{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 142,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.compose import make_column_selector, make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import classification_report, ConfusionMatrixDisplay, roc_auc_score"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "       gender   age  hypertension  heart_disease ever_married      work_type  \\\nid                                                                             \n9046     Male  67.0             0              1          Yes        Private   \n51676  Female  61.0             0              0          Yes  Self-employed   \n31112    Male  80.0             0              1          Yes        Private   \n60182  Female  49.0             0              0          Yes        Private   \n1665   Female  79.0             1              0          Yes  Self-employed   \n...       ...   ...           ...            ...          ...            ...   \n18234  Female  80.0             1              0          Yes        Private   \n44873  Female  81.0             0              0          Yes  Self-employed   \n19723  Female  35.0             0              0          Yes  Self-employed   \n37544    Male  51.0             0              0          Yes        Private   \n44679  Female  44.0             0              0          Yes       Govt_job   \n\n      Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \nid                                                                      \n9046           Urban             228.69  36.6  formerly smoked       1  \n51676          Rural             202.21   NaN     never smoked       1  \n31112          Rural             105.92  32.5     never smoked       1  \n60182          Urban             171.23  34.4           smokes       1  \n1665           Rural             174.12  24.0     never smoked       1  \n...              ...                ...   ...              ...     ...  \n18234          Urban              83.75   NaN     never smoked       0  \n44873          Urban             125.20  40.0     never smoked       0  \n19723          Rural              82.99  30.6     never smoked       0  \n37544          Rural             166.29  25.6  formerly smoked       0  \n44679          Urban              85.28  26.2          Unknown       0  \n\n[5110 rows x 11 columns]",
      "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>gender</th>\n      <th>age</th>\n      <th>hypertension</th>\n      <th>heart_disease</th>\n      <th>ever_married</th>\n      <th>work_type</th>\n      <th>Residence_type</th>\n      <th>avg_glucose_level</th>\n      <th>bmi</th>\n      <th>smoking_status</th>\n      <th>stroke</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>9046</th>\n      <td>Male</td>\n      <td>67.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Yes</td>\n      <td>Private</td>\n      <td>Urban</td>\n      <td>228.69</td>\n      <td>36.6</td>\n      <td>formerly smoked</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>51676</th>\n      <td>Female</td>\n      <td>61.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Self-employed</td>\n      <td>Rural</td>\n      <td>202.21</td>\n      <td>NaN</td>\n      <td>never smoked</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>31112</th>\n      <td>Male</td>\n      <td>80.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Yes</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>105.92</td>\n      <td>32.5</td>\n      <td>never smoked</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>60182</th>\n      <td>Female</td>\n      <td>49.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Private</td>\n      <td>Urban</td>\n      <td>171.23</td>\n      <td>34.4</td>\n      <td>smokes</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1665</th>\n      <td>Female</td>\n      <td>79.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Self-employed</td>\n      <td>Rural</td>\n      <td>174.12</td>\n      <td>24.0</td>\n      <td>never smoked</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>18234</th>\n      <td>Female</td>\n      <td>80.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Private</td>\n      <td>Urban</td>\n      <td>83.75</td>\n      <td>NaN</td>\n      <td>never smoked</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>44873</th>\n      <td>Female</td>\n      <td>81.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Self-employed</td>\n      <td>Urban</td>\n      <td>125.20</td>\n      <td>40.0</td>\n      <td>never smoked</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19723</th>\n      <td>Female</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Self-employed</td>\n      <td>Rural</td>\n      <td>82.99</td>\n      <td>30.6</td>\n      <td>never smoked</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>37544</th>\n      <td>Male</td>\n      <td>51.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Private</td>\n      <td>Rural</td>\n      <td>166.29</td>\n      <td>25.6</td>\n      <td>formerly smoked</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>44679</th>\n      <td>Female</td>\n      <td>44.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Yes</td>\n      <td>Govt_job</td>\n      <td>Urban</td>\n      <td>85.28</td>\n      <td>26.2</td>\n      <td>Unknown</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5110 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('healthcare-dataset-stroke-data.csv', index_col='id')\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Feature Types:\n",
    "- Nominal: gender, hypertension, heart_disease, ever_married, work_type, residence_type, smoking_status\n",
    "- Numerical: age, avg_glucose_level, bmi"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5110 entries, 9046 to 44679\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             5110 non-null   object \n",
      " 1   age                5110 non-null   float64\n",
      " 2   hypertension       5110 non-null   int64  \n",
      " 3   heart_disease      5110 non-null   int64  \n",
      " 4   ever_married       5110 non-null   object \n",
      " 5   work_type          5110 non-null   object \n",
      " 6   Residence_type     5110 non-null   object \n",
      " 7   avg_glucose_level  5110 non-null   float64\n",
      " 8   bmi                4909 non-null   float64\n",
      " 9   smoking_status     5110 non-null   object \n",
      " 10  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 479.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "0"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicate rows\n",
    "df.duplicated().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- gender:\n",
      "Female    2994\n",
      "Male      2115\n",
      "Other        1\n",
      "Name: gender, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "- ever_married:\n",
      "Yes    3353\n",
      "No     1757\n",
      "Name: ever_married, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "- work_type:\n",
      "Private          2925\n",
      "Self-employed     819\n",
      "children          687\n",
      "Govt_job          657\n",
      "Never_worked       22\n",
      "Name: work_type, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "- Residence_type:\n",
      "Urban    2596\n",
      "Rural    2514\n",
      "Name: Residence_type, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "- smoking_status:\n",
      "never smoked       1892\n",
      "Unknown            1544\n",
      "formerly smoked     885\n",
      "smokes              789\n",
      "Name: smoking_status, dtype: int64\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Identify syntax errors in categorical data\n",
    "pd.options.display.max_rows = 999\n",
    "data_types = df.dtypes\n",
    "str_cols = data_types[data_types == 'object'].index\n",
    "\n",
    "for col in str_cols:\n",
    "    print(f'- {col}:')\n",
    "    print(df[col].value_counts(dropna=False)) # No errors\n",
    "    print('\\n\\n')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "               age  hypertension  heart_disease  avg_glucose_level  \\\ncount  5110.000000   5110.000000    5110.000000        5110.000000   \nmean     43.226614      0.097456       0.054012         106.147677   \nstd      22.612647      0.296607       0.226063          45.283560   \nmin       0.080000      0.000000       0.000000          55.120000   \n25%      25.000000      0.000000       0.000000          77.245000   \n50%      45.000000      0.000000       0.000000          91.885000   \n75%      61.000000      0.000000       0.000000         114.090000   \nmax      82.000000      1.000000       1.000000         271.740000   \n\n               bmi       stroke  \ncount  4909.000000  5110.000000  \nmean     28.893237     0.048728  \nstd       7.854067     0.215320  \nmin      10.300000     0.000000  \n25%      23.500000     0.000000  \n50%      28.100000     0.000000  \n75%      33.100000     0.000000  \nmax      97.600000     1.000000  ",
      "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>age</th>\n      <th>hypertension</th>\n      <th>heart_disease</th>\n      <th>avg_glucose_level</th>\n      <th>bmi</th>\n      <th>stroke</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>5110.000000</td>\n      <td>5110.000000</td>\n      <td>5110.000000</td>\n      <td>5110.000000</td>\n      <td>4909.000000</td>\n      <td>5110.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>43.226614</td>\n      <td>0.097456</td>\n      <td>0.054012</td>\n      <td>106.147677</td>\n      <td>28.893237</td>\n      <td>0.048728</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>22.612647</td>\n      <td>0.296607</td>\n      <td>0.226063</td>\n      <td>45.283560</td>\n      <td>7.854067</td>\n      <td>0.215320</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.080000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>55.120000</td>\n      <td>10.300000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>25.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>77.245000</td>\n      <td>23.500000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>45.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>91.885000</td>\n      <td>28.100000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>61.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>114.090000</td>\n      <td>33.100000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>82.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>271.740000</td>\n      <td>97.600000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify errors in numerical data\n",
    "df.describe() # No errors"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "data": {
      "text/plain": "gender               0\nage                  0\nhypertension         0\nheart_disease        0\never_married         0\nwork_type            0\nResidence_type       0\navg_glucose_level    0\nbmi                  0\nsmoking_status       0\nstroke               0\ndtype: int64"
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Address missing values\n",
    "\n",
    "# bmi is a numerical feature -> can impute mean or median\n",
    "# median is imputed to reduce the effect of outliers\n",
    "median_bmi = df['bmi'].median()\n",
    "df['bmi'].fillna(value=median_bmi, inplace=True)\n",
    "df.isna().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAESCAYAAAAWtRmOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAe3UlEQVR4nO3df1RUdf7H8dcw/FABI8ptO6uUKFRuh0LI8mua9kMsU1H8AZNkqbVaK2FpYPljM1dkS2qXXSxb221JIDKtVs9uGZoUa7ayZmXrVqSm2SopFoM6/Jj7/cPDrHQVUGYYZZ6Pczon7nzu3Pd7ON7X3PuZ+WAxDMMQAAAn8fN2AQCAcw/hAAAwIRwAACaEAwDAhHAAAJgQDgAAE8IB57XJkyfr8OHDZ7TPli1bdOedd7bpuFdccYVGjBihUaNGaeTIkRozZoxWrlzperywsFDLly9v9jleffXVJvuc7OT9b775Zn3yySdnVN/evXs1Y8YMSdKBAweUnJx8RvsD/t4uAGiLsrIyrx37pZdeUnh4uCTp8OHDmjZtmhwOhyZPnqyUlJQW9y8vL1dUVNQpH2vN/s3Zv3+/du3aJUm65JJLVFRU1Kbng+/hygHnvJqaGqWlpWnUqFEaPXq05s6dK6fTqTlz5kiSJk2apG+//VY333yz0tPTdfvtt2v9+vX64osvlJqaqhEjRmjkyJF6/fXXTc+9detWDR48WP/6178kSRs2bNC4ceOUmJio5ORkbdu2rVU1hoeHKzMzUy+++KIMw1Bubq4WLlwoSSooKNDIkSOVlJQkm82mL7/8UuvXr9eGDRv05z//WStXrlRubq6mTJmiESNGaNasWU32b3yO0aNHa/jw4Vq1apUk8xVQ488NDQ2aO3euvv76a02ZMkX79u1TbGysJKmurk5PPvmk7rjjDo0YMUKPP/647Ha7pBNXKLm5ubLZbBoyZIieffbZM/tFoUPhygHnvPXr16umpkZvvPGGGhoatGDBAu3du1dZWVlavXp1k3fwUVFRevbZZ1VfX69hw4bp0Ucf1dChQ3XgwAGNGzdOl112met5P/jgA82bN0/PPfecrrzySu3evVvPPPOM/vKXv+jCCy/UF198oXvvvVdvv/22unTp0mKdV155pSorK1VVVeXa1tDQoMWLF2vDhg36yU9+otdff13l5eWaMGGCSkpKFBUVpbvuuku5ubn65ptvtHbtWvn7+ys3N7fJcwcFBWnNmjU6cOCARo8erWuuuea0dVitVi1atEhPPvmkVqxYoX379rkeW7ZsmQ4ePKg33nhDVqtVjz/+uH7zm9+4gujo0aMqKCjQgQMHdNtttykpKUk9evRo3S8KHQpXDjjnxcXF6csvv1RqaqqWL1+uSZMmNTnJnyw+Pl6StHv3bjkcDg0dOlTSiVsrQ4cO1XvvvSdJ+u9//6tp06bp1ltv1ZVXXinpxC2qgwcP6p577tGoUaM0a9YsWSwWff31162q02KxSDpxIm9ktVo1bNgwJScna+HCheratavGjh17yv2vvfZa+fuf+v1a45zBJZdcogEDBmjz5s2tqunHSktLlZycrICAAPn5+Sk1NdX1mkjSLbfc4jrORRddpO+///6sjoPzH+GAc16PHj20fv163X///bLb7br33nu1YcOGU45tfIff0NDgOlk3MgxD9fX1kk6ctF988UWtWbNG27dvlyQ5nU71799fb7zxhuu/4uLi084L/Ngnn3yi7t27Kzg4uMn2p59+Ws8995wiIiK0fPlyPfzww83Wfip+fv/7p+p0OuXv7y+LxaKTl0arq6trsUan09nkdXE6nU32OznYfvz88C2EA855BQUFmjNnjm688UbNnj1bN954oz777DNJJ07yjSf8k0VGRsrf319vv/22pBOf2Hnrrbf0f//3f5Kkbt26qW/fvsrIyNCjjz6qY8eOqX///iorK1NFRYUkadOmTRo5cqSOHz/eYo0HDhzQ008/rcmTJzfZfvjwYd10000KCwvTPffco/T0dNcnj05X+6msWbNG0omJ5s2bN6t///4KDw/X/v37dejQIRmGoXXr1rnGW63WU4bFwIEDVVhYqLq6OjmdTq1cuVIDBgxoVQ3wLcw54JyXmJioDz/8UHfccYc6d+6sSy+9VKmpqZKkYcOGKTU11XSPPiAgQHl5eVq0aJFyc3PV0NCgBx98UDfccIO2bNniGjd69Gi99dZbWrJkiZ544gktXLhQDz/8sAzDkL+/v5YtW2a6Emg0adIk+fn5yWq1SpKSkpJ01113NRkTHh6u6dOn65577lGnTp1c8wGSNGjQIC1ZsqRVr4HD4dDo0aNVV1enuXPnqmfPnpJO3G5KSkpSt27dNHjwYFfw9O7dW0FBQRo7dqyeeeYZ1/NMnz5d2dnZSkxMVH19vWJiYjRv3rxW1QDfYmHJbgDAj3FbCQBgQjgAAEwIBwCACeEAADDpMJ9Wcjqdamg4u7l1q9Vy1vuer+jZN/haz77Wr9T2ngMCrKfc3mHCoaHB0JEjR89q37CwLme97/mKnn2Dr/Xsa/1Kbe+5W7fQU273WDgkJiYqNPTEQbt3765p06YpMzNTFotFUVFRWrBggfz8/FRcXKyioiL5+/tr+vTpGjJkiI4fP67Zs2fr0KFDCg4OVnZ2tmvtHACA53kkHBwOhyQpPz/ftW3atGlKT0/X9ddfr/nz56ukpETXXnut8vPz9dprr8nhcMhms2nAgAEqLCxUdHS0ZsyYoXXr1ikvL09z5871RKkAgFPwyIT0zp07dezYMU2ePFl33323PvroI+3YsUP9+vWTdOKbof/4xz/08ccfKzY2VoGBgQoNDVVERIR27typ8vJyDRw40DX2bBcZAwCcHY9cOXTq1ElTpkzRuHHjtHv3bt13330yDMO14FdwcLCqq6tlt9tdt54at9vt9ibbG8e2xGq1KCys5WWVT72v31nve76iZ9/gaz37Wr+S53r2SDj07NlTl112mSwWi3r27KmwsDDt2LHD9XhNTY26du2qkJAQ1dTUNNkeGhraZHvj2JYwIX1m6Nk3+FrPvtav5LkJaY/cVlq1apVrQbEDBw7IbrdrwIABrgXPSktLFR8fr5iYGJWXl8vhcKi6uloVFRWKjo5W3759tWnTJtfYuLg4T5QJADgNjyy8V1tbqzlz5mj//v2yWCyaNWuWLrzwQs2bN091dXWKjIzUokWLZLVaVVxcrFdeeUWGYegXv/iFEhISdOzYMWVkZKiyslIBAQFaunSpunXr1uwx6+oauHI4A/TsG3ytZ1/rV/LclUOHWZWVcDgz9OwbfK1nX+tXOs9uKwEAzm8d5hvSbdGg06enJx1z1Mv+w7F2Py4AtIRwkNQpwKrLM9e1PNDNdi8ZLnu7HxUAWsZtJQCACeEAADAhHAAAJoQDAMCEcAAAmBAOAAATwgEAYEI4AABMCAcAgAnhAAAwIRwAACaEAwDAhHAAAJgQDgAAE8IBAGBCOAAATAgHAIAJ4QAAMCEcAAAmhAMAwIRwAACYEA4AABPCAQBgQjgAAEwIBwCACeEAADAhHAAAJoQDAMCEcAAAmBAOAAATwgEAYEI4AABMPBYOhw4d0k033aSKigrt2bNHKSkpstlsWrBggZxOpySpuLhYY8aM0fjx47Vx40ZJ0vHjxzVjxgzZbDbdd999Onz4sKdKBACchkfCoa6uTvPnz1enTp0kSVlZWUpPT1dBQYEMw1BJSYkqKyuVn5+voqIirVixQjk5OaqtrVVhYaGio6NVUFCgxMRE5eXleaJEAEAz/D3xpNnZ2UpOTtby5cslSTt27FC/fv0kSYMGDVJZWZn8/PwUGxurwMBABQYGKiIiQjt37lR5ebmmTp3qGtvacLBaLQoL6+KJdjzKWzVbrX7n5evVFvTc8flav5LnenZ7OKxevVrh4eEaOHCgKxwMw5DFYpEkBQcHq7q6Wna7XaGhoa79goODZbfbm2xvHNsaDQ2Gjhw5elY1d+sW2vIgDznbmtsqLKyL147tLfTc8flav1Lbez7d+c/t4fDaa6/JYrFo8+bN+ve//62MjIwm8wY1NTXq2rWrQkJCVFNT02R7aGhok+2NYwEA7cvtcw4rV67Uyy+/rPz8fF111VXKzs7WoEGDtGXLFklSaWmp4uPjFRMTo/LycjkcDlVXV6uiokLR0dHq27evNm3a5BobFxfn7hIBAC3wyJzDj2VkZGjevHnKyclRZGSkEhISZLValZqaKpvNJsMwNHPmTAUFBSklJUUZGRlKSUlRQECAli5d2h4lAgBOYjEMw/B2Ee5QV9fQpjmHyzPXubmilu1eMlyVla2bU3E37s36Bl/r2df6lTw358CX4AAAJoQDAMCEcAAAmBAOAAATwgEAYEI4AABMCAcAgAnhAAAwIRwAACaEAwDAhHAAAJgQDgAAE8IBAGBCOAAATAgHAIAJ4QAAMCEcAAAmhAMAwIRwAACYEA4AABPCAQBgQjgAAEwIBwCACeEAADAhHAAAJoQDAMCEcAAAmBAOAAATwgEAYEI4AABMCAcAgAnhAAAwIRwAACaEAwDAxN8TT9rQ0KC5c+dq165dslqtysrKkmEYyszMlMViUVRUlBYsWCA/Pz8VFxerqKhI/v7+mj59uoYMGaLjx49r9uzZOnTokIKDg5Wdna3w8HBPlAoAOAWPXDls3LhRklRUVKS0tDRlZWUpKytL6enpKigokGEYKikpUWVlpfLz81VUVKQVK1YoJydHtbW1KiwsVHR0tAoKCpSYmKi8vDxPlAkAOA2PXDnceuutGjx4sCRp//79uvjii/Xuu++qX79+kqRBgwaprKxMfn5+io2NVWBgoAIDAxUREaGdO3eqvLxcU6dOdY0lHACgfXkkHCTJ399fGRkZWr9+vX73u99p48aNslgskqTg4GBVV1fLbrcrNDTUtU9wcLDsdnuT7Y1jW2K1WhQW1sUzzXiQt2q2Wv3Oy9erLei54/O1fiXP9eyxcJCk7OxszZo1S+PHj5fD4XBtr6mpUdeuXRUSEqKampom20NDQ5tsbxzbkoYGQ0eOHD2rOrt1C215kIecbc1tFRbWxWvH9hZ67vh8rV+p7T2f7vznkTmH119/Xc8//7wkqXPnzrJYLLr66qu1ZcsWSVJpaani4+MVExOj8vJyORwOVVdXq6KiQtHR0erbt682bdrkGhsXF+eJMgEAp+GRK4ehQ4dqzpw5uuuuu1RfX6/HHntMvXr10rx585STk6PIyEglJCTIarUqNTVVNptNhmFo5syZCgoKUkpKijIyMpSSkqKAgAAtXbrUE2UCAE7DYhiG4e0i3KGurqFNt5Uuz1zn5opatnvJcFVWtjyf4glcfvsGX+vZ1/qVzrPbSgCA8xvhAAAwIRwAACatCocffwmNCWIA6Nia/bTSq6++qlWrVqmiokKlpaWSTqybVF9fr0ceeaRdCgQAtL9mw2HUqFHq37+/nn/+eU2bNk2S5Ofnp4suuqhdigMAeEezt5UCAwPVvXt3PfHEEzp06JD279+vffv2afv27e1VHwDAC1r1Jbi0tDQdOnRIl156qSTJYrHouuuu82hhAADvaVU4fPfddyoqKvJ0LQCAc0SrPq3Us2dPHThwwNO1AADOEa26cigvL9eQIUOa/DW2999/32NFAQC8q1Xh8Pbbb3u6DgDAOaRV4TBnzhzTtqysLLcXAwA4N7QqHO644w5JkmEY+uyzz3Tw4EGPFgUA8K5WhcPAgQNd/z9o0CBNnjzZYwUBALyvVeFw8uRzZWWlvvvuO48VBADwvlaFw7p1//tDOIGBgVq8eLHHCgIAeF+rwiErK0uff/65vvzyS/Xs2VNXXXWVp+sCAHhRq8IhPz9fa9euVUxMjF588UXdfvvtmjJliqdrAwB4SavCYe3atVq5cqX8/f1VV1en5ORkwgEAOrBWLZ9hGIb8/U/kSEBAgAICAjxaFADAu1p15RAXF6e0tDTFxcWpvLxcsbGxnq4LAOBFLYbDK6+8oocfflhlZWX69NNP1a9fP02cOLE9agMAeEmzt5Vyc3NVVlam+vp6DR48WImJifrggw/0hz/8ob3qAwB4QbPhUFpaqt/+9rfq3LmzJKl79+565plntGHDhnYpDgDgHc2GQ5cuXWSxWJpsCwgIUHBwsEeLAgB4V7Ph0KlTJ+3du7fJtr1795oCAwDQsTQ7IT1r1iw98MAD6t+/v3r06KH9+/fr/fffV3Z2dnvVBwDwgmavHKKiolRQUKA+ffro2LFj+vnPf67CwkL16dOnveoDAHhBix9lDQ0NVWJiYjuUAgA4V7TqG9IAAN9COAAATAgHAIAJ4QAAMCEcAAAmrVqV9UzU1dXpscce0zfffKPa2lpNnz5dvXv3VmZmpiwWi6KiorRgwQL5+fmpuLhYRUVF8vf31/Tp0zVkyBAdP35cs2fP1qFDhxQcHKzs7GyFh4e7u0wAQDPcfuXw5ptvKiwsTAUFBXrhhRf05JNPKisrS+np6SooKJBhGCopKVFlZaXy8/NVVFSkFStWKCcnR7W1tSosLFR0dLQKCgqUmJiovLw8d5cIAGiB268chg0bpoSEBNfPVqtVO3bsUL9+/SRJgwYNUllZmfz8/BQbG6vAwEAFBgYqIiJCO3fuVHl5uaZOneoaSzgAQPtzezg0Lspnt9uVlpam9PR0ZWdnu9ZjCg4OVnV1tex2u0JDQ5vsZ7fbm2xvHNsaVqtFYWFd3NyN53mrZqvV77x8vdqCnjs+X+tX8lzPbg8HSfr222/14IMPymazacSIEXrqqadcj9XU1Khr164KCQlRTU1Nk+2hoaFNtjeObY2GBkNHjhw9q3q7dQtteZCHnG3NbRUW1sVrx/YWeu74fK1fqe09n+785/Y5h++++06TJ0/W7NmzNXbsWElSnz59tGXLFkkn/kZEfHy8YmJiVF5eLofDoerqalVUVCg6Olp9+/bVpk2bXGPj4uLcXSIAoAVuv3J47rnn9MMPPygvL881X/D4449r0aJFysnJUWRkpBISEmS1WpWamiqbzSbDMDRz5kwFBQUpJSVFGRkZSklJUUBAgJYuXeruEgEALbAYhmF4uwh3qKtraNNtpcsz17m5opbtXjJclZWtm1NxNy6/fYOv9exr/Urn0W0lAMD5j3AAAJgQDgAAE8IBAGBCOAAATAgHAIAJ4QAAMCEcAAAmhAMAwIRwAACYEA4AABPCAQBgQjgAAEwIBwCACeEAADAhHAAAJoQDAMCEcAAAmBAOAAATwgEAYEI4AABMCAcAgAnhAAAwIRwAACaEAwDAhHAAAJgQDgAAE8IBAGBCOAAATAgHAIAJ4QAAMCEcAAAmhAMAwIRwAACYEA4AABOPhcP27duVmpoqSdqzZ49SUlJks9m0YMECOZ1OSVJxcbHGjBmj8ePHa+PGjZKk48ePa8aMGbLZbLrvvvt0+PBhT5UIADgNj4TDCy+8oLlz58rhcEiSsrKylJ6eroKCAhmGoZKSElVWVio/P19FRUVasWKFcnJyVFtbq8LCQkVHR6ugoECJiYnKy8vzRIkAgGb4e+JJIyIilJubq0cffVSStGPHDvXr10+SNGjQIJWVlcnPz0+xsbEKDAxUYGCgIiIitHPnTpWXl2vq1Kmusa0NB6vVorCwLp5ox6O8VbPV6ndevl5tQc8dn6/1K3muZ4+EQ0JCgvbt2+f62TAMWSwWSVJwcLCqq6tlt9sVGhrqGhMcHCy73d5ke+PY1mhoMHTkyNGzqrdbt9CWB3nI2dbcVmFhXbx2bG+h547P1/qV2t7z6c5/7TIh7ef3v8PU1NSoa9euCgkJUU1NTZPtoaGhTbY3jgUAtK92CYc+ffpoy5YtkqTS0lLFx8crJiZG5eXlcjgcqq6uVkVFhaKjo9W3b19t2rTJNTYuLq49SgQAnMQjt5V+LCMjQ/PmzVNOTo4iIyOVkJAgq9Wq1NRU2Ww2GYahmTNnKigoSCkpKcrIyFBKSooCAgK0dOnS9igRAHASi2EYhreLcIe6uoY2zTlcnrnOzRW1bPeS4aqsbN2cirtxb9Y3+FrPvtavdJ7POQAAzi+EAwDAhHAAAJgQDgAAE8IBAGBCOAAATAgHAIAJ4QAAMCEcAAAmhAMAwIRwAACYEA4AABPCAQBgQjgAAEwIBwCACeEAADAhHAAAJoQDAMCEcAAAmBAOAAATwgEAYEI4AABMCAcAgAnhAAAwIRwAACaEAwDAxN/bBQBARxDStbM6B7X/KfV4XYNHnpdwAAA36Bzkr8sz17X7cXcvGa5qDzwvt5UAACaEAwDAhHAAAJgQDgAAE8IBAGBCOAAATM7Jj7I6nU796le/0n/+8x8FBgZq0aJFuuyyy7xdFgD4jHPyyuGdd95RbW2tXnnlFT3yyCNasmSJt0sCAJ9yToZDeXm5Bg4cKEm69tpr9emnn3q5IgDwLefkbSW73a6QkBDXz1arVfX19fL3P325AQFWdesWetbH3L1k+Fnv2xZtqfl8Pra30HPH581+O9J55Jy8cggJCVFNTY3rZ6fT2WwwAADc65wMh759+6q0tFSS9NFHHyk6OtrLFQGAb7EYhmF4u4gfa/y00ueffy7DMLR48WL16tXL22UBgM84J8MBAOBd5+RtJQCAdxEOAAATwgEAYOIz4eB0OjV//nxNmDBBqamp2rNnT5PHN2zYoKSkJE2YMEHFxcVeqtK9Wup57dq1GjdunJKTkzV//nw5nU4vVeo+LfXcaN68eXr66afbuTrPaKnnjz/+WDabTSkpKUpLS5PD4fBSpe7TUs9vvvmmRo8eraSkJBUUFHipSvfbvn27UlNTTds9cv4yfMRbb71lZGRkGIZhGNu2bTOmTZvmeqy2tta49dZbjSNHjhgOh8MYM2aMcfDgQW+V6jbN9Xzs2DHjlltuMY4ePWoYhmHMnDnTeOedd7xSpzs113OjwsJCY/z48cZTTz3V3uV5RHM9O51OY+TIkcbu3bsNwzCM4uJio6Kiwit1ulNLv+cBAwYYVVVVhsPhcP3bPt8tX77cuPPOO41x48Y12e6p85fPXDk0tyRHRUWFIiIidMEFFygwMFBxcXHaunWrt0p1m+Z6DgwMVFFRkTp37ixJqq+vV1BQkFfqdKeWll7Ztm2btm/frgkTJnijPI9oruddu3YpLCxML730kiZOnKgjR44oMjLSW6W6TUu/5yuuuELV1dWqra2VYRiyWCzeKNOtIiIilJuba9ruqfOXz4TD6ZbkaHwsNPR/Xz8PDg6W3W5v9xrdrbme/fz8dPHFF0uS8vPzdfToUQ0YMMArdbpTcz0fPHhQv//97zV//nxvlecRzfVcVVWlbdu2yWaz6U9/+pM++OADbd682Vuluk1zPUtSVFSUkpKSNHz4cA0ePFhdu3b1RplulZCQcMqVIjx1/vKZcGhuSY4fP1ZTU9PkxT5ftbQMidPpVHZ2tsrKypSbm9sh3l011/Pf//53VVVV6f7779fy5cu1du1arV692luluk1zPYeFhemyyy5T7969FRAQoIEDB3aIhSyb63nnzp169913VVJSog0bNujw4cP629/+5q1SPc5T5y+fCYfmluTo1auX9uzZoyNHjqi2tlZbt25VbGyst0p1m5aWIZk/f74cDofy8vJct5fOd831fPfdd2v16tXKz8/X/fffrzvvvFNjxozxVqlu01zPPXr0UE1NjWvCduvWrYqKivJKne7UXM+hoaHq1KmTgoKCZLVaFR4erh9++MFbpXqcp85fPrOa3W233aaysjIlJye7luT461//qqNHj2rChAnKzMzUlClTZBiGkpKSdMkll3i75DZrruerr75aq1atUnx8vCZNmiTpxMnztttu83LVbdPS77kjaqnnX//613rkkUdkGIZiY2M1ePBgb5fcZi31PGHCBNlsNgUEBCgiIkKjR4/2dslu5+nzF8tnAABMfOa2EgCg9QgHAIAJ4QAAMCEcAAAmhAMAwIRwANro5ZdfbvXY1atXd5gF/9CxEQ5AGy1btszbJQBu5zNfggPcYdeuXZozZ478/f1ltVp1ww036Pvvv9evfvUrxcTE6LXXXpPT6VRaWpoqKyv10ksvKTAwUJdffrkWLlzoep7Dhw/rgQce0EMPPaT4+HgtWLBAe/bskdPpVHp6uq6//novdgnwJTjgjKxcuVJfffWVMjMztXXrVl100UW69957VVZWptWrV2v9+vVatmyZqqqqNH78eK1Zs0YhISFavHixIiIi1KVLF/3zn//UV199pccee0zXXHONCgoK9M0332j27NmqqqrSxIkTtW7dOm+3Ch/HbSXgDIwdO1YXXnihpk6dqpUrV8pqtTZ5vGfPnpKkvXv3qnfv3q6VQ6+77jp98cUXkqT33ntPtbW1rj+u9Pnnn6u0tFSpqalKS0tTfX29qqqq2rErwIzbSsAZKCkpUVxcnH75y19q7dq1+uMf/6iTL779/E683+revbsqKip09OhRdenSRR9++KErOBITE5WYmKiHHnpIr776qiIjI/XTn/5U06ZN0/Hjx7Vs2TJdcMEFXukPaMSVA3AGrr76aj377LOy2WwqKirSxIkT1atXL82aNavJuPDwcM2YMUN33323xo8fr6qqKqWkpLge7927t0aOHKmsrCwlJyfrq6++0sSJE5WcnKyf/exnrpABvIU5BwCACW9PAAAmhAMAwIRwAACYEA4AABPCAQBgQjgAAEwIBwCAyf8DWObI8LmTKBkAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View distributions of categorical features\n",
    "sns.set_style('darkgrid')\n",
    "for col in str_cols:\n",
    "    df[f'{col}'].hist()\n",
    "    plt.title(f\"'{col}' Distribution\")\n",
    "    plt.ylabel('Count')\n",
    "    plt.xlabel(f'{col}')\n",
    "    plt.show()\n",
    "\n",
    "df['hypertension'].hist()\n",
    "plt.title(\"hypertension Distribution\")\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('hypertension')\n",
    "plt.show()\n",
    "\n",
    "df['heart_disease'].hist()\n",
    "plt.title(\"heart_disease Distribution\")\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('heart_disease')\n",
    "plt.show()\n",
    "\n",
    "df['stroke'].hist()\n",
    "plt.title(\"stroke Distribution\")\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('stroke')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Few samples of patients with hypertension, heart_disease, or stroke are present"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD2CAYAAADbPoDqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAeEElEQVR4nO3dfVxT9eIH8M/ZYGaIzadSKlRSfKaXxoWrCCpXL0Sggo6MG3nFSs0e8JpJJJAXlLIkCjXLulkqEkrdrCxTfMBQudmrRCmySEzLEkGSDdvG9v394a8VGuMgbIPO5/0XZztn57Ox1z77nrNzjiSEECAiIsVSuToAERG5FouAiEjhWARERArHIiAiUjgWARGRwrEIiIgUjkVAsuTk5CA+Pt7VMQAAb731FubMmePqGM0aNGgQampq2vxxQ0NDcfTo0Sb/J2+99RZuu+02TJkyBZMnT0ZERATmzJmDs2fPtnmWkpISREZGNjvfqlWrsGvXrjZfP7UNFgHJcs0116Bz586ujkG/Y+9/4u/vj3feeQfbtm3D9u3b0adPH7zwwgtOTvibkpISNDQ0uGz9ZB+LgGQZPnw4/P39AQB79uzBjBkzEBMTg/HjxyM7OxsAsHDhQvznP/+xLZObm4vExEQAwMsvv4y///3viI6OxrJlyxAaGtrsOvft24eoqChMmTIFSUlJCAkJwenTpxvNEx8fjw8//PAPp48cOQKdTofIyEhER0fj4MGDAIDDhw8jNjYWUVFRiImJQVFREQCgqqoKCQkJiI6ORnR0tO15AcCWLVsQExODqVOn4p///CcqKipa9Pr90fJ1dXUYNWoUqqqqbPPpdDrs27cPJpMJy5cvR3R0NCZPnoykpCTo9fpGj/n7/4k9ZrMZer0evXr1sk2np6cjIiICUVFReOKJJ6DX63Hu3DmMHTsW+/btAwBkZ2dj1qxZsFqtCA0NxcqVKxETE4NJkyYhNzf3ivXU1dXh0UcfRWRkJKKiorBixQo0NDRg06ZNOHbsGFasWIGdO3e26HUjJxFELWC1WsXdd98tTpw4IYQQ4scffxRDhgwR1dXV4uDBgyIyMtI27/Tp00VxcbEoKioSYWFh4ueffxZWq1U8/vjjYsKECXbXU1NTIwICAsSXX34phBDirbfeEr6+vuLUqVOioKBA3H///UIIIe6++27xwQcf2Jb7ddpkMomgoCCxZ88eIYQQR48eFZGRkaKmpkaMHj1afP7550IIIY4fPy4CAgLEd999J1atWiVSUlKEEEIYDAaRmJgoLly4IEpKSkRcXJyor68XQgixf/9+ER4e3uxr5evrK6qrq+0u/9hjj4lXXnlFCCHEN998I8aPHy8sFovIyckRTz31lLBarUIIIVauXCnS0tKEEEJMmDBBlJaWNrnegoICMWrUKDF58mQRFRUlAgICRHBwsDh9+rQQQojnn39ePPjgg8JkMgmLxSKSkpJsz3v//v0iODhYfPTRR2LcuHGiurrats6UlBRhtVrFmTNnRGBgoCgvLxeHDh0Sd9xxh+25pKenC6vVKoxGo0hISBAvvfTSH/6fqH1xc3URUcciSRLWrl2LvXv34r333kNFRQWEELh48SICAwNhNBpx9OhRdO7cGTU1NRg9ejSWLVuG8PBwdO3aFQDwj3/8A4cOHbK7nsOHD+OWW27B4MGDAQDR0dHIyMiQnfP48eNQqVQYP348gEvfnt99913s27cP3t7euPXWWwEAAwcOxKhRo/C///0PwcHBuP/++3HmzBmMGTMGCxcuhKenJ/bu3YuTJ09ixowZtse/cOECamtrodVqm81ib3mdToelS5di9uzZKCgowLRp06BSqbB3717U1dXhwIEDAC59i+/Ro4fs5+/v74+XXnoJAGC1WvHiiy/i3nvvxfbt21FUVIQFCxbA3d0dwKVR1Pz58wEAY8eORUREBB566CFs3LgR3bt3tz1mXFwcJElC7969ERwcjOLiYgwbNsx2f1FRETZv3gxJkqDRaDBjxgy8/vrruP/++2XnJtdgEVCL1NfXIzo6GhMnToS/vz+mTZuGXbt2QQgBSZIwffp0vPPOO3B3d8f06dMhSRLc3NwgfndKK7Va3ex61Gp1o2UAQKX64y2Zv5/PbDbblpckqdF8x48fh8ViueJ2IQQaGhrg5+eHwsJCHDx4EIcOHYJOp8O6detgtVoxZcoULFq0CMClD9azZ8/iuuuua/Z5/Dp/U8v7+/ujoaEBpaWleO+99/Dmm2/a5klOTsa4ceMAAAaDAUajUdb6LqdSqRAfH48XXngB1dXVsFqtjV4Dq9Vqe92EEKioqEDPnj3x+eefN9r05Obm1miZy/8ff/S43C/QMXAfAbXIyZMnodfrkZiYiNDQUJSUlMBkMsFqtQK49M199+7d2LFjB2JiYgAA48aNw0cffYS6ujoAwNatW5tdz6hRo1BZWYny8nIAwI4dO3DhwoUrPsS7d++OY8eOAQC++eYbfPXVVwAAHx8fSJKE4uJiAEBZWRlmzpyJW2+9Fd9++y1KS0sBAF9//TU++eQTBAQE4Nlnn8WaNWswceJEPPHEExgwYAC+/vprjB07Fu+//77tVzebN2/GzJkzZb9mzS2v0+mQnp6OQYMGoU+fPrZlNm3aZHttU1JSkJWVJXudl9u7dy9uvPFGdO/eHcHBwdi8eTPMZjOsVis2bdqEoKAgAMD69etRX1+PgoICrF+/3vY6AcB///tfAMAPP/yA4uJihISEXPE8N27cCCEETCYT8vPzMWbMGACXipml0H5xREAtMmjQIIwfPx633347NBoNfH19MWDAAJw8eRLe3t7o1asXhg4dioaGBtxwww0AgNGjRyM2NhZ33nknrrnmGgwcOLDZXyBptVpkZWVh8eLFUKlUGD58ONzc3K5Ybt68eUhKSsK+ffvg4+Nj+war0WiQk5OD5cuXY8WKFXB3d0dOTg569OiB559/Hunp6fjll18gSRIyMzPRv39/zJw5E0lJSYiMjIRGo8GgQYNwxx13QKPR4L777kNCQgIkSUKXLl2watWqK0qpKWPHjrW7/NSpU5GVldXog/6BBx7A008/jejoaFgsFgwZMgRJSUmy/0+HDx/GlClTIEkSGhoaoNVqsXr1aqhUKsybNw9PP/00pk6dahsJpaSk4IsvvsDatWuxdetW3HDDDUhOTsbChQvx9ttvAwBOnz6NmJgY/PLLL1iyZAl8fHwa7ehesmQJMjIyEBUVBbPZjODgYMydOxfApZ+8ZmVlwWw2Izo6WvbzIOeQxOXjb6I2dvToUXz22We45557AACvvfYajhw50uhXOZfT6/VYs2YNHnroIXTu3BllZWWYM2cO9u/fL/sDmNpOaGgonn/+eYwYMcLVUcgBOCIgh+vfvz/WrVuH/Px8SJKEPn36ID09Ha+88grefffdP1xm9uzZtv0Mbm5ucHNzQ3Z2drsqgebyT5482cmJiK4ORwRERArHncVERArHIiAiUjgWARGRwnW4ncVWqxUWC3drEBG1hLt70wdydrgisFgEamvrXR2DiKhD6dXLs8n7uGmIiEjhWARERArHIiAiUjgWARGRwrEIiNrA+fM1SE1Nwvnz510dhajFWAREbWDr1jyUl3+BgoI8V0chajEWAVErnT9fgz17CiGEwJ49uzgqoA6HRUDUSlu35kGISxfmsVqtHBVQh8MiIGql/fv32q6+1dDQgKKiPS7NQ9RSLAKiVgoOHt9oOiRkgmuCEF0lFgFRKw0ZMrzR9NChvIoXdSwsAqJWWrdudaPpl15a5aIkRFeHRUDUSvX19ZdNG1yUhOjqsAiIiBSORUDUSt27d79suoeLkhBdHRYBUSvV1tZeNs0DyqhjYREQtZLVarU7TdTesQiIWkmSJLvTRO0di4ColYQQdqeJ2jsWAVErqVQqu9NE7R3fsUStFBDw10bTgYFjXJSE6OqwCIhayWw2N5o2mUwuSkJ0dVgERK306aefXDb9PxclIbo6LAIiIoVza+sHNJvNSE5Oxvfffw+TyYR58+ahd+/emDt3Lvr16wcAuOuuuxAREYH8/Hzk5eXBzc0N8+bNw4QJPH0vEZGztXkRbNu2DVqtFs888wzOnz+P6OhozJ8/H7NmzUJCQoJtvqqqKmzYsAEFBQUwGo2Ii4tDUFAQNBpNW0ciIiI72rwIwsPDERYWZptWq9U4duwYTpw4gcLCQvTt2xfJyckoLS3FyJEjodFooNFo4O3tjfLycvj5+bV1JCIisqPNi8DDwwMAoNfr8fDDDyMxMREmkwk6nQ7Dhw/Hiy++iNWrV2Pw4MHw9PRstJxer2/rOERE1Iw2LwIAOHPmDObPn4+4uDhERUXhwoUL6Nq1KwBg0qRJSE9Ph7+/PwyG387bbjAYGhVDU9RqCVrttY6ITdRm+B6ljqTNi+DcuXNISEhAamoqRo8eDQCYPXs2UlJS4Ofnh4MHD2LYsGHw8/NDdnY2jEYjTCYTKioq4Ovr2+zjWywCtbX1zc5H5Ep8j1J706tX01+027wI1q5diwsXLmDNmjVYs2YNACApKQnLly+Hu7s7evbsifT0dHTp0gXx8fGIi4uDEAILFixAp06d2joOERE1QxId7AxZZrOF37aoXdHpoq64bcuWd12QhKhp9kYEPKCMiEjhWARERArHIiAiUjgWARGRwrEIiIgUjkVARKRwLAIiIoVjERARKRyLgIhI4VgEREQKxyIgIlI4FgERkcKxCIiIFI5FQESkcCwCIiKFYxEQESkci4CISOFYBERECsciICJSOBYBEZHCsQiIiBSORUBEpHAsAiIihWMREBEpHIuAiEjhWARERArHIiAiUjgWARGRwrEIiIgUjkVARKRwLAIiIoVjERARKRyLgIhI4VgEREQKxyIgIlI4N3t3mkymJu/TaDRtHoaIiJzPbhGEh4dDkiQIIRrdLkkSCgsLHRqMiIicw24R7N69u9F0bW0trrvuOkiS5NBQRETkPHaL4FeffPIJli5dCovFgvDwcHh5eUGn0/3hvGazGcnJyfj+++9hMpkwb948DBgwAElJSZAkCQMHDkRaWhpUKhXy8/ORl5cHNzc3zJs3DxMmTGjTJ0dERM2TtbM4OzsbGzduRM+ePTF37lxs3ry5yXm3bdsGrVaL3NxcrFu3Dunp6cjMzERiYiJyc3MhhEBhYSGqqqqwYcMG5OXl4dVXX0VWVpbdfRJEROQYskYEKpUKWq0WkiShU6dO8PDwaHLe8PBwhIWF2abVajXKysoQEBAAAAgJCUFxcTFUKhVGjhwJjUYDjUYDb29vlJeXw8/Pr5VPiYiIWkJWEXh7e2PlypWora3Fyy+/DC8vrybn/bUk9Ho9Hn74YSQmJuLpp5+27Vfw8PBAXV0d9Ho9PD09Gy2n1+ubzaJWS9Bqr5UTm8hl+B6ljkRWESxduhRbtmzBbbfdhs6dOyM9Pd3u/GfOnMH8+fMRFxeHqKgoPPPMM7b7DAYDunbtii5dusBgMDS6/ffF0BSLRaC2tl5ObCKX4XuU2ptevZr+fJW1j+CRRx6Bl5cXUlNTER8fb/cYgnPnziEhIQGLFi3C9OnTAQBDhw5FSUkJAKCoqAj+/v7w8/PDp59+CqPRiLq6OlRUVMDX17clz4uIiNqAJC4/SOAPHD16FG+//TY+/fRTTJw4EdOmTWty81BGRgY++OAD+Pj42G574oknkJGRAbPZDB8fH2RkZECtViM/Px9vvvkmhBCYM2dOo30LTTGbLfy2Re2KThd1xW1btrzrgiRETbM3IpBVBL/6+eef8eSTT2Lnzp04duxYm4RrKRYBtTcsAuoIWr1p6PDhw0hOTsbdd9+NAQMGYNeuXW0WjoiIXEvWzuLXX38dsbGxWLZsGY8qJiL6k5E1Injuuefw448/IicnByUlJaipqXF0LiIichJZRZCWloYffvgBxcXFMBgMWLx4saNzERGRk8gqgu+++w6PPPIIOnXqhNDQUNTV1Tk6FxEROYmsIrBYLLbNQXq9HioVr2dDRPRnIWtncWJiIu666y5UVVXhzjvvRHJysqNzERGRk8gqgoCAAOzYsQM1NTXo1q0bfzlERPQnYrcI7rzzziY/9PPy8hwSiIiInMtuEWRlZdld+MiRI7j11lvbNBARETmX3SK48cYb7S68cuVKvPHGG20aiIiInKtVP/9pwWmKiIionWpVEXCnMRFRx8cDAoiIFI6bhoiIFE52Eej1enz11Veor//tWgBRUVeeh52IiDoWWQeUffjhh1i7di0sFgvCw8MhSRIeeOABxMbGOjofERE5mKwRwfr165Gfnw+tVosHHniAF6YhIvoTkVUEKpUKGo0GkiRBkiR07tzZ0bmIiMhJZBWBv78/Fi5ciJ9++gmpqakYMWKEo3MREZGTyNpH8K9//QtFRUUYMmQIbrnlFkyYMMHRuYiIyElkjQjOnj0LLy8vhIaGYufOnfjyyy8dnYuIiJxEVhEsXrwY586dQ3Z2NoKCgrB8+XJH5yIiIieRtWmooaEBf/nLX7B27VrccccdyM3NdXQu6iD27duN3bt3ujpGu5OW9rirI7hUaOgkjBsX6uoYJJOsEYHZbEZmZib8/f1x6NAhWCwWR+ciIiInkYSM80RUVlaiuLgYOp0Ou3btwogRI3DzzTc7I98VzGYLamvrm5+RyElyc1/H229vtU1PmxaLGTPiXZiI6Eq9enk2eZ+sEcGvH/qZmZmoqqrCDTfc0DbJiP4E4uJmNppmCVBHI6sIUlJScOrUKQQFBeH777/HkiVLHJ2LqEPp3r07gEujAaKORtbO4pMnT2LTpk0AgIkTJ2LGjBkODUXU0fTu7YXevb04GqAOSdaIwGg04uLFiwCAX375hTuLiYj+RGSNCO655x5MmTIFAwcOxDfffIOHH37Y0bmIiMhJZBXB5MmT4e/vj+rqavTo0QNeXl6OzkVERE4ia9PQqlWr8MYbb2DEiBF46qmn8PLLLzs6FxEROYmsIti9ezeSkpIAAC+88AJ2797t0FBEROQ8sopAkiSYTCYAl44y5rWKiYj+PGTtI5gxYwaioqLg6+uLb7/9Fvfee6+jcxERkZPIKgKdToe//e1vOHXqFG6++WbbwTNERNTxySqCxx+/8kyKmZmZbR6GiIicT9Y+goiICEREROD2229H3759ZV2z+MiRI4iPv3SUZVlZGYKDgxEfH4/4+Hhs374dAJCfn4+YmBjExsZiz549rXgaRER0tWSNCIKDg21/h4SEICEhwe7869atw7Zt22yF8cUXX2DWrFmNlquqqsKGDRtQUFAAo9GIuLg4BAUFQaPRXM3zICKiqySrCD7++GPb31VVVTh37pzd+b29vZGTk4PHHnsMAHDs2DGcOHEChYWF6Nu3L5KTk1FaWoqRI0dCo9FAo9HA29sb5eXl8PPza8XTISKilpJVBO+//77tb41G0+ylKsPCwnD69GnbtJ+fH3Q6HYYPH44XX3wRq1evxuDBg+Hp+dv5sT08PKDX65vNolZL0GqvlRObyGnc3NQAwPcmdUh2i+DXYweWLl3aqpVMmjQJXbt2tf2dnp4Of39/GAwG2zwGg6FRMTTFYhG8MA21Ow0Nl07EyPcmtVf2LkxjtwjCw8MhSVKj24QQkCQJhYWFsgPMnj0bKSkp8PPzw8GDBzFs2DD4+fkhOzsbRqMRJpMJFRUV8PX1lf2YRETUNuwWQVudSuLJJ59Eeno63N3d0bNnT6Snp6NLly6Ij49HXFwchBBYsGABOnXq1CbrIyIi+WRdszgsLAwNDQ22aTc3N/Tp0weLFi3CsGHDHBrwcrxmMbVHaWmXjrVZupTH11D7dNWbhn4VGBiI8PBw+Pv747PPPsOWLVswbdo0ZGRkYPPmzW0WlIiInE/WAWUnTpzAmDFjoNFoEBgYiKqqKowePRoqlazFiYioHZM1ItBoNNi8eTNGjhyJzz77DBqNBseOHeMlK4mI/gRkfaV/9tlnUVlZiWeffRanTp3CihUrUF1d3ezxBERE1P7JGhF069btihPPjRs3DvPnz8fq1asdEoyIiJyjVRv5L1y40FY5iIjIRVpVBJcfbEZERB0Pf/ZDRKRwLAIiIoVrVRFcd911bZWDiIhc5KouVenu7o7evXsjIyPDIaGIiMh5ZI0IjEYjrr/+ekRERODGG2/ETz/9BJPJhMWLFzs6HxEROZisIqipqcGCBQsQHByMBx98EGazGYmJiairq3N0PiIicjBZRaDX61FRUQEAqKiogMFgwPnz51Ffz7OAEhF1dLL2EaSmpmLRokU4e/Ys+vTpg9TUVGzfvh1z5851dD4iInIwWUVw9uxZbN26tdHZRkeMGOGwUERE5DyyNg0dOHAAU6ZMwXPPPYdTp045OhMRETmR7E1DJpMJhYWF+Pe//w2z2Yz169c7OBoRETmD7APKSktL8fHHH6O6uhqjR492ZCYiInIiWSOCiIgIDB48GDqdDsuWLYPZbHZ0LiIichJZRRAZGYl33nkHR44cgRAC7u7u2LFjh6OzERGRE8jaNLRr1y5s2LABISEhyMzMxC233OLoXERE5CSyiqBbt264/vrrYTAYEBgYiJ9//tnRuYiIyElkFYGnpyd27doFSZKQl5eHmpoaR+ciIiInkVUEGRkZ8PLywsKFC1FZWYknn3zSwbGIiMhZZO0s7tKlC4YOHQoASEpKcmggIiJyLl6hjIhI4SQhhHB1iJYwmy2orXXtWU9fe20dKiu/dWkGal9+fT/06+fj4iTU3vTr54NZs+5zdQz06uXZ5H2yNg1RY5WV36Lsq+OwXNvd1VGonZCs7gCA0lPnXJyE2hN1fcf4YQ2L4CpZru2Oi4MjXB2DiNqxzuXbXR1BFu4jICJSOBYBEZHCsQiIiBSORUBEpHAsAiIihWMREBEpHIuAiEjhHFYER44cQXx8PADg5MmTuOuuuxAXF4e0tDRYrVYAQH5+PmJiYhAbG4s9e/Y4KgoREdnhkCJYt24dlixZAqPRCADIzMxEYmIicnNzIYRAYWEhqqqqsGHDBuTl5eHVV19FVlYWTCaTI+IQEZEdDikCb29v5OTk2KbLysoQEBAAAAgJCcGBAwdQWlqKkSNHQqPRwNPTE97e3igvL3dEHCIissMhRRAWFgY3t9/OXiGEgCRJAAAPDw/U1dVBr9fD0/O3kyB5eHhAr9c7Ig4REdnhlHMNqVS/9Y3BYEDXrl3RpUsXGAyGRrf/vhiaolZL0GqvdUhOudzc1C5dPxF1HG5uapd/ZjXHKUUwdOhQlJSUIDAwEEVFRfjrX/8KPz8/ZGdnw2g0wmQyoaKiAr6+vs0+lsUiXH4a6oYGi0vXT0QdR0OD60+dD7SD01AvXrwYKSkpyMrKgo+PD8LCwqBWqxEfH4+4uDgIIbBgwQJ06tTJGXGIiOh3HFYEN910E/Lz8wEA/fv3x8aNG6+YJzY2FrGxsY6KQEREMvCAMiIihWMREBEpHK9QdhVqa89DXV/dYa4+RESuoa6vRm1t+/+VIUcEREQKxxHBVdBqu+G7OguvWUxEdnUu3w6ttpurYzSLIwIiIoVjERARKRyLgIhI4VgEREQKxyIgIlI4FgERkcKxCIiIFI5FQESkcCwCIiKFYxEQESkcTzFxldT1NTzpHNlI5osAAOHe2cVJqD1R19cA6OnqGM1iEVyFfv18XB2B2pnKym8BAP1uvtnFSah96dkhPi8kIYRwdYiWMJvbx/U/iX4vLe1xAMDSpZkuTkL0x+xds5j7CIiIFI5FQESkcCwCIiKFYxEQESkci4CISOFYBERECsciICJSOBYBEZHCsQiIiBSORUBEpHAsAiIihWMREBEpHIuAiEjhWARERArHIiAiUjgWARGRwrEIiIgUjkVARKRwvFQltcq+fbuxe/dOV8dwOds1izvA9WmdITR0EsaNC3V1DPode5eq5MXridqAVtvd1RGIrppTRwRTp06Fp+elVrrpppswd+5cJCUlQZIkDBw4EGlpaVCp7G+t4oiAiKjl2sWIwGg0AgA2bNhgu23u3LlITExEYGAgUlNTUVhYiEmTJjkrEhERwYk7i8vLy3Hx4kUkJCTgnnvuweeff46ysjIEBAQAAEJCQnDgwAFnxSEiov/ntBHBNddcg9mzZ0On06GyshL33XcfhBCQJAkA4OHhgbq6umYfR62WoNVe6+i4RESK4bQi6N+/P/r27QtJktC/f39otVqUlZXZ7jcYDOjatWuzj2OxCO4jICJqIXv7CJy2aWjr1q146qmnAAA//fQT9Ho9goKCUFJSAgAoKiqCv7+/s+IQEdH/c9qvhkwmEx5//HH88MMPkCQJjz76KLp164aUlBSYzWb4+PggIyMDarXa7uPwV0NERC1nb0TAA8qIiBSgXWwaIiKi9qnDjQiIiKhtcURARKRwLAIiIoVjERARKRyLgIhI4VgEREQKxyIgIlK4/wNmOnHhOVvaPAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View distributions of numerical features\n",
    "num_cols = ['age', 'avg_glucose_level', 'bmi']\n",
    "\n",
    "for col in num_cols:\n",
    "    sns.boxplot(data=df, y=col)\n",
    "    plt.title(f\"'{col}' Boxplot\")\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "'avg_glucose_level' and 'bmi' contain many outliers"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x720 with 2 Axes>",
      "image/png": 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iImIRVVBERETCWJkeqE1ERETESqqgiIiIhDGbXX1QRERERCyhCoqIiEgY01U8IiIiIhZRBUVERCSM6SoeEREREYuogiIiIhLG1AdFRERExCKqoIiIiIQxVVBERERELKIERUREREodNfGIiIiEMV1mLCIiImIRVVBERETCmDrJioiIiFhEFRQREZEwpj4oIiIiIhZRBUVERCScGeqDIiIiImIJVVBERETCmK7iEREREbGIKigiIiJhTFfxiIiIiFhEFRQREZEwpj4oIiIiIhZRBUVERCSMqQ+KiIiIiEWCWkHZumBrMN8+ojQd0DTUIYSF129bEuoQwkLVmpVCHULY2LfHE+oQwkJuVl6oQwgfQ+uEOoKIoCYeERGRMKZOsiIiIiIWUQVFREQkjKmCIiIiImIRVVBERETCmS4zFhEREbGGKigiIiJhzDDUB0VERETEEqqgiIiIhDENdS8iIiJiEVVQREREwpjGQRERERGxiCooIiIi4Ux9UERERESsoQqKiIhIGFMfFBERERGLqIIiIiISxgwjMmsNkblWIiIiEtYCqqCsXr2aN998k8LCQv+02bNnBy0oERERCS8+n4+JEyeybds2XC4Xjz32GHXr1vXP//jjj3nzzTex2Wz079+f66677qzvF1CCMnXqVMaNG0f16tX/WPQiIiJyfpWSTrLLly+nsLCQefPmsXHjRqZNm8bMmTP985988kkWL15MTEwMl19+OZdffjnx8fFnfL+AEpQaNWrQpUuXPx69iIiIRKT169fTrVs3ANq2bcuWLVtKzG/SpAlZWVk4HA5M0zznXZgDSlAqVarEhAkTaN68uf8NBw0a9HviFxERkfPIypsFzps3j3nz5vmfDxo0yJ8PZGdnExsb659nt9vxeDw4HEWpRqNGjejfvz/R0dH07NmTChUqnPWzAkpQatWqBcCRI0d+25qIiIhIxDg5Ifm12NhYcnJy/M99Pp8/Odm6dSv/+c9/+Pzzz4mJieGBBx7gk08+oU+fPmf8rIDSrjvvvJOWLVsSFRVF06ZNufPOO3/L+oiIiEiQGDbDssfZtGvXjpUrVwKwceNGGjdu7J8XFxdHuXLliIqKwm63U7FiRY4fP37W9wuogjJ9+nT27NlDu3bt+Oijj1i/fj0PPvhgIIuKiIhIGdCzZ09Wr17N4MGDMU2TKVOmsGjRInJzc/2Vl+uuuw6n00mdOnXo16/fWd8voATl22+/Ze7cuQDccMMNDBw48I+viYiIiPxxpWSgNpvNxuTJk0tMa9Cggf//Q4YMYciQIYG/XyAv8ng8+Hw+gIB63oqIiIj8EQFVUPr27cuQIUNo06YNmzdvpm/fvsGOS0RERAIQqTcLDChBGTFiBBdddBG7du3i2muvLdHxRUREROR8O2uCsmDBAgYMGMD06dP9zTo//PADAPfdd1/woxMREZGzs3AcFCudNUE5MbR9cnKyJcGIiIiIwDkSlBND1vbq1Yvjx49jt9uZP38+11xzjRWxiYiIyDlE6oUrAdWF7r//frZs2cJTTz2F0+lkwoQJwY5LREREyrCAEpTjx49z2WWXcfDgQW655RYKCwuDHZeIiIgEwmaz7mHlagXyIrfbzRtvvEGLFi3YsWNHibH2RURERM63gBKUv//97xw9epTbbruNr7/+mokTJwY5LBERESnLAhoHpX379tSrV4/s7Gy6d+8e7JhEREQkQGV6oLaJEyeycuVKqlat6h/q/sS9eURERETOt4ASlM2bN7N8+XJsEToYjIiISNgqJTcLPN8CWqu6detSUFAQ7FhEREREgAArKAcOHKB79+7UrVsXoNQ28Zg+H6s+msTRA1uxO1xc3P8x4ivX9c/fsXEx362ajc2wUbFGEy665hFM08uK+WPITt+PYbNzcf9HSahaNkbOTejYmqZT7mdtj+Elple9vDuNHr4D0+Mh5a0PSHl9ARgGLWdMpELrJvgKCtl868Pk7twbositYxhwy4Aq1KsZhdtj8uKcNA4ecZd4jctpMPGOJF54L439aUXzKsTamHJPLe6dloLbY4YidEsZwJC/xFCrqh2P1+TtT3I5nOkr8RqnA+4ZFMfsT3I4lO6jc0sXnVu5AHA4DGpXtfP3GcfIK4js7WUA1/eNpXY1B26PyazFWaRllNxWLgfcd30Cby3K4uBRLwB9u0bTtnEUdjusWJfPqo35IYjeOoYBI/olUqeGC4/H5JX30zl01FPiNS6nwbibq/LKgqOkHvYUHa/XVqRGFSc+n8lL89NJS/ec4RMiSFnugzJ9+vRgx3Fe7P5hOV5PAdfcMY9DezaydskT9LrhRQA87ny+XfYcA+79GIcrms/fu489W/8Dponp9XL1HXPZt3013yx7lr8Mez60K2KB5NE3UfP6q/Dm5JWYbjgcNH96LKs6X4s3J48uK+eQtngFiZ0vwFbOxZpug0no1IZmT45hff/bQxS9dTq2Ko/TaTD22X00rhfFjf0qMe3Vg/75DWpHceugKlRKKD6U2jaN4fqrKpFQIaDDKyK0aezE6YAn38mifpKday+NZubC4uEI6lS3M/QvMSTEFRdt/7ulkP9uKRpTaXDPaNZsLoj45ATggqYunA6DKW9mklzTwcCescyYf9w/v24NB8P7xpJYwe6f1qSukwa1nEx9MxOXE3p1jglF6Jbq0CIap8PgkRcO0bCOi+uvSGD6rCP++cm1XIz8ayIV44uPs/bNowGY+OIhmiVHMezKkstIeAmoicfhcPDss88yYcIEVq9ezZEjpfMPfvDn9dRqXDQ8f7W6bTm8b4t/nt3u4urb5+BwFe3APp8Xh8NFfOV6+HxeTJ+PwoJsbDZnSGK3Wu6uvawfcNcp02ObNSBn5148mccx3W7SV68n8aIOJHZtz+FlXwGQ+fUmEtq3tDrkkGjWIJoNP+YCsH13AQ1qlysx3+kweOK1g+w/VFxVMU2TiTP2k53jtTTWUGpYy8H3Pxdtg59TvdStXjI5c9rhpQ+zOZR+6japU91OUmU7qzaVjQEgG9V2smVn0bru2u+hXo1Tt9WMBcc5cKT4l3+LBi72p3m5Y2AF/jY4nk0/Rf62alIvik3biqpEO/YWklzLVWK+ww7TZx0hNa342Fv3fR6vfpAOQJVEB8eyS1amIpVh2Cx7WCmgTxs/fjz9+/ensLCQDh068Pjjjwc7rt+lsCAHV7k4/3PDsOPzFh3khs1GTFxlALasfhtPQS41G3XFGRVDVsZ+5k/vw1cfTKBl1+tDErvVDn74Kab71NKno0IsnmNZ/uferByc8bG/TM/2Tze9Xgy7/ZTlI01MOYPcvOIvOZ/PLDGY4taf8zmaWXI7btqWR3Zu2fhiPKGcyyhR/fCZJavOO/d7ycg6fXWkz5/KsXh1ZDdXnKxclEFu/pm31Y59HjKOl9x/4qIN6tVwMPP948xems0t/eKIdNHlbOTmn3zslRzIdPueQtKPnZrw+nxw28CK3HB1Il9vzrUiVAmSgBKUgoICOnfujGEYJCcnExUVFey4fhdXVHncBSeNcmv6sNkdJz31sXbxE+z7aQ09h/0TwzD47qu3qN24K4MeWEb/ez7iP/PH4nGX3Q7BnuPZOOLK+5/b48rjzsw6ZTo2G6Y38isEufkm0eWKDxObzcBXtnKPgOQXmpRzFZ9lDaPoxHsu0VEG1SvZ2b63DPQT+EV+gUm5qN+2rbLzTLbsKsTrg0NHvbg9EBcTmf0OTsjL9xEdVXzsGQYBH3sz56dz35Op3HxtRaKckb2dgKIM16qHlasVyItcLhdfffUVPp+PjRs34nK5zr1QCFSr146UbV8CcGjPRipWb1xi/lcLH8HjKaDX8Bf8TT1R0fH+qktUTDw+nxvTjPwT75lk/7iT8g3r4kyMx3A6qdStAxlrN5Cx5n9U6XMxAAmd2pC1ZXuII7XG1l15tGte1N7fuF4Ue1LLbvJ6Njv3eWiZXNQ8Wj/Jzv7DgR1DjWo7+HG3+9wvjCA7Uty0blj0HZpc08H+tHNvq59S3LRsULRMQqwNl9MgOy+y++ts311A26ZFTaoN67hIOXju/eSidjFc3b0CAIVuE9MEnxnZ2ymSBdSL79FHH+WJJ54gIyODN954g0mTJgU7rt+lfoue7P9pDf96YTAmJn8eMJUdGxbhLsylSq2WbF33PjXqtWfxqzcA0LLrcFp1u4EvFzzExzOH4vW66djrXpyuyO+A9mtJg6/AHhtDymvz+eGBaXRc+jqGzSDlrQ8oSE3j4EefUblHV7qsnAOGwaabxoU6ZEt8vTmHNk1imHJvTQwMZrx7iG7tYykXZeOzNcfP/QZlxMbtbprVc/LA9XEYwKylOVzYzEmUyzhr35JqFW0cOVa2SlL/21pI82QXY29MwDDgjY+z6NQyiiinwcoNp2/q2vxTIY3rOHl4ZNEy736SRaSfd7/9Po9Wjcsx6fZqYMDL84/SpW0M5aIMvvj69PeD+/a7PEYNrMiEUVWx2w1mf5zBaVqyI44RoWOUGaZ57t18wYIFDBgwwP989uzZDB8+/CxLFJn+UYQfQedR0wFNQx1CWHj9tiWhDiEsVK1ZKdQhhA13YRk4g50HuVl5536RADDnyTqWfl7u6xMs+6yYkZMt+6yzVlAWL17MF198wddff83atWsB8Pl8bN++PaAERURERILMiMx+NmdNULp160aVKlXIzMxk0KBBANhsNmrXrm1JcCIiIlI2nTVBiY+Pp1OnTsyfP5+OHTtaFZOIiIgEKkL7oAS0Vm63m61bt1JQUEBhYSGFhZE/SJCIiIiETkBX8fz888/cfnvxsOaGYfD5558HLSgREREp2wJKUBYtWgRAZmYm8fHxGBHaIUdERCTsROg5OaAE5dtvv2XSpEl4vV569+5NUlJSicuORURERM6ngPqg/OMf/+Cdd96hcuXKjBo1ijlz5gQ7LhEREQmAYbNZ9rBSQJ9ms9lISEjAMAyioqIoX778uRcSERER+Z0CauKpU6cO06dPJyMjg1deeYWkpKRgxyUiIiKBMMrwZcaTJk0iKSmJDh06EBMTw6OPPhrsuERERKQMC6iC4vF4KCgowOstuuumruIREREpJWyReU4OqIJy3333ceTIES6++GJSU1MZO3ZssOMSERGRMiygCkpmZib3338/AD169OC6664LalAiIiISGKMs90Fp2LAh69evB2Dbtm0kJSXhdrs15L2IiIgERUAVlPXr17Nq1SqcTidutxuAXr16ach7ERGRUIvQPigBJSjTpk2jVatWwY5FREREBAgwQXnjjTfYv38/V111FVdddRUVKlQIdlwiIiISiAjtgxJQgvLss89y7NgxFi9ezN13303FihUZOHAgnTp1CnZ8IiIiUgYFnHYdOXKE1NRUMjIySExM5N///rcuNxYREQk1w7DuYaGAKigDBgygXLlyDBw4kLvvvhuXywXAyJEjgxqciIiIlE0BVVAGDBhAeno6zz77LH369OGyyy4D4PXXXw9qcCIiIlI2BVRBmTt3Lq+88gpVqlQJdjwiIiLyW9jKcCfZxMREatasGexYRERERIBzJCjPPPMMAIWFhYwcOZLmzZv7bxR43333BT86ERERObuyeJlx/fr1S/wrIiIiYoWzJij9+vWzKg4RERH5PSJ0qPvIrAuJiIhIWAuok6yIiIiUUhHaByUy10pERETCmiooIiIi4cziIeitogqKiIiIlDqqoIiIiISzCB1JNjLXSkRERMKaKigiIiLhTH1QRERERKyhCoqIiEg40zgoIiIiItZQgiIiIiKljpp4REREwpkuMxYRERGxRlArKB6PGcy3jyiv37Yk1CGEhZEzLw91CGHhw4mrQx1C2Nj2zQ+hDiEs1G3ZMNQhyJnoMmMRERERa6gPioiISDjTZcYiIiIi1lAFRUREJJypD4qIiIiINVRBERERCWcaB0VERETEGqqgiIiIhDFTfVBERERErKEKioiISDjTOCgiIiIi1lAFRUREJJypgiIiIiJiDSUoIiIiUuqoiUdERCSM6TJjEREREYsEXEE5dOgQTz31FBkZGfTq1YsmTZrQpk2bYMYmIiIi51LWO8mOHz+e/v37U1hYSIcOHXj88ceDGZeIiIiUYQEnKAUFBXTu3BnDMEhOTiYqKiqYcYmIiEggDMO6h4UCTlBcLhdfffUVPp+PjRs34nK5ghmXiIiIlGEBJyiPPvooCxcuJCMjgzfeeIOJEycGMSwREREJiM1m3cNCAXeSrV69Ok8//TSmabJx40aqVasWzLhERESkDAs4QXnqqaeoXbs2qampfP/991SuXJknnngimLGJiIjIOZT5cVDWr1/P4MGD2bBhA6+//joHDx4MZlwiIiJShgVcQfH5fGzevJlatWpRWFhIenp6MOMSERGRQJT1cVCuvvpqHn30UUaMGMFTTz3F8OHDgxmXiIiIlGEBV1CGDh3K0KFDAXjooYdwu91BC0pEREQCY0ZoBSXgBGXu3Lm8+eabeDweTNPE6XSybNmyYMYmIiIiZVTAadf8+fN5++23ufjii5k6dSoNGjQIZlwiIiISiLI+kmxiYiJVq1YlJyeHTp06cezYsWDGJSIiImVYwAlKXFwcy5cvxzAM5s6dq6t4REREJGgCTlAee+wxatasyejRo9m9e7eGuhcRESkFTMNm2cNKAXeSjY6OZsuWLRw4cIDu3bvTqFGjYMYlIiIiZVjA6dCECRNITU1l9erV5OTk8OCDDwYzLhEREQlEWe8ku3fvXu6++26ioqK49NJLycrKCmZcIiIiUoYF3MTj9Xr9HWOzs7OxWXzbZRERETmNsj5Q2z333MOQIUM4fPgwgwYNYty4ccGMS0RERMqwgBOUjh07smzZMtLT06lQoQIOR8CLioiISJCYFvcNsUrAdaFPPvmEJUuW8OWXX3LxxRfz+uuvBzMuERERKcMCTlDeeOMNunTpwscff8x//vMfVqxYEcy4REREJBCGzbqHhQL+tKioKADKly+Py+UiJycnaEGJiIhI2RZwR5JatWrRv39/xo8fz4wZM2jdunUw4/pdTJ+PNR9PJv3gVuwOFxf1e5QKler65+/ctITv18zGZrORWK0JXa6agGGzsenLV9j74xf4vG6adRpC4w7XhnAtrGEYcMuAKtSrGYXbY/LinDQOHnGXeI3LaTDxjiReeC+N/WlF8yrE2phyTy3unZaC22OGIvSQSOjYmqZT7mdtj+Elple9vDuNHr4D0+Mh5a0PSHl9ARgGLWdMpELrJvgKCtl868Pk7twbositYQBD+5SndlUHHi/MWpJFWoavxGtcDrjvunjeWpLNwaNeAPp0iaZtIxcOu8GK9Xms2lQQguitZRgw+rZGNKwfi9vtY9rz29h/IN8/v+uFlbhxSB28Xljy2QEWfXrQPy8h3snrz7bj3gmb2bsvLxThW8Yw4P+uTqBODSduj8lrCzM49Mt+c4LLaTB2RCVeWZjJgcMeDANu+msCSZUd+Ex4+f0M0tK9Z/iEyGFSOvqg+Hw+Jk6cyLZt23C5XDz22GPUrVt8Dt68eTPTpk3DNE2qVKnCU0895S9+nE7ACcq0adPIycmhfPnytGrVisqVK/+xNQmCPT8ux+sp4MpRc0nbu5Fvlj5Jj2EvAOBx5/O/5c/R765/4XBFs2LeaFK2/QdnVCxpezZwxS3v4XHn8d2qN0O8Ftbo2Ko8TqfB2Gf30bheFDf2q8S0V4u/CBvUjuLWQVWolFC8i7RtGsP1V1UioULZ6iCdPPomal5/Fd6ckicEw+Gg+dNjWdX5Wrw5eXRZOYe0xStI7HwBtnIu1nQbTEKnNjR7cgzr+98eouitcUETF067wdRZx0hOcjCgR3leWFA8VlLdGg6G9SlPYpzdP61JHScNazmZNusYLif0+lNMKEK3XLc/VcblsjHqgQ20aBLHnSMaMPbx7wGw2w3uuqkBN9/3P/IKvMx8si2rvzlKeqYbu93g73c0prDQd45PiAztm5fD6YCJMw/TsLaToX3jeebt4nvA1a/pZMQ1CVSML96n2jUrB8Ckl4/QrL6L6y8vuYwE1/LlyyksLGTevHls3LiRadOmMXPmTABM02T8+PH885//pG7duixYsID9+/eTnJx8xvc7ZxPPiy++CMB9993HhAkTGD16NFOnTmX06NHnaZXOn0N7/ketxhcBULVOW47s3+KfZ7e7uOLW93C4ogEwfV7sjij2/7SKxOqN+fzdu/js7dup0+TPoQjdcs0aRLPhx1wAtu8uoEHtciXmOx0GT7x2kP2HiqsqpmkyccZ+snMi/xfJyXJ37WX9gLtOmR7brAE5O/fiyTyO6XaTvno9iRd1ILFrew4v+wqAzK83kdC+pdUhW65hbSdbdhUCsCvVQ70aJZNYpx1eWJDlr5wAtEh2si/Nw+0D4rhrYAU27Si0NOZQad08nq/XF500v9+WRdNGcf559WrHsP9AHlk5Hjwek80/HKdNi3gA7hyRzEefpHIkvWxspyb1oti0vaiitiPFTf2arhLznQ6DZ99JJ/Wwxz9t/Q/5vP5hJgCVEx0cyy4byVxpuRfP+vXr6datGwBt27Zly5bic/DPP/9MQkICs2bN4vrrryczM/OsyQkEUEG59NJLARg8ePA5N1KoufOzcUYVH+yGzY7P68Fmd2DYbETHFlV9fvjvO7gLcklq2IWft/yb7MxUeg6bSXbGfj5753b637MUI0Iv2zohppxBbl7xwevzmdhs4Ptl0taf809ZZtO2yC4pn8nBDz8lum7NU6Y7KsTiOVZcJfBm5eCMj/1lerZ/uun1YtjtmN7ITeyiowzyCoqb/Hw+sBng+2XSjn2eU5aJjbFRKd7GP+cdp3KCnbsGxvHwS5kWRRw65WPs5OQW7ws+n4ndBl5f0bzs3OJtlZvnpXx5B30uq0bmMTffbMhg2IA6oQjbctFRBnn5J31HmSW/o7bvOX2i5vPBrQMSubB5OZ57T9WT823evHnMmzfP/3zQoEEMGjQIKBrENTY21j/Pbrfj8XhwOBxkZGSwYcMGxo8fT926dRk1ahQtW7akc+fOZ/yscyYoW7duZevWrX9kfSzjLBeLu7C4865p+rDZi1fR9Pn4dtnTHDuym8uuew7DMIiKSSC+SjJ2h4v4KvWxO6LIz0knOrZSKFbBMrn5JtHlirNhm83wH/gSGM/xbBxx5f3P7XHlcWdmnTIdmy2ikxOAvAKTcq7ipN44KTk5k5w8HwePevH64FC6F7cH4mIMsnIju29TTq6XmOjiZgnDMPD6Tj8vJtpOdraHa6+siQl0aJtIw/qxPHxvU8Y8uoX0TDeRKq/ApFzUSd9RRuDfUS8vyGBurI3Jt1fh78+mUeCO7H3KyqtrTk5Ifi02NrbEBTQ+n88/ZlpCQgJ169alYcOGAHTr1o0tW7acNUE551rt3LmTnTt3snDhQpYuXcqBAwf49NNPWbp06W9aKStUq9OOfdtWApC2dyOJ1RqXmL/6X4/gdRfQY+gMf1NPtbrt2L/9K0zTJPd4Gp7CPKJiEqwO3XJbd+XRrnlRm3/jelHsSY38zonnW/aPOynfsC7OxHgMp5NK3TqQsXYDGWv+R5U+FwOQ0KkNWVu2hzjS4NuR4qZVg6ISfHKSg/2Hz52Q/ZTipmWyE4D4WBtRToPsvAg/kQDf/XiMP3WoCECLJnHs2lP8hb47JZdaSdHExTpwOAzatohny9bj3Dl2E3eN3cRd4zax4+dsHnt2a0QnJ1DU9Ny2SVEHyoa1naQcPPf6XnRBNFddUvQLvtBt4jOLKi9ijXbt2rFyZdE5eOPGjTRuXHwOrl27Njk5OezZsweAdevW0ahRo7O+3zkrKCf6mowcOZJXXnnFP33EiBG/Pfogq9u8B/t3rGHxy0MwTZNu/aewc9Ni3AW5VK7Zgu3rP6B63fZ88saNADTvPIx6LXpyaPc6Fs0ciGn66HzVeGw2+9k/KAJ8vTmHNk1imHJvTQwMZrx7iG7tYykXZeOzNcdDHV6pljT4CuyxMaS8Np8fHphGx6WvY9gMUt76gILUNA5+9BmVe3Sly8o5YBhsuinybwuxYVshzZOdjLkhHgN4c3E2HVtEUc4FKzecPvndvMNN4zpOHvq/eGyGwbvLsikL55KV/z3ChW0TmflkWwzDYMpzW+l5SVWiy9n5eNkBZry2i2cmt8JmGCz57GCZ6XPya+t+yKdVo3I8MqoyhmHw8vsZdGkTTZTLYMW3uadd5tst+dxybQLjb6mM3W7wzuJjuE9tXZQg6dmzJ6tXr2bw4MGYpsmUKVNYtGgRubm5DBo0iMcff5zRo0djmiYXXHABf/7zn8/6foZpBvaV0K9fP2bNmkWFChXIyMhg5MiRLFy48KzLPPG+2gwC9fXKXaEOISyMnHl5qEMICx9OXB3qEMLG1rXfhzqEsFC3ZcNQhxA23p16ap+1YMrY9KVln5XY5hLLPivg60VHjRpF//79iYuLIysri6lTpwYzLhERESnDAk5QevXqRa9evTh69CgJCQnY7UXNIHPnzg2LK3xEREQi0bku/w1Xv3mtKlWq5E9OgFLZWVZERETC2x8eEjTALiwiIiISDBE6btcfrgtF+oBmIiIiYr2ydVMVERGRCKM+KGegJh4RERE53wJOUE7cNPCE6dOnA/DAAw+c34hEREQkYCaGZQ8rnbOJZ8GCBbz//vvs3LnTP4St1+vF4/EwevRoWrduHfQgRUREpGw5Z4Jy9dVX07lzZ15++WVGjRoFgM1mo1KlyL6ZnoiISDiI1D4o50xQXC4XtWrVYv/+/dSsae3wvSIiIlI2BXwVT1xcHMuXL6d+/frYbEXZWv369YMWmIiIiAQgQof7CDhBSU9PZ9asWf7nhmEwe/bsoAQlIiIiZVvACcrbb79d4nlhYdm8BbiIiEhpYv7xEUNKpYATlLlz5/Lmm2/i8XgwTROn08myZcuCGZuIiIiUUQGnXfPnz+ftt9/m4osvZurUqTRo0CCYcYmIiEgZFnCCkpiYSNWqVcnJyaFTp04cO3YsmHGJiIhIAEzDsOxhpYATlBNX8RiGwdy5c0lPTw9mXCIiIlKGBZygPPbYY9SsWZPRo0eze/duJk6cGMSwREREJBCmYbPsYaWAO8lGR0ezZcsWDhw4QPfu3WnUqFEw4xIREZEyLOB0aMKECaSmprJ69WpycnJ48MEHgxmXiIiIBCBSbxYYcIKyd+9e7r77bqKiorj00kvJysoKZlwiIiJShgXcxOP1ev0dY7Ozs/3D3YuIiEjolNmbBZ5w7733ct1113H48GEGDx7MuHHjghmXiIiIlGEBp10///wzNpsNr9dLTk4O48ePD2ZcIiIiEoBIHQflNw11/+qrr1KlSpVgxiMiIiISeIKSmJhIzZo1gxmLiIiI/EZWX11jlXMmKM888wxQdPfikSNH0rx5c4xfyjz33XdfcKMTERGRMumcCUr9+vVL/CsiIiKlR5m9iqdfv35WxCEiIiLiF3AfFBERESl9IrUPSmTWhURERCSsKUERERGRUkdNPCIiImEsUjvJRuZaiYiISFhTBUVERCSMqZOsiIiIiEVUQREREQlj6oMiIiIiYhFVUERERMKY+qCIiIiIWCSoFZTcXG8w3z6iVK1ZKdQhhIUPJ64OdQhhod/ErqEOIWx8NGlNqEMICznH80IdgpyBaaiCIiIiImIJ9UEREREJY6apCoqIiIiIJVRBERERCWNmhNYaInOtREREJKydtYJSWFh4xnkul+u8ByMiIiK/TaSOg3LWBKV3794YhoFpmiWmG4bB559/HtTAREREpOw6a4LyxRdflHiemZlJfHw8RoRecy0iIhJuymQF5YRvv/2WSZMm4fV66d27N0lJSQwYMCDYsYmIiEgZFVAn2X/84x+88847VK5cmVGjRjFnzpxgxyUiIiJlWEAVFJvNRkJCAoZhEBUVRfny5YMdl4iIiAQgUpt4Aqqg1KlTh+nTp5OZmckrr7xCUlJSsOMSERGRMiygBGXSpEkkJSXRvn17oqOjefTRR4Mdl4iIiATAxLDsYaWAmnjuvvtuBg4cyODBg3UFj4iIiARdQBWUUaNG8eWXX3LNNdfw/PPPk5qaGuy4REREJACmaVj2sFJAFZRWrVrRqlUrjh07xsSJE/nLX/7Cli1bgh2biIiIlFEBJSjr1q1j4cKFfPfdd/Tu3ZsHH3ww2HGJiIhIACL1Kp6AEpRZs2YxcOBAHn/8cfVBERERkaALqA/Ks88+y8GDB3n++ef5+uuvSU9PD3ZcIiIiEoBIvYonoATlkUceITU1ldWrV5OTk6MmHhEREQmqgBKUvXv3cvfddxMVFcWll15KVlZWsOMSERGRAJTpCorX6/U362RnZ2OzBbSYiIiIyO8SUCfZe+65hyFDhnD48GEGDRrEuHHjgh2XiIiIBMDq8UmsElCC0rFjR5YtW0Z6ejqJiYm6kkdERESC6qwJyqBBg86YjMydOzcoAYmIiEjgfGVxHJRnnnnmrAtv2rSJNm3anNeARERERM6aoNSsWfOsC0+fPp3Zs2ef14BEREREAuqDciamaZ6vOEREROR3iNSh7v/Q9cLqLCsiIiLB8IcqKCIiIhJakXqZ8R+qoKiJR0RERIIh4ApKdnY2+/fvp3bt2sTExABw5ZVXBi0wERERObdI7YMSUILy73//m5deegmv10vv3r0xDIPbb7+dgQMHBjs+ERERKYMCauJ56623mD9/PgkJCdx+++0sX7482HGJiIhIAEzTsOxhpYASFJvNhsvlwjAMDMMgOjo62HGJiIhIGRZQE0+HDh0YPXo0hw4dYsKECbRq1SrYcYmIiEgAynQflPvuu4+VK1fSrFkzGjRoQPfu3YMdl4iIiJRhASUoaWlpJCUlUatWLV577TWqV69Os2bNgh2biIiInEOZHgflwQcf5MiRI/zjH/+ga9euTJkyJdhxiYiISBkWUILi8Xi48MILOX78OJdffjk+ny/YcYmIiEgAfBY+rBRQE4/b7Wbq1Kl06NCBtWvX4vV6gx3X72IAl3eyUb2igccLH//XS3pW8fyW9Qw6N7PhM+FQhsmSr32cGAu3fDm49XIHsz/zcOR4KKK3jgEM+UsMtara8XhN3v4kl8OZJXc9pwPuGRTH7E9yOJTuo3NLF51buQBwOAxqV7Xz9xnHyCuI7NGEDWBon/LUrurA44VZS7JIyyi5rVwOuO+6eN5aks3Bo0XHRp8u0bRt5MJhN1ixPo9VmwpCEL31Ejq2pumU+1nbY3iJ6VUv706jh+/A9HhIeesDUl5fAIZByxkTqdC6Cb6CQjbf+jC5O/eGKHLrGMDQ3uV/Of5g1tJsDp9mn7p3SAVmLc3m4FEfXVpF0aV1FFB0bNau5mD0cxkRffwZBvzf1QnUreHE7YVXP0jn0NGS5x6X02DsyMq8+kEGqYc9GAbc3D+RGpUd+Ex4eUE6aeml83wl5xZQgjJt2jRWr17NgAEDWL58OU899VSw4/pdmtYxcNgNXvvES63KBr062JmzomjndNjhsgvsvPixB7cXru1mp3Etg237TGwGXPknO+4ysh+3aezE6YAn38mifpKday+NZubCHP/8OtXtDP1LDAlxxQW2/24p5L9bCgEY3DOaNZsLIvrL8YQLmrhw2g2mzjpGcpKDAT3K88KC4qy3bg0Hw/qUJzHO7p/WpI6ThrWcTJt1DJcTev0pJhShWy559E3UvP4qvDl5JaYbDgfNnx7Lqs7X4s3Jo8vKOaQtXkFi5wuwlXOxpttgEjq1odmTY1jf//YQRW+dtk1cOB0wbfZxkpMcDLysPC+8f9I+Vd3O9X1iSTzp+FvzXQFrvitKcq/rVZ5VmyL/+OvQvBxOp8EjMw/TsLaLoZcn8Mzso/759Ws6GdkvkYrxxcde+2blAJj00mGaJUdx/RUll4lUZboPSu3atQGYOnUqhw8fplq1akEN6veqU9VgR2rRL5F9R0ySKhX/0bxeeO0Tjz8JsRng+eX/vTrY+Ha7j6zcyD7gT2hYy8H3P7sB+DnVS93qJfNUpx1e+jCbQ6f55VGnup2kynZWbSq0JNZQa1jbyZZdReu6K9VDvRqnbqsXFmT5KycALZKd7EvzcPuAOO4aWIFNO8rGtsrdtZf1A+46ZXpsswbk7NyLJ/M4pttN+ur1JF7UgcSu7Tm87CsAMr/eREL7llaHHBKNajnYsqvo+NuV6qHur/Yph8PgxfdL7lMn1P3l+PtqY+RX5JrUi2LztnwAdqQUklzTVWK+02HwzNtHST3s8U9b90M+ry3MAKBygp3jWWXkV2eECihBGT9+PCkpKXTt2pX9+/fz8MMPBzuu3yXKaZB/0rnAZxYlIgAmkFO0r9OpqQ2XE3YeMGnbwCAnH3amlo3kBKCcyyjx6+vk7QSwc7+XjKzTb48+fyrH4tX5wQ6x1IiO+tW28pXcVjv2ecjIKlmej42xUa+Gg5c+yOLtT3K4+epYq8INqYMfforp9pwy3VEhFs+x4gqBNysHZ3zsL9Oz/dNNrxfDbj9l+UhTLsogL//kfcosefydZp86oW/XaBatyg12iKVCdDkbuSdvJ9PEdtIZa/ueQtKPnZqA+HwwakAiN1yVwNdb8k6ZL+EjoCaePXv28O677wLQo0cPBg8eHNSgfq8Ct0mUs/i5QdHJ9+TnPdvbqFTBYN5/inbsCxrawITkGnaqVzTod5GdOV94yY7gc3B+oUk5V/E3omGU3E5nEh1lUL2Sne17Tz0JRaq8gt++rXLyfBw86sXrg0PpXtweiIsxykyF7tc8x7NxxJX3P7fHlcedmXXKdGw2zFLav+18yi8wKRdVvE/ZfuPxt21P2Tj+8vJ9JbaTYRQlH4F4aUEG8Z8cY/IdVfn7M4cocEf2sRepA7UFVEEpKCggL68oE83Pzy+1nWT3ppk0qlm0SrUqG6Rlltwpr+xsx2GHuSu8/qaeN5d5efNTL2996uVgusmHqyI7OYGiX2gtk4syufpJdvYfDuzv2ai2gx93u4MZWqmzI8VNqwZFpeXkJEdA2+qnFLd/+8bH2ohyGmTnRfYX5Nlk/7iT8g3r4kyMx3A6qdStAxlrN5Cx5n9U6XMxAAmd2pC1ZXuII7XGjn0eWjUo2j+SkxzsC/D4a1zHwY8/l53jb9ueQto2LepT0rC2i5SD507MLroghqv+HAdAodvENIsqLxKeAqqgDB8+nKuvvppGjRqxY8cO/va3vwU7rt9l616TBjVMRva2Yxjw0WovreobuByQetTkgoYGew/BDX8pKiOv/dHH1pSyt/Nu3O6mWT0nD1wfhwHMWprDhc2cRLmMs/YtqVbRxpFjZesS8w3bCmme7GTMDfEYwJuLs+nYIopyLli54fT9ADbvcNO4jpOH/i8em2Hw7rJsyuJ3ZNLgK7DHxpDy2nx+eGAaHZe+jmEzSHnrAwpS0zj40WdU7tGVLivngGGw6aZxoQ7ZEhu2FdK8vpMHh1fAAN5akk3H5i6iXMZZ+5ZUq2g/5Wq7SLbu+zxaNYxi4m1VMICX38+gS5toykXZ+OKbnNMu8+2WPG4dkMj4W6vgsMHbizM5TatjxInUTrKGaQb21ZmamsrRo0epVKkSSUlJAb35I7PLTrb/Rx06kH3uFwkeT+ms3pU2/SZ2DXUIYeOjSWtCHUJYyDmu/hyBem9aLUs/b/UP1p0/uja3rk9dQE08M2bMYPbs2bRq1Ypp06bxyiuvBDsuERERCYCJYdnDSgElKF988QVjxowB4J///CdffPFFUIMSERGRsi2gPiiGYVBYWIjL5cLtdhNgq5CIiIgEWSBXgYWjgBKUwYMHc+WVV9K4cWN27drFTTfdFOy4REREpAwLKEEZMGAAl112GSkpKdSuXZuKFSsGOy4REREJQKSOgxJQgjJ27NhTpk2dOvW8ByMiIiICASYoffv2BcA0TX744QfS0tKCGpSIiIgEJlLHQQkoQenWrZv//xdffDEjRowIWkAiIiIiASUoq1at8v//8OHDHDlyJGgBiYiISOAi9cLagBKUJUuW+P/vcrmYMmVK0AISEREROWuCUlhYdF+WSZMmWRKMiIiI/Da+sngVT+/evTGMkitumiaGYfD5558HNTAREREpu86aoGhIexEREQmFgPqg9OrVC4+n+J7VDoeDGjVq8MADD9CiRYugBSciIiJnV6YvM+7UqRO9e/emQ4cObNiwgQULFtC/f38ee+wx5syZE+wYRUREpIwJ6G7GP//8M126dMHlctGpUycOHz5M586dsdkCWlxERESCxDSte5yNz+djwoQJDBo0iGHDhrFnz57Tvm78+PE8/fTT51yvgDIMl8vFnDlz2Lp1K3PmzMHlcrFlyxa8Xm8gi4uIiEiEW758OYWFhcybN4/Ro0czbdq0U14zd+5ctm/fHtD7BZSgPP300+zevZunn36alJQUnnzySY4eParxUERERELMxLDscTbr16/3jzzftm1btmzZUmL+hg0b2LRpE4MGDQpovQLqg5KYmHjKDQMvueQS7rjjDl544YWAPkhERETC27x585g3b57/+aBBg/wJR3Z2NrGxsf55drsdj8eDw+EgLS2NGTNmMGPGDD755JOAPiugBOVMjh8//kcWFxERkT/IZ+FQ9ycnJL8WGxtLTk6O/7nP58PhKEoz/v3vf5ORkcEtt9zC4cOHyc/PJzk5mb/+9a9n/Kw/lKD8ehA3ERERKZvatWvHihUr6Nu3Lxs3bqRx48b+ecOHD2f48OEALFy4kF27dp01OYE/mKCIiIhIaJWWcVB69uzJ6tWrGTx4MKZpMmXKFBYtWkRubm7A/U5OpgRFRERE/jCbzcbkyZNLTGvQoMEprztX5eSEP5SgxMfH/5HFRURE5A861/gk4SqgBOXXV/A4nU6qV6/OY489FpSgREREpGwLaByUgoICqlatSt++falZsyaHDh2isLCQBx98MNjxiYiIyFn4MCx7WCmgBCU9PZ17772Xbt26ceedd+J2u7nnnnvIysoKdnwiIiJSBgWUoGRnZ7Nz504Adu7cSU5ODhkZGeTm5gY1OBERETm70nIvnvMtoD4oEyZM4IEHHiAtLY0aNWowYcIEli5dyqhRo4Idn4iIiJRBASUoaWlpvP/++yXuXtyqVaugBSUiIiJlW0BNPGvWrOHqq6/m2WefJSUlJdgxiYiISIBM07DsYaWAm3gKCwv5/PPPmTx5Mm63m7feeivIoYmIiEhZFfBAbZs3b2bVqlUcPXqUXr16BTMmERERCZCVNwu0UkAJSt++fWnatCkDBgzg8ccfx+12BzsuERERKcMCSlCuuOIK/vWvf7Fp0yZM08TpdLJs2bJgxyYiIiLnEKlD3QfUSXb58uW8/fbbXHzxxUydOvW0N/8REREROV8CSlASExOpWrUqOTk5dOrUiWPHjgU7LhEREQmAiWHZw0oBJShxcXEsX74cwzCYO3cu6enpwY5LREREyrCAEpTHHnuMpKQkRo8eze7du5k4cWKQwxIREZFA+EzrHlYKqJNsbGwszZs3B2DMmDFBDUhEREQk4HFQREREpPQp01fxiIiIiFgpqBWUerWdwXz7iLJvjyfUIYSFbd/8EOoQwsJHk9aEOoSwcc0jXUIdQliY2vuVUIcQRmpZ+mmqoIiIiIhYRH1QREREwpjP4rsMW0UVFBERESl1lKCIiIhIqaMmHhERkTCmTrIiIiIiFlEFRUREJIypgiIiIiJiEVVQREREwpjVN/GziiooIiIiUuqogiIiIhLGTA3UJiIiImINVVBERETCmK7iEREREbGIKigiIiJhTFfxiIiIiFhEFRQREZEwpj4oIiIiIhZRBUVERCSMqYIiIiIiYhElKCIiIlLqqIlHREQkjOkyYxERERGLqIIiIiISxtRJVkRERMQiqqCIiIiEMZ8v1BEEhyooIiIiUuqogiIiIhLG1AdFRERExCKqoIiIiIQxVVBERERELKIKioiISBjTSLIiIiIiFlEFRUREJIyZlnZCMSz7JFVQREREpNRRBUVERCSM6SoeEREREYsoQREREZFS56xNPCtWrKB79+7MmzfvlHmDBg0KWlAiIiISmEi9WeBZE5TMzEwADh8+bEUsIiIiIsA5EpR+/foBcNttt/HTTz9RWFhoSVC/l+nzsWzORNL2bcPucNF32GMkVq3rn7/1f8tYu+wVDAzadhtEm4sG4PW6WTp7HMeO7sfrLqRL39to1OayEK5F8BnA9X1jqV3NgdtjMmtxFmkZJVNwlwPuuz6BtxZlcfCoF4C+XaNp2zgKux1WrMtn1cb8EERvLcOA0bc1omH9WNxuH9Oe38b+A8Xr3fXCStw4pA5eLyz57ACLPj3on5cQ7+T1Z9tx74TN7N2XF4rwLWMAQ3uXp1ZVOx4vzFqazeHT7FP3DqnArKXZHDzqo0urKLq0jgLA6YDa1RyMfi6DvIII7fF3koSOrWk65X7W9hheYnrVy7vT6OE7MD0eUt76gJTXF4Bh0HLGRCq0boKvoJDNtz5M7s69IYrcOjr2AhepnWQDuornlltuobCwkAoVKgBgGAYzZswIamC/x/ZNy/G4Cxn+4Dz279rI5+9P49rbZwLg83n58sPp3DDuA1xRMbw2qS+N2l7Gjs0riC6fwJX/9xR52Rm8+Xi/iE9QLmjqwukwmPJmJsk1HQzsGcuM+cf98+vWcDC8byyJFez+aU3qOmlQy8nUNzNxOaFX55hQhG65bn+qjMtlY9QDG2jRJI47RzRg7OPfA2C3G9x1UwNuvu9/5BV4mflkW1Z/c5T0TDd2u8Hf72hMYWGE1l5/pW0TF04HTJt9nOQkBwMvK88L72f559etbuf6PrEkxhV3e1vzXQFrvisA4Lpe5Vm1qaBMJCfJo2+i5vVX4c0peeI0HA6aPz2WVZ2vxZuTR5eVc0hbvILEzhdgK+diTbfBJHRqQ7Mnx7C+/+0hit46OvYkoASloKCAd955J9ix/GH7dqwnuUU3AGomt+Xgni3+eTabnZsnLsVmd5Bz/CimCa6o8jRt15sm7Xr5X2fY7ae8b6RpVNvJlp1F1bBd+z3Uq1FyN3DaYcaC49x0dZx/WosGLvanebljYAWiowzmL8+xNOZQad08nq/XpwPw/bYsmjYq3ib1asew/0AeWTkeADb/cJw2LeJZsfoId45I5qNPUhk2oE5I4rZao1oOtuxyA7Ar1UPdX+1TDofBi+9nMfKq2FOWrVvdTlJlO+8tKxv7VO6uvawfcBdt33qyxPTYZg3I2bkXT2bRj4X01etJvKgDiX9qy+FlXwGQ+fUmEtq3tDzmUNCxF7gyPdR9hw4d+Oqrr0hNTfU/SqOC/Gyioou/AG02Oz6vp/i53cG2DZ/yxmNXU7tRB2x2B65y5YkqF0tBfjYfvvI3Lr7qnhBEbq1yUQa5+cV7tM8E20mDA+7Y5yHjeMlfH3HRBvVqOJj5/nFmL83mln5xlAXlY+zk5Hr9z30+E7uteF52bvH+lZvnpXx5B30uq0bmMTffbMiwOtyQKRdlkHfyPuUzS+xTO/d5yMg6/S/avl2jWbQqN9ghlhoHP/wU0+05ZbqjQiyeY8VVJ29WDs742F+mZ/unm15vmfghpWNPAqqgHD16lClTppRo4pk7d25QA/s9osrFUphf/CvMNH3Y7CVXsckFf6Fxmx4snjWGLWs/onWX/hxPP8DCl+7ggkuuo0XHK60O23L5BSbloorPHoZx7gw8O8/kwNFCvD44dNSL2wNxMQZZuRGauv8iJ9dLTHTxycAwDLy+08+LibaTne3h2itrYgId2ibSsH4sD9/blDGPbiE9021x9Nb59T5lC2CfAoiOMqheyc62PaeesMsaz/FsHHHl/c/tceVxZ2adMh2bDdPrPc07RBYde4Er031Qfv75Zz755JNgx/KH1WzQjh2bV9CsQ1/279pIlZqN/fMK8rJ5/8VRDPrbGzicLlxR0RiGjZzjR5j3zxH0HDyBek07hzB66+xIcdOmcRTrfigguaaD/Wnn/rL7KcVNj47RfLo2j4RYGy6nQXZehB4VJ/nux2N07ViJL1YdpkWTOHbtKU6Ad6fkUispmrhYB3n5Xtq2iGfOwhT+s+aI/zXPT2nDUy9uj/gvyB37PLRp5GTdj4UkJznYdziwE2jjOg5+/Dmyt02gsn/cSfmGdXEmxuPJzqVStw7seuZ1ME2qXtGdA+9/QkKnNmRt2R7qUC2hY08CSlAaN27Mxo0bad68uX+ay+UKWlC/V5O2Pdn942refnIwpmly+Q1T+P6bRbgLcmnbbRAtOl7Ju9OHYrc7qFKzCS06XcXnC6aSn3uc1UteZPWSFwEYeNerOF3lQrw2wfO/rYU0T3Yx9sYEDAPe+DiLTi2jiHIarNxw+itzNv9USOM6Th4eWbTMu59kRWzWfrKV/z3ChW0TmflkWwzDYMpzW+l5SVWiy9n5eNkBZry2i2cmt8JmGCz57CBH0kv3lW7BsmFbIc3rO3lweAUM4K0l2XRs7iLKZfDVxoIzLletop3DmWW7M2PS4Cuwx8aQ8tp8fnhgGh2Xvo5hM0h56wMKUtM4+NFnVO7RlS4r54BhsOmmcaEO2RI69gJnWtoJxbqbBRpmALdBvPLKK8nJySEjI4PExEQMw+Dzzz8/55u/ueK8xFgmrFqlsWYCse2bH0IdQlho1rlsdKQ8H655pEuoQwgLU3u/EuoQwsaqRZdY+nlPL7Quyb//r9YNQB/QJ91///0YhkHdunVxOBw8/vjjwY5LREREAuAzrXtYKaAmnhkzZrBgwQIqVqzI4cOHueOOO5g/f36wYxMREZEyKqAEpXz58lSsWBGAKlWqEB0dHdSgREREJDCR2h/wrAnKM888A4DX6+XWW2+lffv2bN68uVR2kBUREZHIcdYEpX79+iX+BbjsssgeBl5ERCSc+CJ0KNmAbhYoIiIiYiXrrhcSERERCVBAnWRFRESkdIrUTrKqoIiIiEipowqKiIhIGFMFRURERMQiqqCIiIiEMV+EllBUQREREZFSRxUUERGRMGZadzNjS6mCIiIiIqWOKigiIiJhzFQfFBERERFrqIIiIiISxnzqgyIiIiJiDVVQREREwpj6oIiIiIhYRBUUERGRMOaLzAKKKigiIiJS+ihBERERkVJHTTwiIiJhzIzQNh5VUERERKTUUQVFREQkjEXoVcaqoIiIiEjpowqKiIhIGPOpD4qIiIiINVRBERERCWMa6l5ERETEIqqgiIiIhDHTF+oIgkMVFBERESl1VEEREREJYz71QRERERGxhiooIiIiYay0XMXj8/mYOHEi27Ztw+Vy8dhjj1G3bl3//MWLFzNr1izsdjuNGzdm4sSJ2GxnrpOogiIiIiJ/2PLlyyksLGTevHmMHj2aadOm+efl5+fzj3/8g9mzZzN37lyys7NZsWLFWd9PFRQREZEwVlpGkl2/fj3dunUDoG3btmzZssU/z+VyMXfuXKKjowHweDxERUWd9f2UoIiIiEhA5s2bx7x58/zPBw0axKBBgwDIzs4mNjbWP89ut+PxeHA4HNhsNipXrgzA22+/TW5uLl27dj3rZylBERERCWNWdkE5OSH5tdjYWHJycvzPfT4fDoejxPOnnnqKn3/+meeffx7DMM76WUFNUFJSPcF8+4iSm5UX6hDCQt2WDUMdQljIOa79KVBTe78S6hDCwth/3xLqEMLItlAHEBLt2rVjxYoV9O3bl40bN9K4ceMS8ydMmIDL5eLFF188a+fYE1RBERERkT+sZ8+erF69msGDB2OaJlOmTGHRokXk5ubSsmVL3n//fTp06MANN9wAwPDhw+nZs+cZ308JioiISBgzS0knWZvNxuTJk0tMa9Cggf//W7du/W3vd16iEhERETmPVEEREREJYxrqXkRERMQiqqCIiIiEsdLSB+V8UwVFRERESh1VUERERMKYKigiIiIiFlEFRUREJIxFaAFFFRQREREpfVRBERERCWPqgyIiIiJiEVVQREREwpipkWRFRERErKEKioiISBjzqQ+KiIiIiDWUoIiIiEipoyYeERGRMKZOsiIiIiIWUQVFREQkjGmgNhERERGLqIIiIiISxlRBEREREbGIKigiIiJhzKereERERESsoQqKiIhIGFMfFBERERGLqIIiIiISxjSSrIiIiIhFVEEREREJYz71QRERERGxhiooIiIiYUxX8QDHjh0LVhwiIiIifgFVUL755hsmT56M1+uld+/eJCUlMWDAgGDHJiIiImVUQBWU5557jnfeeYfKlSszatQo5syZE+y4REREJACmaVr2sFJACYrNZiMhIQHDMIiKiqJ8+fLBjktERETKsICaeOrUqcP06dPJzMzklVdeISkpKdhxiYiISABMny/UIQRFQBWUSZMmkZSURPv27YmOjuaxxx4LdlwiIiJShgVUQVm3bh1DhgwBIC8vj0cffZTJkycHNTARERE5t0gdqC2gBOW5556jfPny+Hw+HnroIa666qpgx/W79e1oo3qCgccHi9Z6ycgunteirsGfmtrwmXAo02TpN0VlsZv72ClwF70mM9vk47WRWS47wTBgRL9E6tRw4fGYvPJ+OoeOekq8xuU0GHdzVV5ZcJTUwx4MA265tiI1qjjx+Uxemp9OWrrnDJ8QOQwD/u/qBOrUcOL2mLy2MINDR70lXuNyGowdUYlXFmZy4JdtddNfE0iq7MBnwsvvZ5CW7j3DJ0SGE9upbg0nbi+8+kH66bfTyMq8+kGGf5+6uX8iNU5spwXpEb+doGhbjb6tEQ3rx+J2+5j2/Db2H8j3z+96YSVuHFIHrxeWfHaARZ8e9M9LiHfy+rPtuHfCZvbuywtF+JZL6NiaplPuZ22P4SWmV728O40evgPT4yHlrQ9IeX0BGAYtZ0ykQusm+AoK2Xzrw+Tu3BuiyOWPCihBeeGFF7j99tspLCzkueeeo0GDBsGO63dpWtvAYYM3PvVSsxL8pZ2NeSuLkg2HHbq3sfHSEi8eL/y1q43GNQ12HijKPGcvj/wvxhM6tIjG6TB45IVDNKzj4vorEpg+64h/fnItFyP/mkjF+OLdo33zaAAmvniIZslRDLuy5DKRqn3zcjgdMHHmYRrWdjK0bzzPvJ3un1+/ppMR1yRQMd7un9auWTkAJr18hGb1XVx/ecllIlGH5uVwOg0emXmYhrVdDL08gWdmH/XPr1/Tych+iSW2U/sT2+mlwzRLjuL6K0ouE6m6/akyLpeNUQ9soEWTOO4c0YCxj38PgN1ucNdNDbj5vv+RV+Bl5pNtWf3NUdIz3djtBn+/ozGFhZH9A+pkyaNvoub1V+HNKZmMGQ4HzZ8ey6rO1+LNyaPLyjmkLV5BYucLsJVzsabbYBI6taHZk2NY3//2EEVvnTJ5s8Dp06fzzDPP8NZbb1G/fn3S0tL417/+xTPPPGNVfL9JnSrFCcf+o1CjkuGf5/HCm58WJScANqNoWvVEcDpg6KU2hl1mo2alUERurSb1oti0regX2469hSTXcpWY77DD9FlHSE1z+6et+z6PVz8oOslWSXRwLLtsfEk2qRfFpu0FAOxIcVO/Zslt5XQYPPtOOqmHi6tJ63/I5/UPMwGoXEa2VZN6UWw+sU+lFJJ8mu30zNtHS2yndT/k89rCDAAqJ9g5nlU2fiS0bh7P1+uLjqXvt2XRtFGcf1692jHsP5BHVo4Hj8dk8w/HadMiHoA7RyTz0SepHEkvDEncoZC7ay/rB9x1yvTYZg3I2bkXT+ZxTLeb9NXrSbyoA4ld23N42VcAZH69iYT2La0OWc6js1ZQkpOT/f+vX78+HTt2DHpAf4TLib+pBsA0i8qpJ5LLnF+qqBc2NnA5YddBk6oJ8N8ffGzYaVIxDq7rbueFRV4iNCEFILqcjdz84pOmzwc2W9G/ANv3nP4L0OeD2wZWpEPLGP7xduRXTwCiowzyTt5Wphnwtrp1QCIXNi/Hc+9FdvUETuxTxQfNb9lOowYk0qFFNM+9G/nVE4DyMXZycouTMZ/PxG4Dr69oXnZucRKXm+elfHkHfS6rRuYxN99syGDYgDqhCDskDn74KdF1a54y3VEhFs+xLP9zb1YOzvjYX6YXt+ubXi+G3Y7pjezkt0wOdd+vXz/69evHlVdeSW5uLps3b+b48eNcfvnlVsX3mxS6wXVSynVycnJCzwtsJNcwmP9L08/R4/Dd7qIXpWdBXgHERVsVcWjk5fuIjir+0xtG8YnkXGbOT+e+J1O5+dqKRDmNcy8Q5vIKTMqdtK1shhHwtnp5QQajpx/ipn4JEb+t8vJ9lIsqXsffsk+9tCCD0U8f5Ka/Jkb8dgLIyfUSE13c1GUYBl7f6efFRNvJzvZweY/qdLggkeentKFh/VgevrcpFROcVodeaniOZ+OIKx6Pyx5XHndm1inTsdkiPjmJZAFdZjxhwgRSUlLo2rUr+/fv5+GHHw52XL/L3sMmDZOKvuBqVoK0zJLZyRWdbNjtMO9Ln7+pp20Dg57tijZDbDREOSErwvuebd9dQNumRe3/Deu4SDnoPscScFG7GK7uXgGAQreJaRb9So5023cX0LZJFAANazsD21YXRHPVJbFA0bbylYFttW1PYfE+VdtFysFzd6C+6IIYrvpzUfNGWdqnvvvxGH/qUBGAFk3i2LUnxz9vd0outZKiiYt14HAYtG0Rz5atx7lz7CbuGruJu8ZtYsfP2Tz27FbSM8+9L0aq7B93Ur5hXZyJ8RhOJ5W6dSBj7QYy1vyPKn0uBiChUxuytmwPcaTWMH2mZQ8rBdRJds+ePbz77rsA9OjRg8GDBwc1qN9ra4pJcg2D//uLHQP411ovLesZuByQetTkggYGe9NgeI+iXyhfby1q2rm6s8GNPYumfbw2spt3AL79Po9Wjcsx6fZqYMDL84/SpW0M5aIMvvg65/TLfJfHqIEVmTCqKna7weyPM3BH/kU8rPshn1aNyvHIqMoYhsHL72fQpU00US6DFd/mnnaZb7fkc8u1CYy/pTJ2u8E7i49F/LZa930erRpGMfG2Khjg307lomx88c0Z9qktedw6IJHxt1bBYYO3F2dG/HYCWPnfI1zYNpGZT7bFMAymPLeVnpdUJbqcnY+XHWDGa7t4ZnIrbIbBks8Olqk+J+eSNPgK7LExpLw2nx8emEbHpa9j2AxS3vqAgtQ0Dn70GZV7dKXLyjlgGGy6aVyoQ5Y/wDAD6P577bXX8vbbbxMdHU1+fj7Dhg1jwYIF53zzye+WgW+b8+THTamhDiEs2Oz2c79IIrZXfzDs/X5nqEMIC2P/fUuoQwgbl7u3Wfp5f/3bDss+a+E/G1r2WQFVUG644QauvvpqGjVqxI4dO/jb3/4W7LhERESkDAsoQalSpQrz588nJSWFWrVqkZiYGOy4REREJABl8iqeE55//nkSEhJo1aqVkhMREREJuoAqKIZhcMcdd1C/fn1stqKc5r777gtqYCIiIlJ2BZSg9O/fv8Rzw4j8sQpERETCQZlu4vnuu+/8g7b169ePNWvWBDsuERERKcPOWkF59913mTlzJpmZmXz66af+6aX1ZoEiIiJlTaQOK3DWBGXo0KEMHTqUl156icsuuwy73c6rr77K8OHDz7aYiIiIyB8SUBPP119/TXp6Ov/4xz+46KKLmDJlSrDjEhERkQD4fD7LHlYKKEHxeDx06NDBf6NAq4MUERGRsiWgq3jcbjdTp06lQ4cOrF27Fq/uDikiIlIqlOmreKZNm0b9+vW55ZZbSE9P56mnngp2XCIiIlKGBVRBqVevHvXq1QOgb9++wYxHREREfgPTjMxuFwFVUERERESsFFAFRUREREqnMt0HRURERMRKqqCIiIiEMVVQRERERCyiCoqIiEgY8+kqHhERERFrKEERERGRUkdNPCIiImFMnWRFRERELKIKioiISBgzfeokKyIiImIJVVBERETCmPqgiIiIiFhEFRQREZEwZmqgNhERERFrqIIiIiISxnzqgyIiIiJiDVVQREREwpjGQRERERGxiCooIiIiYUzjoIiIiIhYRBUUERGRMKZxUEREREQsogRFRERESh018YiIiIQxdZIVERERsYgqKCIiImFMA7WJiIiIWMQwTTMyG69EREQkbKmCIiIiIqWOEhQREREpdZSgiIiISKmjBEVERERKHSUoIiIiUuooQREREZFSRwlKGFm4cCFPP/100N4/MzOTRYsWnZf3WrlyJfPmzTsv7yWlz6WXXkpBQUGow2Dfvn0MHDjwvLyX1ev0e47nhQsX8vnnnwcpotLnnXfeCfi1wf5+FOspQRG/bdu28cUXX5yX97r44osZNGjQeXmvQJzPL6eCggIWLFgQ8OuffvppFi5cyI8//siMGTPOSwwip/PXv/6Vyy67LNRhWGbmzJmhDkFCKOKGus/Ozuahhx4iKyuLjIwMBgwYQMuWLZk0aRLly5enUqVKREVFMW3aNN5++20WL16MYRj07duX4cOHhzr8c9q0aRMjRowgPT2dIUOGsGDBAt5//30A7rnnHkaMGMGYMWPo0KEDP/30E/Hx8TzzzDM4nU4eeeQR9uzZg8/n45577qFTp05cccUV1KtXD5fLRUZGBlu3bmXevHlcfPHFjB8/noKCAqKionj00Ufxer2MHj2a6tWrk5KSQqtWrZg0aRLr16/niSeewOFwUKFCBZ5++mk+/fRTdu3axf33388bb7zBkiVLcDgcdOjQgQceeIDnn3+effv2cfToUVJTUxk7dizdunUL8dYtcvjwYRYsWMCAAQN+03LNmjWjWbNmQYrq3H697/fo0YPFixezdOlSDMNg0qRJdOnShWrVqp32eDid9PR07r//fgoLC6lfvz5r167ls88+888fM2YMffv25eKLL2blypUsXbqUadOmsWDBAubMmYPP5+Oyyy7jrrvu4uOPP2bWrFm4XC7q1avH5MmT2bdvH2PHjsXhcGC323nyySepVq0a06dP59tvv8U0TW688Ub69OlzzvX/5ptvePbZZ7Hb7dSuXZvJkydz7733Mnz4cDp27MjmzZuZOXMm//znP097LITCxo0bueGGG8jOzuauu+5i+vTpdOjQge3bt1O/fn0qVarEunXrcLlcvPLKK7z00ktUrlyZIUOGhCTeYPr5559L7At/+tOfOHbsGBMnTqR169Z88MEH+Hw+/va3v3H48OFT9qUT0tPTuf3227n77rvp0KFDqflby28XcQnKnj17uPzyy/nLX/7CoUOHGDZsGOXLl+fJJ5+kUaNGPPvssxw6dIgdO3awdOlS3nvvPQzD4MYbb+Siiy4iOTk51KtwVg6Hg9dff539+/dzyy23ULFiRXbs2EHlypXZt28frVu3Jj8/nyuvvJILL7yQJ598knnz5hEVFUViYiJTpkwhIyOD66+/niVLlpCbm8vtt99O8+bN+frrr5k7dy6DBg3innvuYdiwYVxyySX897//5emnn+bee+9l9+7dvP7660RHR9OjRw8OHz7M8uXL6dmzJyNHjuSLL77g+PHj/ni3bdvGJ598wty5c3E4HNx1112sWLECAJfLxWuvvcbq1at54403/nCC8uvkrX79+qecsAoKCk5JYK+77jqGDRtGYmIix48fp1atWuzYsYMZM2Zw5513nvazli1bxsyZM6lYsSJut5vk5GT/9nv22WcZM2YMe/fupaCggJEjR9K3b9/TnkDPFM+7777LRx99hM1mo127djz44IMcOHDglKSxRo0a/phOt+83b96cdevW0aZNG7755hseeughBgwYcMrxcCYvvfQSl112GUOHDmX16tWsXr36nH+Ho0eP8uqrr/Lxxx/jcrmYNm0a+/fv5/nnn+fDDz8kNjaWKVOmMG/ePAzDoEWLFowZM4Z169Zx7Ngxtm7dyr59+5g7dy4FBQUMHDiQrl27UqFChTN+pmmajB8/nvfee49KlSrxj3/8gw8//JABAwbw4Ycf0rFjRz788EMGDhzIggULTnsshEJ0dDSvvPIK6enpDBgwAJ/PxxVXXEH79u3p3bs3Y8eO5d577+X6669nx44dIYnRKmvWrCmxL1SqVIl33nmHiRMnsnDhQipUqMDMmTPJyMhgwoQJp+xLMTExHD16lNtuu41x48bRpk0b3nvvvVLzt5bfLuISlMqVKzNr1iw+/fRTYmNj8Xg8pKWl0ahRIwDat2/P0qVL2b59O6mpqdx4440AHDt2jL1795b6BKV58+YYhkGVKlXIz89nwIABLFy4kKSkJK666iqgKIm58MILAWjXrh0rV67EZrOxfv16Nm/eDIDH4yEjIwOA+vXrn/I527dv5+WXX+a1117DNE2cTicAderUITY2FoAqVapQUFDAqFGjeOmll7jhhhuoVq0arVu39r/Prl27aNOmjX/5E5UdwF9tqF69OoWFhX9425ycvN188834fL5TTlgtWrQ45SR+3XXXAXDllVfSs2dP9u3bx/bt28+YnAA89dRTLFiwgISEBG655ZYS87Kzs/n666/54IMPAFi9evUZT6BnimfhwoWMHz+etm3b8t577+HxeHjiiSdOSRqnT5/u/9zT7fsDBw7kww8/5PDhw1x66aU4HI7THg9nsnPnTvr16wcU/e3O5sRdM1JSUmjUqBHlypUDYNy4cWzevJmGDRv6950LL7yQVatWMW7cOF599VVuuukm4uLiuPfee9m+fTvff/89w4YNA4r21dTU1LMmKOnp6aSlpXHPPfcAkJ+fT9euXbn22mt56qmnyMzMZN26dTz88MM8+uijZzwWrNa+fXsMw6BSpUrExcWxZ88eWrRoAUCFChVo0KCB//+loc9PMF177bWn7AsnO/E9lZKSctp9qU2bNnz11VdUqVIF3y83z9u+fftp/9aJiYkWrpn8XhGXoLzxxhu0bduW6667jrVr1/Lll19SvXp1duzYQcOGDdm0aRMAycnJNGzYkNdeew3DMHjrrbdo3LhxiKM/N8MwSjzv3bs3b7zxBgkJCTz33HNA0UG4detWmjZtyvr162nYsCFQlAiMGjWK/Px8Zs6cSXx8PAA2m83/74kDOzk5mREjRtCuXTt27tzJt99+e9rPB1i0aBH9+vXjwQcf5OWXX2b+/PkkJSX53+fNN9/E4/Fgt9v59ttvueaaa9i6detp3+uPODl5S01NxWaznXLCuuSSS045iZ9wukTtdI4cOUJsbKz/S+6CCy4oMT82Npbx48czfvx4srOzueqqq854Aj1TPFOnTuWNN97g6aefpm3btpimecak8YTT7fudO3fmqaee4tChQ0yYMAHgtMfDmTRu3JgNGzbQrFkzNm7ceMp8l8vF4cOHAfjhhx+AoiR2165dFBYW4nK5+Nvf/saDDz7Izp07yc3NJSYmhm+++Yb69evz+eef0759e+68804WL17Ma6+9Ro8ePejUqROPPvooPp+PF198kVq1ap01zsTERKpXr86LL75IXFwcn3/+OTExMdhsNnr37s3EiRPp0aMHdrud5OTkMx4LVvvuu++AombF3NxcEhMTz/txES5Oty+cfKu4E99TtWrVOu2+BHDNNddwzTXXcPfdd7NgwYJS9beW3y7iEpTu3bszceJEFi1aREJCAna7nQkTJjBu3DhiYmJwOp1Uq1aNpk2b0rlzZ4YMGUJhYSGtW7emWrVqoQ7/N4uKiuLCCy8kPT2dhIQE//RXX32V1NRUkpKS/L9EHn74Ya6//nqys7O57rrr/Af8CXXq1GH79u289dZbPPjgg0ycOJGCggLy8/N56KGHzhhDq1atGDNmjH/7Tp482Z/QNGnShD59+jBkyBB8Ph/t27enR48ebN269bxvi5O/2BMTE4mOjj7lhHW6k/ivlz85UTudhIQEsrKySE9Pp2LFinz33XdUr17dPz8tLY3vv/+eF154gYKCAi655BKuvPLK055AzxTP/PnzmTRpElFRUYwcOZINGzacMWk84XT7vtvtplevXqxZs4a6desC8Mgjj5xyPJzJzTffzN///nc++eQTqlatisNR8itjwIABjBs3jkWLFlGvXj0AKlasyM0338z111+PYRh0796dmjVrctdddzF8+HBsNht16tTh/vvv59ChQ/4+STabjbFjx9K8eXO++eYbrrvuOnJzc+nRo4f/1/KZ2Gw2HnroIW655RZM0/Q36wL079+fHj16sGzZMgAGDx58zmPBKvn5+QwfPpzc3FwmT5581uMs0rVs2fKUfWHfvn3cf//9dOnSxf+6ihUrnnZfOtF007BhQ6666iqmTp3K+PHjS83fWn67MnE343fffZc+ffpQsWJFnn32WZxO51nL9+Fm4sSJ9OrVi86dOwNFl0t+8sknREVFhTgy6yxcuNDfKbegoIA+ffowefJkXnjhhRInrJ9++omJEyeSmJhIQkICP/30E0uXLmXkyJFMnDiRBg0a+Ps9XHTRRTzwwAOn/bxvvvmGqVOnEh8fj8PhoG/fvtSsWZO5c+fyzDPP8Mgjj7B582ZiYmL485//zC233MKqVasCjudf//oXs2fPJjExkWrVqvHYY4+RlpZ2StL46+pNIH7L8fDll1+SmJhI69atWbNmDS+99BKzZ8/+zZ8pIvJblYkE5d///jcvvfQSMTExxMXFMW3atIhpgxwxYgRVq1YtcRVGWUxQJHCnOx7Gjx/PsWPHSrwuNjaW+++/n3HjxmG32/H5fDz00EO0atXK8pjnzZvH4sWLT5l+3333/a4kTURKvzKRoIj8Hps3b+app546ZXqfPn38HWtFRCQ4lKCIiIhIqaPeQiIiIlLqKEERERGRUkcJioiIiJQ6SlBERESk1FGCIiIiIqXO/wMYDeD3bFfbqwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View heatmap of correlations between features\n",
    "corr = df.corr()\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize=(10,10))\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The highest correlation found in the dataset is between bmi and age, with a correlation of 0.32.  The highest correlation between the target variable, stroke, and any of the numerical features is a correlation of 0.25 with age. Age is also weakly correlated with all of the other numerical features."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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5eDOqClOw8UADUvQqlGYasbO2nb00mde+6XpJcrgcs5ZHMSGlzLlGY9bIQtwr8OrXfleDddtOYt6YEp6xmj+2FMN7pqIqP1kyMWtzeQGDSjw3EnUEUtepbrBi5aZqzBvDJLhf2FzNPh93TA9eWQaHLxA1Yd1ZkjAE56Ocx8VN55RIzGN30U5HC2V11CAJr0uSuFxiNVqCNBZ0lRB1AzDKf8W0Snj8AV4itqrQjGlVBZj5tx/Y+y6cUA58X8MaA3ZeKSePExYXWh0ezL6wiBfXpylzUlcASHv1JHS0ZmsNlk8dgP31VshlQGGqHj30SvTQKdHojiMxG6GOYHpVHtUYF4dyD2olo+yXfrpfxHeUolfD7vHj+lejE+F1liQMwfkm50vv2nNdBPNYmKbD4okVkAWDSDdq8Oy1A0WQwk5nHI0SyuqwQQoCIwqSsPbmYahvdyMrScMqYyCcIOVy9QhDUlKOBg11M3d0CRAMwqRR8RTarN8U4U8hr5zcY9HHe7B86gDMefvnKPMqw+AcIz64dQRq2py8OaizuLBmaw1eu6EKXl+ADVeVZw1i8gAS3jgxXK0OD3qYtUjSKERzrpBBZGTmjSmBgkLwJkWHPfvCIpGxKkzT4baLi9n50KrkuPuyUqy9eSgarHwUVJ8sE5ujINft6rakCUNwnsl50bv2XJEYIZSEiviPb27nKf6PbhsZOpp3Ie10hFBWh5PfcuCrI+EqVqnGJtyYOjd0JFfIsfeUBe1uP5s/GJxtBIIM6oagiMjv3912HCN6MXz+XIUWkFDIchnCdNERCr3C0FO9yCC2Ojwwa5TIMIfmLRCex2M2j2ToiFw336gKh3o4c1lvdWPN1hr2+YJBYM3WGvRM08OfrOcV3kmFf2SysLH666wqtLu8SDeKjfHTXx7EW7OHozhdz8utdEcjGqEkNMd5JudFs3fg7IWvQve1uLyot3rxwIfRO0RJURHHnBzuqmftYPL7hNXLg3FKNTYhTq5U6Gj99pNodXgwb0wJeqbokKpWwO31UU8Ebq+PhXUShbZyxiDqfbOTtMgXKOGIDo5OGZdBbHV4RbUPD4/vh3Sjhr2e1BymGzVs7J475t2nrLh73Q48enUFnt90KGLimeQqWh0efH+sBc9trJZU7jaXF1qFmpdbKck0oTBNh5pmJ++6XdkRL1FHcJ7J+dC7tjuabUe773+OtLJGABBj2rnzKFXcEwv7aCAQ7NpnjZXtkiPCKlbS2ISLQ7/vij4Y0ycTa26swtPTBorw/wRbT/6/yeEFAKiUCpHRfG7TISiVCvgDQV5sfv22E1g0sZx338UTK5CfJE66nrK6qe+gzuqOm8kzVa8S1T6s3nIYSVolqC+GU9vg8Qfw4JVlvDHPHc3UBri8ATz0j92YNiQfQDhXIVVHMG9MCd7bdhJA2BhzhexbEm4jdRH3vb8Dt13MsLxy35c7EMS201actHs7XXOfQy5kQmKR86F37dkKX3HvG0m5CyGUJZmmDieHpRq3nJVQXehkkiSghaizuPDutuO8BGleig5EL7U4olMlWN2MorR5/dTvNljd0Cj5c9gvNxnvbTuOJ6cOgNPjg06txJpvj6A4wyBqC2nQKKjvQKtSMEgngwYZughoMDlzEqq3ulCYqhdVOC+aWI7H/rUPBxtsUQn37r6sFPPGlCDVoMbxFifWfhcms2Pi/QbMGV0MAPh0Vx0vV0HqCISd0NZvP4m7LyvF018e5O1bhVwGuUIuMq6LPtmDl2cOwY/HWpGsUyJVr+YljzujPwFXEobgfJNzpW1iBIkFH98VoRThfaMpd48vgNVbjiBFrxYlCGM1rg0UT7arKYOpwlFotOe5bmgBS8yWY9bCeEFPzHuHSYY/MK4PXQkrw56uQa3E98dacEHvNOp3/UFgV61FRKNQ2+bGgdNW1qjUtrnx1aFGEX1DukGFhRPKsejjPTyE0dEmGx76aG9kUISA2bMwTYd7Lu+DWy8uQiAIyGXMu260eUSGmua0PP3lQcy+sAh2j5PaEOdQCAJKYJ5alQJeXwCADKkaBVLVejS6/CL+oxS9EsunDoDd44NBrYRKKcPX1U1INWqo+2XHCQtWbqrGc9cPwlOf7+flZVZuPoTe6QPDIbYzlIQhOB+li4tLzlQiol26EPXEve/67Sfx53FlaHZ4qEVTXAVAEnu3XlyEAblmhqwsRuOaaeI/K8Gw3/TGtm5FdUk9T7+cJBg1Stz3ftg7/d3wAraiFWCGRUPKACScU46HPtqFmmYnvjvciEUTy7FwA0dhjy/Hq1sOo9Hm4V0nSaLAjAi3D3GyXoX124/zkrSrvq5mwzCRTlrCnMj4ylz86f2dovXHrTwmTolUSEohB96jwFS5je9pMM/Hr+mPLJMaZp2ahzy76TeFaLZ7sWDDXj4aSQYca3ZQ94svQP4OUvMyrU5PpxmCRI4gIZ0ukbhRzqSLVbz3dfsZj5/GPyM8PdRZXHhuYzVUclnMsXgAyE/W8e5Jw7B31vNFEqnn0SvlMKgUPO80Q+CB2j1+FilDYuprttYgL0WPNTcNwwtfVbOJy201Fry37ThWzxyCZ68biGevGwS7x4uL+2RiypA8fLqrjoGHji5GQapBNBfPbjwEfyDIFlGt3nIEt6zZjt//9QeMLsvGBz+dxMpN1XhhM3NPF+edSeVthDmRaB3NuCdDEpLiilYlR3GmCVOGMPxD88aU4InJ/bF86gBezwMag+gDH+7Cf460YvLLW+HxBfDRbSOx5sYq9Othps5FTrKemsNZNqUSv+2XhTU3ViE7SUvNy5i0nWMEgMSJICFdIRHCV12KeuLc1+L2sV45uUdH+XYiiVwuw4j8MPVyklaF90KUCzE9XyeFySI9j8XlxeIJ/aDXqGB3+1CQRodjCpEyuWYtWh0eHnoFYIyB2xtAj2QNth1rE3nMJ1odWLmpGnNG05EyLl+AitTiFn2RMXCrhadV5cETCKLR5efNk5DZk/yWhuYhlcTeUK+FZL0a88eW8jh/5o4uwWOC/gYv/W4Qsk1ankEVGhxCg1GQomPJ6l66bhD6pumxo9FOnQuH24c6iwuf7a7Dk1MHQA6ghzlM352apsfP9Tbqb9vsHsDYOSCRhCFISNeIRPjqjBVwNMUZum8sfDudkXQPBIKSlAvUNokCSoITbU5R4VpHwkiRnkellOFYq4ulXChM0/HCOx/vqBWFe5ZMqkC+SQUZ6Eo1zaCGVimjerkrpjEngj5ZdBikXMbkOKVCMuR7iyaW48WvqqN26MozqbBkUgUbHvp4Ry2enFKJ6kYbGxYsTNOjwerGnFHF0CjkuPaVMBnck1MrMW9MCeweP8qyTFj2+X6eEdCq5ChM1olgrMx1dRhfmQuTVgGTVoUln4RDP3dfVgq7z4+mZg/SDGrqXDTa3MgxazGuIodXbMZ9vgyjhr0POdV8vKOWpQrpDEl0KEuISLp0ns6wi1isv43UNpHO7NmxpLslAIxf+R/Rfah89pCmJOAaDUnEUTQjKPE8wu5YAKPAlkyqwA/HWmFQK9A/z4RAQIZWuxcpBhXksiD6pOhR3erEzyfbRfHpih4mOLwB3PbmT6Jhzh1TzD77wgnlWPV1NdtSkvQYaHF5cdPr20TzxkU2lWQaUZpuQIvTyzvdUeeJgxrKMetwsN6KBRzD9siEcrz1fQ0uKs0UJYAL03RYPnUAmmxu5CRpUW91SxtnzhznmDX4pdaKhz/ajdkXFlETy9x18OTUStS2OmH3MIV6xZlGLP/8AMZX5lJ/yz6fAth8uA0LNoRRUIsnVmBU72TAH3F58iTRoSwh546cAeopHtpmi8uLx6+pFBWU8e7VCUl3GmrI5Q1gQK5ZVD1LY6SMSPXAlVgat+gVnO/LcMrhwymLE4BMNMaaZidsLh+GFSTDrFfj+2MtvPDI/LGlyNBroBVUEgeDTCVxweVlCIJ+WvAHwvOw6OM9WHvzMDTbPcg0asIhD434BDNvTAmv5SRRhl5fIHo4MQDkG1TIN6hwxOpmjQD57iMf78GTUwegusEqCudcK+BDevTqCsy/rATtbj/kMkCt5OQQuJ3S3D72FCKVl1DKmd+m6NWobXWKTjWrZwzGkRYH9bd1Vjea7B4YtSrWCJDPFmzY3akQ5YQhOB/kf41kroMKOF7a5sI0HV7+/RCGP6iLYLZC1BAg3SYxWmtD8ltamEzKCBIPWiEDemca8dTnB1DT7ERVoRnzxvSB1e1HdpKGGpYgFb5H2t34+/c1PHji37+vwYBcMwKBgAixcvdlpVAp5XB5fFgwvh8Wc8IhhOWT+3zfVDexXjG3P3DvdANL4yHE3ZPfkhAaNZwoER9vstHnWQ7g4uJ0Xv/fyYPzRJQZz286hPGVuXhhczUqc5NQmmnCD6es6JGkRV6SCo12Zi+6/AGWUqNPFr0WpWe6gb0PDUTwwa0jUJgsztloVXLsqLVErEpOtKr8NUmCZI6VeGmba5qd+OOb28OeU5zNaWIxvgQ1FEuuQYoeuiyLYaRUyICB+WYA4BRRhdsz0pTBQQ6mfd6YElw3tACf7qrDlMEF+MPaMISVxNtJiIbkABAAnD4/FZ7o8vmRodfwFKVWKYdOJce8d35mje3T0wfiUIMNRekGanydnBBS9GocabJTwy6NTjHunrxbbyBIhXG6/UHRPAFMspW2TnqYtXALWm+atQrqs+cma/HAuDKU5Zhw8HQ72t1+HKy3oneGEWu2HsW2GgtevWEIm7ug1W7MHV2CU6EQa6QCRxoF931X9IHHF8Cc0cVnVPAYqyRyBOe4NLp8bJcjIhHjyJ0g5+w8RTGK+5odmPX6NtHP1txYFeYMElyPxiVPuktFC8M0Onxoc/uRYVDC6g5IN2yX4D+i9fgldMrcmDpRlLSchxBls2zqAAQBHusn+eyNm4ahxe5mxsjxbLVqJWa99gPl+0NRlKThzfncMcX46JdaUeJy6dX9ka5XieaNnBDqLC7cOapYOhauU0q+2211Viz+ZB9LZhcMAh/8dBLTqvLofYXlwNfH2vDQP8LzuvSa/sgxqaFVKfGXT/dieFEGZDJgaM8U/HHtdmqugjCkkjwO4V0qyjDij2u3Y+3sYfjDmnDugiCb8lP0ONbswCc7azFpYC6e28j0H3h5i/jZ37llOHINKvx02sY2CjKoFchO0uLPH+5i18ldY0pxtNnOq4khRICxSiJHcB7LeUMy1x3SmbTNEbjkZ40sFDVYWf7FAVw3tABPfHaAp8BT9OqIiBbhfaoKzfjbDUNhcXqojJQk8chtufjBrSOQYVBi8cQKXsKQFoZxuH3s/3PF5Q2gyebGsBwTM6bj4THde3kptcWkzeUDTBrenAdlQJpezSvUu/k3vSBHEKlqBQ9Km2HU4J73d7AnhIi0HzoljFoFr/LWoGHi69kmLdTKcPxMJgPUShkvF8HLFQUBk0bJqyy2ubyY/+8DaHV4eMb2wSvLqGM60mRn/5+bx3l24yG8OGMwAKC+nV+/QGo35oxmDB5xHqryk2HUqkStKeePLUUgCFS3udhTEgDcOaoYz2w8yK4/o0aBFocn9kZCHZBfmSY5/6S7mqScn8InEIsHEhqJS75HKIEoDBf0MGsB8Ju0R4r9CikMcsxajC7Lxs1v/Mh619FyBkRRuvxBvPDVIVY59O9hxtJP94rCMHaPDxkSuPpMiZaRSoWMWgGcF3pebk6n1uGFw+vnKaV5Y0qgU4u7lwlPPKRhO20tt7j92H/aJhpDDxMTl7/z0hKeEVw4vhxv/8A3gsQ5anT6eIqV3Ico85WbD7EoIan8g1tQyMblXbJ7GKiORknnRxrZKxVX9c1k111qmh4/1FlFrSmTtUp8dagRuWam7mD9dqb+xCQIV80dU4zVWyhr7I8jkKHpHBWeqCw+VyXEiGhxefD4NfQq3V+dRGM1jYOlMlLiNtmgphY7JYeML9ezjcZcGonuWoqRkhusJYqy3upCTbOTUWSbqnGkyYbrhhbw1sWDV5ahJMsEmQx4ccZgHnvloonlkMuAbaetqG3nI518/qBkBbBQbB4/9bs2j5+ao1m5men3+9Ytw3FVvyxeFTZJ5jfZPWhz+/DOj8dF121xetFo94lQM4s+2YOLSjNF8xTp3WqUch5K6Pa//4x739+BhRP4DKmEQZT2TrQqORqtjPF949sjWDyxgvfbv1zdH3JyFORIj1BxHnl/CrkMbS4fVm85gvs/2IVXvzmCmSMKkWPWIjdZL1ontOeptbhF76ej8ivUJueBnAX0y/kgMcFHY0QkRWqW3igBCW20unnfJd+JdGLj3kdoNAi1APfkwW25yDX6bj/fy7d7/Fi//STrYRrUCji9ATZmTa6lVsqgUyrh9Pnwu78yYSghj74rFnhmSKwuH/W7NrcPAb/4OjXNTjTbPfAHgzCqFVArGWy9Xq2AWafmNQO6+7JSvP7tMR7Tp8Pjp8JHU/Rq9M0OJ9m5PFImAfsqmcue6QYqSmj9doYh9WC9FVqlHHoOJQc3ZMjNFbHQYIMSb80ejgabG1qVAgs37BbldxAE8oz8ordeaQbcte4X3lomldU1zeIqZNrzmDrRGUwYgnNQOhX98j8knZkvoYWRyIa/YWQhdeMRQ/Dxjlo2Xr9+u5iYjBuO4t6HXIdcl9BDvzV7eIiWmd9ykWv080wqPDm1EtUNTLVsWbYJaqWMTRQLE7EpejVOtDqQa9YhRS/HXz4Nk8wJ2yhGCtkIJcdMN6BZJg00cjkVFeX2BbC/3gpfIIhl/96PmmYn7r28lGUaJe/x6S8PYs6oYiz//CB73RS9Gm4/v48wrTk9N2buEiCDuAgeKZTQqTaGFoOMmVBAa5VyKGTAo5PKeWSEqQR84GfqF7QKGQ/UIXJSAsClvZLZ/IlMTvfyC1J1MGiUvOddv/0kHryyDE32cF4mVa+GyxtHNVkUSRiCc1ASCWK6dGq+hJJ4JlzyWSYNcpN1VNRQebYJRq0KSz/dy/Mq540pQWmmkVEWeiXbzjDdoMGIAiaB2ur04LFr+rNoEK1KjnvH9uG3TQwITjQgIUIvggHwYvPcpKeCo1gIoZtQ2RH0DmEnXT1zCOxuH3qYdSjOMPKe9/Fr+sPi8gKQ8aCZuQLPlowjz6QCguB9Vpimw22XFPMUNhlHVpKWusZJHkOrYipxvz/WApkMvHoFKWI/onTNWjW1CO7xa/pDIZfzEFLEE18+dQB73+uGFuBAXTsLy102pRKVmQZmDiScsJj2LKfobX+ri7qWj7c48cFPfEOtVsqgUSl4737+2FIY1Ap0lvx6tco5LIkEMV06vSmPMIyEsKcnhU5KDRGIbauxYFuNhXe5v84awsAgIyRM4wrzcUKEQgoDl5dBGK29eRiabG5kGDWsooiF0I2gcPbUWbH/tBVDe4a91WSdGk99sR/baiyiEEej3Ycv9p7CyzOHoM3uRUaSGkcb7dhx2o40vRrv/hguTBtZlIrZAuI/Mo40g5qO9U/WYc2NVWy1M7f5+9PTB0KtkCGIKE2H9AoqLLc4RYud9XTyN7fPjzmjiyGXAUUZBgQDwBOT+8OgVcKgjp5KjXfPmrVKat0BMdZrttawxYLDe6ayNSFkvCu+OIg3Zw+POq5YJWEIzkE5Z7qQnWsVzd3ZlCdCrkGqm5ZerUCj04flXxxglWGfLBOWf76/Q2E+bohQ2P0L4FftEu6ghz/aLZnALkzVYc7oYhjVCuQka3Hr2nB8PttcjlVf72Hj23NHl6C2zY06i4vnbVtcXgzMT8Mf126nQme5yqxP1iDqOBRySBaJqRQyuP2Aze3j9UyoaXbi7nW/4LUbqmDW0HMARq0K+5odMGlVPIOkVcrRandjR71d0gAVpOqhVyuQadJi3+l2PPrPfawBemBcX+xssCPHpJXcAxl6BS90Ryq9M/TKcE0Jhw8pO0mL/nkmLJ86AP5AEAq5DKu3HGbzI60OD/adtuKFzdVQjy2lGz6bG4WJxjT/wxIED49NLVLqajmbFc2RDNA50JQnVauiKrFUnQotDo8oBv3ncWWwun0st/767SdjCvNxww290g1UBUaw9FyEjtvvx6vfiL97ss3Jq0JO0atRZ2Gw8Is+3sOrX+CeILjetkalZJ+NBp3l/s7i8FBZMytyzAgCMGn5WH+TVokdJ9qw9NMDeP56uhGxuf0oTtaKHKUlkypw93u/8AwZqbG47eIiNDs8+OWkBUkaBR69uoJXbLZkUgXufX8H+1syNwBwbVUB7l4XgR02pNybHR624x03fNOSbkCqWiHqoqZVMcRxL3x1iHffRpsHrQ4Pb/wef4AOB+5E9tGEIYgmZ8MrFuCxu1UJh+Rs9R2OaoDO5H101m+NGpRlG3lKrCjdgFSNAnavkheWSdGr4fD6sTKkUMmGzzJF38TccENtm0OUACUGhvTPXb/9JFodHuhVchHhHrezlstLJ7vTcMjVXN4AeqXrMWd0Mb473Mh626pQArfO4pI8eSjYywRx2yXFovaTp9udeH/7Sdx0QS8AYSSWQaPEuh9PAABUCpkoQTytKg++YBAnbV6MKAifDI1aFWsEyBiIQTJqFKK6hwevLMPbtwyH1eml/vadH4/jz1f1hdPNkPZxDeZ963eyyf0cswY7Tlnx0D/E7KMkfNO/RxVS1QqcsHqxcvMhXl7pha/CnEbknbw8cwgAYMFHu3n9joWOx8IJ5TBpOg/9nzAEkeQsecVxK+EuMFZnK2Ed8dkj0BB0JoV1rL/9bb8sNDt9SNYo2BCVzeXlzRvNa3524yFc0jst6lxk6BR49tqB2FPXjtxkPZZz+tb2zzWjptkuMjCHG21Y+ukBXi5CitBNSHZHCNLI3wa1Eh/vqMWdo0pYGmuhUaF5qhcVp6NvtgkmrQq3rOHHthd9vAfLpg7ARaWZbNKc+1sC45TJZGzDGFoI6tGrK1CSYYTd64dCIYfHx3+JxCDlpehxH4duw+UNYOmn+/HaDVVswxguGR+pM/gTJcFNjEF1ow3HW50Y2SsVz4cMs5RRtLkZZE+rU3xSnDu6BHI5//unLS68+FU1L8fR6vDAqFHi6ekD0e7yQqdW4tUth5GbVIpkGnVKByRhCCLI2fKK41LCXWSszlbCOtKzA+jw+4j6LiMY00i/HVqYwvAySdBcRKRViGENkXCDUBneP64P1cDMGcWcDri5CMhkVEI3Oad149zRJTgd4pciyt6oUeDey8tEivTZjYdw68VF+Gp/g6jh/OKJFXjis33YVmPBk1P6i549Ra9Gkk6Jkkwj/m90MeQyGVupu+VAA1vherTRhoI0PW69uAglmSbRGB76x24e1z+tr0Nxpon9vnD+HaF7mrR8qGa0RDs3xPaqIARF2y9JoXecpFVRr/tkCK1Evt8zzYCnpw1EfpJK8sRDvtuZezHiSvR4xP1BiajV//sIlrPlFcejhLvKWJ2thHWkZz+T9xHxt1FOGpF+WwK+COctKkY/RgPEbUg/INcsiZyh9fjNMmlEoYX5Y0vhDwQxZ3QxC6+85/IyzBldDINaAX8giFNt0uGf/j3MGNkrFQ98uIsH03zhq0O45/IyjOhthUmr5OUIdCo5TBolS/JGFPj67SehVspEUNP5Y0sxpk8mTrQ6qWMghc9c40QMA2k3+fsRBdT5T9Grsa/ZgSSditeqUiGB75fJwtXb7a5wOO7dbccxeXAetThw4YRypOqZddlq91D5nI4329kxzRtTgr2nLHji3wfY9UeclHvH9unSvRhx94wbNw4ymQxCglKZTIaNGzd2zgjOYTlbXnE8SrjLjFV3InQ4EvHZIevw+5CigDZqVdjRYEd1o00UC47U35j89seaViRrlGEFLpi3LJNGhNHnUoTEY4AIqRlT1SpdGS2cm3qrm21OT1A0GoUcj32xn6eQH/vXPgDAzBGFbMhJWIVMrmvUKOD1BVjaC664PAzxXbPNLeIIEiapSa4CgKjAbMUXB/HaDVXIlaCV5j6ryxtARY8krLmxivWg6ywubDvagsUTy3ndyhZPrMDST/diW40FD17ZBxqlgs33lEr0FeiTZcJz1w3C0SZ+OG7u6BIo5eHiwJUzBsPp8QGQ4Y1vj2BATjmgZPpW0PicijKMrDFes7UGU4bkiZ25btiLETXFpk2beH+3tbXBbDZDxg0u/g9LVIXcVYnkOF58lxqrzkToxDpXEZ79TE4pwt8Wpunwf6P5sW8uxQHXmNJ+O0cQN+eF4wTzJvU8tI5l0QwQebe0uZCipwBkPPZOly+Arw7U8mgVDGqGVmH2hUW8EMa6beKq1jSDGqk6FfwB8WmnME0HvZp57mSDBq//9wgvQfrOj4wHzU1ScwnduEJCOMUpWlERm9Az/3hHLVL1ahQa1TwPevrQAjz+2T7RqWXakHxsq7EgJ5mfQ8gxa6n4/qX/2ocHr+qLp788yHtfz206hNduHIonp1YiJ0mLZzceYOsv5o4ugcXlRYZWCV+Azuc0Z1QxW9GsVcmhDSXsIztzna9/Y3IZf/zxRyxatAh+vx/jxo1Djx49MG3aNOp3vV4vHnzwQdTW1sLj8eD2229HcXExHnjgAchkMpSUlGDhwoWQy+VYt24d3nnnHSiVStx+++0YNWpUpz6cpHSCUuryRHKMSvi8qDmId66knp32PgRVvLG+S5NOhetf/Z63MbkUBzxjKvitUavi9QCOGo6TeJ5op7lo71YEMU5SUekpaAVWC8eX49Uth7Gzth0AowD/OqsKrU4vL4Rh1Cjg8Qd5qJtHr65AqpaZZ6GBFIZ3Fo4vx6ot1TxYJ0EnESRQrlkHvUZJ7aKWY9KI6BnSjGocaXRg5eb9vPt4/X7R+6ppc1FPLRkh6OXRJn6BGQnBvXZDFdqcXuw+ZWUTxcLvkve19UgzLyRF6i+e23QIa28ehn3NDrj8QepvPSH8LzkhcJ+dGz7satBKTIbgmWeewZtvvon/+7//w2233Ybrr79e0hBs2LABycnJWLZsGVpbW3HNNdegrKwMd911F4YPH44FCxZg48aNGDhwINauXYv169fD7XZjxowZuOCCC7o+99BJSumswSsp4zsbIRyeRJnTTp0r7vuIF2bL+e2ORvqmzgjREkfqb7yvmd5jNt5wXNTTXDRHROLZRWvVEe6tS8a66JM97IkAYDxqnYoplOOGMGgUyA/9I9QvV69EUAY2rFKWZWKNAPc+wvqE1TOHsHxBXM9biKtfMqkiXJAVBLQKGQwqBYJBGV76ulp0n9dvHCp6162CZDCZY32IvpmG0VcrZdAoFVAp/DBrFfjd8AK4fIGotRzk+bjP+/3RFiz//CBeu7GK+tvhvVLxxJT+0KuVeGXLYVzcJ1O0/rpD18QERJXL5UhOToZMJoNGo4HBYJD87rhx4zBv3jz2b4VCgT179mDYsGEAgIsvvhjffvstdu7ciUGDBkGtVsNkMqGgoAD79+8/w8eJLlKT2uj0xXWdaOiWbpXQou+bpj8rpHTR5rSr5upM3iWpDuaKViVHj2Qtnb46RAtOKldpv403HEc8fnItYf4AgOS7jefZpea/OtTm8tVvjuDOUSVY+ule/HyijRfCkKJArrO6ccTixp/e34nnNjLUyvvrrdTvCvsrWN0+/OXqClGoZMGG3WzCevaFRVi5+RAaHT4R/fgNr/2Aa6sKkEP6JYR+3+7yip493cAU/3HneN6YEtSFEFKEQJB8Xpimw52XMmG/hz/aA38QWBmijl7++X4R7fTdl5XCqFFgzuhizBldjBS9mn1erUoOZ+j5jFqFiO564YRyHG204f71u3Df+ztwZf8cXFicJlp/3aFrYjInBQUFeOqpp9DW1obVq1ejR48ekt8lRsJms2Hu3Lm466678MQTT7B5BYPBAKvVCpvNBpPJxPudzWaLOhaFQobk5I5hZxUKOdrcfuqktrn9KMlOivlaPSjxUa1Kjh4p+g6PjyuBQBAn2pxosLqRadIgP1kHubx7cjMKhTyuZzhU0xpxTrtqrqLdN5JkBUGtDs5N1iE/RQ+/P4D9DTacbnchL0WHmmYH7nlvBxsCEXYLWzKpAkUZRigU8RX5XG7Soe+cC+N+z/E8u9T8cz3ZBR/txpxRxTBpVKLr0n57sMGGVL2aF0aSauAuTGAnaVT4/lgLdfzOUIc1mQwwa1Vo9/hx2uYRJfMJ9JJ7oskyaUXrydrqgEGtEFUwD8hLxlu3JEOvVuDRf+5hcwilWSa2hkBYA1LT7MQLXx3CO38YAYfHD5VChn117Vj6KT/pTp5zyaQK/GvnKQCA3R3Aqq+rebmKVV9XY9qQfAAMrNbp9cPq8iHdoEGSKbwOulrXADEagkWLFuG9997DkCFDoNPpsGTJkojfr6urw5133okZM2ZgwoQJWLZsGfuZ3W5HUlISjEYj7HY779+5hkFK/P7gGfUsTtbQj4rJGgVz3RjzB2YFqPFbswJn3u/3bNI7IP6exdHmtKvmKuq7jCAmOVMNLKwONsmBtnYHjw6ACY8cESkEooiCQWDl5kMozzJ26KhulgNmMxOzbm938j8U8NMY1XLUWdySnPu0Z6fNP63NZbZZK+q6RatqJcou2aDG7ZcUsYnkujYHFk0sx0IOQmfRxHK8+FU4GbpwQjlSDSpc0DuVSoNRa2Ew+iTfcOPrP4rGzBZ2hU40WpUciydVoIdRGX720D6uaXPC6vZBp1LA7vHDHwBe2HwYT1xTgb5peuxrdqC2LVzd7HSH+y3QoLM1zU60Ozzom6bHIYuLNQJkDp/deAirfj8Et15cBKfHh+uGF2LzwSY0Wt10hJUvQGWL5e53sxJUxlezMr79c8Y9i+fNm4fp06fjuuuui4oYampqws0334wFCxZg5MiRAIB+/frh+++/x/Dhw7FlyxaMGDEClZWVeOaZZ+B2u+HxeHD48GGUlpbG/FAdlcjwxDgUcBfG5s+Z/EOMEjVhHe9cxWiMzyhRHgQGZxuRb9aKxnTC5uXF1GnhkZpmJw7WW1nEB4DOry+h8NMsnFCOt7+vgcXlFZGcDcw3AwD2NTv48yaYf61aifvW7xC1ucwwabCvrp2n+FsdHhjUCjw9nalw5sIcm20u2D1i+oYXZgzCzycsKMsyYf32E7jn8jI4PT7o1Ep8uP0EeqbqgGAwIg3G+MpcEZxUWNglPNFwiwOF+3ju6BK2FWRhmg46DZPrSdKrcOeo3izJnBAuGymH4/DQowv1IZivViXHqt8PwZzRxaLCNXKtYJBexMZrd+rw8egpwo7HoE5bbzFd5bbbbsOHH36IFStW4LLLLsOUKVMkw0OrVq1Ce3s7XnzxRbz44osAgP/3//4fHn30UaxYsQJFRUW44ooroFAoMHPmTMyYMQPBYBB33303NJrOI1GSlAhKKRqcj3atriBAOy/6EQiU9Yj8KIo+1rmK5zR0psZYYkz1Vpdo/mMJeXR2fckJq1ec5P2YSfI+9q99OG1xsUq4ME2HvBQ9bv/7z/R54zzr3hYHle7A4vDgjW9rcPslRbyTEgAs/mQvr2o3GARS9Bos/mQfb3xLP92Pp6YNwMpNTBP3rUdbsPlgE4BwQ5mbQtTUXBoMyGW4f/2uqI3uSWEX7UTD7Vks3MfPbTqEZVMHoK7NgSSdGjP/9gPPAJGwE7dpj9RpSBGalHQJJlOjVok5o4uxfvtJWF0+9oRDI7tbufkQJgzIjbjfm+we6mmiM/VBTFfp378/+vfvD4vFgkceeQSXX345du/eTf3uQw89hIceekj072+++abo36ZPn47p06fHOeROkA7C+bpLzvl+BBGUtaSiF5C2KWRAvdUdF51DdxnjbEHzd5pCkMTsd2J9Cc0gubwBOD0+TB6cx6NpHl+ZKzIavHnjhJgyTBr8cqJZ1LjlqWkDsWB8X/Qw66CSA002xriesLh4rRvJs5f3SKKOj1T8Cqttp1Xl4Z0f+W0iF2zYjZeuGwR3IAi1UoY7RxVHzDf0yWKom5d9vl90oonWs/hAvRVyGbDiS/5Jg0vAR+Cjr8yqgs3lxbFmB+aMKobLF2BPQ+XZJqSm6eH0+HD3ZaVsbQExFI/+cx9aHR7MH1sKq4uZt5pmJ57fdIjajc7i8VHDZLR2p7TPO0Ni0m7btm3DBx98gF27dmHcuHG4//77O20A55KcKwo47pBHNzOkdoQU76fTNuypa+cVJb309RG0Ojwx0zl0lzHOM/G7cLU6PMhN1mHemBLYPX4Y1Aqkm1S47/Iy2N0+fvOSTszvCA0SwKxHnVop8pgjchrplVQK5HXbatjip0evrsA9HBpn7pgzdEZR7UZ51iC0uHzU8SlDHjO3FafLF4DX74dWqRCfRFxeFIfQOiQBT0vIL5xQjto2B+QA/m90Cc+7XjalEgq5jIfqop3gApA+aRAhRq8wWY+71+2Q1AdmrRpv/cBUbBel61HT4uDxHa344iDuvTwc7vb4gnD7SHtJWdiJ0Skj7vfuqBWSBYX8ERT5v//7P0yfPh0XXnjhWa8q9nr9Z5Qsjvjbs5ykFY4lrNwjhDy6YMzR5mlfswOzXt8m+vc1NzKMjkJp8fix6VATNeH4xGcHoFXJw/FQlx+TV28Vbb5uz49wPOh0o4blqweA+8f1AQDR84wuSYc/EOT1rj2j8UfIEYwqy8TLnAT2nNHFePWbI6L7vhXqYsUtgCOfrbl5GJpD3c0eDxHFxTTm0Nx4AgHsPGlhTyZaFcMPhGAQSz89wFuLyWY9dtVaqON4a/ZwUc9fgIFy3nd5GfbXW2FQK9CvRxIcbh9ykrSwebxoc/hZQ6xSyLD0X/tQ0+wUdYXjhpKmDMmjzpOQwO6C3mnI0Smxu8kOuzvAM/gV6QZRUemiCeW4/4Ndoql6YnJ/3P/BLmrdBI1evc3t57HZshKrPoggZ5wsfvrpp/Hhhx/i+eefx/Dhw1FSUoLU1NT4RnE+yLlQnMUZSywhj7ORWI735NTi9FLL65eFmBe5Hv85UykdCPeX3dfs4FW80qiNn914CAPzzPD6Ap13ognwK2qzTFqYNHLkJpWKOIw+3lGLhePLseiTPTzl5/b60ObyUcd0rMmOP63fxX6XVMRGHDPHOKXo1aJ8Qo5Zi4IUPV763SBkm7RML+PQrYUU3eQ+NpcXNtAT8vvrrVi//SRmjihkyeoIoojLejpvTAmuG1qAJz47wGvS0+rwYEethUUbSYX5Wu1utlVlXooOOSGyuCYOcIB8FxkQJeGbnV7qnjCECtei9Vkm+70kO4nHZstKF+UjicS0MhcuXIjMzEx8++23qKiowP33349XXnmlc0dyrkgXT3inCMc7kCpdr7O6u8wQ0JT1s9cOBEBBrACwS6ArHCHMOM+InEvGOCRCw+dw0xWrw+NHjkQ4Jxa2UanPiUEikkzpq2zUqrD00708YjmlHLB7mWppGn2DNsQJRJKp3EY1Uoadm8Cus7jw0tdHMK0qD/1yTJBBhma7G9e+/53Y66XMI7mPSimHVklv/0lD1dAQRVzHAmCMiM3lRWWmAQ6Pnw33tDo8KEo38NeXUYljbR7Ut7uRnaRBoVkN+IETdnGy/uGPduOt2cPD7yOkLxRyGb31plKOOaOLkWvWnfWQZySJaQTHjx/HX/7yF2zfvh2jR4/G6tWru3pcCZESQSho5YxB1A2kVyu6bgxBMcPmwUY7e7QXHnt7JNGVY6PNTff4zzFjLDR8rQ6P5Jwr5PSaiZjgyWcQ5jOo5LhhZC/ct34n27tgxZfS9A2Ea4iIyxvuLEbi80zlqoxnrIQJbMKI+sSU/jje4hR16SJeb7KZ7kDMG1OC+e/thFopE2HlCZyUMHISkcqHEMeCvI90gzqiY8FCTWvocy6VrG+wuUMJ37AhT9UoRHUpepUCj2zYgzqLC3NG01lcJfOP3Zz3i8kQ+P1+tLS0AGAqhuXy+KonE9J5IgwFnWx1SPbP7VLhKOtosNsMrVgBPD65EllGFdt5LJpXfFaNAcXw5SbrqAqt1eHBs9cO7BA8Oa7mOUYNTrQ5Me/dX3insg9uHQGL28dCNMl1FmzYjbU3D0OTzY10owZPfLaPJZwDGKV0aUkGRvZMQb3Vgz++uZ2nGEszDKi3ulkuplgS2ABQmmmE1evHxgMNyDJq2BaTdVY3dtRaeMlVEs6xubww6VTYVWsJJ245fQ2kEEUtHGSTsI5FyrGQeifv3DIcSRJJ57117Ww+gWuouXUpXCpsgI48kwx5noVcZUyG4K677sL111+PxsZGXHvttXjwwQe7ZjQJiSpCVM0b39bgtouLqP1z4140IUVzSMixH+eYAMGxN1q4R2AEzpmEPVcEyoQ8z2mbB7+cbOMptHnv/oIPbh0RTpzHCE+Ot3mOkNuf3FcqT9Hu9MKgUsCgUuAPF/XG7lO/8OY436hCo1OGm9ZsFylGkkytKjRT0TyvbjmMUWWZPMVZmZuE64cX4obXwtXBSyZVYGCuCR5/AM9t5OPiSTiHVPy+9DVDYZ1pUuOOS4vZimUaoogYq/JsU1zhxJMWN3WuTrS5sPRf+6hOFrf3My3OT2smQw1JSYyx0enD8i8O8GC2y784gJeu67wCMqHEdNVhw4bh3//+N1paWpCSknLWkUO/ZhHGWessLvzt26N4etrAMD65I1W7Z6CAY0oeRwr3cMZk1KrOjarqaKeS0PO0uf0ihSYV+402T5E+p50WaA3oyXhp19lRa+F5sh/dNjJUyxFeM1LGiNQGMMiiGvZ0IUxg98tJYmHCw3qmUGPsy6cOwP56a9S5aHV48MLmatw5qpgXciIUHzxMfmj8qQLjG+1d6lRyepJXreB1heuXkwSjRon73uf3fk7Rq2Fx0+tjRhSEacK5ifNoIU+Ly0sv+Av1NugKiRjjufbaa3Hdddex/91xxx24/vrrcd1113XJYBISXWiMlfeO7YN8oyo6+6iAxXHy6q347mQ7u1E6yuQZE4umlAjG9NWhRkmvuNskwjwJJdOkYZ+biFTsN9o8RfpcSkELG9Bzm9ZwrzNvTAne23aS/d1963fCHwiG1wyYMIknEMS8McU8Zk+StCWyrcYCp9uHqmwT8g0qJCsV6JumZ06hHNle04qbf9NLxBJq9/jYYrNY5kKK84ecHjq65gGG2I7GTuoNWT6SA9Er5UjVqni9n7mV0rNe34bJL2/FpkNNuP/D3bj9nZ/w1ZE2zPjr97j97z/j+r9+j+9OtKPF48e+ZgcaXX7J/jIalZLa31ij6jpHKGIdQW1treQPc3NzsWPHDgwYMEDyO10hXVpHcL5IBzHFjS6fJMa9ye6JqzYg6phibBgjHJMUHr47TwSR5kk4hqQkHT7fVx/7SSrau5P4XKq+got/p+HSm+weqJRyzH+P78kCnHdLOQ2SEEirw0NtDE+bi2j1IkAoET2pAj8ea4VOJYdcJkN+qh49krTI50BNuXNh8fhw0+vbOrQmor5LObD1ZDvL2SSXAb3SDXjtP0fZHAr7fUF4TkhGSL5LWm9yTzGxvq82tx8yWRCP/Ws/LirN5PU3JkR5HZUO1xHk5uZGvPBTTz2FNWvWdGxUCem4dBBVEykGfcZV1YL4aKwNY4RjojUB7+46gniqm+VyWXxw17jeXdhllKqvKM0woCo/mZp3CSfz/TxPFuC/W6mw02s3VMGsUYooJqTeh1S9yPIQrLMwTYc7Li0WNa8/0eLAve/tEK8R8gx6pYhgr3emMdy0JoJEfZcBYGReEvLMOrZWw+Ly4GCDjfq83HftCdCh25Fab5IQGy/kKTAwUnUSWaau42I7IxcrhqLkhJxDEm8P3I4q4HiK3Gg5D0JLEFPOowskUrN6Wp1Ep8FdJfI0BLFTmkFPNlLj4hyJ9m5PWekJU4fHj2KzFhl6IzXWLRSpehG1Uo5Vvx+MNEOY7I18Rvr2RlojLS4/j2BPq2IqmFvSDUglMGmJPEBMDg6neBAA8kNoNilgA9fAStU+EHI82mfcuSEhT25yuE+WiVqweEnvNPoL7gQ5I0OQSBqfXxK1B27I25Esc49R4vGoaWMiOQ9wina6U+JqVk+TDsJfpQyoZDgh1nmJgtoi3dqESkuvVjA8UXVhnqj9p63ol5OEwdlG0f2l6kXykjQoyU7C1sNN1HXh8oWfl7dGQvPY7PTyCPZc3gBWfHEQ/XtUMYYgEgliRxycGA27VF3Emq01EesiuHOTblCLksNzxxTHvH86S85+SVtCuk9igHFGLHOPUeIKM50rlcQRaLWlmtW/NXs4DrUJoLbxoq8493VLVIlTwwnRFALFGEkpt9RQwpRWi9Li9uNIk53njc8bU4LsJC0sDr6ho9WLcBPAwnXBbV4/Z3QxPt5RS23YvmhCueSJBYh+Au2y9UVZuwq5LAxhNSh5J6k2l4caYnP5g7zkcCAYuQ9CV0giNPRrk26o2o3bCzvblcRRaLWlmtV/dahR5KnHxf0kuK+wKQogHU4QFZjFCwUWFKeVZRuptSjVbS5q3D8/RY85b/8sunYkpctdF6T6Wcj5Q+L+3Hlssrup85ITipnHUsfSZetLeG2EQnUy4Lvj4ndAg+wKOZjORp4sZkNgs9lQW1uL/Px86PVMTHLChAldM6qEnN9yjnr5ksilKMpb6oTD7ZBFvh9PWEx4X25TlGjhhEjKPpYKZdpvf9svS6SkpOL+9hCdg+jakZQuZ13Qqp9Xbj6E3ulMPYxKJWeL5d787riI95+rGM8V+niuRHoHXORPo8sHjZofmquzuLBp/2m2ViNSXqazJCZD8Nlnn2HVqlXw+/0YN24cZDIZ7rjjjrPTVCYh8cnZoms4h738aMglIDojqlSHrHiUkvC+pICJFFwZ1Arkpuh4NAuEA8jlD0oqGqnnqbO6oxbtCauhI/FE0eYqqoTWhXCMOWYtrq0q4OVhuLDVf+48heVTB0AmY8bEVYznDGMtR6I6BJz1OW9MCc8BKEzTYcqQAl4XtXOCYuL111/HunXrMHv2bNxxxx2YMmUK7rjjjq4ZUULE0lFlfq7SNXSDxBOikWpkYtSGk9VCpk8ujwz5frzoq3SDhsehAwAf76hFD7MWSRoFW4/xwa0jYHF5eRxAkRKKUs9DKovjSUbS4v7zx5bitf8eEz17PCI0mLS+vc9uPIRbLy7Ce9tO4sr+Obg3hKShJc3PiRNohOcDpCG7do8f67efZFFDpVkm/EmAGurq6vqYriqXy6FWqyGTySCTyaDT6bpkMN0u3ektnwVlfjZ6FZwrEk+IxuX1iWKyhMsf4NMN03hkImHNIymlDL1C1EBlyaQKprCKIKY4lARcDqBICUWLyyt6Hm6IKa5kJOV5hHUFj1/THxaXF0Km0kgiNJgKOR13PyDXjJG9UkVhJNE6PpMTaBfogWgOgXB9EjoNgCmqPCdRQ1VVVbjnnntQX1+PBQsWoH///l0ymG6V7vSWz5IyPxfaPp4tiSdEY9aq8e6246IevpcWD6JfPAiMyGd4ZBpsbmQaNfyq2BiVUqPDR+Xiob3bWArv2GQrZLzn6ZNlwtJ/7eMxYcaVjBQ8D6kraLC5oVUpsHDDbmqLS6pwlC63LsKkVVGrdHNMmq5dx12lB6I4BNzToFGjwMPj+2HJJ3sZoyhRg3DWUUPz58/Hli1b0LdvX/Tu3RujRo3qsgF1l3Snt3y2lPm5mESjylnwyITfjeTh08Yba+V0JInn3UoV3j05dQAO1lsRDDI0zuVZg5ChU/KeZ96YYl5l8RkV7XGeffaFRZL9B6hrM5LSRaQ+DrIuW8ddqgcoiKJGVxipdc/lffCn98O1Ki/+bjC0CjnS9Cpe97lzBjXU0NCAHj16IC8vD6+++iqys7PRt2/frhlRd4hMupqyK7zls6XM4+0k1qXSBaynESWeuHGcMebOUh7xvFvau7y2qgCPcTz9HLOWZcLkettmvRoGjZLXW/i6oQUwqOTIN8TH1sl9dqkGMVLruqN4/65MBnf1aYPMI2neJEUjXtPsxB1//wmfzLkQZgWgVsp5cF61smt7wMT0pPfffz/++Mc/4q233sIVV1yBpUuXYu3atV06sC6TkOKpbrR1m7fc2co85k0QjK+TWKdKlCYqHcLdxyvxxI0jeW8CZdhZyiOudxslYc1lwiQe5uKJFZABcHh9+GTHKV7oa83WGpRnm8L0FESiGGbhs8ezrjuM9+eE4ggfkIigroMS996M9fQqmEchQR1JhgtpxBusbng0CnavcMd01pPFPp8PQ4cOxapVq/Db3/4Wb731VpcMpjuEKJ4UvbrbijY6U5nHjYjgbK5oHbLikkgbQoLNkttEpSO4+y6VKN4bVxl2Wsgt3ncbIWHNbY6eY9ZixrBCXpcxGoMobbzx1FTEm2vo8Lx1UiiOJnHtzThOr8J5DATppychjXimSYNTrfQCxrOeLPZ6vXjsscdQVVWF7777Dn6/v0sG0x1CFE+dxYW139WwXtLwninI7KrYeScqcyJS3mok6TSlG2VDdEYTlW7NY8TgvRFKCZvLiyyTpvNCFR1FuwSlmTB/N7yALb4i4ydQTG4lNG288dRUkFzDy78fAlkwGHVdd9Qh6upTY6x7M55x0OaRts7lsvD/L5tSifxkHTxeOpndWU8WP/744/jvf/+LadOm4csvv8SyZcu6bEBdLVzFU2dx4YXN1aEyfibxFQ/qIa4Ye2cVWHV1J7EYJNqG6EgTlbNZDBSr98allCD9gc+UoO+MhHfaCyuPDKOGOv7+PcxYc2NVRGVHq2347nAjj3mVy8MUse0oZbzCUKU/EMS+psj5qi4/Nca4N88kuS/Vs1hIIy6Xy6BSAAsnlPNoqBdOKIdK2XUknzHNYn5+PgDgscceQ8+ePZGVldVlA+pqoTFL3j+uL6obbLjloqLIvUHPgQKtM/GOOkvpRtsQUgZH6P3Ei7vvKonVe+NSSpD+wEMLU86IoE8kHXQ0uO/WrKcXlCVplShKCnHaS3ntgtqGwjQd7ryUzrzaIaeGKF1KD2apvXROnBrjHIdwr0XqWSykEW+ye7Hq62peTmfV19V4ckol6jzuLgF5ROxQRuTBBx9EUlISqqqq8MMPP6CtrQ1PPvlk540iDumUDmWhzVZnc+N0u5vF75JCokF5SShOFhfNNbp8uP2dn0WVoF3ZVFoo+5odndtJjKJ0o3Vyk+qWFY3LhvDqE0+w22kvhBKai1NWN3bVWrBu20nUWVxs03WeRza+HG//UMN2rQKYOR/ZO73zut6dqaMhB05YvWhzeXDa4sZizrqeN6YEvylKQ48o7UOFHb2E/YIB8buOxXAJ11Q8XeDOBQcs7nHIgJ9Oh+m75TKgXIK+myvJyXp8fagRt6zZLvps7phiOh15jNLhDmVEampq8Pe//x0AcNlll53fPYs5C9esVbGbBQj3Bn1r9nDqT89GU2mhpBs7sZNY6O94RepkoZDLooYPUtP1nbupO7FimyRULy3LxPrtDEbf6fZBr1HijW+P4KLSTF77ws72SM8oFi5IqBam6bBi+kBUN9jg9gVYlFA0QyA8HUlBROus7ri8+mj3IdelhnvOgVNjvONodPo6jPyR4neikRx2lt6J6SputxtOpxM6nQ4ul+u8TRYHAkHRwn14fD+8+8NxdoO7vAHYXN5wiT9HpJpKSxmOrhCFDFSGSoW86+KHIqFsiBMWFyat2ho1fNCpib9Orth+duMhLJs6AGqlHFqlguV7IQafQLm59Rg/1gj6EYTG1RHj1GT3IEWvxuTBebxetbHEwoXPU9PsxPx1v7AJepHhirOjl/BvvVpxRu8y7nBPZ+XYOlWk99yZ5DVo/E5SJIfdaghmzZqFSZMmoaSkBNXV1Zg7d26n3Ly75USbU7Rwl3yyFyumD8SST/aizuKKuBiFvOHkGlKGoyuk3urGmq01sWHCu1IEsFSu9xMvmqKji7orKrYP1FsxtGcK1eCvuXkY1txYFbkeA/yQgEIGyY5eQskyaUQc/fPHlsKsV0ctAIyUoBflguLo6PXxjlpR4pI0rak/g6LMswoS6AberzPKa0SpGYnrWjFKTDtv4sSJqKqqQnNzM9LS0tCjR49OG0B3Sn27i7pw959ux+TBeSxqqMM46FCMNlpv1zORdIOGR1AlGsNZkHg82bj7AUe5b0RFFGHDS3XLKsk0we7yUa/r8vjQN00fsR5DIZdRO3r1TNGF++tKiD8QFDWBWfHFQelWldx5lQgZXlCUiqv6ZvLWdLwVvvV2N7VpjT/Qecqu28I93cT7dcaGLkLNSFcYzZgMwcqVK2Gz2fDAAw9g7ty5qKiowK233to5I+hGMUrQ8/oDQEUPE/NCO4qDlgFfHW0TMUle2iu5U41B1AV2FvoP0DzZeWNKkBXqIBVp/BH7AZ8JHDbKho/ULUuqU5hRw5z6IhkglVJO7eg1MM8c1RBIXTeWVpVSIcNUnYq9L6k94dYcCMdPq/DN0Cth1qpFDk6HlJ1ofXZvuKfbeL8609B1g9GMyRBs2rQJH3zwAQDgueeew3XXXXdeGgK3z89j+SOxt3e3HceFvfsjUswPQMQXcsLmpTJJvjV7OPI7M2wUaVGcJXQFzZN9duMhXNI7Ler4pfoBnykcNuqG54yj1eXD7DVhmmNap7C7LyuFPxjAvmYHTFoVCtN0qGl2smMhBkiKw4r0140kUoZN2KqyzuoWzU291Y1Pd9XxEtyvbDnMhAwFCXopQ0f15KNU9caloLqZ8ZfmEHUr71cnFYJSr9XJ8xWTIZDJZPB4PFCr1fB6vedtr+JUvRof/nQCK6YPxP7T7fAHgE37T+OBcX3xY00b/IFA9HiuxAupt9LDTvVWV+cagghjOFv9B2LaXBKeoFQ/4JhyBjRFpFei0eFDTZsTt1xUhPXbT7KxVdF1Q/NY0+bkjYF0Cls2dQAO1FuhVcqhU8kx828/sgrsyamVqG11wu7xs3kA5mRIZ8rMoZyOhPNCq1imtarUU04WWSYNruyfw0twk1NZLC0xpU6VJq0Ky784ENGgRlRQoWsdqmmFTinHG1uPsvktAPy6nc46zUYwOGeCujujcM+5AoGVkJi0w3XXXYcJEyagtLQUR44cwS233NLV4+oSyU/W4YaRvfDEZ/swvjIXfbIM6JGsxd3rfuFtnljiuULJNtEhX1kmbWc/hqR0TCGfeegoqqcUaWOeabGQIJYq9F4J2iISEID27lodHhxqsGLlJqaj19JP97Ofp+jVqG11ihQpQEd8xMNdQyqWm+wemHQq7Kq18JrAkHCPUISnshS9Gk6vHycsLhg0CpbnCQgbutduqILXF4h6quTOIRCHoaZc65EJ5Wh3etDuZgzozb/pxcCv44WixsiQSsbLzeEIjeD8saXwBmNg5D2DEE2j04flXxyQNoJnWWIqKAOAlpYWnDhxAvn5+UhNTe3qcUnKGReUWRzsApIr5LiFExIAmM322g1VKDbHqcAVwObDbViwIZwjWDyxAqN6JwPdhLbtaKGXcLNFKygTSTTuoUjFQ2eARRc/P/0+hDf/2WsHIt+sFSsPuTi/s2xqJfKSdTjd7kKSVoV739/JKsL4iqwiYM2jFVXFUZTELTTMMWsxc0QhtUMZl3Qu3I9Agwy9Ao0On6ipPHcOCUAhVjy81PNxk9+k0E0lQ6cVmEUqugSA+z/czQIbtEo5TBolHvtsf+d76px1oNMq8FONheWAIgZWWLwa996LQ864oOzPf/6z6N8ee+yxjo/obArHg9zRaO9wPFcojXYf1m2rYWK0Hh90aiXWfHsEFdnGjh17O+C5Rzu6xh06inUMUTwl4Uklx6zF5MF5ONLihNusxYiCzkmESZ2IKnqY8NFtI6UhnwHg0l7JLM1xD7MOx1scuP5VeiP1qDz8McZzY6FlHpxt5BivCBxBnJBHpP6/RAEvmVSBu9/7BTXNTjZh//BHu3HLRUXUMSk4NRSxhkNiSX6TRLrXF4g5RBgLQyqtFzRzGpTxUHd3jirGys37Y98TsUoMRIbdXYMUSWJ60quuugoAEAwGsXfvXjQ0NET9zY4dO7B8+XKsXbsWe/bswW233YaePXsCAK6//npcddVVWLduHd555x0olUrcfvvt3d75TKqCTzKeG0Ga7B5sq7FgW83Pon/vyLE34velFHScChmIcMw/o5gmP+nODf/QvFWCroopERYHJBRg3meyTgWHNxA51h0A8g0q5BtUVHgoV5F2VivByAoL7FxIGhVulbxezYY8pAzVgFyGdI7g0kmye3xlLg/oQHu2S0syeORosRTPxZr8dnj8yJHYi7Q5lUrIkyS6VC/oDD0z7mevHciessqyTbywmfBaHZVYiQzZGiROLkVUoNgNEtOTXnTRRez/X3zxxbj55psjfv+VV17Bhg0b2Cb3e/fuxU033cT7XWNjI9auXYv169fD7XZjxowZuOCCC6BWdx8ePq54LpE4KzLTDeq4PfGI39cpIxcsRVAc8cTj4xqzLHIRFfekQvNWY0ZXxQEJ5Xry89/biVaHJ+ZYt5TBJIo0y6TplFaCERVWNMgxxeN8b9tJzL6wCH2yTJIODknQcxFPXMMh1WMg36gKF00G6WOI9X0Ik985Jk1cFfMGjYL6fCSJHrEXtE4Jjy8gqvMQhs1oCfl4JFYiQwJ17mgRYmdJTIbgP//5D/v/jY2NaGpqivj9goICPP/88/jTn/4EANi9ezeOHj2KjRs3orCwEA8++CB27tyJQYMGQa1WQ61Wo6CgAPv370dlZWXEaysUMiQnx19BGwgEcbzVifp2NzJNGuQn6yCXy3C5SYe+cy5Eg5X/71LX+PJAI+55L5xcfmraQFzWJwNJScBT0waKPuudacL2E21UxdLm9qMkO0l0n0M1rZLf12qU1IKlkkwj8lMiz0tSIIjnrx+EXbUWdsFV5JrRO9MEuVyGQCCIE21OVB9vhUwmo3pKtDGfaHVEHROZ5/31VuqzNdjc6J9rjjj+mhaHyDgt/+IAnr12EBwePzJNGowty8Qncy7E8RYHfjnZxtvgz23i90QoTNMhSa/GIQv/3fcI0DdtYZoBhanM8/RMN6LvnAvRaHMjwxh53UR6HprC+mTOhex9uELeT4PVDb1aIfI4Scgjx6wVKXOyFiM9n8sb7tNx68VFKM8xo3eGQfLZaO/jvvU7eeMn773R5ka6QY399VZe8vvRqytQlGHETyct1Ir5AXnJKMrkx7azgnSjkZWkRXKyPuL+UauVEU97wmvFI9z3Yw6d9ojBpdFQk3dSa3F2eE93lsRkCP75z3+y/69Wq7F06dKI37/iiitw8uRJ9u/KykpMmzYNFRUVeOmll/DCCy+grKwMJlP4BRsMBthstqhj8fuD8SdTonguZjlgNjPhoPZ2p+RlGl0+VtEDzCK6571fWC95WA+jKCzT3u5EskZJD1loFNRnEX6fVL06PD402oB3fjwuWsgD88wwRdNDMsDl8fMW3LIplWi3Ms8sRcLG9ZRoY65vd0kWUXHHZJYDuRIhgEyjJup7FXZuyjFrcW1VAa57RRz3V8qA5zZW837PjXWTuDjtt2YlsGRShchTNyvBG6NZDhQWpKCt3YE9de3hgqskFRrtnFNjKBErPEVKdaI61eqAWdiilnIC4P6W68lLNYwha9us4DeKF9JItDo80KkU6JmihVkuvSdiGr8M8Hh8CAYBm8uHN74Nw0cNagUarW7850gz0vRqqJUyUcU8bb2Z5Ex1s7Da2SRn3k+k/SY15oJUPeaMLhZdK2ah6JglkyqwcvMh1DQ7JWmo29udMe+fM5UOJ4s9HsZyL1q06IwGMHbsWCQlJbH/v2TJElRVVcFut7PfsdvtPMPQmdJZ+PpYknu0sEy8+ONIVa9ScL5YEtyR5gFAVE9Jasx2j586L7Qx5ZlUVCUbSw9aYWiLFmYizyNFZXFhcTpKM03ITNLgvhDmnvvb126oglapwLs/8r3TlZsPoTyLA/ULhQhPnLLgcKOd9zyLJ1Zg3bYabKuxiBQCDzobB6adFnPm/pYo/zASSA2FXIZ6qxuiQklKLkmKRiJSaKIjsGEuedqNv+nJQ9E8enUFnt/EnyepivnBOdJJ9Ej7ze0P0k97qXpkGtQdBivQ9hYJeZL3QWpc2MkJSUz7p4sZAyJqwXHjxkEm4y+iYDAImUyGjRs3xnyT2bNn4+GHH0ZlZSW2bt2K8vJyVFZW4plnnoHb7YbH48Hhw4dRWlrasaeIIhEVeKgKNZYJNklQVBi1UWLbseCPBS+a0DgL4Xwub4Aa4kg5A2KyJruH/X/hZyQuHmmDxJR05zxfebYJ7/5hBE5ZnHE1IhducIVcGr3TN10vorK489ISzPrbDxEN6n+PtODVb46IPiPzJ4ThPjl1gCi8s2DDbjw5dQC21fzMKgTyvrjGioZpF8XFQ/MmLJBbv/0k7r6slKdI7x3bh4nlG1XRE/0CpyVDp0SWQUNfnxJKKEOn4CVeeYV1Eii1d7cdx5+v6gu314+TrQ5eH+uH/rEbb98yHFanN+aKeVrVbpZJA7VSzjNs6hB1rMvrE4XN5o4ugdMjVtDxiNTesrm8TJ8QGfBTHT0PEHX/dEMOIaIh2LRpU6fc5JFHHsGSJUugUqmQnp6OJUuWwGg0YubMmZgxYwaCwSDuvvtuaDTxo3ViESnPJcukiQsZI7WI3F4fgHiqh2WiP6XGIbXAClN1mDO6GEkaBVKNmpi4eqQ8UJNOBZeP3ieVJBgBSHMwRUu6R0MgxWAEyP25BtWkVfEgeWTM6Qa16Ls6jRIzQ0aAzKHQoBJEi9RnxNPlKjinm05QF1YsYRZQIAydrWlzIlmvwqe76qSZZKN41DoVXdnFdAKmKHcqyEDi3ZEmQzqVAh/9Usvz4onQYMPXVhWI6L2JwXV5A2ixe1CZYeCNIerzRIFqkvf3wa0jYNaq8e6247w5f3fbcbgG5vLakFLrTSJItNNRi9svSUYYbf9E+m28ha9SElNc5IorroDPF17YSqUSOTk5uO+++1BeXk79TV5eHtatWwcAKC8vxzvvvCP6zvTp0zF9+vSOjDsukToq+gPBuEJGUovo0uJBkQcQreAqQtWh1AI72ebEyk3VuH9cHzz0j90xPYMUMuPHYy147duayLQDkSTKiSfqRo7E2hpJYckQ2QBxvN5tp+lJai4+nqtkhZ9xr8tVcHqJeLROHZ77wjQdSrNMmD+2FCWZRjz+2T68sNkpmYehGRwyJmKgFHLwqp3Jb8k7iBjCjKLcucpP6t1xQ4ZcZS7E80cL5XENrhRah/Y8KXo1Wl3MSSnDqOFBg6WgmuSkKGTy5CKZUvRqHGmys7Tq0ZxDItHCvy1Ob0QyQrJ/aD2wo/22MyQmQzB8+HCMGzcOVVVV+Pnnn/Hee+9hypQpePTRR/H22293ykC6VEKK6pM5F+JUq4NVVPua4uO5ydApOkQHG00RRup8VpyqE8XUF04ox9vfMws3Va+mPsPRVieQoud5M1K9DKYMyWNpB269uAgD85KRbaSEgiIp7Aiw1YiKSa+UZm0NRjlJxBJyC4kUBchFxekYkGvGjloLLxSkVUlj57kK7pUth0V8/YsnVWDNt0cAMEbgjkuLJb3gSHkYqXnrn5sEpVwmOafRQpixKPdoJ1JuURhXmXP3T6yhPNIzQYo+g0YZPmtkIUsUSAvzSXrngjWjUsox/71w1fjkwXkipRtTPjEIjMhPYosShSHPqHmA0P4pyU4S9cCOJwfXUYnJEBw9ehS/+c1vADBG4cUXX8TIkSOxcuXKThtIl0rIq2xz+2PG/lMlDsXDlWgeWqTOZ40OH1ZuPiRqZD2+Mhc7a9slPdK9dVbc894OnuJMN9J7GWhDIYU6iwvPbazGW7cMZ5PfrMiBr4+1sacPkty7pGdy3ElegoLyBIKoafdg5WZ6XYFWIaPCRZ+eNjBMjSAV0hCIVJK6Z5IaMKnh8Ph5sEZJ7Dz43l+jzQOFLIjlUwfA7vHBoFZCpZTh8n7ZGNE7A2VZJtwrSEoLFadUHsYgodDTjRroFXLJYjSLyxsxhBkP3bVU0ZuwKIx8xts/nP3S5vZDp5JTQzblOUm49eIiyQS10KBMqxIra+6c0qCaUifFRlf4vQPSrTmjcitReK64e+9Milc7s/BVSmIyBGq1Gm+//TYGDRqEn3/+GWq1Grt37z4/WlZGSjR1hE0wUqWnhEQzOFKdz1odHnj8AXh84ZvIZIDHF0RBCpMjUCvlokVPvCOhNyMVGuKKViVHJidJRcIyeq0SzwuMFUnu5ekp+RHOb7NMGjapqFcrkMTpFS2VtK23umBQKXjzQmLMMfcuEISVLi1KlvTYpFhMpeLEJBlZkmnioY/IHBKlNGd0MfXdchUnLw8jYyjN660upBk1ePDKMjYERN6Xz+9HhkklXYwGWcQQZjx015XZBtx5aQmPQ4t7IuX+lrp/OJ7u3rp26vrLNKlRmJwpve8EDphDgo6ChPKkoJrREHoub6DDVePRTv0dKl4lYzyD38YqMRmC5cuXY9WqVdi4cSNKS0vx5JNPYufOnVHrCc4FkXpBH902Ev5AEEk6Jd64aShsLh+zIbugS1I0gyO1MXfUWqBXK6hNXyxOD1ZuqkZhmg4PXtkXy6cOgC8QhEIuwytbDlOrZ6VCQ9Oq8th7LptSifxkHdqtzphYKBttbrEhEBjfwjQd/m90CVZvOYLZFxbhmS+lvTkyjiyTFlqFPGqMOVK1s5QDwFYwc3UJ18BH8e64jcmjKXryPDSlK9rQFPK7uy8rxbwxJbB7/Oz7Ks4oj1o9GymEGWvFr16twIl2L2sEyH0WfbwHt15chJ217dCq5Hj8mv7IMqnx236RGzudsjip669nmh5V2SaeEYwUejxp90qG+YShvKgOm8DIdLRqPBZoeaTQUUTpYCQiHonJEKSkpIiI5y655BLceeedeOGFFzpvNF0gUommPfU2PPjhLr6nFQgiQ2fs/EFEeZGRNubsC3ti+ecHeRvx2Y2HMGdUMQDmdFDT4sCKL/isho02j4h6WarNpTAWLpfLIiYquQo7iQKdFf52fGUuG1KSOnpzE7NsXUEQMcWYO0JMFkmiUQYL11Qk73r99pOYP7aU9354ipNz8tBrlCLl/tYPNbjn8jIcrLdCJgPUShlyzRqcao/MtxNRcQjWo0Grwp5TYrprvVqJUxYndf8M7ZmKl343CNkmLYwaOeosoXqFCHj3bJOWt/5IiFAhl6PR7Ud1sx1/ep9fkEXr8BcxzBcIh6ViFsEpvyNKN6aaigjORbxj7Gxn9Qzo9YD29vbOGkeXCe0FTavKY40AwC+eyjdru4YfPNKLjJDAMmlU1A3v8oW9ZKJkyGdEYf/1P0eweGIFMgxKwC+N+6bFwqU8HCHKxuMThweFv9Uo6XQG3L8vKk5H32xTxJBNRLgoZQyx9lEWSqTkvRANQ+PmWTihHKu+ZpSdWilDhknDg3nq1AoUpzA8XFzlIKwWpsEtl0yqQJvLC5OWnhtiUTfRFAfn8+o2J4JB8MaoVyngcHtFSXaSqP0DJ1E7f2wpXvvvMaiVMlG4ilV24CtwWqHkvDElvLoCSQ6qAJ8tNi7vmiaxQmkjyFlj/u0kOSONJyw2OxeF9oJKMo1UJRcIIiZFEbPE8zIlElhSyWDicUp52AWpOsZ7/+oQijMGsptJSLj17LUDxWOEtIdTnGnCnNHFbNx55PSBokcRJhgH5psjKs5lUyqRpFbAqVJAq5CLjGTMcFGOZJk0uP2SIjTZPazRu/2SImofZeG70muUvBg7wDzriF7Ms3LXVJ3FhV9ONOP1m4ahyepGdpIG/qAfkwbmMuyWgmQxmUdaNbewWliKoG/OqGK8t/2ECK0khbqJJmatGn/7djf7vvwB4G/fHmVOQHolz/umJWpXfHEQc0YVwxkaH03ZJZvBU+B2jw9/WLtd5IxxT5wub4QOf4EwWyz5u0MSrcYlVoly6u8+5t+OSRe4vueYCJALyRqFZDtBuQxi77KjlvkMXibXc3f7/Hj62oF4/NN9bNEO1+OUSm4db3GyG+p0O7OZhF6JFGb6cpMOGXoFFbb61Of7WQ77B8b1RX27Gxq5nDcvQlbNwjQdFo4vx6JP9rBUCCumD4RcBhSYtThhcWHSqq30eZKouI7l2G4X8CoJE+NS72rh+L7UEwFbOMhZU65AEIcb7bjxtXDF8pJJFZhQkYU6i1vUKF5YUMYl9hMaSalQWIZRg5pmJ1Z9XY3lUwdgf701ZloIqfUmmVMQeN8KCdhqhlGDegl66Ca7B+zMhxT4PgmAhDC30tUd/s6YfkakH+gnCdLnWoi+orESnI2Ws//7hgAQY3QpnuW8MSUoSjfw0SJGDU60OaWLSyIYCamX+doNVTBrVFENitBzf3h8P1hdXjg8fmQnqfHM9IE43c40UREmt7iFUQRuCIi9EinMdN85F8Lj4cNW+2SZ8PGOE7jn8jIEgwySidvik5dMFSQya5qdWLWlGk9OHYCD9VYEg8ATn+3DS9cxKBYyv9wxSHUvI1WfjFBOpKF3ctzigsvr54UapIpwhO8q3aQV8RBJNRFxev3UpO1bs4ejb5oeja5wxXa0zmHESK69eRiabG6kGNTUUJheo2TnVaeSY1gBpU9APBItGRkEtAoZDCoFFIIEPndMvSROrzRlJ3XiJOwavFxRR739GETKU6+zuqM7f3E4e76AH7ddXIxFn4RPcI9MKEcQQT41TIQxdWq0QiBndFWz2dxZ4+he4Sz8OqsbWpUCaqUMyVqlKKEjjFtGUlLcRSD1MgmXjWjBcIyKUasSGZEln+zFsqkDcLLVAavbj9v//j1POX5w6wgct7igUsixOOR5a1VyLBxfDl+AieMLN59UWKmh3QWfP4Ca5vCpojI3CdcPK8Sf3t/B5h+kPBbas9c0O1Ed6gHM9TgjFfUBiOkEwzXMkZBOLi+9CEeYT5DLQKXg5jYRIfeR6uhFQhrRejEIC8ruHdsHPU1q9DSq0ebzY9HEcizcEFYeiyaWo9XuBhA7BUhMIpVTEMzp4on9qBDQJqsLXn8gQv0CX6Ri6gWpemquqKskEmJPWFwndP5o+1TKc5fJ5KwRIN99JIS+4t7ncpMu/vqmTpCYDIEQMaRSqZCdnY1HH320SwbVneLw+rGz1oJ1205iWlWeqJ2cMG6ZolfD4mZ6u0ZaBJEKcUQLhsKVQlMuB0LeNEFWkH+f9y5DhZ2iV2HZv/fjnsvLeO0y77u8DEDsmGm9Rgm1IHx2UWkmu5ClDAjxosgxmNv8pDBNxyaEudDASFWwsZ5gyDxGQzoRxclKqFLaHQhi/thSXhgsHuoH2ntmQxocp6OmTYzAcXmlC8oCAcDt9fOSuG6vH/4Aw6D54JVlcAeC0Zuu00R4mpWgyhY+q93jh0Gt4I3JoFagb44JSpkcf/l0L9uuVa9W4o1vj2BEr1T8SDpvGRhIaoPVjWS9GvMvK0G7289yJfXQK9FDF2Ih7mIjANANEkl+A5FPqFL7lNrsyBZbEV/fORd2rL7pDCUmQ+B2u5Gfn4+qqirs2LEDu3btQmpqKu6//36sWrWqa0bWlSLhOfoE8VxATBg2a2QhbnpjG9UTJEaChJXuubwPDw63cHw53v4hzGVDFkw0imEgnCCOVPmYZdLg8vIcHspk/thSJHO8PC6WOdWghl6tFDXU9vgDyE1S83IEwnh1NC+KS71MuP9ncpg/iZcVicgv1hMMMUDCeDz3/Yk2EwezTzvhxEr9sOVAg+jIv3hiBfKTVAA5fBA8e7KeOm9SXn2L00vlE1r1+yF49rpBOCHoqyzFFyQSyvqXosoWnpbyUvR4ZcthXFSaySaWX/r6CJ64pgJ9MzS4dmghb/0tnliBpZ/uxbYaC8sAyy1Oe3h8PwQA2Nx+LP10HwvRJePsDuSMkKlUo+A3g5A6oUrtU5rnnm5Ui75LOKjmjGag4Ou3n0RDuwvmZG2X1w0IJSZD0NLSghUrVgBg2lbefPPNuOuuu/C73/2u60bW2SJjOmnVt7tgc/tR2+ZEaaaRXdBunx9lOUkR45ZCxAQNVkcoo2lhpUWf7MHsC8OFOGTBCD1fGsXw3ZeV4vVvj2HKkDzqGHUaJVocHuQn63mLuiTTAD/xGgU5j3ljivGPX2pFFaiDC1Nwwurl5QhKMsPtD2nIH25BksvL52I3alVsNTD5nORLtColNu0/Ta2CjfUEQwzQvDHF1M8vKErFVX35lasnrF7WyEXr8RuJa4h7UiK/W7BhNzU8EJOnx1F+bj/dsFkcXigUMqz97hjvvsu/OID7Li/D/norjGoFKvPM8PkCMeWvpKiys0waKsyTnJYqc5Pw/67qC6vHT6ULWbCBue62GgvGV+aKitOWfLKXNcRciG53IWe4xYFEtCq5qF4m3aCm7lMaAo6mtIPBAAuYcHkZAMVtl/A5qOaNKYFJF4Zxd2XdgFBiMgQ2mw2HDx9G7969cfjwYdjtdrS2tsLhiLNT2NkSGcPnfaTJzlvQj0wox/vbj2N4UQaCMobW96Hf9sWj/9wn8rKq8pN5HqdwEdBgdTQ4HM07pcUEU/RKHn+Nw+MFwIQdHrumP44221lYZM80A+59fwdqmp2oKjRj/tgyWJwe5CRpUW91Y/Lq73iLjRinddtOUjf53e/+wnABcagtjrfY8edxZXjss/1sUnPljMHw+vwwaVW47/2dvJh6il4Nd6jGoMVBPxYfb3XiVJsTs37TC099foDakCRa1SfXAK3bRueYKU7WshwzROqtrqgnHClPnavQ4+KmkQFBGR+vH5Qx/07LcTwwrg91XAcbbKziJPkPUnNwr0CxrNlag1aHh42/n7I4oZTLqWPmInbIM6Qb1HjnRz6U9p0fj2NaVR6+2t+A64cX8u5Jqz4nv5OaK/Lv3IR8pyJnIpwsYqmXYdcj+OFSWjMgKc/doFJh84EjeHnmELTZvchM0uCm138U6YuKXDMUQUiG6rpKYprRBQsW4L777kNDQwNycnKwYMEC/Otf/8Jtt93WdSPrRGl0+rCnrl0U/3/p62r88eLePN6bpdf0x0e3jQwdr8MvNlWAAOH2dh2Qa0YQ0ouciFYlx/CeKRhTOpxXPi/0FG/6TSGa7V4s2LCXt6mfu24A1AoFth1v5SGK5o8thccXRI5Zi9Fl2bj5jR8lTyVc41RnceHTXXV446ahaLIxhWzHm+2YMiQP/XKScPslRTyem/ljS/HcdYOw/7QVpVlGLPlkD2qanZg3pphH3CU8HUl56sdbnPjrf44wz3btIFgcdMQKr+qTE9oSGiDCoPraDVXw+gJsl659TeIYeoYxclGYVIMYIYy13RvAq9/EVuR2wurl5XfId0nRlLCi2R8M8hyTwjQd7h/XF9UNNtxyURF+OdGMx6f0x+k2F7KTtfhjBFw+l11U6n0Q8kHuM0gV1+UmazHkij6ie0r1eAAYR0sq5El+3+rwIN8gTYwniZyRUvZRThZSiVlSbZ9l0jAn6ibmRP3stQN5YAW2GRCFnJArGXoFxvbrwc4XrXhw8uA8nGp1Yp/TgpxkHQ412LqtmX1MhqChoQHvv/8+5PLwQunfv3/XjKgLhBQVCRfW+Mpc1ggAzOcPfrgLb99CYIJ8eKJQYbc6PCjJMKIy04BGJ72xC9ElhWk6LBhfjj2nrHB6feiXk4SeKTo2njuiIKzgUvRq3EjxFl67oQoGJbDs3wd4n6344iBmX1gEACJUyjs/Mh2hDtZbATAnmbJsE56Y3B/JBjXq2py44bUfY6r0XPHFQbx9y3CYe6bgTx/sZBOkOrUCD/22LxqsbqaIKtuEZf8Ox7bXbTuJB68s4xV3perVWBUyZgTW2Tct1Kibu9gVwDGLB/XtbmSbNXB5fbhlzU+sgeEaIIAhHNMoFUAwiIONDjzwYVgBvPS7QTBr1ai3upBu1ODp6QOw77QVgSBg1Cjw2o1D0Wh1QyZj+JoiNYghJ0W5DHj8mkrefZZNqWQMkCCJKzyFkHdEEEYWlxczhhXyu45d3gdrbx6KNqcPDVY35ocgu4TeWkqxkGsTRyRFr0ZJqBhQrZSLyOzmjy0F0chcL9jlD1KZcZ+cOgCn2+jPI6QLWbn5EABARenIJoQ6G7VK7Gt2SIIIqMiZCMo+KhkcJVz37LUDoVXI4FbIsK/eLnq3NEeRKgKEERdmzM0vCGHFD17ZBydbnV3aiEYoMRmCb7/9Fs8++yxGjx6NqVOnIj8/v0sG01WSbtCI4ss5Zi0KUnXUhXyowYY/rd8ljksKkq05SVrolHLsa3Igy6Shog/8gSAeuLIPTFoV7nzrJ96LrWm2Y+mnB1CYpsO9l/dhPYCybBMVvujw+OGVYF7UKOXw+Pmf0SgK5o0pQV2bA0s/PYD7x/VhNyUNkUMLbbXYPXD7A3hgXF88/tk+Nhl8+yXFvIUrDBE4vfy6iLsvK+WNn8qtrgA2H27jJRcXTSzHyF6p2HywiRoKWjKpAne/9wvGV+byEsClmUacbvfg9r//zCrSOy9lxkyMIDmqk3dnDrX/NGlVvMYnRJkQD7swTcdrFC9VIJctQSdMEEZaTuKe3Gf55wfw5s3DEAwCDe0udl2Mr8xlYaVAZIABOaHdx1kHd19WildmDsH3x1rZBGmvdL0oJyLFjHva4kSOWUe954VcupAkFcqzBqHN7YfD48PDHzF5Mo1SjuJMI574bB8LdV48sRzrtx/Huu11KEwT9+GQir9HUvYxkcEJwo/HWp34bH8DSjJNrBEQXpfqtHAlChKQewoVwopzkvWiOpbObkQjlJhDQx6PBxs3bsTixYvh9Xrx+uuvd8mAukIydAr0y0lilQbZ+KfanPTEa6i7lNB7oBFHceOwBM8v5Au6c1SxiHHz2Y2HsGzqAADAjSN7osHq5inK+67og1e/OcrGf6dV5cEbCCJJR/eUhvdKRbPdjXljirFuG9PXVgq3TgjruE1tIsVvuffhIoOIsh9fmYtHPuYnTLkhgsmD80QK7ukvD0rDOkNyzOIRJRcXbtiDl2cOweaDTaJQkFGrwt3v/YKaZqfoeW65uDdrEAGEEpd7JI3gii8OSnbhAvgeNgAs2LAbj1/THy5/ULJAzqiWU+sCTBrGhW6WUFpbqptE4xA+X6QEPi1/9fSXB/HyzCFYuSkcwll78zD0NPJJ26TW2+CCFAQDfpGyXjyxAveF8lVcI1iSnYRD9VYq6VyuWYfjrU688FU1Fk+qwLrtdahpdmLl5kMxxd8jFYXpNYroJwtO+LHFE24LectFRR3mq4qGBCT5BcIazB2fQ6L9aWc2ohFKzFmXnTt34j//+Q+am5txxRVXdNmAukSCwOBsI0oyjajMNcPjD+D2v/+EFL2aunlOtoaT4FzvodEl9jy4XjPB8/dN06PR7YdaKcOdo4pRkEI/eTjcTKFNcaZRxLuy7N8H8Kcr+uCVb47yQjYPXtmHCrfccaIVSz89wFMACjnjCd9ycW843T7oNUq8suUwS1gn5DGKFNoiXjIXX02UfTRG0WidqaQ8vXoJhs02h5f9u9XhgVmjRIaZCSlwaxe4zyPsLcwds9T4pbpw0TzsuaNLsKPWAodEN6k6qxt6lRweX4CXLJbLgBaHD3VetySRnD8gHofw+eosLmzafxqv3zQMDaHw4qkWJt+Tn6KnjmlfnZX3d7PdEzYEIQkiKOI0WjihHApZEHkmLXqatewJOd2oYUEL5JpcriFhP4xWhwdapQLPbDzEGljuu61pdoabvzODoUqkorD3JAAEUkaF2xbSqKFTwEvyVXEkFoTRvWP7oCRdjza3jzf+Jrub+jypZ7ug7KqrrkJZWRmmTZuGv/zlL/B6vdF/dK5JEMhP0eNkiwMNIQVDEr5EmZVlmdBid+Olr4+wP+N6DyctdMVEvAWu0VApgNsuKcaij/fglouKqC+20cZUiEq1ostL0WP51Eq2JR8AtLv9+HiHGPI5vjKX/R05bRi1CvRKN6C6wcrG5m+6sBesTub9NdtcLEUyravTo1dXoMXmxpzRxZL4am6DFeHzVRWm4Ikp/VGQqqdSJQzvlYrRpRmSFaTZSfQNnhLqf6BV8YuqTLpwIdv67Sfx53FlaHYweYmcZHpYJpIRFDZqIY8v1SHrxd8Nht3tFxXTaVUMK6hGqcBf/rUvHAIIGZSHPwpDCoVKlxtD5875xztq8eSUSlQ3MuFEo1qBHsk6HufR4onl2HKgAVdUZFOfj5MbhlYlR6ZRrODsngBWfV0t6pC39Or+gB488jehISbjJVxD9VY3Pt1VhyenDoAcAGTA6q8P84r2Uji9LSRzAgKJROPObcE6INcctecIdy/6/EGsDMFpybM8u/EQLumdFnVMQuNERRiF6GwC/gCemT4Qe08zrMAquQyLJ5VjwUfhdXD3ZaVweXwArQlUJ0hMhmD8+PH46KOPsGPHDgSDQahUKvz73//ukgF1mYTqCOQKOUxaJS+EQsITr8ysQovdLWpZSBZONNQDd+E22b3shqZ5A49MKMdLIeI4g5p+fNWpFWgXxGjXbz+JG3/TU1QEJlQWB+qtGJhnxmmLS5R0GpSfjDmji5GXoofHz3ioSrkcpVlGtgmKXMYcUV/7ll9dS0OEfLKzVqTAFk0sx/66drS7/XB7xSGEhRPKseyz/TjYYGP5g1jEh1GBY20etDm9WHpNf17fiEUTy3Ha4sSc0cUwqBVwegO8oipCyOfxBeH2h/MS04fkYPHECjbU9PGOWvbv9dvFyewMkwZWl48N/Xy8o5ZlXpXysH863oZXvzmCx67pj9PtLtg9fvZaerWSTboTEYakhERyw3qm4OGPdvNyRVqVHEN7pmBsWSaOtzhE75ab3F+wYQ+ev24QqhttoudLM6iRbdax13z06goYVHLsa3YgK0kDhzeA0+0MyRyXaiT8vOIwRTRqhGS9Glf2zxHlrBptHrQ6PFg0sRwN7U7q3osowch9iEkL1jU3VrEhnUYXHZrJbQvpksjHxRIaohmnRyb0AwDYvX4kBYL4qc6Gee/+gtJMI343opD3Lp+Y0h/3Xl6KNqcPwSDw1g81uCjEzdUVEpMh+PLLL7F27Vq89NJLGDduHN54440uG1CXiEQdATe+v3BCOY422fDS10d48EPuQjRrVVSelTVba9j/J5BDboiAe/IozTLiYL0Nm/bXYdnUAWi2uantJueNKYFaIYNSLucZLYAxSCS8UJZlwrLP94uURTAIyGUyagL4xRmDWc6feWNK8N62k5g8OA+LP9nLIoECQeCv/z2KyYPzeMliLiJk4YRy5Jo1GFmUiic/2896jeU5SWi0urDiy/DzPPTbvnj9pqGot7ihkMuwesth7KxtR45Zy+MPqio049qhhazRqCo04683VKHF7kW6UQ2X14s9p2wAmCrX5Z/v5z3foo/34MmpAyADeAm3C0uz8dTn4TGWZpmw5lumY5pJq4BKqeBtxCWTKvDKj0fYWPcjE8rRbCUGUUH1+oNBJndwut0lSmC/8NVBDMhP5dFR9MkSgwK4RHJJGiWuG1ogWhcubwAbaxqi0qG4vAGma52MnqyXycA0l0nSosHqxqRVW0XoMalaBlpOJ2LBHBiCPtp6fHnmEKgVcrz343H8trIHtYgvqgTpNO7cMacb1FHhpBm6MOsu+Z2UYROJBMS4ye5BjlmDX2qtLFiBa7iF+SuXN4D71+/C6plD8MOxVijlYI1Ih+hEYpCYO5RlZmbCbrdj+PDheO655zpvBN0gUnUEJISy/7QVq76uxrQh+VArZdAoFfD6AhDCR3MMSuSm6FglbFArUJJlxPXDCuD2BbBmaw0LOUzV80vK6ywu/PU/R7Bi2gB8srMWd15aglkhyoXFE/tR+VuCQaDF7kFJlgn3XF6Cpz4/hGlVediwoxazflMEp9sHlVKOWSN7spBSrnEqyzZRPRp7KOnEVR6pehUVL64RYMurClNYbzwvRYugPwiVQoaDDTbsrGUaFT1//SAeNYLLG8Ar3xzB0mv6Iwhgf70VjTZmo04enMcrWBreK5VtekLqImaH6hFINSY5rhOF9vq3x3gFTAQqy312p9vH82znjC7GthoLttVYQsl8frJbWGlLCMKIASUnD2IoyKmMlnh++KPdeHLqALTZ3Wy4kBbCIHOcolfD5vJCrZCjf54Jq2cOQYvdizSDGlq1DNuPWSRPJcLkvkGjwMCCFNwXIgskn7/1Qw2WTR0Al5vxOJd+ug+zLyxCQYoOpyxO1kC9sbVG1GFNCh4bsWAOgFUiCfrjsVa8+g1TT5Ju1MDi8Ij2nkgiFIlFMkhSCCNuFTypqDdqFHh4fD8s4dQZRXp2yd7oWiVO2L0illqy94T5K/L5tppWdr0tmVSBRz7eK0rCd5YxiMkQmEwmfPnll5DJZHjnnXfQ0tLSOXfvJpGqI3B5mYStTAZMGJCLZJ0S917eB18daqQXcgSAkXlJyDPr0GBz43izHfvrrPD4mQ2Ym6yBUcvESoMyUJO6CoUcy6cOYHl3AIar5c5RvXljS9arcP8HO3k9CJ6YUgGHx4+cJB3veP3YNf3x2o1VaLB6kGFUQ62Uo2eaHlkScEWtUs7jN5HJgOIsI7U4aPXMIezvFk4ox/J/H0CjzYPbLi7Cj8da2fj0Y5P7488fMCEct4+f8yAw1tkc+g2iOE1aBc8A5U/pz/5WiHoaX5nLKlEyxqe/ZJqiLP/8IDtOg1oBu4df1yHV4Icoz2hK1eXlJ48XfbwnFEr0sCecOotL+loACtIMuO1NcdEXF5306NUVLPKpqtCM6VWFbDiL1KIATO1GtOT+vDGMIW+xu6lG/miDFfd/uAcPXtmH+jlBSb3232N4ZeYQBAOR4bEnrF489fkB3qnyqc8P4OlpA5GcxA+7cMdMiBif3cjUGzzx2YHIyi4G+gkhf5A65NBIIYy+OtSI5zZWY+6YYp7DkGPWYvaFRajoYUJhsk7y2aMZGKtEHlAmk16bXJDAys2H2JalAL91amdITFd59NFHcfz4cdxzzz3429/+hkceeaRTbt5dkm7QwCixcewexiNSyIFeGUbsP22NXMhBkmMmFawuHy/u/eSUSnx/rAV2jx9l2SZelytuUleIomHyFIexYlolvL4ANGollv17H4/RctXX1Vh6TX/o1DLc9S7/GPnnD3fhbzcOxeOf7ucVptCw2EsmVeDRf4U9i3ljSqCQAY0STUXsbh9e+t0gpBrU+Ms/92FnbTvuH9cHDi+/6ctDv+2LV2dVobbVidxkHS90QoOxPreJOY2Zdfw+vXq1UlJBSynZzFCYQqtikE1VhSk43e7Cqt8NwcKPd6Om2Yk3vj3Cg25+vKOW/Zv8lqagpP52eQP4saaFVeAk1i3Fh3Sy1cGD63Kvk5esYxPyLq8f1w0tgN3jx9CeKaxxJsaU1KLQEstLJlWg1R5O7ueYtfjLP/fh3iv6UOf/bzcOxZzRxeiTkxSxQrjV4YFGpUCzzQ29LyAJj212eEQG5e7LSuH2+/FjTSuMGkXEgjKXNwCvP8j+/8c7TyLLXIyGdjdyzFoY1XLUt7sl6zq4LLQ0/qAPbh0hmccgSpcG8/zrf46wHeWknj2agVk5Y5Ck4X5ly2ERrJg7L7R6IB4vUydITFcxGo3o14+JUT3wwAOdcuPulAy9Aj1SdKIj7rwxJchO0sLtY8IlrQ4v1mw9xnvR7/x4HIPyk0WMjsLmKyl6NWrbnOwiJ2EMGgKERhzHhUH+3GDH6LJskYdm9/jR5qAX+LTYPPjL1RUssZ1MxlTLun18GmOnx8dyCBEv7JnpA5Gko3sl6UYNehrVaHT5YXF5cecoJslc3WBlwwcpejWsLh9u4fSxXTi+HKu2MKETKfjogXorXv2Gz5lT2+ZglQUZA82LF45xzuhiaJVyaBRyzAglj6sKzVh6TSVa7B5kJmngcHlY/qYkrQpqpQy3XlwEvVocAlg8sQIvfBUeA5fTiPwb12N7diNzekrSKJGdpEFdu5uXmLV7/JKeXw2nm5w2lP9ZuamaV4QkNKYksfzKrCp8f7SFeXYl0CNZD7PHB7NWBZ1ajsv6ZUuumeYQak3KCdAoGXDEs9cORLPNg3anDwaNEqWZRjYMSL7bZPfArFWJDM7TX4brMZZPq8SarUyurCBVh9o2J682Q6ti2sgCwKjSdIzpm8OGT4U5vT+PK2NCTSEo9JYDDSzzryoUXiPXrcxNwi0X98aRFifykrUimgiu0qURPj56dQUy9Ersa5TunZFupBsYdSiptvrrwyLDvXhiOVINahRnGJGsV7FADWHeb/LgvIitUztDfhUdyhodPvzp/Z2YN6YE8y8rQU6yHm6fH8l6FRqtHqqHwiXyupFTcUrIu060OnHLRUVYvz1cvEVDgLw8cwhOW1w43hJe9NFYC5P14g313KZDeOOmYXCHjIyQ/x4ADjZYeR7Z3DH8QjZAjPxxeQNQKWTwBfjsiESZe/x+fH/KirwULeaOKcX/4yB4SNe0XEol5KJP9rDPLgUfrSpMwbCeQ/Dmd+Gk9Bvf1uDuy4qxfOoABBHEo1f3x0P/2CXy4rnK4UA90/DmzlHFWLmZyU1U5iZh6pACzObwLj0xpT8arW60OX3ol6PE4k/2saeWytwkLJ86AJAB2SYNClPUKM4YiHqrCxkmDY402nhoMhpS64djrfh4R62oypokZrlGjvtZIBjkhepICIrrndJOQ1xSwJ7pBmz45QTGD8iHTCaDQaPEsxsPYFuNRdIblUGGlZukvdWqnil45w/DcbDBxoMycueRrD+VUg6LU2xwuIV3uck6qJUythZj5ohC3pzOG1MCeSi2deOFvSS5kz746SQcXj8vV7RwQjke+HAX76S7ZmsNMoxqzBzZk+dN/+Wa/njwyjI02DxUsAWN8LHF5ZfsMZJuUItqJMgYiOysbQe+r2H3RG6KDsea7Jjz9s883bN++0kA4CXszYLwKfku2zq1E+RXYQjIsU0mA3yBMJpk7phiUQJZWBErVMhcagGuQqBt1JpmJ063uXCizYlPdtbyKhQ37T/N6zer5iRl7RJl/Q1WF745WC/idF80sRzrt5/A5CH5PIUslRcRJhTNejXcXj/W/3ScbSpCmtr8pjiDjZ1+xKGsBoDVWw5jfGUuDjVYqffZXtPKUjAIjcy8MSX48we70OrwYMmkCmSa1JCNLkaSRgG5XM4yWham6bBi+kBUN9jg9gXwXqgak8xbdpIWz2xk8gPck8ftlxazrTQBRiGdanNJhiXGVeTw7vl/o0vw0D/Cc/zglWX4241VaGj3INuswfJ/05FatCrrp788iL/dOBSn2pzITdbi6ekD0e7yIjdZhyONNh7nD1d5bDnQwM4buQe5LqlBIIl1wj3EVXgLx5fD46uheqMLJ5Rj9ZbDAICTrQ78v6v6otHmZnM+JVnGUOW9gk2Yk3msb3fzTtZLJlVg9ZYjGF6URh0jWZOvfnOEd1LctP80Vs+sgsXJJIdf2XIYvx9RCABotUv3NKYl5Fd9XY3HJleiod2NVIMKb353FNOq8jAwL5l3SgaA5zYexMLx5ViwYS87RnI9IeEj6c1d0+ZEYYqO14ubnBwzDErsa3Cwpx0SCl6zlTn9E7G4vLA4fTjR6kSPZB1LJkiegat71mxlkvkH6q0oy0nCrZTQHa11akflV2EIiCXvG4qFkrLxXLOO59UDzCQXpjIxWynOH1rFqVRsuMXhYZqXCMJEj03uj9MhzHQghNogyZ8UvbiJhVbFVBZOHJjPhmDIGBZuYBb60Sa7aPPEklBssXuwcMMezBpZKMJ4k3CIXs33SggTpsPjg0ZJh1OS0Am3Z7FaIcPeunYeUubhj3bzwiEr/sHvdzx/3S+8U8zsC2XIT9Eh26zF3787ynRkc/tQkKbHR7/UoqbZKSrbpykPbpUu1+BPG5LPGgHy3aWf7se8MSVsInPhhHI02sSooSlD8qhecYvNg1NtLhg0Spajicv1RO7DpQC5qDSTNc5quQxPTq1ENYePikvsJ+QeIqeyJ6cOwNy3fwa+r8HqmUNQZ3ExXq7byyK39GoF1Eq5KDdGwjDcU/LvhhewRoDc5+GPdmPV74fA7Qvwwq+0wjsyplNtDIfTrWv5/TtKM4146XeDkCYRaqE1ZyIn95s5J/dFE8tRlK6D1e2netOQMZuYy8DbEuqudwsHtXZtVQHrUNAcxwUbdmPtzcOgUSt49BlkvGWhxjNJGgVSDBp2f8mjkAS2Ojw4FGrtunxaJfW7hKm1M+RXYQgyDArcM7YPrE4f7ri0GCWZRnYzCsNBWpUcJ9ucPJy9EN4nTBqWZZtQkCJuIn/fFX3g8QXwh4t78zz1FL0apy1i75Qkf/zBoCifMX8sQ9LW7qJDzWQywOMP8DYPrVr4kQnlcHqYQinitdw1poRXgZlr1kGvUWLpv/axz80N/5ANMp/TvJ7r6UmFPKobrCjONOK5jcLipAAvHBJpgxSm6ZBmUMMXCECnUmDiwHzc+94OngJ48atqUZFeJBoMIT9/hlFD/W5eip59nlVfV+O+y8tg9/jQEupeNWVIHvpkmXj3JR4nl7OfhNTSDPT7EDFrFRhdlo0/vb8DKXo1brqgpySxn9TzOT1M+OZggw3fH21lFVVhmo5lpe2ZZuB5nCl6NZxeP+4aU4ITbU68u+04G7qTmpt6iwsLP96DP48rw5xRxXD5ApIQ1+oGK/wBUPm3EDK2l/dLx+KJ5SwfFHm3bp8fqYKEL+3kvnDDHqy5eRh0Kplkopy8n6lD8vDfw80IBJnaDqnrSq3NJpsHXn8Af72hCk9/wYTjyHhf//YIttVYGAfnSzr7KBGiW4juqCpIQVV+MhQKeiGrvhMJ6H4VhuCUzcdL5Ao3EfEMCTc+t9OWEN5HSxoCwKYDDRiYb2YRBDK5DB/+dBKTBufB4vCK8gnCZh/vbjuOoT0HYF+zAwFZEBqFuH2eze1HkgQfjVwGEa9Kq8MDg1rBbsxgEHjp62qMr8zlJSetboZyglRgEmVHuJJIB7dIG2/RJ3vwtxuH4kSLAz2SdThKCXkMzDdDLotcnQ3QewAHg2AZQ2e9xk8gcqtpX/yqGo9O6g+nz48F4/uxNONSJ7aqwhQo5fyGI1JJXeKhkfUTRDCkmNSsN15VaOYpsGlV4ndNQmpOj4P6rP3zzCI0z+TBeSJPnMb9LxxzhlGDeWOKWdpvQIxC4SalhZTI5FlJ5NIkASrITNLglouK8Ldvj7Lr674rSqnfHVKYIhn6ITUuJVnJeOErPrXFi19Vh1hlj+KRCeVsCE4KjPCf6iYUpNKNEaFZuek3hbB7wgg4bq8GmnGlPc++0+2sflg0sRwTB/pR3+5hx7utxiIyIrQ84aNXV8Di8GL2hUV49ZujKLnGgL5perR4/NT8Q6qu8+gmfhWGoN3lFR1R393G5+mv6JGEl34/BA9+sEsUCioNJbu0Sjn0KoUowbX0X/vQ6vBg3pgS9CjRom+aHs1eP4b3Tmc3Mtf4CLHz5LOfjzPEcSuvH4THPhP3ql09cwiUCjGn+31X9MGg/GT0yTIhN1mLitwqtNg8SDep8eqWI+iXmwyZjPGKzVoV+mYzz6OQMUlGsinIfYJB4PsjjbxcRLQN4vIG8FNNK5Z/flAy5DFvTAl+ONJMTfgS4yoMo5H8QpvTiyenDMANr/0gui6XDO7aqgL8YW04bv709IE41GCDVikTQWkfvboCLm8AHl8Aq2cOwZ5T7bB7/NCo6JXeXAfhuU2H8NqNQ+HyBnCy1cEao+FFGTwF1jvdQH3Xcjnwr511opDhIxPK4QvF1GwuH9tOtSCFHsYkld4MZQbfg543pgT/7x+70erwsCdKgO7pkncrBfV9LQQ1zTRp8Njk/jjaxOmQl26ATi2HQgbc/JtebGc6Wv+BJZMqcKCuHTkS/ZtJcxyZDFRqC/Lv728/zoS62lzomW6gghH8AaC+3UW9T3aSBmturIJcIeeFWrnU5uS7kU7YwnWxcAMT+lrwEZN/KAvttfIck8job9p/Gm/cNAz1VheSdSq89p+j2Hywib0vqWBO1Sp4haxyGZCbokOqVkHl6OqI/CoMgcsbnad/wfh+yE/RUUvT9WoFS9dbmKbDyzOHwO72Yf9pKy9sxOUMt7r9opgt8eDykvVsqID72ZMhWuojlFi/yxuA1eVjK5i5ntJHP9eiX7YJKrkMzXYvFoc6h5EEopD2eNnn+9kQzvyxpeiZZmCflXglN/6mCMs4lAw6tYINV5HvCjdXQZoBc0YXS+Ll7R4/u9BXzxwCi9OLrCQtjjXZMK0qj6XMuJcTgrr5N73wy8k2BIKAz0/nfiEbiwaxvHvdL3hq2gDsrbPi8701eHLqAByst2JArhk2jw9z3/lZRKtQmKbDXZeV8mg8/sIJk5H7bj3STAUNcBXY89cPoirWl2cOoaKtuBXMkSDIJIw5JFTpHQwCX+6rw+qZQ2CjrE0urbbQg+Z6p5KtN0NQU38wiEYBZfr8saVwenx4ecsRzB9biorQicasU+Pv3zNz7nT7UJiux/FmO1Z8yVDBC5Xq/LGlKAytIWGIjawx0l9hdFk2G84qTNOJjCAXCCCEgz4wrgxahRw90vTY0WgXMfRu3ncar984FHaPD49d0x9/DiHlWh0eFKUb8PYtw3G6ncn33L9e7Di6vX5Roryq0Ezdi3a3FwfrbVDIgFFlmdhfb2NbixIUYaPDxyvU8weYQr1uLyg73yXXzK9opHk9iz/Zi1dnVVEtfoDTuemesaU43GCDQa1k49pEXN4wZ3h9u5vKZV6WbYLLR68yPNZkByCO9ZN7H22yw+sPiDjduW0huZtg2pB8kTFauGEPW4nr8jK8+6/MrMITk/tDr1HijW+PYHhRBryBgMiT/fO4MjwzfSAabG785Zr+PCjpkkkVeCpkYKRaIZLwz/56Jl5dlm1CMBiEjzORDk84BzJrZCGvcE3quiT5LRUicHj87ImBAKaMWiX+752fWS9YCP195suDeGxyJU60OJBmVFMdhFjooWkJfJc3gB0nLPAF6IaNTAetkloYxiSfyWTArlobTltc0GuUojxMil6NqsIUPHPtQGSbNWxSHQgzYxKOJroHrYVZo4THFxSFqFZ8cRAvzhjM/v9z1w7Cyk3VePDKPlSSOXJ6IjmpsmwTjjTaoVHIMe+dcOMgrnLntul88Kq+PI6pmmaml8Gam4ahweZGlomhwyYK+vVvj7G5r+OtTnzw00n0yTKi3upGVpIGvxvBB0ksnljOVvULmw4R5ZynV6HG7qGui8wkjShRPrwoAy9yTooAE+qaNDCXDTc+PL4flkwqh1oph1wW3hN1Vjf1dFRndScMQTySoeVzj0gpDJvbhzSDSsT5o9coWH6d0wLoHNdDqyo0I0mnwrbTVvRI1lK5zPOTtWxCSLjZeqYz3pBRo+AliwmE7VCDTdRmUIoSefaFRZKJvawkLe/vBqsL93+wi4cEyjRpRNwoj322H09OHQC72wcEg5KFarTOYQsnlOPt72uobfn8QVCVfV4K32OmXffRqyuQolfhicn9kZOspaKXMpM0eODKPkg3qiEDkJ+sQ5PNw1OiNKN9ssWBE61OeH3+iB4nmR9CD82FavoDdKPu9oX7MUgZTCnvvCCVCRNkm7VYyqkSf/DKMuSn6uHzB6kJ61s5YUohV9K1VQV47F/7AIipUZZMqkCyVgGn2webBFiBy18FGbDmxiqYdCqWGZZ8JiTG8weY/yrzzHhuY9jAEOX+6qwq2D0+WF1+HjiBgBHIfddvP4k2pxcKGaBSyHDv5X1wX6g/tFopQ1l2EqobbNCr5Zg6JI91nOhIoD3syRFgmg69dP0gZGj4JHgen59ae+P3M+Fk7jzRwsF3X1bK2/PtTi9SdCqolXK4fEG0ef1IVipglMgLGjvJCAC/EkNAaGrf+cMIbDrAtKCjTezeunb0MOtgVCvQ7mYWWDAI7DxpYQuWPtlZK0ryTh6ch++PNGJ6VSHLISQVJ6+6eRh6JqkpnZ3KWY9aq2L4g9bcNBRWtw+n290shI0soFdmDsEpiwt6tVIyXCKV9OyRrOXRK+tUCioSSEjolqJnCmf6ZJtwx99/El2XbHDi7REcNOGwnzQwF/4AH6opbMvHVfbCTk3kuq/MqkKbw4PsJC3qLA7sOMkk405ZnLjrslI88+VBXmGRyxuAUa2A1w889I8dIoNj1Chw+yVFPJrm2y8pQpvDw3priyf2Yyg02pzIMWup9NCD85NRlm1CwO/HM9MHYnddO8p7mEUIMK4Rkeoqxr2ucJ6zkrToYdbhoY92sUYvRa+GXC7D1iPN0KsVvEQ5Fcb5Mb/g7ylOUdXa7xhPvV9OEtIMagSCfkx4keHXeXP2MOqYkvUqzBldjI931MKkUaDYrMW+ZnolrkxGT0qTugdStVzT7MSpNidqWpy8tqMub7himYvuO21xYsGGvazxmn9ZCRRyOcx6FW9dP/TbviyAoiSTDhGvFgAD6mxuuH1B5HF6ZwSDMmrtzR2XliDNxIeA08KA5BnWbz8pchoXTSxHtkkDKAGX10/lLXNTqMA7Kr8OQwAAQaBvlgknWhxY/vl+6sSu/Y7BTT85dQDa6xmIG0FBAECqXiVqLn73ZaUozjTg0tJ0HpGcVLOZBqsbBqWcZTgklMjECJDv/fnDXXhr9nCk6NS4862fRQto9cwhONHqRFm2SZISuY5SybpwQjke5FRgPj65PzQqOe4aU8JjnST3IWEk4lXe894O3HJRkeQGJ9Lq8GD/aSsvhDWkMAXtTh8v6elw+0Te+Ke76vDijMFQCNA85Lq7ay144rMDePDKPtCq+BA6q9OLxyf3x38Pt7AnuiONNvTrwefT4RocjULOQ44QxUISl4yXuBcvzhiM+9fvQo5Zi5su6CmC95LE7LwxJRjW04BymAAE8cmOU2zYRSmXodHqYguNNu0/jRXTBmDvaSu0SoYwj4QbaJXUCyeUI4ggdpy08N75rRf1QiAUU7K5/ZADeP66QbC6fZJ9rknBH1E8pC6i1eFBjlmHFzdX42CDDQsnlLO0EkeabDzEDpt3CvWWeGxyf6iVCnx/yorMJA11bcpl0sgztu4BpCeHMqYOcs9uPIRXZlaxDs7KzYcwvjIXOlUQy/4R5iUidCjcqmQaRJyE/VL0arh8fri9QXx1ohHFmUaG3C4AqFUyjOmbLQp9/b9/7MYDgk6CdW1O6jMo5XJqjcvCDXuw5qZh2GdzwKBV4lhTO16eOQStdi9bMDeyKBWdJV1mCHbs2IHly5dj7dq1qKmpwQMPPACZTIaSkhIsXLgQcrkc69atwzvvvAOlUonbb78do0aN6qrhAADkchlG5Cdh8cQKHDjNTOyPx1oRDILHecL1Bh6f3B9apQJPTO6PXhkGlvsECCvlN2cPR6PVJXrRNM8pw6RBk90jokSmdXayubzwCgqjyGdcilqCnSfKneDUC9ONaLS6RL0LuF5kncWFBz6gw2pdXiZhC4i7ckWK1RPFSNpaEiNCQ1A5vT5qCO1kqwMOr59aT0Gu2yvDiOoGm0iBc3M3pFbi/nFl1NPFqt8z7Ko0VlDSU5r8m53TMzZZG6IhcPtg0CrhcHl5v33txqEwqBQw6VSYNChXpCzWbz/J9sFosrlZMEKOWYsXZwzGTyfa0C/bhL/99wjrMGiVclidHiTrVBhSmMJTssWZRuw+1S6ai4oeSThYb6O+L26O48WvqrFsygDUW93IMKrh8Pjxu+GFbN7olot7Y+7bP6PB6sF3hxvx5NQBCAaCot4Spy0uloWWtjbvvbwP+mQZ0Uaho3B5A6yXy10HUuuNRgJI9sTdl5UiiCBSdHzgAk3pCiHiZG3STi3zxpSgLlWPHK0SSSolSx+fn6LHiVYHa1DkMjk27T/NJsp7Zhioz1CWZcLOUxbRXJRmGuHy+VHX7kZaIIgLS7N4+2fxxHIg2EmQIXSRIXjllVewYcMG6HRMB6THHnsMd911F4YPH44FCxZg48aNGDhwINauXYv169fD7XZjxowZuOCCC6BWd11fToDJwP8xtOnX3jwMr34jhp2NLEpDIAgMyjej2e7BA6GFPVeiGrDZ5ka2iZ+QpkHN5o4ugcfrkySoEv6dblDD4aPHmLmbeOGGPXhlVhVOW1w41uzAyk3VLFfSbRcXhcfq8/MMTqRqW9K1rUeIGTPXrOM9G42Yy+H2scyXWUkaqJWMZYiUx3CGNqJwYz49fSBqWuxIM6ol0TtqpZz621dnVaGiRxKON9thdfswZUge0gziJuytDg9sLh/sUs3CQz2lyZwrQ5Zu1shCNDu8WPDxXp6CmDWyEE98xnifBFFEi0Fz4+SLPt7DMzitDg+ONNmwchNTz1HbxqB11Ao5+uaYcLDehj117VDIgHsu74PTbc5QGJPehOjlmUOQl6qPyHGkU8lhVCsx67UfRAgqErIhtUvrt5/ETRcw3D23XFTEGjCp9bRwwx5eRbPL54NCIUOGiU5JnZcSZmI1aZWwunwwahQiioxoJIBPf3kQT08fiECQny+ROl0QBlgu99Cdo4pFp5ZnNx5CRa4ZtW1OZCdp0SfbgFSrBnI5eAn6VocbvxvRk20TW5CmowJRdBo5O35yn8rcJFw/nJ/ToXWfe/PmYegs6RJDUFBQgOeffx5/+tOfAAB79uzBsGHMoC+++GL897//hVwux6BBg6BWq6FWq1FQUID9+/ejsrKyK4YEAPD7A2h0enHHpcXolW6Ax0+PvdlD1ZhalYIl2wKkqwF1agWMGjkv7k8r5np323EMyO8Pb4BfOSxMMBLP1xsE7B4fHr26gsd7Q0tUtjm80KoUIk6jj3fWYu6YUvxwrBX5KfqYNgVJYhLSOaKUuL/VCvjeHW4fssw6NNiYOPuKLw7igXF9saeuHSWZ9AY5pZlGeCWQM4cabFjxBROSmlbFVOwGAR5KwyqRuLS6ffD5A0g1ath5+67QzGtVScIsq7ccluzp28KpF1k8sQKb99cBAApT9Zj/3g6RgiAKnauUhIVEOWYtJg/OQ0FI4W050MCsk1BdB2mRCYDXPJ2moEltwMpN1Sjg9HHgzUXI0HEhx6SDHLfgb/7YUqZISa9mw1vkBLRqC3NaeGJyf5j1DKspQwoIXvc8jVJOHcM2Tghq/thS5Cbr0ObwiJBnC8eXw+v3Iz+FqR53eP149J/7kKJX4/ZLitj1lqpX8ZBcUnvCHwwizcDvKihVWEgYYLncQ9LV2n7c/vefUZimwz1j+6C60YaSTBMeGNcHQTBh4dxkHQ7Wh0+r/bIHUbmIHh7fV+Q03npxbxG8nNZ9rsHqRqGxcxznLjEEV1xxBU6ePMn+HQwGIQtpJoPBAKvVCpvNBpPJxH7HYDDAZrNFvbZCIUNysj7uMQUCQXy2rx73cyggVv1+MH450SyKvZX3SMLKTdXokzWItxC4JGBcZWLSKvHnf+zGHy7qzbIWmrUquP1+UXWtze3HnloLNEp+RzKlHCwZmUmrhMvrw7WvfAeXl18YNSDfjAWURKVcBjTb3bjt4mLe+B6ZUA5vSCt5/D7e+KU2xeD8ZCybOgBvfHsE91xeBoCJVxNDN3lwHrXgbfnUAbwEmz/AfG7U0HtBKBUyqFXihHZhmo7tq5ykUaA0Own766wAgnhiSn/cvz6kPJT0KmWzVon/Hm7hJRgZzzrAvp8csxbL/32ATUzS4t5ur5/F6L/w1SGGHM9sgEwmkzxBaFVyliKZ4OFJCEcYaiB1AneEegwQT10RMuLBIFjlQPO2V3zBeL0A0GhzU+dCo5QjSatlT2dEhB3kSJ3B/R/sEoWv5o4uwS8nmGJHWriHeOdFEuEPrqfOrWcoTNPhxRmDYXF6oVDI2UTriVYn9Gol/hRC/UwenMcbLwBc3i8dr904FA3tbmSbtVj27308B+i7w41IM6jRYHWjMteMh64qw2kr45wJEWAEFAGAB3SQWrfmUEXvdUMLcLqd3xN84YRyyGQeyAQntCSdispFlGpQ467LGG9/9cwhaLC62bniSopezRanAcx+TDNqOqQLadItyWK5nMOsabcjKSkJRqMRdrud9+9cwyAlfn8QbW2OuMfQ6PKxRgAIFclYXbisb44o9kbyj0K+motKM7FqC7/snTSMmfWbIlEzjKpCM4vM0KuVeGXLYUwamIucZKbXLreT08rNDKqGS2VBjoKkMGrOqGLsq2un9rFVKeTIS9azSoU8I7dAaeQtw3jjT9apIlajzh1dArfPx0JnSW+D3hlG6kIlMFuAOf0snlSBlZsYKm7asVipkEMp59P3EuX4h7XbWC+YxO+1Kqbwj7CAphjUuO+KPrw2nfPHlsLp9Yu8ucmD8/DCV4fZOderlTjYYGPf6/vbw+gPvTpcT8HduFaXDyWZRpj1dDRWllmLZ68bhJpmu4gieVWI2iOWjmuv3TgUT0zpD7NOFfX0Rmow3vzuOC9cV5imwyMTK2B3+WDSKvHIxH64/U0GdCAV4hQmX4kHyi12JOEebhtPchryBwL487gyNDvC6CsutYXwPjXNTtzx1k94ccZg7DjZhokDcmHSKZGfrINBrWCrqgvT9HhgXB9kJenQ7vTCrFehxe7GTSGSuapCM6ZVFfCS6osnluMBToe/RRPLkWlSw+72w+MP8JwwnUrOmwsCdDBpFdSIgdXtxZzRxajINbPsr+TZFn3M1Ok02fg9Ho402USJ/yem9IfNxbCRtto9KMliejGYBTQewuI08jzpekVcujAjQ1q/dosh6NevH77//nsMHz4cW7ZswYgRI1BZWYlnnnkGbrcbHo8Hhw8fRmlpafSLdVCa7B4ROiUnWc+2TwTCGOJXZlUBAI632HmKRiGnl7232D0IBMQhgNFl2TwjM29MCfr2MGHfKasIU7x4Qj/kJOt5hV3CxvEZRg2e2XiId0wm5ebHmmww6+gVvWTjtTq8vPHfP64PFHI/W2xTa3Hy0BOkBwKBzhJl/crMIdSFSiClZMO4Qh41AKQb1aw3nqpXI0mvxGmLGxqlnHdkLs0ysYlVmhe8+JO9vMQel+QsGARe++8xXD+sAACfs6iiRxIyjWr2JMOlxjZxyN244+f4L6EcgRx3vrUdD17ZR2SA7ruiD/bVtcMfCLJGgIx50cd7mNOeIEEqpdwbrC7cv34X3pw9lB+Oi5Cgr7O48NYPNXhlZhUcHh+sbj9u5xjQRRPLMbJXKjYfbGIbzgivJUy+kn3i8oaLHYWfkb8P1Fth1Cggl8lEVcdcod3npxNtePWbI7j7slLUtTnZmhZSVU0cAi6EmoSy7B4/+mSZRNDMBQJjRagfmmxW3poiY+KuKUKgOGFALj7eUctz/N7ddpwtAsuXCMdlGDVotLl568/q8iE5JdznIEWvRpvDg/vX85vZr9lag9xkjYivipZ7efuW4UjSdw7xXLcYgvvvvx8PP/wwVqxYgaKiIlxxxRVQKBSYOXMmZsyYgWAwiLvvvhsajabLxpBl0ohirCWZYs/W5WUKy+aMLoZGKYdBGy4wK5Uoe1fIZJAp+BuVBo8jEDdhf+DSTCP8kLEcOcSL9Pr9vPvoNUo2LnpJSQYarC5kGDXYW9eOFV8ewi0XFUXc4EJyNW7B1pzRxbzEHxmzxenFc9cNRFaSFnWtdnzwSx0cHh/P+5RKBq+eOYSlSrjj0mLcG2LR5L6HB8b14R2Z54wOe6uxwAYf+2y/iHitZ7oBje0uUU8B4Slr1RbmtCKXyUSe3XObDrFhFxJie2Ezwz/zyc46zL6oF88YZyVpEQwGAdDDRggZbO7861R0hdzDrMMTk/tDp1KyBkeK56aWg6q5bmgBFArmdDbv3R9EiuPlmUOw+WATTBplTMlXsm60KqYAjkhhmg6lWfwwRTAI+PxBrNwsDl9FI23UhnILb/1Qg8cmV7IhNVI9LBUWo3Vy48650FjJAeQm66jfzTXr2DDg+p+O4/HJlWiyudFzdCnbGEk4frNORSUNTNKp8Pfva0T0IEsmVaBnugZ1bUEk61SY+w4fFs49haUa6vC3G4eisd0NlUTu5XS7C3n6c5yGOi8vD+vWrQMA9OrVC2+++aboO9OnT8f06dO7agg88QeCosV0pJEOq9tX184usNWfhJtH5Ji1os24aGI5FAoZ6lqcWDF9APafDqEEUugL7vtjLezCJ3LLxb2x5tsjLNSMnAjmjenDjmnemBK2IbdWJUej1Y09dVYMzlew8VMaoyF34b64uZp3PHV6fKJnF/69+5SF3cSLJzKN0+VyOd76IezFcxFFRFL0ajg9gaib+o2tNbzEuTBvEYvnSojXyGZ7KhR2W/b5AcmNBjAdvhyhUIGUInliSv8QnYgfD1xVhoZ2D3LMGtzw2o+icT09fSD8ApQKd03pVPyevTRSNqYA7v+39+XxVVVX28+547nzzXAzkJBAyJwwJoADIoMiWAYrIKKCgG/92UJFUQRpARmKIpZBsaIiTlXrAK3Dq9YKSrSIFq1MAiEEQuZ5uPP8/XHuPjkjRuUtfs1Zf3HJveeeu8/ee+211rOeJ8zm6h/+5UAsHp+DYDiK3GQzewpWUYBZr2EPLaT4eK8pBwaZJsMODwNxjTPp8RiHR0rYv8CdN+S5P/lpN9fWwjE5vOjpoSlFePXLKozOS7rgJitH2ghIawpcSPSJeyC4EKUz93W8WYe4iBg9RmtVMOmZrVCjAqYNSUezy4/yRheSLDpsnjkYETCp1KVvHWEjZr1WJdrsV08pAq1T4arcJFHaj+huEDSZ3JxLtdG4ckASDla2stoTUv0YyRYaF8t6TUOZlLj0G4dqRFq13I1TiPgghaRn5pSg3RNEik2P47VdqG73Mtq0IR3L4bJ4fLYsnbJPAAnVqYDpwzJE6lKhaIQHxzzb4oJBq0YoEsV3DUyRk9u4Vt/pw8sHmQ06P8UMu1GHsxyJxfImFyhE8eycUtR1MipJ5D6knMiKSfno8oXYk9+Tn1bgkRsH4XSTk0egJfytJEWw+PXusJfAFeONOl5DWX2nD8//8xyLnR+UbmMLt9/H9ggwC4Kww6ooIBqNYGZJXzjMekm2TjYtGEtn3fvGt7KRlE6tQnW7l312Nw/PwMYPT4mEQggSyB+KoKnLK8uuOr0kHbu/rmE3YJtBh617T4uQJL/7RQF7vw/+9ShLd3A6VtMY2McGnYbCQ+8e520OmQkGWGgt4k3SwkZxsdOjNxASpThJ/0Kzy4/mWMFyekk6ilKtcPoCmDYkTUQKSO7xoVhePCjDkXW+vfu7MhMM7O/hqnjJsZ5K8TeR12Sjl5q7G6cPRF2Hj0VjZSeZcaKuC+FIRLJJj0vEuPS6PNAaJuXS6goAUWBkZhw6/CEeas3jD4s2e5IGVKukaUuI85JLz9EalYhji1tnIve4blox+nK6nH+q9RpHkGgSY/fbPQG4fUF2IeYlW3hiLIB4ArZ7GBHvug6m/V24SZHN4pOTYlWy1ZOL8NpXVWh2BXgT12bU4Z43xJq/L85nILdmnRoWvRY17T4U9mEIuuSI2Oo7fXju80osGpuN000uvHmohrfRbPm4AtNL0rF9HwOV+/0vCtDk9LMpjidvGYrjdU7kpVhwroVf9Lx7XA66fCEMcJhxpsnFpkYcZh3uuzYXFc0MNXF6nJGnnuULdndD13f4kJ9iwb3js7FlL9Pv0O4JwBVgYKpPzB7KFm59wTDS7DSrL2vSqdHHTvNOlEuvy0N1LD1i1KnR4Q2J7pnL1kly6tx0lhQabM3UIrS5fd0UE1MK0TfBhK2zhiDBrGMdn16jEgkdrZiUjxfmD8fnFa0oybBjxV+PcXofuvMVJNUnRJJwi5dxRh3YV1EgJ5lJZ5rUWvzqqixW7pDk1O9941tc3j9etNmtnVoMpy+AReOyYTFI91R8V98FXyjC6615YvZQVrYR4KfuiPmCEWQ5TKhp80husjv2d6ftbh7OcBoJhZ7k+L/UKrHOhvBAQEjzSIe2SadGuzvIez9pRGz3BLD+hiIWcZRk1Yu6+jf9/RSPvmLl5EI0uPyw0Vpe9CrXf0JRwOicRBi0alkNYxVFSTZLhiNR9I0TsxOvefc4np83HK3uAKOpbdMBF49hovc4AodJzaOUJQNv0KqxOaaUtHh8Ns/jS51ICfZcqDpGUg9P3TqMlw7h8hLtKOsWhSFsj+WNTrS6xNGKLxhBYxcjdH/X6CwcrYuJWwQiPPZHKSK2lZMLYdYx2r9ELJwYN79r1qsRDEdFxb3XvjqP311fwNYAyP08vu80Xpo/At5gmAfnWz25ALUdPln1LPJ5Lp589ZQi/PrqLGz44CTWTC3CSwcYZEmL0ycq3K6bVoyBaTQSTDq0ugO8jl4VgI1/ZxaysHmL0AMQ+owUqx4d3hDbIEdObBkx+nGSdiEiKMuuY6CzuUlmaLVqfBEL1e1G+wXpoTd8cBLPzi1FtsMMg07Nbv5lp5p48N7MBINo47z3mly4Yn0EUupmXAnJR24ciK03DUGnN4hUO832sAipvq0GLV45eJYRe4lh5YWb0PobitHm8iMSVeHJW4eivMEFdyAMk07N4+KRq2uY9Bq4AmFkJGh46Ss1ophZ0peJgiVSQ6QLPj1OWp8gN8mC6SXpMOnU2Hl7KTo8QSRb9Khu9/Cu89txObx5IFVT2DRjMGo7PGhzB/H7v/FTULUdft5cJSd3RqPZBw1F4ZvzHShMMeOZOSVocwfRxy7dFKdVUzxHxN0fttw0BIvGZSPLYUaXNyDqTrcadUzWQGI/aHUzaKbTjU60ploxLMXMI8H7KdY7HIEK+LSyg+X3UauAYRlx2PzRKUwfls6ebE06NdZOK8YqTlOYhdaw+P4UG40XPj+Lq3KTEJahfnD5wywFr5QgSVocjcXjs9Ev0cSeRIQoHKAbAz738kxe0QsUeAuTi3s+2+JGYR8LTje6UN/pg5oC7hmfi617y1lK3buu7hY4XzYxDxs/FOPJ/zhzMIIyefNGpw8efxi/HZcNFUXBHQgjK4lf/OaG9dwiLhdPvuZdRtHssRmD4bBosHh8HlrdAaTaaJH4zMq3j+GOUVmw0WqEOdh6YRQWiXaH4xZaDQut5aX91k0rxt4TNThU1YkVk/JE4IG7x+XwUknOWGfxwnE5qGzubg6SIhTk/l5fkOGWeWD3Ud6p+KrcJNYJAAwC7U+fVvBSJa9+VYWlsd4NqSI8qXPs+Ya5TznVvU/KW/BJeQsPBJCfagfAnP4NWjU7r620Fl3eAKsTIEX50U2dIF3XMOjUuDwrAc//8wxyku0sLPqNr6sx94oslDc64QtFcKSmCU/PKYHHH8aJhi5eF7zUdf8giB42zxyM+986jPsm5PGK9cFQGDcPz2CauSRqVr4gg2xSURB1ekvNVaJ7IOz76DMmG4vfOMK+liq6R6OMUL3UPZDfTFh3t+3lf9Zm0gOQrjNpKIpHstcvzoD4iyRX2SscQbUzyJ6WyMNePD4bY/KTRI1RmQkGPDOnBF+da0d+sgXPlJ3BVblJDO7eqMPl2YnY/I9y2bwyoSEQsmqSCffojMF4uqwS90/Iw8M3DkRzVwAOi040oVZPKUKH248sh5m3CQkXJtCNezbr1Tjd6BItpk0zB6OsvAWFKRZeR2yiDE21JxCGQSfdTEOBEjUdyRXGuUVcbtMO+Xu7O4Bnys7grjEDEIkA/mAYTU6/5LUoSnpMuR293E7cO0ZliTRxV759DM/PG47zrR6kxRl4ylTCDYHWMoRnAKBT85uD5AgFicOmtSpeY9Cad4+zQADh56pavTjf6uYt8BSbHovGZaMgxSr7Pd9HD0I6snOSLHhi9lB8Vt6AKwYkIivRjFQ7jcZOj6Qo+/dx8XiDYVG68aUvqhAMR7H7m2pRQ+PqyXxW3bVTi7Dq7WOYPqwv3jxUIyIb/OPMwTjR4ERBigXr/1csBlTR7MbMkr5ssxmxzAQDll2Xj+8anLKsu9Eok1KXGtPMeANbT0gw6fDU/kpR3WLyoDSevkdVqxc79leIah7LJuZBr5FeP+QwJDeX/zhzMKrbPSIKl3uvycW5NjfvvUQE62JYr3AEjU4fr3Bj1quhUVFIstKigmJVqxedXuYkqNUw3OWEnjgYjuCVL6vgC0oXV5dcm4tzrczDqm2XZhusi7EQPvbRKTw2YzAWv/4tNk4fiNeIklOMznZn2RncdlkmtCppDhkuJG/t1GI8+elp3D8hX3Jy7bithKWJ4N5TgkxRMcmqR4cnINlMUxNrYOGeTuW6WgtSrGwRl5v3HpRmxZ2jByAKYM20IpxqcLFdvXLiM3nJFvZ7hWNKOIEY+GIFu1nKjf+yPUcviNogY0rSVX4J9k65jYaLouFe91yLG4FwRBJAMDjdzo5TvFGLSJTRTLAbpREueckWEUU3+R61CjyaBPLs1k4twrLdh7tpzm8ciPsn5KLDG+JRMcvRROQlW7Bx+kCk2mg8U1Ypqmv4QxGmQe49QfH0PT6en+D781IskpFHklUPV6wJLs2u59OlHK6FPxRBRpwBl/ePx7xR/dHuDiLZqsPJBhd7yMlMMIg6xUm0NL0kXXJMazq8rDNeN60I86/IFPXlSM2pqlYvyhudbNRFa1WIMzGfuxD9uNzzc/lCcPnDLLEfF0U4MsvBe68noNBQ/yBLsxlEnC1EoUuqoFgVO6GtnlwAXygim/smCJ2MeANqO7ygNSo8tZ/ZPISYcXJCy0ww4qUFw/G3b2pg1DOspn3jDOj0BVn6XSBGOmfWwxuUPn0W97HhidlDkWzVY+dnTO1Bo5LGsHtjE0YIzQxGopL1BV8wAhWlwuuHzouaaQglN5czp90bwP0T8vDYR/wO37XvfccL6+8cnYVPTzZh9sjuvPdTtw5jFywgXfMgutA3lUovYsIJJISBSr3XYWF6VeQgh8My7Hh6TgleOXgWI7McuGyAAxZaI0JFCRc5yXXfMSoLT8W6iAl9Ba1VISvRxEQ/gtrC2qlF0GspppuW1kCjApa+dZhN5UmlHja8fwK3X54p63z/cEOxqMOc22AVZ9ShQZBWIlFm/0RpmohoFFi2+6jonriiSf0TTSJufyE1QtmpJuSnWGLU2GEekdq2vafx9G0lqO7wotMbwKzhmQKN6YHQayhkxBsAqpsRQFgbqmr14qn9FWzqq7bDy67ZnugOr3yb6cbv8nslx0L4msu6u3h8DpbvPhqjsx/E1kuuHJCAP350knVsNhkn3+zyo+wUs0Z4KMKYsBP3vQmmi0fQSUWj0YtUbvjPWDAY/sEUE83+EG58muHtIeIywlPZ5EFpeO7zSnbAj9R24YnZQ3knbIB5AMJ84lO3DoM/GIEvFGaZSldPLoA3Fq5L5V3JKZ7bAs/lbyH6rRZajQUvHBLdw2MzBuNko5PXxr/+hiKedgF5767bS1Hd5kWcWQdfIMSie4r7WNHlDaCmwweNio9+yUwwiMJ87oIR0vOunVqIfolmNHT5EGfQ4YHdR3gbAgBsvHEgUuw0r56wddYQ3PP6t7z3pdpoPBJrvz/V6MSbMVIzqZPu8on5SLUb0OEOoJ/DhE0fnsDILAfijVrYjFpeQ9nG6QMRb9Ljy7NtLOMmSQ1yF9vovCTs/ro7bXF1bgLONHl4J8wtNw2GiqLgDUagVlF4NkbFTOzu8dlsxHbvNbkoTLUgHOXTXZPns3h8DjZ+yPD4rJiUD4eFxvF65lrlDR247bL+aHMHoVFTeGY/8z3LJjI9JsINTR2DpS7bc1Q0piuuL8CpRicLZBCugWlD0mDUqRGJQBQJqlTAIx+cAsBQp9w9Phft7gAC4ShvsybIF3cgzMKIuQ5TCIMUggoenT4QD+w+KtrcuWtvZP84lpkTYJBM3OcFMECPe8bnYOve07hrdBaP9qIg1YJ4ox5nW91IsdFYLtAdJmN1tsUtWhMLx2TzKFmIQp7LF+ZRcpP7/fOCEWh2+dE3zojj9V1saikzwYBFY3PYsctMMGDFpEKcanQiy2HCpr+fFPUNcLMAa6YWoSDFjFS65w1ll5xi4lIbV5ZQSjKOFHHvGJWF3V+fx52jB+Bko1M2vcDNfa+eUoTqNg82fHASKyblY8tNQ3C8vgsFfaxYtvtIDNMv1QJ/TNQCT1StTDo1HBY9Fv/l31h2XR5WTMrnqWf1SzTxcM8rJuVj1eRCeIMRbJ89FJXNLnT5w1BTQJqdRiQKVHcwgiP9HSb2t5yo70KWwwSzTo1Uu5HNGQNgO293zRuOA2daeYiPO0Zl8XKncUYdWt1BrHrnKza9I6Xler69G6JHLF6CHlqnoaBTqxCJgqctwCqUxfogMuKNqGhy8XRuyUIVMlZa9Wr4gxG2g5jWMtDT7bOH4tuaTpbr/9aRmbCbdDDpuqF/wgJjnFGHc60eUaG22RVgo8rsWG8DrWHgoL96+WusmVIkOZ/ijTr2uu5AGBtiz4E44zsFOg7NrgDcgTCvJ4Hkp1dcX4BEMz/lJ+SqkQUy2GmoVCoe2k0qEiTC8UQ7mTuvhR2/wsLsmnf5qSJhXYbUVriFf+7mTlFAm5tfiOXWhrhOMTPRiNuvyIROqxYh41IsNKy0BhqK4s1VKV6flZMLkWDSQaOioFaDpYmw0lqoKLCHL66uciDMpI/LKlrw+N4K7JxbIqovbP+km27dQmt5fTdSqLucJAuPgub+CfnAReop6xWOgNtDICUZR4q4e76pwbwr+rFpC7l8dUlmHO+B3DFqAHzBCDZ8cJJl4ByYZsOCK/qj1ROQzQcKW+ApikLfOAP6J5iwdPdh3DEqCyk2Azq8QdFEJvrAws2DW8TVaSjcdXU2WxQlqCFh4TnLYUaHRMMdkQok+c/MBAMemzEYHkG6SlhglErvkJOiw8Lv5zjX6halGn4zJpslFBMuinZPAMFwBNXtXph0Gvzh/e7O78mD0vBkTCA8I86Auk4vG03cPyGXPcmR8d7091O4f0Iudn9dgzmXZbIwYiEqSLgpSXHbcEXlV0/pLpDePT6bhdrKFTGNsa5W4ThK5dy5TVZSPQgUAI0aePjGgTjb4pZsApMDMjw9pwQVjS5Jag4SCXILqD3p+L3QvOemFxePz0aiWY8/fXoGgHhzz0wwYNXkQrgDISQLlM+4tSHyHdv2nsbWm4bA6Qtj68f8Mdz8j3I8N7eUEQ7Sa3hzVQqpte49Rp3OoFPhaG0XL8IhtCUARFKvJKpfNC4bgbAYZVjV6kWXNxi7R/nCP3m2p5ucPPEcBtV2cWh5eoUjcBjU2PDLgVjx16No6vLxsP1lp5pwVW4SfMEwVk4u4MEppTa0NTHUAze0VcWShL5gBFEwoaqVVqMqzNQXvo8DiLw26tU4WudFnEnHSmLKnbrIJJFDeZDNgtv5KMV2uW3vafzplmGymxTpsKS1DFY70axDFPxcqXBDICd30i1M+OOX7T7K8OBwxvS5z8/iwUn5PHroO2WgqM99XonF43NYxk2PgCJDLtp7+WAVHDIIKYteixuHpfPqIfFGfpFQuCnJFZpzk8xsVLl2WjH+da6dJ2JeKyEdylXhEo7jhfQi3j1cK2KOXTu1CB0eP/RaFYKc2pbwfs+2uCWvS6QrMxMM2HzTEFQ0uVi5T3JqFjZ+9WReS/1dSv2LS1DH3dxTbTRuGcEnnVs3rRjbP2FSq3IUIaFIFBlxBskO8/ouH+5/80gsJVPAzj+zXpqe45vqDknoKXetCTujudHR9luGSgIFjHp5KU5u5oGrzEfWxEsXUZhG9f1v+S+wKDAg0YQl1+TAatDivSMM1QOtUWHxNbl470gtHnjrCJa8cRizSjNYeUYuRn/RuGw8OmMwm8cHuh+IUde9WWpjTiEaE2fxBbsRRrS2W41o9ZQivHekln29eHwOTtR1Yvu+Chyr7WShYxfaDIALbxY93VjcgTBqOzxYNbmQd4/rphXDE2AodxeNzUYkEsGBylY0dfmwkvNeUoTmGrdbuNUdZDUEyJjeOToL22cPxdppxdjzTTVONDhR3e5FY5c0fDQj3oA7R2chxUbjD+9/h+37KpgCPed70+xGSZqCG4els46Oa7SWaYSy0GosuKI/u/BMNP+9oXBUFCFIXcsSK/ZOGZTG8lVZDd3XevFAFYxaRodi0bhshvXVTuMv/zovO45Sr4f1teP+Cfkob+jAS/NH4PHZQ/Di/BE4eKYF9V0BmPUaXvQjvN9AjApCeF0Cbaxq9WLJG9/CH4rgsY/K8dT+SvxxJrMGcpIs7Gel5vXi8TnY800N+/fF4/l/XzO1CF9WNktSSmz+Rzlmlqaz90j+duvIDFFz48q3j+HR6YOw7eYhuDI7UfL3lDc5sWzPUez8rBJzLstk1zWtVbHpuEAoiqo2D+5/6zCW7T6K8kan5LWiUekIhxTD5SDUJDoKhkK46+psPPd5Jbbvq8DOzypx19XZaHf7ZZ8zoU65c3QW9GqV6NqdMe6oi2G9IiIAgE5PAJ2+MF756rTsqZG0qj94fQHKG50AGK9NRNjltIWdviBorQqPzhiEuk7m7w2cDU3IAeSw6FHT7mH5W8ipiyCOhBPuQkgFOXGZaBQsFPL7rlXT7sHGD08hM8GA7bcMgzcQAgUKnkAQ8SY9zrf7kJNsQVVrN7UF99SYaNby1NnIgvfGKA36Chgf6zt9eHxvBVvk+/XVWWwNRK5/wazX4PKsBJZjHhBHbFWt0iddigLqZU7jqTYaGg2FaBTYdeAsWxTkdvyS6xCTI/cjOg5Lrs1lT1jeQLcYUH2nD7sOnMWqyUU43eRCv3gjujg8Piadmve97x6uFVFfcL/n0ekD0eYOwOsPo5ny46rcRDR0BdDs9Ivg0lxcutR1pRS+yGGj3RNAOAqWloRbs1JRwPZbhuK7OieGZdhxpqmb26rdE4Ax1rh2vL6L17Etpy5HCOqIw/EFI7LRXLMzgNNNLlhotQguKkQCcaPKpdflwWzQiAgRpeYUd2yE0FNuPUEu6rcbmC020WKQ1C4gadyeNNMJU0WGi9RDAPQiR5Bo0kOtgkgchEySTTMG49myM5hYnPq9sC3hw7YZGDk8ty+IDi/jpY06NS+PSTiAdtxWgiPVHchMNCE3SQN3IASHWY/f/U2sOsaNJribDqEDWDQuG4PSbSIoI1kEOg3Fy7+/e7hWtGC4IWdVqxeLXv2GXTArJuWDjqn0cHsoAOYkdbKhC2k2Bvb4AkdknSx4wtsuV2uJRpnCsJ5TzJOCTK6cXIhQmKHE5jpitng8txRfnm2TpQnPS7agpt0DC63hdaOm2GgsefNbdvPnFvr2nqhnRIU6fMhI4NMfkAMD4U4S6jhs/kc5tsaoBIx6LTZ8wC++rn3vOFZPKYJGReHhD7vFcrr8YXz03Xnsmjcc1W0eGHUavHu4mqUhIDDZ6SXpMOvVaHYFeJoIa6YWIcGkRV2nV1Q85QIZ8pItsdpWN8fWYzGdXuG4LR6fjXijDg0xpJ5OQ4EWFF+XXpeH1746j+I+Fuw6cJb3W3cdOMvSqhCraHazUYnwWRGCOq7DMeo1PElM8l4ALPb/978oYOGiUuuJRJVEjnPuc19JHgS5WQAVBTR2+ZAWZ8Q943NgM2qx/oYinG/zimovcoeDvvEMOKNNogbnC0bQP9GEZRPzkWTVYeUvCmDUaWDQqrGGA70m7xWCVGgthYtlvQI+CgCggBNtPhyqauOJTBO7e3w2kq00nik7IwvbIoVMLjcMQVu0uQPwhSK8xffwjQPR0OmDO8AgeDISjNBQFAOFtNNsumTx+Gw8zck9CmGSmQkGrJxchA5PAEkWPVbGahQAA8ncupdJf+SnWNh8szsQZpgjvQHUdPrYzS/dRqNPnBEHz7ZhWF87b8EIewOE0D/uqVGY3xWiHACw9AZSsM81U4vgD4VhN+pZx0ssM8GA+ybk41yLG9lJZmyMwff+fMcIXjcweT7P3V6KmnYv+jtM+PpcuyztQqqNZqK2DkY28vG95ThU1YlUG415V/TjdXI+NKUIT8Vgjism5SEkAakckMQU2YVQTQDYPnsom3Ne+Oq/RX//48zBsBg0qGh0ia6rUYGVhSQHkesHpYICxd6jHLzy+XnDcbyuk+2T4f6NwFS5oi9kft1zTS5bXOZ215IIp1+CCUdqO1GcasU9HHQZufaWm4Yg0aJFZbNHknmVu4E/PacEJ+q7RBQg3PVi1at5mtPcaxFeKADY+OEp0TqVWk8zS9ORZjPAqNfgsY/E0EzhafvO0VlIj6MRjVIi8j4C+yYQYWLs+ok34HybF3u+qcG91+Tggd1H8fqdIyWpy7mQ0LvH5SA/1Qy1SsWDWJP3cjuY3ztSiz/OHIJMkwIf/WEWBUZk2BGV4YsPR4B1733HmxAA44kLU608XnqhIP2cyzKRlWjmNfFINe1wGRC5IinCcJQI3784fzg6PEFEosC6946zk487ibkMliQyIPbE7KFY9e53kpsFAJj03UVAYfFOCvpH0FVefwh1nV5eM5AcXwsgLh6TiGHyoDQ0OaXRSmTCL+FsOt5gWJKxUUWB1bklamekyY/rnNo9AfzrXBtv4dV2+CVz0IRa+bGPytHlD0sqVa2fNhDWRIPkfKK1Kpxs9CI/RTpK0WlUMGjVktEpVxaSwC0z4008eK8cIqfDE0SCTColM8GIReOykZ9swe6vq9lOdrtRh7oOL++Uf+81ueznNv+jHM/dzqj2hWQ5tkLwhyJ45WB3h7yV1qLF5ReJzJ+q78IjH5xCaaYNz91eivpOH/rYaVQ2uXkF+c0xJ0C+g9CJnG5y8rQYACZdR8gUHWYdW0iX6uGRgmZyT9ubpg9CepwBgXBUxHvFhX0LqaRJ1M+Fw2YmmLDt5iFQCSRZ5dJXu+YNx5GaDsmu/ocFzMhd3gDwAxzBhaz3OAIKqOvyIYoo1t8wkKc6RCYGd0IQo7UqIMpsNAT9wqU5fmhKEago0+7NXSByqkpkknBFUuo7ffjgaHcqwqjXYM/X1Ygz6aFRUWh2+rB8YgG6vAzjITfl1O7xs6kUYbgthw5x+ULIdpjh8ncrjQmLd3IbTYUAwkYWlC8YYWkkhJMcYFIK6pgqO0UxqSWKYii2hWiKg2eaUZRqRZc3yEN8nGl2scVWEuHQGhWO1nSyKSjiFIljkxJbIb+FOC+pHHScUYfCVIYiw6BVYcEV/XnNZ4w6mBv9Es2SCzwSy6kL6w0k9eULhqECWF1eLlZeShbSJQFBlnIwJr1alpqCwBwNehVGZCWwkZiU0yeINYL3b3Ex42g1yOg1W/XQqlUob3LxOuSF+gP7TjZg7hVZWD4pDw4L09wXiQK0Ro31HCiw3PyLRKMoTLGiySnujHaYdbBm2GHUa3DfWwz8uiTDLuqyljq0XJWdiNK+diSadaju9OGT8mYMcJgvqGkhR8DHFfTZ9PcTOFTViS2zBuODo/WskyQEltw6TigcRasrgMJUK/588CwvdUeka7mII4NWqRH8MKOAgzVdqOnwwhsMw6xT47EZg1He5EQ4Ah69RFGqjZ3oJLeaYNEhP2KBw6rHpg9P8E6GT+2vwB9uGAid4HTwfWgf7r9TbTQmDUzl6RuvnlKExz46iUAoirmXZ/L6BNZNK0a7248ufxgOC43nPjuDO0ZlIcdh4gnthCPSedjqdg/WvncCi8dn42/fMifdzARjjzYaLoOosBloYLoNi8ZloyjVimanj92ESUqN+/uWXJuLAQ4TEs06xJl0vI1y4ZhsHlSQOJwXD1Th11dnie6RiOfoNMzz2vDByW6O+lhBu3+iSaQ1QRy/EDrLajC/2q3BvPS6PLwwbziq2jww6TQw6SnQWi2aXQFkOcwsbw9p7LrnGiZ1wWUYrevwwGHR49vqDjYFM39Uf2z9uJyFI3M568nvU1GAhebfo1yBMRqNwu0PSZ4o3YEQtu+rwFO3DhOhoC40V2ktQ2dwmllKkt/rDUZwXtATQmtV+M2YbLangjTIPfDWYSwen4OmLr8sxFVu/jnMeoSjUWx4g8+au20vQ5FekGDEiVYPK7yz5Npcyd/GjQDWTi1iwReBcBRVrR5ZWhlupCts6qM1zEVXXJ+PJAvNOgEA6BNb48L64479FQiEoph/ZT9W5pOs8df/VYVDVZ0ozbRhZmmGSOch8SJFA0AvcQTN3hAe+8cpLBqbg6o2D7r8YTz//glRnnvV5EJ0ev08WmqLQYs5MoUlYi5/CHaVhnfyE6J5uHnKReOy8e7hWkmRFICfEgAg+tvKt4/hsRmD0dXoxMn6LkwZlIYdZZUiBSxaqxZxAC2fmI+nyxh00huHatiwWYh62P11jSgNI8UgSpBJd4/LQWds469qdaNfopEdR2FemURHT9w8FJ5AhIfOmTwoTdT4xUV8WAxauANhuANhFPex4axAPOfea3Lx7JwSNDr9sBt1bEpNrts5N8kCfWwzu1BTEREr4cp2PvlptxjN6slFeOfbOhyp7QKt7WYuBWLRD4D+idJMsoRGm2xoxBmQ7+nwBGA1aCTThzvnluLg2TaoKGaOURRgNWgleaLW3zAQANDl5XfmymkM0LHDzZJrc+EPMXxVupisJTcqM+nUqGv34JnPzuLBSbksX3+8SYsooiw99Ih+3dQQXL1sQMz9JOXo7h6Xg2anD9UyhI6d3iBg0fEaSOWK0qWZcVg0LhsmnRpajQq3xta4XEp004zBqGhyYoDDhD/G0q9qSrqp7/l5w9Hi8rNOAAC8wYjsGjdoVTyNEbLGX14wArUdPqTY9Hjs7wJtk/0VeGr20Iu2g/cKR9DpC+K+CfnwBULIT7HgdKMT7Z4AC+mkKGZCJ5r1PEHphWOz8WCMOwiQDyuNOjWqWr14hcMgmhFvgNWgxaa/n5LMU66/oRjZiSbkJFlgN2rZDlhiBJ8s15Vc3uTkURfPvTwT7kBYNDFJN/DJGBe7UavCrSMz4IuJkX9wtB47bivByYYurJpciLWxaEKnoeAw63kLnssgSn57XrIFd4zKwuuHzrMoIVqrwrKJ+RiVnYizLW5AJjoKRqIiUR65SCo32YwtNw0BrQVSrHY0O/2wG7Si3P6Wj8vx2IzBuP/NI5hQmIhHbhyEJqcfCSYdUqx6rIlpUJNx+8P7JwAAvxkzgP2tcgLnBBPuC4pJ3Oq7vPjV6AGoaHKiX6IJO8uYDlkuxHDTjMGSzX+ERpv8X5JFz7KRBsIRPH+gCrdf3o+tf5DN/ZOTTRiQZEa2w4x4kxZ/PngWC0YxDk/YHbz+hmK8/AVzADAJSPSykyySp/wsh5nFsLtjxIVyjJdkPjU5g3hgN597iBSLl0/MZ2Gt4UiUl3YRom6Io+NCT18/dB5rphYjySotCEOkOB1GNQtnljvQnG50Yfu+Ciwcm42tsTSYnP62L8hoGez8rBLLJ+azjo3WqrH0ujwecmv1lCK4/OLuZzm4LEVBFh57tsWNB2JEf0Ler7vH5aDFHYBDf3G28P9+R0ABjc4gtuw9hcmD0mDQMhQRJIQlaY2VkwtFYu497fjTa1RocfvR6QviVAPDUdTi8iPZymykOUliSoLf/+0Y74RJCskE3fLrq7NwusmJNJtBEjrHTdFs23saj80YjD+8f0K0oBeOGQAVxdAa24xM8Y57gl48PgeIRvHIB6eQaqPZXodEs56leSAmRDkQJkxS/Obm3zd+eBIvzB+B6na+NjL3WrRGBa2MdqvwdbxJh2AojBZXECvf/lY2nUAW147bhsIfirCqYpUUkOUw4cX5w3Gu1YM+NgOPGO9Pn55hER9GnTyfvfB7pDpk199QjE4fAyPmRhc+GSZZQqNNvkerUfNojReNzUZaTEWNOPlBaVbMHpmJO17s5k5aPbkIVc1ObN13BgvHDuA58UgkguklmRifnwq7UcNDv909PltSY2D2iAz2WW+5aQjLH8TVETBo1ejyhZAeZ8Sj0wfh92/zD07culh/hwnzr+wniUQjqTyy8asowG7U8iRAl16Xhwf3HMXCMVmiPojVk4sQDDNOqtkTYkWoLLQaCWad6ECTbDMAEHejXwjq7AtG8MiHJ3maD3eNzoJQIGfV28fR7gnwCPZaXD7RdYlaoFmmd4ZElXJUIy/OH46LZf/1jqDZG8KWvadETWQvLhjOtpUbdRo8W3YGY/OTerQpccXSHRY9OrxB/P1YgyTF8KA0m4ibBxCfMDf/o5yF982/gjndS4nRtMd0AoTNP1o1hekl6bDomXRBXacPWYlGVDa7WZ52spgINS4A/OVf5/G7SYUAulEPm2cORm2HdPidEW9kQ+qBaVasuD4fNoMWj/39FK+YVXaqCcFwBH3tBhgEqRfyeygK+OvX1Vg7tRir3mFOke8erpUUf69uc2OAw4LfcNhVI1FItu1Ho0x6pLLZzbt/py8EgzaEZbuPilJFXMTHnm8uTFVM5kE0CskO2d//7Rhemj+CYWI16dhoz0xLO5g2AaqmjgOP9gWZhqraDr5YyZ2jB4h1bd9jVN9WXF8gYs3NTDBg6YR8VHd4YdJreOm4SFQ6xdEv0cRe2x0Iid5LnKCcRjT5rD6WO1dREKVAuGm/345jnjMxvVbNnr5VFKCJPWSbUY8XD1SK+Prvm5CPL+uc0GkotkbAnPiPicZ8y01DAEDEPXahhjJ2DcQZ2GY0ohNAFNme+ydTAH7ykwqsefc4CwCxm3RYP60I59u9LAlinImBTt9zTc4Poh4h9+HyhQCLwjXUI2txB0RNZHFGHQ5Xd4qasBJMWl7D1buHayX54IUdf0/PKcGY/CQRj8+qd46zEUFPTpjpccwmm5dqFUk/btvLkILp1Cos38OneKa1KtiMWpY8jWDtpfKdQmHue6/JhUGnYjSMdWoU9rHiuzonCvtI3zPhHvIGw6hsduOp/ZWYf0UmJg1MFWk7//5vRznNWt18QiadBp5AEHqtCqlxJrxxqIqHpnj7mxr2NXHSt47MRKugKUeoAUy+10Qz9MdOX0jkTPsnMvf/xqEaEasrFzufYqPxxM1Dcbi2U1T8prXdtOFTBqdJLtLPz7Swp+kVk/LR5QshGI6IlKeWXpeHQCiCReOyRUyf5LtSY9q44WiUPX3KwTjrO7w42+pBbpIZ/zN6ALz+EGxGLZqdftZxCCMpqWYorkOitSroY8+d+14pJyiVOs2KORR/UJoTqDDVgufnDed1jZPPChXX7olRVlxbyC+83j0uBy5/CCcbnTzILkVJs5g6YxFbveDAQ6DOO24rgS8Yxnf1XTzHRmtVqO30sogwqZSNStX92wh/U2mmDbOGZ/KK45tjDurFA1WiyILLNEC+V7gWUy+SEwB6gSMgHcXcQbxxWLroZLJt72lsvmkwdn1eiRfmD0dVqwdWWguthmIfUGGKBesk5PNcPkblSe7ULzzNyZ0wAQZy+MTsoZLX6vKGUNvhYQnpyLXuvSYXnd5QjyGg3Ehky8flPMew5NpcvPjFOeg0FO+kLucE7xiVhYAE+yOXbjgQiqKm3SvK0xp0AVAUcKiqE4eqGMjh8kl5yEmx8hb54vE5sJt0MGr5IbRQA5h87/0TcpGXbJXMxz87p5Qdi0A4ynMUG345EA9enw+AYhXiAKb4nZHQXfwe0S8Ofz54FvdNyJcN60nqjkA2A+EIvP4wKETZPhRaw3BTbRI40B37uzfRNVOLcLK+C6l2I5ZxTq5PzB4q+b2JFj3cgZBI2ITbtyIszJK0DBfmSRwScWQaFcVi9vedbMDTc0rQKSg6k3EWSpSGI4yjk4OemvUatLoDLKMusTijDiUZdjw2cxDijDqs5fTSSBV0n583HH3tBug0KraPQIqiesm1uejvMGHrrCFItdMozbTxCrukPgFEYKW1shBkuZQN6QOhtSroYoMx94osXuMkd23Wd/qwo4yBh+Ymm6FXq9DEOXhIHUg3TR8Eh0GjiNf31BwGNUoz4ngTUC7UOtngxLj8FLQ4/VgWEx5fcm0uxuQ44PIFYdRroNNQWDg2m5eKCEeBM03OC576DbH8OkEjpdho0QQjoWBTlzifSGtVaOzywiXR3PSP7+qxeHyeJNNiTyIRYYqK6Ma+cagKz88bjianH3FGLZYJBDyYVJEBahllNDJGUo53y8fl2HX7cIzop+0RpfCWm4YAVJR3ii9M7ZZY5H5vqs0gibv3BSNs7v7WkRksmor8bcVfj7JNZLSWL1+4dmoR4gxatHqCaOj0YnpJBoKhKAKhsMhhrp5chNe+YjYLUsQXRia7v65BuyeAByfm8wqir31ZxWrvFqcyePnNHzOoLu7vebbsjGSe3OsPoX+CGb96+ZBoDIljlooAuDDP0kwbNvxyIFpcAeycW4p2T0DE/FnV4kaWwyw5v4ZnxvMkSus6fdi+rwJPzymRTIF8V9/FoqVIYVkKwktSNHIHnKO1ndjw/knQWqZL+aUFIxCNRHE7p9ZF5rhQ5AU4j0NVnez3eGJ8W/EmLbtu85MtvIOQ3D5yrsXN7h2EhjoiEcEJnfFzn1fi2bml6PAE8dR+PmXLa19W4bm5pQiHI0g06S6qEwB6gSNAFChINOCRXw7E8r8yhSw5orZwhKER2DZrCIDuSbNzbikKEow45/RJinN3ePx445AYnfD7XxSgwxNE3zgj7ntTnLPlonm4oeDOz8+K0AhLr8vDzs/OQqehcN+1uazKmI1W4+YRmezC5y6Y3V/X4Pe/KECT0y9Kf3B/t9AxnGhgEBJ3j8vB0ZoObPjgFJZPzJMWm2nzoiD1wqkvuQVzpLYTWz8u57XtB8IREWFaKByF0x+CUaeC3aRFi5u5j1MNTvz66iw8tb+S5/iiUYBSST9jkmd2WKSRGg6znpfOIafgJzncSY/fPAidniCLHHrvCN8x7/7mPP5n9ACUNzpR1MfKkvGR7yDU31+f7xBx8WQmGBBn0qGv3QCrQYOHPzzLfpb7e47UdkH3zXkmB93pg0GnwUsHKnFFtkMW+UIcM4kAnp83HOfbPOifYMLOzytw34R86NSA0xfmFaG50YQvyEAb7xydhWRbWHJj9wTDvGI34V3SqoE+dpqXAjFq1dgRc5JcLW4pCC9Xi0Hq2abECsC+YAQP7jmK5+cNR7AH9bnV7zC5/H+da2ejodWTi9Ds9PM0LRaNk4YgC++juI8Vm28aDJc3yIombb+FH8HJ9YHoNBTsRq1kzSaO1sBBx7bsi0wM1DtoqCPAlRk2vLRgBJ6YPRQmHcPGSNIxZPPc800NfMFITPCBMV+QaZ8/1OAEKJWkOLfdyOTq9GoVSzG85Joc6DVqbP+kAicanKLJWNXqhU6jQn6KBaOzE6FSdSsltXsC0Gu6F9C6aYUYkm7Hg9fnY/PMIeiK5b6376uAKxBmYYLknkj+Ns2uh16jZt/7dFklDDomqiG/m0sbTP6PICQe33caebHu2v4OM5Zelyc5ZjXtngvSbMvRK/eJwTSf/PQ0HrlxEDbeOBBXDEjAr69mioe7v65BNAps/6QCD7x1BKcanKht9/F+jzsQxvwrMnn3VNPhwZ8+qcBDU4p497RmahH0sXpIqo2WvCeHRY/Hbx7KqL19UsFSBs8qzUBJhh1P3ToUyVYj2+tAUWALk9v3VWDPNzUYl5+CB946jO37KnDny1/zqM3JM2p2+vHc55W4a3Q2O04k5zz/hX9h2Z6jWPDiIfazUpTPt17WD9/VdaG63YszTU7cVNoXOrWKJWnjfiet5Wvr3joyE4FQGMt2H4U/FMbQDKbTOBqlJB3XjcPSefcfiQId7gALaV00Lht3jMrCS19UocMd4L33uwaGkvs3r/wbnZ4gdGoV+sYZEI4AO8oqeYXlnBgQQy7VSlHS9NZSRfZ2dwAWWiv5nIWHn8ZY1PLc55W4eXgGKmIU1k+XdVNYC58BSdlw74Owwy554zBaPUE2NfjM/jO89xJm1kVjs3lj1+IM4ERdJ9ZNK+Zdd/0NxVCrKJxo9aDZF2Y6+y6i/fdHBByr7/ChscuLTl8Y8UYtXlwwAg2dXqgoFZ4tO8N2DpLCGMAszmgUOF7vRL+EsGTh6VyLGzcOS2cpCACmB2EzB60gWXjVqnCkthMefwhPfnKGd6p88pMzmF6SzghZj8jErc99CV9QXACWC5Nzk8wY0T9OVHRe+fYxvLRgBFpdfiSa9ThR38XrACYMnIQimtA30FoVHv7lQIa90aRDmzuAcCSK6SXpSLMbRWyWz5Z1s2rSWrWoRrJyciGikQiWT8oDBYqnSLbk2lyWJZLLaSSlrEVO18Ji65HaLiDW1xGNRqGmKHj8QazYw5DsPTSlQPJEdqrRifQ4o6g/4fF9p/HyghGgokCT0y96luR1TwuoVoOWrUmRccpNtvDyyMLPvnyQ0XHITbKgrsMDjYqCN8igv5icNiWCBhOk2bppxfAGQmzKJtmqR0eMC7/DG2I39Ciki9BcNT1ay0B/W9x+yZNrs8vPe82FOhMm1up2L09wibw3xkICh0UnuWai0e4cvpDzS1hkT7Lo4QwEJVFowvpcio1mx4YbnQufweuHzmPbrCE4WtedyvvTLcPQ6g6gut3DI9jjpuOO1HYh4Vg9XpjPpFpTrDQe/fAErzZBa1Uw0xps/vg0VkzKx7NzS1HX4YWF1iAUDuN/v2tkI/vCVCuGpZiVGsEPtWZvCC8fPItZwzPZDZqcJF4/dB6zSjPQ6Qvi5uEZqI+dLAhLI8mPrpiUJ6mNCgChCH9D1mv4YaAwJ7t6chHOt7pFHDnEyKT/n9EDZItM3PcKF4xKRaGhwye5qJudflh0ahhjndN3js7CAIcJLn+YJ7XHpTvwBSN48K9HY5s94zi4mw4XM714fDbKm1w8MXfCOSNkFJUq/JEc7rI9R3mpLrnmuhaXn9dcRxb5kdouPPDWYey4rQR6jQovHOg+fT5ddlaEtSdpitkjMiQd/mcx/dmXFgxnx1z4bIXABHKP3ALqqsmFqGpzIzPBhKmD01gpS7m+CPLZdk8AtEaNmg4PolGgpt0rQKGI1eeemVMCvUbNozsg97Fr3nAsGpcNvUYFnYbCk59U4Pl5pdLziRNNkHnx0hdVIgdPlMPIe+V0DqTWxNppxdgYE22XokxfObkQKVY9rvmfkWhy+rHhg+65SlBc5HtXTynCyYYu1HcFeKm7wWk2uAIhXn1u9ZQikB11SLodW2PcW0KtZFqrwtzL+6Gyxc2Tbw2GI4hEoiJWY64DTbXRGJufhANnmL6WM00u3DqyH5pd3fQid4/LYSVjieztMtJQJiEx2y/OgPiLpEnQaxxBpy+IBaMG8Ngsud7+8X1MU1a7J4ABSSZsmzUEDqseCziFJil0zLa9p/HEbKZ5ibuA+iea2NdEmObO0VkoSLHiWF0XdpQxOVlAGrtMNrR7xufwFmWcgU8oJpdrrGn38O6BGLOoKcx94RAj03d9AXKTLDDTGizbLYasLhqbzX6WTGypgu6ad4/j+XnD8c8zraC1avz+FwVY/7/dXbz3XpOL2nYPki16nGroYhEiPUE2kWfU4vZL/p5Uu4EViufKKpKxeHDPUfZU3OxiahHtngDUFFCQYkUkGsWpRiebptBpKFmHDwDPf36WPWVKcRpJ3WN2kgXbbh4Mi16LNTH0CxkX0tdRkGqV/GxBilUU8eSnWEQUDVLj6AmE0ekN8ZwA+dvBylbWgRLa7QiiolrX0uvyUJRqRSTa3Ww2szQd9Z0+vPpVFbbGamqJJh1aPd1CO/nJFmyS0DlQUd11ij/dMgxnW9xIjzNiwwffsaCBqlYvntpfwTt9b99Xgekl6dj5WSW2zRqCP98xEg1dPiSadPj7sTqsnVaMDncQdpMWL3x+FkXpdpbgkP3doQje+XctC0826DTYWXYGi8fnYESGHXEmnQgKvXh8DrKTmC5rDUUhHNvcyQa9+PVvJYVpMhMMyI8xog5Kt+Fci1u0ma+4vhBHazvZZ/vAdflYNC4bx2s6EG/WYeusIUi26rF8zxHR2hySblMcwQ81o16L8zInZFLMjALY+Xklpg1Jw+N7K/Do9IG89/tC0jjoSCSKBJOWdzqq7/CIWuZpjRqBUJg9+ROWSYJdZlEBZh3OtHqYPKKeTwdQ0IdPByDFOZNk1aPZ6WdPO1zY2eopRWh2MguzqtWLZ8vOYNnEAjR2+SWZFpOtNFswJc1akSh4OHWjXsOm1sjG8uiMQXji5qFo8wTgMOvQ5gnKNulcqNDMfUZvHKoWKaHde00u2mKpCF8ogo++rsGzc0rgCURwvL6TF6pv/4Shc2h1+RFv0mHHp2dQmGZHQaoFBalWtnYSjYr5nbhO8ZPyFsQZtXhx/gi0uQM40dCFdTEhkVQbLdpI199QDJcvgBSbAb95hc+EyWX5XDu1UPJ5GXRMSKBRAQuu6I8dZZWiA4LcOBq0aujU0nw7BNroCzK0239eMIIRSrLwqUXiTTqW0oR8lttU6Q2G8fznZzEmP4kX3UnpUCy5NhfhSJR1bGveO477JuQjEI6I1P8Y8ECU972kfrX49W95yJ/VU/ha4oQiwxcI4bfjslnqFPK3hwUw6FSLHg5ag2ZfSPbZk+8ikRQ3lSeMcIiTuP8CDK+EEYAbza7/3xPQaSgsuTYXB2Nd8WdbXOxz565NObqPH2O9wxFQDI20SUagPRrDVVMAbh2ZyU5UIeMjeb/wtUmvgValwqtfdXPBpMcbsfFDvjLV64fOY2ZJXwDMaaFfoom3yZp0aqQZaeZ+Y4VnbzCIhWOy2eJk37iBIs6Zp/ZXYvkkJrowaNWwGbSobffB6QvjNQ7/ETn9jM5LAsAs1InFfTBnlzSpHq1lmErJRCWMiAvH5cBh0YuYFPslGtmTeYc7gAdiKY9lE/NkESByEY0wh5uXbEF0UBpe/1cVD+Hx6ldVPI6jFZPyQVEUE65znEmqjcas0gzM5fxWks568hMv+7rTG0CcQSfaZH1BBlG0aFw2zHqmYe32579ieaRIFCLF0aRVU2hzhdBM+SVTTiT1E2/SY9NH/DmzY38F7/cRgfeQgFlWbhyttAaUDF0y1+KMOvhDEagoFbZ+XM4ePCJRYOvH5Vgai17JPVTHoM4GrRoNnT7cOjKTHSdi5IBD0HFC+CWxaCQKm1GL0kwb5l6RxesWNsVOvJkJBiyfWIDTnPoVIXAEmHEi6CuyyT5wXR5a3UFeLYL8TUiVoo7lvlpklMQIl5IvGMGRWN1s0bjuVB5XjjYj3oBUG80S7AHyEZtaRWHjjQN5KnfLJuaJ9IgNOialeLyeSbe+e7gWCSYdLpb1CkfQ5g+jut2Dv/zrvGQHJWFmTLJq0ery4+F/nGS9ulDqUarTWK9VwahRY8WkAhyv70IkCtS0eUTEX4RPiNAyczfSNVOLEAFwotUDC63Fhg+YHPrCsdm8iWzUaUT1hMwEA1KtNFQUBYoCHv/4NI7UdmHpdbkifnhaq8JVuYwjuGNUPxGWntvyL+QPWvn2MeycWwqVihJ1Ua959zienVvKEnlt/6S7cB5vlN5YM+INuKk0HQkmLR6fNRRH6jpBaxgReG56595rclkK6VQbDbePQXVpVMAdV/ZnC3txRh3cgTDbWc11bFJFXG7TG3m9aGy2bAqKdJRyT3dks7tzdBbSbAYY9BqR4hqtjfWQICCZcirpF4dIFNCoKV4ag6Q1pPo84k1aXuTR7gnAQmt4kWGKjYbTH4QnEBEdHl76gtHgBbpP7ne8dAj3XJMjqeltptXYfstQ2A1aLH3rCPssuDxLUjw97Z4AEsw69A0YkGDRScIvy5tcuGJAHGaWZojWRKJFh203D0YgFOX1Miwen4NOb4B1kNyOXjJWJp0GbR7ppre+sS7+aBT4qrIVwzLsqGzzIMkiTWhHKKZprQqeGN2GEIbOpSmR6q2Rui4AVMf6VYhlOcQstaunFPF4l1ZPKeIV8H+q9Q5H4O1OSxCvbdCqUNIvDkerOzB5UBqe2FeOe8bnYteBbtx2VasXO/ZX4KUFI+Dzh6DRqNDi9PGpEvxB1LV7sfnjciwam8N7eJtmDGLzvyadGgMcJswekYGsRJOIJ2Z1jI5CKPoixOALG4kIf//c57/iLZJmVwBvHKpmOyzJ39ZOK8aTsWJeilUab56bbGY1nMWt+SFZeoO2mHiJ8J7lOHbOtzHIkXuvyWWgtvukibzIIiQbFpc7iShpAdJiQMSxyRVxuYuJpMLa3f4LRinC0119pw+P761gUyWS6cOofLPczrlMt7PDLGapFZ7cfUGmk90dCOP5f54TIc1WXF+AvnEGtq/gN2NyWGSYEIwgRYOeEW/EkjfEyKWXF4xAUbwRzb4wu5kLnatcrcsTCKG63Qt/MCwqAJN5PijNyuM/Imti26yhKG90S57qN88czEYErx86z9bcyO8zxpg5pebeuVYPnvykgiXvIzKSKyblyUZPwtRWgkknTuXFmgmL+gz43ohtybW5ONfqFt2jVkXJUlZzDy0755YCBkWhrMfm8nc3ldR3+tgFIdQcJagY7oKpavWizR1ASbIZbcEwjtV1iaQStYEwJg9KE+Gvl751RAQb3DRjsGyDlVSBlHyOvJ80Eu2aNxzNTj8cFn5BmyySTTMGo6bdg0A4wttUHWYtnrp5KFrcAVAqaeZPi16DiianZNFMq6YASMt96mIbtvCkpFGLSee4qnBbPi7H03NKAEAEwyXXJpu5cIFwc+xy45oRb7hgWpD7OsVGw2bQYPU73/HgsEJRG6lrXd4/HlqNSlJLOBqVrzF9da4N2/dVwKRTX7A2Qa5l1GtgM4gjQ5LeJF3xi8fnoKrVLblBr7+hGANTLCjta4eHc19CpT1yH63uAPqZdXAY1Ng0fRCW7j4iGm8SHXE7pQmLKTnxlmba8EyMnuJkg4tNQ3bInNw7vAF5JoBGJy8iaHV25/wJYEIKncRFNgnJ+7r8fLEZ8htWTS7A87eXYsmb3TxfC8dmi5oJd5QxKarzbeIaoVGrxtabhuBYjF2V1qjYaJb73jaPdHpKeGhxcfqdfqr1Ckcgx3ES5o81fEFpqcokM9MwFg5HRVQJm/9RjkUxyomePLxTjU4Ufk8nLvde3pRYxNNLMvBgjKBLDnJ4KtaxzN1YyPfsufMyFCQYUemUPvnqtSoU9LHhVy+JaQqenVuKuk6vCJu9bloxshMNeGleKZItemQ7zFi6m0E6VLV0S0wOcJhR3ugSMVS2OJmCr9zJPTfJDDkaZ/LM5DrGU2009BqVCOoo5PVZPD4HWhWQYONTPgs7SuVEU1SIglarRF3hJJqYXpJ+wXnoltmEA7E3cDe4do9aTDExpQhmWs1GJma9Bn/69AwPjNDlCyLZQqOvRQtEgPgEI2rcQfa+9Bpp7iSTvruj9bJ0K/bceRlavEHRe9s9ARyr65IknaO1KowvSMGy3Uex4voCfspTxlETKuYLjRs5OD0zp4T97WlxBvzxo1MsOunp20pARaMMPYNRg6Jk5jDklBhzKQebaTcAoHjzgNtMyDWKAryBMKv+R5zErgNn2aiFFOrbY5s+F3lmo6WlRoWHlmSFdO6HmV2vES3OVZML8XRMPIQYrVVhSF87+xDIBkcWzfcVknry8KKx+oHwlCJVIB2T40BhqhUaNcWmmFQUoEYUj/xyIDq8QTjMeskT6JVZ8bIbS4s7AAetgZ3WSKpNlTc4ZT97uLoDj31UjtJMG3bdPhyd3gBvY7EnGAFwNotYd+c9b36LyYPSoNeoJBuJjDoNI6wuI/Ze3e5hiNok/pYdQ7AU9rGJEDuLx+cg2aRDOAJeMb8o1QqnrxvqSH67QauBg+4+9fqCsdoQZ9MlpzuhaMqY7KHo9AWhobqJCotTrWzjlZwD4eLspX4fF6FDmp1uKk3HPyuaeUCAlw5U4oGJBSjuY8V39V2sEwCYDSeO1iDXHus25jzadIuWRWPVdngkDwdmLkwxCjhoDcIQy1aunlyEHWXdTuDucTmw0GrsuG0YzHoNW18Qfs+LBypFh4vVU4qws+wMml0BydqesD8hGIpgRIad3exJ5Cvi5okw9++gNTwnCEg7eZbgDeDNC7mDx8j+8bDqNbzDEPk9hM+J1jLAhhfnD8e/qzuRk2RmSfUyEwxYf0Mxr77ITemSiC7drOU9x59iVDTK3ap+/hYMhtHBaSXvkVHANw0uVLa4EW/UoU8cDbc/hLMtHt4D3/DLgbgqy4bqjiAanT7eBgcAzb4wbnzmC8m0xXtHarFobA4P2khCUPLgH/nlQCRbdKC1GnZjJFqnVlrDNhaRyXdZuhXN3jB+/Zdv2PdGo8B7R2rx1M1DGd4RitFj5k447mel7nfPnZexn/2mwcUWuFUU0//Q2OUT5YrJZ1+9YyRcvuAPI77i3GOcUScSJ1l/QzGe2HeaXQRyRXadhhJpPnAb2TITDLhvQh4qmlzs7ykiHZjgj1NmggH3XZuHiubu92YnmXF5upV53hTThNjiDsBMa7Hhg+9Y3nnyDAjM+ELPy6RTw0JrWL4nq16NglQr3IEwEk063PfWYRY2KQW3XH9DMdpcjD61SafG4HQbQqEIki16HG1w8sZp3bRijOlvB6Lyc0L2eamAamcQrZ4A/KEIyhud3ePiMCEr0YxkvVr0mYM1XTgdG+/8ZAte4PDzc+dqTooVpxuc7HwktaBWT4D97DuHqzF5cF94AyHYDYzM5T2vH2af10NTiqAGYKa1uPfNb0WU1ey8/iGmAj4928Fbt4/OGITsBCNaXBJOhDMvLvgMBHPIatSirtOHkw1O3lrLcZjR5vIj1aaH0x9Bk8uPJLMefa1aNLtD3Y7MpEF1l/S+1FNzOCyyf/uPOoIbbrgBFgtzM+np6bjrrruwfPlyUBSFnJwcrF69GiqV6oLX+FGOAAAooDMM1MWamuqcPgTDgEZFwR1gKCdyE2jgQtBciU2XbO42WguHUYNmD+fhCV+TCSVxnW2zhqCvje7Re0WLmjPhfspnoxSFVe8ck92Qv3cz+Z7x5y6gcCQqP06C12oVhUanH4kmHRrdfvy7upNdTEP72pBs0n//mEuNU08Xl8w45jpMaPWGYNerL9qz3TZrCJIsejR0xe5JuCFwf09s85a8f7k58X2mYg4HwVCUBURoNRTjTKXGhnMPfWwGnG/zSM43u82Ijk6PyBmvnVoMKhpFskWP8mY377NP3ToUNlon/n09mdc/xC40jhfrsxRwrMUNtz/CjqtJr0Jxool3z3a78cftbz2wn4Uj8Pv9mDVrFv72t7+x/3fXXXdh/vz5GDlyJFatWoWrrroK11577QWv86MdAQSD/GMXyo/93E+5zk/5zh/xPR3+MLO5XWhTvVR2scb/In2v5ML9Tz3b/yv7KRvj943ThX7v/+/j9n3Wg3v+r3cEhw8fxgMPPIC0tDSEQiEsWbIEv/3tb1FWVgaKovDxxx/jn//8J1avXn3B61w0R6CYrCnj1HNTxqpnpoxTz+xSOYL/WLGYpmnccccdmDlzJs6dO4df/epXiEajoGKwGpPJBKfT+b3XUasp2O3GH3UParXqR3+2N5kyTj03Zax6Zso49cwu1Tj9xxxB//79kZmZCYqi0L9/f9jtdhw/fpz9u9vthtVq/d7rhMNRJSL4PzZlnHpuylj1zJRx6pldqojgPyZM89Zbb+GRRx4BADQ2NsLlcuHKK6/El19+CQAoKytDaWnphS6hmGKKKabY/4H9x2oEgUAADz74IOrq6kBRFO6//37ExcVh5cqVCAaDyMrKwvr166FWX5hWVakR/N+bMk49N2WsembKOPXM/uuLxRfLFEfwf2/KOPXclLHqmSnj1DNTHIFiiimmmGKXxHqHeL1iiimmmGKypjgCxRRTTLFeboojUEwxxRTr5aY4AsUUU0yxXm6KI1BMMcUU6+WmOALFFFNMsV5uiiNQTDHFFOvl1isUyiKRCB566CGcOnUKOp0O69evR2Zm5qW+rZ+FBYNBrFixArW1tQgEAvj1r3+N7OzsH6wT0VustbUVN954I3bt2gWNRqOMk4w9/fTT2LdvH4LBIGbPno0RI0YoYyWwYDCI5cuXo7a2FiqVCuvWrbtkc6pXPImPP/4YgUAAr7/+Ou677z6W80gx4J133oHdbserr76KZ599FuvWrcPDDz+Me+65B6+++iqi0Sj27t17qW/zZ2HBYBCrVq0CTTNyj8o4SduXX36Jf//733jttdfw8ssvo6GhQRkrCdu/fz9CoRD+8pe/YOHChdi6deslG6de4Qi+/vprXHXVVQCAIUOG4NixY5f4jn4+NnHiRCxevJh9rVarcfz4cYwYMQIAMHr0aBw4cOBS3d7PyjZu3Iibb74ZSUlJAKCMk4x9/vnnyM3NxcKFC3HXXXdhzJgxylhJWP/+/REOhxGJROByuaDRaC7ZOPUKR+ByuWA2m9nXarUaoVDoEt7Rz8dMJhPMZjNcLhfuvvtu3HPPPT9KJ+K/3fbs2YP4+Hj2QAFAGScZa29vx7Fjx7Bt2zasWbMG999/vzJWEmY0GlFbW4tJkyZh5cqVmDNnziUbp15RIzCbzXC73ezrSCQCjaZX/PQeWX19PRYuXIhbbrkFU6ZMwaZNm9i/9VQn4r/ddu/eDYqi8MUXX+DEiRNYtmwZ2tra2L8r49RtdrsdWVlZ0Ol0yMrKgl6vR0NDA/t3ZawYe+GFFzBq1Cjcd999qK+vx+23345gMMj+/T85Tr0iIhg2bBjKysoAAN9++y1yc3Mv8R39fKylpQULFizA0qVLMWPGDABAYWGhohMhsFdeeQV//vOf8fLLL6OgoAAbN27E6NGjlXGSsJKSEnz22WeIRqNobGyE1+vF5ZdfroyVwKxWKywWhhHUZrMhFApdsrXXK9hHCWqovLwc0WgUGzZswIABAy71bf0sbP369fjggw+QlZXF/t/vfvc7rF+//gfpRPQmmzNnDh566CGoVKofrKfRW+zRRx/Fl19+iWg0invvvRfp6enKWAnM7XZjxYoVaG5uRjAYxNy5c1FcXHxJxqlXOALFFFNMMcXkrVekhhRTTDHFFJM3xREopphiivVyUxyBYoopplgvN8URKKaYYor1clMcgWKKKaZYLzfFESimmGKK9XJTHIFiiimmWC83hWdBMcV6aC6XC7/73e/gdDrR3t6OmTNnori4GGvWrIHJZEJCQgL0ej0eeeQRvPzyy3jvvfdAURSuv/56zJ0791LfvmKKyZriCBRTrIdWVVWFX/ziF5gwYQIaGxsxZ84cmEwmPProo8jJycGWLVvQ2NiIiooKvP/++3j11VdBURTmzZuHUaNG8bq3FVPs52SKI1BMsR5aYmIiXnzxRXz00Ucwm80IhUJoampCTk4OAIZj5/3330d5eTnq6uowb948AEBnZyfOnz+vOALFframOALFFOuh7dq1C0OGDMEtt9yCgwcPYv/+/UhJSUFFRQWys7Nx+PBhAEBWVhays7Oxc+dOUBSFF154QSE6VOxnbYojUEyxHtrYsWPx0EMP4d1334XdbodarcaqVauwYsUKGI1GaLVaJCcnIz8/H5dffjlmz56NQCCAQYMGITk5+VLfvmKKyZpCOqeYYj/BXnnlFUyaNAnx8fHYsmULtFotFi1adKlvSzHFfpApEYFiiv0ES0hIwIIFC2A0GmGxWBQ9bMX+vzQlIlBMMcUU6+WmNJQppphiivVyUxyBYoopplgvN8URKKaYYor1clMcgWKKKaZYLzfFESimmGKK9XL7fzbhG3YC2CH1AAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize correlations between age and other numerical features\n",
    "cols = df.select_dtypes(include='number')\n",
    "for col in cols:\n",
    "    if col != 'age':\n",
    "        sns.scatterplot(data=df, x='age', y=col)\n",
    "        plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEECAYAAAArlo9mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAYbElEQVR4nO3df3BU9b3/8dfZX0k2vxPyA/A6JYK0fL1tb0VCW4ELVya2HVtauYJIrHba72i1EC8igRkSantFh47jlPst1PbbaeVHaQU640j/KAQuUXAo/UE7gFYvFLSQH4RsINlNyO6ec/9IiAkhEjG7Z8nn+ZjJbJKz2c/7bE5e55P3nj3HchzHEQBg1PO4XQAAIDkIfAAwBIEPAIYg8AHAEAQ+ABjC53YBQ7FtW/E4BxABwIfh93uHXJaygR+PO2pri7hdBgDcUIqKsodcRksHAAxB4AOAIQh8ADAEgQ8AhiDwAcAQBD4AGILABwBDpOxx+KPF/v17tXfvbldraGtrkyTl5eW5WockzZkzV7NmzXG7DCg1tk0pdbZPE7ZNAt8AbW2tktz/gwKuhu0zeaxUvQBKNBrnnbYjpLZ2pSTpu99d63IlwGBsnyPrg95pm5AZfjQaVXV1tc6cOSOPx6Pvfe978vl8qq6ulmVZmjRpkmpra+Xx8BICACRLQgJ///79isVi2rZtmw4cOKAXXnhB0WhUVVVVKi8vV01Njerq6jR37txEDA8AuIqETLEnTJigeDwu27bV0dEhn8+nY8eOadq0aZKkmTNn6uDBg4kYGgAwhITM8IPBoM6cOaMvfOELCoVC2rhxow4fPizLsiRJmZmZam9v/8DH8Hot5eUFE1GecXy+ntOl8nwiFbF9Jk9CAv/nP/+57rzzTi1btkwNDQ36+te/rmg02rc8HA4rJyfnAx+D0yOPnFgsLkk8n0hJbJ8jK+mnR87JyVF2ds+gubm5isVimjJlig4dOiRJqq+v19SpUxMxNABgCAmZ4T/00ENatWqVFi1apGg0qieeeEK33XabVq9ereeff15lZWWqqKhIxNAAgCFwHL4BOM45tTQ0nNUz/1krObbbpaSE86GQJKkwP9/lSlKA5dHj3/kPTZ78iet+iKQfhw9gaPF4XI1NjZqUG9WYdEI/K6Ons1xkdbhcibsudls6Fgqou7s7YWMQ+IBL5t7Upeklifvjxo3lb20+HQsFEjoGb3UFAEMQ+ABgCAIfAAxB4AOAIQh8ADAEgQ8AhiDwAcAQBD4AGILABwBD8E5bwCVvhvzK9DkqzoirMN2Wj+mXcWxHOt/lUXOnJ+HvspUIfCDpvF6v/H6/9p2V9p1NlyR5LKkwQypOi6o4I977YaskI66ijLgy+Eu9YV2KS+c6vWrq9Ki509v74VHzJb9aOi3F+51Oyev1yudL3C+bzQhIsrFjx2nz5u0KhUJqampQU1OjGht7bpsaz+oPjQ1qPxse8DPZaVJxekwl6fEBO4TijLhyA456LyYHFziO1B613g/yy7ddPjV3+dTWNfD+wYx0lZSMVdnYcfpsyViVlpaqpGSsSkpKVVBQKK/Xm7BaCXzABR6PR4WFhSosLNSUKbcNWh4Oh6/YGTSpqalB7zSc0RunW9X/rOYBr3rCPz026L8DWkUjI25LrZc8auoX6uc6PWrq8qu506Ou2MD7F+TnqWTseP1LaU+Ql/belpSMVVZWVt/lXpONwAdSUGZmpsrKJqqsbOKgZdFoVOfONaupqUGNjY29O4YmNTWc0dHGJnX3u5yoZUmF6VJxelQlV/xnUEyraIBLcV0xS788Uw+opVOK97tyiM/nVXFRsUomjtf/6Z2hX56pFxeXKBBIfD/+evDrBm4wfr9f48aN17hx4wctcxxHoVDrgDbRO+/8TW++eVzHQ4NPxTwhO6Y0b0peAymp4o70zgX/oO97vV5NnvwJfW7yJ/pm6sXFJQlvvSQKgQ/c4OLxuM6fbxkQ8o2NDWpqPKumpkZ1dg1sIuen97we4Di2bIfmvyRZkibndstjWWq+5Fdrp+So57k9fvyoTpx4WyXFJSopHT+g515SUqqiouKEvtA6km6MKgHDXbrU1dfHf7+N06imhjNqbmlRPB7vu6/XIxVlOCpOj2rimLiK0+2+Vk5RRlxpN97ENOmittTS29rp6dt3qjnSoTPH39WRP1qK9juyxuPxaExhQe/OYOyA9k5JSYkyMoLurcgVCHwgBTiOo4sXL74f5H2z9QY1NZxV6MKFAfcP+qXidFv/lBHVZ8b3BHpJb6gXpNvyMHH/SPweaWymrbGZtqTogGW2I7V1e9Qc6XdETmenmk836Y2/edVxRecsNztbxaVjVVo6btALuHl5eUl9AZfAB5KsszOi11+vH3br5baMmIoLemfpwZ7bLB+HYrrFY0kFabYK0mx9PD82aHk4aulcV+9/BxGPmju71NzSquP/eFuv97aKLktLC1zRKirV9OmfV25uXkJqJ/CBJDt//rxefPH/ydfbeim63HrpPZSyOMNWUXpcAVovN6RMv6NMf1wfy44PWta/VdTc1dsq6uzQ2Tff1ZE/WYrGpXHjbtI//3NeQmoj8AGX/N9PtHMRc8N8UKvorZBPz/w5N6Hj85YMAEgByWjREfgAYAgCHwAMQeADgCEIfAAwBIEPAIYg8AHAEAQ+ABiCwAcAQxD4AGAIAh8ADEHgA4AhCHwAMASBDwCGIPABwBAEPgAYgsAHAEMQ+ABgiIRd4vDHP/6x9u7dq2g0qvvvv1/Tpk1TdXW1LMvSpEmTVFtbK4+H/Q0AJEtCEvfQoUP685//rF/+8pfatGmTGhsbtXbtWlVVVWnr1q1yHEd1dXWJGBoAMISEzPBff/113XrrrXrsscfU0dGhp556Sr/+9a81bdo0SdLMmTN14MABzZ07d8jH8Hot5eUFE1GecXw+ryTxfKaICxfS3S4BKSwzMy1hf6sJCfxQKKSzZ89q48aN+sc//qFHH31UjuPI6r1Kb2Zmptrb2z/wMeJxR21tkeuuwbbt6/7Z0SYWi0uSWls7XK4kdbjZTmxv75IkOY5kO66VgRTj9G4L4fClj5R9RUXZQy5LSODn5eWprKxMgUBAZWVlSktLU2NjY9/ycDisnJycRAzd56WX/r927XoloWPcaBYs+IrbJaSEkpJS/dd//cTtMrTheLY2HHe7CpgkIYF/++2366WXXtLDDz+s5uZmdXZ26rOf/awOHTqk8vJy1dfXa/r06YkYeiCPV5dKP5n4cVKcJ3JekmQHC12uxH2+0CnZjrvT6uzsHN133yJXa0gl+/btkSTNnn2Xy5WkhuLikoQ9dkICf/bs2Tp8+LDmz58vx3FUU1Ojm266SatXr9bzzz+vsrIyVVRUJGLoASyPV93j/yXh4+DG4em6KMnd1lZubq7+/d/vd7WGVHL06F8lieckCRJ2WOZTTz016HubN29O1HAAgGvgQHgAMASBDwCGIPABwBAEPgAYgsAHAEMQ+ABgCAIfAAxB4AOAIQh8ADAEgQ8AhiDwAcAQBD4AGILABwBDEPgAYAgCHwAMQeADgCEIfAAwBIEPAIYg8AHAEAQ+ABiCwAcAQxD4AGAIAh8ADEHgA4AhCHwAMASBDwCGIPABwBAEPgAYgsAHAEMQ+ABgCAIfAAxB4AOAIXxuF5BIjmPL294oxxvo/fBLXr9ksZ8zguNIdkxWPCor3i3Fu2XFIqN8qweGNro3/XhMwbd+O+jbltcv+Xp2ArbHJ9vTszNwvAGp9/b9HcT7yxxvQPL55XgCkscrWZYLK2UIOy4rHu0J6b7A7rm1er/Xf9nlW48dlceOSrFuObFuSc6gh7YySpO/PkAKGHbgnzp1SqdPn9bkyZNVUlIiK8XD7itfuVef//xMRSJhRSKdikTC6uyMKBKJDPpeOBxWOBJRJNKmzosRRbu7rz2A5ZHlC0jegGyvX7bHL8fj79tRXN45KNopb6Q18St8A7DiMcXTsuWk5wwK8ctBbcW75cSikh275uN5vT6lZwQVDGYomJOprMw8BYNBZWQEFQxmKhgM9n1c/l5GRoZycnKSsLZA6hlW4G/evFm7d+/WhQsXNG/ePL377ruqqalJdG0fSX5+gfLzC655v+7u7n47gp6dQUdHh1pbW9TS0qLz58+ppaVFLS3n1NYWkuP0zhgdW060S4p2yZLkHeLx7WC+bG/aiK3XjcyKdysQ+vsH3mfwfFzKyspWYeEYjRkzpve2SIWFY5Sbm9svyHuCPT09PeUnI4BbhhX4u3bt0tatW/Xggw/qoYce0r333pvouj6yv/71iI4e/asikUhfoHd2RtQRDvfO8CPq6uxUPH7tmaQ8Plm+gJz0HNkev+y+9k//ds9V2j9evxxfuhx/RuJX+EYQj8qKRmTFumXZUVmxbqn3tu/rq7RnwtGowmfO6PTpD95ZSJJlWUpPz1B6RlCZmUFlBjOvmO1nasyYIt1995eSsMJAahlW4F+e1V6eOQUCgcRVNEL+9KfD2rXrFVn+tJ62y+Wg9gTkeDPlZOTLyRoqpAM97RlfQPL4e/r1+Oi8fjne3KvO4ofFcfr18fu9EHtFe+hSPKqL8W6pvVtWW5s89rn320XRLhUVlRD4MNKwAv9LX/qSHnjgAZ09e1bf+ta3dNdddyW6rhFh+QK6+OkH3C4DI8Wyel5s9wWue6eRfmK/pI6RrAq4YQwr8CsrK/W5z31Ob7/9tsrKyjR58uRE1wUAGGHDCvyVK1f2fV5fXy+/36/S0lI98MADys3NverPnD9/Xl/72tf0s5/9TD6fT9XV1bIsS5MmTVJtba08Ho6FB4BkGlbqXrp0ScXFxfriF7+o8ePHq6mpSd3d3VqxYsVV7x+NRlVTU6P09HRJ0tq1a1VVVaWtW7fKcRzV1dWN3BoAAIZlWIHf2tqqJ554QjNmzNDjjz+uaDSqqqoqtbe3X/X+zz33nBYuXKji4mJJ0rFjxzRt2jRJ0syZM3Xw4MERKh8AMFzDaul0dHToxIkTuuWWW3TixAmFw2GFQiFFIpFB9925c6cKCgo0Y8YMvfjii5J6jvK5fIRPZmbmkDuK/rxeS3l5wQ+zLgOkpfmv+2cxunk8no+0bWFk+Xw9R8HxO0m8YQV+TU2Nli9frubmZo0dO1arV6/Wb3/7Wz3yyCOD7rtjxw5ZlqU33nhDb775plasWKHW1vffaRoOh4f1Tsd43FFb2+AdynBduhS97p/F6Gbb9kfatjCyYrG4JPE7GSFFRdlDLhtW4B87dkzhcFiBQEDnz5/Xk08+qd/97ndXve+WLVv6Pq+srNSaNWu0bt06HTp0SOXl5aqvr9f06dM/5CoAAD6qYfXwX375ZW3atEmzZs3S2rVrNXHixA81yIoVK7R+/XotWLBA0WhUFRUV11UsAOD6DWuGn5+fr+LiYoXDYZWXl+uHP/zhsB5806ZNfZ9v3rz5+ioEAIyIYc3ws7OztWfPHlmWpW3btg3oyQMAbgzDCvzvf//7GjdunJYtW6ZTp05pzZo1CS4LADDShtXSycrK0pQpUyRJ1dXVCS0IAJAYnN8AAAxB4AOAIQh8ADAEgQ8AhiDwAcAQBD4AGILABwBDEPgAYAgCHwAMQeADgCGGdWoFAKPP/v17tXfvbrfL0KlTJyVJtbUrXa1jzpy5mjVrjqs1JBqBD8BVeXkFbpdgDAIfMNSsWXNG/YwWA9HDBwBDEPgAYAgCHwAMQeADgCEIfAAwBIEPAIYg8AHAEAQ+ABiCwAcAQxD4AGAIAh8ADEHgA4AhCHwAMASBDwCGIPABwBAEPgAYYlRfAMWJx5T27iG3y3Cdp7NNkmRn5LlaRyrwRFqknHS3ywBcMWoD3+fzKy0QUKDthNuluC7afUmS5L903uVKUkNGer7bJQCusBzHcdwu4mqi0bja2iJulzEqXL449He/u9blSgAkWlFR9pDL6OEDgCEIfAAwBIEPAIYg8AHAEAQ+ABhixA/LjEajWrVqlc6cOaPu7m49+uijmjhxoqqrq2VZliZNmqTa2lp5POxrACCZRjzwX3nlFeXl5WndunUKhUL66le/qo9//OOqqqpSeXm5ampqVFdXp7lz54700ACADzDi0+y7775bS5cu7fva6/Xq2LFjmjZtmiRp5syZOnjw4EgPCwC4hhGf4WdmZkqSOjo6tGTJElVVVem5556TZVl9y9vb26/5OF6vpby84EiXZySfzytJPJ+A4RJyaoWGhgY99thjWrRoke655x6tW7eub1k4HFZOTs41HyMed3in7QiJxeKSxPMJGCCp77RtaWnRN77xDS1fvlzz58+XJE2ZMkWHDvWcxKy+vl5Tp04d6WEBANcw4oG/ceNGXbx4UT/60Y9UWVmpyspKVVVVaf369VqwYIGi0agqKipGelgAwDVw8jQDcPI0wBycPA0AQOADgCkIfAAwBIEPAIYg8AHAEAQ+AFeFQq2qqalWKBRyu5RRj8AH4Krt27fprbeOa8eObW6XMuoR+ABcEwq1at++OjmOo3379jDLTzACH4Brtm/fJsexJUm2bTPLTzACH4BrXnvtvxWLxSRJsVhM9fX7XK1ntCPwAbhmxox/lc/Xc9Jen8+nmTNnu1zR6EbgA3DN/PkLZVk9MeTxeHTvvQtdrmh0I/ABuCY/v0CzZ/+bLMvS7Nl3KT8/3+2SRrWEXAAFAIZr/vyFeu+9d5ndJwGBD8BV+fkFevrpZ90uwwi0dADAEAQ+ABiCwAcAQxD4AGAIAh8ADEHgA4AhCHwAMASBDwCGIPABwBAEPgAYgsAHAEMQ+ABgCAIfAAxB4AOAIQh8ADAEgQ8AhiDwAcAQBD4AGILABwBDEPgAYAgCHwAMQeADcFUo1KqammqFQiG3Sxn1CHwArtq+fZveeuu4duzY5nYpox6BD8A1oVCr9u2rk+M42rdvD7P8BCPwAbhm+/ZtchxbkmTbNrP8BEta4Nu2rZqaGi1YsECVlZU6ffp0soYGkKJee+2/FYvFJEmxWEz19ftcrWe0S1rg79mzR93d3frVr36lZcuW6dlnn03W0ABS1IwZ/yqfzydJ8vl8mjlztssVjW6+ZA30xz/+UTNmzJAkffrTn9bRo0eTNbSr9u/fq717d7taw6lTJyVJtbUrXa1DkubMmatZs+a4XQZSxPz5C7VvX50kyePx6N57F7pc0eiWtMDv6OhQVlZW39der1exWKxv734lr9dSXl4wWeUlTDAYkM/ndbWGwsJCSXK9Dqnn+RgNv1eMjLy8oO6+u0KvvvqqKiru1oQJ490uaVRLWuBnZWUpHA73fW3b9pBhL0nxuKO2tkgySkuoO+64U3fccafbZaSU0fB7xci55575+p//Oal77pnPtjECioqyh1yWtB7+Zz7zGdXX10uSjhw5oltvvTVZQwNIYfn5BXr66WeVn5/vdimjXtJm+HPnztWBAwe0cOFCOY6jZ555JllDAwAkWY7jOG4XcTXRaJx/7wDgQ0qJlg4AwF0EPgAYgsAHAEMQ+ABgCAIfAAyRskfpAABGFjN8ADAEgQ8AhiDwAcAQBD4AGILABwBDEPgAYAgCHwAMQeCPclw8HqnuL3/5iyorK90uwwhJOx8+3NH/4vFHjhzRs88+qw0bNrhdFiBJ+slPfqJXXnlFGRkZbpdiBGb4o5ypF4/HjeHmm2/W+vXr3S7DGAT+KDfUxeOBVFBRUfGB17bGyCLwR7kPe/F4AKMXgT/KcfF4AJcx1RvluHg8gMs4PTIAGIKWDgAYgsAHAEMQ+ABgCAIfAAxB4AOAIQh84AqbN28e9n137typH/zgBwmsBhg5BD5wBU4uh9GKN17BaH//+9+1cuVK+Xw+eb1eTZ8+XRcuXNCaNWv0yU9+Ujt27JBt21qyZInOnTunX/ziFwoEAvrYxz6mp59+uu9xWltb9e1vf1tLly7V1KlTVVtbq9OnT8u2bVVVVam8vNzFtQR68MYrGG3Lli06efKkqqur9Yc//EGFhYV6+OGHdeDAAe3cuVO7d+/Whg0bFAqFdN999+k3v/mNsrKy9Mwzz+jmm29WMBjU4cOHdfLkSa1atUqf+tSntHXrVp05c0bLly9XKBTS4sWLtWvXLrdXFaClA7PNnz9f+fn5+uY3v6ktW7bI6/UOWD5hwgRJ0nvvvaeJEyf2nXn0jjvu0DvvvCNJeu2119Td3S3btiVJb7/9turr61VZWaklS5YoFospFAolca2Aq6OlA6PV1dXp9ttv1+OPP65XX31VP/3pT9X/n16Pp2dOdNNNN+nEiROKRCIKBoP6/e9/37czmDdvnubNm6elS5fq5ZdfVllZmUpLS/XII4+oq6tLGzZsUG5urivrB/THDB9Gu+222/TCCy9o0aJF2rZtmxYvXqxbbrlFTz755ID7FRQU6Dvf+Y4efPBB3XfffQqFQrr//vv7lk+cOFFf/vKXtXbtWi1cuFAnT57U4sWLtXDhQo0fP75vxwG4iR4+ABiCaQcAGILABwBDEPgAYAgCHwAMQeADgCEIfAAwBIEPAIb4X4hcIrkQgA2UAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_cols = ['hypertension', 'heart_disease', 'stroke']\n",
    "\n",
    "for col in box_cols:\n",
    "    sns.boxplot(data=df, y='age', x=col, notch=True)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Those who have had hypertension, heart disease, or stroke are older, on average."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "text/plain": "gender                object\nage                  float64\nhypertension           int64\nheart_disease          int64\never_married          object\nwork_type             object\nResidence_type        object\navg_glucose_level    float64\nbmi                  float64\nsmoking_status        object\nstroke                 int64\ndtype: object"
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize correlations between age and other categorical features\n",
    "cat_cols = df.select_dtypes(include='object')\n",
    "for col in cat_cols:\n",
    "    sns.barplot(data=df, y='age', x=col)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Those who have been married are older, on average. Those who are self-employed are older, on average, and those who have never worked are younger, on average."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEECAYAAAAoDUMLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAbGUlEQVR4nO3de1Bc5f3H8feyGxJgVzANpaZIBFJib5ZLepfYEKmKTb1s4oIZUkda660ZG294KWKMgGPjmKYRapSieAkkY6tYdZTBimWqZjGrobZqSLMhxsbEZI27EJdlz+8Pt/srGshGcyAkn9eMs5zznMv35JH9cM7Z86zFMAwDERE55sWNdwEiInJkUCCIiAigQBARkQgFgoiIAAoEERGJsI13AZ9HOBxmaEgfkhIRORSTJlkPOH9CB8LQkIHP1z/eZYiITCipqY4DztclIxERARQIIiISoUAQERFAgSAiIhEKBBERARQIIiISoUAQERFAgSAiIhGmBEI4HKaqqgqXy0V5eTler3dYe0dHB06nE5fLRWtra3T+H/7wB1wuF+effz7r1q0zo7QJ49VX3dx66028+qp7vEsRkWOEKU8qt7e3EwwGaWlpwePxUFdXR319PQCDg4PU1tayfv16EhISKCsrY+7cuWzZsoWNGzfy6KOPMjAwQGNjoxmlTRjr1j3Cv/+9hf37B8jPnz3e5YjIMcCUQOju7qawsBCA3Nxcenp6om29vb1kZGSQnJwMQEFBAW63mzfeeIOcnByuuOIK/H4/11133UH3Y7VaSElJNOMQxl0w+FH09Wg9RhE5spgSCH6/H7vdHp22Wq2EQiFsNht+vx+H4//H0UhKSsLv97N371527NhBQ0MD27dv57LLLuOZZ57BYrGMuJ+jeSyj/w7adzQfo4iMj5HGMjIlEOx2O4FAIDodDoex2WwHbAsEAjgcDlJSUsjKyiI+Pp6srCwmT57Mnj17+MIXvmBGiSIi8gmm3FTOz8+ns7MTAI/HQ05OTrQtOzsbr9eLz+cjGAzidrvJy8ujoKCAF198EcMw2LlzJwMDA6SkpJhRnoiIHIApZwjFxcV0dXVRWlqKYRjU1NTQ1tZGf38/LpeLyspKKioqMAwDp9NJWloaaWlpbNiwgQULFmAYBlVVVVitBx6zW0REDj+LYRgT9htmBgeHjtrr61dddTn/+c8OvvSl6dx99z3jXY6IHEX0fQgiIjIqBYKIiAAKBBERiVAgiIgIoEAQEZEIBYKIiAAKBBERiVAgiIgIoEAQEZEIBYLIYaYvN5KJypSxjESOZfpyI5modIYgcpgNDOwf9ioyUSgQREQEUCCIiEiEAkFERAAFgoiIRCgQREQEUCCIiEiEAkFERAAFgoiIRCgQREQEUCCIiEiEAkFERAAFgoiIRCgQREQEOIaGv7YfN4WEyZPGu4yYWa2W6GtqqmOcq4nNwEeD+PdphE+RicqUQAiHw1RXV/Pmm28SHx/P8uXLmTFjRrS9o6OD1atXY7PZcDqdXHDBBQCce+65OBwfv/mlp6dTW1t72GpKmDyJgmsfPGzbM5tj94dYgW27P5wwdXffuRg/CgSRicqUQGhvbycYDNLS0oLH46Guro76+noABgcHqa2tZf369SQkJFBWVsbcuXM57rjjAGhubjajJBEROQhT7iF0d3dTWFgIQG5uLj09PdG23t5eMjIySE5OJj4+noKCAtxuN//6178YGBjg4osvZvHixXg8HjNKExGREZhyhuD3+7Hb7dFpq9VKKBTCZrPh9/ujl4UAkpKS8Pv9TJkyhYqKChYuXMjWrVv5xS9+wTPPPIPNNnKJVquFlJREMw5BPiP1x/D7P/r3kInElECw2+0EAoHodDgcjr6xf7ItEAjgcDjIzMxkxowZWCwWMjMzSUlJYdeuXZxwwgkj7mdoyMDn64+ppolyY3aii7U/jmZDQ0b0Vf8eciQa6f3QlEtG+fn5dHZ2AuDxeMjJyYm2ZWdn4/V68fl8BINB3G43eXl5rF+/nrq6OgB27tyJ3+8nNTXVjPJEROQATDlDKC4upquri9LSUgzDoKamhra2Nvr7+3G5XFRWVlJRUYFhGDidTtLS0liwYAE33HADZWVlWCwWampqRr1cJCIih5cp77hxcXEsW7Zs2Lzs7Ozoz0VFRRQVFQ1rj4+PZ8WKFWaUIyIiMdCTyiIiAigQREQkQoEgIiKAAkFERCIUCCIiAhxDo53KxDY1eRLW+CnjXUZMJuJItQBDwf3s+WBwvMuQcaRAkAnBGj+Fbcu+Od5lxCS0ZypgI7THO2FqBsio2gQoEI5lumQkIiKAAkFERCIUCCIiAigQREQkQoEgIiKAAkFERCIUCCIiAigQREQkQoEgIiKAAkFERCIUCCIiAigQREQkQoEgIiKAAkFERCIUCCIiAigQjlhGnG3Yq4iI2RQIR6j90/MYtH+J/dPzxrsUETlG6M/PI1QoOZ1Qcvp4lyEixxCdIYiICGBSIITDYaqqqnC5XJSXl+P1eoe1d3R04HQ6cblctLa2Dmt7//33Oe200+jt7TWjNBERGYEpgdDe3k4wGKSlpYWrr76aurq6aNvg4CC1tbU0NjbS3NxMS0sLu3btirZVVVUxZcoUM8oSEZFRmBII3d3dFBYWApCbm0tPT0+0rbe3l4yMDJKTk4mPj6egoAC32w3AHXfcQWlpKV/84hfNKEtEREZhyk1lv9+P3W6PTlutVkKhEDabDb/fj8PhiLYlJSXh9/t57LHHmDp1KoWFhdx7770x7cdqtZCSknjY65fPTv0xsan/jm2mBILdbicQCESnw+EwNpvtgG2BQACHw0FzczMWi4W///3v/POf/+T666+nvr6e1NTUEfczNGTg8/XHVFNqquPgC8nnFmt/HCr139gwq//kyDLS75MpgZCfn8/zzz9PSUkJHo+HnJycaFt2djZerxefz0diYiJut5uKigrOPPPM6DLl5eVUV1ePGgYiR6opVmPYq8hEYUogFBcX09XVRWlpKYZhUFNTQ1tbG/39/bhcLiorK6moqMAwDJxOJ2lpaWaUITIuzjspwDN9iZx5ov7alonFYhjGhP0zZnBw6JAuGRVc+6DJFR3buu9czK5dH5qy7dRUB9uWfdOUbcvHMqo2mdZ/cmQZ6ZKRHkwTERFAgSAiIhEKBBERARQIIiISoUAQERFAgSAiIhEKBBERARQIIiISoUAQERFAgSAiIhEKBBERARQIIiIScUiB8MEHH5hVh4iIjLOYhr9+5ZVXWLZsGUNDQ5x55plMnz6dhQsXml2biIiMoZjOEFauXMlDDz3EtGnTuPTSS3n00UfNrktERMZYTIEQFxdHSkoKFouFyZMnk5SUZHZdIiIyxmIKhIyMDFasWIHP5+Pee+9l+vTpZtclIiJjLKZAuPXWW5k+fToFBQUkJCSwfPlys+sSEZExFtNNZbfbTVlZGQADAwPcdtttLFu2zNTCRERkbMV8U3nTpk289tprLFy4kPT0dLPrEhGRMRbTGcLq1au5/PLLCQaDrFy5kuzsbLPrEhGRMTZqIKxYsQKLxQJAZmYmL774Io8//jgAS5cuNb86EREZM6MGQlZWVvTnzMxMvvOd75hekIiIjI9RA+G8884DIBQK0dLSwubNmznppJOiN5hFROToEdNN5aqqKvr6+vjhD3/IO++8w80332x2XSIiMsZiuqns9Xp5+OGHATj99NMpLS01tSgRERl7MZ0hfPTRRwwMDACwf/9+hoaGRl0+HA5TVVWFy+WivLwcr9c7rL2jowOn04nL5aK1tRWAoaEhbrjhBkpLS1m0aBHbtm37LMcjIiKfUUxnCD/72c8455xz+MpXvsLmzZtZsmTJqMu3t7cTDAZpaWnB4/FQV1dHfX09AIODg9TW1rJ+/XoSEhIoKytj7ty5vPbaawCsXbuWl19+mdra2ug6IiJivpgCITU1ldbWVvr6+khPT+f4448fdfnu7m4KCwsByM3NpaenJ9rW29tLRkYGycnJABQUFOB2uznrrLP40Y9+BMCOHTuYNm3aZzkeERH5jGIKhFWrVvHwww+TkpIS00b9fj92uz06bbVaCYVC2Gw2/H4/Docj2paUlITf7/+4GJuN66+/nueee47f/e53B92P1WohJSUxpppkbKg/Jjb137EtpkCwWCxcccUVZGZmEhf38W2H0R5Ms9vtBAKB6HQ4HMZmsx2wLRAIDAuIO+64g2uuuYYLLriAv/zlLyQmjvw/6NCQgc/XH8shkJrqOPhC8rnF2h+HSv03NszqPzmyjPT7FFMgOJ3OYdP/fXp5JPn5+Tz//POUlJTg8XjIycmJtmVnZ+P1evH5fCQmJuJ2u6moqODPf/4zO3fu5Je//CUJCQlYLBasVmss5YmIyGEQUyBs2rSJqqqq6PR1113HueeeO+LyxcXFdHV1UVpaimEY1NTU0NbWRn9/Py6Xi8rKSioqKjAMA6fTSVpaGj/+8Y+54YYbWLRoEaFQiBtvvJHJkyd/7gMUEZHYjBoIDz/8MPX19fh8Pp599tno/IMNbhcXF/ep4bH/d52ioiKKioqGtScmJrJy5cqYCxcRkcNr1EBYtGgRixYtoqGhgXnz5mG1WlmzZg2LFy8eq/pERGSMxPRg2ssvv8yePXu4++67OfXUU6mpqTG7LhERGWMxBUIoFGL27Nns27ePs88+m3A4bHZdIiIyxmIKhP8+XTx79mxeeumlgw5dISIiE09MgVBXV0dmZiaXXHIJe/bs4c477zS7LhERGWMxfez0pJNO4qSTTgKgpKTEzHpERGScxHSGICIiRz8FgoiIAAoEERGJUCCIiAigQBARkQgFgoiIAAoEERGJUCCIiAigQBARkQgFgoiIAAoEERGJUCCIiAigQBARkQgFgoiIAAoEERGJUCCIiAigQBARkQgFgoiIAAoEERGJUCCIiAigQBARkQibGRsNh8NUV1fz5ptvEh8fz/Lly5kxY0a0vaOjg9WrV2Oz2XA6nVxwwQUMDg5y44038s477xAMBrnsssuYN2+eGeWJiMgBmBII7e3tBINBWlpa8Hg81NXVUV9fD8Dg4CC1tbWsX7+ehIQEysrKmDt3Lp2dnaSkpHDnnXeyd+9ezjvvPAWCiMgYMiUQuru7KSwsBCA3N5eenp5oW29vLxkZGSQnJwNQUFCA2+3mzDPP5IwzzoguZ7VaD7ofq9VCSkriYa5ePg/1x8Sm/ju2mRIIfr8fu90enbZarYRCIWw2G36/H4fDEW1LSkrC7/eTlJQUXXfJkiVcddVVB93P0JCBz9cfU02pqY6DLySfW6z9cajUf2PDrP6TI8tIv0+m3FS22+0EAoHodDgcxmazHbAtEAhEA+Ldd99l8eLFnHPOOcyfP9+M0kREZASmBEJ+fj6dnZ0AeDwecnJyom3Z2dl4vV58Ph/BYBC3201eXh67d+/m4osv5tprr2XBggVmlCUiIqMw5ZJRcXExXV1dlJaWYhgGNTU1tLW10d/fj8vlorKykoqKCgzDwOl0kpaWxvLly9m3bx/33HMP99xzDwBr1qxhypQpZpQoIiKfYDEMwxjvIj6rwcGhQ7qHUHDtgyZXdGzrvnMxu3Z9aMq2U1MdbFv2TVO2LR/LqNpkWv/JkWVM7yGIiMjEo0AQERFAgSAiIhEKBBERARQIIiISoUAQERFAgSAiIhEKBBERARQIIiISoUAQERFAgSAiIhEKBBERARQIIiISoUAQERFAgSAiIhEKBBERARQIIiISoUAQERFAgSAiIhEKBBERARQIIiISoUAQERFAgSAiIhEKBBERARQIIiISoUAQERHApEAIh8NUVVXhcrkoLy/H6/UOa+/o6MDpdOJyuWhtbR3W9tprr1FeXm5GWSIiMgqbGRttb28nGAzS0tKCx+Ohrq6O+vp6AAYHB6mtrWX9+vUkJCRQVlbG3LlzSU1NZc2aNTzxxBMkJCSYUZaIiIzClDOE7u5uCgsLAcjNzaWnpyfa1tvbS0ZGBsnJycTHx1NQUIDb7QYgIyODVatWmVGSiIgchClnCH6/H7vdHp22Wq2EQiFsNht+vx+HwxFtS0pKwu/3A3DGGWewffv2mPdjtVpISUk8fIXL56b+mNjUf8c2UwLBbrcTCASi0+FwGJvNdsC2QCAwLCAOxdCQgc/XH9OyqamfbR9yaGLtj0Ol/hsbZvWfHFlG+n0y5ZJRfn4+nZ2dAHg8HnJycqJt2dnZeL1efD4fwWAQt9tNXl6eGWWIiMghMOUMobi4mK6uLkpLSzEMg5qaGtra2ujv78flclFZWUlFRQWGYeB0OklLSzOjDBEROQSmBEJcXBzLli0bNi87Ozv6c1FREUVFRQdcNz09/VMfRRUREfPpwTQREQEUCCIiEqFAEBERQIEgIiIRCgQREQEUCCIiEqFAEBERQIEgIiIRCgQREQEUCCIiEqFAEBERQIEgIiIRCgQREQEUCCIiEqFAEBERQIEgIiIRCgQREQEUCCIiEqFAEBERQIEgIiIRCgQREQEUCCIiEqFAEBERQIEgIiIRCgQREQEUCCIiEmFKIITDYaqqqnC5XJSXl+P1eoe1d3R04HQ6cblctLa2xrSOiIiYy5RAaG9vJxgM0tLSwtVXX01dXV20bXBwkNraWhobG2lubqalpYVdu3aNuo6IiJjPZsZGu7u7KSwsBCA3N5eenp5oW29vLxkZGSQnJwNQUFCA2+3G4/GMuI6IiJjPlEDw+/3Y7fbotNVqJRQKYbPZ8Pv9OByOaFtSUhJ+v3/UdUYyaZKV1FTHiO2f1H3n4kM8EjlUh9IfhyqjapNp25aPmdl/cuQz5ZKR3W4nEAhEp8PhcPSN/ZNtgUAAh8Mx6joiImI+UwIhPz+fzs5OADweDzk5OdG27OxsvF4vPp+PYDCI2+0mLy9v1HVERMR8FsMwjMO90XA4THV1NW+99RaGYVBTU8Mbb7xBf38/LpeLjo4OVq9ejWEYOJ1OFi1adMB1srOzD3dpIiIyAlMCQUREJh49mCYiIoACQUREIhQIIiICKBDG3Msvv8ysWbN46qmnhs2fP38+lZWVB1znscce47e//e1YlCcR27dvJz8/n/Ly8uh/v//97w/rPsrLy+nt7T2s25SR9fX18atf/Yry8nJKS0uprq7G7/ezY8cOOjo6APWJPug/DrKysnjyyScpKSkB4M0332RgYGCcq5JPmjlzJs3NzeNdhhwG+/fv5/LLL2f58uV861vfAuBPf/oTV199NWeccQZbtmyhqKhonKscfwqEcXDyySezdetW9u3bx3HHHccTTzzB/Pnzeffdd3nooYd49tlnCYVCOBwOVq1aNWzd5uZmnnzySSwWCyUlJSxerKevx9KKFSvYsGEDhmFw0UUXcdZZZ1FeXs6sWbN4++23SUxMZPbs2fztb39j3759NDY2YrVauemmm/jwww/Zu3cvCxcu5MILL4xu88MPP+Smm25i7969ANx8883MmjVrvA7xqPTXv/6Vb3/729EwADjvvPN46KGHqKqqYtq0aeTl5QGwevVqdu/ezcDAAHfddRcnnnjiiP1+/PHHs2/fPu6//36sVut4Hd5ho0tG46S4uJjnnnsOwzB4/fXXycvLIxwO4/P5aGpq4pFHHiEUCrFp0/8P17B582aeeuopHnnkER555BHa29vZsmXLOB7F0W3z5s3DLhk98cQTbN++nbVr1/Lggw/S0NDAvn37ADjllFN44IEHCAaDTJkyhT/+8Y/MnDmTDRs24PV6Ofvss2lsbKShoYGmpqZh+2loaOB73/sezc3N3HbbbVRXV4/9wR7l+vr6yMjI+NT8GTNmsGzZMn7yk58wb948AE477TQefPBB5syZwzPPPMMLL7wwYr/Pnz+fpqamoyIMQGcI42b+/PlUV1dz4oknMnv2bADi4uKYNGkSS5cuJTExkf/85z+EQqHoOm+99RY7duzgoosuAuCDDz5g27ZtZGVljcchHPU+eclozZo1/OMf/6C8vByAUCjEjh07APj6178OwHHHHcfMmTOjP3/00UdMmzaNBx54gGeffRa73T6sT+Hjfn3ppZd4+umnAaJvNnL4pKWl8frrr39q/tatW/nBD34wbN43vvENAKZNm8bu3bt56623Ruz3zMxMkysfWwqEcXLiiSfS399Pc3MzS5cupa+vD7/fT3t7O+vWrWNgYIDzzz+f/31uMCsri5kzZ3LfffdhsVhoamrSEB9jKCsri+9+97vcdttthMNh7rnnHtLT0w+6XmNjI7m5uVx44YW89NJLvPDCC5/a7k9/+lPmz5/P+++/z7p168w6hGPWvHnzaGho4PXXX+eUU04BYN26dUydOhWbzUY4HB5x3dH63WKxjEn9Y0WBMI5KSkp4/PHHyczMpK+vD6vVSkJCAueffz7x8fGkpqby3nvvRZc/+eST+f73v09ZWRnBYJBTTjmFtLS0cTyCY0tRURGvvPIKF154If39/Zx++unDRugdydy5c6murqatrY2UlBSsVivBYDDafumll3LTTTfR2tqK3+/nyiuvNPMwjklJSUk0NDRQU1ODz+djaGiIWbNmcdddd7F9+3bq6+ujZ3mf9Fn7fSLS0BUiIgLoprKIiEQoEEREBFAgiIhIhAJBREQABYKIiEQoEETGQGdn54iDF4ocKRQIIiIC6ME0kRHt37+f6667jvfee48TTjiBDRs2cP/997N8+XIAUlJSot8XvmbNGiZNmsT27dspKSnhsssuo7e3lxtvvJGEhAQSEhJITk4G4Omnn6apqYm4uDgKCgq45pprWLVqFRs3bqS/v5/bb79d3ycu40JnCCIjaGlpIT09nbVr13LllVfy/vvv85vf/IZbbrmF5uZm5syZw3333QfAjh07WLVqFS0tLdF5K1euZMmSJTQ1NUVH0vT5fKxatYqmpiYeffRRdu7cSVdXF/DxEAlr165VGMi40RmCyAh6e3uZM2cOANnZ2UydOpXe3l5uvfVWAAYHB6ODm+Xk5GCz2bDZbEyZMgWAt99+OzpuTn5+Plu2bGHbtm3s2bOHSy65BIBAIEBfXx9w9A2UJhOPAkFkBDk5OWzcuJHTTz+dbdu2sXfvXr72ta9xxx13MH36dLq7u9m1axdw4EHOsrKy2LhxI3PmzKGnpweA9PR0TjjhBBobG5k0aRKPPfYYX/3qV2lvbycuTifsMr4UCCIjWLBgAZWVlSxatIjp06czefJkqquruf766xkaGgLg9ttvHzYA4f+65ZZb+PWvf83999/P1KlTmTx5MlOnTuWiiy6ivLycoaEhvvzlL3PWWWeN5WGJjEiD24mM4NVXX6W/v59TTz2VrVu38vOf/5z29vbxLkvENAoEkRHs2rWLpUuXMjg4SCgUYsmSJdF7CiJHIwWCiIgA+tipiIhEKBBERARQIIiISIQCQUREAAWCiIhE/B/Uv9yEQ//a7QAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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WgSAilzxri+Z8cE+XRq2h3GqBoCDKjx9v1Fq6bPwAfmAg6JSRiIgACgQREfFRIIiICKBAEBERHwWCiIgACgQREfFRIIiICKBAEBERHwWCiIgACgQREfFRIIiICKBAEBERHwWCiIgACgQREfFRIIiICKBAEBERHwWCiEg9CP3W9FKkQBARqQddPF5u8Hrp4vE2dik/WEB+QtPr9TJ16lQOHDhASEgIWVlZ3HDDDf72DRs2sHDhQqxWKykpKQwaNAiXy8WkSZM4ceIETqeTp556invvvTcQ5YmI1LsEY0jwmMYu40cJSCCsX78ep9NJXl4eBQUFZGdnk5OTA4DL5WL27Nm8+eabtGjRgsGDB9OtWzc2btxIZGQkc+bM4cyZM/Tr10+BICLSgAISCNu3b6dz584AJCYmsnv3bn9bYWEhMTExREREAJCcnEx+fj49e/bkgQce8N/PYrEEojQREalBQALBbrdjs9n88xaLBbfbjdVqxW63Ex4e7m8LCwvDbrcTFhbmX3b06NGMGTPme7djsQQRGdmy3utvqtRX9Uv9Wb/Un/Xnh/ZlQALBZrPhcDj8816vF6vVWm2bw+HwB8Rnn33GiBEjePjhh+nTp8/3bsfjMZSWltXYHhUVXmPb5ai2vqoL9WdV6s/69WP6U31Z1ff1ZU39FZBvGSUlJbFx40YACgoKSEhI8LfFx8dTVFREaWkpTqeT/Px8OnbsyBdffMFjjz3G+PHjGTBgQCDKEhGRWgTkCOG+++5j06ZNpKWlYYxh1qxZrFu3jrKyMlJTU8nIyGDYsGEYY0hJSSE6OpqsrCzOnTvHK6+8wiuvvALAkiVLaN68eSBKFBGRbwlIIAQHBzN9+vQqt8XHx/v/7t69O927d6/S/vzzz/P8888HohwREakDXZgmIiKAAkFERHwUCCIiAigQRETER4EgIiKAAkFERHwUCCIiAigQRETER4EgIiKAAkFERHwUCCIiAigQRETER4EgIiKAAkFERHwUCCIiAigQRETER4EgIiKAAkFERHwUCCIiAigQRETER4EgIiKAAkFERHwUCCIiAigQRETER4EgIiKAAkFERHwUCCIiAigQRETER4EgIiKAAkFERHwUCCIiAigQRETER4EgIiKAAkFERHwUCCIiAgQoELxeL5mZmaSmppKenk5RUVGV9g0bNpCSkkJqaiqrV6+u0zIiIhJYAQmE9evX43Q6ycvL49lnnyU7O9vf5nK5mD17NkuXLiU3N5e8vDxKSkpqXUZERALPGoiVbt++nc6dOwOQmJjI7t27/W2FhYXExMQQEREBQHJyMvn5+RQUFNS4jIiIBF5AAsFut2Oz2fzzFosFt9uN1WrFbrcTHh7ubwsLC8Nut9e6TE2aNbMQFRVeYzvA9jlDf8SeNC3f11d1EZO5qx4qaRrqoz83jdpUD5U0DT+2P7ts/KCeKrn0/dC+DMgpI5vNhsPh8M97vV7/C/u32xwOB+Hh4bUuIyIigReQQEhKSmLjxo0AFBQUkJCQ4G+Lj4+nqKiI0tJSnE4n+fn5dOzYsdZlREQk8IKMMaa+V+r1epk6dSoHDx7EGMOsWbPYu3cvZWVlpKamsmHDBhYuXIgxhpSUFIYMGVLtMvHx8fVdmoiI1CAggSAiIpceXZgmIiKAAkFERHyaVCB4PB6GDRvG4MGDOXv2bINsc8uWLYwdO7ZBtnXe8ePHGTRo0AUvt3HjRjIyMgJQ0aVjwYIFrFq16oKXGzt2LFu2bAlARY2vMZ7DF4vq9v03v/kNa9eurfb+GRkZ/i+/NEVN6nudJSUlnDlzpsYHU0REatakAmHKlCl88sknZGZm8stf/pLx48djt9vxeDw888wz3HnnnTz00EO0bduWkJAQYmNjKSoq4syZM5w9e5aHH36Yd999l6NHj/LCCy+QmJhIbm4ub7/9NkFBQfTu3ZuhQ4eSkZFBaWkppaWlDBs2DIAPP/yQ1atXM3/+fADS0tKYP38+11xzDQCnT59mzJgxGGNwuVxMmzaNsLAwxo4dS+vWrTl+/DgPPvgghw4dYu/evXTt2pVx48axd+9eZsyYgcViITQ0lBkzZvj31+PxkJGRwY033sgTTzxRba2FhYVMmjSJFi1a0KJFC/8V4j/G2rVr+eCDD6ioqODYsWP84he/oH///hw4cICsrCwAIiMjmTVrFi+//DIdOnSgX79+lJSU8OSTT7J27Vrmzp3Ltm3bMMbw6KOP0qtXL9LT07nyyis5d+4cv//977FYLAC8++67LFmyBKvVyk9+8hN+/etfs3Dhwu997JYuXcp///d/Y7Va6dSpE+PHj/fvQ1FREePGjWPmzJn85Cc/YfLkyZw5cwaA559/nvbt27Ny5UrWrFlDVFQUX3755Y/ut/p29OhRnnvuOaxWKxaLhZSUFP785z/TrFkzPv/8c9LS0ti8eTP79+9n6NChPPzww2zatIl58+YRGhrqf4zOKy8vZ+TIkfz85z+nb9++1T5GK1eu5K233iI4OJikpCQmTpzYiD0QWI8//jjNmjXj+PHj9O7dm6eeesrf9s9//pOsrCzmz5/PSy+9REhICCdOnODUqVNkZ2dz880385e//IXly5cTEhJC27ZtmT59OoMGDeJ3v/sdV1xxBXfccQcrVqzgpz/9Kf369WPu3Lk899xzXHvttRQXF3PLLbcwbdq0ht1p04QUFxebgQMHGmOMyc7ONsuWLTPGGPP555+bbt26GY/HY7p162b27NljjDFm/vz5ZvLkycYYY1599VUzevRoY4wxb775psnKyjKHDh0yaWlpxu12G4/HY9LT001hYaGZOHGiee2114wxxmzevNmMGTPGeL1e88ADD5jS0lJz6NAhM3z48Cq1vf/+++bpp5825eXlZteuXSY/P98UFxebO+64w5w7d86cOnXK3HLLLebMmTOmoqLC3HnnncYYY/r162f27t1rjDHmvffeM6NGjTLFxcWmX79+ZsyYMWbFihXGGFNjraNGjTIffvihfx8nTpz4o/v5j3/8o3nssceMMcYcPXrUPPDAA8YYYwYOHGgOHTpkjDFm9erV5sUXXzSHDx826enpxhhjlixZYnJzc83//d//mTFjxhhjjKmoqDB9+/Y1Z8+eNf/5n/9p3n333e9sb9SoUebtt982xhjzpz/9yZw9e/Z7H7v9+/ebAQMGGKfTabxerxkxYoTZsGGDmT9/vpk5c6ZJSUkxR48eNcYY8+tf/9qsXLnSvz9paWnm3Llz5v777zeVlZXG6XSahx56yGzevPlH9119WrFihZk+fbpxOp3mo48+Mrm5uaZ3797G6XSanTt3mnvuucdUVlaaY8eOmb59+xqv12u6detmPv/8c2OMMcuWLTPZ2dlm8+bN5oknnjBDhw4169evN8aYGh+j/v37m507dxpjjFm5cqVxuVyNsu/15fz/32+aM2eOWbt2renVq5dxuVzG4XCYpKQkY4wxEydONPPmzTOpqanmiy++8N+Wk5NjjDEmLy/PTJkyxZw+fdr06NHDfPXVV8YYY2bOnGlyc3PNggULzJ/+9Cfzj3/8w/Tp08csXrzYHDp0yIwZM8YUFxebn/3sZ+arr74ybrfbdO3a1Zw6daoBe8OYJnWE8E2FhYX06dMHgOjoaGw2G6dPnwYgNjbWf7+f/vSnAISHh9OuXTsAIiIiqKys5ODBg3z66ac8+uijAJw9e5Zjx459Zx0AQUFB9O3bl7fffpvjx48zYMCAKu333HMPn3zyCU8//TRWq9X/buP6668nPDyckJAQrr76aiIjI/3rAzh16hQ33XQTALfffjtz584F4MCBA9hsNsrKygBqrPXQoUPceuutwNcXDB45cuSHdmkVHTp0AKB169Y4nU7g6z4//47G5XIRGxtLfHw8Ho+HEydO8Ne//pVly5aRl5fHnj17SE9PB8DtdvPpp58C3+1XgOeee45XX32VVatWERcXR48ePYDaH7sjR45w22230axZMwA6derEoUOHgK8/Szn/rvp8323evJn/+Z//AeDcuXMcOXKEdu3aERISAuDvw4vJgAEDWLJkCY8//jjh4eHcdddd3HjjjTRr1ozw8HBiYmIICQnx98mZM2ew2WxER0cDXz+fXnzxRbp27crWrVtp3769/7E8ePBgtY/R+YEpf/Ob35CYmIi5xL+13rx5c/8+n1dWVkZoaCgJCQlYrVasVivNmzf3t2/atAmHw1FlJIXz/0evvfZaduzYQXFxMe3atfMPx3P77bfz4YcfMmTIEBYtWkTr1q0ZO3Ysubm5GGO4//77AYiJifEvExUVRWVlZUD3/9ua1IfK3xQfH09+fj4AJ0+e5Ny5c/4X2+Dgf+32+Rfe6sTFxdGuXTtef/11cnNz6d+/v/8K6uqWS0lJ4Z133mHbtm106dKlStuWLVu45pprWLp0KU899RQvvvji924f4JprrmH//v0AbNu2jbZt2wJw8803s3jxYv7yl7+wf//+GmuNi4tj586dAPU6YGB1dcfGxvLCCy+Qm5vL+PHj/X0wYMAA5syZQ7t27bjiiiuIi4vjjjvuIDc3l+XLl9OrVy/atGlT43rz8vIYNWoUK1asAOC9996r8b7nxcXF8fHHH+N2uzHGsG3bNn/YPPLII0yaNIkJEybg8XiIi4vj0UcfJTc3l3nz5tGnTx+uv/56Dh8+TEVFBR6Ph3379v24DguAv/3tbyQnJ7N8+XJ69uzJkiVLau2TK6+8ErvdzqlTpwDYunWr//nUtWtXXn75ZebNm8fJkydrfIxWr17NtGnTWLFiBfv27fM/ty5V8fHx7Nu3z98nlZWVbNu2DYfDUWNfjhw5kkcffZSpU6f6b/v2fdu0aUNhYaH/DdvWrVuJjY0lISGB48eP8/HHH9OlSxfKysr429/+xj333FPtehpakz1CePLJJ5k0aRL/+7//S0VFBdOnT7/gsZE6dOjAnXfeyeDBg3E6ndx6663+d1fViY6OJiwsjMTExO9sq0OHDowdO5bly5cTHBzMiBEj6lRDVlYWM2bMwBiDxWKpcs63efPmTJ06lYkTJ7JmzZpqa/3Vr37F2LFj+f3vf0+rVq0IDQ29oD64EOdr8Xg8AMycOROAnj17MnPmTHJycgDo3r07W7du5eGHH6asrIwePXpUGdjw22699Vb+67/+i8jISMLCwujatas/HGrSvn17evXqxeDBg/F6vSQnJ9OjRw9/uP77v/8777zzDkuWLGH48OFMnjyZ1atXY7fbGTlyJK1ateKZZ54hLS2NVq1a0aJFi/roonr1b//2b4wfP54FCxYQHBxMeno6H3/8cY33DwoKIisri1GjRhEUFERERASzZ8/2HzldffXVjBo1ikmTJvG73/2u2seoffv2DBgwgCuvvJLo6Ghuu+22htrdgLDZbGRkZPDkk0/SvHlzXC4X6enpxMTE8NFHH9W43MCBA3nnnXdYt25dte2tWrVi1KhRDB06lODgYGJiYvjlL38JfH20cPz4cYKDg7n99ts5fPgwYWFh/s+wGpOuVK5n54PohhtuaOxSREQuSJM9ZdTQKioq6N+/Px06dFAYiMglSUcIIiIC6AhBRER8FAgiIgIoEERExEeBIPIt1Q14tm/fPl5++eWAbve9997j5MmTNbaXlpbW+DVHkfqgQBCpg5tuuomRI0cGdBuvv/46dru9xvYDBw6wYcOGgNYgl7cme2GaXH4COdhbdHQ0b7zxBr/97W+5//77SUpK4ujRo1x11VUsWLAAl8vFhAkTOHXqFK1bt2bbtm18+OGH1dZZWVnJM888g91up6KigvHjx1NeXs6+ffuYOHEif/jDH1iwYAG7d+/G4XAQHx/P7NmzWbRoEfv37ycvL4+dO3fSu3dv7rnnHjZu3Mhf//pXsrOzycjI4NixY1RWVjJs2DB69+7dUN0vTYACQZqMjz76iJtvvpmMjAzy8/MpLCzk888/56233mLPnj0888wz/tMyI0eOZPDgwUyZMoVVq1YRHR3N8uXLycnJoWvXrpSVlTF8+HCGDh3KvffeW+W3EIqLi1m+fDmtW7cmLS2NXbt28c9//pM2bdowf/58CgsLeeihh2qs89ixY3zxxRcsW7aML7/8kk8++YSuXbty0003MXXqVJxOJ1dccQWvvfYaXq+XBx98kJMnTzJ8+HDeeOMNUlNTqx0ywm63s2XLFv74xz8CX4+5I3IhdMpImozzQyo8/vjjrFy5EovFcsGDvZ0fxmHr1q1UVlZ+Z+Az+HpMoNatWwNfD+5XWVlJYWEhSUlJwNfj47Rq1arGOm+88UaGDBnCuHHjmDZtGl6vt0p7aGgop0+fZty4cWRmZlJWVobL5apxfecvJbLZbEyZMoUpU6YwduzYamsXqY0CQZqMQA729k3VrTMhIcH/rv3YsWO1jktz4MABHA4HixcvJjs72/8bF0FBQRhj2LhxI5999hkvvvgi48aNo6KiAmMMwcHB/vAICQmhpKQEgL179wJfj4y7Z88eFi5cyOLFi5kzZw5ut7suXScC6JSRNCGBHOztF7/4Ra3bHjBgABkZGQwZMoTrrruu1kEE27Zty8KFC3nrrbdo1qwZo0ePBqBjx45MmDCBnJwcXnnlFQYNGkRISAjXX389p06dIiYmhoMHD7Js2TIGDhzIpEmTWLdunT/EoqKiKCkp4T/+4z9o2bIljz322AUP6CiXNw1dIVIPduzYQVlZGXfffTeffPIJjz/+OOvXr2/sskQuiAJBpB6UlJQwbtw4XC4Xbreb0aNH89lnn/H2229/577jxo2jY8eOjVClSO0UCCIiAuhDZRER8VEgiIgIoEAQEREfBYKIiAAKBBER8VEgiIgIAP8PSvSL7NaplpwAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize correlations between stroke and other categorical variables\n",
    "for col in cat_cols:\n",
    "    sns.barplot(data=df, y='stroke', x=col)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Those who formerly smoked seem to be more likely to have had a stroke than those who have never smoked or are currently smoking. Those who have been married before are also more likely to have had a stroke than those who have never been married. Those who are self-employed also seem to be more likely to have had a stroke than those who have a private or govt. job."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create 2 explanatory visualizations\n",
    "ax1 = sns.barplot(data=df, y='age', x='stroke')\n",
    "ax1.set_ylabel('Age')\n",
    "ax1.set_xlabel('Has had a stroke before?')\n",
    "ax1.set_xticklabels(['No', 'Yes'])\n",
    "ax1.set_title('Age vs. Stroke History')\n",
    "plt.show()\n",
    "\n",
    "ax2 = sns.barplot(data=df, x='ever_married', y='stroke', order=['No', 'Yes'])\n",
    "ax2.set_ylabel('Proportion of Sample w/ Stroke')\n",
    "ax2.set_xlabel('Has been married?')\n",
    "ax2.set_title('Stroke Probability vs. Marriage History')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Those who have had a stroke are around 30 years older than those who have not, on average.\n",
    "\n",
    "Those who have been married have around a 4x higher probability of having had a stroke than those who have not been married."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "outputs": [
    {
     "data": {
      "text/plain": "gender                 0\nage                    0\nhypertension           0\nheart_disease          0\never_married           0\nwork_type              0\nResidence_type         0\navg_glucose_level      0\nbmi                  201\nsmoking_status         0\nstroke                 0\ndtype: int64"
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recreate dataframe to prevent data leakage during modelling\n",
    "ml_df = pd.read_csv('healthcare-dataset-stroke-data.csv', index_col='id')\n",
    "ml_df.isna().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "outputs": [
    {
     "data": {
      "text/plain": "0    0.951272\n1    0.048728\nName: stroke, dtype: float64"
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check class imbalance\n",
    "ml_df['stroke'].value_counts(normalize=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "outputs": [],
   "source": [
    "# Split data into train and test subsets\n",
    "X = ml_df.drop(columns='stroke')\n",
    "y = ml_df['stroke']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "outputs": [],
   "source": [
    " # Instantiate median imputer\n",
    "median_imputer = SimpleImputer(strategy='median')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "outputs": [],
   "source": [
    "# Instantiate transformers\n",
    "scaler = StandardScaler()\n",
    "ohe = OneHotEncoder(handle_unknown='ignore')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "outputs": [],
   "source": [
    "# Create pipeline to impute + scale numerical columns\n",
    "num_pipe = make_pipeline(median_imputer, scaler)\n",
    "\n",
    "# Create tuples matching transformations to columns\n",
    "cat_cols = ['gender', 'hypertension', 'heart_disease', 'ever_married', 'work_type', 'Residence_type', 'smoking_status']\n",
    "num_cols = ['age', 'avg_glucose_level', 'bmi']\n",
    "\n",
    "cat_tuple = (ohe, cat_cols)\n",
    "num_tuple = (num_pipe, num_cols)\n",
    "\n",
    "# Create final preprocessing object\n",
    "# ohe categorical columns, impute + scale numerical columns\n",
    "preprocessor = make_column_transformer(cat_tuple, num_tuple, remainder='drop')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "outputs": [],
   "source": [
    "# Define function to evaluate categorical models\n",
    "def evaluate(y_true, y_preds, y_proba=None):\n",
    "    print(classification_report(y_true, y_preds))\n",
    "    ConfusionMatrixDisplay.from_predictions(y_true, y_preds, cmap='Blues')\n",
    "    if y_proba != None:\n",
    "        print(roc_auc_score(y_true, y_proba))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:55:32] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankit\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "{'xgbclassifier__eta': 0.3,\n 'xgbclassifier__lambda': 1,\n 'xgbclassifier__max_depth': 5,\n 'xgbclassifier__min_child_weight': 2}"
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune XGBoost model\n",
    "# Since we are predicting stroke, false negatives are worse than false positives\n",
    "# scoring metric should be recall\n",
    "eta = [0.01, 0.03, 0.07, 0.1, 0.2, 0.3]\n",
    "min_child_weight = [0.5, 1, 2, 3]\n",
    "max_depth = range(3,10,2)\n",
    "lambdas = [0, 1]\n",
    "\n",
    "params = [{'xgbclassifier__eta':eta,\n",
    "           'xgbclassifier__min_child_weight':min_child_weight,\n",
    "           'xgbclassifier__max_depth':max_depth,\n",
    "           'xgbclassifier__lambda':lambdas}]\n",
    "\n",
    "xgb_pipe = make_pipeline(preprocessor, XGBClassifier())\n",
    "\n",
    "xgb_gs = GridSearchCV(xgb_pipe,\n",
    "                      param_grid=params,\n",
    "                      n_jobs=-1,\n",
    "                      cv=3,\n",
    "                      scoring='recall',\n",
    "                      error_score='raise')\n",
    "xgb_gs.fit(X_train, y_train)\n",
    "\n",
    "xgb_gs.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97      1198\n",
      "           1       0.40      0.07      0.13        80\n",
      "\n",
      "    accuracy                           0.94      1278\n",
      "   macro avg       0.67      0.53      0.55      1278\n",
      "weighted avg       0.91      0.94      0.91      1278\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate tuned XGBoost model\n",
    "xgb_tuned = xgb_gs.best_estimator_\n",
    "evaluate(y_test, xgb_tuned.predict(X_test))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "outputs": [
    {
     "data": {
      "text/plain": "{'randomforestclassifier__criterion': 'gini',\n 'randomforestclassifier__max_depth': 12,\n 'randomforestclassifier__min_samples_leaf': 1,\n 'randomforestclassifier__min_samples_split': 2,\n 'randomforestclassifier__n_estimators': 150}"
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune random forest model\n",
    "n_estimators = range(50,601,100)\n",
    "criterion = ['gini', 'entropy']\n",
    "max_depth = range(3,15,3)\n",
    "min_samples_split = range(2,20,4)\n",
    "min_samples_leaf = range(1,20,4)\n",
    "\n",
    "params = [{'randomforestclassifier__n_estimators': n_estimators,\n",
    "           'randomforestclassifier__criterion': criterion,\n",
    "           'randomforestclassifier__max_depth': max_depth,\n",
    "           'randomforestclassifier__min_samples_split': min_samples_split,\n",
    "           'randomforestclassifier__min_samples_leaf': min_samples_leaf}]\n",
    "\n",
    "rf_pipe = make_pipeline(preprocessor, RandomForestClassifier(random_state=42))\n",
    "\n",
    "rf_gs = GridSearchCV(rf_pipe,\n",
    "                     param_grid=params,\n",
    "                     n_jobs=-1,\n",
    "                     scoring='recall',\n",
    "                     cv=3)\n",
    "rf_gs.fit(X_train, y_train)\n",
    "\n",
    "rf_gs.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97      1198\n",
      "           1       1.00      0.03      0.05        80\n",
      "\n",
      "    accuracy                           0.94      1278\n",
      "   macro avg       0.97      0.51      0.51      1278\n",
      "weighted avg       0.94      0.94      0.91      1278\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate tuned random forest model\n",
    "rf_tuned = rf_gs.best_estimator_\n",
    "evaluate(y_test, rf_tuned.predict(X_test))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "outputs": [
    {
     "data": {
      "text/plain": "{'kneighborsclassifier__leaf_size': 5,\n 'kneighborsclassifier__n_neighbors': 3,\n 'kneighborsclassifier__p': 1,\n 'kneighborsclassifier__weights': 'distance'}"
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune KNeighbors model\n",
    "n_neighbors = range(3,40,5)\n",
    "weights = ['uniform', 'distance']\n",
    "leaf_size = range(5,60,10)\n",
    "p = [1,2]\n",
    "\n",
    "params = [{'kneighborsclassifier__n_neighbors':n_neighbors,\n",
    "           'kneighborsclassifier__weights':weights,\n",
    "           'kneighborsclassifier__leaf_size':leaf_size,\n",
    "           'kneighborsclassifier__p':p}]\n",
    "\n",
    "knn_pipe = make_pipeline(preprocessor, KNeighborsClassifier())\n",
    "\n",
    "knn_gs = GridSearchCV(knn_pipe,\n",
    "                      param_grid=params,\n",
    "                      n_jobs=15,\n",
    "                      scoring='recall',\n",
    "                      cv=3)\n",
    "knn_gs.fit(X_train, y_train)\n",
    "\n",
    "knn_gs.best_params_"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.98      0.96      1198\n",
      "           1       0.17      0.05      0.08        80\n",
      "\n",
      "    accuracy                           0.93      1278\n",
      "   macro avg       0.56      0.52      0.52      1278\n",
      "weighted avg       0.89      0.93      0.91      1278\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate tuned KNN model\n",
    "knn_tuned = knn_gs.best_estimator_\n",
    "evaluate(y_test, knn_tuned.predict(X_test))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Out of the 3 models, the tuned XGBoost model had the highest recall of 0.07 with a similar accuracy to the other models of 0.94 (which would be below a dummy model's accuracy of 0.95)."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19:57:06] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankit\\anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97      1198\n",
      "           1       0.22      0.03      0.04        80\n",
      "\n",
      "    accuracy                           0.93      1278\n",
      "   macro avg       0.58      0.51      0.51      1278\n",
      "weighted avg       0.89      0.93      0.91      1278\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate tuned XGBoost model with PCA\n",
    "pca = PCA(n_components=0.95)\n",
    "pca_xgb = make_pipeline(preprocessor, pca, XGBClassifier(eta=0.3, reg_lambda=0, max_depth=9, min_child_weight=1))\n",
    "\n",
    "pca_xgb.fit(X_train, y_train)\n",
    "evaluate(y_test, pca_xgb.predict(X_test))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "PCA reduced the recall of the tuned XGBoost model from 0.07 -> 0.03"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
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    "False negatives are worse than false positives for stroke predictions, since false negatives lead to a lack of proper treatment and may be dangerous while false positives only lead to an unnecessary checkup.\n",
    "\n",
    "The recommended production model is the tuned XGBClassifier model, since it had the highest recall of 0.07 with an accuracy similar to the other models of 0.94.\n",
    "\n",
    "Further research should be conducted before using this model in the real world. A recall of 0.07 is still low for stroke predictions, leading to dangerous false negative errors."
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