dmhw_02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d6c98af9",
   "metadata": {},
   "source": [
    "# 데이터마이닝 수업 과제02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "007a75fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>mfr</th>\n",
       "      <th>type</th>\n",
       "      <th>calories</th>\n",
       "      <th>protein</th>\n",
       "      <th>fat</th>\n",
       "      <th>sodium</th>\n",
       "      <th>fiber</th>\n",
       "      <th>carbo</th>\n",
       "      <th>sugars</th>\n",
       "      <th>potass</th>\n",
       "      <th>vitamins</th>\n",
       "      <th>shelf</th>\n",
       "      <th>weight</th>\n",
       "      <th>cups</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100%_Bran</td>\n",
       "      <td>N</td>\n",
       "      <td>C</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>68.402973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100%_Natural_Bran</td>\n",
       "      <td>Q</td>\n",
       "      <td>C</td>\n",
       "      <td>120</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>33.983679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All-Bran</td>\n",
       "      <td>K</td>\n",
       "      <td>C</td>\n",
       "      <td>70</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>59.425505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>All-Bran_with_Extra_Fiber</td>\n",
       "      <td>K</td>\n",
       "      <td>C</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>93.704912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Almond_Delight</td>\n",
       "      <td>R</td>\n",
       "      <td>C</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>34.384843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name mfr type  calories  protein  fat  sodium  fiber  \\\n",
       "0                  100%_Bran   N    C        70        4    1     130   10.0   \n",
       "1          100%_Natural_Bran   Q    C       120        3    5      15    2.0   \n",
       "2                   All-Bran   K    C        70        4    1     260    9.0   \n",
       "3  All-Bran_with_Extra_Fiber   K    C        50        4    0     140   14.0   \n",
       "4             Almond_Delight   R    C       110        2    2     200    1.0   \n",
       "\n",
       "   carbo  sugars  potass  vitamins  shelf  weight  cups     rating  \n",
       "0    5.0     6.0   280.0        25      3     1.0  0.33  68.402973  \n",
       "1    8.0     8.0   135.0         0      3     1.0  1.00  33.983679  \n",
       "2    7.0     5.0   320.0        25      3     1.0  0.33  59.425505  \n",
       "3    8.0     0.0   330.0        25      3     1.0  0.50  93.704912  \n",
       "4   14.0     8.0     NaN        25      3     1.0  0.75  34.384843  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#01_한 번에 계산\n",
    "import pandas as pd\n",
    "\n",
    "# CSV 파일을 데이터프레임으로 읽어옴\n",
    "cereals_df = pd.read_csv('https://raw.githubusercontent.com/reisanar/datasets/master/Cereals.csv')\n",
    "cereals_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbc13f5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     77.000000\n",
      "mean     106.883117\n",
      "std       19.484119\n",
      "min       50.000000\n",
      "25%      100.000000\n",
      "50%      110.000000\n",
      "75%      110.000000\n",
      "max      160.000000\n",
      "Name: calories, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calories 변수에 대한 요약 통계 계산\n",
    "calories_summary = cereals_df['calories'].describe()\n",
    "\n",
    "# 결과 출력\n",
    "print(calories_summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "faeebc56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  106.88311688311688\n",
      "Median:  110.0\n",
      "Standard Deviation:  19.48411905682084\n",
      "Minimum:  50\n",
      "Maximum:  160\n"
     ]
    }
   ],
   "source": [
    "#01_각각 계산\n",
    "calories_mean = df['calories'].mean()\n",
    "calories_median = df['calories'].median()\n",
    "calories_std = df['calories'].std()\n",
    "calories_min = df['calories'].min()\n",
    "calories_max = df['calories'].max()\n",
    "\n",
    "# 결과 출력\n",
    "print('Mean: ', calories_mean)\n",
    "print('Median: ', calories_median)\n",
    "print('Standard Deviation: ', calories_std)\n",
    "print('Minimum: ', calories_min)\n",
    "print('Maximum: ', calories_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95070eed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM     RM   AGE  LSTAT  MEDV\n",
       "0  0.00632  6.575  65.2   4.98  24.0\n",
       "1  0.02731  6.421  78.9   9.14  21.6\n",
       "2  0.02729  7.185  61.1   4.03  34.7\n",
       "3  0.03237  6.998  45.8   2.94  33.4\n",
       "4  0.06905  7.147  54.2   5.33  36.2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#02\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# CSV 파일을 데이터프레임으로 읽어옴\n",
    "housing_df = pd.read_csv('https://raw.githubusercontent.com/reisanar/datasets/master/BostonHousing.csv', usecols=['CRIM', 'RM', 'AGE', 'LSTAT', 'MEDV'])\n",
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b803e882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn0AAAIUCAYAAABviJpfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAACPIUlEQVR4nOzdd3QU1dsH8O/sJtn0RjqQRgm9JZTQQQkdURRUOogiKGqsqK8US7CjIOVHRxFQQAXRQKSF3psQ6SEEEkgvpO/e94+QJZvdlIEsySbfzzl7Tnb2zuwzszuzT565c0cSQggQERERUY2mqOoAiIiIiMj4mPQRERER1QJM+oiIiIhqASZ9RERERLUAkz4iIiKiWoBJHxEREVEtwKSPiIiIqBZg0kdERERUCzDpIyIiIqoFmPRVgZkzZ0KSJMycOfORvaevry8kSUJ0dPQje0+ismzcuBGdOnWCjY0NJEmCJElVHRLVMLt374YkSejZs2dVh0JULVS7pG///v148cUX0aRJEzg4OEClUqFu3boYNGgQli5dirt371Z1iNXO77//jpkzZ+LUqVNVHcpDKUpMV65cWWa7nj17PvKk2ZCZM2dWeQymKiIiAk8//TQOHz4Mb29vdOnSBV26dCl3vujoaG2CWPyhVCrh7OyMbt264YcffkBBQcEjWAvDTGl/LL49y/uHsKjd7t27H0lstUnRMa34w9bWFnXr1kWPHj3w1ltv4ciRI0Z5bx7Hahezqg6gSFZWFsaPH49ffvkFAGBpaYkGDRrAysoKN2/exNatW7F161Z89NFH2LZtG1q2bFnFEVcfv//+O1atWgVfX1+0adPGYJsGDRrA0tIS5ubmjza4GmzWrFkAwAPmA1i4cCEA4KuvvsKbb775QMsICgqCSqUCAOTl5eH69evYt28f9u3bhw0bNmDbtm2wsLCotJgrqiL7Iz0a1tbWCAgIgLe3d1WHUiH169fXxpqXl4fk5GTs27cPkZGR+Prrr9GzZ0+sXLkSPj4+lfaePI7VLtUi6cvPz0dISAj2798PDw8PfP7553jmmWdgZWWlbXP+/Hl8//33WLZsGa5cucKkT6YdO3ZUdQhEWv/99x8AYMCAAQ+8jF9//RW+vr7a50IIrFu3DqNHj8bu3buxdOlSTJky5WFDJRPWoUMH7XfNFEyYMEEv+UpPT8emTZswc+ZM7N69Gx06dMCxY8dQv379qgmSTFq1OL07a9Ys7N+/H+7u7jh48CDGjBmjk/ABQLNmzbBo0SLs2rULbm5uVRQpEVWG7OxsANDbzx+GJEl47rnn8NRTTwEA/vnnn0pbNlFVsbe3x7hx43DixAm0bNkSd+7cwZgxY6o6LDJRVZ70paWl4fvvvwcAzJ07V+c/d0O6du2Kzp07603funUr+vXrBxcXF6hUKvj5+WHKlCm4ceOGweUUv7Bh165d6N+/P1xcXHT6rBTvXL5x40Z0794djo6Oev1fkpOT8cEHH6BFixawsbGBnZ0dOnXqhCVLlkCj0VR4W6jVavzxxx+YMGECmjdvDgcHB1hbW6Np06Z45513kJiYqNO+qD/OqlWrAADjx4/X6RNS/D/Gsi7kyM/Px7x589ChQwfY29vDxsYGrVu3xqeffoqsrCy99kXvW/RZ/fTTTwgKCoK1tTWcnZ3xzDPP4OrVqxVe78oWGxuLadOmoXHjxrCysoKjoyN69eqFDRs2GGwfHx+PefPmoW/fvvD19YWlpSWcnJzQo0cP/Pjjj3rtiy7EKVKyL07RNl65ciUkScK4ceOQnZ2N6dOnw9/fH1ZWVggICMC8efO0y0hKSsJrr70GHx8fWFpaonnz5qX2bZQbL6D7mQkhMG/ePLRs2RLW1tZwc3PD6NGjERMTU8EtrOvu3bv45JNP0KpVK9jY2MDe3h4dO3Y02LeuqO9S0Tby8/Mz+H19GEWnvvLy8gy+npSUhHfeeQcBAQGwsrKCk5MTevbsiTVr1kAIYXCeLVu2oG/fvnBxcYG5uTlcXV3RqlUrvPrqq4iKigIgb398kDiKf59yc3Mxc+ZMNGzYEJaWlqhfvz5CQ0OrrM+zEAI//fQTevToAUdHR1hZWaFJkyZ49913kZycrNe+5DHEkNIu7rl+/Tpeeukl+Pv7Q6VSwc7ODv7+/njyySexbt06nbalXcjxsMewkydPYvDgwXBycoKtrS06deqkPb4Y66IkZ2dn7Xdr9+7dOHTokM7rxjqOyf1dompOVLE1a9YIAMLV1VXk5+c/0DLee+89AUAAEPXq1ROBgYHC2tpaABBOTk7i6NGjevP4+PgIAOKzzz4TCoVCODk5ifbt24t69eqJXbt2CSGEdplz5swRAIS7u7to3769cHV1FdeuXRNCCPHvv/+KunXrCgDCwsJCNGvWTDRo0EBIkiQAiKefflpoNBqd954xY4YAIGbMmKEz/caNGwKAUCgUwtPTU7Rr1040adJEWFpaCgDC19dXxMfHa9vHxcWJLl26CDc3NwFANGrUSHTp0kX7WLZsmd76FsVdJCsrS/Tu3Vu7rk2bNhWtWrUSCoVCABBt2rQRiYmJOvNcu3ZNABA+Pj7abe/j4yNat24tVCqVACA8PT1FQkKCrM+xKMYVK1aU2a5Hjx4Gt58QQuzevVs4ODgIAMLKykq0bNlS1K9fX7t+b775pt48H3/8sbZ9gwYNRFBQkPD29tbOM3nyZJ32y5YtE126dNG+Xnybd+nSRcTFxQkhhFixYoUAIJ577jkRHBwslEqlaNWqlfD19dXOO2vWLHH79m3RqFEjYWFhIdq2bSu8vLy0ry9fvvyh4xVC9zN7+eWXBQDh7e0tAgMDtd8vV1dX8d9//5W57Uu6c+eOaNmypfZ726pVK9G0aVNtLH369BHZ2dna9q+88oro0qWL9nsSFBRk8PtamqL1MPRdLtKzZ89SP+tLly5pvw8WFhaiXbt2wt/fX7vMMWPG6O2v8+bN077u4eEhgoKCRKNGjbTb7dtvvxVCyNsfHySOou/T888/L7p37y4kSRLNmzcXAQEB2v21T58+5W5DuduzSFG7ouNjEY1GI55//nnt6/7+/qJdu3bCwsJC+527cuWKwff18fEp9/1Kzufi4iIACGtra9GyZUvRpk0b4ezsLACI1q1b67TftWuXACB69OhR6vvLPYZFRERo29jb24ugoCDh6ekpAIhvvvnGYNzlKeuYVlLHjh0FAPHhhx/qTDfWcUzu7xJVb1We9E2dOlUAEEOHDn2g+bds2SIACDMzM/HTTz9pp6elpYknn3xS+6XMysrSma8owVAqlWLWrFnahFOj0YicnBwhxP2DjoWFhfjf//6nPQjn5+eL/Px8kZmZKRo0aCAAiGnTpom0tDTt8s+dOyeaN28uAIj58+frvHdpSV9qaqpYuXKlSEpK0pmekpIiXnnlFQFAjBs3Tm8bjB07ttxkqbSk78033xQAhJeXlzh+/Lh2+qVLl0STJk0EADF8+HCdeYoOmGZmZsLe3l789ddf2tfi4uJEq1atBADx7rvvlhpPWTE+aNJ38+ZN4ezsLCRJEp999pn2cxRCiP3792uT8y1btujMt3fvXrFz505RUFCgM/306dPaBGb37t16cZR3cC/6kTY3NxctW7YUV69e1b62du1a7QE6JCRE9OrVS9y+fVv7+qeffqr94SkZ14PEW/wzMzc3F2vXrtW+lpiYKB5//HEBQHTo0EEv2SjLsGHDBADRvHlzcfnyZe30o0ePCnd3dwFAvPPOO3rzlfZ9LE9pSUpubq64ePGimDZtmgAgHBwcdLa3EIX7dlBQkDYJKP5D9ffffwsbGxsBQCxYsEA7PT8/Xzg5OQkzMzPx22+/6SwvPz9fbNmyRezZs0dnenn744PEIYTu96lZs2biwoUL2tcOHjwo7O3tBQDx999/l7kNi6uMpK8oKbazsxPbt2/XTi9KggGIjh07GnxfuUlf0XFw7NixIiMjQ+e1qKgosXjxYp1p5SV9co9h6enpwsPDQwAQ48eP1/6uaDQaMX/+fG0yaMykr+iY3bdvX53pxjqOPejvElVPVZ70DR06VAAQb7zxxgPNX3RQee211/Reu3v3rva/wpJVhKIfncGDB5e67KKd4dVXXzX4+vfffy8AiCeffNLg66dPnxaSJAl/f3+d6aUlfeWpX7++sLa21quIPmjSl5aWpq2IlvxBE0KII0eOCABCkiSdH/TiPxRff/213nybN28WAESrVq1krV9RjBV9lNx+oaGhZX6Xiv5B6N27d4Vj+ueffwQAMWnSJL3XKpr0SZIkTpw4ofd6cHCwNvG7efOmzmsFBQXaJNXQvHLjLf6ZTZs2TW++27dva/9z37lzZ4Xe6+LFi9qKtqEYf/nlFwFA2NjYiPT0dJ3XKiPpK+0xfPhwERUVpTdvRESEACBUKpW2ilHcF198oU1EihLfuLg4AUC0bdu2wjGWtz8+SBxC6H6fDJ29KPr+G/p8S1OR7VnyUTzp02g02oplUcWzuNjYWG3Fb8eOHXrvKzfp69u3rwAgTp8+XaH1Ky/pk3sMW7RokQAgmjRpYvDMVNFnb8ykb+7cubK/kw9zHCtPab9LVD1V+dW7GRkZAAAbGxvZ82ZmZuLgwYMAgFdffVXvdWtra0yaNAlhYWHYvn07JkyYoNemIh1iS2uzadMmAMALL7xg8PVWrVrB19cXV69eRWxsLOrVq1fuewHAzp07sWXLFly8eBEZGRnafoFpaWnIysrCpUuX0LRp0wotqyz79u1DVlYWvL298cQTT+i93r59ewQHB+PgwYOIiIhAgwYN9NpMnDjR4HwAHrhfX6NGjcq8WOfs2bNIT0/Xm17e59GvXz9YWFjgwIEDKCgogJnZ/a9/RkYG1q1bh3379iEuLg7Z2dkQQiA3NxcAcPr06QdaFwBo27Yt2rZtqze9TZs2OHjwIPr37w8vLy+d15RKJVq1aoWbN2/i6tWrevM/TLxTp07Vm+bm5oann34aP/30E7Zt24ZevXqVu14REREQQqBr164G12/YsGGoV68eYmNjsX//fvTr16/cZcpRfMgWjUaDW7du4caNG9i6dSv8/f3x6aefQqG43215+/btAIBnnnkGHh4eesubPHky/u///g/Xr1/HhQsX0KRJE7i6ukKlUuHixYs4ffo0Wrdu/dBxP0gcxbVp0wZBQUF68z3sfld8exqyf/9+vWlRUVG4ceMGLC0tMWnSJL3X69ati2HDhmHt2rXYvn07evfu/UCxFSm6YnXDhg1o2bJlpfSdk3MMi4iIAACMHj1a5/hRZPz48dp+d8ZS9FtZ9NtZnDGPY4/qd4mMq8qTPjs7OwB4oA7Ily9fhkajgUqlgr+/v8E2zZs3BwBcvHjR4OsV+ZKW1ubs2bMAgI8++gifffaZwTZFnVxv3rxZbtKXl5eHESNG4Pfffy+znaGO0Q+iaJs0adKk1INn8+bNcfDgQYPbz8XFBQ4ODnrTixK2zMzMB4rr/fffx7hx40p9vWfPntizZ4/OtMzMTG3H4xdffLHM5efk5CApKQnu7u4ACjtlDxo0CLdu3Sp1nofZ5oaSZQBwdXWt0Oslt+PDxGtubo6GDRsafK3oe17avlJSUbtmzZoZfF2hUKBJkyaIjY3FxYsXKz3pKzlkS1FMI0eOxJw5c5CXl4evv/66wvHa2dmhfv36uHz5Mi5evIgmTZpAqVRi2rRp+PLLL9GuXTt06dIFvXr1Qrdu3dC1a1dYWlrKjvtB4iiutO/Lw+53hrZncYaOEUXr4u3tXeo/7uUdg+WYOnUqVq1ahY8//hirV69Gv3790K1bN/Tq1UvvH6eKkHsMu3TpEoDCf+gNKW16ZSqKyd7eXme6sY5jj/p3iYyryq/erVu3LgDg2rVrsuct+vK7urqWmrQU/bAb+q8IqFiFsbQ2aWlpAIDjx49j//79Bh9F71s0REVZ5syZg99//x0eHh5YvXo1oqOjkZOTA1F4Gl57x4L8/Pxyl1URRduvrKpaWduvtO1SvLryqBR9FgBK/Sz279+vvaKz6PNQq9UYPnw4bt26hQEDBmDPnj1ITExEQUEBhBDag/zDbHNra2uD04u+s+W9Lopdyfmw8dapU6fUz6e8faWkh/3+GEPjxo2xYsUKAMD8+fNx+/Zt7WsPGu+cOXMwd+5cNGjQAHv37sXs2bPRp08fuLu7Y/r06doqSkUZe78r/n0xtkf9HWjTpg0iIyMREhKCmzdvYvHixRg1ahTq1auHvn37aq+krii5x7Ci4kRRsaKk0qZXpqKr7Itvc2Mexx717xIZV5VX+jp37owffvjB4Cm38tja2gIAEhISIIQwmPgVHfSNsTPa2toiNTUVly5dKrV6IseaNWsAFA7N0LdvX73XSxt+5kEVbb87d+6U2saY268yFa0LUPifaUXvPHLkyBFcvnwZPj4+2LRpk97prcre5g/rYeNNSkqCRqMx+KNW9D2o6GddXb8/LVq0gJ2dHTIyMnD69GmEhIQAePB4FQoFXnvtNbz22muIjo5GZGQk/v77b2zatAlz5sxBRkYG5s+fX+H4qut2exAPui6G/qEprqwzP506dcK2bduQmZmJ/fv3Y9euXfj555+xfft29OnTB//++y8cHR3lrkqFFCWJpVVTH8U/N/v27QNQOPB0EWMexx717xIZV5VX+gYMGABbW1vcuXOn1HHUStOwYUMoFArk5uaW2o/l3LlzAAorAJWt6PTMv//+WynLKzo9aWgcwqSkJNy8edPgfA/ar6Vom0RFRZV68DXm9qtMDg4O2tM7RTFXRNE2DwwMNNif6WH6wBjDw8abn5+PK1euGHytqEpS0c+6qN358+cNvq7RaLR3Q3jU35+i73PxU07lxZuRkaH9ASstXl9fX4wZMwZr167F5s2bAQDLly/XGY+zvP2xMuKoLorii4mJKTURMnQMKUqeEhISDM5z+fLlct/b1tYWffv2xZw5c/Dff/+hQYMGuHnzJv7++29Z6yBH0TqcOXPG4OtFXX6M5cSJEzh69CgAYODAgdrpxjyOPejvElVPVZ70OTo6ai/CeP3118u96ff+/ftx4MABAIU7fdEXsfhAt0Wys7OxdOlSADD4H8rDKhr5//vvv6+UUypFdycofkqqyNdffw21Wl3mfBU5hVxc165dYW1tjRs3buCPP/7Qe/3YsWM4ePAgJElCnz59ZC27KhR9HnPnzq3wPGVt8/z8/DKX9aDb/WE8TLxFFixYoDctISEBv/76KwBoK2PlCQkJgSRJ2LdvH06ePKn3+qZNmxAbGwsbGxvtKaBH4cyZM9oEpHhf36JjwK+//or4+Hi9+RYvXozc3Fz4+PggICCg3Pfp1KkTgMLPPyUlRTu9vO9FZcdRlZo2bQpvb2/k5ORoj7XF3bp1Cxs3bgSgewyuU6cOHBwckJ2dbfCfNEPLKou1tbX21pxl9Wl7WEXHwZ9++sng8bi0AdUrQ3JyMsaOHQsAeOyxx3QqfcY8jj3o7xJVT1We9AGFI4MHBwfj9u3bCA4Oxo8//oicnBydNhcvXsTUqVPRs2dPnVMJ7777LoDCH7Kff/5ZOz0jIwNjxoxBQkICfH198eyzz1Z63EWjwu/atQsjR45EXFyczuuZmZn45ZdfEBoaWqHlde3aFQDw5ptvan+0hBBYvXo1vvrqq1I7jRf9sEVGRspKPu3t7fHyyy8DAF555RWdH+4rV65oDzDDhw8vtfN4dfLuu+9qR60PDQ1FamqqzuvJyclYvnw5PvnkE+20Tp06wczMDPv378fq1au109PS0jBy5EiDB7oiRdu95EUlxvQw8QKAmZkZFixYoE3wgMLtMmrUKOTk5CAoKKhCV+4ChZX2okR7zJgxOtX2EydOYNq0aQAKv1uP6jTlhQsXMH78eACFFygVv8q1d+/eaN++PXJzc/Hcc8/pHEe2b9+uvfH8e++9p63WnT9/Hi+99BKOHj2qs2/l5ubi008/BVB4B5A6depoXytvf3yQOKorSZLw9ttvAwBmzJihc4/v27dv49lnn0VeXh46deqk872SJEmbBIaGhupUCVetWoXly5cbfL+XX34Z69ev17tTUGRkpPa927VrVzkrZ8Bzzz0HDw8PnD9/HpMnT9b+TgkhsHDhQp3foMqSnp6OVatWoV27dvj333/h4eGhl1wa8zj2oL9LVE09utFhypaRkaEd6BX3xi5r0aKFaN++vXa8MqDwjhtnz57Vmbf4HTnq168vgoKCtAOcOjk5iSNHjui9X0XGCUMFxi+KiooSfn5+Aigcsbxp06aiY8eOonHjxkKpVAoYGJi0tHH6jh07pjPSe2BgoPbuDKNHj9aO5VRycNTLly/rjH7frVs30aNHD51xwsq6I0evXr2069qsWTPRunVrbeytW7cu844cD7PtSqqMO3Ls27dPOzZj0aDIHTt2FP7+/tox5UaMGKEzz1tvvaWNt+guFVZWVsLc3FwsXLiw1HWdPXu2AAoH+G7btq3o0aOH6NGjh94dOcaOHWtwPcobr7G08d4eJF5Dd+Tw8fERQUFBwsrKSgAQderUEefPnzcYS2mK35FDqVSK1q1bi2bNmmnje/zxx3XuyFGkMsbpK343j86dOwtfX1/tnSlcXFzEyZMn9ea/dOmSqFevnnacvHbt2omGDRtqlzl69GidsfFOnjypfc3R0VG0a9dOtG3bVnvXFwsLC52BfYWo2P4oNw4hyv8+lTYmXUW3Z2XdkaNhw4Y6d+Tw9vbWuyOHEIXHTltbW4F7Yzm2a9dOe2eLou9xyWNI69atBVA4qHLTpk1Fhw4ddMb3HDVqVIW2ycMcwyIiIrTr5uDgINq3b689Tn/99dfa3wI5io5p9evX136nO3ToIBo2bKj9TgMQvXr1EjExMQaXYazj2IP+LlH1VG2SviKRkZFi4sSJonHjxsLW1lZYWFgILy8vMXDgQLFs2TK9O2sU2bJli+jTp49wcnISFhYWwsfHR0yePLnUHaSykj4hCkdpnzNnjujYsaOwt7cXKpVK+Pr6it69e4uvvvpK7z3K+rE/fPiw6NOnj7C1tRU2NjaiTZs24vvvvxcajabMnWvbtm2iR48ewt7eXpvcFF9+Weubl5cnvvvuO22yXHT7sk8++UTcvXtXr311TvqEKExEPvjgA9G6dWtha2srrKysRMOGDUX//v3FggUL9G4ZpNFoxNy5c0WTJk2EhYWFcHFxEYMHDxaHDh0qc13z8vLEjBkzREBAgM5I/EXb2FhJ34PEW3y6RqMR3333nWjRooWwtLQULi4uYuTIkSI6OtpgHOXJzMwUs2fPFi1atBBWVlbCxsZGtG/fXsybN0/k5eUZnMcYgzPb2tqKVq1aiffee0/n7iYlJSQkiLfeeks0atRIqFQqYW9vL7p37y5+/PFHvUQrMzNTLFmyRDzzzDOiUaNGwtbWVtja2opmzZqJyZMn6wxaXlx5+6PcOISovkmfEIXfydWrV4tu3bppj4GNGjUSb7/9tt4/jcWdOHFC9OvXT9jZ2QkbGxvRuXNn7R1zDB1Ddu7cKV577TXRrl074erqqj3W9+3bV2zevFlvuxkj6RNCiOPHj4uBAwcKBwcH7fd97dq1IjMzU5sMylF0TCv+sLa2Fp6enqJbt27izTffNFi8KM5YxzEhHvx3iaofSYhHeH0/EVWJ6Oho+Pn5wcfHp9x+s0T0YI4fP46goCC0bt0ap06dqupwiPRUiz59REREpq5ojMhHeeESkRxM+oiIiCpo165dWLdunc6g3Pn5+fjmm2+wcOFCKBQKg7ekI6oOqnxwZiIiIlNx/fp1jB8/Hubm5vDz84O9vT0uXryovR94WFgY2rRpU7VBEpWClT4iIqIK6tatG1555RU0btwYCQkJOHXqFCwtLTF48GBs27YN7733XlWHSFUgMjISgwcPhpeXFyRJKvdexUDhMDmBgYGwtLSEv78/Fi1aZPQ4WekjqgV8fX0f6T1ZiWqqBg0aGLwZANVud+/eRevWrTF+/HgMGzas3PbXrl3DgAEDMGnSJPz000/Yv38/pkyZAldX1wrN/6B49S4RERFRJZEkCb/99huGDh1aapt3330Xmzdv1t7+EgAmT56M06dP4+DBg0aLjad3iYiIiErIzc1Fenq6zqP4BTwP4+DBg3q3vOzbty+OHTuG/Pz8SnkPQ6rN6d2t5tX7HpO1yZ3tF6o6BLpnhMXGqg6B7lEkxpXfiB6ZT1JerOoQ6J5PxllU2XsbM3c4+sFz2tsiFpkxYwZmzpz50MuOj4+Hu7u7zjR3d3cUFBQgMTERnp6eD/0ehlSbpI+IiIioupg+fTpCQ0N1pqlUqkpbfsl7axf1tjPmPbeZ9BEREZFJksyNlyCpVKpKTfKK8/DwQHx8vM60O3fuwMzMDHXq1DHKewLs00dERET0SAUHByMiIkJn2vbt2xEUFARzc3OjvS+TPiIiIjJJCjPJaA85MjMzcerUKe09l69du4ZTp04hJiYGQOGp4jFjxmjbT548GdevX0doaCiioqKwfPlyLFu2DG+99ValbRtDeHqXiIiI6CEcO3YMvXr10j4v6gs4duxYrFy5EnFxcdoEEAD8/Pzw119/4Y033sAPP/wALy8vfP/990Ydow9g0kdEREQmSjKvHicse/bsWeYA+CtXrtSb1qNHD5w4ccKIUelj0kdEREQmSe5p2NqueqTIRERERGRUrPQRERGRSTLmkC01ESt9RERERLUAK31ERERkktinTx5W+oiIiIhqAVb6iIiIyCSxT588rPQRERER1QKs9BEREZFJYp8+eVjpIyIiIqoFWOkjIiIikyQpWemTg0kfERERmSQFkz5ZeHqXiIiIqBZgpY+IiIhMkqRgpU8OVvqIiIiIagFW+oiIiMgkSUrWruTg1iIiIiKqBVjpIyIiIpPEq3flYaWPiIiIqBZgpY+IiIhMEq/elYdJHxEREZkknt6Vh6d3iYiIiGoBVvqIiIjIJPHeu/Kw0kdERERUC8iq9MXExFSonbe39wMFQ0RERFRRkoK1KzlkJX1+fn7av4UQAABJknSmSZIEtVpdSeERERERUWWQlfRJkoR69eph3LhxGDx4MMzM2CWQiIiIqgaHbJFHVtYWGxuLVatWYeXKlVi0aBFGjRqFiRMnomnTpsaKj4iIiIgqgayT4R4eHnj33XcRFRWFDRs2ICUlBR07dkSnTp2wZMkSaDQaY8VJREREpEOhlIz2qIkeuAdk165dsWzZMly6dAnW1taYPHkyUlNTKzE0IiIiotJJCsloj5rogZO+AwcO4IUXXkDjxo2RmZmJH374AY6OjpUYGhERERFVFll9+uLi4rB69WqsWLECKSkpGDlyJA4cOIDmzZsbKz4iIiIigzhkizyykj4fHx94eXlh7NixGDJkCMzNzaFWq3HmzBmddq1atarUIImIiIjo4chK+goKChATE4OPP/4Yn3zyCYD74/UV4Th9RERE9CjU1L53xiIr6bt27Zqx4jBZzl2D4P/mRDi0awFLLzccGzYFtzfvqOqwahwhBPb9OR+n961HTlY6PH1bI+S5j+Dq1ajUeU7t/QX/Hv4dCbcuAQA8vJujxxOh8PK7X4k+GL4YF05uR3L8VZhZWKKuf1v0fPIt1PHwN/o6mapfdh7CqvC9SEzNQIO6bnjruYFo19jPYNuTF6Px3YZwRMclICcvH551HDGsZweMCumqbbN533HMWL5Rb95Di2dBZW5utPWoCdYfOIOVe04iMeMuGrg7450h3dDOr265852MvoWJizahoXsd/PLGczqvpWfnYn74Qez49wrSs3NR19kebw7sim5NfY20FjVL7zZKBDVWwMoCiE0U2HJIjTupotT2zbwl9GilhLO9BKUEJGUI7P9Xg1NX74+GYWEGPN5OiWbeCthYAnHJAlsPq3EzqfTlEhki+/Qu6VLaWCP9zAXErtqEwF/nV3U4Ndbh7UtwdMcKDBw7B85uvjjw90Ks/248Js0Kh8rS1uA8MRcPo1nQQNRt0A5m5hY4tH0p1n8/AS98tBV2Tu732hxBux4j4enbEhqNGpF/fIv130/ECzO2wkJl/ShX0SRsO3IGX67diumjh6BNQx9s3H0Er3y7Chs/eR2edRz12lupLDCidzAa1/eAlcoCJy9F45NVv8PKwgLDenbQtrO1UuG3z0J15mXCV7bwUxfxxZa9+GBoT7Tx9cSGw/9iyrIt+O3NkfB0sit1vozsXHy4LgIdGtZHckaWzmv5BWpMXvI7nG2t8NXo/nB3sEV8aiZsVPwsKqJbCwU6N1Ng074CJKYDPVsrMC7EDHM35SOvwPA82XnA7jNqJKYJqDVAQD0FnuyqRGaOwOVbhUndk12UcHOUsGFvAdKzBNo0UGJ8XzN893s+SnyEtU5NHVrFWGQlfSX77pWmNvXpS9gWiYRtkVUdRo0mhMDRHavRuf9kBLQNAQAMHPs55r3TGeeP/Im23Z81ON+QiV/rPO8/6hNcOLEN0RcOomWnoQCAEdOW6bQZOCYM378djPiYc/Bu1L7yV8bE/bRtH4Z2C8RT3Qu3zdvPD8LBc5fw667DmPZ0X732TXy80MTHS/vcy8UJO4+fw8lL0TpJHyDBxaH0RIX0/bj3FJ5s3wxPdSy8kO6dId1x4GIMfjl0Fq/171zqfB9v2oX+bQOglCTsOndV57Xfjp5HWlYOVk19GuZKJQDAy8neeCtRw3RupsSeM2qcjylM1jbuVeO9ZxVo7a/A0YuGx7G9Fq9brTsYpUHbhgr4uCtw+ZYaZkqgmY8Ca3YWIPp2Ydudp9Ro6i2hY4AS/5xkdyqqOFlJX5s2bSBJkl4/vuLYp48qW1piLO6mJ8C36f1TgmbmFqjfqD1uXj1ZatJXUn5eNjTqAlhZO5TaJjc7AwDKbFNb5RcUIOr6LYwf0ENneqfmDXH68vUKLeO/67dw+nIMpjzVR2d6dm4e+r/9BTQaDQLqe2LKk310kkXSlV+gRtTNO5jQK1BnenAjb5yOjit1vt+PnkdsUho+ezYES3Yc1Xt9z/lraOXjibDf9mDX+atwsrHCgLaNMb5nIJS8SrJMTraAnbWkrc4BgFoDRMcLeLtJOHqxYsvx95TgYi9hW3zh76hCApQKCQUlflbzCwAfd1a52KdPnirp05ebm4vc3FydaflCA3OJBxXSl5meAACwsa+jM93G3gXpybcqvJw9v30NW0d3+DY1XAURQmDHhjDUaxgI17qNHzzgGiolIwtqjQbODrqn0+vY2yEp7VKZ8/Z9cw5SMu5CrdbgpSce01YKAcDX0xWzJg5Dw7oeuJuTg58jDmB82GKsm/UqfNxdjLIupi7lbjbUGoE6trpdEOrYWSGxlPN91xNS8d3fB7Di5WEwUxo+1sYmp+HWlVgMaBuAHyYMwfXEVIT9vgcFaoHJfToYnIcK2VoVJh+Z2bpFkcxsAUfbshMTlTnwznBzmCkBjQC2HFTjSlzhcvIKgJg7GvRqrURCagEyc4BWfgrUc5WQlG6cdTElHLJFnirp0xcWFoZZs2bpTHtOcsZIJQ/wBJw7vBnhP8/QPn9m6mIAhVVkHWVUnEs6tG0Jzh/diudDV8PMXGWwTcS62bgTexGj3v5ZftC1iATdz0EIgZIfTUnL33sRWbl5OHvlBr7fEI76bnXQv1NrAECrBt5o1cBb27ZNQx88N+sHrPvnIN4dObjS469JDO0Shj4LtUaD6Wu34eU+HeHr6lTq8jQCcLa1wkfDekGpUKBZPTckpN/Fqj0nmPSV0NpfgSHBSu3zH/8p7LRX8qgkSfrTSsrLB37YnA8LcwkNPCX076BESqbQnvrdsLcAT3Yxw7sjLKDWCMQlCZy5qoFXHSY8JI+spK88mzZtwsyZM8vt+zd9+nSEhup22t7pHFhKa6ptGrbujQl+rbXPCwryAACZaYmwdXDTTr+bkQQb+/L/UTi8fRkOhi/Gs6+vgFu9JgbbbF/3MS6d2YmRb/4EeyePh1yDmsnJzhpKhQJJaRk605MzMuFsb/himiJ1XZ0BAI3qeSApPQOL/9ihTfpKUigUaO5XFzG3kyon8BrIycYKSoWkV9VLzszWq/4BwN3cfJyLvYP/biVgzh97AAAaISAE0O69+Vj4whPo2LA+XO2sYaZU6JzK9XdzQmJGFvIL1DA3U+otu7aKitHgRsL9fnpm9y4osLOSdKp9NpYS7maXnfYJAMkZhX/FJwu4Okjo3lKJa/GFiWRyBrAsvADmZoVVwcxsYEQPJVIyePUuT+/KIzvpW7JkCbZv3w5zc3O89tpr6NixI3bu3Ik333wTFy5cwOjRo8tdhkqlgkqlW23hqV0qorK01bkiVwgBG3tXREfth4d3MwCAuiAPNy4dRc8n3ypzWYe3L8WBvxZi+LRl8PRpqfe6EAIR6z7GxVMReD70Rzi61K/clalBzM3M0NTHC4fOX0bvwPt34Tl07jJ6tm1W4eUIAeQVlHIpIwo/kwsxcWhUj8l3aczNlGha1w2HLt3AYy0aaKcfuhSDns31hxuyVVlgQ+jzOtN+OXgWRy7fwFejB6Cuc+HFGm18PfH3qYvQaAQU935MryemwtXOhglfCXkFRYlaEYGMLIEGXhLikguTMaUC8PWQsP2Y4Ys4SiMBMLS58wsKH5YWQMO6Cmw7xv7zJI+sTOurr77C1KlTce3aNfzxxx/o3bs3PvvsMwwfPhxDhw5FTEwMFi9ebKxYqyWljTXsWzeBfevCCpK1Xz3Yt24Cy/qeVRxZzSFJEto/NubemHoRSLh5EVtXTYe5hSWadRikbbdlxTvY/dv9K3YPbVuCyM1z0X/MZ3CoUxeZaQnITEtAXs5dbZvta2fh3JHNGDLxa1hY2mjb5OflPNJ1NBWj+nbFb5HH8PveY7h66w6+WrsV8clpePrelbjfb9iGD5f8qm2/fsdB7DkVheu3E3H9diL+2HscP27bi4HBbbRtFv+xAwf+vYjYO8m4EHMLs1ZswsUbcdplkmGju7XBpiPn8NvR87h6Oxlfbt6LuNRMPNOpBQDgu78P4IN12wEACoWERh51dB7ONlZQmZmhkUcdWFsUDskyPLglUu/m4PPNkYhOSEFk1DUs3XkMIzrr/8NE+g6cV6NHKyWaektwc5TwVFcl8guA08XG3BvWVYk+7e5ndN1bKtDAU4KTLeDiAHRupkCbhgqcunJ/noZeEhrVLWzTwFPCxH5mSEwTOHFJXjJZE0kKyWgPuRYsWAA/Pz9YWloiMDAQe/fuLbP9mjVr0Lp1a1hbW8PT0xPjx49HUpJxz3DIqvQtW7YMixYtwoQJE7B792707t0bO3fuxOXLl+Ho6GikEKs3h8AWCN7xo/Z5s6/eBwDcWL0JZyZOr6qwapyOIZOQn5eL7WtnIScrDV5+rTFi2nKdimB6chykYhXjE3vWQl2Qj9//N01nWV0GvoJug18FAJyMXAsA+Pkb3Qr1gDFhaNX5KWOtjsnq26EV0jKz8L/NO5GYloGGdd0x7/Wx8HIp7CeWmJaB+ORUbXuNEJi3cRtuJqTATKlAPdc6ePXpvni6x/2ELiMrBx+v+h1JaRmwtbJEE28vLH33RbTwZ9W1LP3aNEZaVg7+988RJKTfRUOPOvhhwmDtECuJ6XcRn5opa5kejnZYNOkJfLllL575di3c7G0wsmtrjO/J7jcVsfdfDczNJAzpZAZLFRCbILBye4HOGH2OthJEsV5+FmYSBgcr4WAN5KuBxDSBXyPV+Df6fkJnaSEhpJ0S9jZAdi5w7roGESfU0PDsbrWxfv16vP7661iwYAG6dOmCxYsXo3///jh//jy8vb312u/btw9jxozBt99+i8GDB+PmzZuYPHkyXnjhBfz2229Gi1MSZY2/UoK1tTX+++8/7QqoVCpERkaiY8eODx3IVvOAh14GVY472y9UdQh0zwgL/TtVUNVQJJY+FAo9ep+kvFjVIdA9n4yzqLL3vvhcP6Mtu/Ha8Aq37dixI9q1a4eFCxdqpzVt2hRDhw5FWFiYXvuvvvoKCxcuxJUrV7TT5s2bhy+++AI3btx4uMDLIOv0bk5ODiwtLbXPLSws4OrqWulBEREREVWl3NxcpKen6zxKDjcHAHl5eTh+/DhCQkJ0poeEhODAgQMGl925c2fExsbir7/+ghACt2/fxoYNGzBw4ECjrEsR2RdyLF26FLa2hafUCgoKsHLlSri46F5BOW3aNEOzEhEREVUaY47TZ2h4uRkzZmDmzJk60xITE6FWq+Hu7q4z3d3dHfHx8QaX3blzZ6xZswYjRoxATk4OCgoKMGTIEMybN69S16EkWUmft7c3lixZon3u4eGBH3/8UaeNJElM+oiIiMjojHnvXUPDy5UceaS4kmPJFo5haji+8+fPY9q0afjoo4/Qt29fxMXF4e2338bkyZOxbNkyg/NUBllJX3R0tJHCICIiIqo+DA0vZ4iLiwuUSqVeVe/OnTt61b8iYWFh6NKlC95++20AQKtWrWBjY4Nu3brhk08+gaencUYAkVUX3blzJ5o1a4b0dP17v6SlpaF58+blXqJMREREVBmqw5AtFhYWCAwMREREhM70iIgIdO5s+LafWVlZUJQ4Na1UFg7lI+P6WtlkJX1z587FpEmTYG9vr/eag4MDXnrpJXzzzTeVFhwRERFRdRcaGoqlS5di+fLliIqKwhtvvIGYmBhMnjwZQOGp4jFjxmjbDx48GJs2bcLChQtx9epV7N+/H9OmTUOHDh3g5eVltDhlnd49ffo0Pv/881JfDwkJwVdfffXQQRERERGVx5gXcsgxYsQIJCUlYfbs2YiLi0OLFi3w119/wcfHBwAQFxeHmJgYbftx48YhIyMD8+fPx5tvvglHR0f07t27zByrMshK+m7fvg1zc/PSF2ZmhoSEhIcOioiIiMiUTJkyBVOmTDH42sqVK/Wmvfrqq3j11VeNHJUuWSly3bp1cfbs2VJfP3PmjNE6HxIREREVVx369JkSWUnfgAED8NFHHyEnR/++pNnZ2ZgxYwYGDRpkYE4iIiIiqkqyTu9++OGH2LRpExo3boxXXnkFAQEBkCQJUVFR+OGHH6BWq/HBBx8YK1YiIiIirZpakTMWWUmfu7s7Dhw4gJdffhnTp0/XXlYsSRL69u2LBQsWlDomDREREVFlqi4XcpgK2bdh8/HxwV9//YWUlBRcvnwZQgg0atQITk5OxoiPiIiIiCqB7KSviJOTE9q3b1+ZsRARERFVGE/vysO6KBEREVEt8MCVPiIiIqKqxD598nBrEREREdUCrPQRERGRaZLYp08OVvqIiIiIagFW+oiIiMgk8epdeZj0ERERkUnihRzycGsRERER1QKs9BEREZFJ4uldeVjpIyIiIqoFWOkjIiIik8Q+ffJwaxERERHVAqz0ERERkUlinz55WOkjIiIiqgVY6SMiIiKTxEqfPEz6iIiIyDTxQg5ZuLWIiIiIagFW+oiIiMgkSRJP78rBSh8RERFRLcBKHxEREZkkDs4sD7cWERERUS3ASh8RERGZJA7ZIg8rfURERES1ACt9REREZJrYp08Wbi0iIiKiWoCVPiIiIjJJ7NMnD5M+IiIiMkmSxBOWclSbpO/O9gtVHQLd4xYSUNUh0D1r/uZ+UV38veNyVYdAxSyyeaGqQyCt1VUdAFVQtUn6iIiIiGTh6V1ZWBclIiIiqgVY6SMiIiKTxNuwycOtRURERFQLsNJHREREJolDtsjDSh8RERFRLcBKHxEREZkmjtMnC5M+IiIiMkk8vSsPU2QiIiKih7RgwQL4+fnB0tISgYGB2Lt3b5ntc3Nz8cEHH8DHxwcqlQoNGjTA8uXLjRojK31ERERkmqrJkC3r16/H66+/jgULFqBLly5YvHgx+vfvj/Pnz8Pb29vgPMOHD8ft27exbNkyNGzYEHfu3EFBQYFR42TSR0RERPQQvvnmG0ycOBEvvFB4e8C5c+di27ZtWLhwIcLCwvTah4eHY8+ePbh69SqcnZ0BAL6+vkaPs3qkyEREREQySZJktEdubi7S09N1Hrm5uXox5OXl4fjx4wgJCdGZHhISggMHDhiMe/PmzQgKCsIXX3yBunXronHjxnjrrbeQnZ1tlO1UhEkfERERUQlhYWFwcHDQeRiq2iUmJkKtVsPd3V1nuru7O+Lj4w0u++rVq9i3bx/+/fdf/Pbbb5g7dy42bNiAqVOnGmVdivD0LhEREZkmI/bpmz59OkJDQ3WmqVSqUttLku6VxEIIvWlFNBoNJEnCmjVr4ODgAKDwFPHTTz+NH374AVZWVg8ZvWFM+oiIiIhKUKlUZSZ5RVxcXKBUKvWqenfu3NGr/hXx9PRE3bp1tQkfADRt2hRCCMTGxqJRo0YPF3wpeHqXiIiITJKkkIz2qCgLCwsEBgYiIiJCZ3pERAQ6d+5scJ4uXbrg1q1byMzM1E67ePEiFAoF6tWr92AbowKY9BEREZFpkhTGe8gQGhqKpUuXYvny5YiKisIbb7yBmJgYTJ48GUDhqeIxY8Zo2z///POoU6cOxo8fj/PnzyMyMhJvv/02JkyYYLRTuwBP7xIRERE9lBEjRiApKQmzZ89GXFwcWrRogb/++gs+Pj4AgLi4OMTExGjb29raIiIiAq+++iqCgoJQp04dDB8+HJ988olR42TSR0RERKapGt2GbcqUKZgyZYrB11auXKk3rUmTJnqnhI2Np3eJiIiIagFW+oiIiMgkSTL73tV23FpEREREtQArfURERGSaqlGfPlPASh8RERFRLcBKHxEREZkkyYi3YauJmPQRERGRaSrl3rZkGFNkIiIiolqAlT4iIiIyTTy9Kwu3FhEREVEtwEofERERmSb26ZOFlT4iIiKiWoCVPiIiIjJJHLJFHm4tIiIiolqAlT4iIiIyTRJrV3Iw6SMiIiLTxHvvysIUmYiIiKgWYKWPiIiITJLE07uycGsRERER1QKyKn2rV6+uULsxY8Y8UDBEREREFcY+fbLISvrGjRsHW1tbmJmZQQhhsI0kSTUm6RNCYN+f83F633rkZKXD07c1Qp77CK5ejUqd59TeX/Dv4d+RcOsSAMDDuzl6PBEKL79W2jYHwxfjwsntSI6/CjMLS9T1b4ueT76FOh7+Rl+nms65axD835wIh3YtYOnlhmPDpuD25h1VHVaNIoTAgb/m48z+9cjNSoeHb2s8PvwjuJSxX5zZ/wvOHf4diff2C3fv5ug2JBSevq0Mtj+8bTH2bv4G7XqNQe+nPzDKetQkI/o7o09ne9hYKXDpei6W/JqAG/F5Zc5jbaXAyEHO6NTKFjbWCtxJKsDK3xNx4nwWAKBZA0s88ZgTGtRXwdnBDHOWxOHI2buPYnVqDKtOj8G62wAo7BxQcOcmMv9cg/zoiwbb2j09CVaB3fSmF9yORfLc940dKtUSsk7vNm3aFBYWFhgzZgz27NmDlJQUvUdycrKxYn3kDm9fgqM7VqDPsx9h7HsbYOvggvXfjUduTmap88RcPIxmQQPx/BurMeaddbB39sT67ycgI+V2sTZH0K7HSIx+9xeMeG0FNBo11n8/EXm5WY9itWo0pY010s9cwLnXZld1KDXWkYglOL5zBR4b/hFGvrMBNvYu+HX+eOSVsV/cuHgYTYIGYsRrq/H8W+tg7+SJDfMnICP1tl7buOtncHr/erjWDTDmatQYTz7uiMG9HLHk1wS8+3UsUtMLMGOqFyxVpVdAzJTAzClecHM2x5fL4/HqJzFYsO4OklMLtG1UFgpE3yxMIEk+VcuOsB04End3bUbyvI+QH30RDuPegsKhjsH2mVt+QuKnr95/hL0GTVYmcs8efcSRmxhJYbxHDSRrrc6dO4etW7ciOzsb3bt3R1BQEBYuXIj09HRjxVdlhBA4umM1OvefjIC2IXCt2xgDx36O/LwcnD/yZ6nzDZn4Ndr1HAn3+k1Rx6MB+o/6BEJoEH3hoLbNiGnL0KrzU3D1agT3ek0wcEwY0pNvIT7m3KNYtRotYVskLs6Yi/jfI6o6lBpJCIETu1ajY9/JaNwmBK5ejdF/9OcoyMtB1NHS94uB479G2+4j4XZvvwgZWbhfxBTbLwAgL+cu/lr5Nvo+/wlU1g7GXp0aYVAPR2zcnozDZ+4iJi4P36+5DZW5hO6BdqXO07uTPWxtlJizJA7/XctBQkoB/ruag+hb96uDJ6OysHZr4XJJPutu/ZB9bA9yju2BOuEWMv9cA01aMqw69TbYXuRmQ5OZpn2Y1fODZGmN7OORjzhyqslkp7IdO3bE4sWLERcXh2nTpuGXX36Bp6cnRo4cidzcXGPEWCXSEmNxNz0Bvk27aqeZmVugfqP2uHn1ZIWXk5+XDY26AFZl/IDlZmcAQJltiKqDtCTD+0W9hu1x81rF94uCe/uFZYnv/D+/zIZ/8x7wadK50mKuydzrmMHJwQyn/rt/lqCgADh3JRsBfpalzte+hQ0uXMvBpGdcsfwTX8x9rz6G9XFi96jKolTCzMsXeZf+1Zmcd+kszL1L7wZRnFVQD+RfOQdNapIxIqw5JMl4jxrogeuXVlZWGDNmDGbNmoUOHTpg3bp1yMqqOacnM9MLT2nY2OuW4m3sXXA3PbHCy9nz29ewdXSHb1PDP2JCCOzYEIZ6DQPhWrfxgwdM9AjcLdov7PT3iywZ+0XkH1/D1sFdJ7n779hW3I45h25PvFk5wdYCjvaF3bJT09U601PT1drXDHF3MUdwGxsoFBI+WRyHX7enYEhvRwzr62TUeGsLhbUdJKUSmsw0nemazHQo7Mr/515h5wCLxq2QfXSPsUKsORQK4z1qoAcap+/mzZtYtWoVVqxYgbt372LUqFFYuHAhnJwqdsDIzc3Vqwrm56lgbqF6kHAqxbnDmxH+8wzt82emLgZQeGGKjlIuYDHk0LYlOH90K54PXQ0zc8PrFrFuNu7EXsSot3+WHzSRkZ0/shkRa+/vF09NKdwvSv4XXNqFXYYciViC/45txYjX7+8X6Slx2LnhUzz9yvJS9xUCugfZ4qURbtrnny6+ZbBd4cdT+meikIC0DDUWrbsDjQCu3siFs4MZhvZ2xK/hKZUcNemowL5i2a4bRE4Wcs8ffwQBUW0iK+n75ZdfsGLFCuzZswd9+/bF119/jYEDB0KpVMp607CwMMyaNUtn2pAxMzB03ExZy6lMDVv3xgS/1trnBQWFfVsy0xJh63D/IHs3Iwk29i7lLu/w9mU4GL4Yz76+Am71mhhss33dx7h0ZidGvvkT7J08HnINiCpfw1a94el7f79Q39sv7qbr7hdZGUmwrsB+cfSfZTi8bTGeeXUFXOve3y9ux5xDVkYSfvz8Ke00oVEj9vJRnNyzBm98dxYKhbzjTE105OxdXIy+oX1ublaYfDvaK5FSrNrnYKfUq/4Vl5KuRoFaQFMs/4iNz4OTgxnMlEBB6bNSBWiyMiDUaihsdat6Clt7aDLL7wNvGdQdOSf3A2p+EOWqoRdcGIuspO/ZZ5+Ft7c33njjDbi7uyM6Oho//PCDXrtp06aVuZzp06cjNDRUZ9q6g1X7373K0hYqS1vtcyEEbOxdER21Hx7ezQAU/uDduHQUPZ98q8xlHd6+FAf+Wojh05bB06el3utCCESs+xgXT0Xg+dAf4ehSv3JXhqiSWFjawsLAfnH9v/1wr39/v4i9fBTdnyh7vzgSsRSHwhfi6VeWwaPEfuET0AljP9iiMy38x+mo4+6P9iGTmPDdk5MrEJ+brzMtJa0ArQOscS22MCE3UwLNG1jhx82l9wX772o2ugXaQZLuF5683MyRnFbAhK8yqNUouBUNi0YtkFesWmfRsAVyo06UOau5XxOYuXgg7RhP7VLlk5X0eXt7Q5Ik/Pxz6aciJUkqN+lTqVRQqXSTPHMLOZEYnyRJaP/YGBwMXwwnN184u/ngYPhimFtYolmHQdp2W1a8AztHd/R8srAf0qFtS7B3y3cYPOFrONSpi8y0wj5QFiprWFjaAAC2r52F80f/xLCXF8DC0kbbRmVlB3OL0jtfU/mUNtawaeitfW7tVw/2rZsgLzkNOTfiqjCymkGSJLTrNQaHty2Gk6svHN18cHjbYphZWKJp+/v7xV+r3oGtozu63+ufdyRiCfb/+R0GjvsaDs51cffed9783n5hYWkLVy/dPq3mKmtY2jrqTSddf+5JxbA+TohLyEdcQj6e6uOE3HyByOMZ2jbTRrkhKU2NNVsKE8HwfekY0N0RE59ywdbINHi5mmNYHydsjbzfB83SQoKHq7n2uVsdM/jWtUBmlgaJKfeHdiHDsvaGw374SyiIvYb8mMuw6tATCsc6yD68EwBg0/cZKOydkPHr/3Tms2rfA/kxl6G+fbMqwjY9vPpIFllJX3R0dLltbt6sOV/UjiGTkJ+Xi+1rZyEnKw1efq0xYtpynYpgenKczr3/TuxZC3VBPn7/n27i22XgK+g2+FUAwMnItQCAn78ZrdNmwJgwtOr8FOjBOQS2QPCOH7XPm31VOKjpjdWbcGbi9KoKq0bp0GcSCvJz8c/6wv3C07c1nn5luU5FMD1Fd784FVm4X2xeqrtfBA94BV0GvvrIYq+JfvsnFRbmCrz4jCtsrAsHZ5694BZycu+fu3VxMtc5lZuUWoBZC25hwlMu+PY9eySnqbF1Txp+++d+f74G3pb4eFpd7fMJT7kCAHYeTsf8NXeMv2ImLvfsYWTa2MLmsSegsHNEwe1YpK38Wns1rsLOEUpH3QuiJJUVVM2DkPHnmqoImWoBScjpgV2G+Ph4fPbZZ1iyZAmys7Nlz79iV2VEQZXBLYSD4lYXt/6+UNUh0D1/b75c1SFQMYtsOAB7deEWVrFbtBpDzh/zjbZsyydeMdqyq4qsHpCpqakYOXIkXF1d4eXlhe+//x4ajQYfffQR/P39cfDgQSxfvtxYsRIRERHRA5J1evf9999HZGQkxo4di/DwcLzxxhsIDw9HTk4O/v77b/To0cNYcRIRERHpqqGDKBuLrKRv69atWLFiBR5//HFMmTIFDRs2ROPGjTF37lwjhUdERERUiho6iLKxyNpat27dQrNmhcM0+Pv7w9LSEi+88IJRAiMiIiKiyiOr0qfRaGBufv8SfqVSCRsbm0oPioiIiKhcPL0ri6ykTwiBcePGacfYy8nJweTJk/USv02bNlVehERERET00GQlfWPHjtV5PmrUqEoNhoiIiKjCeBs2WWQlfStWrDBWHERERERkRLKSPiIiIqJqg1fvysKtRURERFQLsNJHREREpolX78rCSh8RERGZJklhvIdMCxYsgJ+fHywtLREYGIi9e/dWaL79+/fDzMwMbdq0kf2ecjHpIyIiInoI69evx+uvv44PPvgAJ0+eRLdu3dC/f3/ExMSUOV9aWhrGjBmDxx577JHEyaSPiIiITJMkGe8hwzfffIOJEyfihRdeQNOmTTF37lzUr18fCxcuLHO+l156Cc8//zyCg4MfZitUGJM+IiIiogeUl5eH48ePIyQkRGd6SEgIDhw4UOp8K1aswJUrVzBjxgxjh6jFCzmIiIjINBlxyJbc3Fzk5ubqTFOpVNq7khVJTEyEWq2Gu7u7znR3d3fEx8cbXPalS5fw3nvvYe/evTAze3SpGCt9RERERCWEhYXBwcFB5xEWFlZqe6nEKWEhhN40AFCr1Xj++ecxa9YsNG7cuNLjLgsrfURERGSShBGHbJk+fTpCQ0N1ppWs8gGAi4sLlEqlXlXvzp07etU/AMjIyMCxY8dw8uRJvPLKKwAAjUYDIQTMzMywfft29O7duxLX5D4mfUREREQlGDqVa4iFhQUCAwMRERGBJ598Ujs9IiICTzzxhF57e3t7nD17VmfaggULsHPnTmzYsAF+fn4PH3wpmPQRERGRaXqA8fSMITQ0FKNHj0ZQUBCCg4Pxv//9DzExMZg8eTKAwqrhzZs3sXr1aigUCrRo0UJnfjc3N1haWupNr2xM+oiIiIgewogRI5CUlITZs2cjLi4OLVq0wF9//QUfHx8AQFxcXLlj9j0KkhBCVHUQALBiV1VHQEXcQgKqOgS659bfF6o6BLrn782XqzoEKmaRzeyqDoHucQtbXWXvnb17rdGWbdXzOaMtu6qw0kdEREQmyZgXctRE1eNkOBEREREZFSt9REREZJqqyYUcpoJbi4iIiKgWYKWPiIiITBP79MnCSh8RERFRLcBKHxEREZkmBWtXcnBrEREREdUCrPQRERGRSeI4ffIw6SMiIiLTxCFbZOHWIiIiIqoFWOkjIiIikyRY6ZOFW4uIiIioFmClj4iIiEwTL+SQhZU+IiIiolqAlT4iIiIySezTJw+3FhEREVEtwEofERERmSb26ZOFSR8RERGZJp7elaXaJH0jLDZWdQh0z5q/L1R1CHSPV/+Aqg6B7ln11aCqDoGK+dJxaVWHQPfMruoAqMKqTdJHREREJAfvvSsP66JEREREtQArfURERGSa2KdPFm4tIiIiolqAlT4iIiIySQLs0ycHK31EREREtQArfURERGSSeBs2eZj0ERERkWli0icLtxYRERFRLcBKHxEREZkkDs4sDyt9RERERLUAK31ERERkknghhzzcWkRERES1ACt9REREZJrYp08WVvqIiIiIagFW+oiIiMgksU+fPEz6iIiIyCTx3rvyMEUmIiIiqgVY6SMiIiKTxNO78nBrEREREdUCrPQRERGRaeKQLbKw0kdERERUCzDpIyIiIpMkoDDaQ64FCxbAz88PlpaWCAwMxN69e0ttu2nTJvTp0weurq6wt7dHcHAwtm3b9jCbokKY9BERERE9hPXr1+P111/HBx98gJMnT6Jbt27o378/YmJiDLaPjIxEnz598Ndff+H48ePo1asXBg8ejJMnTxo1TvbpIyIiIpMkqkmfvm+++QYTJ07ECy+8AACYO3cutm3bhoULFyIsLEyv/dy5c3Wef/bZZ/jjjz+wZcsWtG3b1mhxstJHREREJklICqM9KiovLw/Hjx9HSEiIzvSQkBAcOHCgQsvQaDTIyMiAs7OzrPWXi5U+IiIiohJyc3ORm5urM02lUkGlUulMS0xMhFqthru7u850d3d3xMfHV+i9vv76a9y9exfDhw9/uKDLwUofERERmSQByWiPsLAwODg46DwMnaotIpU41SyE0JtmyNq1azFz5kysX78ebm5uD71NysJKHxEREVEJ06dPR2hoqM60klU+AHBxcYFSqdSr6t25c0ev+lfS+vXrMXHiRPz66694/PHHHz7ocrDSR0RERCbJmH36VCoV7O3tdR6Gkj4LCwsEBgYiIiJCZ3pERAQ6d+5cauxr167FuHHj8PPPP2PgwIGVvm0MYaWPiIiI6CGEhoZi9OjRCAoKQnBwMP73v/8hJiYGkydPBlBYNbx58yZWr14NoDDhGzNmDL777jt06tRJWyW0srKCg4OD0eJk0kdEREQmqboM2TJixAgkJSVh9uzZiIuLQ4sWLfDXX3/Bx8cHABAXF6czZt/ixYtRUFCAqVOnYurUqdrpY8eOxcqVK40WJ5M+IiIiooc0ZcoUTJkyxeBrJRO53bt3Gz8gA5j0ERERkUkSqB6VPlNR4Qs5BgwYgLS0NO3zTz/9FKmpqdrnSUlJaNasWaUGR0RERFSa6jA4symp8Fpt27ZNZ5DCzz//HMnJydrnBQUFuHDhQuVGR0RERESVosKnd4UQZT4nIiIiepR4eleemlm/JCIiIiIdFa70SZKkdzuRitxehIiIiMgYamrfO2ORdXp33Lhx2tGoc3JyMHnyZNjY2ACA3k2Ja4Jfdh7CqvC9SEzNQIO6bnjruYFo19jPYNuTF6Px3YZwRMclICcvH551HDGsZweMCumqbbN533HMWL5Rb95Di2dBZW5utPWoCYQQOPDXfJzZvx65Wenw8G2Nx4d/BBevRqXOc2b/Lzh3+Hck3roEAHD3bo5uQ0Lh6dvKYPvD2xZj7+Zv0K7XGPR++gOjrEdt4tw1CP5vToRDuxaw9HLDsWFTcHvzjqoOq8Yzb9UZFu16QrKxhyYpHrmRf0B961rpMyiVsOgQAvMm7SBZ20NkpiL36A4UnD/y6IKuQXq1ViKwsQJWFkBsosCfh9VISC29O1RTbwndWyrhbC9BKQFJGQIHzmlw+qpG20YhAb3aKNHKTwFbKyAjGzh1WY09ZzRgRyuSo8JJ39ixY3Wejxo1Sq/NmDFjHj6iamLbkTP4cu1WTB89BG0a+mDj7iN45dtV2PjJ6/Cs46jX3kplgRG9g9G4vgesVBY4eSkan6z6HVYWFhjWs4O2na2VCr99VuJefkz4ynUkYgmO71yBfqPnwMnNF4fCF+LX+eMx8aNwWFjaGpznxsXDaBI0EHX92kFpboGjEUuxYf4EjPtwK+wcde+HGHf9DE7vXw/XugGPYnVqBaWNNdLPXEDsqk0I/HV+VYdTK5g1agNV9yeQu2sT1LeuwbxlMKyemIS7P30BkZFqcB7L/mOgsLZDzj+/QJOaCMnaFlAoH23gNUTXFgoEN1Pgt/0FSEoHerRSYGwfM3z/Wz7yCgzPk50LRJ5VIyFNQK0BAuopMLSLEndzBC7fEtrlBjVW4Ld9BbiTKuDlIuHJLmbIyQcORWkML7iWYJ8+eSqc9K1YscKYcVQ7P23bh6HdAvFU9/YAgLefH4SD5y7h112HMe3pvnrtm/h4oYmPl/a5l4sTdh4/h5OXonWSPkCCi4OdscOvUYQQOLFrNTr2nYzGbUIAAP1Hf46F0zsj6uifaN3tWYPzDRz/tc7zkJGf4OKpbYi5cBDNOw7VTs/LuYu/Vr6Nvs9/goPhC422HrVNwrZIJGyLrOowahWLdt2Rf+4I8s8dBgDkRv4BpU8AzFt2Rt6Bv/TaK30CYFavATJXfArkZgMAREbKI425JgluqkTkWTWiYgqTtU371HhnhAKt/BU4dtFwchZ9W7dWdyhKgzYNFPB2U+DyLTUAoL6rAv/d0ODizcK2qXcFWvoJeNVhwkPy8GS4AfkFBYi6fgvBzXVPHXZq3hCnL1+v0DL+u34Lpy/HoF2A7ung7Nw89H/7C/R9cw6mzV2F/67fqrS4a6q0pFjcTU+Ab9P7p8rNzC1Qr2F73Lx2ssLLKcjLhkZdAEtr3fsa/vPLbPg37wGfJqXfGJuo2lMooXCrB3WM7tBZ6usXoPT0NTiLmX9zqG/fgEVQb9hM/Ag2Y96DqutgQMlx++VysgXsrCVtdQ4A1BogOl6gvmvFkzN/Dwku9hKu376fJMbc0cDfU4E69oXP3Z0k+LhJuHSTJ3c5Tp88Fd6z4+LiMH/+fHz66acAgK5duyIrK0v7ulKpxO+//466detWfpSPWEpGFtQaDZwddE8b1rG3Q1LapTLn7fvmHKRk3IVarcFLTzymrRQCgK+nK2ZNHIaGdT1wNycHP0ccwPiwxVg361X4uLsYZV1qgrvpCQAAG7s6OtNt7F2QnlzxpDnyj69h6+Cuk9z9d2wrbsecw+h39ftaEpkSycoGkkIJTVamznSRnQmFjeGzCwr7OlB6+QHqAmT/uQKSlQ0sew2DZGmNnH/WP4qwawxbq8LE7m62biJ2N0fA0abspE9lDrz1jDnMlIBGAH8eUuNK3P3l7P1XA5WFhFeHmkMIQJKAHSfUOHutdp/aBXh6V64KJ30LFizQuQPH6dOnMWHCBDg7OwMA/v77b3z77bf46quvyl1Wbm6u3oUf6rx8qCyqV982qcSXSQiB8i5YXv7ei8jKzcPZKzfw/YZw1Herg/6dWgMAWjXwRqsG3tq2bRr64LlZP2DdPwfx7sjBlR6/qTp/ZDMi1s7QPn9qyuLCPyT9z6OijkQswX/HtmLE66thZl54MVJ6Shx2bvgUT7+yXDuNyOQZ2C9K3VXu7VPZ4WuAvBwAQG7kZlgOHAPs2gioS+mIRmjlp8Dg4Pt9H9fsKNxWhjZ1eUeqvHxg4ZZ8WJhJ8PeU0K+9EikZQnvqt4WvAq39FdgQqcadVAFPZwn92ysLL+i4wsSPKq7CSd+WLVvw5Zdf6kx77bXX4O/vDwDo1KkTQkNDK5T0hYWFYdasWTrT3h//DD6YOKKi4RiVk501lAoFktIydKYnZ2TC2d7wRQNF6roWJsGN6nkgKT0Di//YoU36SlIoFGjuVxcxt5MqJ/AaomGr3vD0vb/N1AV5AIC76YmwdXDTTs/KSIK1ffkV0qP/LMPhbYvxzKsr4Fq3iXb67ZhzyMpIwo+fP6WdJjRqxF4+ipN71uCN785CwQ7tZCJE9l0IjRoKGzsUTwMkK1uIrAzD89xNh8hM0yZ8AKBJvg1JUkCyc4RITTRy1KbrvxsaxCbe39JKZWECbWslIbNYtc/GUve5IQJAckbhX/EpAq4OhVf0Rt8uTCT7Bimx96wa/0YXvt+dVAFHW6BbS2WtT/oEh46TpcJJX3R0NBo0aKB93qdPH+1wLQAQEBCAa9fKGBagmOnTpyM0VPcKVvVx/U7GVcXczAxNfbxw6Pxl9A5srp1+6Nxl9Gxb8fsLCwHkFZT+n7IQAhdi4tConsdDxVvTWFja6lyRK4SAjb0rrv+3H+71C7e/uiAPsZePovsTb5W5rCMRS3EofCGefmUZPHxa6rzmE9AJYz/YojMt/MfpqOPuj/Yhk5jwkWnRqKG5Ewuld2MUXPlXO1np3RgFV88ZnEUdFw2zRq0Bcwsgv/CfK4WTK4RGU+rVvlQor6AoUSsikJEl0NBTQnxyYZKnVAC+HhIijstMzCRAWezwY67UrxZqNOCJTZKtwklfQUEB0tLStM83bdqk83pKSgoUiop1fFSpVNrx/opkVbNTu6P6dsWHS35FM9+6aNXAG5v2HEV8chqevncl7vcbtuFOSjo+mfQMAGD9joPwqOMIX09XAMCpi9fx47a9ePaxYO0yF/+xAy0b1Ie3mwvu5uRg7T8HcfFGHKaPGvLoV9CESJKEdr3G4PC2xXBy9YWjmw8Ob1sMMwtLNG0/SNvur1XvwNbRHd2feBNA4Snd/X9+h4HjvoaDc13cTSvsG2iusoaFpQ0sLG3h6tVY573MVdawtHXUm07yKW2sYdPwfncGa796sG/dBHnJaci5EVeFkdVceSciYdn3Oahvx0ITFw3zlp2gsHNC/tmDAACLzgOgsHVAzva1AID8Cydg0aEPLPs8i7xD2yBZ2kDVdTDyzx/hqd0HcDBKjW6tlEjKEEhKB7q3VCC/ADhTbMy9p7oqkZ4F/HOi8Mrcbi0UuJUkkJwhoFQAjesp0KaBAlsOqbXzXIjVoHtLJdIyReHp3ToSOjdX4sSl2l3lAwAhmPrKUeGkLyAgAAcOHEDbtm0Nvr537140blxzfij7dmiFtMws/G/zTiSmZaBhXXfMe30svFycAACJaRmIT07VttcIgXkbt+FmQgrMlArUc62DV5/ui6d73B+uJSMrBx+v+h1JaRmwtbJEE28vLH33RbTwr/+oV8/kdOgzCQX5ufhn/SzkZKXB07c1nn5luU5FMD0lDlKxK65ORa6FuiAfm5dO01lW8IBX0GXgq48s9trKIbAFgnf8qH3e7Kv3AQA3Vm/CmYnTqyqsGq3g0inkWllD1bEPJGt7aJLikP3HUu0wLAobe0h2jvdnyM9D9m+Loer5JKyffR0iJ6twGQf+rpoVMHH7/tXAXClhUEczWKqAmwkCqyMKdMboc7CRdPojW5hLGNRJCXtrIF8NJKYJbNx7/1QuAGw9rMZjbYFBncxgY1k4OPOxixrsPq0GkRySqGBv+C+//BJz5szBrl270KqV7h0NTp8+jd69e+O9997D22+//UCBZO3n1ZPVxZrsYVUdAt3j1Z+DRVcX3b8aVH4jemS+dAyr6hDontljLarsvS9dqdgwag+iUQMfoy27qlS40vf666/jzz//RGBgIPr06YOAgABIkoT//vsPERER6NSpE15//XUjhkpERERED6rCSZ+5uTkiIiLwzTffYN26ddi9ezcAoFGjRvj4448RGhqKc+fOoU2bNkYKlYiIiOg+jtMnj6whpy0sLPDee+/h1KlTyMrKQlZWFvbs2QN7e3sEBwcjMDDQWHESERER6RCQjPaoiR74PiM7d+7EqFGj4OXlhXnz5qF///44duxYZcZGRERERJVE1g0WY2NjsXLlSixfvhx3797F8OHDkZ+fj40bN6JZs4qPX0dERET0sGpqRc5YKlzpGzBgAJo1a4bz589j3rx5uHXrFubNm2fM2IiIiIioklS40rd9+3ZMmzYNL7/8Mho1amTMmIiIiIjKxUqfPBWu9O3duxcZGRkICgpCx44dMX/+fCQkJBgzNiIiIiKqJBVO+oKDg7FkyRLExcXhpZdewrp161C3bl1oNBpEREQgI8PwDb2JiIiIjEEIyWiPmkj21bvW1taYMGEC9u3bh7Nnz+LNN9/EnDlz4ObmhiFDeA9ZIiIiourogYdsAQrvx/vFF18gNjYWa9eurayYiIiIiMrFcfrkeaikr4hSqcTQoUOxefPmylgcEREREVUyWeP0EREREVUXNbUiZyxM+oiIiMgkMemTp1JO7xIRERFR9cZKHxEREZmkmjq0irGw0kdERERUC7DSR0RERCZJwz59srDSR0RERFQLsNJHREREJolX78rDSh8RERFRLcBKHxEREZkkXr0rD5M+IiIiMkk8vSsPT+8SERER1QKs9BEREZFJ4uldeVjpIyIiInpICxYsgJ+fHywtLREYGIi9e/eW2X7Pnj0IDAyEpaUl/P39sWjRIqPHyKSPiIiITJKAZLSHHOvXr8frr7+ODz74ACdPnkS3bt3Qv39/xMTEGGx/7do1DBgwAN26dcPJkyfx/vvvY9q0adi4cWNlbJZSMekjIiIiegjffPMNJk6ciBdeeAFNmzbF3LlzUb9+fSxcuNBg+0WLFsHb2xtz585F06ZN8cILL2DChAn46quvjBonkz4iIiIySUJIRntUVF5eHo4fP46QkBCd6SEhIThw4IDBeQ4ePKjXvm/fvjh27Bjy8/Plb4gK4oUcRERERCXk5uYiNzdXZ5pKpYJKpdKZlpiYCLVaDXd3d53p7u7uiI+PN7js+Ph4g+0LCgqQmJgIT0/PSlgDfaz0ERERkUnSGPERFhYGBwcHnUdYWFipsUiSbnVQCKE3rbz2hqZXJlb6iIiIyCQZc8iW6dOnIzQ0VGdaySofALi4uECpVOpV9e7cuaNXzSvi4eFhsL2ZmRnq1KnzkJGXjpU+IiIiohJUKhXs7e11HoaSPgsLCwQGBiIiIkJnekREBDp37mxw2cHBwXrtt2/fjqCgIJibm1feSpTApI+IiIhMUnUZsiU0NBRLly7F8uXLERUVhTfeeAMxMTGYPHkygMKq4ZgxY7TtJ0+ejOvXryM0NBRRUVFYvnw5li1bhrfeeqtSt09JPL1LRERE9BBGjBiBpKQkzJ49G3FxcWjRogX++usv+Pj4AADi4uJ0xuzz8/PDX3/9hTfeeAM//PADvLy88P3332PYsGFGjZNJHxEREZmk6nQbtilTpmDKlCkGX1u5cqXetB49euDEiRNGjkoXT+8SERER1QKs9BEREZFJktv3rrZjpY+IiIioFmClj4iIiEySRlR1BKaFSR8RERGZJJ7elafaJH2KxLiqDoHu+XvH5aoOge5Z9dWgqg6B7ol868+qDoGK2dlvSFWHQPfMHtujqkOgCqo2SR8RERGRHNVpyBZTwAs5iIiIiGoBVvqIiIjIJAleyCELK31EREREtQArfURERGSSNLx6VxZW+oiIiIhqAVb6iIiIyCTx6l15mPQRERGRSeKFHPLw9C4RERFRLcBKHxEREZkk3oZNHlb6iIiIiGoBVvqIiIjIJGnYp08WVvqIiIiIagFW+oiIiMgkccgWeVjpIyIiIqoFWOkjIiIik8Rx+uRh0kdEREQmiffelYend4mIiIhqAVb6iIiIyCTx9K48rPQRERER1QKs9BEREZFJ4pAt8rDSR0RERFQLsNJHREREJom3YZOHlT4iIiKiWoCVPiIiIjJJvHpXHiZ9REREZJIEB2eWhad3iYiIiGoBVvqIiIjIJPFCDnlkVfqUSiXu3LljrFiIiIiIyEhkVfoEe0wSERFRNcG0RB726SMiIiKqBWT36du2bRscHBzKbDNkyJAHDoiIiIioIljpk0d20jd27NgyX5ckCWq1+oEDIiIiIqLKJzvpi4+Ph5ubmzFiISIiIqowjeA4fXLISvokiRuXiIiIqgee3pVH1oUcFbl699SpUw8aCxEREREZiaykb+zYsbCystKbnpaWhgULFqBdu3YIDAystOCIiIiISiOE8R7GkpKSgtGjR8PBwQEODg4YPXo0UlNTS22fn5+Pd999Fy1btoSNjQ28vLwwZswY3Lp1S/Z7y0r6VqxYATs7O+3znTt3YtSoUfD09MS8efMwYMAAHDt2THYQRERERLXB888/j1OnTiE8PBzh4eE4deoURo8eXWr7rKwsnDhxAv/3f/+HEydOYNOmTbh48eIDjZQi+0KO2NhYrFy5EsuXL8fdu3cxfPhw5OfnY+PGjWjWrJnsAIiIiIgehKndhi0qKgrh4eE4dOgQOnbsCABYsmQJgoODceHCBQQEBOjN4+DggIiICJ1p8+bNQ4cOHRATEwNvb+8Kv7+sSt+AAQPQrFkznD9/HvPmzcOtW7cwb948OYsgIiIiqpUOHjwIBwcHbcIHAJ06dYKDgwMOHDhQ4eWkpaVBkiQ4OjrKen9Zlb7t27dj2rRpePnll9GoUSNZb2SK1h84g5V7TiIx4y4auDvjnSHd0M6vbrnznYy+hYmLNqGhex388sZzOq+lZ+difvhB7Pj3CtKzc1HX2R5vDuyKbk19jbQWNceI/s7o09keNlYKXLqeiyW/JuBGfF6Z81hbKTBykDM6tbKFjbUCd5IKsPL3RJw4nwUAaNbAEk885oQG9VVwdjDDnCVxOHL27qNYnRrFvFVnWLTrCcnGHpqkeORG/gH1rWulz6BUwqJDCMybtINkbQ+RmYrcoztQcP7Iowu6FnHuGgT/NyfCoV0LWHq54diwKbi9eUdVh1UjTXjOB0P6esLO1gznL2bgm0WXcC0mq9T28z5rjbYtHfWmHziahHdm/6s3fdTT9TF5rD9++SMW3y+9UpmhmyRhxCFbcnNzkZubqzNNpVJBpVI98DJLG/bOzc0N8fHxFVpGTk4O3nvvPTz//POwt7eX9f6yKn179+5FRkYGgoKC0LFjR8yfPx8JCQmy3tBUhJ+6iC+27MWk3kFY/9qzaOfnhSnLtiAuJaPM+TKyc/Hhugh0aFhf77X8AjUmL/kdt1LS8dXo/vjj7VH4aFhvuDnYGGs1aownH3fE4F6OWPJrAt79Ohap6QWYMdULlqrSd3gzJTBzihfcnM3x5fJ4vPpJDBasu4Pk1AJtG5WFAtE3CxNIejBmjdpA1f0J5B3dgayfv4H61jVYPTEJkp1jqfNY9h8Ds/qNkPPPL7i7eg6yw3+CJuXOowu6llHaWCP9zAWce212VYdSo40cVh8jhtbDN4sv44XQE0hKycO3s1vBykpZ6jzvf3YOQ0Yf0D5GTz2KArXArv36x6QmjewwpJ8nLl/LNOZq0D1hYWHaiy2KHmFhYQbbzpw5E5IklfkouubB0PB3QogKDYuXn5+PZ599FhqNBgsWLJC9TrIqfcHBwQgODsZ3332HdevWYfny5QgNDYVGo0FERATq16+vc6GHKftx7yk82b4ZnurYHADwzpDuOHAxBr8cOovX+ncudb6PN+1C/7YBUEoSdp27qvPab0fPIy0rB6umPg1zZeFBwMtJXpZeWw3q4YiN25Nx+ExhFe77Nbex4hM/dA+0w/YD6Qbn6d3JHrY2Skz/NhZqTeG0hJQCnTYno7JwMqr0/8KpfBbtuiP/3BHknzsMAMiN/ANKnwCYt+yMvAN/6bVX+gTArF4DZK74FMjNBgCIjJRHGnNtk7AtEgnbIqs6jBrvmSF1sfqXGEQeTAQAfPrtf9j8Y2eE9HDDH+FxBufJyNQ9Jj3W3Q25uWrs2qeb9FlZKjDjzSb4Yt5FjB3hY5wVMEHGvMp2+vTpCA0N1ZlWWpXvlVdewbPPPlvm8nx9fXHmzBncvn1b77WEhAS4u7uXOX9+fj6GDx+Oa9euYefOnbKrfIDMpC8mJgb169eHtbU1JkyYgAkTJuDChQtYtmwZ5syZg/feew99+vTB5s2bZQdSneQXqBF18w4m9NIdfia4kTdORxvecQHg96PnEZuUhs+eDcGSHUf1Xt9z/hpa+Xgi7Lc92HX+KpxsrDCgbWOM7xkIpUJW0bVWca9jBicHM5z6735yVlAAnLuSjQA/y1KTvvYtbHDhWg4mPeOKDi1tkJ6pxt7jmfjtnxST6/xbbSmUULjVQ96xnTqT1dcvQOnpa3AWM//mUN++AYug3jBvEgjk56Hg6jnkHvwbUBcYnIeouvNyt4SLswpHTt7/Bya/QODUv6lo0cS+1KSvpEF9PLAj8g5ycjU600MnN8KBY8k4djqVSd8jIudUrouLC1xcXMptFxwcjLS0NBw5cgQdOnQAABw+fBhpaWno3Ln0glJRwnfp0iXs2rULderUqdhKlCAr0/Dz89M7nRsQEIAvvvgCsbGxWLt27QMFUd2k3M2GWiNQx9ZaZ3odOyskZhiuCl1PSMV3fx/AZ8+FwExpeLPGJqfhn7OXoRYCP0wYgkmPtcfqyFNYsoPD3JTF0b7wf5PUdN17Oqemq7WvGeLuYo7gNjZQKCR8sjgOv25PwZDejhjW18mo8dYmkpUNJIUSmizd000iOxMKG8NVf4V9HSi9/KCs44HsP1cgJ/J3mDVqBctewx5FyERG4exkAQBITtXtZ5ySmqd9rTxNG9mhga8ttmzX7dv1WDdXBDS0w+JVV0uZs/bSCOM9jKFp06bo168fJk2ahEOHDuHQoUOYNGkSBg0apHPlbpMmTfDbb78BAAoKCvD000/j2LFjWLNmDdRqNeLj4xEfH4+8vLL7tZckq9JX1h05lEolhg4diqFDh5a7HEOdI0V+PlTm5nLCMbqSp9eF0J8GAGqNBtPXbsPLfTrC17X0hEIjAGdbK3w0rBeUCgWa1XNDQvpdrNpzApP7dKjk6E1X9yBbvDTifkfXTxcbHoCy8LMo/TupkIC0DDUWrbsDjQCu3siFs4MZhvZ2xK/hPJ1YqQwcG0o9XNzbibLD1wB5OQCA3MjNsBw4Bti1kdU+Mgl9erjh7amNtc/fmX228I+S33tJKuswpWNQiAeuRGci6tL9vuNuLiq8NqkhQj86g7x8nqIoyRRvw7ZmzRpMmzYNISEhAIAhQ4Zg/vz5Om0uXLiAtLQ0AIVD5RWdQW3Tpo1Ou127dqFnz54Vfm/Z4/RVhrCwMMyaNUtn2gcj+uPD5wZURTh6nGysoFRIelW95MxsveofANzNzce52Dv471YC5vyxBwCgEQJCAO3em4+FLzyBjg3rw9XOGmZKhc6pXH83JyRmZCG/QA1zs9I7+9YmR87excXoG9rn5maFSYKjvRIpxap9DnZKvepfcSnpahSohc5/bLHxeXByMIOZEigofVaqIJF9F0KjhsLGDsVPRklWthBZhi96EnfTITLTtAkfAGiSb0OSFJDsHCFSE40cNdHD23ckCecv3j9LY2FeeFx3drJAUsr96ouTg7le9c8QlUqBx7q5YdmaaJ3pAQ1t4exkgaVz73c3MlNKaN3cAU8NqoveT0VCowGZEGdnZ/z0009ltileZPP19a3QbXArQnbSt3TpUtja2pbZZtq0aWW+bqhzpNi+TG4oRmNupkTTum44dOkGHmvRQDv90KUY9Gzur9feVmWBDaHP60z75eBZHLl8A1+NHoC6zoWdLdv4euLvUxeh0QgoFIWJzPXEVLja2TDhKyYnVyA+N19nWkpaAVoHWONabOHB00wJNG9ghR83J5W6nP+uZqNboF3hP9r39hcvN3MkpxUw4assGjU0d2Kh9G6Mgiv3h5dQejdGwdVzBmdRx0XDrFFrwNwCyC/8PBVOrhAaDURG6qOImuihZWercTNb90CSmJyL9m2ccOlqYXcHMzMJbVo4YlEFTsv27uoKc3MFtu3W7eR/7HQqRk/V7SP+/usBuB6bjTUbYmp9wmeKlb6qJDvpW7RoEZTK0hMUSZLKTfoMdY7MqWandkd3a4MP1kegWT03tPb2wMbD5xCXmolnOrUAAHz39wHcScvEp8+GQKGQ0MhDt1Ols40VVGZmOtOHB7fE2v1n8PnmSDzXpRViElOxdOcxPN+l9SNdN1P0555UDOvjhLiEfMQl5OOpPk7IzReIPH6/mjRtlBuS0tRYs6UwEQzfl44B3R0x8SkXbI1Mg5erOYb1ccLWyDTtPJYWEjxc73/33OqYwbeuBTKzNEhM4WnGisg7EQnLvs9BfTsWmrhomLfsBIWdE/LPHgQAWHQeAIWtA3K2F/b5zb9wAhYd+sCyz7PIO7QNkqUNVF0HI//8EZ7aNRKljTVsGt4ftd/arx7sWzdBXnIacm5U7AIDKt+vm29i9DPeiL2VhRu3sjFmuDdyc9XYvuf+cEQfvhGAhKQ8LF6tO47loD6e2HsoEekZuvtAdrZab5y/nBwN0tPzyxz/j8gQ2UnfsWPHDA4sWNP0a9MYaVk5+N8/R5CQfhcNPerghwmDtUOsJKbfRXyqvLGSPBztsGjSE/hyy1488+1auNnbYGTX1hjfM7D8mWu53/5JhYW5Ai8+4wob68LBmWcvuIWc3Pv/5rk4meucyk1KLcCsBbcw4SkXfPuePZLT1Ni6Jw2//XO/P18Db0t8PO3+gNsTnnIFAOw8nI75azhuXEUUXDqFXCtrqDr2gWRtD01SHLL/WKodhkVhY687Zl9+HrJ/WwxVzydh/ezrEDlZhcs48HfVrEAt4BDYAsE7ftQ+b/bV+wCAG6s34czE6VUVVo2zZuMNqCwUCH25EexszXH+Yjre+OgMsotVBN1dLfUuEqjvZYXWzR3w+v+decQRmz6OxCCPJGScKFYqlYiLizNK0pfzx/zyG9Ej8fyOflUdAt2zqsHCqg6B7ol868+qDoGKCev3v6oOge7Zt6VHlb33UiPeWOaFx4y37KpSaVfvEhERET1KTEvkkTVO34wZM8q9iIOIiIiIqh9ZSd+rr76K5ORknWnnzp3D+PHjMXz4cPz888+VGhwRERFRaTQa4z1qIllJ39SpU/HNN99on9+5cwfdunXD0aNHkZubi3HjxuHHH38sYwlERERElUMI4z1qIllJ36FDhzBkyBDt89WrV8PZ2RmnTp3CH3/8gc8++ww//PBDpQdJRERERA9HVtIXHx8PPz8/7fOdO3fiySefhJlZ4fUgQ4YMwaVLlyo3QiIiIiIDWOmTR1bSZ29vj9TUVO3zI0eOoFOnTtrnkiTp3VOXiIiIiKqerKSvQ4cO+P7776HRaLBhwwZkZGSgd+/e2tcvXryI+vXrV3qQRERERCVphPEeNZGscfo+/vhjPP744/jpp59QUFCA999/H05OTtrX161bhx49qm6QRiIiIiIyTFbS16ZNG0RFReHAgQPw8PBAx44ddV4PCQnBjh1GHB6biIiI6B7j3jRCMuKyq4as07sA4OrqiieeeEIv4QOAVq1accgWIiIiompIVqWPiIiIqLqoqVfZGguTPiIiIjJJNfXOGcYi+/QuEREREZkeWZW+p556qszXi4/hR0RERGRMPL0rj6ykz8HBodzXx4wZ81ABEREREVHlk5X0rVixwlhxEBEREclSUwdRNhb26SMiIiKqBXj1LhEREZkk9umTh5U+IiIiolqAlT4iIiIyScKonfpq3m3YmPQRERGRSeKFHPLw9C4RERFRLcBKHxEREZkkXsghDyt9RERERLUAK31ERERkkjTs1CcLK31EREREtQArfURERGSS2KdPHlb6iIiIiGoBVvqIiIjIJLHSJw+TPiIiIjJJGmZ9svD0LhEREVEtwEofERERmSShqeoITAsrfURERES1ACt9REREZJIE+/TJwkofERERUS3ASh8RERGZJA379MnCSh8RERFRLcBKHxEREZkk9umTh5U+IiIiMkkaYbyHsaSkpGD06NFwcHCAg4MDRo8ejdTU1ArP/9JLL0GSJMydO1f2ezPpIyIiInpEnn/+eZw6dQrh4eEIDw/HqVOnMHr06ArN+/vvv+Pw4cPw8vJ6oPeuNqd3P0l5sapDoHsW2bxQ1SHQPV86Lq3qEOienf2GVHUIVMz0cP5mVB8XquydhTFLckYQFRWF8PBwHDp0CB07dgQALFmyBMHBwbhw4QICAgJKnffmzZt45ZVXsG3bNgwcOPCB3r/aJH1ERERE1UVubi5yc3N1pqlUKqhUqgde5sGDB+Hg4KBN+ACgU6dOcHBwwIEDB0pN+jQaDUaPHo23334bzZs3f+D35+ldIiIiMklCGO8RFham7XdX9AgLC3uoeOPj4+Hm5qY33c3NDfHx8aXO9/nnn8PMzAzTpk17qPdn0kdERERUwvTp05GWlqbzmD59usG2M2fOhCRJZT6OHTsGAJAkSW9+IYTB6QBw/PhxfPfdd1i5cmWpbSqKp3eJiIjIJGmM2KdPpbKs8KncV155Bc8++2yZbXx9fXHmzBncvn1b77WEhAS4u7sbnG/v3r24c+cOvL29tdPUajXefPNNzJ07F9HR0RWKEWDSR0RERPRQXFxc4OLiUm674OBgpKWl4ciRI+jQoQMA4PDhw0hLS0Pnzp0NzjN69Gg8/vjjOtP69u2L0aNHY/z48bLiZNJHREREJsnUBmdu2rQp+vXrh0mTJmHx4sUAgBdffBGDBg3SuYijSZMmCAsLw5NPPok6deqgTp06OssxNzeHh4dHmVf7GsI+fURERGSShMZ4D2NZs2YNWrZsiZCQEISEhKBVq1b48ccfddpcuHABaWlplf7erPQRERERPSLOzs746aefymxTXgVTTj++4pj0ERERkUnSmNjp3arG07tEREREtQArfURERGSSTO1CjqrGSh8RERFRLcBKHxEREZkkYw7OXBOx0kdERERUC7DSR0RERCaJXfrkYdJHREREJknw9K4sPL1LREREVAuw0kdEREQmiYMzy8NKHxEREVEtwEofERERmST26ZOHlT4iIiKiWoCVPiIiIjJJrPTJw0ofERERUS3ASh8RERGZJBb65GGlj4iIiKgWYKWPiIiITBL79MnDpI+IiIhMkuDgzLLw9C4RERFRLSAr6Zs7dy6Sk5ONFQsRERFRhWk0wmiPmkhW0jdr1ix4eXlh+PDh2L59O8uqRERERCZCVtIXHx+PZcuWITk5Gf3794ePjw9mzJiBa9euGSs+IiIiIoOEEEZ71ESykj6VSoWRI0fin3/+wZUrVzB+/HisXr0ajRo1wuOPP45169YhNzfXWLESERER0QN64As5fH19MWvWLFy7dg3h4eFwd3fHxIkT4eXlVZnxERERERkkNMJoj5qoUq7eVSgUkCQJQghoNJrKWCQRERERVaIHTvquX7+OWbNmwc/PDyEhIbh16xaWLFmCuLi4yoyPiIiIyCBW+uSRNThzTk4ONm7ciOXLl2PPnj3w9PTE2LFjMWHCBPj7+xsrRiIiIiI9mhp6wYWxyEr6PDw8kJOTg0GDBmHLli3o27cvFAqO70xERERU3clK+j766COMGTMGLi4uxoqHiIiIqEJq6mlYY5GV9IWGhgIALl26hD/++APR0dGQJAl+fn4YOnQoT/ESERERVVOykj4ACAsLw//93/9BCAE3NzcIIZCQkID33nsPn332Gd566y1jxFllerdRIqixAlYWQGyiwJZDatxJLf0/i2beEnq0UsLZXoJSApIyBPb/q8Gpq/evarYwAx5vp0QzbwVsLIG4ZIGth9W4mcT/WCrKqtNjsO42AAo7BxTcuYnMP9cgP/qiwbZ2T0+CVWA3vekFt2ORPPd9Y4daI/VqrURgsf3iz8NqJJSxXzT1ltC9pe5+ceCcBqeL7RcKCejVRolWfgrYWgEZ2cCpy2rsOaMB94yyTXjOB0P6esLO1gznL2bgm0WXcC0mq9T28z5rjbYtHfWmHziahHdm/6s3fdTT9TF5rD9++SMW3y+9Upmh1zrOXYPg/+ZEOLRrAUsvNxwbNgW3N++o6rBMVk0dRNlYZCV9u3btwocffoj/+7//w2uvvQYnJycAQHJyMubOnYv33nsPHTp0QPfu3Y0S7KPWrYUCnZspsGlfARLTgZ6tFRgXYoa5m/KRV2B4nuw8YPcZNRLTBNQaIKCeAk92VSIzR+DyrcIv55NdlHBzlLBhbwHSswTaNFBifF8zfPd7PjJKP07TPaqWHWE7cCQy/liF/OuXYNWxFxzGvYXkb6dDk5ak1z5zy0+4G/7L/QkKBZxf+xS5Z48+wqhrjq4tFAhupsBv+wuQlA70aKXA2D5m+P63MvaLXCDyrBoJxfaLoV2UuFtsv+jaQoGgxgr8tq8Ad1IFvFwkPNnFDDn5wKEoDgVVmpHD6mPE0Hr4dO4F3LiZhbEjfPDt7FZ47uWjyM5WG5zn/c/OwdxM0j53sDfHiu+DsGt/gl7bJo3sMKSfJy5fyzTaOtQmShtrpJ+5gNhVmxD46/yqDodqGVlXYSxatAgvvPACZs6cqU34AMDZ2RmzZ8/GhAkTsHDhwkoPsqp0bqbEnjNqnI8RuJMqsHGvGuZmQGv/0jfbtXiBqBiBhDQgOQM4GKXB7RQBH/fCecyUQDMfBbYdVyP6tkByBrDzlBopmQIdA5SPatVMmnW3fsg+tgc5x/ZAnXALmX+ugSYtGVadehtsL3KzoclM0z7M6vlBsrRG9vHIRxx5zRDcVInIs2pE3dsvNu0r3C9albFfRN8u3C8S04CUjMIk7naKgLfb/Xnquyrw3w0NLt4USL0LnL9emBB61ZFKXS4Bzwypi9W/xCDyYCKuxWTh02//g0qlREgPt1LnycgsQHJqvvYR1MYJublq7Nqnm/RZWSow480m+GLeRWRklpLRkywJ2yJxccZcxP8eUdWh1AgajTDaoyaSlfQdOXIEo0ePLvX10aNH49ChQw8dVHXgZAvYWUvaKgQAqDVAdLyAt1vFf4T8PSW42EuIji+sVCgkQKmQUFDiH/D8AsDHnT9u5VIqYebli7xLuqeg8i6dhbl3owotwiqoB/KvnIMmVb8qSGUra7+o7ypjv/Ao3C+u375fwYu5o4G/pwJ17AufuztJ8HGTcOlmzTz4VgYvd0u4OKtw5GSKdlp+gcCpf1PRool9hZczqI8HdkTeQU6ubkU1dHIjHDiWjGOnUysrZCKqQrJO796+fRu+vr6lvu7n54f4+PiHjalasLUq/AHLzNb9wcnMFnC0LfvHTWUOvDPcHGZKQCOALQfVuBJXuJy8gsIft16tlUhILUBmDtDKT4F6rhKS0o2zLjWJwtoOklIJTWaaznRNZjoUdg7lz2/nAIvGrZC+vuZUpB+lov3ibon94m6OgKNN+fvFW8/c3y/+PHR/vwCAvf9qoLKQ8OpQcwgBSBKw44QaZ6/x1G5pnJ0sAADJqXk601NS8+DuZlmhZTRtZIcGvraY871un9jHurkioKEdXnjjeOUES2QEvHpXHtmDM1tYWJT6urm5OfLy8kp9vUhubi5yc3N1phXkSzAzV8kJp1K19ldgSPD906s//lN4KqPk10mS9KeVlJcP/LA5HxbmEhp4SujfQYmUTIFr8YVzbthbgCe7mOHdERZQawTikgTOXNXAqw7HPHwoFejQa9muG0ROFnLP84esIlr5KTC42H6xZofh/aK0acXl5QMLt+TDwkyCv6eEfu2VSMkQiL5dOGcLXwVa+yuwIbLwYilPZwn92ysLL+i4wsQPAPr0cMPbUxtrn78z+2zhHw9yoLpnUIgHrkRnIupShnaam4sKr01qiNCPziAvnz+qVH3xQg55ZF+9u3TpUtja2hp8LSMjw+D0ksLCwjBr1iydad2e+BDdh/6f3HAqTVSMBjcS7v+wmCkLqxZ2VpJOtc/GUtKrcpQkUNifDxCITxZwdSi8cvFafOEPZnIGsCy8AOZmhdWPzGxgRI/CH0AqmyYrA0KthsJWt6qnsLWHJrP8UqllUHfknNwPqA13cCdd/93QIDbx/n6hvLdf2BrYL0pWxUvS2S9S7u8X0bcL94u+QUrsPavGv9GF73cnVcDRFujWUsmk7559R5Jw/uIx7XML88J/FJ2dLJCUcv8fbicHc73qnyEqlQKPdXPDsjXROtMDGtrC2ckCS+cGaqeZKSW0bu6ApwbVRe+nIsHbrBOZHllJn7e3N5YsWVJum/JMnz5dO+Zfkc/WV21/tryCoh+kIgIZWQINvCTEJRf+mCkVgK+HhO3H5B3tJBRewFFSfkHhw9ICaFhXgW3HmIiUS61Gwa1oWDRqgbxi1TqLhi2QG3WizFnN/ZrAzMUDacf2GDvKGqO0/aKhp4T4EvtFxHGZWYAEKIvtF+ZK/eKURlO4/1Ch7Gw1bpa4IjcxORft2zjh0tXCq2vNzCS0aeGIRauulru83l1dYW6uwLbdt3WmHzuditFTda9uf//1AFyPzcaaDTFM+KjaEPwyyiIr6YuOjq6UN1WpVFCpdE/lmpmX/1/po3bgvBo9WimRlC60Q1PkF0BnbLFhXZVIzwIiThQeiLu3VOBmokByhoBSCTSuq0CbhgpsPnj/QN3QS4IkAYlpAs52hae5EtMETlzil7cisvaGw374SyiIvYb8mMuw6tATCsc6yD68EwBg0/cZKOydkPHr/3Tms2rfA/kxl6G+fbMqwq4xDkap0a2VEkkZhftF95aF+8WZYvvFU/f2i3/u7RfdWihwK+nefqEAGtdToE0DBbYcur9fXIjVoHtLJdIyC68K9qwjoXNzJfeLcvy6+SZGP+ON2FtZuHErG2OGeyM3V43te+5o23z4RgASkvKwePU1nXkH9fHE3kOJSM/QvTI3O1utN85fTo4G6en5ZY7/R+VT2ljDpuH94oi1Xz3Yt26CvOQ05NyIq8LIqDaQfXq3Ntn7rwbmZhKGdDKDpQqITRBYub1AZywyR1sJolh9wsJMwuBgJRysgXx1YWL3a+T9U1YAYGkhIaSdEvY2heOXnbuuQcQJNdgftWJyzx5Gpo0tbB57Ago7RxTcjkXayq+1V+Mq7ByhdKyjM4+ksoKqeRAy/lxTFSHXKPv+1cBcKWFQx8L94maCwOoI3f3CwUbS6WtjYS5hUCcl7IvtFxv36u4XWw+r8VhbYFAnM9hYFg7OfOyiBrtPswJeljUbb0BloUDoy41gZ2uO8xfT8cZHZ3TG6HN3tdQ7vtT3skLr5g54/f/OPOKIazeHwBYI3vGj9nmzrwoHiL+xehPOTJxeVWGZrJo6tIqxSEJGL8gBAwZg7dq1cHAo7E/16aefYurUqXB0dAQAJCUloVu3bjh//rzsQD5cWf0qfbXVtAsvVHUIdM/8JkurOgS6Z+eGg1UdAhUzPfzFqg6B7hmYf6HK3nvEW9eNtuz1X/kYbdlVRdblotu2bdO56vbzzz9HcnKy9nlBQQEuXKi6D5+IiIhqDyGE0R7GkpKSgtGjR8PBwQEODg4YPXo0UlNTy50vKioKQ4YMgYODA+zs7NCpUyfExMTIem9ZSV/JjcBLpYmIiIgq7vnnn8epU6cQHh6O8PBwnDp1qswbXwDAlStX0LVrVzRp0gS7d+/G6dOn8X//93+wtKzYeJxF2KePiIiITJKpDc4cFRWF8PBwHDp0CB07dgQALFmyBMHBwbhw4QICAgIMzvfBBx9gwIAB+OKLL7TT/P39Zb+/rEqfJEmQJElvGhEREdGjJjTCaI/c3Fykp6frPEreWEKugwcPwsHBQZvwAUCnTp3g4OCAAwcOGJxHo9Fg69ataNy4Mfr27Qs3Nzd07NgRv//+u+z3l1XpE0Jg3Lhx2uFWcnJyMHnyZNjY2ADAQ28MIiIiourA0I0kZsyYgZkzZz7wMuPj4+Hm5qY33c3NrdTb2N65cweZmZmYM2cOPvnkE3z++ecIDw/HU089hV27dqFHjx4Vfn9ZSd+YMWN0KnujRo0y2IaIiIjI2DTCeON4GrqRRMkxhovMnDlTL0Es6ejRwgHPDZ0hFUKUeuZUc28A6ieeeAJvvPEGAKBNmzY4cOAAFi1aZLykb+XKlXKaExEREZkkQzeSKM0rr7yCZ599tsw2vr6+OHPmDG7fvq33WkJCAtzd3Q3O5+LiAjMzMzRr1kxnetOmTbFv374KxVdEVtI3YcKEcttIkoRly5bJCoKIiIhIrupyIYeLiwtcXFzKbRccHIy0tDQcOXIEHTp0AAAcPnwYaWlp6Ny5s8F5LCws0L59e70h8S5evAgfH3ljCcqu9Pn4+KBt27YcroWIiIhIhqZNm6Jfv36YNGkSFi9eDAB48cUXMWjQIJ0rd5s0aYKwsDA8+eSTAIC3334bI0aMQPfu3dGrVy+Eh4djy5Yt2L17t6z3l5X0TZ48GevWrcPVq1cxYcIEjBo1Cs7OzrLekIiIiKgyVJdKnxxr1qzBtGnTEBISAgAYMmQI5s+fr9PmwoULSEtL0z5/8sknsWjRIoSFhWHatGkICAjAxo0b0bVrV1nvLSvpW7BgAb799lts2rQJy5cvx/Tp0zFw4EBMnDgRISEhHL6FiIiIqAzOzs746aefymxj6GzqhAkTKtTNriyyxukDCjs2Pvfcc4iIiMD58+fRvHlzTJkyBT4+PsjMzHyoYIiIiIgqyhRvw1aVHuqOHEWDNQshtJcUExERET0KzD3kkV3py83Nxdq1a9GnTx8EBATg7NmzmD9/PmJiYmBra2uMGImIiIjoIcmq9E2ZMgXr1q2Dt7c3xo8fj3Xr1qFOnTrGio2IiIioVKZ4IUdVkpX0LVq0CN7e3vDz88OePXuwZ88eg+02bdpUKcERERERUeV4qNuwEREREVUVYcTbsNVEvA0bERERUS3wUFfvEhEREVUV9umTR/bVu0RERERkeljpIyIiIpPESp88TPqIiIjIJGl4IYcsPL1LREREVAuw0kdEREQmiad35WGlj4iIiKgWYKWPiIiITJLQsE+fHKz0EREREdUCrPQRERGRSWKfPnlY6SMiIiKqBVjpIyIiIpMkOE6fLEz6iIiIyCRpeHpXFp7eJSIiIqoFWOkjIiIik8QhW+RhpY+IiIioFmClj4iIiEwSh2yRh5U+IiIiolqAlT4iIiIySRyyRR5W+oiIiIhqAVb6iIiIyCSxT588TPqIiIjIJHHIFnl4epeIiIioFpCEEKyNVoLc3FyEhYVh+vTpUKlUVR1OrcfPo/rgZ1F98LOoPvhZUFVg0ldJ0tPT4eDggLS0NNjb21d1OLUeP4/qg59F9cHPovrgZ0FVgad3iYiIiGoBJn1EREREtQCTPiIiIqJagElfJVGpVJgxYwY75FYT/DyqD34W1Qc/i+qDnwVVBV7IQURERFQLsNJHREREVAsw6SMiIiKqBZj0EREREdUCTPqIiIiIagEmfSXEx8fj1Vdfhb+/P1QqFerXr4/Bgwdjx44dAABfX19IkgRJkmBlZYUmTZrgyy+/RPHrYaKjoyFJEk6dOqXz3MzMDDdv3tR5v7i4OJiZmUGSJERHRz+q1TRp48aN034GZmZm8Pb2xssvv4yUlBRtm6LPad26dXrzN2/eHJIkYeXKlY8w6prlwIEDUCqV6Nevn95reXl5+PLLL9GuXTvY2NjAwcEBrVu3xocffohbt25p2xX/HIs/DC2zths3bhyGDh1q8LWTJ09i0KBBcHNzg6WlJXx9fTFixAgkJiZi5syZBrdx8UfRccfQZ1raZ1T8UdsUbZPJkyfrvTZlyhRIkoRx48bptC3rO17yN8XX1xfDhw/Hzp07tW2+/vprODg4ICsrS+89c3Jy4OjoiG+++abyV5ZqHCZ9xURHRyMwMBA7d+7EF198gbNnzyI8PBy9evXC1KlTte1mz56NuLg4REVF4a233sL777+P//3vf+Uu38vLC6tXr9aZtmrVKtStW7fS16Wm69evH+Li4hAdHY2lS5diy5YtmDJlik6b+vXrY8WKFTrTDh06hPj4eNjY2DzKcGuc5cuX49VXX8W+ffsQExOjnZ6bm4s+ffrgs88+w7hx4xAZGYnjx4/jiy++QFJSEubNm6eznKLPsfhj7dq1j3p1TNadO3fw+OOPw8XFBdu2bUNUVBSWL18OT09PZGVl4a233tLZtvXq1dMev4oe9evXB2D4M/3uu+902gLAihUr9KbVNvXr18e6deuQnZ2tnZaTk4O1a9fC29tbp21FvuNFn8mFCxewevVqODo64vHHH8enn34KABgzZgyys7OxceNGvVg2btyIrKwsjB492ghrSjWNWVUHUJ0U/Zd25MgRnaSgefPmmDBhgva5nZ0dPDw8AAAvvPACFi5ciO3bt+Oll14qc/ljx47FihUrMH36dO20lStXYuzYsfj4448reW1qNpVKpf0M6tWrhxEjRuhV7kaOHIlvv/0WN27c0PlhGzlypF7yTRV39+5d/PLLLzh69Cji4+OxcuVKfPTRRwCAb7/9Fvv27cOxY8fQtm1b7TwNGzZE3759UXKEqOKfI8l34MABpKenY+nSpTAzKzyc+/n5oXfv3to2tra22r+VSqXO8atIaZ+pg4MDHBwcdNo6OjrW+s+sXbt2uHr1KjZt2oSRI0cCADZt2oT69evD399fp21FvuPFPxNvb290794dnp6e+Oijj/D0008jICAAgwcPxvLly/WSu+XLl2PIkCFwdXWtxDWkmoqVvnuSk5MRHh6OqVOnGqwCOTo66k0TQmD37t2IioqCubl5ue8xZMgQpKSkYN++fQCAffv2ITk5GYMHD37o+Guzq1evIjw8XO8zcHd3R9++fbFq1SoAQFZWFtavX6+TwJN869evR0BAAAICAjBq1CisWLFCm8ytXbsWffr00Un4iquNpwONycPDAwUFBfjtt9/0Emo5yvpMybDx48frnElYvnx5pR5bXnvtNQgh8McffwAA/r+d+wtp6g3jAP61zYY2hOkK5k3IZm01Qm9imJELa8UuonSksVFteFWLyMIoSAtqRHZR5B9ocxp1k0lB9ods5KIIu7BVoCWBVjeLCpPsIhue30VuuM7M/WwV7Xw/cG4877v3Pb5nO895zvsel8uFYDCI4eHhWJmRkRHcu3cPLpcrZe1SemPQN+XVq1cQBAF6vX7WsnV1dVAqlVAoFDCbzRAEAXv27Jm1XmZmJux2O9ra2gB8/5Gw2+1JBYwUr7u7G0qlEllZWdBqtRgYGEBdXZ2onNPpRHt7OwRBwJUrV6DValFUVPTnO5xGfD4f7HY7gO+PrsbHx2NzXoeGhrB06dK48ps3b4ZSqYRSqURJSUncvug4Tt+Y9U6eyWTCoUOHsG3bNqjVamzcuBGnTp3Cu3fv/tfn/GxMKTGHw4EHDx5gZGQEr1+/xsOHD2P/w+nmeo7n5uZi0aJFsTmXFosF+fn5cU80/H4/8vPzsX79+lQdFqU5Bn1Tone1yWQiDhw4gFAohGAwCLPZjMOHD4suZjNxuVzo7OxEOBxGZ2cns05zZDabEQqF0NfXB7fbDYvFArfbLSpntVoxPj6O+/fvp/xOXIpevnyJx48fo6qqCgAgl8uxdevW2I0MIP4ONTc3IxQKwel0iiaiR8dx+jZ9/izN7vjx4wiHw2htbcWyZcvQ2toKvV6P58+fJ1U/mTElMbVaDavVio6ODvj9flitVqjValG5XznHBUGIfZ9kMhm2b9+O9vZ2TE5OQhAEdHR0YMeOHZDJZCk9NkpfnNM3pbCwEBkZGRgcHJxxlVyUWq2GTqeDTqdDV1cXdDodTCYTysvLZ23HaDRCr9ejuroaBoMBRqMxtsqXkrdgwQLodDoAwNmzZ2E2m3H06FHRHbRcLofD4UB9fT36+vpw9erVv9HdtOHz+RCJROIWHwmCgMzMTIyOjqKwsBAvXryIq6PRaAB8z1z8aPo40tzl5eXBZrPBZrPB4/GguLgYjY2NsakNPzPbmKpUqt/Z9X+a0+nE7t27AQBNTU0Jy8z1HP/48SPev3+PgoKCuPY8Hk9sZe+bN2+wc+fOOfScpIqZvim5ubmwWCxoamrCly9fRPs/ffqUsJ5KpYLb7cb+/fuTngPjdDrR29vLrFMK1dfXo7GxMe6VIFFOpxPBYBCbNm3iBewXRCIRXLhwAadPn47LWjx9+hSLFy/GpUuXUF1djZ6eHjx58uRvd1ey5s+fD61Wm/B37EfJjCnNbMOGDZiYmMDExAQsFktKP/vMmTOYN29eXBJCq9VizZo18Pv9aGtrQ1lZGbRabUrbpfTGTN80zc3NKCkpwcqVK3Hs2DGsWLECkUgEPT09aGlpweDgYMJ6u3btwsmTJ9HV1YXKyspZ26mpqYHNZku4OITmpqysDMuXL8eJEydw7ty5uH0GgwEfPnxAdnb2X+pdeuju7sbo6ChcLpdoRWdlZSV8Ph8ePXqEGzduYO3atWhoaMDq1auhUqkwNDSEW7duiR5Dff36FeFwOO5vcrk84WMyqRsbGxM9FXj27Bnu3LmDqqoqLFmyBIIg4Pr167h586bodUWJJDOm0UwWiclksth1YaZHrMmc458/f0Y4HMa3b98wPDyMixcvwuv1wuPxiLKELpcLNTU1AACv15vKwyEJYKZvmoKCAvT398NsNqO2thZGoxHr1q1DIBBAS0vLjPUWLlwIh8OBhoYGTE5OztpO9AsffcUCpca+fftw/vx5vH37VrQvLy8PWVlZf6FX6cPn86G8vFwUHABARUUFQqEQBgYGEAgEcPDgQfj9fpSWlsJgMGDv3r1YtWoVrl27Flfv9u3b0Gg0cVtpaekfOqJ/S29vL4qLi+O2u3fvIjs7G7W1tSgqKoLJZMLly5fh9XqTem9bMmPa39//Ow4nbeTk5CAnJ2fG/cmc40eOHIFGo4FOp4PD4cDY2BgCgUDCxWkVFRVQKBRQKBTYsmVLyo+H0luGwHX5RERERGmPmT4iIiIiCWDQR0RERCQBDPqIiIiIJIBBHxEREZEEMOgjIiIikgAGfUREREQSwKCPiIiISAIY9BERERFJAIM+IiIiIglg0EdEREQkAQz6iIiIiCSAQR8RERGRBPwHDZdBbEPJVw0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 변수들 간의 상관관계 계산\n",
    "correlations = housing_df.corr().round(2)\n",
    "\n",
    "# 히트맵 생성\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(data=correlations, annot=True,cmap = 'coolwarm')\n",
    "plt.title('Correlation Heatmap of Boston Housing Data', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a2a324",
   "metadata": {},
   "source": [
    "## 해당 히트맵을 통해 각 변수들 간의 상관관계를 파악할 수 있음\n",
    "### MEDV(주택 가격 중앙값)은 RM(방의 수)과 양의 상관관계를 가짐\n",
    "### LSTAT(하위계층의 비율)은 MEDV와 음의 상관관계를 가짐\n",
    "### CRIM(인구 1인당 범죄 발생 수)은 MEDV 와 강한 음의 상관관계를 가짐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "406e0325",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}