AIM-PIbd-31-Yakovlev-M-G/lab_3/lab_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "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": 11,
   "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": 11,
     "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": 12,
   "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eNX6gq7z99tt4/fXX8fjjj2PRokXw9PSETCbD888/X+tfQI2pKob33nsPXbt2rXGf6xOQ8vJypKWlYdiwYbc9riAI+OOPPyCXy295TABIT08HAPj5+d3ymD4+Pli7dm2Nj9+YGPTu3RtvvfWW2bZPP/0Uv/76a43lExMTsXr1anz//fc1/jMzGo3o1KkTPvzwwxrLBwUF3TT2ujIajRg2bBheeumlGh9v27at2f3p06fjoYceMtt2syHGb7zxBgYMGICKigrExsZi4cKFyMvLq/HX760MHz4cc+fOBQCkpKRg6dKluOuuu3D06FGzX+z1UdfyU6ZMwXPPPYeUlBTodDocOnQIn376qdk+r7zyCg4cOHDLUZIGgwHDhg1DTk4OXn75ZYSHh8PJyQmpqamYNm1ajZ/ZnTt3IiYmBm+88UadYq6Pun6ugMrP7Ouvv44nnnii2vvmdjp37owPPvgAAEz9agYPHoxjx46ZfVbvu+8+zJo1C6IoIjk5GQsXLsQ999xT44zId/reqIub1YofOXIETz31FJycnPDWW2/hoYceMkuq6nOdmyMmO2SyceNG3HXXXaZfPVXy8vLMqoJDQ0Nx+PBhVFRUNOivhhv/2YiiiAsXLqBz586m8wKAq6sroqOjb3u8EydOoKKi4pYJXtVxRVFEq1atavUPNiEhAYIg1Pgr7vpj7tq1C/369avVP0y1Wl3tOd2qE/G8efPQtWtXPPzwwzc9/4kTJzB06NA7alqsjdDQUBQVFdXqNQEqf9HeuO/1v3iv16lTJ9O+I0eOhEajwZo1a+q8BIO/v7/ZOdu1a4e+ffti06ZNN00oQkJCAFSOtmrdurVpe3l5OZKTk03Ha9WqlWm/G505cwZOTk5mn59HHnkEc+bMwY8//ojS0lIoFIpqr6NarUZMTAwSEhJMyfWJEyfw4osvmvaJj4/HuXPnsGbNGkyZMsW0fefOnTe9DtHR0XBzc8Mbb7xx03gbaqK8un6ugMqRb5mZmTWOlrwdDw8Ps9d48ODBCAgIwKpVq8wGRbRo0cJsP2dnZ0ycOLHGZr/rX9uqJuAq11+r698rNzpz5gzUanW19/j58+dNxweACxcuwGg0Vrv+w4YNw+eff46ysjJs2rQJ06dPN43MA+p3nZsj9tkhE7lcDlEUzbZt2LABqampZtvGjRsHrVZb7dcogGrl6+K7775DYWGh6f7GjRuRlpaGkSNHAgAiIyMRGhqK999/H0VFRdXKZ2VlVYtdLpfXOKz7emPHjoVcLsebb75ZLX5RFE0jMoDKoZ8//fQTevXqdctfTOPHj4fBYMCiRYuqPabX6++oD0pMTAx+/fVXvPPOOzdNZMaPH4/U1FR89dVX1R4rLS1FcXFxvc9f07liYmKwffv2ao/l5eU16NpQRqMRMpnsjhO4qqHZtxqGHx0dDaVSiY8//tjsffHNN98gPz/fNLOvt7c3evTogTVr1pg1JSUlJeG3337DyJEjzX5xq9VqjBw5Et9//z3Wrl2Lu+++u8bRTzKZDB07dkR0dDSio6OrzcRcdczrYxNFEcuXL7/lc+/atSt8fX3x1VdfoaSkxLT9r7/+wtGjR2/7eamtunyugMr+WYsXL8bs2bNvWWtaW7V5jYH/1RjXVCvSrVs3+Pn5YeXKlWbHufFa+fv7o2vXrlizZo3ZZ/vUqVPYsWNHtRGpQGW3gut98sknAGD6f1elb9++kMvlcHJywsqVK7F//36zz3Vdr3NzxZodMrnnnnuwcOFCPPbYY+jbty/i4+Oxdu1as1+1QGU1/HfffYc5c+bgyJEjGDBgAIqLi7Fr1y4888wz1TrQ1Zanpyf69++Pxx57DBkZGVi2bBnatGljauKQyWT4+uuvMXLkSEREROCxxx5DYGAgUlNTsWfPHri6uuL3339HcXExVqxYgY8//hht27Y1mw+kKkk6efIkYmJiEBUVhdDQULz11luYN28eLl26hPvvvx8uLi5ITk7GL7/8gunTp+PFF1/Erl278Prrr+PkyZP4/fffb/lcBg0ahKeffhpLlixBXFwchg8fDoVCgfPnz2PDhg1Yvnw5HnzwwXpdpx07dmDYsGG3rEmZPHky1q9fj3/961/Ys2cP+vXrB4PBgDNnzmD9+vXYvn37bWu8ioqKsG3bNrNtVb9c9+3bB4VCgcDAQMydOxe//fYb7rnnHkybNg2RkZEoLi5GfHw8Nm7ciEuXLtV7KHNcXBycnZ2h1+sRGxuL7777Dvfdd1+NX0y3cvHiRXz//fcAKjt2f/rpp3B1db1lJ2Vvb2/MmzcPb775Ju6++27ce++9OHv2LD777DP07NnTrEPxu+++i+HDhyMqKgpPPvmkaei5vb09Fi9eXO3YU6ZMMb3+NSXEtREeHo7Q0FC8+OKLSE1NhaurK3766adqfXdupFAosHTpUkybNg39+vXD1KlTkZOTg+XLlyMwMLDakG+DwWD2PoiLiwNQ2bxyfSd6g8GA1NRUHDlyBL169ar156rKsWPHoFarb9ocejsZGRmm11ir1eKLL76AnZ1dteRNo9Fg27ZtpmasxYsXIyQkBN26datWu2xnZ4d3330XU6ZMwYABAzBx4kRTE1mLFi3MrtV7772HkSNHIioqCk888YRp6Lmbm1uNNVXJycm49957cffddyMmJgbff/89Hn30UXTp0uWmz3HEiBGYNGkSXnrpJYwZMwb+/v51vs7NVlMO/SJpVA2f/Oeff265X1lZmfjCCy+I/v7+ooODg9ivXz8xJiamxmGdJSUl4quvviq2atVKVCgUop+fn/jggw+KSUlJoijWb5jxjz/+KM6bN0/08fERHRwcxNGjR5sNn61y/PhxcezYsaKXl5eoUqnEkJAQcfz48eLu3bvNzn27243DkX/66Sexf//+opOTk+jk5CSGh4eLM2fOFM+ePSuKoig+++yz4sCBA8Vt27ZVi+nGoedVvvzySzEyMlJ0cHAQXVxcxE6dOokvvfSSePXqVdM+dR16LgiCGBsba7a9pteovLxcXLp0qRgRESGqVCrRw8NDjIyMFN98800xPz+/2vluPN7trt+qVatM+xcWForz5s0T27RpIyqVSlGtVot9+/YV33//fbG8vFwUxfq9J6pudnZ2YkhIiPjvf/9bzM3NFUWxbkPPrz+WWq0Whw8fLsbExNy2rChWDjUPDw8XFQqF6OvrK86YMcMUw/V2794t9uvXT3RwcBBdXV3F0aNHi/Hx8TUeU6fTiR4eHqKbm5tYWlpaqzhqGnqekJAgRkdHi87OzqJarRafeuop8cSJE9Ven5ren+vWrRO7du1qem88/PDD4qVLl8z2mTp1aq0+S9ffbnwf3u5zJYr/e7999NFHZmVv9rm60Y3vV3d3d7Ffv37i1q1bzfa7fh9BEEQ/Pz9x7NixYmJioiiK1YeeV1m/fr3YrVs3UaVSiZ6enuKECRNq/N+0a9cus/fAmDFjxISEhBqfU0JCgvjggw+KLi4uooeHhzhr1qxq7wXcMNWEKIqiVqsVvb29xQceeMBse22uc3MmiOIdtDsQNYC9e/firrvuwoYNG+pd23G9S5cuoVWrVkhOTr5p/4MFCxbg0qVLWL169R2frzlq2bIlFixYUOOMuXR7er0eAQEBGDNmTLU+ctZs9erVWL16dbXZlel/qiaqzMrKarDJG+n22GeHiKiJbdq0CVlZWWYdi4mo8bDPDtmcqtEVt+pA3LlzZ9PyF1R3gwYNMk1GSbV3+PBhnDx5EosWLUK3bt0waNAgqUNqUIGBgTUu2UIkNSY7ZHPUarWpo+LNjB07tomisU1r1qyROgSr9Pnnn+P7779H165dbbIJddiwYbed14pICuyzQ0RERDaNfXaIiIjIpjHZISIiIpvGPjuonEHz6tWrcHFxafSp9YmIiKhhiKKIwsJCBAQEmC1sfSMmOwCuXr3aoAsjEhERUdO5cuUKWrRocdPHmewAcHFxAVB5sVxdXSWOhoiIiGqjoKAAQUFBpu/xm2GyA5iarlxdXZnsEBERWZnbdUFhB2UiIiKyaUx2iIiIyKYx2SEiIiKbxmSHiIiIbBqTHSIiIrJpTHaIiIjIpkma7CxYsACCIJjdwsPDTY+XlZVh5syZ8PLygrOzM8aNG4eMjAyzY2g0GowePRqOjo7w8fHB3Llzodfrm/qpEBERkYWSfJ6diIgI7Nq1y3Tfzu5/Ic2ePRtbtmzBhg0b4ObmhlmzZmHs2LE4cOAAAMBgMGD06NHw8/PDwYMHkZaWhilTpkChUODtt99u8udCRERElkfyZMfOzg5+fn7Vtufn5+Obb77BDz/8gCFDhgAAVq1ahfbt2+PQoUPo06cPduzYgYSEBOzatQu+vr7o2rUrFi1ahJdffhkLFiyAUqls6qdDREREFkbyPjvnz59HQEAAWrdujYkTJ0Kj0QAAYmNjUVFRgejoaNO+4eHhCA4ORkxMDAAgJiYGnTp1gq+vr2mfESNGoKCgAKdPn77pOXU6HQoKCsxuREREZJskTXZ69+6N1atXY9u2bfj888+RnJyMAQMGoLCwEOnp6VAqlXB3dzcr4+vri/T0dABAenq6WaJT9XjVYzezZMkSuLm5mW5cBJSIiMh2SdqMNXLkSNPfnTt3Ru/evRESEoL169fDwcGh0c47b948zJkzx3S/aiExIiIisj2SN2Ndz93dHW3btsWFCxfg5+eH8vJy5OXlme2TkZFh6uPj5+dXbXRW1f2a+gFVUalUpkU/ufgnERGRbbOoZKeoqAhJSUnw9/dHZGQkFAoFdu/ebXr87Nmz0Gg0iIqKAgBERUUhPj4emZmZpn127twJV1dXdOjQocnjJyIiIssjaTPWiy++iDFjxiAkJARXr17F/PnzIZfLMWHCBLi5ueGJJ57AnDlz4OnpCVdXVzz77LOIiopCnz59AADDhw9Hhw4dMHnyZLz77rtIT0/Ha6+9hpkzZ0KlUkn51KgeNBoNtFptvcqq1WoEBwc3cERERGQLJE12UlJSMGHCBGRnZ8Pb2xv9+/fHoUOH4O3tDQD46KOPIJPJMG7cOOh0OowYMQKfffaZqbxcLsfmzZsxY8YMREVFwcnJCVOnTsXChQulekpUTxqNBuHt26O0pKRe5R0cHXEmMZEJDxERVSOIoihKHYTUCgoK4Obmhvz8fPbfkcixY8cQGRmJiS+/B9/g0DqVzdAkYe3SuYiNjUX37t0bKUIiIrI0tf3+lnxSQaLr+QaHokVYhNRhEBGRDbGoDspEREREDY3JDhEREdk0JjtERERk05jsEBERkU1jskNEREQ2jckOERER2TQmO0RERGTTmOwQERGRTWOyQ0RERDaNyQ4RERHZNCY7REREZNOY7BAREZFNY7JDRERENo3JDhEREdk0JjtERERk05jsEBERkU1jskNEREQ2zU7qAIikptFooNVq61VWrVYjODi4gSMiIqKGxGSHmjWNRoPw9u1RWlJSr/IOjo44k5jIhIeIyIIx2aFmTavVorSkBBNffg++waF1KpuhScLapXOh1WqZ7BARWTAmO0QAfIND0SIsQuowiIioETDZIZuRmJjYJGWIiMi6MNkhq1eQkwUAmDRpUr2PUVRU1FDhEBGRhWGyQ1avtKgAADD66VfRrnNkncomHtmHP9YsR1lZWWOERkREFoDJDtkMr4CQOve7ydAkNVI0RERkKTipIBEREdk0JjtERERk05jsEBERkU1jnx2yaXqjEcU6A+xkAlQKGexkzO+JiJobJjtkU0RRxNX8MpxJK8DlnBIUlunNHnext0MLDweEeDoh1NtJoiiJiKgpMdkhm1FgUODHI1eQVaQz2y6XCTAaRYgACsv0SEwrRGJaIeztZPCCD+TOntIETERETYLJDlk9IwCPodNxXKcGdDoo5ALCfFzQ1tcZ3i4qOCjkAIAyvRGZBWW4kluKcxmFKCzTIxVeCJj+JZLLK9Beb4DKTi7tkyEiogbHZIesWrneiAQEwbVHewBAuJ8LBoSp4ais/tZ2UMgR4uWEEC8n9A31wqXsYuw9kYRChSM0env836HLGNLOB629nZv6aRARUSNib02yWmUVBvx8PAV5cIaxvAwdVdkYEeFXY6JzI5kgoLXaGZ1xGZk/LYKDoEexzoDfT6bhj1Np0OkNTfAMiIioKTDZIatkMIrYEp+GjAId7KBHxrpX4CXX3b7gDQQApRcOI9I+E5EhHhAAnMsowg+HNUjP5xISRES2gMkOWaW/zmchJbcUCrmATtCgPO3cHR1PLgD926jxUI8WcLW3Q0GZHhtiryA+Jb+BIiYiIqkw2SGrc/pqPk5cS0JGRPjBCXWv0bkZfzcHPNorGG18nGEUgT/PZmLP2UwYjGKDnYOIiJoWkx2yKoVlFdh3LgsA0KeVJ0IboTOxSiHHqI5+iAr1AgCcTMnHprhUlFawHw8RkTViskNWQxRF/HkmExUGEf5u9ujVqvHmxxEEAb1aeuKezv5QyAWk5Jbiv/9cQXZRw9UiERFR02CyQ1bjXEYRLmWXQC4IGBruA0EQGv2cod7OGN8jCK72dsgvrcD6oynQ5JQ0+nmJiKjhMNkhq1CuN5qar3q29ICXs6rJzq12VuGRnsEIcLdHucGIX+NSkZBW0GTnJyKiO8Nkh6xC3JU8lFYY4OagQI+WTb+8g4NSjge6BqKtb2XH5Z0JGUjM58eHiMga8L81WbyyCgNiL+cCAKJae0Eua/zmq5rYyWW4O8IPPUI8AAAJ+XbwGvUcKgwcqUVEZMm4XARZvKOXc1FuMELtrERbX2mXchAEAf3aqOHqoMCeMxlw7jQMi//OwfedKuBqr6jTsTQaDbRabb1jUavVCA4Ornd5IqLmgskOWbRinR4nruQBqKzVaYpOybXRKdANuuxU/JWqx8kM4KHPY7DqsZ4IcHeoVXmNRoPw9u1RWlL/zs4Ojo44k5jIhIeI6DaY7JBFO5mSD71RhK+rCq3UTlKHY8bPQUT62pcRMeNTnM0oxP0rDuDbaT3RMdDttmW1Wi1KS0ow8eX34BscWudzZ2iSsHbpXGi1WiY7RES3wWSHLJbeYER8auVMyZHBHhZTq3O9isyLeGeoGh8cLcG5jCI8/EUMPp3YHXe186lVed/gULQIi2jkKImImjd2UCaLdSajEKUVBrjY2zXKTMkNxdtJjo0z+qJfGy8Ulxvw5Jqj+PGIRuqwiIjoGiY7ZJFEUUScJg8A0LWFO2QSjcCqLVd7BVZN64Vx3VvAYBQx7+d4vLvtDIxcU4uISHJMdsgiXcktRXZxORRyAREBrlKHUytKOxnef6gzno8OAwB8tjcJz/83Djo919QiIpISkx2ySPHXVjXv4O8KlUIucTS1JwgCno9ui/cf6gI7mYDfTlzF5K+PILe4XOrQiIiaLSY7ZHFKyw24qC0CAEQE3H5kkyV6MLIF1jzeCy4qOxy5lIP7VhzA2fRCqcMiImqWmOyQxTmbUQijCHi7qODt0nRrYDW0fm3U2DijL4I8HaDJKcHYzw5gx+l0qcMiImp2mOyQxalaZLODv3X01bmVdn4u+G1mf0S1rhypNf3/YvHJ7vMQRXZcJiJqKkx2yKLklQvIKtRBJgDtfF2kDqdBeDgp8d0TvTA1KgQA8MHOc/ggJg+CwnprrYiIrAmTHbIol4sr35Kt1c5wUFpPx+TbUchlePO+jnhnbCco5AIOppTBb+K7KNZLHRkRke1jskOWQ5Ah5Vqy0z7ANmp1bvRIr2D88FQfuKlkUPqGYk+6Aqm5pVKHRURk05jskMVQBbZHmVGAyk6GEE/LWgerIfVs6Yl3o9XQpV+Azijg5+MpOHVtWQwiImp4THbIYji26wcAaO3tBLmFz5h8p7yd5MhY+zJaOBpgFIHdZzKx52wmDJxxmYiowTHZIYtgFEVTshPmY5tNWDcS9Tr08jIgKtQLQOUK75uOp6K0nDMuExE1JItJdt55553K2Weff960raysDDNnzoSXlxecnZ0xbtw4ZGRkmJXTaDQYPXo0HB0d4ePjg7lz50KvZ69Pa3M2uwJ2Ll6wE0QEeTpIHU6TEQSgV0tPjOnsD4VcQEpeKdb9o4G2SCd1aERENsMikp1//vkHX3zxBTp37my2ffbs2fj999+xYcMG7Nu3D1evXsXYsWNNjxsMBowePRrl5eU4ePAg1qxZg9WrV+ONN95o6qdAd+jglcpOugEORtjJLOJt2aRaezvj4R5BcHNQoKBMj/VHryApq0jqsIiIbIKd1AEUFRVh4sSJ+Oqrr/DWW2+Ztufn5+Obb77BDz/8gCFDhgAAVq1ahfbt2+PQoUPo06cPduzYgYSEBOzatQu+vr7o2rUrFi1ahJdffhkLFiyAUqmU6mlRHRiNIg6llAEAAh2NEkcjHS9nFR7uGYSt8WlIyS3F5pNpuKudNzq3cL9pmcTExHqdS61WIzg4uJ6REhFZF8mTnZkzZ2L06NGIjo42S3ZiY2NRUVGB6Oho07bw8HAEBwcjJiYGffr0QUxMDDp16gRfX1/TPiNGjMCMGTNw+vRpdOvWrcZz6nQ66HT/ayYoKChohGfWPGk0Gmi12jqVScqpQHapEUZdCXwdJH9LSspBIccDXQOx51wmTqUWYM/ZLJSWG9CrlScE4X+dtgtysgAAkyZNqt95HB1xJjGRCQ8RNQuSfrOsW7cOx44dwz///FPtsfT0dCiVSri7u5tt9/X1RXp6ummf6xOdqserHruZJUuW4M0337zD6OlGGo0G4e3bo7SkpE7l3PpNgHv/iSi7FIdS//BGis56yGQChrTzgZPSDoeTc3AoOQcVRhH9Qr1MCU9pUWWCPvrpV9Guc2Sdjp+hScLapXOh1WqZ7BBRsyBZsnPlyhU899xz2LlzJ+zt7Zv03PPmzcOcOXNM9wsKChAUFNSkMdgirVaL0pISTHz5PfgGh9a63J/pdsgtB0ov/oOybi0bL0ArIggC+rT2gr1Cjn3nshB7OReiKKJ/G7VZDY9XQAhahEVIGCkRkeWTLNmJjY1FZmYmunfvbtpmMBiwf/9+fPrpp9i+fTvKy8uRl5dnVruTkZEBPz8/AICfnx+OHDlidtyq0VpV+9REpVJBpeK6RI3FNzi01l/AxTo9cjXJAIDSpKMAHmrEyKxP1yB3yARgz9ksHNPkQYCAfm28pA6LiMiqSDbsZejQoYiPj0dcXJzp1qNHD0ycONH0t0KhwO7du01lzp49C41Gg6ioKABAVFQU4uPjkZmZadpn586dcHV1RYcOHZr8OVHdXc6ubPJyRikMxbkSR2OZOrdwx5B2PgCAWE0ujl7mdSIiqgvJanZcXFzQsWNHs21OTk7w8vIybX/iiScwZ84ceHp6wtXVFc8++yyioqLQp08fAMDw4cPRoUMHTJ48Ge+++y7S09Px2muvYebMmay5sRLJ2mIAgAc4zPpWOrVwQ4XBiL8uaHEwKRuhcJc6JCIiq2HRQ18++ugjyGQyjBs3DjqdDiNGjMBnn31melwul2Pz5s2YMWMGoqKi4OTkhKlTp2LhwoUSRk21ZTCK0ORU1ux4Mtm5re4hHijTG/DPpVwkwQ/2rXtIHRIRkVWwqGRn7969Zvft7e2xYsUKrFix4qZlQkJCsHXr1kaOjBrD1bxSlBuMcFDI4VxRJnU4ViGqtReKdQYkpBXA+96XUGRkkkhEdDvNb6pashhVtTohXo6w7WU/G44gCBgS7gM3FEOmcsQpnReKdVwehYjoVpjskGSqkp1gT0eJI7EucpmA9khBRfYV6EQ5/jiVztXSiYhugckOSaK0woDMwspZrIOY7NSZHYzI/Hkx5DAiNa8UBy7UbdZqIqLmhMkOSSKlqmOykxLOKovqOmY19DkpCFfmAQCOX8nD+YxCaQMiIrJQTHZIEppcNmE1BLVdGXqEeAAAdp3JREFphcQRERFZHiY7JIkrOaUAgCBPB4kjsX59WnvBz9Ue5Xoj++8QEdWAyQ41ufzSCuSXVkAQgBburNm5U3KZgJEd/aC0kyG9oAyHk7OlDomIyKIw2aEmd+Vafx0/V3so7fgWbAiuDgpEh1cuKXH0Ui7S8zlvERFRFX7TUJO7wv46jSLM1wXtfF0gAtiekI4Kg1HqkIiILAKTHWpSoigiNbeyv04LD/bXaWiD23nDSSVHXkkFDl5gcxYREcBkh5pYfmkFissNkAsC/FztpQ7H5tgr5Ihu7wsAiEvJQ1p+qcQRERFJj8kONamUvMovX19XFezkfPs1hpZeTmjv5wIA2H0mk6OziKjZ47cNNamr15qwAtmE1agGhHnDXiFDdlE5jmtypQ6HiEhSTHaoSVXV7AS6M9lpTA5KOQaGeQMADiXnIK+kXOKIiIikw2SHmkxBaQUKy/QQBMDfjclOYwv3c0GQhwMMRhF7zmZBFNmcRUTNE5MdajKp12p1fFxUnF+nCQiCgLvCfSCXCdDklOBsOtfOIqLmid841GSqkh3Omtx0PByV6NXKEwCw/7wWpRUGiSMiImp6XG6amkxVshPgYVtDzhMTE5ukTH1FBnvgXHohsovLEZOUjbbyJjs1EZFFYLJDTaKkXI+8ksoVuQNspL9OQU4WAGDSpEn1PkZRUVFDhXNTcpmAu9r5YOOxFMSn5sPHT2j0cxIRWRImO9QkqtZq8nRSwl5hG1ULpUUFAIDRT7+Kdp0j61Q28cg+/LFmOcrKmmYNq0APB7T1dca5jCLE5drG9Sciqi0mO9Qkrl5LdvzdbKsJCwC8AkLQIiyiTmUyNEmNFM3N9W+jxsWsYmTrZHBsP6jJz09EJBV2UKYmUbVsgS0mO9bCxV6Bni0rOyt73PU4Siu4UCgRNQ9MdqjRGYwiMgp0AIAATiYoqe7B7nCyE2Hn4oWfEhu/vxARkSVgskONLqtQB4NRhL1CBncHhdThNGt2chk6u+sBAL+dK8YlbbHEERERNT4mO9TorpqasBwgCBwJJDV/BxGlF2OhNwKLNidIHQ4RUaNjskONLs2GOydbI0EAcnZ/BblQuSr6njOZUodERNSomOxQoxJF0dQ52Vbm17EF+pwUjA5zAgAs3JyAcj07KxOR7eLQc2pUhWV6FOsMkAmAj6tK6nDoOl3sUrHf3hPJ2mK8tf5v3B/uXKtyarUawcHBjRwdEVHDYbJDjSq9oLIJS+2sgkLOikRLUDXz85NTJ8Gp4xCoR8/B6n8y8PbTD8BQlHPb8g6OjjiTmMiEh4isBpMdalRVyY6fK/vrWIrrZ35u2ykSezOMyIEj+s5dhZ7qWy8UmqFJwtqlc6HVapnsEJHVYLJDjapqmQhfdk62OF4BIQhqG4HhfmVY988VaErk6KUOQaAH+1YRkW1huwI1GoNRRGZh5WSCrNmxXL6u9ugY4AoA2HsuE0ajKHFEREQNi8kONZrs4srJBJV2Mng4cjJBS9Y3VA2VnQzaonLEp+ZLHQ4RUYNiskONpqoJy8/VnpMJWjgHpRx9Q70AADEXs1FSrpc4IiKihsNkhxpNVedkXw45twodA93g7ayCTm/EwaRsqcMhImowTHao0WTks7+ONZEJAga38wYAnL5aYKqZIyKydkx2qFHo9AbklJQDqOwAS9YhwN0B7f1cAAB7zmZCFNlZmYisH5MdahQZBZW1Oi72dnBScYYDa9KvjRpKuQyZhTqcvlogdThERHeMyQ41igxOJmi1nFR26N3aEwBwIEmLsopbTzRIRGTpmOxQo8i8VrPD9bCsU5cW7vB0UqKswogYdlYmIivH9gVqFJmFlTU7Pi6s2bFGcpmAwW298fPxVJxMzUd7f1f4cRZsomZNo9FAq9XWq6zUCwgz2aEGpzMABWWV87T4uLBmx1oFeToi3M8FZ9ILsetMBib05FpYRM2VRqNBePv2KC0pqVd5qRcQZrJDDS6vvHICQTcHBewVcomjoTsxMMwbl7NLkF1UjlhNLgKkDoiIJKHValFaUoKJL78H3+DQOpW1hAWEmexQg6tKdlirY/0clHIMbKvG9tMZOJKcg6G+UkdERFLyDQ5Fi7AIqcOoM3ZQpgaXW175tmLnZNvQztcFIV6OMBhFHMuxA8ClP4jIujDZoQaXa6rZYYdWWyAIAoa084FCLkCrk8G5y3CpQyIiqhMmO9SgZPbOKDGwGcvWuDooENW6cqFQj7seR04p594hIuvBZIcalNK3suMaOyfbni5B7vBQGiFTOeGzo/lcSoKIrAaTHWpQSr8wAKzVsUUyQUAPTwNEfTmOpenwwxGN1CEREdUKkx1qUEq/NgDYOdlWuSpF5O5bAwB4a3MikrXFEkdERHR7THaoQZmSHXZOtlmFR39DRx8lSisMmP3fOOgNRqlDIiK6JSY71GAKdUYo3P0AsBnLtol4tqc7XFR2iLuSh8/3JkkdEBHRLTHZoQZzMa8CAOBkJ7Jzso3zdpJj4f2VE4st330eJ1PypA2IiOgWmOxQg0nKqUx2PJRs1mgO7u8aiFGd/KA3inj+v3EoKddLHRIRUY2Y7FCDuZhbmey4KzkkuTkQBAGL7+8EHxcVLmYV441fT0sdEhFRjZjsUINJyq2q2WGy01x4OCmx/JFukAnAxtgU/BSbInVIRETVMNmhBpFfUoGM4spZdVmz07xEhXrhuaFtAQCvbTqFC5lFEkdERGSOyQ41iFNX8wEAFblpUPJd1ezMGtIGfUO9UFphwKwfjqGsgstJEJHlsJM6ALINJ1Mqk53yjAsAvKQNhpqcXCZg2SNdMWr5XziTXog3fz+NJWM7V9tPo9FAq9XW6xxqtRrBwcF3GioRNUNMdqhBnEq9luykXQDQW9pgSBI+LvZY9nA3TP72MH48cgV9Wnvhvq6Bpsc1Gg3C27dHaUlJvY7v4OiIM4mJTHiIqM4kTXY+//xzfP7557h06RIAICIiAm+88QZGjhwJACgrK8MLL7yAdevWQafTYcSIEfjss8/g6+trOoZGo8GMGTOwZ88eODs7Y+rUqViyZAns7JjHNaX41Otrdqi56h+mxqy72uCTPy9g3s/xiAhwRRsfFwCAVqtFaUkJJr78HnyDQ+t03AxNEtYunQutVstkh4jqTNKMoEWLFnjnnXcQFhYGURSxZs0a3HfffTh+/DgiIiIwe/ZsbNmyBRs2bICbmxtmzZqFsWPH4sCBAwAAg8GA0aNHw8/PDwcPHkRaWhqmTJkChUKBt99+W8qn1qwUlFVAk1P5a708g7PpNnfPR7dF7OVcHEzKxozvj+HXWf3gqPzfvxrf4FC0CIuQMEIiam4k7Uo6ZswYjBo1CmFhYWjbti0WL14MZ2dnHDp0CPn5+fjmm2/w4YcfYsiQIYiMjMSqVatw8OBBHDp0CACwY8cOJCQk4Pvvv0fXrl0xcuRILFq0CCtWrEB5ebmUT61ZOZNWCABQO8phLONInOZOLhOw/JFu8HFR4XxmEV75OR6iyBF6RCQdixk3YzAYsG7dOhQXFyMqKgqxsbGoqKhAdHS0aZ/w8HAEBwcjJiYGABATE4NOnTqZNWuNGDECBQUFOH365hOc6XQ6FBQUmN2o/k5fG4nVyp1Nh1TJ20WFTyZ0g1wmYFPcVfxwRCN1SETUjEme7MTHx8PZ2RkqlQr/+te/8Msvv6BDhw5IT0+HUqmEu7u72f6+vr5IT08HAKSnp5slOlWPVz12M0uWLIGbm5vpFhQU1LBPqplJuFqZLLZyV0gcCVmS3q298NKIdgCAN39LMC0nQkTU1CRPdtq1a4e4uDgcPnwYM2bMwNSpU5GQkNCo55w3bx7y8/NNtytXrjTq+WxdQtq1ZMeDyQ6Zmz6wNaLb+6LcYMT7MbmQqZykDomImiHJkx2lUok2bdogMjISS5YsQZcuXbB8+XL4+fmhvLwceXl5ZvtnZGTAz88PAODn54eMjIxqj1c9djMqlQqurq5mN6qfcr0R5zMq++mwGYtuJAgCPnioC4I8HZBRbIDX6Nlg9x0iamqSJzs3MhqN0Ol0iIyMhEKhwO7du02PnT17FhqNBlFRUQCAqKgoxMfHIzMz07TPzp074erqig4dOjR57M1RUlYRyg1GuNjbwdtRLnU4ZIHcHBX4fGIkFDLAMawPzhVa3L8dIrJx9f4pXlxcjH379kGj0VQb+fTvf/+7VseYN28eRo4cieDgYBQWFuKHH37A3r17sX37dri5ueGJJ57AnDlz4OnpCVdXVzz77LOIiopCnz59AADDhw9Hhw4dMHnyZLz77rtIT0/Ha6+9hpkzZ0KlUtX3qVEdVPXX6eDvCkEQJI6GLFXHQDc83s0VX8QW4HSeHOG5pQj0cJA6LCJqJuqV7Bw/fhyjRo1CSUkJiouL4enpCa1WC0dHR/j4+NQ62cnMzMSUKVOQlpYGNzc3dO7cGdu3b8ewYcMAAB999BFkMhnGjRtnNqlgFblcjs2bN2PGjBmIioqCk5MTpk6dioULF9bnaVE9VPXX6RDgCkAnbTBk0Ya3dsQHazbBueMQ/HEqDRN6BcNJxaZPImp89fpPM3v2bIwZMwYrV66Em5sbDh06BIVCgUmTJuG5556r9XG++eabWz5ub2+PFStWYMWKFTfdJyQkBFu3bq31OalhXV+zA2RJGwxZNEEQkLNjBQK6DUZBObDtdDoe6BYIGWsEiaiR1avxPC4uDi+88AJkMhnkcjl0Oh2CgoLw7rvv4pVXXmnoGMlCiaJommOnsmaH6NbECh16q/VQyAWk5Jbi0MVsqUMiomagXsmOQqGATFZZ1MfHBxpN5YRhbm5uHMbdjKTmlaKgrPKLK+za+kdEt+OqAIaGV86H9c+lXCRriyWOiIhsXb2SnW7duuGff/4BAAwaNAhvvPEG1q5di+effx4dO3Zs0ADJclU1YYX5uEBpxxE2VHvt/FzQuYUbAGDH6XQUlHLCQSJqPPX6hnr77bfh7+8PAFi8eDE8PDwwY8YMZGVl4csvv2zQAMlymXdOJqqbAWFq+LqqUKY34o9T6TAYOQEPETWOenVQ7tGjh+lvHx8fbNu2rcECIuth3jmZqG7sZDKM6uiPH45okF5QhiPJOYgK9ZI6LCKyQfWq2RkyZEi1mY2p+WHNDt0pVwcFhob7AAD+uZSD1NxSiSMiIltUr2Rn79691SYSpOYlv7QCKde+mNqzZofuQJivC9r7u0AEsD0hHboKg9QhEZGNqXevUs6W27xVNWG18HCAmwMXAKU7M7itD9wcFCgs0+PPs5kQuYAWETWgek9f+sADD0CpVNb42J9//lnvgMg6mJqwWKtDDUBpJ8PdEX5YH3sF5zKK0NKrkDWGRNRg6p3sREVFwdnZuSFjISti6pzM/jrUQPzc7NGnlRdiLmZj79ksBLiz1pCIGka9kh1BEDB37lz4+Pg0dDxkJapqdiIC3CSOhGxJj5YeuJxdjKv5ZdiZkIFx3QPZZE5Ed6xefXbYnt68leuNuJBZCIA1O9SwZIKA4RF+UMgFpOaVIu5KntQhEZENqFeyM3/+fDZhNWPnMwtRYRDh5qBAgJu91OGQjXFzUKB/GzUA4GBSNnJLOPKTiO5MvZqx5s+fDwDIysrC2bNnAQDt2rWDt7d3w0VGFuv6yQTZxECNoVOgGy5kFeFKTil2JmQgihWIRHQH6lWzU1JSgscffxwBAQEYOHAgBg4ciICAADzxxBMoKSlp6BjJwnAyQWpsgiAgur0vlHIZ0vLLcL6Qa68RUf3V6z/I7NmzsW/fPvz222/Iy8tDXl4efv31V+zbtw8vvPBCQ8dIFuY0l4mgJuBqr8CAtpXNWafz5LDzaiFxRERkrerVjPXTTz9h48aNGDx4sGnbqFGj4ODggPHjx+Pzzz9vqPjIwoiiiEQOO6cmEuHviguZRbicXQL1qNlcLJSI6qXezVi+vr7Vtvv4+LAZy8al5JaiUKeHUi5DGx92UqfGJQgCosN9oRBEqALaYcv5YqlDIiIrVK9kJyoqCvPnz0dZWZlpW2lpKd58801ERUU1WHBkeaqasNr6OUMhZz8KanzO9nbo5FG5XtYPpwqhyeYPKiKqm3o1Yy1btgx33303WrRogS5dugAATpw4AXt7e2zfvr1BAyTLwmUiSAotnYw4ePwkENIZr/wSj/97ohdHAhJRrdUr2enUqRPOnz+PtWvX4syZMwCACRMmYOLEiXBwcGjQAKnpaTQaaLXaGh+LScwBALgYCnDs2DGzxxITExs9NmqeBAHI3vYJWj3zFf6+oMWG2BSM7xEkdVhEZCXqlezs378fffv2xVNPPdXQ8ZDENBoNwtu3R+lN+l4F/utb2Ln5YPGLM/BGyuka9ykqKmrMEKmZ0uel4eEIF/zfyUIs3pKIwe284ePCSS2J6PbqlezcddddSEtL49pYNkir1aK0pAQTX34PvsGhZo+VG4DfUytXup/+8iIobuiyk3hkH/5Ys9ysLxdRQ7q3rROOZws4lVqAN39LwIqJ3aUOiYisQL2SHa6NZft8g0PRIizCbNuVnBIgNRVuDgq0ahdWrUyGJqmpwiOJ1afJsiGaOeUyAUvHdca9nx7Alvg03Hc6HcMj/O74uERk2+qV7ABATEwMPDw8anxs4MCB9Q6ILFdWkQ4AoHZWShwJSaUgJwsAMGnSpHof406bOSMC3DB9YGt8vjcJr/96Cn1CveBqr7ijYxKRbat3svPAAw/UuF0QBBgMhnoHRJYrq7Ay2fF2UUkcCUmltKhyNN7op19Fu86RdSrbkM2czw0Nw7ZT6UjWFmPJ1jNYMrbTHR+TiGxXvZOd9PR09tlpZqpqdrydmew0d14BIdWaOW+nIZs57RVyvDO2Ex7+8hB+PKLBfV0D0Ke1V4Mdn4hsS71mheP8Fs2P3mBEbnE5ANbskGXo3doLj/YOBgD856eTKKtgjTIR1axeyQ47KDc/2cXlMIqAvUIGZ1W9KwSJGtR/RobD11WFS9klWLbrvNThEJGFqleyYzQa2YTVzFzfhMWaPbIUrvYKLLqvIwDgq78u4lRqvsQREZElqleys2TJEnz77bfVtn/77bdYunTpHQdFlkfLzslkoYZH+GF0J38YjCJe/ukkKgxGqUMiIgtTr2Tniy++QHh4eLXtERERWLly5R0HRZaHI7HIki24NwJuDgqcvlqAL/dflDocIrIw9Up20tPT4e/vX227t7c30tLS7jgosiyiKHIkFlk0bxcV5o/pAABYvus8zmcUShwREVmSevU0DQoKwoEDB9CqVSuz7QcOHEBAQECDBEaWI7+0AhUGEXKZAA9HTihI0rnVLMwhoohIfxVi03R4Zk0M3h7iBbmssn+ZWq1GcHBwU4VJRBamXsnOU089heeffx4VFRUYMmQIAGD37t146aWX8MILLzRogCS9qiYsLyclZDJ2TqamV9uZm+XOXgh48jOcz3FC9MzFKDjyCwDAwdERZxITmfAQNVP1Snbmzp2L7OxsPPPMMygvr5x7xd7eHi+//DLmzZvXoAGS9ExNWOyvQxKpy8zNyUUyHMsBvIY8jocfnYyStCSsXToXWq2WyQ5RM1WvZEcQBCxduhSvv/46EhMT4eDggLCwMKhU/DK0ReycTJaiNjM3B4oitHFXockpwakSV/QJCm2i6IjIUtWrg3IVZ2dn9OzZEx07dmSiY8PYOZmsiSAIGBruA4VcwNX8MiQV3dG/OSKyAfWeCvfo0aNYv349NBqNqSmrys8//3zHgZFlKCnXo1hXOQ2/mskOWQlXBwX6t1Fjz9ksnMqTw87NV+qQiEhC9frJs27dOvTt2xeJiYn45ZdfUFFRgdOnT+PPP/+Em5tbQ8dIEqpqwnJ3UEBpx1/IZD06BbqhhbsDDKIAr5H/hpHL3BA1W/X69nr77bfx0Ucf4ffff4dSqcTy5ctx5swZjB8/nh0AbQw7J5O1EgQBQ9v7QC6IsA/pgp1JJVKHREQSqVeyk5SUhNGjRwMAlEoliouLIQgCZs+ejS+//LJBAyRpsXMyWTN3RyUi3CqbYdecLMQlbbHEERGRFOqV7Hh4eKCwsHKG0sDAQJw6dQoAkJeXh5IS/nqyJdrCyv5Y7JxM1qqNixFll0+iTC9i9vo46Ll2FlGzU69kZ+DAgdi5cycA4KGHHsJzzz2Hp556ChMmTMDQoUMbNECSToXBiNySa8kOa3bISgkCoN3yERwVAo5r8vDZ3iSpQyKiJlav0ViffvopysrKAACvvvoqFAoFDh48iHHjxuG1115r0ABJOtlF5RABOCjkcFTKpQ6HqN4MhVl4qrsblh/Ow/Ld5zGorTe6BLlLHRYRNZE6JTsFBZWzmNrZ2cHZ2dl0/5lnnsEzzzzT8NGRpKr66/i4qCAIXCaCrNvAYHsklfpj88k0zP5vHDb/uz8clfWefYOIrEidPunu7u61+tIzGAz1DogsR9VILDWbsMgGCIKAxfd3wtFLubioLcbbWxPx1v2dpA6LiJpAnZKdPXv2mN0XRRGjRo3C119/jcDAwAYNjKRnGonFzslkI9wcFfhgfBdM/Powvj+kweC2PojuwAkHiWxdnZKdQYMGVdsml8vRp08ftG7dusGCIumJIqDlHDtkg/q1UePJ/q3w9d/JeHHjCWz99wAEuDtIHRYRNSJOiUs1KtQDeqMIO5kAd0eF1OEQNaiX7g5H5xZuyCupwLM/HkcFh6MT2bQ7SnauXLmCkpISeHl5NVQ8ZCHyyyvfGmpnFWTsnEw2Rmknw6cTusNFZYfYy7n4YMc5qUMiokZUp2asjz/+2PS3VqvFjz/+iCFDhnA9LBuUV1GZ4LAJi2xVsJcj3n2wM2asPYaV+5LQu5Un7gr3kTosImoEdUp2PvroIwCVoxrUajXGjBnDeXVsVH75tWSHnZPJho3s5I8pUSH4LuYy5qyPw9bnBsDfjf13iGxNnZKd5OTkxoqDLExeOWt2qHl4ZVR7xF7OxemrBXj2h+P44ak+UNqxOyORLeEnmqqRO3lAZxQgAPByVkodDlGjslfIseLRyv47Ry/nYvGWBKlDIqIGxmSHqlH4Vk4j4OGohELOtwjZvpZqJ3z4cFcAwJqYy9gYmyJtQETUoPhNRtUofSqTHbULa3Wo+RjWwRfPDQ0DALzySzziU/IljoiIGgqTHapGea1mh/11qLl5bmgYhob7oFxvxNP/dxTZ1ybWJCLrxlXwqJqqmh2OxCJbkpiYWKv9poULSEyV42p+GaZ+sR8fj2uL1i1DGjk6ImpMTHbITGmFEXYe/gBYs0O2oSAnCwAwadKkWpdReAXBb/IHOJUF9HnmfRxdORfBwcGNFSIRNTJJk50lS5bg559/xpkzZ+Dg4IC+ffti6dKlaNeunWmfsrIyvPDCC1i3bh10Oh1GjBiBzz77DL6+/1u8T6PRYMaMGdizZw+cnZ0xdepULFmyBHZ2zOXq6nK+HoIgg71chKOS14+sX2lRAQBg9NOvol3nyFqXSy0RcEgLOHa+G9/FXMZrTHaIrJak32b79u3DzJkz0bNnT+j1erzyyisYPnw4EhIS4OTkBACYPXs2tmzZgg0bNsDNzQ2zZs3C2LFjceDAAQCAwWDA6NGj4efnh4MHDyItLQ1TpkyBQqHA22+/LeXTs0rJuRUAAHeFKHEkRA3LKyAELcIiar1/CwBFsYk4lWeHb+MK0L9rJga34wzLRNZI0g7K27Ztw7Rp0xAREYEuXbpg9erV0Gg0iI2NBQDk5+fjm2++wYcffoghQ4YgMjISq1atwsGDB3Ho0CEAwI4dO5CQkIDvv/8eXbt2xciRI7Fo0SKsWLEC5eXlUj49q3QxrzLZcVMy2SFq62JE0cmdMIrAsz8cx7mMQqlDIqJ6sKjRWPn5lUM9PT09AQCxsbGoqKhAdHS0aZ/w8HAEBwcjJiYGABATE4NOnTqZNWuNGDECBQUFOH36dI3n0el0KCgoMLtRpaRrNTseSq4CTSQIQPb2FejgrUShTo/HV/8DLUdoEVkdi0l2jEYjnn/+efTr1w8dO3YEAKSnp0OpVMLd3d1sX19fX6Snp5v2uT7RqXq86rGaLFmyBG5ubqZbUFBQAz8b61RWYcCVfD0AwIM1O0SVjHq83NcDIV6OSMktxfTvjqKswiB1VERUBxbTA3XmzJk4deoU/v7770Y/17x58zBnzhzT/YKCAiY8AM6kF8IgAoaSfDjIuRgiUZWUi2fxYs8w/Gd3KY5p8jBl5R7MjfKAXCbcspxareYoLiILYBHJzqxZs7B582bs378fLVq0MG338/NDeXk58vLyzGp3MjIy4OfnZ9rnyJEjZsfLyMgwPVYTlUoFlYrDqm8Un1rZjFiefgFCeCeJoyGS3o3D1lUtIuD78CIcSQXufuVr5OxcecvyDo6OOJOYyISHSGKSJjuiKOLZZ5/FL7/8gr1796JVq1Zmj0dGRkKhUGD37t0YN24cAODs2bPQaDSIiooCAERFRWHx4sXIzMyEj0/lSImdO3fC1dUVHTp0aNonZOVOpfwv2QGY7BDVNGw9pUTAYa0Il+73oM9ddyPcreb+bRmaJKxdOhdarZbJDpHEJE12Zs6ciR9++AG//vorXFxcTH1s3Nzc4ODgADc3NzzxxBOYM2cOPD094erqimeffRZRUVHo06cPAGD48OHo0KEDJk+ejHfffRfp6el47bXXMHPmTNbe1NHJazU7uvQLEkdCZFmuH7beAoD9lTzsO5eF0/l2CAj0RQd/V2kDJKJbkjTZ+fzzzwEAgwcPNtu+atUqTJs2DQDw0UcfQSaTYdy4cWaTClaRy+XYvHkzZsyYgaioKDg5OWHq1KlYuHBhUz0Nm1BWYcD5a8Nqy5nsEN1S1yB3FOn0iL2ci92JGXBUyNFS7SR1WER0E5I3Y92Ovb09VqxYgRUrVtx0n5CQEGzdurUhQ2t2zqQXQm8U4aqSwVCYJXU4RBavX6gXinV6nEkvxJb4NNzfLRCB7uzYT2SJLGboOUmrqnNyaw+FxJEQWQdBEBDd3hctvRyhN4r4Le4qMgvLpA6LiGrAZIcA/K9zcqiHRQzQI7IKcpmAUZ38EejugHKDEZuOX0VuMWduJ7I0THYIwP9qdkJZs0NUJwq5DGO6+MPHRYXSCgN+Pp6KgrIKqcMiousw2SGUVRhMa/6EeigljobI+qjs5LivawA8HBUo0unxy7FUlHKSZSKLwWSHTJ2TPZ2UUDvyLUFUH45KOzzQLRAu9nbIK63AXxkKyJzcpQ6LiMBkh/C/JqyOgW4QhFtPf09EN+dir8C47i3grLJDoV6A7yOLkVfGKh4iqTHZIVPn5E6BnBiN6E65OSgwrnsgHOQilOoQLNiXg2yulE4kKSY7ZKrZ6RToJnEkRLbB3VGJAT4V0BdmQ5Ovx8SvDyOHo7SIJMNkp5m7vnNyRyY7RA3GRQFkrHsF7vYynEkvxIQvD3EeHiKJMNlp5s5e1zmZs78SNSx9TioWDvaCr6sKZzMKMX5lDFLzSqUOi6jZYbLTzLFzMlHjauFqhw1P90ULDwdcyi7B+JUxuKQtljosomaF0+U2c/HsnEzUqBITE9EewBv9XLBgXzlS80px/6f7sWCQJ4Ldbj6Jp1qtRnBwcNMFSmTDmOw0c+ycTNQ4CnIqF9SdNGmSaZvM0R2+Dy9Cnk8rPLvpIrJ+Wghd6pkayzs4OuJMYiITHqIGwGSnGWPnZKLGU1pUAAAY/fSraNc50rS93AAcyDIiB64ImPweennpEegompXN0CRh7dK50Gq1THaIGgCTnWbs9NUC6I0i1M7snEzUWLwCQtAiLMJsW1CYEX+cSkeythiHtAoMbuuNLkHu0gRI1Aywg3IzFnclDwDQNcidnZOJmpBCLsM9nfzR8Vpfub3nsvD3BS1EUbxNSSKqDyY7zdhxTS4AoFuwh8SREDU/MpmAIe180DfUCwAQezkXW+PTUa43ShwZke1hM1Yzdn3NDhE1PUEQ0LOlJ5xVdtiVmIELWUXIjS1HTw6OJGpQTHaaqaxCHVJySyEIQOcW7JxMJKX2/q5wc1BgS3wasovK8WeJAvYhXaQOi8hmsBmrmaqq1QnzcYaL/c3n+iCiphHg7oBHegbBx0WFcqMAn/ELsflcMfvxEDUAJjvNlKm/ThD76xBZChd7BR6KbIFgRwMEmRzfxhVgxvfHkF9aIXVoRFaNyU4zdVyTBwDoFuwuaRxEZM5OLkMPLwNydn0JOxmw7XQ6Ri3/C8eu/UAhorpjstMMGYwiTqbkAQC6MtkhsjiCABTG/obFQ7wQ7OmI1LxSjF8Zg5X7kmA0slmLqK6Y7DRD5zMLUVxugJNSjjAfF6nDIaKbCPNUYvO/+2N0Z3/ojSLe+eMMJn97mCunE9URk51mKO5aE1bnFu6QyziZIJElc7VX4NMJ3fD2A51gr5DhwIVs3P3Rfqw/eoWdl4lqiclOM8T+OkTWRRAEPNo7GFv/PQDdgt1RqNPjpY0n8eSao8gsKJM6PCKLx2SnGeJkgkTWqbW3Mzb+qy/+MzIcSrkMu89kYhhreYhui5MKNjOFZRU4l1m50jk7JxNZH7lMwL8GheKudj54YUMcTqUW4KWNJ/HzsRS8/UAntPZ2hkajgVarrdfx1Wo1V1onm8Nkp5k5mZIPUQRaeDjAx8Ve6nCIqJ7a+blg0zP98O2BZHy08zwOXczB3cv/wuTuaiyZOhSlRYX1Oq6DoyPOJCYy4SGbwmSnmWETFpHtsJPLMH1gKEZ29Mdrm05h37ksfHMkE+4PL8VQtRFtgwPqdLwMTRLWLp0LrVbLZIdsCpOdZoYrnRPZniBPR6x+rCd+P5mG138+gXx1MOIBiHpX9G+jhkohlzpEIkmxg3IzIooia3aIbJQgCLi3SwA+vtsbhSe2AwBOXS3Ad4cu41xGITswU7PGZKcZSckthbaoHAq5gIgAV6nDIaJG4KKSIWfbJxjoUwEPRwVKyg3441Q6Np9MQ1GZXurwiCTBZKcZOX6tVqeDvyvsWa1NZNO87UU82jsYvVp5QiYAF7XF+L9DlxGfms9aHmp22GenGTl2ubK/DpuwiKxDYmLiHZWxk8kQ1doLbX2csSsxE+kFZfjzTCbOpRdiaHsfuDsqGzJcIovFZKcZib2W7ES29JQ4EiK6lYKcLADApEmT6n2MoqIi099ezio81KMFTlzJw8GkbKTkleL7wxpEtfZCtyB3yLhsDNk4JjvNRJFOj9NX8wEAPVtyJBaRJSstKgAAjH76VbTrHFmnsolH9uGPNctRVma+jIRMENAt2AOtvZ2x+0wGruSU4u8LWpzLKMSICD94Ov2vlqc+NUoAJyQky8Vkp5mI0+TBeG0yQX83B6nDIaJa8AoIQYuwiDqVydAk3fJxNwcFHugaiIS0Avx1XovMQh1+PKLBgDA15Nl3VqPECQnJUjHZaSaOXMoBAPRkExZRsycIAiIC3BDi6YQdiem4klOKPWez4AFXyBzdMXLyzDrXKHFCQrJkTHaaiaPXkp0ebMIiomuc7e3wQNdAxF3Jw4GkbOQaXRDw+KeAp6HONUpEloxDz5uBCoMRxzV5AFizQ0TmhGt9eR7pGQRHlEHu5I5TOi/EJGXDyCHqZCOY7DQDCVcLUFphgJuDAm28naUOh4gskNpZha64hMJjmwFUNn3/cjwVxTpOREjWj8lOM/BPVRNWiAeHmBLRTckgImfnSoQrc6GQC0jJLcWPRzRIzS2VOjSiO8JkpxmoSnZ6tmITFhHdnq9dKR7pGQxPJyWKyw346XgKTqTkceZlslpMdmycKIo4eqlyMkHOr0NEteXppMQjPYPQztcFogjsPZuFP89mwmBkwkPWh8mOjTufWYTs4nI4KOToFOgudThEZEUUchlGRPiifxs1AOBUagF+OZ6KknL24yHrwmTHxh26mA2gcsi50o4vNxHVjSAIiAzxwL1dAqCUy5CaV4p1/1xBVqFO6tCIao3ffjYuJqky2enT2kviSIjImrVSO+HhnkFwd1CgsEyPjbEpuJxdLHVYRLXCZMeGGY0iDidXdk7u05qdk4nozng6KfFwzyC08HBAucGIX09cxanUfKnDIrotJjs27HxmEXLYX4eIGpC9Qo77uwaivV9lx+XdZzJx4IIWHKhFlozLRdgw9tchosYglwkY1sEXrg4KHE7OwdHLuUh3lANyhdShEdWI34A2rCrZYX8dImpogiCgT2svDOvgC5kApJTI4fvwWyjUGaUOjagaJjs2iv11iKgpdPB3xX1dA6EQRNgHReA/u7XsuEwWh8mOjTqbUcj+OkTUJII9HTHYVw99fibSigwY+9lBxF3JkzosIhMmOzbqwAUtAKBXK0/21yGiRueqFJH+fy+gtYcdsovL8ciXMdiZkCF1WCQhURQtZokRdlC2UTvjrwAAWjmU4dixY7Uul5iY2FghEZGNMxTnYtFgL3yVYMTes1l4+v+OYsG9EZgS1VLq0KiJGIwizmUUIimrCJezSwAAjnI7qO+fh6uFenSXKC4mOzboQvIlHErSQlDY4+3npuLNrEt1PkZRUVHDB0ZENs9BIcPXU7rjtU2nsO6fK3jj19NIzS3Fy3eHQyYTpA6PGlF+aQX+OJWGjALz2bULjDI4tesHKV9+Jjs26ODZNAgKeyigx6z5H0Kowxss8cg+/LFmOcrKyhovQCKyaXZyGZaM7YQgT0e8t/0svth/Eal5pXj/oS6wV8ilDo8aQbK2GNtOpaPcYITKToYuQe4IVTtBYSfDhQsXsPnHVfB+8A3J4mOyY4NOZpQDAPwcBQS1jahT2QxNUmOERETNjCAImHlXGwS42+OljSex+WQaMgt0+HJKJNwdlVKHRw0oNbcUW+LTYDCK8Hezx90d/eBq/785l/wdRBTG/ga5bL5kMbLnqg06kVFZhehjbxkdw4io+XqgWwuseawXXFR2OHIpB2M/P4grOSVSh0UNJLtIh99PXoXBKKK12gnjurcwS3QshaTJzv79+zFmzBgEBARAEARs2rTJ7HFRFPHGG2/A398fDg4OiI6Oxvnz5832ycnJwcSJE+Hq6gp3d3c88cQTzbq/SX5JBZJyKwAAPvac3IuIpNe3jRobZ/SFv5s9LmYV44HPDuJkSp7UYdEd0lUY8OuJq9DpjfB3s8fIjn6QW2i/LEmTneLiYnTp0gUrVqyo8fF3330XH3/8MVauXInDhw/DyckJI0aMMOtPMnHiRJw+fRo7d+7E5s2bsX//fkyfPr2pnoLFibmohVEEKrKvwJGNlERkIdr5ueCXZ/oh3M8F2iIdHv7iEP48w6Hp1mzvuSwUlunh5qDAmC4BsJNbbmORpJGNHDkSb731Fh544IFqj4miiGXLluG1117Dfffdh86dO+O7777D1atXTTVAiYmJ2LZtG77++mv07t0b/fv3xyeffIJ169bh6tWrTfxsLMO+c1kAgNLk4xJHQkRkzs/NHhv+FYUBYWqUVhjw5Jqj+L9Dl6UOi+rhfEYhzqQXQgAwIsIXDhbe8dxif/snJycjPT0d0dHRpm1ubm7o3bs3YmJi8MgjjyAmJgbu7u7o0aOHaZ/o6GjIZDIcPny4xiQKAHQ6HXS6/w2NKygoaLwn0oREUcSeM9eSnYtHAdwtbUBERDdwsVfg22k98crP8dgQm4LXN53ChYxCTO3igryc7HodU61WIzg4uIEjpZsp1unx55lMAEDPlp7wd3OQOKLbs9hkJz09HQDg6+trtt3X19f0WHp6Onx8fMwet7Ozg6enp2mfmixZsgRvvvlmA0csvTPphUgvKINSDpRp4qUOh4ioRgq5DO8+2Bkt1U54b/tZrIm5jK/+exIZPy+GUVf3dbUcHB1xJjGRCU8TOXBBizK9Ed4uKvRqZR1rL1psstOY5s2bhzlz5pjuFxQUICgoSMKIGsaes5WZdicfFc4bKiSOhojo5qqGprfxccZzPx4Dgjsj7PnvMMBfgGsdBvNkaJKwdulcaLVaJjtNIFsnIDGjEAAwJNzHYjsk38hikx0/Pz8AQEZGBvz9/U3bMzIy0LVrV9M+mZmZZuX0ej1ycnJM5WuiUqmgUqkaPmiJ7T1b2YTV3V+FnyWOhYioNkZE+GHxEC88tyEBZW4+2Jclw6iOfgjxcpI6NLqRIENcTmXfnIgAV/i52kscUO1ZbNfpVq1awc/PD7t37zZtKygowOHDhxEVFQUAiIqKQl5eHmJjY037/PnnnzAajejdu3eTxyyl/NIKxF7OBQB097O9RI6IbFcrdwXSvpsNL5UR5Xojfo27iuOaXItZRJIqOXcehrwKGVR2MvQN9ZI6nDqRtGanqKgIFy5cMN1PTk5GXFwcPD09ERwcjOeffx5vvfUWwsLC0KpVK7z++usICAjA/fffDwBo37497r77bjz11FNYuXIlKioqMGvWLDzyyCMICAiQ6FlJ4+/zWhiMItr4OMPX2WIr7IiIamQsyccAHz3O6dVISCvA/vNaZBXpMKSdj0UPaW4uyg0i3PpNAAD0buUJR6V1fc9IGu3Ro0dx1113me5X9aOZOnUqVq9ejZdeegnFxcWYPn068vLy0L9/f2zbtg329v+rOlu7di1mzZqFoUOHQiaTYdy4cfj444+b/LlIrapn/OC23gC4rhURWR+5AES394GXkxJ/X9AiMa0Q2qJyjO7kDzcHy5uVtznZfqEYdi5qOMpFdGrhJnU4dSZpsjN48OBbVlMKgoCFCxdi4cKFN93H09MTP/zwQ2OEZzX0BqNpcq4h7X2AfI3EERER1Y8gCOge4gG1iwrbTqUjq1CHH49oMCLCD63U7McjhWKdHj+dqRwlF+5mgJ3M+mrarKseimp09HIucksq4O6oQK+Wnjh5gskOEUkjMTGxQcoEezpiQq8gbI1PR3pBGX47cRW9WnmidytPyATrGAFkK1YfvIQCnREVOVcREqSWOpx6YbJjA3YmXKvVCWfbNhFJoyCncjTopEmT6n2MG9c1dLFXYFxkIP46p8XJ1HwcSc5BRkEZRkT4WfyMvbYiv7QCX+xLAgDk/b0Wsq7PSRxR/TDZsXKiKGJHQuUEisM73Hy4PRFRYyotqpyJfvTTr6Jd58g6lU08sg9/rFlutu5hFTuZDHeF+8DPzR5/nsnE5ewS/HhEg1Ed/eHnZj1Dn63V139dREGZHsFudricuB8Akx2SwJn0QlzJKYXKToaBba2zepGIbIdXQAhahEXUqUyGJum2+7T3d4XaWYUt8WnIL63Ahtgr6Buqhg9Hpzea7CIdvv07GQDwSIQL/oL1Xmy2eVi5Hacrm7AGhHlb3VBAIqK68HZRYUKvIIT5OMMoAn9f0OJAlh1kjtY3OsgarNyXhOJyAzoFuqF3oHXP38Zkx8qZmrAifG+zJxGR9VPZyTGyox+GXluqIKNMBv/HPsGJDN3tC1OtZRSU4buYyhXpXxjeFoKVdwpnsmPFLmmLcfpqAeQyAUPDfW5fgIjIBgiCgI6BbnikZxBcFUbYOXti4b4cvLf9DPQGo9Th2YRP/7wAnd6Ini09MKitt9Th3DEmO1ZsS3waAKBvqBe8nK27ipGIqK7Uzirc5atHYdwfEAGs2JOE8V/EQJNdInVoVu1KTgnW/VM5hckLw9tZfa0OwGTHqv1+4ioA4J7O/rfZk4jINtnJgJztK/BClDtcVHY4psnDyOX7sf6fK1xbq54+3n0eFQYRA8LU6NPautbAuhkmO1bqQmYRzqQXwk4mYEQEh5wTUfPWL8gBW58bgF4tPVFcbsBLP53E0/8Xi+wi9uWpi6SsIvx0LAVAZa2OrWCyY6W2nKxswuofpoa7o1LiaIiIpBfk6Ygfp/fBf0aGQyEXsCMhAyOW7cfuxAypQ7MaH+08B6MIRLf3Rdcgd6nDaTBMdqzU5pNVTVjNa3V3IqJbkcsE/GtQKDbN7Ie2vs7QFpXjiTVHMe/neBTr9FKHZ9ES0wqw+doP6ReGt5U4mobFZMcKnUkvwPnMIijlMg45JyKqQUSAG36b1R9P9m8FAJWzLn/8Fw5dzJY4Msv1wY5zACr7gbb3d5U4mobFZMcK/RRb2Z56V7g3XO0VEkdDRGSZ7BVyvHZPB/zwZG8EuNnjcnYJHvnyEN749RRreW4QdyUPuxIzIBOA2cNsq1YHYLJjdfQGI345XtmENa57C4mjISKyfH3bqLFt9kBM6BUEAPgu5jJGLNuPgxe0EkdmGURRxNI/zgAAxnZvgVBvZ4kjanhcX8DK/HVeC22RDp5OSgxux4kEiYgAIDEx8bb7PNQSCLP3xGdH85GSW4pHvz6MoS1VeKybOxwVdf/tr1arERwcXI9oLcues5mIuZgNpZ0Mz0eHSR1Oo2CyY2U2XhsSeG+XACjtWDFHRM1bQU4WAGDSpEm1LiMoHeAxaBpcuo/G7ks6bD8Rj+ztn6Is+Vidzu3g6IgziYlWnfDoDUYs2VpZq/NYv5Zo4eEocUSNg8mOFckvqcDOhMohlA9GsgmLiKi0qAAAMPrpV9Guc2Sdyh6NO4oLshZQuPvBd/xCtHA0oIuHAfby25fN0CRh7dK50Gq1Vp3sbIhNwfnMIrg7KvDM4DZSh9NomOxYkd9PXkW53oh2vi6ICLCtnvJERHfCKyAELcIi6lQmQ5OEvz6chaiXVuOq3hkpJXJklivQP1SNjoGuNrFMwq0UlFWYRmA9OyQMbg62O+CF7SBWQhRFrD1cuVbJQz1a2PyHkIioKYgVZWijLMAjPYPg46JCud6IP89mYkNsCrQ2Pvvysp3noS3SobXaCZP6WG/tVG0w2bESx6/kITGtACo7GZuwiIgamI+rPR7uGYRBbb2hkAtIyy/Dj0c0OHBBiwobXEn9THoB1sRcAgAsuDcCKrtatN1ZMSY7VmLtocpanXs6B3B5CCKiRiATBHQNcsfkPiEI9XaCUQSOXs7FdzGXcS6j0GYWFhVFEW/8ehoGo4iRHf0wsK231CE1OiY7ViCvpNy0PIStVzUSEUnNxV6BezoHYExnf7jY26FIp8cfp9Lx07FUZBVaf9PWD0c0OJKcA3uFDK/d00HqcJoEOyhbgY2xKdDpjejg72pTC7MREVmy1t7OCPZ0ROzlXBy9nIvUvFL8eESDjoFuCJE6uHq6klOCxVsq5yR6aUQ4At0dJI6oaTDZsXAGo4jvYi4DACb2CWbHZCKiJmQnl6F3ay+0D3DFgfNanMssQnxqPs4ICrh0vwd6o/U0bRmNIl7ccAIl5Qb0aumJaX1bSh1Sk2EzloXbcTodmpwSeDgqMLYbOyYTEUnB1V6BkZ38Ma57INTOSlSIAjyH/QvPbcvC5pNXraI/zxf7L+Jwcg4cFHK891BnyGTN58czkx0L99VfFwEAk/qEwEFp273liYgsXQsPR0zoGYyuHnoYivOQVmTArB+O474VB3DAgtfaOnBBi/e2V86U/Po9HRDi5SRxRE2LyY4Fi72cg2OaPCjlMkyOstYWYiIi2yKTCQh1MSL1y6cwvoMznJRynEzJx8SvD2PyN4dxKjVf6hDNXM0rxbM/HodRrJx9v2pB1OaEyY4F+2p/MgDg/m4B8HGxlzgaIiK6nlheikc6umDfS3dhWt+WUMgF/HVei3s++RvP/ngc5zMKpQ4ReSXleGzVP8gpLkdEgCveur9js+z7yWTHQl3ILMT2hHQAwJMDWkscDRER3YzaWYUF90Zg95zBuK9rAADg9xNXMXzZfsxcewyJaQWSxFWk02Pqqn9wNqMQPi4qrJwUCXtF8+wOwdFYFurj3RcgikDvQBWKUs/jWGrtyyYmJjZeYEREVKNgL0csf6Qbpg9sjY93n8f20xnYEp+GLfFpGNbBFzMGh6J7sEeTxJJXUo7p38XixJU8uDsq8P2TvRHkaZsrmtcGkx0LdCGzEL+fqJxE8Je3pmN9ZnK9jlNUVNSQYRERUS1EBLjhi8k9cCa9AJ/+eQFb4tOwMyEDOxMy0DXIHY/3b4WRHf2gkDdO48olbTEeX/0PLmqL4ayyw5rHeqGtr0ujnMtaMNmxQB/vvgARQMm5GIx/7Bn4BofWqXzikX34Y81ylJWVNU6ARER0W+F+rvj00e54PrMIX+xLwq9xVxF3JQ///vE4/FztMalPMB7o3qLBJvYTRRGb4lKx4LcE5JdWIMDNHt9M64n2/q4NcnxrxmTHwpzLKMTv15aGyDvwI3yj30eLsIg6HSNDk9QYoRERUT208XHGew91wUt3h+OHwxr836HLSC8ow/s7zuH9HefQq5UnHugWiFEd/eHmqKjXORLTCrB02xnsPZsFAOgS5I6vpkRycMs1THYszJKtiaa+OuszL0odDhERNRBvFxWeiw7Dvwa3xpaTaVh/9AoOXczBkeTK2/xfT6NPqBcGhqnRp7UX2vq6QGl386auvJJy7D+vxYajV/DX+co5fpRyGf49tA2eHhTaaM1k1ojJjgX5+7wWe85mwU4mYHJnV6yXOiAiIrql+g4I8dfp8FIPFbLa++AvTSn2a0qhyddj/7ks7D9XWTtjJwNauNpB7SCHh4MMMkGAKALFRjmySkVcyCxC1WoVMgEY2ckfs6Pboo2Pc0M9PZvBZMdCGIwiFm+t/NBM6hOCABfrX1mXiMhWFeRUJiSTJk2q5xEEAOZLTCi8gmDfqjscWnWDMiAcsHfGpTw9LuXpb3qUdr4uGBzujYm9QhDs1XxHW90Okx0LsTH2ChLTCuBib4d/Dw3DpbOnpA6JiIhuorSocu6c0U+/inadI+tUtmoQya3KiiJQbChHYYWAUr0AnREQRQHFBTk4vn09li1+HaP6dYW/W/NYtfxOMdmxADnF5Xjnj8o1S/49JAyeTkpckjYkIiKqBa+AkHoPIqlP2ZTzp/FX3B/o5vcWE506YO8lC/D21kTkllQg3M8F0/q1lDocIiIim8JkR2IxSdnYGJsCQQDeHtuJveeJiIgaGL9ZJVSs02PezycBABN7BzfZNOJERETNCfvsSOitLQm4lF2CADd7zB0RLnU4RERkJeo75F2tViM4OLiBo7F8THYksuN0On48cgWCAHwwvivcHOo3ayYRETUfdzrk3cHREWcSE5tdwsNkRwJX80rxn5/jAQDTB7RGVKiXxBEREZE1uJMh7xmaJKxdOhdarZbJDjWusgoD/vV9LHKKyxER4Io5w9tKHRIREVmZ+gxbb87YQbkJiaKIN349hZMp+XB3VGDlpEio7ORSh0VERGTTmOw0oa//Ssb6oymQCcAnE7ohyJNTexMRETU2JjtNZNPxVNPaV/NGtseAMG+JIyIiImoe2GenCew5m4kXN5wAADzRvxWeHNBK4oiIiKi5qs+w9foOdbcUTHYa2e7EDMz4/hj0RhFjugTg1VHtIQiC1GEREVEzc+crtQNFRUUNFU6TYrLTiLadSsezPx5DhUHEyI5++OChLpDJmOgQEVHTa4iV2svKyhojtEbHZKeR5BSX44X1cagwVNbofDS+C+y47hUREUnsTlZqt1ZMdhqJp5MSnz7aHT8dvoApYUacPBFXp/LW3j5KRERkKZjsNKJQxzJ8+8wwrCgpqfcxrLV9lIiIyFIw2WlEWq0WpSUlmPjye/ANDq1TWWtvHyUiIrIUTHaagG9waLNrHyUiIrIU7DFLRERENo3JDhEREdk0JjtERERk05jsEBERkU2zmWRnxYoVaNmyJezt7dG7d28cOXJE6pCIiIjIAthEsvPf//4Xc+bMwfz583Hs2DF06dIFI0aMQGZmptShERERkcRsItn58MMP8dRTT+Gxxx5Dhw4dsHLlSjg6OuLbb7+VOjQiIiKSmNXPs1NeXo7Y2FjMmzfPtE0mkyE6OhoxMTE1ltHpdNDpdKb7+fn5AICCgoIGja1q9uOU86ehK63bLMpV8+ykXzqHJCdHlmVZizg3y7Isy7JsXctmpSQDqPxObOjv2arjiaJ46x1FK5eamioCEA8ePGi2fe7cuWKvXr1qLDN//nwRAG+88cYbb7zxZgO3K1eu3DJXsPqanfqYN28e5syZY7pvNBqRk5MDLy8vCIIgYWSWq6CgAEFBQbhy5QpcXV2lDsdm8Lo2Hl7bxsHr2jh4XetHFEUUFhYiICDglvtZfbKjVqshl8uRkZFhtj0jIwN+fn41llGpVFCpVGbb3N3dGytEm+Lq6soPYiPgdW08vLaNg9e1cfC61p2bm9tt97H6DspKpRKRkZHYvXu3aZvRaMTu3bsRFRUlYWRERERkCay+ZgcA5syZg6lTp6JHjx7o1asXli1bhuLiYjz22GNSh0ZEREQSs4lk5+GHH0ZWVhbeeOMNpKeno2vXrti2bRt8fX2lDs1mqFQqzJ8/v1rzH90ZXtfGw2vbOHhdGweva+MSRPF247WIiIiIrJfV99khIiIiuhUmO0RERGTTmOwQERGRTWOyQ0RERDaNyQ6ZrFixAi1btoS9vT169+6NI0eO3HTfr776CgMGDICHhwc8PDwQHR19y/2bs7pc1+utW7cOgiDg/vvvb9wArVhdr21eXh5mzpwJf39/qFQqtG3bFlu3bm2iaK1HXa/rsmXL0K5dOzg4OCAoKAizZ89GWVlZE0VrHfbv348xY8YgICAAgiBg06ZNty2zd+9edO/eHSqVCm3atMHq1asbPU6b1TArVJG1W7dunahUKsVvv/1WPH36tPjUU0+J7u7uYkZGRo37P/roo+KKFSvE48ePi4mJieK0adNENzc3MSUlpYkjt2x1va5VkpOTxcDAQHHAgAHifffd1zTBWpm6XludTif26NFDHDVqlPj333+LycnJ4t69e8W4uLgmjtyy1fW6rl27VlSpVOLatWvF5ORkcfv27aK/v784e/bsJo7csm3dulV89dVXxZ9//lkEIP7yyy+33P/ixYuio6OjOGfOHDEhIUH85JNPRLlcLm7btq1pArYxTHZIFEVR7NWrlzhz5kzTfYPBIAYEBIhLliypVXm9Xi+6uLiIa9asaawQrVJ9rqterxf79u0rfv311+LUqVOZ7NxEXa/t559/LrZu3VosLy9vqhCtUl2v68yZM8UhQ4aYbZszZ47Yr1+/Ro3TmtUm2XnppZfEiIgIs20PP/ywOGLEiEaMzHaxGYtQXl6O2NhYREdHm7bJZDJER0cjJiamVscoKSlBRUUFPD09GytMq1Pf67pw4UL4+PjgiSeeaIowrVJ9ru1vv/2GqKgozJw5E76+vujYsSPefvttGAyGpgrb4tXnuvbt2xexsbGmpq6LFy9i69atGDVqVJPEbKtiYmLMXgcAGDFiRK3/J5M5m5hBme6MVquFwWCoNuO0r68vzpw5U6tjvPzyywgICKj24WzO6nNd//77b3zzzTeIi4trggitV32u7cWLF/Hnn39i4sSJ2Lp1Ky5cuIBnnnkGFRUVmD9/flOEbfHqc10fffRRaLVa9O/fH6IoQq/X41//+hdeeeWVpgjZZqWnp9f4OhQUFKC0tBQODg4SRWadWLNDd+ydd97BunXr8Msvv8De3l7qcKxWYWEhJk+ejK+++gpqtVrqcGyO0WiEj48PvvzyS0RGRuLhhx/Gq6++ipUrV0odmlXbu3cv3n77bXz22Wc4duwYfv75Z2zZsgWLFi2SOjQiE9bsENRqNeRyOTIyMsy2Z2RkwM/P75Zl33//fbzzzjvYtWsXOnfu3JhhWp26XtekpCRcunQJY8aMMW0zGo0AADs7O5w9exahoaGNG7SVqM971t/fHwqFAnK53LStffv2SE9PR3l5OZRKZaPGbA3qc11ff/11TJ48GU8++SQAoFOnTiguLsb06dPx6quvQibjb+r68PPzq/F1cHV1Za1OPfBdSFAqlYiMjMTu3btN24xGI3bv3o2oqKiblnv33XexaNEibNu2DT169GiKUK1KXa9reHg44uPjERcXZ7rde++9uOuuuxAXF4egoKCmDN+i1ec9269fP1y4cMGUQALAuXPn4O/vz0Tnmvpc15KSkmoJTVVCKXLpxXqLiooyex0AYOfOnbf8n0y3IHUPabIM69atE1Uqlbh69WoxISFBnD59uuju7i6mp6eLoiiKkydPFv/zn/+Y9n/nnXdEpVIpbty4UUxLSzPdCgsLpXoKFqmu1/VGHI11c3W9thqNRnRxcRFnzZolnj17Vty8ebPo4+MjvvXWW1I9BYtU1+s6f/580cXFRfzxxx/Fixcvijt27BBDQ0PF8ePHS/UULFJhYaF4/Phx8fjx4yIA8cMPPxSPHz8uXr58WRRFUfzPf/4jTp482bR/1dDzuXPniomJieKKFSs49PwOMNkhk08++UQMDg4WlUql2KtXL/HQoUOmxwYNGiROnTrVdD8kJEQEUO02f/78pg/cwtXlut6Iyc6t1fXaHjx4UOzdu7eoUqnE1q1bi4sXLxb1en0TR2356nJdKyoqxAULFoihoaGivb29GBQUJD7zzDNibm5u0wduwfbs2VPj/8yqazl16lRx0KBB1cp07dpVVCqVYuvWrcVVq1Y1edy2QhBF1jMSERGR7WKfHSIiIrJpTHaIiIjIpjHZISIiIpvGZIeIiIhsGpMdIiIismlMdoiIiMimMdkhIiIim8Zkh4iIiBrF/v37MWbMGAQEBEAQBGzatKnOxxBFEe+//z7atm0LlUqFwMBALF68uE7H4EKgRERE1CiKi4vRpUsXPP744xg7dmy9jvHcc89hx44deP/999GpUyfk5OQgJyenTsfgDMpEZNGmTZuGvLy8ar8I9+7di7vuugu5ublwd3eXJDYiqj1BEPDLL7/g/vvvN23T6XR49dVX8eOPPyIvLw8dO3bE0qVLMXjwYABAYmIiOnfujFOnTqFdu3b1PjebsYiIiEgSs2bNQkxMDNatW4eTJ0/ioYcewt13343z588DAH7//Xe0bt0amzdvRqtWrdCyZUs8+eSTda7ZYbJDRDYhLy8PTz75JLy9veHq6oohQ4bgxIkTpscXLFiArl27mpXZu3cvBEFAXl4eACA7OxsTJkxAYGAgHB0d0alTJ/z4449N+CyImg+NRoNVq1Zhw4YNGDBgAEJDQ/Hiiy+if//+WLVqFQDg4sWLuHz5MjZs2IDvvvsOq1evRmxsLB588ME6nYt9dojIJjz00ENwcHDAH3/8ATc3N3zxxRcYOnQozp07B09Pz1odo6ysDJGRkXj55Zfh6uqKLVu2YPLkyQgNDUWvXr0a+RkQNS/x8fEwGAxo27at2XadTgcvLy8AgNFohE6nw3fffWfa75tvvkFkZCTOnj1b66YtJjtEZPX+/vtvHDlyBJmZmVCpVACA999/H5s2bcLGjRsxffr0Wh0nMDAQL774oun+s88+i+3bt2P9+vVMdogaWFFREeRyOWJjYyGXy80ec3Z2BgD4+/vDzs7OLCFq3749gMqaISY7RNRsnDhxAkVFRaZfg1VKS0uRlJRkuh8fH2/6JwoABoPBbH+DwYC3334b69evR2pqKsrLy6HT6eDo6Ni4T4CoGerWrRsMBgMyMzMxYMCAGvfp168f9Ho9kpKSEBoaCgA4d+4cACAkJKTW52KyQ0RWr6ioCP7+/ti7d2+1x64fqdWuXTv89ttvpvuHDx/GpEmTTPffe+89LF++HMuWLUOnTp3g5OSE559/HuXl5Y0ZPpHNKioqwoULF0z3k5OTERcXB09PT7Rt2xYTJ07ElClT8MEHH6Bbt27IysrC7t270blzZ4wePRrR0dHo3r07Hn/8cSxbtgxGoxEzZ87EsGHDqjV/3QqTHSKyet27d0d6ejrs7OzQsmXLm+6nVCrRpk0b0/2UlBSzxw8cOID77rvPlAAZjUacO3cOHTp0aJS4iWzd0aNHcdddd5nuz5kzBwAwdepUrF69GqtWrcJbb72FF154AampqVCr1ejTpw/uueceAIBMJsPvv/+OZ599FgMHDoSTkxNGjhyJDz74oE5xMNkhIouXn5+PuLg4s21Vvxbj4+PRp08fREVF4f7778e7776Ltm3b4urVq9iyZQseeOAB9OjRo1bnCQsLw8aNG3Hw4EF4eHjgww8/REZGBpMdonoaPHgwbjWdn0KhwJtvvok333zzpvsEBATgp59+uqM4mOwQkcXbu3cvunXrVuNjAwcOxJ49e7B161a8+uqreOyxx5CVlQU/Pz8MHDgQvr6+tT7Pa6+9hosXL2LEiBFwdHTE9OnTcf/99yM/P7+hngoRSYAzKBORVWvZsiVWr15tmnGViOhGnFSQiKxahw4dzEZYERHdiDU7REREZNNYs0NEREQ2jckOERER2TQmO0RERGTTmOwQERGRTWOyQ0RERDaNyQ4RERHZNCY7REREZNOY7BAREZFNY7JDRERENu3/Af4+AkF503h1AAAAAElFTkSuQmCC",
      "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:  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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5SEpKQteuXZ87X5VKhT///BPa2trPnCcAJCcnAwBsbW2fOU9ra2uEhYWVOP7p8ODq6op58+bJ2latWoVff/21xOkvX76M0NBQbNq0qcQPvMLCQjg7O2PJkiUlTu/g4FBq7eVVWFiIrl27YurUqSWOb9iwoWx4zJgxGDBggKyttMerZ86cCQ8PD+Tl5SEmJgZz5sxBenp6iX9FP0u3bt0wZcoUAMCtW7ewYMECdOrUCX/99ZfsL/+KKO/0/v7++Oyzz3Dr1i3k5OTgxIkTWLVqlazP559/jmPHjj3z6c+CggJ07doV9+7dw7Rp09C4cWMYGxvj9u3bGD58eInv2YiICERHR2PmzJnlqrkiyvu+Ap68Z2fMmIGRI0eqHTfP06JFC3z11VcAIN3n4+Xlhb///lv2Xu3Tpw8CAwMhhEBCQgLmzJmDnj17lvjN0y96bJRHaWfXT506hdGjR8PY2Bjz5s3DgAEDZMGrIttZiRiIqFx+/vlndOrUSfrrqUh6errstHP9+vVx8uRJ5OXlVepfH09/IAkhcP36dbRo0UJaLgCYmZnB29v7ufM7e/Ys8vLynhkCi+YrhICjo2OZPoQvXboElUpV4l+Dxee5f/9+uLu7l+lD1crKSm2dnnXjc1BQEFq1aoX33nuv1OWfPXsWXbp0eaHLmGVRv359PHz4sEz7BHjyl/HTfYv/5Vycs7Oz1NfX1xeJiYnYsGFDuX8Ow87OTrbMRo0aoX379ti1a1epoaNOnToAnjxFVq9ePak9NzcXCQkJ0vwcHR2lfk+7cuUKjI2NZe+fQYMGYeLEifjpp5/w6NEj6Orqqu1HKysrREdH49KlS1IAP3v2LCZPniz1OX/+PK5evYoNGzbA399fao+IiCh1O3h7e8Pc3BwzZ84std7K+rLB8r6vgCdP9N29e7fEp0Cfp3r16rJ97OXlBXt7e6xfv172IEetWrVk/UxMTDBkyJASLzEW37dFl5uLFN9WxY+Vp125cgVWVlZqx/i1a9ek+QPA9evXUVhYqLb9u3btijVr1uDx48fYtWsXxowZIz1xCFRsOysR7yGictHW1oYQQta2fft23L59W9bWv39/pKamqv1VC0Bt+vLYuHEjHjx4IA3//PPPSEpKgq+vLwDAxcUF9evXx+LFi/Hw4UO16VNSUtRq19bWLvGR9uL69esHbW1tzJ49W61+IYT0pAnw5LHXHTt24O23337mX14DBw5EQUEB5s6dqzYuPz//he6JiY6Oxq+//or//ve/pYadgQMH4vbt2/juu+/Uxj169AhZWVkVXn5Jy4qOjsbevXvVxqWnp1fqb3kVFhZCS0vrhUNe0WPpz/oKAm9vb+jp6WHFihWy4+KHH35ARkaG9A3KNWrUQJs2bbBhwwbZZav4+Hj89ttv8PX1lf3lbmVlBV9fX2zatAlhYWHo3r17iU91aWlpoXnz5vD29oa3t7faN14XzbN4bUIILF++/Jnr3qpVK9jY2OC7775Ddna21H7kyBH89ddfz32/lFV53lfAk/vF5s+fjwkTJjzz7GtZlWUfA///zHNJZ1dat24NW1tbrF27Vjafp7eVnZ0dWrVqhQ0bNsje2xcuXMC+ffvUnrQFntzCUNzKlSsBQPq8K9K+fXtoa2vD2NgYa9euRVRUlOx9Xd7trFQ8Q0Tl0rNnT8yZMwcjRoxA+/btcf78eYSFhcn+OgaenPLfuHEjJk6ciFOnTsHDwwNZWVnYv38/Pv74Y7Wb/srKwsICHTp0wIgRI3Dnzh0sW7YMTk5O0uUULS0tfP/99/D19UWzZs0wYsQI1KxZE7dv38ahQ4dgZmaG33//HVlZWVi9ejVWrFiBhg0byr4vpShInTt3DtHR0XBzc0P9+vUxb948BAUF4caNG+jbty9MTU2RkJCAnTt3YsyYMZg8eTL279+PGTNm4Ny5c/j999+fuS6enp4YO3YsQkJCEBsbi27dukFXVxfXrl3D9u3bsXz5crz77rsV2k779u1D165dn3lG5oMPPsC2bdvw0Ucf4dChQ3B3d0dBQQGuXLmCbdu2Ye/evc89c/bw4UOEh4fL2or+Aj58+DB0dXVRs2ZNTJkyBb/99ht69uyJ4cOHw8XFBVlZWTh//jx+/vln3Lhxo8KPccfGxsLExAT5+fmIiYnBxo0b0adPnxL/83qW//3vf9i0aROAJzejr1q1CmZmZs+8sbpGjRoICgrC7Nmz0b17d/Tu3RtxcXH4+uuv0bZtW9lN0AsXLkS3bt3g5uaGUaNGSY/dGxgYYP78+Wrz9vf3l/Z/SaG5LBo3boz69etj8uTJuH37NszMzLBjxw61e4mepquriwULFmD48OFwd3fHsGHDcO/ePSxfvhw1a9ZUe9y9oKBAdhzExsYCeHIpp/iN/wUFBbh9+zZOnTqFt99+u8zvqyJ///03rKysSr30+jx37tyR9nFqaiq++eYb6OjoqAW8xMREhIeHS5fM5s+fjzp16qB169ZqZ6l1dHSwcOFC+Pv7w8PDA0OGDJEux9WqVUu2rRYtWgRfX1+4ublh5MiR0mP35ubmJZ7xSkhIQO/evdG9e3dER0dj06ZNeP/999GyZctS19HHxwdDhw7F1KlT0atXL9jZ2ZV7OyvWq3ykjaquokdHT58+/cx+jx8/FpMmTRJ2dnbC0NBQuLu7i+jo6BIfac3OzhZffPGFcHR0FLq6usLW1la8++67Ij4+XghRsUesf/rpJxEUFCSsra2FoaGh8PPzkz06XOTMmTOiX79+wtLSUujr64s6deqIgQMHigMHDsiW/bzX049i79ixQ3To0EEYGxsLY2Nj0bhxYxEQECDi4uKEEEJ88sknomPHjiI8PFytpqcfuy/y7bffChcXF2FoaChMTU2Fs7OzmDp1qvj333+lPuV97F6lUomYmBhZe0n7KDc3VyxYsEA0a9ZM6Ovri+rVqwsXFxcxe/ZskZGRoba8p+f3vO23fv16qf+DBw9EUFCQcHJyEnp6esLKykq0b99eLF68WOTm5gohKnZMFL10dHREnTp1xKeffiru378vhCjfY/fF52VlZSW6desmoqOjnzutEE8es2/cuLHQ1dUVNjY2Yty4cVINxR04cEC4u7sLQ0NDYWZmJvz8/MT58+dLnGdOTo6oXr26MDc3F48ePSpTHSU9dn/p0iXh7e0tTExMhJWVlRg9erQ4e/as2v4p6fjcsmWLaNWqlXRsvPfee+LGjRuyPsOGDSvTe6n46+nj8HnvKyH+//G2dOlS2bSlva+e9vTxWq1aNeHu7i7++OMPWb/ifVQqlbC1tRX9+vUTly9fFkKoP3ZfZNu2baJ169ZCX19fWFhYiMGDB5f42bR//37ZMdCrVy9x6dKlEtfp0qVL4t133xWmpqaievXqIjAwUO1YwFNfsyGEEKmpqaJGjRrinXfekbWXZTsrmUqIF7h+QfSKREZGolOnTti+fXuFz5oUd+PGDTg6OiIhIaHU+yGCg4Nx48YNhIaGvvDylKhu3boIDg4u8ZuJ6fny8/Nhb2+PXr16qd2z9zoLDQ1FaGio2rdY0/9X9GWfKSkplfYFmPR8vIeIiKgK2rVrF1JSUmQ3QxPRy8N7iEiRip4aedZNzy1atJB+ioTKz9PTU/pCTyq7kydP4ty5c5g7dy5at24NT09PTZdUqWrWrFniz+cQaRoDESmSlZWVdHNlafr16/eKqnkzbdiwQdMlvJbWrFmDTZs2oVWrVm/k5dquXbs+93u/iDSB9xARERGR4vEeIiIiIlI8BiIiIiJSPN5DVAaFhYX4999/YWpq+tJ/4oCIiIgqhxACDx48gL29veyHykvCQFQG//77b6X+0CURERG9Ojdv3kStWrWe2YeBqAxMTU0BPNmgZmZmGq6GiIiIyiIzMxMODg7S/+PPwkBUBkWXyczMzBiIiIiIXjNlud2FN1UTERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHi6Wi6AKI3hcuUjZougf5PzCJ/TZdARK8ZniEiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLFYyAiIiIixWMgIiIiIsVjICIiIiLF02ggCgkJQdu2bWFqagpra2v07dsXcXFxsj5eXl5QqVSy10cffSTrk5iYCD8/PxgZGcHa2hpTpkxBfn6+rE9kZCTeeust6Ovrw8nJCaGhoS979YiIiOg1odFAdPjwYQQEBODEiROIiIhAXl4eunXrhqysLFm/0aNHIykpSXotXLhQGldQUAA/Pz/k5ubi+PHj2LBhA0JDQzFz5kypT0JCAvz8/NCpUyfExsZi/PjxGDVqFPbu3fvK1pWIiIiqLo3+lll4eLhsODQ0FNbW1oiJiUHHjh2ldiMjI9ja2pY4j3379uHSpUvYv38/bGxs0KpVK8ydOxfTpk1DcHAw9PT0sHbtWjg6OuKrr74CADRp0gRHjx7F0qVL4ePj8/JWkIiIiF4LVeoeooyMDACAhYWFrD0sLAxWVlZo3rw5goKCkJ2dLY2Ljo6Gs7MzbGxspDYfHx9kZmbi4sWLUh9vb2/ZPH18fBAdHV1iHTk5OcjMzJS9iIiI6M1VZX7tvrCwEOPHj4e7uzuaN28utb///vuoU6cO7O3tce7cOUybNg1xcXH45ZdfAADJycmyMARAGk5OTn5mn8zMTDx69AiGhoaycSEhIZg9e3alryMRERFVTVUmEAUEBODChQs4evSorH3MmDHSv52dnWFnZ4cuXbogPj4e9evXfym1BAUFYeLEidJwZmYmHBwcXsqyiIiISPOqxCWzwMBA7N69G4cOHUKtWrWe2dfV1RUAcP36dQCAra0t7ty5I+tTNFx031FpfczMzNTODgGAvr4+zMzMZC8iIiJ6c2k0EAkhEBgYiJ07d+LgwYNwdHR87jSxsbEAADs7OwCAm5sbzp8/j7t370p9IiIiYGZmhqZNm0p9Dhw4IJtPREQE3NzcKmlNiIiI6HWm0UAUEBCATZs2YfPmzTA1NUVycjKSk5Px6NEjAEB8fDzmzp2LmJgY3LhxA7/99hv8/f3RsWNHtGjRAgDQrVs3NG3aFB988AHOnj2LvXv3Yvr06QgICIC+vj4A4KOPPsL//vc/TJ06FVeuXMHXX3+Nbdu2YcKECRpbdyIiIqo6NBqI1qxZg4yMDHh5ecHOzk56bd26FQCgp6eH/fv3o1u3bmjcuDEmTZqE/v374/fff5fmoa2tjd27d0NbWxtubm4YOnQo/P39MWfOHKmPo6Mj9uzZg4iICLRs2RJfffUVvv/+ez5yT0RERAAAlRBCaLqIqi4zMxPm5ubIyMjg/URUKpcpGzVdAv2fmEX+mi6BiKqA8vz/XSVuqiYiIiLSJAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjyNBqKQkBC0bdsWpqamsLa2Rt++fREXFyfr8/jxYwQEBMDS0hImJibo378/7ty5I+uTmJgIPz8/GBkZwdraGlOmTEF+fr6sT2RkJN566y3o6+vDyckJoaGhL3v1iIiI6DWh0UB0+PBhBAQE4MSJE4iIiEBeXh66deuGrKwsqc+ECRPw+++/Y/v27Th8+DD+/fdf9OvXTxpfUFAAPz8/5Obm4vjx49iwYQNCQ0Mxc+ZMqU9CQgL8/PzQqVMnxMbGYvz48Rg1ahT27t37SteXiIiIqiaVEEJouogiKSkpsLa2xuHDh9GxY0dkZGSgRo0a2Lx5M959910AwJUrV9CkSRNER0ejXbt2+PPPP9GzZ0/8+++/sLGxAQCsXbsW06ZNQ0pKCvT09DBt2jTs2bMHFy5ckJY1aNAgpKenIzw8/Ll1ZWZmwtzcHBkZGTAzM3s5K0+vPZcpGzVdAv2fmEX+mi6BiKqA8vz/XaXuIcrIyAAAWFhYAABiYmKQl5cHb29vqU/jxo1Ru3ZtREdHAwCio6Ph7OwshSEA8PHxQWZmJi5evCj1KT6Poj5F83haTk4OMjMzZS8iIiJ6c1WZQFRYWIjx48fD3d0dzZs3BwAkJydDT08P1apVk/W1sbFBcnKy1Kd4GCoaXzTuWX0yMzPx6NEjtVpCQkJgbm4uvRwcHCplHYmIiKhqqjKBKCAgABcuXMCWLVs0XQqCgoKQkZEhvW7evKnpkoiIiOgl0tF0AQAQGBiI3bt3IyoqCrVq1ZLabW1tkZubi/T0dNlZojt37sDW1lbqc+rUKdn8ip5CK97n6SfT7ty5AzMzMxgaGqrVo6+vD319/RdeL95TUnXwnhIiInoWjZ4hEkIgMDAQO3fuxMGDB+Ho6Cgb7+LiAl1dXRw4cEBqi4uLQ2JiItzc3AAAbm5uOH/+PO7evSv1iYiIgJmZGZo2bSr1KT6Poj5F8yAiIiJl0+gZooCAAGzevBm//vorTE1NpXt+zM3NYWhoCHNzc4wcORITJ06EhYUFzMzM8Mknn8DNzQ3t2rUDAHTr1g1NmzbFBx98gIULFyI5ORnTp09HQECAdJbno48+wqpVqzB16lR8+OGHOHjwILZt24Y9e/ZobN2JiIio6tDoGaI1a9YgIyMDXl5esLOzk15bt26V+ixduhQ9e/ZE//790bFjR9ja2uKXX36Rxmtra2P37t3Q1taGm5sbhg4dCn9/f8yZM0fq4+joiD179iAiIgItW7bEV199he+//x4+Pj6vdH2JiIioatLoGaKyfAWSgYEBVq9ejdWrV5fap06dOvjjjz+eOR8vLy+cOXOm3DUSERHRm6/KPGVGREREpCkMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeDoVnTArKwuHDx9GYmIicnNzZeM+/fTTFy6MiIiI6FWpUCA6c+YMevTogezsbGRlZcHCwgKpqakwMjKCtbU1AxERERG9Vip0yWzChAno1asX7t+/D0NDQ5w4cQL//PMPXFxcsHjx4squkYiIiOilqlAgio2NxaRJk6ClpQVtbW3k5OTAwcEBCxcuxOeff17m+URFRaFXr16wt7eHSqXCrl27ZOOHDx8OlUole3Xv3l3W5969exgyZAjMzMxQrVo1jBw5Eg8fPpT1OXfuHDw8PGBgYCDVSURERFSkQoFIV1cXWlpPJrW2tkZiYiIAwNzcHDdv3izzfLKystCyZUusXr261D7du3dHUlKS9Prpp59k44cMGYKLFy8iIiICu3fvRlRUFMaMGSONz8zMRLdu3VCnTh3ExMRg0aJFCA4OxrffflueVSYiIqI3WIXuIWrdujVOnz6NBg0awNPTEzNnzkRqaip+/PFHNG/evMzz8fX1ha+v7zP76Ovrw9bWtsRxly9fRnh4OE6fPo02bdoAAFauXIkePXpg8eLFsLe3R1hYGHJzc7Fu3Tro6emhWbNmiI2NxZIlS2TBiYiIiJSrQmeIvvzyS9jZ2QEA5s+fj+rVq2PcuHFISUmp9DMvkZGRsLa2RqNGjTBu3DikpaVJ46Kjo1GtWjUpDAGAt7c3tLS0cPLkSalPx44doaenJ/Xx8fFBXFwc7t+/X+Iyc3JykJmZKXsRERHRm6tCZ4iKBxBra2uEh4dXWkHFde/eHf369YOjoyPi4+Px+eefw9fXF9HR0dDW1kZycjKsra1l0+jo6MDCwgLJyckAgOTkZDg6Osr62NjYSOOqV6+uttyQkBDMnj37pawTERERVT0VOkPUuXNnpKenV3Ip6gYNGoTevXvD2dkZffv2xe7du3H69GlERka+1OUGBQUhIyNDepXnvigiIiJ6/VQoEEVGRqp9GeOrUK9ePVhZWeH69esAAFtbW9y9e1fWJz8/H/fu3ZPuO7K1tcWdO3dkfYqGS7s3SV9fH2ZmZrIXERERvbkq/NMdKpWqMusok1u3biEtLU26f8nNzQ3p6emIiYmR+hw8eBCFhYVwdXWV+kRFRSEvL0/qExERgUaNGpV4uYyIiIiUp8I/3fHOO+/IblQu7uDBg2Wax8OHD6WzPQCQkJCA2NhYWFhYwMLCArNnz0b//v1ha2uL+Ph4TJ06FU5OTvDx8QEANGnSBN27d8fo0aOxdu1a5OXlITAwEIMGDYK9vT0A4P3338fs2bMxcuRITJs2DRcuXMDy5cuxdOnSiq46ERERvWEqHIjc3NxgYmLyQgv/66+/0KlTJ2l44sSJAIBhw4ZhzZo1OHfuHDZs2ID09HTY29ujW7dumDt3LvT19aVpwsLCEBgYiC5dukBLSwv9+/fHihUrpPHm5ubYt28fAgIC4OLiAisrK8ycOZOP3BMREZGkQoFIpVJhypQpak94lZeXlxeEEKWO37t373PnYWFhgc2bNz+zT4sWLXDkyJFy10dERETKUKF7iJ4VYoiIiIheNxUKRLNmzXrhy2VEREREVUWFLpnNmjULAJCSkoK4uDgAQKNGjVCjRo3Kq4yIiIjoFanQGaLs7Gx8+OGHsLe3R8eOHdGxY0fY29tj5MiRyM7OruwaiYiIiF6qCgWiCRMm4PDhw/jtt9+Qnp6O9PR0/Prrrzh8+DAmTZpU2TUSERERvVQVumS2Y8cO/Pzzz/Dy8pLaevToAUNDQwwcOBBr1qyprPqIiIiIXroKXzIr+oHU4qytrXnJjIiIiF47FQpEbm5umDVrFh4/fiy1PXr0CLNnz4abm1ulFUdERET0KlToktmyZcvQvXt31KpVCy1btgQAnD17FgYGBmX6MkUiIiKiqqRCgcjZ2RnXrl1DWFgYrly5AgAYPHgwhgwZAkNDw0otkIiIiOhlq1AgioqKQvv27TF69OjKroeIiIjolavQPUSdOnXCvXv3KrsWIiIiIo3gb5kRERGR4lXokhkAREdHo3r16iWO69ixY4ULIiIiInrVKhyI3nnnnRLbVSoVCgoKKlwQERER0atWoUtmAJCcnIzCwkK1F8MQERERvW4qFIhUKlVl10FERESkMbypmoiIiBSvQvcQFRYWVnYdRERERBpToTNEISEhWLdunVr7unXrsGDBghcuioiIiOhVqlAg+uabb9C4cWO19mbNmmHt2rUvXBQRERHRq1ShQJScnAw7Ozu19ho1aiApKemFiyIiIiJ6lSoUiBwcHHDs2DG19mPHjsHe3v6FiyIiIiJ6lSp0U/Xo0aMxfvx45OXloXPnzgCAAwcOYOrUqZg0aVKlFkhERET0slUoEE2ZMgVpaWn4+OOPkZubCwAwMDDAtGnTEBQUVKkFEhEREb1sFQpEKpUKCxYswIwZM3D58mUYGhqiQYMG0NfXr+z6iIiIiF66Cv+WGQCYmJigbdu2lVULERERkUZUOBD99ddf2LZtGxITE6XLZkV++eWXFy6MiIiI6FWp0FNmW7ZsQfv27XH58mXs3LkTeXl5uHjxIg4ePAhzc/PKrpGIiIjopapQIPryyy+xdOlS/P7779DT08Py5ctx5coVDBw4ELVr167sGomIiIheqgoFovj4ePj5+QEA9PT0kJWVBZVKhQkTJuDbb7+t1AKJiIiIXrYKBaLq1avjwYMHAICaNWviwoULAID09HRkZ2dXXnVEREREr0CFbqru2LEjIiIi4OzsjAEDBuCzzz7DwYMHERERgS5dulR2jUREREQvVYUC0apVq/D48WMAwBdffAFdXV0cP34c/fv3x/Tp0yu1QCIiIqKXrVyBKDMz88lEOjowMTGRhj/++GN8/PHHlV8dERER0StQrkBUrVo1qFSq5/YrKCiocEFEREREr1q5AtGhQ4dkw0II9OjRA99//z1q1qxZqYURERERvSrlCkSenp5qbdra2mjXrh3q1atXaUURERERvUov9FtmRERK5TJlo6ZLoP8Ts8hf0yXQG6BC30NU5ObNm8jOzoalpWVl1UNERET0ypXrDNGKFSukf6empuKnn35C586d+ftlRERE9ForVyBaunQpAEClUsHKygq9evXi9w4RERHRa69cgSghIeFl1UFERESkMS90DxERERHRm4CBiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBSPgYiIiIgUj4GIiIiIFI+BiIiIiBRPo4EoKioKvXr1gr29PVQqFXbt2iUbL4TAzJkzYWdnB0NDQ3h7e+PatWuyPvfu3cOQIUNgZmaGatWqYeTIkXj48KGsz7lz5+Dh4QEDAwM4ODhg4cKFL3vViIiI6DWi0UCUlZWFli1bYvXq1SWOX7hwIVasWIG1a9fi5MmTMDY2ho+PDx4/fiz1GTJkCC5evIiIiAjs3r0bUVFRGDNmjDQ+MzMT3bp1Q506dRATE4NFixYhODgY33777UtfPyIiIno96Ghy4b6+vvD19S1xnBACy5Ytw/Tp09GnTx8AwMaNG2FjY4Ndu3Zh0KBBuHz5MsLDw3H69Gm0adMGALBy5Ur06NEDixcvhr29PcLCwpCbm4t169ZBT08PzZo1Q2xsLJYsWSILTsXl5OQgJydHGs7MzKzkNSciIqKqpMreQ5SQkIDk5GR4e3tLbebm5nB1dUV0dDQAIDo6GtWqVZPCEAB4e3tDS0sLJ0+elPp07NgRenp6Uh8fHx/ExcXh/v37JS47JCQE5ubm0svBweFlrCIRERFVEVU2ECUnJwMAbGxsZO02NjbSuOTkZFhbW8vG6+jowMLCQtanpHkUX8bTgoKCkJGRIb1u3rz54itEREREVZZGL5lVVfr6+tDX19d0GURERPSKVNkzRLa2tgCAO3fuyNrv3LkjjbO1tcXdu3dl4/Pz83Hv3j1Zn5LmUXwZREREpGxVNhA5OjrC1tYWBw4ckNoyMzNx8uRJuLm5AQDc3NyQnp6OmJgYqc/BgwdRWFgIV1dXqU9UVBTy8vKkPhEREWjUqBGqV6/+itaGiIiIqjKNBqKHDx8iNjYWsbGxAJ7cSB0bG4vExESoVCqMHz8e8+bNw2+//Ybz58/D398f9vb26Nu3LwCgSZMm6N69O0aPHo1Tp07h2LFjCAwMxKBBg2Bvbw8AeP/996Gnp4eRI0fi4sWL2Lp1K5YvX46JEydqaK2JiIioqtHoPUR//fUXOnXqJA0XhZRhw4YhNDQUU6dORVZWFsaMGYP09HR06NAB4eHhMDAwkKYJCwtDYGAgunTpAi0tLfTv3x8rVqyQxpubm2Pfvn0ICAiAi4sLrKysMHPmzFIfuSciIiLl0Wgg8vLyghCi1PEqlQpz5szBnDlzSu1jYWGBzZs3P3M5LVq0wJEjRypcJxEREb3Zquw9RERERESvCgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKZ6OpgsgIiKq6lymbNR0CfR/Yhb5v5T58gwRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESlelQ5EwcHBUKlUslfjxo2l8Y8fP0ZAQAAsLS1hYmKC/v37486dO7J5JCYmws/PD0ZGRrC2tsaUKVOQn5//qleFiIiIqjAdTRfwPM2aNcP+/fulYR2d/1/yhAkTsGfPHmzfvh3m5uYIDAxEv379cOzYMQBAQUEB/Pz8YGtri+PHjyMpKQn+/v7Q1dXFl19++crXhYiIiKqmKh+IdHR0YGtrq9aekZGBH374AZs3b0bnzp0BAOvXr0eTJk1w4sQJtGvXDvv27cOlS5ewf/9+2NjYoFWrVpg7dy6mTZuG4OBg6OnpverVISIioiqoSl8yA4Br167B3t4e9erVw5AhQ5CYmAgAiImJQV5eHry9vaW+jRs3Ru3atREdHQ0AiI6OhrOzM2xsbKQ+Pj4+yMzMxMWLF0tdZk5ODjIzM2UvIiIienNV6UDk6uqK0NBQhIeHY82aNUhISICHhwcePHiA5ORk6OnpoVq1arJpbGxskJycDABITk6WhaGi8UXjShMSEgJzc3Pp5eDgULkrRkRERFVKlb5k5uvrK/27RYsWcHV1RZ06dbBt2zYYGhq+tOUGBQVh4sSJ0nBmZiZDERER0RusSp8helq1atXQsGFDXL9+Hba2tsjNzUV6erqsz507d6R7jmxtbdWeOisaLum+pCL6+vowMzOTvYiIiOjN9VoFoocPHyI+Ph52dnZwcXGBrq4uDhw4II2Pi4tDYmIi3NzcAABubm44f/487t69K/WJiIiAmZkZmjZt+srrJyIioqqpSl8ymzx5Mnr16oU6derg33//xaxZs6CtrY3BgwfD3NwcI0eOxMSJE2FhYQEzMzN88skncHNzQ7t27QAA3bp1Q9OmTfHBBx9g4cKFSE5OxvTp0xEQEAB9fX0Nrx0RERFVFVU6EN26dQuDBw9GWloaatSogQ4dOuDEiROoUaMGAGDp0qXQ0tJC//79kZOTAx8fH3z99dfS9Nra2ti9ezfGjRsHNzc3GBsbY9iwYZgzZ46mVomIiIiqoCodiLZs2fLM8QYGBli9ejVWr15dap86dergjz/+qOzSiIiI6A3yWt1DRERERPQyMBARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIxEBEREZHiMRARERGR4jEQERERkeIpKhCtXr0adevWhYGBAVxdXXHq1ClNl0RERERVgGIC0datWzFx4kTMmjULf//9N1q2bAkfHx/cvXtX06URERGRhikmEC1ZsgSjR4/GiBEj0LRpU6xduxZGRkZYt26dpksjIiIiDdPRdAGvQm5uLmJiYhAUFCS1aWlpwdvbG9HR0Wr9c3JykJOTIw1nZGQAADIzM8u13IKcRxWsmCpbefddRXB/Vx3c38rC/a0s5dnfRX2FEM/vLBTg9u3bAoA4fvy4rH3KlCni7bffVus/a9YsAYAvvvjiiy+++HoDXjdv3nxuVlDEGaLyCgoKwsSJE6XhwsJC3Lt3D5aWllCpVBqs7NXKzMyEg4MDbt68CTMzM02XQy8Z97eycH8ri1L3txACDx48gL29/XP7KiIQWVlZQVtbG3fu3JG137lzB7a2tmr99fX1oa+vL2urVq3ayyyxSjMzM1PUG0jpuL+VhftbWZS4v83NzcvUTxE3Vevp6cHFxQUHDhyQ2goLC3HgwAG4ublpsDIiIiKqChRxhggAJk6ciGHDhqFNmzZ4++23sWzZMmRlZWHEiBGaLo2IiIg0TDGB6L333kNKSgpmzpyJ5ORktGrVCuHh4bCxsdF0aVWWvr4+Zs2apXb5kN5M3N/Kwv2tLNzfz6cSoizPohERERG9uRRxDxERERHRszAQERERkeIxEBEREZHiMRAplJeXF8aPH6/pMugViYyMhEqlQnp6eql9QkNDn/t9W8HBwWjVqpU0PHz4cPTt27dSaqQX87z3tEqlwq5du8o8v7IcM/T6e95xUZHj4OnPideFYp4yI1Ky9u3bIykpqcxfUEZvnqSkJFSvXl3TZdBrRkmfHQxERAqgp6dX4reyk3Jw/7958vLyoKur+1KXoaTPDl4yI9y/fx/+/v6oXr06jIyM4Ovri2vXrgF48jswNWrUwM8//yz1b9WqFezs7KTho0ePQl9fH9nZ2a+8dqXy8vLCJ598gvHjx6N69eqwsbHBd999J33ZqKmpKZycnPDnn38CKPm0d2hoKGrXrg0jIyO88847SEtLU1vOf//7X9jY2MDU1BQjR47E48ePn1lXYWEhQkJC4OjoCENDQ7Rs2VJ27NDLVVhYiKlTp8LCwgK2trYIDg6Wxj19aeT48eNo1aoVDAwM0KZNG+zatQsqlQqxsbGyecbExKBNmzYwMjJC+/btERcX92pW5g3z7bffwt7eHoWFhbL2Pn364MMPPwQA/Prrr3jrrbdgYGCAevXqYfbs2cjPz5f6qlQqrFmzBr1794axsTHmzZsHJycnLF68WDbP2NhYqFQqXL9+vUy1paam4p133oGRkREaNGiA3377TRpX0mfHd999BwcHB+mzY8mSJSVebv/xxx9Rt25dmJubY9CgQXjw4EGZ6tGYSvk5eXrteHp6is8++0wIIUTv3r1FkyZNRFRUlIiNjRU+Pj7CyclJ5ObmCiGE6NevnwgICBBCCHHv3j2hp6cnzM3NxeXLl4UQQsybN0+4u7trZD2UytPTU5iamoq5c+eKq1evirlz5wptbW3h6+srvv32W3H16lUxbtw4YWlpKbKyssShQ4cEAHH//n0hhBAnTpwQWlpaYsGCBSIuLk4sX75cVKtWTZibm0vL2Lp1q9DX1xfff/+9uHLlivjiiy+EqampaNmypdRn2LBhok+fPtLwvHnzROPGjUV4eLiIj48X69evF/r6+iIyMvLVbBgF8/T0FGZmZiI4OFhcvXpVbNiwQahUKrFv3z4hhBAAxM6dO4UQQmRkZAgLCwsxdOhQcfHiRfHHH3+Ihg0bCgDizJkzQgghHTOurq4iMjJSXLx4UXh4eIj27dtraA1fb0Wfnfv375fa0tLSpLaoqChhZmYmQkNDRXx8vNi3b5+oW7euCA4OlvoDENbW1mLdunUiPj5e/PPPP2L+/PmiadOmsmV9+umnomPHjmWqC4CoVauW2Lx5s7h27Zr49NNPhYmJiUhLSxNCCLXPjqNHjwotLS2xaNEiERcXJ1avXi0sLCxknx2zZs0SJiYmol+/fuL8+fMiKipK2Nrais8//7yCW+/VYCBSqKJAdPXqVQFAHDt2TBqXmpoqDA0NxbZt24QQQqxYsUI0a9ZMCCHErl27hKurq+jTp49Ys2aNEEIIb2/vKn+gv2k8PT1Fhw4dpOH8/HxhbGwsPvjgA6ktKSlJABDR0dFqH2qDBw8WPXr0kM3zvffek32oubm5iY8//ljWx9XVtdRA9PjxY2FkZCSOHz8um2bkyJFi8ODBL7C2VBZPHxNCCNG2bVsxbdo0IYQ8EK1Zs0ZYWlqKR48eSX2/++67EgNR8f/A9+zZIwDIpqOy69Onj/jwww+l4W+++UbY29uLgoIC0aVLF/Hll1/K+v/444/Czs5OGgYgxo8fL+tz+/Ztoa2tLU6ePCmEECI3N1dYWVmJ0NDQMtUEQEyfPl0afvjwoQAg/vzzTyGEeiB67733hJ+fn2weQ4YMUQtERkZGIjMzU2qbMmWKcHV1LVNNmsJLZgp3+fJl6OjowNXVVWqztLREo0aNcPnyZQCAp6cnLl26hJSUFBw+fBheXl7w8vJCZGQk8vLycPz4cXh5eWloDZSrRYsW0r+1tbVhaWkJZ2dnqa3oZ2nu3r2rNu3ly5dl+xyA2g8dl6VPcdevX0d2dja6du0KExMT6bVx40bEx8eXfcWowoofEwBgZ2dX4v6Pi4tDixYtYGBgILW9/fbbz51n0aXykuZJzzdkyBDs2LEDOTk5AICwsDAMGjQIWlpaOHv2LObMmSN774wePRpJSUmy2xHatGkjm6e9vT38/Pywbt06AMDvv/+OnJwcDBgwoMx1Fd/HxsbGMDMzK3Ufx8XFqR0rJR07devWhampqTRc2rFYlfCmanouZ2dnWFhY4PDhwzh8+DDmz58PW1tbLFiwAKdPn0ZeXh7at2+v6TIV5+mbKVUqlaxNpVIBgNo9Cy/Lw4cPAQB79uxBzZo1ZeP4+0mvRknHxIvuf00eU2+aXr16QQiBPXv2oG3btjhy5AiWLl0K4Mn7Z/bs2ejXr5/adMWDq7Gxsdr4UaNG4YMPPsDSpUuxfv16vPfeezAyMipzXS/7uKmseb5sDEQK16RJE+Tn5+PkyZNSqElLS0NcXByaNm0K4MmB7OHhgV9//RUXL15Ehw4dYGRkhJycHHzzzTdo06ZNiW9SqrqaNGmCkydPytpOnDhRYh9/f/9S+xTXtGlT6OvrIzExEZ6enpVbMFWqRo0aYdOmTcjJyZHC6unTpzVc1ZvPwMAA/fr1Q1hYGK5fv45GjRrhrbfeAgC89dZbiIuLg5OTU7nn26NHDxgbG2PNmjUIDw9HVFRUZZcuadSokdqx8qYcOwxECtegQQP06dMHo0ePxjfffANTU1P85z//Qc2aNdGnTx+pn5eXFyZNmoQ2bdrAxMQEANCxY0eEhYVhypQpmiqfKujTTz+Fu7s7Fi9ejD59+mDv3r0IDw+X9fnss88wfPhwtGnTBu7u7ggLC8PFixdRr169EudpamqKyZMnY8KECSgsLESHDh2QkZGBY8eOwczMDMOGDXsVq0Zl8P777+OLL77AmDFj8J///AeJiYnSk0pFZ4Ho5RgyZAh69uyJixcvYujQoVL7zJkz0bNnT9SuXRvvvvuudBntwoULmDdv3jPnqa2tjeHDhyMoKAgNGjR45qXtF/XJJ5+gY8eOWLJkCXr16oWDBw/izz//fCOOG95DRFi/fj1cXFzQs2dPuLm5QQiBP/74Q3bK09PTEwUFBbJ7hby8vNTa6PXQrl07fPfdd1i+fDlatmyJffv2Yfr06bI+7733HmbMmIGpU6fCxcUF//zzD8aNG/fM+c6dOxczZsxASEgImjRpgu7du2PPnj1wdHR8matD5WRmZobff/8dsbGxaNWqFb744gvMnDkTgPzyDFW+zp07w8LCAnFxcXj//feldh8fH+zevRv79u1D27Zt0a5dOyxduhR16tQp03xHjhyJ3NxcjBgx4mWVDgBwd3fH2rVrsWTJErRs2RLh4eGYMGHCG3HcqIQQQtNFEBGRZoWFhWHEiBHIyMiAoaGhpsuhcjpy5Ai6dOmCmzdvSg9UvCqjR4/GlStXcOTIkVe63MrGS2ZERAq0ceNG1KtXDzVr1sTZs2cxbdo0DBw4kGHoNZOTk4OUlBQEBwdjwIABryQMLV68GF27doWxsTH+/PNPbNiwAV9//fVLX+7LxktmREQKlJycjKFDh6JJkyaYMGECBgwYgG+//VbTZVE5/fTTT6hTpw7S09OxcOFC2biwsDDZY/zFX82aNavwMk+dOoWuXbvC2dkZa9euxYoVKzBq1KgXXRWN4yUzIiKiN9CDBw9w586dEsfp6uqW+f4kpWAgIiIiIsXjJTMiIiJSPAYiIiIiUjwGIiIiIlI8BiIiIiJSPAYiIiIiUjwGIiIq1fDhw9G3b19ZW0pKCpo3bw5XV1dkZGRopjAiokrGQEREZZaSkoLOnTvD0NAQ+/btg7m5uaZLIiKqFAxERFQmqamp6NKlC/T19RERESELQ0uWLIGzszOMjY3h4OCAjz/+GA8fPgQAREZGQqVSlfoqcvToUXh4eMDQ0BAODg749NNPkZWVJY2vW7eu2rSTJ0+Wxq9Zswb169eHnp4eGjVqhB9//FFWv0qlwpo1a+Dr6wtDQ0PUq1cPP//8szT+xo0bUKlUiI2NldpmzJgBlUqFZcuWSW1XrlxB165dYW5uLtVRrVq1Urdb0fqnp6er1bNr1y5pOCcnB5MnT0bNmjVhbGwMV1dXREZGSuNDQ0PVlvN0zaUtCwDS09OhUqlk8ySi/4+BiIieKy0tDd7e3tDR0UFERITaf8xaWlpYsWIFLl68iA0bNuDgwYOYOnUqAKB9+/ZISkpCUlISduzYAQDScFJSEgAgPj4e3bt3R//+/XHu3Dls3boVR48eRWBgoGw5c+bMkU07a9YsAMDOnTvx2WefYdKkSbhw4QLGjh2LESNG4NChQ7LpZ8yYgf79++Ps2bMYMmQIBg0ahMuXL5e4zrdu3cKyZcvUftvrww8/RF5eHo4dO4akpCRZWHoRgYGBiI6OxpYtW3Du3DkMGDAA3bt3x7Vr1ypl/kT0HIKIqBTDhg0THTt2FK1atRK6urqiXbt2Ij8//7nTbd++XVhaWqq1Hzp0SJT0sTNy5EgxZswYWduRI0eElpaWePTokRBCiDp16oilS5eWuLz27duL0aNHy9oGDBggevToIQ0DEB999JGsj6urqxg3bpwQQoiEhAQBQJw5c0YIIYS/v78YOXKk2nINDQ1FWFiYNLx+/Xphbm5eYl3F1/n+/fuydgBi586dQggh/vnnH6GtrS1u374t69OlSxcRFBRU6nKerrm0ZQkhxP379wUAcejQoVJrJVIyniEiomeKiopCYWEhYmNjcf36dbUfkASA/fv3o0uXLqhZsyZMTU3xwQcfIC0tDdnZ2WVaxtmzZxEaGir78UkfHx8UFhYiISHhudNfvnwZ7u7usjZ3d3e1sz9ubm5qwyWdIfr777+xc+dOzJ07V22co6Mjdu7cWeZ1K4vz58+joKAADRs2lG2Dw4cPIz4+XuqXkZFRph/orFWrFkxNTeHo6IjRo0fz5neiMtDRdAFEVLXVq1cPBw4cgJWVFb7++msMHToUfn5+aNGiBYAn97H07NkT48aNw/z582FhYYGjR49i5MiRyM3NhZGR0XOX8fDhQ4wdOxaffvqp2rjatWtX+jo9z6RJkzB58mTY2dmpjfvhhx8wbNgwmJqawtDQEPn5+TAwMHih5T18+BDa2tqIiYmBtra2bJyJiYn0b1NTU/z999/S8O3bt+Hl5aU2vyNHjsDU1BQ3btzAqFGj8MUXX2DevHkvVCPRm46BiIieydnZGVZWVgCAAQMG4JdffoG/vz9OnToFPT09xMTEoLCwEF999RW0tJ6cdN62bVu5lvHWW2/h0qVLcHJyqlCNTZo0wbFjxzBs2DCp7dixY2jatKms34kTJ+Dv7y8bbt26tazPb7/9hqtXr2LPnj0lLqtdu3bo3bs3oqKisGnTJuzcuRNffvllheou0rp1axQUFODu3bvw8PAotZ+WlpZsG+nolPwR7ujoiGrVqsHJyQkDBgxAdHT0C9VHpAQMRERULqtXr0bz5s0xe/ZszJ8/H05OTsjLy8PKlSvRq1cvHDt2DGvXri3XPKdNm4Z27dohMDAQo0aNgrGxMS5duoSIiAisWrXqudNPmTIFAwcOROvWreHt7Y3ff/8dv/zyC/bv3y/rt337drRp0wYdOnRAWFgYTp06hR9++EHWZ+HChVi5cmWpZ7Z27NiB0NBQxMTEoHbt2rC2ti7TOubk5ODx48eytry8PBQWFqJhw4YYMmQI/P398dVXX6F169ZISUnBgQMH0KJFC/j5+ZVpGU8v68aNG/jzzz/RoUOHck1PpES8h4iIysXCwgLfffcdFixYgJMnT6Jly5ZYsmQJFixYgObNmyMsLAwhISHlmmeLFi1w+PBhXL16FR4eHmjdujVmzpwJe3v7Mk3ft29fLF++HIsXL0azZs3wzTffYP369WqXk2bPno0tW7agRYsW2LhxI3766Se1s0hOTk6yM03FXb16FaNGjcLmzZvLfSnP1tYWhoaG0gsABg4ciKioKADA+vXr4e/vj0mTJqFRo0bo27cvTp8+XaFLhkXL8vDwQMuWLcu9P4iUSCWEEJougojoZVOpVNi5c6faN29rUt++fTF+/PgS7wMioleLZ4iIiDRET09Puu+KiDSL9xAREWlIeW8+J6KXh4GIiBSBdwcQ0bPwXC0REREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKR4DERERESkeAxEREREpHgMRERERKd7/A86U0ST7SqYDAAAAAElFTkSuQmCC",
      "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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
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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": 17,
     "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": 18,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Study\\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"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "      <th>price_per_sqft</th>\n",
       "      <th>DAY(date)</th>\n",
       "      <th>MONTH(date)</th>\n",
       "      <th>WEEKDAY(date)</th>\n",
       "      <th>YEAR(date)</th>\n",
       "    </tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<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": 21,
     "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Оценка качества"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Study\\AIM-PIbd-31-Yakovlev-M-G\\kernel\\Lib\\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": [
      "\n",
      "Время обучения: 0.29 секунд\n",
      "Метрики:\n",
      "MSE: 2205809457.9675403\n",
      "RMSE: 46966.04579872081\n",
      "R²: 0.9623494755346685\n",
      "MAE: 35440.813340503635 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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",
    "from sklearn.metrics import r2_score, mean_absolute_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "X = feature_matrix.drop('price', axis=1)\n",
    "y = feature_matrix['price']\n",
    "\n",
    "#Делим на выборки\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "#Начнем обучение\n",
    "model = LinearRegression()\n",
    "start_time = time.time()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "#Вычесляем показательную способность\n",
    "y_pred = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\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()\n",
    "print(f\"Время обучения: {train_time:.2f} секунд\")\n",
    "print(\"Метрики:\")\n",
    "print(f\"MSE: {mse}\")\n",
    "print(f\"RMSE: {rmse}\")\n",
    "print(f\"R²: {r2}\")\n",
    "print(f\"MAE: {mae} \\n\")\n",
    "\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()], color=\"black\")\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "kernel",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}