLly3Hp0iWMGjUKAQEBdV0jGcG/gwmyvQ7RnVzsrLFqfAc42lSMsDzr17PsoUVkxmoVdhITEzFkyBAAgLW1NYqLiyGRSDBjxgx8++23dVog1T2dTtBfxmJPLKLqNfV0xDfjIiGXSvDHmTR8tuuK2CURUS3VKuy4urqisLAQAODn54fz588DAPLy8lBSUlJ31ZFRXFUWobBMAxsrKcK8HcUuh8hkdW3qgQUjKnpordiTgI0nU0SuiIhqo1YNlHv27ImdO3ciPDwcI0eOxH/+8x/s2bMHO3fuRL9+/eq6RrqP5ORkKJXKB15/d1JFIG3iIsfZM7H3XT8uLq62pRGZvVEd/JGcU4Iv9iZg9qZz8HOxRdemHmKXRUQ1UKuw88UXX6CsrAwA8O6778LKygpHjhzBk08+if/+9791WiDdW3JyMsJatEBpDc6ouQ2cDse2gxD9xzpEvrH2gZ9XVMR5g6hhmvloc1zPLcEfZ9Lw4o8x2PRyVzT15FlRInNRo7BTUFBQ8SS5HA4ODvr7L7/8Ml5++eW6r47uS6lUorSkBGPfWgKvgJAHes7OdDkKyoGBjw2D36gn7rt+3PH9+Hvtcn3AJWpopFIJljwVgbS8UsRcv4mJa05g88vd4MEu6URmoUZhx8XFBRLJ/edQ0mrZTbO+eQWEoHGzVvddT63RoSA5EQDQOqw57BX3fwtk3lqfqCGzsZLh22cjMfzLI0jOLcHkH07i58mdYWMlE7s0IrqPGoWdvXv3GtwXBAGDBw/Gd999Bz8/vzotjIwjs6Di7IyjjfyBgg6RpairtmezOtpj9p5SnE7Ow3Nf7cWsrq6QSSXw8PDg0BtEJqpG33a9evWqskwmk6Fz585o0qRJnRVFxpOeXxF2fJxsRK6EqH4U5GYDAMaNG1dn21Q0bgWvpz/A8TRg0H9XI3fHStja2eFSXBwDD5EJ4r/2DUzGrTM73s4MO9QwlBZVtC0cMvVdhEZE1tl2U0skOKoU4Ng2Cq3adsDBxROhVCoZdohM0EOFnZSUFJSUlMDd3b2u6iEjEgQBGZVndpxtRa6GqH65+wY+ULu2B9UYgM2NPOy7nI1kNIJDxIA62zYR1a0ahZ3PP/9c/7NSqcTPP/+Mvn37cj4sM5FfWo7Sci1kEgk8HDn5J9HDatPYBcUqDU5cuwm3gdNwIq0M7duLXRUR3alGYeezzz4DAEgkFY3xHn/8cY6rY0YqL2E1clRALq3V4NlEdIcuTdyRla3E9WIZPom+iQ7hNxEZyDnniExJjcJOUlKSseqgelDZOJntdYjqjkQiQXs3LeLOngJCHsH4VdFY0NcDjZ3qvkkke3wR1Q4bKDcg/7bXYdghqktFN7Oh3LoIXqMXAL6hePmX88j4cRa0Rbl1uh/2+CKqHYadBqJcq4OySAWAZ3aI6lppUQGEchXa2BfghlxAkbMXWr22Br28NLCqoyvGmcmJWLd4Fnt8EdWCyYedoKAgXL9+vcryl19+GStXrkTv3r2xf/9+g8emTp2Kr7/+ur5KNAtZhSroBMDeWgZHDiZIZBRePn7o3DYYG06mIF8NnCp2xhNtfdlGjkhkJv8JPHHiBNLT0/W3nTt3AgBGjhypX2fy5MkG63z88cdilWuyMm5rr/MgU34QUe0421rhiba+sJZJceNmKf65kAlBEMQui6hBM/l/8Rs1amRwf9GiRQgJCTEYzdnOzg7e3t71XZpZSc8vBcBLWET1wdPRBkMifLA1NhXxWUWwj1eiZzMP/qNBJBKTP7NzO7VajR9//BHPP/+8wR+NdevWwcPDA61bt8bs2bNRUlJyz+2oVCoUFBQY3CyZwWCCThxMkKg+BLjZ4dGWXgCA2JQ8nErOE7cgogbM5M/s3G7Lli3Iy8vDhAkT9MueeeYZBAYGwtfXF2fPnsVbb72Fy5cvY9OmTXfdzsKFCzFv3rx6qNg0FKk0KFZrIZEAnk4KscshajDCvJ1QotLiYIIShxKUsFfIEObtJHZZRA2OWYWdVatWISoqCr6+vvplU6ZM0f8cHh4OHx8f9OvXD4mJiQgJCal2O7Nnz8bMmTP19wsKCuDv72+8wkVWeVankYMCVjKzOplHZPbaB7qiSKXB6ZQ87LyYCTtrOQLc7MQui6hBMZtvvuvXr2PXrl2YNGnSPdfr1KkTACAhIeGu6ygUCjg5ORncLJl+MEHOdE4kih7NPNDc0wE6AfjrbDqyC1Vil0TUoJhN2Fm9ejU8PT0xZMiQe64XGxsLAPDx8amHqswDZzonEpdEIsGjrbzQ2MUWaq0OW2JTUVBaLnZZRA2GWYQdnU6H1atXY/z48ZDL/73ylpiYiA8++AAxMTG4du0afv/9dzz33HPo2bMnIiIiRKzYdGi0OmQVcDBBIrHJpVI8FuEDdwdrlKi12BKbitJyrdhlETUIZhF2du3aheTkZDz//PMGy62trbFr1y4MGDAAYWFheP311/Hkk0/ijz/+EKlS05NVqIJWEGBrJYOLrZXY5RA1aAorGYa18YODQo6bJeX440waNFqd2GURWTyzaKA8YMCAagfl8vf3rzJ6MhmqbK/j68LBBIlMgYONHMPa+mJjzA2k55fh7/MZGBLhAyk/n0RGYxZndqj2KgcT9HHm+DpEpsLdQYHHI3whk0pwVVmMfZezOcoykREx7FgwQRCQlseZzolMkZ+rLQa2qhh08FxqPk5cuylyRUSWi2HHguWXlqO0XAuZRAJPRw4mSGRqmnk6onfziilxoq/m4GKaZY/mTiQWhh0LVtlex9NJATkHEyQySW38XRAZ6AoA2HUpE9eUxSJXRGR5+A1owdLyKtvr8BIWkSnrFuKOMG9HCAKw7Xw6Mm+NjUVEdYNhx4L92xOLjZOJTJlEIkH/Fl4IcLNDuVbA1tg05JWoxS6LyGIw7FgoVbkWOcUVfyw5TQSR6ZNJJRgS7oNGjgqUlmuxJTYNJWqN2GURWQSGHQuVfus0uLOtFewVZjGcElGDZy2X4ok2vnCykSO/tBy/n0lDOQcdJHpoDDsWKv1Wl3NfttchMiv2CjmGtfODjZUUmQUqbDuXDq2OY/AQPQyGHQuVVjmYINvrEJkdVztrDG3jC7lUgms5JdhzKQscc5Co9hh2LJBOJ+h7c7AnFpF58nG2RVRrb0gAXEwvwMV8mdglEZkthh0LpCxSoVwrwFouhbu9tdjlEFEtNWnkgL5hngCASwUyOLQZJHJFROaJYccCpeX/e1aHk38SmbfWfs7oFOwGAHAb8BKOpXIMHqKaYtixQJWTf/py8k8ii9Ap2A1B9lpIpDJ8dvQmTl7LFbskIrPCsGOB0vPZXofIkkgkErRz06Ik4TjUWuCFtSeRkFUodllEZoNhx8IUlpWjsEwDiQTw4mCCRBZDKgGUvy9GMzcr5JeWY/z3JzitBNEDYtixMJVndRo5KGAt56+XyJII5Sq828MNTTzskZpXivHfH0dBWbnYZRGZPH4bWphUTv5JZNGcFFKsfb4jGjkqcCmjEFN/iIFKoxW7LCKTxrBjYSrDjp8rGycTWSp/NzusnvAI7K1liL6agzc2noWOoywT3RXDjgUpK9cip6hi8k8/jpxMZNFa+znj62cjIZdK8MeZNCzYFid2SUQmi2HHgqTdOqvjamcFO2tO/klk6Xo0a4SlI9sAAL47lISv9yeKXBGRaWLYsSD6S1g8q0PUYAxr54d3BocBABb9fQk/HUsWuSIi08OwY0EYdogapik9Q/By7xAAwLtbzmFrbKrIFRGZFoYdC6HRAVmFKgCALxsnEzU4swaG4tnOgRAE4PUNZ7A7LlPskohMBsOOhchRSSAIgKONHE42VmKXQ0T1TCKRYN7QVhjezg8anYCX1p3CkUSl2GURmQSGHQuhVFX8KnkJi6jhkkol+PipCDza0gtqjQ6T155EbEqe2GURiY5hx0IoVRWzmzPsEDVsVjIpVoxph64h7ihWazH+++O4nMF5tKhhY9ixBDI5civDDtvrEDV4NlYyfPtcB7T1d0F+aTnGrTqGq9lFYpdFJBqGHQug8G4OHSSws5bBxZbtdYgIcFDIsWbiIwjzdkR2oQpj/ncUScpiscsiEgXDjgVQ+LcCAPi62EIikYhcDRGZChc7a/w4qROaeTogs0CF0d9GM/BQg8SwYwFs/FsDYHsdIqrKw0GBn6d0ZuChBo1zCpg5rU6Awq8FAIYdooYgLq52c2DN7myHOfvKkFKgwpMrD2B+b3f4Olb9CvDw8EBAQMDDlklkUhh2zFxSXjmkCjtYSQS4O1iLXQ4RGUlBbjYAYNy4cbXehtTOBV5jPkKuRyBe3HAJWRveQ7nScHoJWzs7XIqLY+Ahi8KwY+YuZlfMcu6uECBlex0ii1VaVAAAGDL1XYRGRNZ6O2Va4GCWDgWO7giavBLdGmngphAAAJnJiVi3eBaUSiXDDlkUhh0zd1FZEXY8bHQiV0JE9cHdNxCNm7V6qG00DtFiS2wqMgtUOKxU4PE2PmjsaldHFRKZHjZQNmM6naA/s+Nx6z8zIqL7sbGSYUS7xmjsYgu1VoctsWlstEwWjWHHjF1ML0CRWoBOVQJXa4YdInpw1nIpnmjri2APe2h1Av48m4brxfxKIMvEd7YZq5zkryzlPKRsrkNENSSXSTEk3AehXo7QCcDJHDmcuoyCIPCfJ7IsJh125s6dC4lEYnALCwvTP15WVoZp06bB3d0dDg4OePLJJ5GZmSlixfXrcEIOAKDs+hmRKyEicyWTSjCwlRciA1wBAK49n8NXJ/Oh0bIdIFkOkw47ANCqVSukp6frb4cOHdI/NmPGDPzxxx/YuHEj9u/fj7S0NIwYMULEauuPWqPD8aRcAAw7RPRwJBIJujfzQFtXDQSdFruSSjHph5MoVmnELo2oTph82JHL5fD29tbfPDw8AAD5+flYtWoVPv30U/Tt2xeRkZFYvXo1jhw5gqNHj4pctfHFpuShtFwLJ4UU5dnXxS6HiCxAiKMO2ZsXwFoG7Lucjae+jsaNmyVil0X00Ew+7MTHx8PX1xdNmjTB2LFjkZxcMQBWTEwMysvL0b9/f/26YWFhCAgIQHR09D23qVKpUFBQYHAzN4cTKtrrhHtaA+D1dSKqG6UJx/BBb3d4OFgjLr0AQ784jKNXc8Qui+ihmHTY6dSpE9asWYPt27fjq6++QlJSEnr06IHCwkJkZGTA2toaLi4uBs/x8vJCRkbGPbe7cOFCODs762/+/v5GfBXGUdk4OcJTIXIlRGRpmrlbY+v07mjt54TcYjXGfXcM/3f0Ohsuk9ky6bATFRWFkSNHIiIiAgMHDsS2bduQl5eHDRs2PNR2Z8+ejfz8fP0tJSWljiquH8UqDU4n5wEAwr04RQQR1T0/F1tsnNoVQ9v4QqMT8N6W83hn83moNFqxSyOqMZMOO3dycXFB8+bNkZCQAG9vb6jVauTl5Rmsk5mZCW9v73tuR6FQwMnJyeBmTo5fy4VGJ6Cxqy28HTgINhEZh621DMtHt8XbUWGQSICfjydj5NfRSMllOx4yL2YVdoqKipCYmAgfHx9ERkbCysoKu3fv1j9++fJlJCcno0uXLiJWaXxHbrXX6RbiIXIlRGTpJBIJXuwVgu8nPAJnWyucvZGPIZ8fxI4L924uQGRKTDrsvPHGG9i/fz+uXbuGI0eOYPjw4ZDJZBgzZgycnZ3xwgsvYObMmdi7dy9iYmIwceJEdOnSBZ07dxa7dKOqHF+na1N3kSshooaiT6gntv2nB9oFuKCgTIOp/xeDD/68CLWG4/GQ6TPpsHPjxg2MGTMGoaGhGDVqFNzd3XH06FE0atQIAPDZZ5/hsccew5NPPomePXvC29sbmzZtErlq48otVuNiekXvsa48s0NE9cjPxRa/TOmCSd2DAQCrDiVh5NdHOK8WmTyTbvCxfv36ez5uY2ODlStXYuXKlfVUkfiiEyvO6oR6OaKRowLm1bSaiMydtVyK/z7WEh2D3fDGxjM4c+uy1vuPtcTTj/hDIuHcNWR6TPrMDlV1+FaXc17CIiIxDWjlje2v9UTnJm4oUWvx9qZzmPp/McgtVotdGlEVDDtmho2TichU+LrY4qdJnfHO4DBYyST452ImBi47gN1xDWeOQjIPDDtmJDWvFNdySiCTStCpiZvY5RARQSqVYErPEGyZ1g3NPB2QXajCC2tPYsYvscgr4VkeMg0MO2akcoqIiMbOcLSxErkaIqJ/tfJ1xh+vdMfUnk0glQCbT6ei/6cHsP08u6iT+Bh2zMiBK9kAgO5NeQmLiEyPjZUMswe3wG8vdUUzTwcoi1R48ccYTP/pFHKKVGKXRw0Yw46Z0OoEHIyvOLPTO7SRyNUQEd1duwBX/Plqd0zrEwKZVII/z6ZjwGcH8OfZNM6vRaJg2DETsSl5yC8th5ONHG0au4hdDhHRPSnkMswaGIYtL3dDqJcjcorVmP7TaUz5vxhk5JeJXR7VMUEQUFhWjus5xTh7Iw9x6QVIUhajsKxc7NIAmPg4O/Sv/bcuYfVo1ghyGTMqEZmH8MYVbXm+2JuAL/cmYOfFTBxNzMHbg8Mw5pEASKUcl8ecFas0iMsoQFxaIXLv0iDdxUoO566jkVMi3iSyDDtmojLs9OIlLCIysri4uDrfZm93ILi/O748mY/43HK8u/k8tsamYdGIcDRp5FDn+yPj0mh1OJWchxO3JqYGAKkEcLa1goudNbQ6ASVqDZRFauSVS+HSYxzyVeJNLcKwYwZyi9U4eyMPANCrOcMOERlHQW7FP1Xjxo0z3k4kUrh1Ho5GfZ/H8aRcDFp+EP/p1wxTejaBFc9am4WM/DJsv5CB/NKKS1SejgqE+zmjmZcDFHKZwbolag1OXYjH/v17ETzyWTHKBcCwYxYOxmdDEIAwb0d4OdmIXQ4RWajSoop594ZMfRehEZFG2UdmciLWLZ6FdYvfxLorFR0vluy4jD/PpmPxk+GIYJtEkyUIAs7cyMfB+GzoBMBBIUe3pu4I9XK86zQhdtZyBDnosGnbckg+eK6eK/4Xw44Z2H+Zl7CIqP64+waicbNWRt2Hp70cPzzfDptPp2L+nxcRl16AYSsP44XuwZj5aChsrWX33wjVG51OwJ7LWbiQVhGImzZyQP+WnlXO5JgqnjM0cVqdgH232uv0bu4pcjVERHVHIpFgRPvG2DWzF4a28YVOAP53MAkDlx3AoVtDbZD4NFodtp1Px4W0AkgA9GzmgcHh3mYTdACGHZMXm5KH3GI1nGzk6BDkKnY5RER1zsNBgc/HtMP3EzrA19kGybklGLfqGGZtPMMpJ0Sm0enwx9l0JGYXQyaRYHC4D9oFuJrd7PYMOyauckK9XqGebLxHRBatb5gX/pnZC+O7BEIiATbG3ED/Tw/gr7PpHIxQBFqdgL/PZSA5twRWMgmeaOuLpp7m2XOObXZM3J5LWQCAfmG8hEVEluNe3duf8AdCbSu6qd8oUGHaT6fQ0VeBye2d4W73YJdOPDw8EBAQUFflNjiCIGDnxUxcVRZDJpXg8Qhf+LvZiV1WrTHsmLAbN0twKaMQUgmniCAiy1Cj7u0yOZy7jIJz55E4ngYcTUrCzX1rUBS7HcC9z/TY2tnhUlwcA08tHU7MweXMiu+fIeE+Zh10AIYdk1Z5VqdDoBtc7KxFroaI6OHVpnt7vho4latDLuzhPnAaQoe+hPZuGjhaVb9+Zfd2pVLJsFMLZ2/kIeb6TQDAoy28EOxhL3JFD49hx4TtjqsIO31b8BIWEVmWmnRvbwyghSDg7I18HElUQqmSYnemAh2D3RAZ4AoZp5yoM9dzirHv1nAnnZu4IczHSeSK6gZbvJqoIpUG0VdzALC9DhGRVCJBW38XjOsUiEB3O2h1AqITc7D+RDIyCzixaF3ILy3H3+czIABo4e2IjkFuYpdUZxh2TNS+y1lQa3QI9rA329bvRER1zcnWCk+08cXAll6wsZJCWaTGLydScDA+G+Va8eZeMncaHfDn2TSoNDp4OSnQN8zT7LqX3wsvY5moHRcqupwPaOVlUW84IqKHJZFIEObjhAB3Oxy4osTlzEKcSs5DQlYR+rXw4n/xtRCTK4OyRA1bKxmGhPtAbmFDnVjWq7EQKo0We281Th7YylvkaoiITJOdtRyDWntjaBtfOCjkKCjTYPPpVJzOlUFipRC7PLPh+Mhw3CiR6XteOdrcpeW3GWPYMUFHEnJQpNLAy0mBtpwUj4jonoI97PFs50BENHYGAFwtksFnwue4nMPRl+/nTKYKrr0nAAB6NmsEP1dbcQsyEoYdE7TjQgYAYEBLb0jZy4CI6L6s5VL0CfXE8HZ+sJUJsHLzw7t7cvDJP5eh1rAtT3VSckvwafRNSKQyBNpr9WHREjHsmBitrmLUSoCXsIiIairAzQ79fcpRdGEvdAKwYk8CRnx1GPGZhWKXZlJK1VpM/b8YFKoFqNKvoJ2b1qLbhzLsmJiT13KRU6yGs60VOjWxnG5/RET1xVoK5Pz5Cd7o4gIXOyucTy3AkBWH8N3Bq9DpOMeWIAiYveksLqYXwEkhRfbmBZBZbs4BwLBjcv48mw4AeLSlFyf+JCJ6CF39bfHPaz3RO7QR1BodPvwrDmO/O4bUvFKxSxPVqkNJ2BKbBplUglldXKAtVIpdktHx29SEaLQ6/H2+Iuw8FuEjcjVERObP08kGqyc8go+Gt4atlQzRV3Mw6LMD+DXmRoOcSf1IghIL/74EAPjvkBZo5dkweq0x7JiQY0m5UBap4WpnhW5NPcQuh4jIIkgkEoztFIi//9MD7QNcUKjS4I2NZ/DijzHIKVKJXV69ScktwfSfT0OrEzCivR8mdA0Su6R6w7BjQv48mwYAGNTam5ewiIjqWJCHPTZM7YJZA0NhJZNgx4VMDFx2ALtudQqxZIVl5Zi09iRyi9Vo7eeEBcPDLbpB8p34jWoiyrU6/H2+osv5YxG+IldDRGSZ5DIppvVpis0vd0NzLwcoi9SY9MNJvPXrWRSWlYtdnlFotDq88vNpXM4shKejAt8+2wE2VjKxy6pXDDsm4nCCEnkl5fBwsEanYPbCIiIyptZ+zvh9endM6dkEEgnwy8kURC0/iGO3JmC2JB/+FYd9l7NhYyXFd+M7wNfFMgcOvBeGHRPxe2zFJayo1pY3JwkRkSmysZLhncEtsH5yZzR2tcWNm6UY/b+jWLAtDmXlWrHLqxM/RF/DmiPXAADLnm6LiAY6Kj+/VU1AsUqD7bdGTR7Wzk/kaoiIGpZOTdzx93964OkO/hAE4NsDV/HEF4dxIS1f7NIeyr7LWZj7+wUAwJuDQjGodcPt5ctZz03AjgsZKFFrEeRuh/YBLmKXQ0RkEeLi4mq0/tNNgGCFK746mY/LmYV44otDGNXSEcPD7CGrZuoeDw8PBAQE1FW5dep08k28vO4UdAIwMrIxXuoVInZJomLYMQGbTqUCAEa0b9ygWscTERlDQW42AGDcuHG1er7U1gnuA6fDLrQrfjpfiNX/xCDn7+UoV143WM/Wzg6X4uJMLvDEpRdgwuoTKFFr0b2pBz5qYD2vqsOwI7L0/FIcTqwYvXI4L2ERET200qICAMCQqe8iNCKyVtsQBCC5WIMzN2WAb3P4vfAFwpx0CHXWQiYBMpMTsW7xLCiVSpMKO/GZhXh21XHkl5ajfYALvn0uEtZytlgx6bCzcOFCbNq0CZcuXYKtrS26du2KxYsXIzQ0VL9O7969sX//foPnTZ06FV9//XV9l1srW2PTIAhAxyA3+LvZiV0OEZHFcPcNRONmrWr9fH8AbVQa7L2UhavKYsQVyJCltUX/Fl7wqrsy68yFtHw8u+o4covVaOHjhNUTO8LO2qS/5uuNSce9/fv3Y9q0aTh69Ch27tyJ8vJyDBgwAMXFxQbrTZ48Genp6frbxx9/LFLFNSMIAn6NuQEAGN6eZ3WIiEyNg0KOxyJ8MLi1N2ytZMgpVuOXkymIyZFBauskdnl6MddzMebbo8gtViPczxk/TeoEZ1srscsyGSYd+bZv325wf82aNfD09ERMTAx69uypX25nZwdvb+/6Lu+hnbx+EwlZRbC1knEuLCIiEyWRSNDMyxGN3exwMD4bcemFuFYsg+/kb/B3QjHatBWqbcBcX34/k4Y3Np6BWqND+wAXrHm+I5xsGHRuZ9Jndu6Un1/RDdDNzXDQvXXr1sHDwwOtW7fG7NmzUVJScs/tqFQqFBQUGNzE8PPxZADA0Da+cOQbk4jIpNlayTCgpTeeimwMZysdZLaO+N+pAjy+4hAOJyjrfWJRjVaHT/65jFd/Pg21RodHW3rhx0mdGHSqYTZhR6fT4bXXXkO3bt3QunVr/fJnnnkGP/74I/bu3YvZs2fj//7v/+7bAn/hwoVwdnbW3/z9/Y1dfhX5JeX462zFDOejO9b//omIqHb8XGzRz1uDnH++hL2VBBfTCzD2u2MY/e1RHE/KrZcaUvNKMeZ/R7FiTwIAYHKPYHw9LpJtdO7CbI7KtGnTcP78eRw6dMhg+ZQpU/Q/h4eHw8fHB/369UNiYiJCQqofV2D27NmYOXOm/n5BQUG9B54tsalQaXQI83ZEW3+Xet03ERE9HIkEKDq9DV98MRf7lXb46VgyjiXlYtQ30eje1AMzHm2GyMC6n/pHo9VhbfR1fLbzCopUGjgo5FgwIhxD23BOxXsxi7Azffp0/Pnnnzhw4AAaN258z3U7deoEAEhISLhr2FEoFFAoFHVe54MSBEF/CWtMx4AGP/4BEZG5craRYe7QVpjaqwm+2JOADSdTcChBiUMJSrTxd8GEroGIau3z0BNv6nQCdlzIwPLd8biUUQgAaBfgguVPt0OAO3vy3o9Jhx1BEPDKK69g8+bN2LdvH4KDg+/7nNjYWACAj4/pNvg9ce0mLmUUwsZKimFt2QuLiMjc+Tjb4qPh4XixVwi+2JOAzadTcSYlDzN+ycN7Wy4gqrU3BrX2RpcQ9xpdasosKMMfZ9Kw/kQKErKKAAAudlZ4a1AYnu7gD6mIDaPNiUmHnWnTpuGnn37C1q1b4ejoiIyMivmjnJ2dYWtri8TERPz0008YPHgw3N3dcfbsWcyYMQM9e/ZERESEyNXf3ZojSQAqBhF0tmNDMiIiS+HvZofFT0Vg1qBQrD+ejJ+PpyA1rxQbY25gY8wNWMukaOPvjNZ+zmjh7QRvZxs0clRALpVAJwDKIhXS8kpxIa0Ap5Jv4lxqPirbPTvZyDGhaxAmdguGq721uC/UzJh02Pnqq68AVAwceLvVq1djwoQJsLa2xq5du7Bs2TIUFxfD398fTz75JP773/+KUO2DSc0rxY4LmQCA8V2DxC2GiIiMwsNBgel9m+Hl3k1x8vpN/HEmDXsvZ+HGzVKcuHYTJ67dfOBtRQa64om2vhjezo89d2vJpMPO/brx+fv7Vxk92dT9X/R1aHUCuoa4I8zbdAakIiKiuieVStAx2A0dg90gCAKuKosRm5yHc6n5SMwuQlaBCjnFKuhufd252VvDx9kGTTzsERnkhkeCXOHjbCvui7AAJh12LE2pWov1JyoaJk/gWR0iogZFIpEgpJEDQho54MnIe3e2obplNuPsWIJfT91AXkk5Grvaol8LU5xZhYiIyPLwzE490Wh1+PZAIgBgUvdgUYcWJyKiuhEXF2fU7Xt4eJjUrOrmimGnnvx1Lh0puaVws7fG04/wjUtEZM4KcrMB4L4j9j8sWzs7XIqLY+B5SAw79UAQBHy1r+KszsSuQbC1frjBpYiISFylRRVzKg6Z+i5CIyKNso/M5ESsWzwLSqWSYechMezUgz2XsnApoxD21jI81yVI7HKIiKiOuPsGonGzVmKXQffBBspGJggClu2KBwCM6xzIQQSJiIjqGcOOke24kIFzqfmwt5ZhSs8mYpdDRETU4DDsGJFWJ+CTf64AAJ7vHgx3B/EmHyUiImqoGHaM6PczqYjPKoKTjRyTevCsDhERkRgYdoykXKvDZzsr2upM7RUCZ1u21SEiIhIDw46RaHUChrX1RaC7HSZ2CxK7HCIiogaLXc+NxMZKhpkDQvFqv2aQy5gpiYiIxMJvYSNj0CEiIhIXv4mJiIjIovEyFhERkQkz5mSjxp7I1FQw7BAREZmg+ppsFACKioqMvg8xMewQERGZoPqYbDTu+H78vXY5ysrKjLJ9U8GwQ0REZMKMOdloZnKiUbZrathAmYiIiCwaww4RERFZNIYdIiIismgMO0RERGTR2EDZyJKTk6FUKo22/YYyRgIREVFtMewYUXJyMsJatEBpSYnR92XpYyQQERHVFsOOESmVSpSWlGDsW0vgFRBilH00lDESiIiIaothpx54BYRwjAQiIiKRsIEyERERWTSGHSIiIrJoDDtERERk0Rh2iIiIyKIx7BAREZFFY9ghIiIii8awQ0RERBaNYYeIiIgsGsMOERERWTSGHSIiIrJoDDtERERk0Swm7KxcuRJBQUGwsbFBp06dcPz4cbFLIiIiIhNgEWHnl19+wcyZMzFnzhycOnUKbdq0wcCBA5GVlSV2aURERCQyiwg7n376KSZPnoyJEyeiZcuW+Prrr2FnZ4fvv/9e7NKIiIhIZHKxC3hYarUaMTExmD17tn6ZVCpF//79ER0dXe1zVCoVVCqV/n5+fj4AoKCgoE5rKyoqAgDciL8AVWlJnW67UmZyIgAg49oVJNrbcR8NYB/1tR/ug/vgPriPupB9IwlAxXdiXX/PVm5PEIR7ryiYudTUVAGAcOTIEYPls2bNEjp27Fjtc+bMmSMA4I033njjjTfeLOCWkpJyz6xg9md2amP27NmYOXOm/r5Op0Nubi7c3d0hkUhErEx8BQUF8Pf3R0pKCpycnMQux+Lw+BoPj61x8fgaD49t7QmCgMLCQvj6+t5zPbMPOx4eHpDJZMjMzDRYnpmZCW9v72qfo1AooFAoDJa5uLgYq0Sz5OTkxA+dEfH4Gg+PrXHx+BoPj23tODs733cds2+gbG1tjcjISOzevVu/TKfTYffu3ejSpYuIlREREZEpMPszOwAwc+ZMjB8/Hh06dEDHjh2xbNkyFBcXY+LEiWKXRkRERCKziLDz9NNPIzs7G++//z4yMjLQtm1bbN++HV5eXmKXZnYUCgXmzJlT5TIf1Q0eX+PhsTUuHl/j4bE1Pokg3K+/FhEREZH5Mvs2O0RERET3wrBDREREFo1hh4iIiCwaww4RERFZNIadBmjlypUICgqCjY0NOnXqhOPHj9913f/973/o0aMHXF1d4erqiv79+99zfarZ8b3d+vXrIZFIMGzYMOMWaMZqemzz8vIwbdo0+Pj4QKFQoHnz5ti2bVs9VWt+anp8ly1bhtDQUNja2sLf3x8zZsxAWVlZPVVrPg4cOIDHH38cvr6+kEgk2LJly32fs2/fPrRv3x4KhQJNmzbFmjVrjF6nRaubGarIXKxfv16wtrYWvv/+e+HChQvC5MmTBRcXFyEzM7Pa9Z955hlh5cqVwunTp4W4uDhhwoQJgrOzs3Djxo16rtw81PT4VkpKShL8/PyEHj16CE888UT9FGtmanpsVSqV0KFDB2Hw4MHCoUOHhKSkJGHfvn1CbGxsPVduHmp6fNetWycoFAph3bp1QlJSkrBjxw7Bx8dHmDFjRj1Xbvq2bdsmvPvuu8KmTZsEAMLmzZvvuf7Vq1cFOzs7YebMmcLFixeFFStWCDKZTNi+fXv9FGyBGHYamI4dOwrTpk3T39dqtYKvr6+wcOHCB3q+RqMRHB0dhbVr1xqrRLNWm+Or0WiErl27Ct99950wfvx4hp27qOmx/eqrr4QmTZoIarW6vko0azU9vtOmTRP69u1rsGzmzJlCt27djFqnuXuQsPPmm28KrVq1Mlj29NNPCwMHDjRiZZaNl7EaELVajZiYGPTv31+/TCqVon///oiOjn6gbZSUlKC8vBxubm7GKtNs1fb4zp8/H56ennjhhRfqo0yzVJtj+/vvv6NLly6YNm0avLy80Lp1ayxYsABarba+yjYbtTm+Xbt2RUxMjP5S19WrV7Ft2zYMHjy4Xmq2ZNHR0Qa/CwAYOHDgA/+dpqosYgRlejBKpRJarbbKyNJeXl64dOnSA23jrbfegq+vb5UPItXu+B46dAirVq1CbGxsPVRovmpzbK9evYo9e/Zg7Nix2LZtGxISEvDyyy+jvLwcc+bMqY+yzUZtju8zzzwDpVKJ7t27QxAEaDQavPjii3jnnXfqo2SLlpGRUe3voqCgAKWlpbC1tRWpMvPFMzv0wBYtWoT169dj8+bNsLGxEbscs1dYWIhnn30W//vf/+Dh4SF2ORZHp9PB09MT3377LSIjI/H000/j3Xffxddffy12aRZh3759WLBgAb788kucOnUKmzZtwl9//YUPPvhA7NKIquCZnQbEw8MDMpkMmZmZBsszMzPh7e19z+cuXboUixYtwq5duxAREWHMMs1WTY9vYmIirl27hscff1y/TKfTAQDkcjkuX76MkJAQ4xZtJmrz3vXx8YGVlRVkMpl+WYsWLZCRkQG1Wg1ra2uj1mxOanN833vvPTz77LOYNGkSACA8PBzFxcWYMmUK3n33XUil/F+6try9vav9XTg5OfGsTi3x3diAWFtbIzIyErt379Yv0+l02L17N7p06XLX53388cf44IMPsH37dnTo0KE+SjVLNT2+YWFhOHfuHGJjY/W3oUOHok+fPoiNjYW/v399lm/SavPe7datGxISEvQBEgCuXLkCHx8fBp071Ob4lpSUVAk0lcFS4JSLD6VLly4GvwsA2Llz5z3/TtN9iN1CmurX+vXrBYVCIaxZs0a4ePGiMGXKFMHFxUXIyMgQBEEQnn32WeHtt9/Wr79o0SLB2tpa+PXXX4X09HT9rbCwUKyXYNJqenzvxN5Yd1fTY5ucnCw4OjoK06dPFy5fviz8+eefgqenp/Dhhx+K9RJMWk2P75w5cwRHR0fh559/Fq5evSr8888/QkhIiDBq1CixXoLJKiwsFE6fPi2cPn1aACB8+umnwunTp4Xr168LgiAIb7/9tvDss8/q16/sej5r1iwhLi5OWLlyJbuePySGnQZoxYoVQkBAgGBtbS107NhROHr0qP6xXr16CePHj9ffDwwMFABUuc2ZM6f+CzcTNTm+d2LYubeaHtsjR44InTp1EhQKhdCkSRPho48+EjQaTT1XbT5qcnzLy8uFuXPnCiEhIYKNjY3g7+8vvPzyy8LNmzfrv3ATt3fv3mr/jlYez/Hjxwu9evWq8py2bdsK1tbWQpMmTYTVq1fXe92WRCIIPN9IRERElottdoiIiMiiMewQERGRRWPYISIiIovGsENEREQWjWGHiIiILBrDDhEREVk0hh0iIiKyaAw7REREZBQHDhzA448/Dl9fX0gkEmzZsqXG2xAEAUuXLkXz5s2hUCjg5+eHjz76qEbb4ESgREREZBTFxcVo06YNnn/+eYwYMaJW2/jPf/6Df/75B0uXLkV4eDhyc3ORm5tbo21wBGUiMmkTJkxAXl5elf8I9+3bhz59+uDmzZtwcXERpTYienASiQSbN2/GsGHD9MtUKhXeffdd/Pzzz8jLy0Pr1q2xePFi9O7dGwAQFxeHiIgInD9/HqGhobXeNy9jERERkSimT5+O6OhorF+/HmfPnsXIkSMxaNAgxMfHAwD++OMPNGnSBH/++SeCg4MRFBSESZMm1fjMDsMOEVmEvLw8TJo0CY0aNYKTkxP69u2LM2fO6B+fO3cu2rZta/Ccffv2QSKRIC8vDwCQk5ODMWPGwM/PD3Z2dggPD8fPP/9cj6+CqOFITk7G6tWrsXHjRvTo0QMhISF444030L17d6xevRoAcPXqVVy/fh0bN27EDz/8gDVr1iAmJgZPPfVUjfbFNjtEZBFGjhwJW1tb/P3333B2dsY333yDfv364cqVK3Bzc3ugbZSVlSEyMhJvvfUWnJyc8Ndff+HZZ59FSEgIOnbsaORXQNSwnDt3DlqtFs2bNzdYrlKp4O7uDgDQ6XRQqVT44Ycf9OutWrUKkZGRuHz58gNf2mLYISKzd+jQIRw/fhxZWVlQKBQAgKVLl2LLli349ddfMWXKlAfajp+fH9544w39/VdeeQU7duzAhg0bGHaI6lhRURFkMhliYmIgk8kMHnNwcAAA+Pj4QC6XGwSiFi1aAKg4M8SwQ0QNxpkzZ1BUVKT/b7BSaWkpEhMT9ffPnTun/yMKAFqt1mB9rVaLBQsWYMOGDUhNTYVarYZKpYKdnZ1xXwBRA9SuXTtotVpkZWWhR48e1a7TrVs3aDQaJCYmIiQkBABw5coVAEBgYOAD74thh4jMXlFREXx8fLBv374qj93eUys0NBS///67/v6xY8cwbtw4/f0lS5Zg+fLlWLZsGcLDw2Fvb4/XXnsNarXamOUTWayioiIkJCTo7yclJSE2NhZubm5o3rw5xo4di+eeew6ffPIJ2rVrh+zsbOzevRsREREYMmQI+vfvj/bt2+P555/HsmXLoNPpMG3aNDz66KNVLn/dC8MOEZm99u3bIyMjA3K5HEFBQXddz9raGk2bNtXfv3HjhsHjhw8fxhNPPKEPQDqdDleuXEHLli2NUjeRpTt58iT69Omjvz9z5kwAwPjx47FmzRqsXr0aH374IV5//XWkpqbCw8MDnTt3xmOPPQYAkEql+OOPP/DKK6+gZ8+esLe3R1RUFD755JMa1cGwQ0QmLz8/H7GxsQbLKv9bPHfuHDp37owuXbpg2LBh+Pjjj9G8eXOkpaXhr7/+wvDhw9GhQ4cH2k+zZs3w66+/4siRI3B1dcWnn36KzMxMhh2iWurduzfuNZyflZUV5s2bh3nz5t11HV9fX/z2228PVQfDDhGZvH379qFdu3bVPtazZ0/s3bsX27Ztw7vvvouJEyciOzsb3t7e6NmzJ7y8vB54P//9739x9epVDBw4EHZ2dpgyZQqGDRuG/Pz8unopRCQCjqBMRGYtKCgIa9as0Y+4SkR0Jw4qSERmrWXLlgY9rIiI7sQzO0RERGTReGaHiIiILBrDDhEREVk0hh0iIiKyaAw7REREZNEYdoiIiMiiMewQERGRRWPYISIiIovGsENEREQWjWGHiIiILNr/A3IouxfW7jkiAAAAAElFTkSuQmCC",
      "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:  503651.6890625\n",
      "Средняя цена в контрольной выборке:  515548.73125\n",
      "Средняя цена в тестовой выборке:  502023.62175\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def plot_price_dist(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_dist(train_data, 'Распределение цены в обучающей выборке')\n",
    "plot_price_dist(val_data, 'Распределение цены в контрольной выборке')\n",
    "plot_price_dist(test_data, 'Распределение цены в тестовой выборке')\n",
    "\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": "markdown",
   "metadata": {},
   "source": [
    "### Преобразование целевой переменной в категории дискретизацией"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "labels = [\"low\", \"middle\", \"high\", \"very_high\"]\n",
    "num_bins = 4\n",
    "hist1, bins1 = np.histogram(train_data[\"price\"].fillna(train_data[\"price\"].median()), bins=num_bins)\n",
    "bins1,hist1\n",
    "pd.concat([train_data[\"price\"], pd.cut(train_data[\"price\"], list(bins1))], axis=1).head(10)\n",
    "pd.concat([train_data[\"price\"], pd.cut(train_data[\"price\"], list(bins1), labels=labels)], axis=1).head()\n",
    "train_data['price_category'] = pd.cut(train_data[\"price\"], list(bins1), labels=labels)\n",
    "test_data['price_category'] = pd.cut(train_data[\"price\"], list(bins1), labels=labels)\n",
    "val_data['price_category'] = pd.cut(train_data[\"price\"], list(bins1), labels=labels)\n",
    "\n",
    "sns.countplot(x=train_data['price_category'])\n",
    "plt.title('Распределение цены в обучающей выборке')\n",
    "plt.xlabel('Категория цены')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Конструирование признаков\n",
    "1. Прогнозирование цен недвижимости. Цель технического проекта: Разработка модели машинного обучения для точного прогнозирования рыночной стоимости недвижимости.\n",
    "2. Оценка влияния факторов на цену недвижимости Цель технического проекта: Разработки модели для анализа данных для выявления факторов, которые больше всего влияют на цену\n",
    "\n",
    "### Конструирование признаков\n",
    "Унитарное кодирование - замена категориальных признаков бинарными значениями."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Столбцы train_data_encoded: ['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15', 'price_category_low', 'price_category_middle', 'price_category_high', 'price_category_very_high']\n",
      "Столбцы val_data_encoded: ['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15', 'price_category_low', 'price_category_middle', 'price_category_high', 'price_category_very_high']\n",
      "Столбцы test_data_encoded: ['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15', 'price_category_low', 'price_category_middle', 'price_category_high', 'price_category_very_high']\n"
     ]
    }
   ],
   "source": [
    "categorical_features = ['price_category']\n",
    "\n",
    "\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",
    "\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())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ручной синтез\n",
    "Создание новых признаков на основе экспертных знаний и логики предметной области.\n",
    "Новый признак будет - цена недвижимости за фут."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2.0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "6252  1150900080  20150427T000000  846450.0         4       2.50         3710   \n",
       "4684  9268200600  20140519T000000  413500.0         2       1.00          770   \n",
       "1731  7883603750  20141209T000000  337000.0         3       1.75         1400   \n",
       "4742  3330501545  20141201T000000  330000.0         2       1.00          950   \n",
       "4521   993001332  20140903T000000  407000.0         3       2.25         1430   \n",
       "...          ...              ...       ...       ...        ...          ...   \n",
       "6412   925069071  20150126T000000  750000.0         5       3.75         3500   \n",
       "8285  9268200315  20140828T000000  456000.0         3       2.00         1870   \n",
       "7853  1822300040  20140507T000000  420000.0         2       1.50         1040   \n",
       "1095  7202290160  20150114T000000  435000.0         3       2.50         1560   \n",
       "6929  2621700010  20140508T000000  569000.0         4       2.25         2250   \n",
       "\n",
       "      sqft_lot  floors  waterfront  view  ...  zipcode      lat     long  \\\n",
       "6252      7491     2.0           0     0  ...    98029  47.5596 -122.016   \n",
       "4684      4000     1.0           0     0  ...    98117  47.6959 -122.364   \n",
       "1731      6000     1.0           0     0  ...    98108  47.5283 -122.321   \n",
       "4742      3090     1.0           0     0  ...    98118  47.5510 -122.276   \n",
       "4521      1448     3.0           0     0  ...    98103  47.6916 -122.341   \n",
       "...        ...     ...         ...   ...  ...      ...      ...      ...   \n",
       "6412    101494     1.5           0     0  ...    98053  47.6745 -122.054   \n",
       "8285      8442     1.5           0     0  ...    98117  47.6964 -122.365   \n",
       "7853      3500     1.5           0     0  ...    98144  47.5880 -122.304   \n",
       "1095      3987     2.0           0     0  ...    98053  47.6870 -122.043   \n",
       "6929     41688     2.0           0     0  ...    98053  47.6695 -122.050   \n",
       "\n",
       "      sqft_living15  sqft_lot15  price_category_low  price_category_middle  \\\n",
       "6252           3040        7491               False                  False   \n",
       "4684           1420        5040               False                  False   \n",
       "1731           1030        6000               False                  False   \n",
       "4742           1230        4120               False                  False   \n",
       "4521           1430        1383               False                  False   \n",
       "...             ...         ...                 ...                    ...   \n",
       "6412           3250       38636               False                  False   \n",
       "8285           1640        6174               False                  False   \n",
       "7853           1340        1213               False                  False   \n",
       "1095           1600        3152               False                  False   \n",
       "6929           2350       37920               False                  False   \n",
       "\n",
       "      price_category_high  price_category_very_high  price_per_sqft  \n",
       "6252                False                     False      228.153639  \n",
       "4684                False                     False      537.012987  \n",
       "1731                False                     False      240.714286  \n",
       "4742                False                     False      347.368421  \n",
       "4521                False                     False      284.615385  \n",
       "...                   ...                       ...             ...  \n",
       "6412                False                     False      214.285714  \n",
       "8285                False                     False      243.850267  \n",
       "7853                False                     False      403.846154  \n",
       "1095                False                     False      278.846154  \n",
       "6929                False                     False      252.888889  \n",
       "\n",
       "[2000 rows x 26 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_encoded['price_per_sqft'] = df['price'] / df['sqft_living']\n",
    "val_data_encoded['price_per_sqft'] = df['price'] / df['sqft_living']\n",
    "test_data_encoded['price_per_sqft'] = df['price'] / df['sqft_living']\n",
    "test_data_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Масштабирование признаков\n",
    "Это процесс изменения диапазона признаков, чтобы равномерно распределить значения. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "numerical_features = ['bedrooms']\n",
    "\n",
    "scaler = StandardScaler()\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])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Конструирование признаков с применением фреймворка Featuretools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Study\\3 курс 5 семестр\\AIM\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\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"
     ]
    },
    {
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       "<p>6362 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
       "id                                                                          \n",
       "1121059105   378500.0 -0.382609       2.50         2860     43821     2.0   \n",
       "1026069163   630000.0 -0.382609       2.50         2460     38794     2.0   \n",
       "3751601501   382450.0 -0.382609       2.50         2220     20531     2.0   \n",
       "4322300340   265000.0  0.706185       1.50         1740     12728     1.0   \n",
       "7701960130   820000.0 -0.382609       2.50         2980     18935     1.5   \n",
       "...               ...       ...        ...          ...       ...     ...   \n",
       "8645500900   279000.0  0.706185       2.00         2200      7700     1.0   \n",
       "9528104660   905000.0  0.706185       3.50         2980      3000     2.0   \n",
       "5100402668   495000.0 -0.382609       1.00         1570      5510     1.0   \n",
       "1338300180  1127312.5  0.706185       2.25         3960      8640     2.0   \n",
       "7167000020   792500.0  0.706185       2.50         4290    175421     2.0   \n",
       "\n",
       "            waterfront  view  condition  grade  ...  sqft_lot15  \\\n",
       "id                                              ...               \n",
       "1121059105           0     0          4      9  ...       65340   \n",
       "1026069163           0     0          3      9  ...       51400   \n",
       "3751601501           0     0          3      8  ...       19249   \n",
       "4322300340           0     0          4      7  ...       11125   \n",
       "7701960130           0     0          3     11  ...       18225   \n",
       "...                ...   ...        ...    ...  ...         ...   \n",
       "8645500900           0     0          3      7  ...        7700   \n",
       "9528104660           0     0          3      9  ...        4545   \n",
       "5100402668           0     0          4      7  ...        6380   \n",
       "1338300180           0     2          3      9  ...        8640   \n",
       "7167000020           0     0          3     10  ...       63162   \n",
       "\n",
       "            price_category_low  price_category_middle  price_category_high  \\\n",
       "id                                                                           \n",
       "1121059105               False                   True                False   \n",
       "1026069163               False                  False                 True   \n",
       "3751601501               False                   True                False   \n",
       "4322300340                True                  False                False   \n",
       "7701960130               False                  False                 True   \n",
       "...                        ...                    ...                  ...   \n",
       "8645500900                True                  False                False   \n",
       "9528104660               False                  False                False   \n",
       "5100402668               False                   True                False   \n",
       "1338300180               False                  False                False   \n",
       "7167000020               False                  False                 True   \n",
       "\n",
       "            price_category_very_high  price_per_sqft  DAY(date)  MONTH(date)  \\\n",
       "id                                                                             \n",
       "1121059105                     False      132.342657          8            7   \n",
       "1026069163                     False      256.097561         22            4   \n",
       "3751601501                     False      172.274775         16            7   \n",
       "4322300340                     False      152.298851         12            1   \n",
       "7701960130                     False      275.167785         17           10   \n",
       "...                              ...             ...        ...          ...   \n",
       "8645500900                     False      126.818182         20            6   \n",
       "9528104660                      True      303.691275         27            8   \n",
       "5100402668                     False      315.286624         18            2   \n",
       "1338300180                      True      284.674874         29            7   \n",
       "7167000020                     False      184.731935         16            6   \n",
       "\n",
       "            WEEKDAY(date)  YEAR(date)  \n",
       "id                                     \n",
       "1121059105              1        2014  \n",
       "1026069163              2        2015  \n",
       "3751601501              2        2014  \n",
       "4322300340              0        2015  \n",
       "7701960130              4        2014  \n",
       "...                   ...         ...  \n",
       "8645500900              4        2014  \n",
       "9528104660              2        2014  \n",
       "5100402668              2        2015  \n",
       "1338300180              1        2014  \n",
       "7167000020              0        2014  \n",
       "\n",
       "[6362 rows x 28 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "\n",
    "# Удаление дубликатов по идентификатору\n",
    "train_data_encoded = train_data_encoded.drop_duplicates(subset='id', keep='first')\n",
    "\n",
    "#Создание EntitySet\n",
    "es = ft.EntitySet(id='house_data')\n",
    "\n",
    "#Добавление датафрейма в EntitySet\n",
    "es = es.add_dataframe(dataframe_name='houses', dataframe=train_data_encoded, index='id')\n",
    "\n",
    "#Генерация признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='houses', max_depth=2)\n",
    "\n",
    "feature_matrix"
   ]
  }
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