Stroke prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "291b2f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "901ecea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"healthcare-dataset-stroke-data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2492094f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\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>1</th>\n",
       "      <td>51676</td>\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>2</th>\n",
       "      <td>31112</td>\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>3</th>\n",
       "      <td>60182</td>\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>4</th>\n",
       "      <td>1665</td>\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>5</th>\n",
       "      <td>56669</td>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53882</td>\n",
       "      <td>Male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>70.09</td>\n",
       "      <td>27.4</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10434</td>\n",
       "      <td>Female</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>94.39</td>\n",
       "      <td>22.8</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27419</td>\n",
       "      <td>Female</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>76.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60491</td>\n",
       "      <td>Female</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>58.57</td>\n",
       "      <td>24.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0   9046    Male  67.0             0              1          Yes   \n",
       "1  51676  Female  61.0             0              0          Yes   \n",
       "2  31112    Male  80.0             0              1          Yes   \n",
       "3  60182  Female  49.0             0              0          Yes   \n",
       "4   1665  Female  79.0             1              0          Yes   \n",
       "5  56669    Male  81.0             0              0          Yes   \n",
       "6  53882    Male  74.0             1              1          Yes   \n",
       "7  10434  Female  69.0             0              0           No   \n",
       "8  27419  Female  59.0             0              0          Yes   \n",
       "9  60491  Female  78.0             0              0          Yes   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2        Private          Rural             105.92  32.5     never smoked   \n",
       "3        Private          Urban             171.23  34.4           smokes   \n",
       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
       "5        Private          Urban             186.21  29.0  formerly smoked   \n",
       "6        Private          Rural              70.09  27.4     never smoked   \n",
       "7        Private          Urban              94.39  22.8     never smoked   \n",
       "8        Private          Rural              76.15   NaN          Unknown   \n",
       "9        Private          Urban              58.57  24.2          Unknown   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  \n",
       "5       1  \n",
       "6       1  \n",
       "7       1  \n",
       "8       1  \n",
       "9       1  "
      ]
     },
     "execution_count": 3,
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   "source": [
    "data.head(10)"
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    {
     "data": {
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       "      <th>5100</th>\n",
       "      <td>68398</td>\n",
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       "      <td>82.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>71.97</td>\n",
       "      <td>28.3</td>\n",
       "      <td>never smoked</td>\n",
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       "      <th>5101</th>\n",
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       "      <td>0</td>\n",
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       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>97.95</td>\n",
       "      <td>24.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5102</th>\n",
       "      <td>45010</td>\n",
       "      <td>Female</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>77.93</td>\n",
       "      <td>21.7</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5103</th>\n",
       "      <td>22127</td>\n",
       "      <td>Female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>82.85</td>\n",
       "      <td>46.9</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5104</th>\n",
       "      <td>14180</td>\n",
       "      <td>Female</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>children</td>\n",
       "      <td>Rural</td>\n",
       "      <td>103.08</td>\n",
       "      <td>18.6</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5105</th>\n",
       "      <td>18234</td>\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>5106</th>\n",
       "      <td>44873</td>\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>5107</th>\n",
       "      <td>19723</td>\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>5108</th>\n",
       "      <td>37544</td>\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>5109</th>\n",
       "      <td>44679</td>\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",
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      "text/plain": [
       "         id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "5100  68398    Male  82.0             1              0          Yes   \n",
       "5101  36901  Female  45.0             0              0          Yes   \n",
       "5102  45010  Female  57.0             0              0          Yes   \n",
       "5103  22127  Female  18.0             0              0           No   \n",
       "5104  14180  Female  13.0             0              0           No   \n",
       "5105  18234  Female  80.0             1              0          Yes   \n",
       "5106  44873  Female  81.0             0              0          Yes   \n",
       "5107  19723  Female  35.0             0              0          Yes   \n",
       "5108  37544    Male  51.0             0              0          Yes   \n",
       "5109  44679  Female  44.0             0              0          Yes   \n",
       "\n",
       "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "5100  Self-employed          Rural              71.97  28.3     never smoked   \n",
       "5101        Private          Urban              97.95  24.5          Unknown   \n",
       "5102        Private          Rural              77.93  21.7     never smoked   \n",
       "5103        Private          Urban              82.85  46.9          Unknown   \n",
       "5104       children          Rural             103.08  18.6          Unknown   \n",
       "5105        Private          Urban              83.75   NaN     never smoked   \n",
       "5106  Self-employed          Urban             125.20  40.0     never smoked   \n",
       "5107  Self-employed          Rural              82.99  30.6     never smoked   \n",
       "5108        Private          Rural             166.29  25.6  formerly smoked   \n",
       "5109       Govt_job          Urban              85.28  26.2          Unknown   \n",
       "\n",
       "      stroke  \n",
       "5100       0  \n",
       "5101       0  \n",
       "5102       0  \n",
       "5103       0  \n",
       "5104       0  \n",
       "5105       0  \n",
       "5106       0  \n",
       "5107       0  \n",
       "5108       0  \n",
       "5109       0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.tail(10)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7eeeb7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5110, 12)"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
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   "cell_type": "code",
   "execution_count": 6,
   "id": "57c948cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5110 entries, 0 to 5109\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 5110 non-null   int64  \n",
      " 1   gender             5110 non-null   object \n",
      " 2   age                5110 non-null   float64\n",
      " 3   hypertension       5110 non-null   int64  \n",
      " 4   heart_disease      5110 non-null   int64  \n",
      " 5   ever_married       5110 non-null   object \n",
      " 6   work_type          5110 non-null   object \n",
      " 7   Residence_type     5110 non-null   object \n",
      " 8   avg_glucose_level  5110 non-null   float64\n",
      " 9   bmi                4909 non-null   float64\n",
      " 10  smoking_status     5110 non-null   object \n",
      " 11  stroke             5110 non-null   int64  \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 479.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cdd7b67f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace(to_replace=\"Urban\", value=1, inplace=True)\n",
    "data.replace(to_replace=\"Rural\", value=0, inplace=True)\n",
    "\n",
    "data.replace(to_replace=\"Yes\", value=1, inplace=True)\n",
    "data.replace(to_replace=\"No\", value=0, inplace=True)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f6b2893",
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   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>1</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>1</th>\n",
       "      <td>51676</td>\n",
       "      <td>Female</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>0</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>2</th>\n",
       "      <td>31112</td>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>0</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>3</th>\n",
       "      <td>60182</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>1</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>4</th>\n",
       "      <td>1665</td>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>0</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>5</th>\n",
       "      <td>56669</td>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>1</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53882</td>\n",
       "      <td>Male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>0</td>\n",
       "      <td>70.09</td>\n",
       "      <td>27.4</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10434</td>\n",
       "      <td>Female</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Private</td>\n",
       "      <td>1</td>\n",
       "      <td>94.39</td>\n",
       "      <td>22.8</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27419</td>\n",
       "      <td>Female</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>0</td>\n",
       "      <td>76.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60491</td>\n",
       "      <td>Female</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Private</td>\n",
       "      <td>1</td>\n",
       "      <td>58.57</td>\n",
       "      <td>24.2</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease  ever_married  \\\n",
       "0   9046    Male  67.0             0              1             1   \n",
       "1  51676  Female  61.0             0              0             1   \n",
       "2  31112    Male  80.0             0              1             1   \n",
       "3  60182  Female  49.0             0              0             1   \n",
       "4   1665  Female  79.0             1              0             1   \n",
       "5  56669    Male  81.0             0              0             1   \n",
       "6  53882    Male  74.0             1              1             1   \n",
       "7  10434  Female  69.0             0              0             0   \n",
       "8  27419  Female  59.0             0              0             1   \n",
       "9  60491  Female  78.0             0              0             1   \n",
       "\n",
       "       work_type  Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0        Private               1             228.69  36.6  formerly smoked   \n",
       "1  Self-employed               0             202.21   NaN     never smoked   \n",
       "2        Private               0             105.92  32.5     never smoked   \n",
       "3        Private               1             171.23  34.4           smokes   \n",
       "4  Self-employed               0             174.12  24.0     never smoked   \n",
       "5        Private               1             186.21  29.0  formerly smoked   \n",
       "6        Private               0              70.09  27.4     never smoked   \n",
       "7        Private               1              94.39  22.8     never smoked   \n",
       "8        Private               0              76.15   NaN          Unknown   \n",
       "9        Private               1              58.57  24.2          Unknown   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  \n",
       "5       1  \n",
       "6       1  \n",
       "7       1  \n",
       "8       1  \n",
       "9       1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41705ef2",
   "metadata": {},
   "source": [
    "## Correlation between the different features and the target variable\n",
    "The correlation between different features can be best understood by the following Heatmap:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7eede8da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_25292\\1212894860.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(data.corr(), cmap=\"BuPu\", annot=True)\n"
     ]
    },
    {
     "data": {
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nwMBAOceR59/Z2VmBLyxWHBgYIOcnsw+e7hcYEKCMGZ/9OBcUGKg83qaLszk6OsrR0VGuuXLJ3d1dVStW0KGDB1XQzy+hTcM7ljptOllZWenfFxaC/PfeHaVN7xj7ToqeeumU2UWSlDWnh25fv6Q/1i81GRxImcpWTpld5JTZRTk88mpCvy+078+NKvvhZ4nSFiSetOkdZGVlrTsvLPB3NzgoxmyCN3Hr5nUd3r9XvYaMSvCx8G44ODrK2traZGFQSQoODIxzvRknZ+dYygcYyx/Yt0/BQUGqXb2y8f2IiAh9P+5bLVm0QGs2xJ6THv9v787jakr/OIB/bouKSmlBlkpaFdmX7CK7LDMYS6Kx73uDMAzDMIzsZiZrGLux78PPPihbWgwyI1ISUWl5fn+kw+W20HK77uf9et0XnfOcc55z73PP+d7znOd7VEdxfUPINDSQ8CJObvqrF3HQ/6RzzkP87+BWWDtUxYPwm3j18jkWTewrlRfp6Tjyx6+4cGw3xsxbVzA7Q1lq2qw5XFze3eTzJiUjt39sTKzcAxqfxcbAzsFR4Trexbofxq7vYl1TU1OkpKTgxYsXcqMHnr13XDE1NcXNG/IpZjKPQ1nFzNlZF/A7fv9tDVb9+jvs7O0/eXnKP5mxyYcPH46Pi4OR8ec/lHzDGn94du8Lt2YZ5yJL68qIiY7Cri3r2TlA9AVQdgz7NDoaw77tD5dqrvCdNiOf9opIffGZAyru4sWLcn9fuHABtra20CzAh8j5+voiPj5e7jVh0uQC215B0S5WDI5OTrh04YI0LT09HZcuXkDVaq4Kl6nq6ipXHgAunD+Pqq4ZF3PLlS8PU1NTXLr4rkxCQgJuXL+OalmsM3O7APDm7UPdSLVoaWmjrKUt/gkJkqalp6fjn5AglLdR/INdEZEukJaakn0ZIZCaQxkqmrS1tWFj54AbVy9L09LT03H92mXYOSl+1sSnOHloHwyNjFGznlue10WFQ1tbG/aOTrh86d25PD09HZcvXYRL1WoKl3GuWg2XL8mfhy5dOC+Vb9O+AzZu24n1W7dLLzMzc/Ty8sYvK1YV3M5QodHS0oaFpS3ufXjOuROE8pU+4Zwj0pGaknE+qVa/BYbMWI7B05dJLwMjEzTw6Io+Y37I712gXChRogQqWlpKLxubytnEmIqPFxmxbpVsY13HKlWgpaUtV+b+vXuIioqSblip6uqKiPAwuYs658+dg76+PirZfNpzTQJ++xVrVq7A8lWrUcXZOecFqEBpa2ujkp09blyTj01u5DE2SU5KgkxD/i5ODQ1NiE+4IY2Iii5lxrDRT55gqI83HJycMHXmbGho8LImUV5x5ICKi4yMxNixYzFo0CBcvXoV/v7+WLhwYYFuU0dH56Phv4mpaQW6zYLSx6sfpn3nC6cqznB2ccGmDeuRmJiITp07AwCm+k6Gubk5Ro4ZCwD4pncf+PTzwvq1AWjUuAkOHTyA2zdvwm9GxkgKmUyGXn36Ys2qVahY0RLlypfHMv8lMDM3R7MWLQAAN64H49aNm3CtUQOGJQ3xb+RDLPP3R4UKFeRGDdyNiMi4kys+Hq9evcKdkIyHDDo45v6HPxWeBq26YNdvC2BhZYvy1vY4f2wX3iQnoYZbKwDAjl9/gqGxCVp27Q8AOL1/Cyys7FDKvCzSUlIQduMygi8cR4fewwEAb5KT8Ne+zXBwrQeDkqXwOuEFLp74Ey/jYuBcq5HS9pPypkPXnvCf/z1s7B1ha++EfTu3IDkpCc1btwcALPlxBkqZmqG3zzAAGQ/7+vdBRt7O1NQUxMY8xb2IMOjq6aFsuQrSetPT03Hi8D40bdkOmpo8tauSnn36Yta0KXB0qgInZ2ds3bQRSYmJaNfJEwAwc6ovzMzNMXTkGABA9296Y4iPNzatXwu3Ro1x9NBBhNy+hcl+MwAAJY2MUNLISG4bmlpaMDExhaWVtTTtcVQUXsTH48njKKSnpyHszh0AQPmKFVG8ePEC32/Km/otO2PX7wthYWmLctb2uHBsN1KSk1HdLeNuu52/LYChkQncu3oDAM4c2AoLS1sYvz3nhN+4jOsXTqBdr4xzTnF9QxTXl885r6GpCf2SxjAtU75wd44UehdjrlQQY7pL5Qb290bzFu7o0asXAKCPl1e2sa6BgQE6d+2ChfN/RMmSJVFCXx8/zpmNqq6uUgdC/QZuqGRjgymTJ2H0uPGIjYnBMv9f8HXPb1CsWDFp25mxauLr14iLi8OdkJCMjvG3D5UM+HUNli/1x9z5C2BhUQ4xTzOelVC8eHEUL1GiwN9DUqx9155YNn8WbOwcUdneCft3bUVyUhKaeWTc4e8/byZKmZqh14ChAD6ITVJSFcYmNes1xM7AtTA1L4MKlta4FxGGP3dsRnOP9srZSSo0uiV0UbZyWenv0talYV3NGi+fJSDmoeLno5BqUkYMm9kxUMbCAiPGjMfzuHejKLMasUDqgVmF8oZXEFRc3759kZiYiDp16kBTUxOjRo3CwIEDlV0tleHRpg3inj3DiqX+iImJgb2DA5avWiWdWKKiPrYy2QAARwhJREFUoiCTveuJdq1eHXPmz8eyJUvgv3gxKlpaYpG/Pyrb2kpl+g0YgMTERMyaMR0vX75E9Ro1sHzVaqlDRVdXD8ePHcOKZUuRmJgIUzMzuDVsCJ9BP8v9wBo+ZDCiHj2S/u7RrSsAIOjW7QJ9T+jzuNRpgtcv43Fi9wYkvIhDmQqV0GfMbCnFQ/yzaLnciW+Sk7Bv41K8iIuBtnYxmJatgK4+E+FSpwkAQKahgZjHD7Fl+TG8TniB4iUMUM7aDgMmL4B5OStl7CLlA7dmLREf/xxb1q7G87hYWNvYYercxdLQ/ZjoJ5C9d/dLXOxTjB/cR/p777ZN2LttE6pUrYHvf36Xf/r61UuIiX6MFm06FN7OUL5o6dEGz+PisGbFUsTGxMDW3gGLlq+Eydt0dY8/OA9Vda2O7+fMw6pl/ljp/wsqVLTE/EVLYFPZNqtNKLR6+VIc+HOP9HffHhm5Wpet+R01a9fJhz2jguRcpwleJcTj5J6NSHjxDGUq2KD36FnvzjmxH59z9m9ahhdxMdB6e87pMmACnN+ec0g19Bvg816M+eKjGBMAHj6MRNzzdxdLPNq0RdyzOKxYuuRtrOuI5atWy11EGT/JFzKZBsaNHoU3KW/QwM0N3031k+ZrampiyfIV+OH7mfDq1RN6enro0MkTQ4ePkKtfj25dpP/fvnULB/fvQ1kLCxw8ehwA8MfWLUhJScH4MaPklhs0dBiGDBueP28SfTK3pi3x4vlzbF23Bs/jYmFlY4spcxa9F5s8ljuexMU+xcQh71KQ/bltE/7ctglOVatj5sKM2GTA8HHYsnY1fl3yE+Kfx6GUiSlatvNEt94DCnfnqNBVrmWLOafmSn/7LPoWAHB87TH84r1YSbWigqCMGPbShfP492Ek/n0YiY4eLeTmXQi6mT87RqSGZEIIoexKkOpT1ZEDVLj2XohUdhWoiKtimXW+bKJM5Ux5hynl7NDfD5VdBVIBnvWtlF0FKuLCH8UruwqkAqZa9sm5EKm9da93KbsKpAKM9bRzLkRy7hThc7WDRUllVyFHHDlARERERERERERERCqHaYXyhk/uICIiIiIiIiIiIiJSM+wcICIiIiIiIiIiIiJSM0wrREREREREREREREQqRwbmFcoLjhwgIiIiIiIiIiIiIlIz7BwgIiIiIiIiIiIiIlIzTCtERERERERERERERCpHxqxCecKRA0REREREREREREREaoadA0REREREREREREREaoZphYiIiIiIiIiIiIhI5ciYVyhPOHKAiIiIiIiIiIiIiEjNsHOAiIiIiIiIiIiIiEjNMK0QEREREREREREREakcZhXKG44cICIiIiIiIiIiIiJSM+wcICIiIiIiIiIiIiJSM0wrREREREREREREREQqR8a8QnnCkQNERERERERERERERGqGnQNERERERERERERERGqGaYWIiIiIiIiIiIiISOUwqVDecOQAEREREREREREREZGaYecAEREREREREREREZGaYVohIiIiIiIiIiIiIlI5MhkTC+UFRw4QEREREREREREREakZdg4QEREREREREREREakZphUiIiIiIiIiIiIiIpXDrEJ5w5EDRERERERERERERERqhp0DRERERERERERERERqhmmFiIiIiIiIiIiIiEjlMKtQ3nDkABERERERERERERGRmmHnABERERERERERERGRmmFaISIiIiIiIiIiIiJSPTImFsoLjhwgIiIiIiIiIiIiIlIz7BwgIiIiIiIiIiIiIlIzTCtERERERERERERERCqHSYXyhiMHiIiIiIiIiIiIiIjUDDsHiIiIiIiIiIiIiIjUDNMKEREREREREREREZHKkTGvUJ5w5AARERERERERERERkZqRCSGEsitBqu91Spqyq0Aq4FJotLKrQEWcZWkDZVeBVEA6QxfKhWJamsquAqkAY30dZVeBirjo+ERlV4FUAI8llBtexTsruwqkAvaKfcqugsr5N+61squQpfLGxZVdhRwxrRARERERERERERERqRxmFcobphUiIiIiIiIiIiIiIlIz7BwgIiIiIiIiIiIiIlIzTCtERERERERERERERKpHxsRCecGRA0REREREREREREREaoadA0REREREREREREREaoZphYiIiIiIiIiIiIhI5TCpUN5w5AARERERERERERERkZph5wARERERERERERERkZphWiEiIiIiIiIiIiIiUjky5hXKE44cICIiIiIiIiIiIiJSM+wcICIiIiIiIiIiIiJSM0wrREREREREREREREQqiHmF8oIjB4iIiIiIiIiIiIiI1Aw7B4iIiIiIiIiIiIiI1AzTChERERERERERERGRypExq1CecOQAEREREREREREREZGaYecAEREREREREREREZGaYVohIiIiIiIiIiIiIlI5zCqUNxw5QERERERERERERESkZtg5QERERERERERERESkZphWiIiIiIiIiIiIiIhUjox5hfKEIweIiIiIiIiIiIiIiNQMOweIiIiIiIiIiIiIiNQM0woRERERERERERERkQpiXqG84MgBIiIiIiIiIiIiIiI1w84BIiIiIiIiIiIiIiI1w7RCRERERERERERERKRyZMwqlCccOUBEREREREREREREpGbYOUBEREREREREREREpGaYVoiIiIiIiIiIiIiIVA6zCuUNRw4QEREREREREREREakZdg4QEREREREREREREakZphUiIiIiIiIiIiIiItXDvEJ5wpEDRERERERERERERERqhp0DX7j79+9DJpMhKChI2VUhIiIiIiIiIiIioiKCaYWKoH79+uH58+fYvXu3squiFrZuDsS6gN8RGxMDO3t7TPpuCpxdqmZZ/ujhQ1i+1B+P/vsPFS0tMXLMWDRq3ESaL4TAimVLsWv7Nrx8+RLVqlfHd9P8YGlpJbeeM3/9hdUrlyM8LAzFdHRQs1YtLFqy9KPtPX/+HN27dkb0kyc4fe4CDAwN823fKX+dPLgLR/dsQfzzZyhvVRk9BoyEta2jwrJnju7Dhb8O41HkPQBAxUp28Oz1rVz5tf5zcf7UYbnlnFxrY9S0nwpuJ6jA7d3xB7ZvXo+4Z7GoZGOLoWMmwt7JWWHZ+//cxYbfViI8NATRj6MwaOQ4dP76G7ky+3Ztw77d2xEdFQUAqGhdCb36fYva9d0KfF+oYPy58w/s2LwBcc9iYW1jiyGjJ2TZRh7cy2gjEaF3EP04CgNHjIXnB21k/67t2L97O548zmgjltaV0LOfD2rXYxtRZXu2b8Ufm9bh2bNY2FS2w/Cxk+BQJetjydo1yxF+JwRPHkdhyKjx6Nqjl1yZ69eu4I9N6xEeehuxMTGY+ePPcGvSrDB2hfLRH5sDsX5tRlxra2+Pib45x7Urlvoj6tF/qFAxI65t+F5ce+LYUWz/Yyvu3L6F+Ph4BG7bAXsH+dhmoLcXrvx9WW5a16++xnd+M/J136jg/PlBbDIkm9jkwQexycAsYpP9u7fjSdS78843jE1UyvYtm7FxXQCexcagsp09xk36DlVcXLIsf/zIYaxevlQ6lgwbNQYNGjVWWHbe7JnYtX0bRo+fhB69+wAAHv33HwLWrMTfly7hWWwMTM3M0Lpte/T7dhC0tbULZB9Jeao0qoLOE7rCpqYNTCxM8IPnbFzcc0HZ1SIVJmNeoTzhyAEVlpKSouwqqLzDBw9i4fx5GDRkKAK3bYedvQOGDhqIZ7GxCssHXbsG34kT4Nm5CzZv24GmzVtg7MgRiAgPl8qs/f03bN60Ed/5Tcf6wC3Q09PDsEEDkZycLJU5dvQIpvpOQkfPzti6YxcCNmxEm7btFG5zpt9U2NrZ5e+OU767fPYEtq9djnZf98OUn9agvKUNlsyagBfxcQrLh90KQu2GLTB25iJMmrMMxqbm+OX78YiLfSpXrkr1Opj/6w7p5TPGrzB2hwrIX8ePYM3Sn9HbeyCW/rYJlSrbYcrY4Xge90xh+eTkJJSxKIf+g0fA2MREYRlTs9LoP3gE/H/biCW/boBrjdqY6TsW9/+5W5C7QgUko40swjf9voX/rxtRqbIdpo0bkXUbSUpC2bLl4T1oOIxLZdFGzM3hPXg4lvy6Ab+sWY9qNWphlu84PLjHNqKqTh47jJVLFqLPgEFYuTYQlWztMHnMUMQ9U9xOkpKSUNaiPHyGjkQpE9MsyiSikq0dRozzLciqUwE6cuggfv5pHgYOHopNf2yHnZ0DhmcT1wYHXcOUSRPg2aULAt/GteNGyce1iYmJcK1eAyPGjMt22527foXDJ/+SXiPHjs/XfaOC89fxI1i99Gf08h4I/982wbqyHaZmE5skvY1NvHOITbzfi02q1aiN733H4gFjE5Vw9PBB/LJwPnwGDcG6zdtga2eP0UMH4dkzxceS60HX4Oc7ER08O2Pdlm1o3Kw5Jo4ZibsR4R+VPXXiGG5evw4zM3O56Q/u30N6usDkqX4I3LEbo8ZPws7tf2CF/+KC2EVSMp0SurgX/A9WDVup7KoQEdg5oFTbt2+Hi4sL9PT0YGJiAnd3d0yYMAHr1q3Dnj17IJPJIJPJcOrUKSk90NatW9GkSRPo6upi06ZNSE9Px/fff4/y5ctDR0cHrq6uOHToUJbbTEtLQ//+/eHg4IDIyEgAwJ49e1CjRg3o6uqiUqVKmDlzJlJTUwvrbVCqjevXoku3r9CpcxfY2FTGFL/p0NXVxe5dOxWW37xxAxq4NYRX/wGoZGODYSNGwtHJCVsCNwHIGDUQuGE9vh04CM2at4CdvT1mzfkRT6OjcfL4cQBAamoqfvpxLkaPm4CvuveApZUVbGwqo1XrNh9t748tW/DyxUv07eddcG8C5Ytjf25DQ/d2cGveBhYVrNBr0FgU09HFueMHFJYfMHoqmrb2RAVrW5Qpb4m+QyZACIE7N67KldPS0kZJYxPpVULfoDB2hwrIzi0b0bpDZ7Rq1xGW1pUwYsJ30NHVxeF9exSWt3esgm+HjUZTdw9oaxdTWKZew8aoU78hylWoiPIVLdFv0DDo6hXHnds3CnJXqIDs2roJrTt4olW7jqhoXQnDx/tCR1cXR/bvVVjezrEKBgwbhSbuHtAupriN1HVrjNrvtRGvgW/byC22EVW1Y/NGtO3YBa3bd4KltQ1GT5wCHR1dHNq3W2F5B6cqGDRiDJq1bJ3lHZh16jdE/0HD0LBp8wKsORWkjevXonPXr9CxcxdUsqmM7/ymQ1dPF3uyiWvruzVEX+8BsK5kg6EjRsLByQl/bN4klWnXoSMGDhmKuvXqZ7ttXT1dmJqaSS99ff183TcqOLu2bEQbBbHJkWxiEx/GJl+0zRvWo1OXbmjv2RnWNjaYNNUPurq62Ld7l8LyWwM3ol4DN/Tu1x/WlWwwaNgI2Ds6YfuWQLly0U+eYOGPczFzzjxoasknsajv1hDTvp+Nug3cUK58BTRu2gy9+vbDqbe/oenLcvXQFWyathEXdp9XdlWICOwcUJqoqCj07NkT/fv3R0hICE6dOoUuXbpg+vTp+Prrr9G6dWtERUUhKioKDRo0kJabPHkyRo0ahZCQEHh4eOCXX37BwoULsWDBAly/fh0eHh7o2LEjwsM/7qVPTk7GV199haCgIJw5cwYVK1bEmTNn0LdvX4waNQq3b9/GqlWrsHbtWvzwww+F+XYoRUrKG4Tcvo269epJ0zQ0NFC3Xn1cDw5SuMz14CDUrS//46h+AzdcDw4GAPz377+IiYmRK2NgYADnqlWldd4JuY3oJ0+goSFDj25d0LJpYwwbPFDuLi0AuHs3AmtWLsesuXOhIeNXtShLTUlB5N1QOFatKU3T0NCAQ9Wa+Cfsdq7W8eZNMtLSUj+6+B92KwjjvT3hN6IPNq36GQkv4/O17lR4UlJSEB52B9Vr1ZGmaWhooHqtOgjJp4u0aWlpOHXsMJKTEuFYJes0ElQ0paSkICLsDlxr1pWmaWhowLVWHdy5dT1ftpGWloa/jh1GEtuIykpJSUFYaAhq1JZvJzVq18Xtm/nTTkj1pKS8wZ3bt1Hng7i2Tr36uJFdXFsv67j2Uxzcvw/NGzXA1507wn/xz0hMTPzkdVDhy4xNXD+ITVwLIDZJSkqEA887RV5KSgpCQ26jdl35Y0ntuvVw47riY8PN68GoXVf+WFKvfgO58unp6Zg51Re9vfqhUuXKuapLQkICDEsypS4R5UwmK7ovVcBnDihJVFQUUlNT0aVLF1haWgIAXN7m8NPT00NycjLKlCnz0XKjR49Gly5dpL8XLFiASZMmoUePHgCAefPm4eTJk1i8eDGWLVsmlUtISEC7du2QnJyMkydPomTJkgCAmTNnYvLkyfDy8gIAVKpUCbNmzcLEiRMxffp0hXVPTk6WS5EDAGkaWtDR0fnct0Mp4uKeIy0t7aPh9SYmJrh/7x+Fy8TExKDUB8NnTUxNERsTI80HoHCdmWX+ffgvAGDl8mUYN3ESLCzKYcO6tfjW2wu79x9AyZJGePPmDXwnTMDoceNRtqwF/nu7DBVNCS/jkZ6eDgOjUnLTDUsa4/F/kblax84Nq1DS2FSug6FK9TqoXq8xTM3L4unj/7A78Ff4z56ESXOWQUNTM1/3gQrei/jnSE9Lg9EHqV+MSpng4YP7eVr3vbvhGDPYG2/evIGenh6mzVkAS+tKeVonFb7MNmJcSv5YYmRcKh/aSATGDXmvjfzwEyqyjaik+OdxCtuJcT4cS0h1PX8b15p8QlwbqyCuLWXyLq7NrdZt26GMhQXMzMwRHhYK/0U/48H9+1iweMmn7QQVunfnHfl2YFzKBP/mQ2wylrGJynkeF/f2N/IHbcLEBPfv31O4jKJjifEHx5INAb9BU1MTX3/TO1f1eBgZiW1bAjFiDFOUEREVNHYOKEm1atXQokULuLi4wMPDA61atUK3bt1gbGyc7XK1atWS/v/ixQs8evQIbm7yD3Zyc3ND8Ad3/PTs2RPly5fHiRMnoKenJ00PDg7G2bNn5UYKpKWlISkpCa9fv0bx4sU/qsPcuXMxc+ZMuWnfTZ2GKX6KOxNInhDpAACfgYPg3rIVAGDm7B/g0aIZjh4+jG5fd8eSxYtgXakS2nXoqMyqUiE5tHMTLp89gXEzF0O72LtOttoNW0j/L2dZCeUsbTB12DcIvRUk14lAVL6iFZYHbMarhAScOXUMC3+Yjvn+a/gjnCTlK1pi6e+BePUqAf87eRwLf5iB+f6r2UFARHnW5auvpf/b2tnB1MwMQ3z64+HDSFSoUFGJNSNlKl/RCsvexib/Y2yi1u7cvoWtgRuxbvM2yHJxG230kycYM2wQmrdsBc+u3QqhhkRE6o25SpREU1MTR48excGDB+Hk5AR/f3/Y29vj3j3FvfGZSpQo8Vnba9u2La5fv47z5+VzuiUkJGDmzJkICgqSXjdu3EB4eDh0dXUVrsvX1xfx8fFyr/GTJn9WvZTJ2NgImpqaeBYrf3dUbGwsTEwVP6zP1NT0o4e6xcbESOVN3/6b3TpNzcwAAJVsbKT5xYoVQ/ny5fE4KgoAcPniBRw7chi1qrmgVjUXDPLpDwBo1sgNK5b6f9b+UsHRNygJDQ0NvHwu/+C2F/FxKPnBaIIPHdmzBYd2BWLUtJ9Q3som27JmZSygb1gSTx//l+c6U+EzLGkEDU1NPP/gYW7Pn8XCOIsHhOaWtrY2LMpXgK2DI/oPHgFrGzvs3rY5T+ukwpfZRj58qOzzuGcf3ZH3qaQ2Yu8I78HDUamyHfZsZxtRRSWNjBW2k7hnsVk+HJS+fEZv49pYBTGoaRbnGBMFce2z2Jgs4+DccnHJSB3zMDJ3oydJed6dd+TbQVw+xybeg0egko0d9jA2KfKMjI3f/kb+oE1k8xtZ0bEk7r1jSdDVq4h79gyebVrCrWY1uNWshsdRj7Dk55/g2aaV3HJPo6Mx7Nv+cKnmCt9pM/Jvx4joiyYrwi9VwM4BJZLJZHBzc8PMmTNx7do1FCtWDLt27UKxYsWQlpaW4/KGhoawsLDA2bNn5aafPXsWTk5OctOGDBmCH3/8ER07dsRff/0lTa9RowZCQ0NRuXLlj14aGoqbh46ODgwNDeVeqpZSCAC0tYvB0ckJFy9ekKalp6fj0sULqFrNVeEyVau54tKFC3LTLpw/j6rVqgEAypUvD1NTU1x8r0xCQgJuXr8urdPRqQqKFSuG+/fuS2VSUlLw6L9HKGthAQBYsOgXbN2xC1u278SW7TvhN/N7AMBv6zage89v8rrrlM+0tLVR0cYeIe89TDg9PR13rl9BJTunLJc7vHsz9m/fgJHT5sOqskOO24mLjcarly9Q0pgXf1SRtrY2bO0cEHTlsjQtPT0dQVcuw7GKS75uS4h0pKS8ydd1UsHT1tZGZTsHBF+5JE3LbCP5nac5XaQj5U1Kvq6TCoe2tjbs7B1x9e+L0rT09HRc+/sSnJyZz1tdaWsXg4OTEy5/ENdevnABLtnFtRfl49qL78W1nys09A4AwMzULE/roYLH2IQ+pK2tDXtHJ1y+JH+OuXzpIlyqKj42OFethsuX5I8lly6cl8q3ad8BG7ftxPqt26WXmZk5enl545cVq6Rlop88wVAfbzg4OWHqzNlZXo8gIqL8xbRCSnLx4kUcP34crVq1grm5OS5evIinT5/C0dERSUlJOHz4MEJDQ2FiYiI9H0CRCRMmYPr06bCxsYGrqysCAgIQFBSETZs2fVR2xIgRSEtLQ/v27XHw4EE0bNgQfn5+aN++PSpWrIhu3bpBQ0MDwcHBuHnzJmbPnl2Qb0GR0LtvP/hN8YVTFWc4O7sgcON6JCYmopNnZwDAVN/JMDc3x8gxYwEAPXv3wbfeXli/NgCNGjfB4YMHcPvWTUybkZFmSSaT4Zs+ffHr6lWoaGmJcuXKY/nSJTAzN0ezFhkpYvT19dHt6+5YuXwpypQpg7IWFlgX8DsAoGUrDwBAhYryQ7Cfx8UByHgmhIEhH8pUFLl3+Apr/efCysYeVraOOL5vO94kJ6FB8zYAgIAlc2BUyhSdew8EABzaFYg/twRgwOipMDErg/i4jLttdHT1oKtXHEmJr7Hvj3WoUb8xDI1K4enjR9i5YRXMypSDk2ttpe0n5U2XHr2x4IfpsHVwhL2jM3b9EYikxES0apeRQuynWX4wMTND/8EjAGR0HEbez8gVnZqSgpin0bgbHgo9veKwKF8BAPD7Sn/UrucGs9JlkPj6FU4ePYTr167gh5+XKmcnKU86d++Fn+fMgK2DE+wcq2DPtkAkJyaiZdsOAIAFs/1gYmoO78HDAXzcRmKfPv2ojQSsXIpa9RrAvHQZvH79GqeOHsKNa1cwayFHoqmqrj17Y/4sP9g7OMG+ijN2bglEUlIiWrfvBAD4ceZUmJqZw2foSAAZ7eTB27zzqakZx5KIsFDo6emh3Nu0L4mvX+O/fx9K24h69B8iwkJhYGiI0mXKFvIe0ufo3bcfpk/xhWMVZzi7uCBwQ0Zc2/FtXOv33WSYmZtjxGj5uHbDugA0bNQERw5lxLVTpr9LHxof/xyPo6LwNDoaAPDg/n0AGXcKm5qa4eHDSBzavx8NGzVGSSMjhIeFYuH8eahRsxZs7e0L9w2gz9K5R28sfC822f3H2/PO29hkwdvYxDuL2CRWQWwSsNIfteq5vT3vvMKpt7HJbMYmKqFnn76YNW0KHJ2qwMnZGVs3bURSYiLadfIEAMyc6gszc3MMHTkGAND9m94Y4uONTevXwq1RYxw9dBAht29hst8MAEBJIyOUNDKS24amlhZMTExhaWUN4F3HQBkLC4wYM176/Qsgz6OZqOjRLaGLspXfxRalrUvDupo1Xj5LQMzDp0qsGZF6YueAkhgaGuL06dNYvHgxXrx4AUtLSyxcuBBt2rRBrVq1cOrUKdSqVQsJCQk4efIkrKysFK5n5MiRiI+Px7hx4xAdHQ0nJyfs3bsXtra2CsuPHj0a6enpaNu2LQ4dOgQPDw/s27cP33//PebNmwdtbW04ODjAx8enAPe+6PBo0wZxcc+wYqk/YmNiYO/ggGUrV0kByOOoKLk7FlyrV8ecefOxzH8Jlv6yGBUtLfHzEn9Ufu/97td/ABITEzF7xnS8fPkSrjVqYNnK1XKjK0aPGw9NTU1M9Z2M5OQkOLtUxerff4dhNh1BVLTVdmuOhPjn2LslAC+eP0N568oYOXU+DN+mFXoW80Qux+bpw3uQmpqCVQvkn9XR/msvdOjuDQ0NTfz34B9cOHUYr18nwMjYBI7VaqNTz/7Q1i5WqPtG+adJi1aIfx6HDb+uRNyzWFSqbIfZC/2lBwFGP3kMmca7dhIb8xTDvN+NFtqxeQN2bN4AF9ea+GnpagAZnYc/zfZDXGwMipfQh7WNLX74eSlq1K5XuDtH+aJJi1Z48TwOG35710a+X/CujTx98hgasnfnpWcxTzGify/p7x1bNmDHlg1wca2Bef4ZbST++TMs/GE6nsXGoMTbNjJroT/biApr5u6B+Lg4rP11BeJiY2Fja4+5i5bJHUvej19iY55isFcP6e9tgeuxLXA9qlaviZ+X/woACL1zG+OHfSuVWblkIQCgVdsOmDjt+8LYLcqjVq3bIO7ZM6xclhHX2jk4wP+DuFb23vGjmmt1/PDjfKxYugTL3sa1C3+Rj2v/OnkSM6dNkf72nTAOADBwyFAMGjoc2trauHThPDa/vcGmdJkyaNGyJQYMHFxIe015lRmbbPx1JZ49i4VNZTvMyiY2eRbzFMOziE3mvxebLJjtJ3femc3YRGW09GiD53FxWLNiKWJjYmBr74BFy1dKDzz/8FhS1bU6vp8zD6uW+WOl/y+oUNES8xctgU1lxdckFLl04Tz+fRiJfx9GoqNHC7l5F4Ju5s+OUZFRuZYt5pyaK/3tsygj/ji+9hh+8V6spFqRSsvF80woazIhhFB2JUj1vU7JOQ0S0aXQaGVXgYo4y9IGyq4CqYB0hi6UC8W0NJVdBVIBxvqqlxqTCld0fKKyq0AqgMcSyg2v4p2VXQVSAXvFPmVXQeXEJRbddKnGetrKrkKOmMSNiIiIiIiIiIiIiEjNMK0QEREREREREREREakcJhXKG44cICIiIiIiIiIiIiJSM+wcICIiIiIiIiIiIiJSM0wrREREREREREREREQqR8a8QnnCkQNERERERERERERERGqGnQNERERERERERERERGqGaYWIiIiIiIiIiIiISOUwq1DecOQAEREREREREREREZGaYecAEREREREREREREZGaYVohIiIiIiIiIiIiIlI9MiYWyguOHCAiIiIiIiIiIiIiUjPsHCAiIiIiIiIiIiIiUjNMK0REREREREREREREKodJhfKGIweIiIiIiIiIiIiIiNQMOweIiIiIiIiIiIiIiNQM0woRERERERERERERkcqRMa9QnnDkABERERERERERERGRmmHnABERERERERERERGRmmFaISIiIiIiIiIiIiJSQcwrlBccOUBEREREREREREREpGbYOUBEREREREREREREpGaYVoiIiIiIiIiIiIiIVI6MWYXyhCMHiIiIiIiIiIiIiIjUDDsHiIiIiIiIiIiIiIjUDNMKEREREREREREREZHKYVahvOHIASIiIiIiIiIiIiIiNcPOASIiIiIiIiIiIiIiJVq2bBmsrKygq6uLunXr4tKlS9mW37ZtGxwcHKCrqwsXFxccOHDgk7fJzgEiIiIiIiIiIiIiUjkyWdF9fYqtW7di7NixmD59Oq5evYpq1arBw8MD0dHRCsufO3cOPXv2xIABA3Dt2jV4enrC09MTN2/e/LT3TwghPq2qRB97nZKm7CqQCrgUqviARpTJsrSBsqtAKiCdoQvlQjEtTWVXgVSAsb6OsqtARVx0fKKyq0AqgMcSyg2v4p2VXQVSAXvFPmVXQeUkphbda5J6n/CbpG7duqhduzaWLl0KAEhPT0eFChUwYsQITJ48+aPy3bt3x6tXr7Bv37s2U69ePbi6umLlypW53i5HDhARERERERERERER5aPk5GS8ePFC7pWcnPxRuTdv3uDKlStwd3eXpmloaMDd3R3nz59XuO7z58/LlQcADw+PLMtnhZ0DRERERERERERERKSCZEX2NXfuXJQsWVLuNXfu3I/2ICYmBmlpaShdurTc9NKlS+Px48cK9/rx48efVD4rWp9UmoiIiIiIiIiIiIiIsuXr64uxY8fKTdPRKVqp6Ng5QERERERERERERESUj3R0dHLVGWBqagpNTU08efJEbvqTJ09QpkwZhcuUKVPmk8pnhWmFiIiIiIiIiIiIiEjlyGRF95VbxYoVQ82aNXH8+HFpWnp6Oo4fP4769esrXKZ+/fpy5QHg6NGjWZbPCkcOEBEREREREREREREpydixY+Hl5YVatWqhTp06WLx4MV69egVvb28AQN++fVGuXDnpmQWjRo1CkyZNsHDhQrRr1w5btmzB33//jdWrV3/Sdtk5QERERERERERERESkJN27d8fTp0/h5+eHx48fw9XVFYcOHZIeOhwZGQkNjXdJgBo0aIDAwEBMnToV3333HWxtbbF79244Ozt/0nZlQgiRr3tCREhOTsbcuXPh6+tb5B40QkUD2wjlBtsJ5QbbCeUG2wnlBtsJ5YRthHKD7YRyg+2EqGhg5wBRAXjx4gVKliyJ+Ph4GBoaKrs6VASxjVBusJ1QbrCdUG6wnVBusJ1QTthGKDfYTig32E6IigY+kJiIiIiIiIiIiIiISM2wc4CIiIiIiIiIiIiISM2wc4CIiIiIiIiIiIiISM2wc4CoAOjo6GD69Ol8qA5liW2EcoPthHKD7YRyg+2EcoPthHLCNkK5wXZCucF2QlQ08IHERERERERERERERERqhiMHiIiIiIiIiIiIiIjUDDsHiIiIiIiIiIiIiIjUDDsHiIiIiIiIiIiIiIjUDDsHiPKoadOmGD16dJbzrayssHjx4kKrDxFlL6fvrDrie5JB1d+Hfv36wdPTU/pb1feHVNPatWthZGSU5/XIZDLs3r07z+tRVffv34dMJkNQUFCWZU6dOgWZTIbnz58XWr1I9eSmLakSZZzbPjy/qquCeO/z65xBX5Yv7bhFVNSxc4Aoj3bu3IlZs2YpuxpE9AVQ1o9PHseUq6B+APFzJWXo3r07wsLClF2NQtGvXz/IZDLIZDJoa2vD2toaEydORFJSUp7XXaFCBURFRcHZ2Tkfalp0qHunDxHJU6dzxpeOnWhEqktL2RUgUnWlSpVSdhWISMWlpaVBJpMpbfs8jinPmzdvCmzd/Fy/LG/evEGxYsWUXQ0AWdclJSUFenp60NPTU0KtlKN169YICAhASkoKrly5Ai8vL8hkMsybNy9P69XU1ESZMmXyqZZEREWTup0zKCNW0NbWVnY1iOg9HDlAlEfvD6+Mjo5Ghw4doKenB2tra2zatEm5laNCd+jQITRs2BBGRkYwMTFB+/btcffuXWn+uXPn4OrqCl1dXdSqVQu7d+/+6I7hmzdvok2bNtDX10fp0qXRp08fxMTEKGFvvlzp6emYOHEiSpUqhTJlymDGjBkAgP79+6N9+/ZyZVNSUmBubo7ffvsNQMZ3fvjw4Rg+fDhKliwJU1NTTJs2DUIIaZnk5GSMHz8e5cqVQ4kSJVC3bl2cOnVKmp85hHrv3r1wcnKCjo4O+vfvj3Xr1mHPnj3SnaiZyzx8+BBff/01jIyMUKpUKXTq1An379+X1pd5p86CBQtQtmxZmJiYYNiwYUhJSZHKLF++HLa2ttDV1UXp0qXRrVs3ad6Hw8Tj4uLQt29fGBsbo3jx4mjTpg3Cw8M/qv/hw4fh6OgIfX19tG7dGlFRUZ/7kRQZWbUNAHj+/Dl8fHxgZmYGQ0NDNG/eHMHBwdL8u3fvolOnTihdujT09fVRu3ZtHDt2TG79VlZWmDVrFvr27QtDQ0MMHDgQ1tbWAIDq1atDJpOhadOmOdYzLS0NY8eOlY41EydOlGuDwMefa3ZtID09HXPnzoW1tTX09PRQrVo1bN++XW57AwYMkObb29vjl19+kdveqVOnUKdOHZQoUQJGRkZwc3PDgwcPpPl79uxBjRo1oKuri0qVKmHmzJlITU3NcV+Lqqzes/T0dJQvXx4rVqyQK3/t2jVoaGhI70lO7WnGjBlwdXXFr7/+Cmtra+jq6uZYp6ZNm2LEiBEYPXo0jI2NUbp0aaxZswavXr2Ct7c3DAwMULlyZRw8eFBaJjefbeYx5ocffoCFhQXs7e2lES9bt25FkyZNoKuri02bNilMEZHTZx8eHo7GjRtDV1cXTk5OOHr0aK4/B2XT0dFBmTJlUKFCBXh6esLd3V2qf07fq7i4OPTq1QtmZmbQ09ODra0tAgICACgeUXTgwAHY2dlBT08PzZo1kzsPZPrf//6HRo0aQU9PDxUqVMDIkSPx6tUrab6VlRXmzJmD/v37w8DAABUrVsTq1avl1vHvv/+iZ8+eKFWqFEqUKIFatWrh4sWL0vzP/S5bWVkBADp37gyZTAYrKyvcv38fGhoa+Pvvv+XKLl68GJaWlkhPT5fSJ+3fvx9Vq1aFrq4u6tWrh5s3b37SvheU7GK/Bg0aYNKkSXLlnz59Cm1tbZw+fRoAEBUVhXbt2km/HwIDAz8pLemdO3fQsGFD6ftz7NixbEdoKPqOZsaj7/vzzz9Ru3Zt6OrqwtTUFJ07d5bm5RQnPHjwAB06dICxsTFKlCiBKlWq4MCBA9L8/Ix1s4u5Xrx4AT09PbljHgDs2rULBgYGeP36NYCc4yx6JzU1NcsY2MrKCrNnz0bfvn2hr68PS0tL7N27F0+fPkWnTp2gr6+PqlWryn3fmVZI9Wzfvh0uLi7Q09ODiYkJ3N3dMWHCBIW/Y7KKFdLT0/H999+jfPny0NHRgaurKw4dOpTlNtPS0tC/f384ODggMjISwJcXVxIplSCiPGnSpIkYNWqUEEKINm3aiGrVqonz58+Lv//+WzRo0EDo6emJRYsWKbWOVHi2b98uduzYIcLDw8W1a9dEhw4dhIuLi0hLSxPx8fGiVKlSonfv3uLWrVviwIEDws7OTgAQ165dE0IIERcXJ8zMzISvr68ICQkRV69eFS1bthTNmjVT7o59QZo0aSIMDQ3FjBkzRFhYmFi3bp2QyWTiyJEj4uzZs0JTU1M8evRIKr9z505RokQJ8fLlS2l5fX19MWrUKHHnzh2xceNGUbx4cbF69WppGR8fH9GgQQNx+vRpERERIX766Seho6MjwsLChBBCBAQECG1tbdGgQQNx9uxZcefOHREfHy++/vpr0bp1axEVFSWioqJEcnKyePPmjXB0dBT9+/cX169fF7dv3xbffPONsLe3F8nJyUIIIby8vIShoaEYPHiwCAkJEX/++adcnS5fviw0NTVFYGCguH//vrh69ar45Zdf5N6TzOOYEEJ07NhRODo6itOnT4ugoCDh4eEhKleuLN68eSNXf3d3d3H58mVx5coV4ejoKL755puC+dAKSXZtQwgh3N3dRYcOHcTly5dFWFiYGDdunDAxMRGxsbFCCCGCgoLEypUrxY0bN0RYWJiYOnWq0NXVFQ8ePJC2YWlpKQwNDcWCBQtERESEiIiIEJcuXRIAxLFjx0RUVJS0vuzMmzdPGBsbix07dojbt2+LAQMGCAMDA9GpUye5/cn8XHNqA7NnzxYODg7i0KFD4u7duyIgIEDo6OiIU6dOCSGEePPmjfDz8xOXL18W//zzj9Tut27dKoQQIiUlRZQsWVKMHz9eREREiNu3b4u1a9dK+3769GlhaGgo1q5dK+7evSuOHDkirKysxIwZMz7/A1Oy7N6z8ePHi4YNG8qVHzdunNy0nNrT9OnTRYkSJUTr1q3F1atXRXBwcI51atKkiTAwMBCzZs0SYWFhYtasWUJTU1O0adNGrF69WoSFhYkhQ4YIExMT8erVKyFEzp+tEBnHGH19fdGnTx9x8+ZNcfPmTXHv3j0BQFhZWYkdO3aIf/75Rzx69EgEBASIkiVLSsvm9NmnpaUJZ2dn0aJFCxEUFCT++usvUb16dQFA7Nq163M/nkLh5eUl9527ceOGKFOmjKhbt64QIufv1bBhw4Srq6u4fPmyuHfvnjh69KjYu3evEEJI729mfBAZGSl0dHTE2LFjpXNP6dKlBQARFxcnhBAiIiJClChRQixatEiEhYWJs2fPiurVq4t+/fpJdbS0tBSlSpUSy5YtE+Hh4WLu3LlCQ0ND3LlzRwghxMuXL0WlSpVEo0aNxJkzZ0R4eLjYunWrOHfunBAib9/l6OhoAUAEBASIqKgoER0dLYQQomXLlmLo0KFyZatWrSr8/PyEEEKcPHlSABCOjo7iyJEj4vr166J9+/bCyspKOi/lZt8LSnax39KlS0XFihVFenq6VN7f319umru7u3B1dRUXLlwQV65cEU2aNMn174fU1FRhb28vWrZsKYKCgsSZM2dEnTp15L4/H7alD7+jQgixa9cu8f6lgX379glNTU3h5+cnbt++LYKCgsScOXOk+TnFCe3atRMtW7YU169fF3fv3hV//vmn+Ouvv4QQeY91P4xZcoq5unXrJnr37i23jq5du0rTchtnvf9dV1c5xcCZx5eVK1dK5xtDQ0PRunVr8ccff4jQ0FDh6ekpHB0dpfavqD1S0fXo0SOhpaUlfv75Z3Hv3j1x/fp1sWzZMvHy5UuFv2OyihV+/vlnYWhoKDZv3izu3LkjJk6cKLS1taXv7fvHraSkJNG5c2dRvXp16bzxJcaVRMrEzgGiPMoMUENDQwUAcenSJWleSEiIAMDOATX29OlTAUDcuHFDrFixQpiYmIjExERp/po1a+R+sM2aNUu0atVKbh0PHz4UAERoaGhhVv2L1aRJk48u2tWuXVtMmjRJCCGEk5OTmDdvnjSvQ4cOchcXmjRpIvejRgghJk2aJBwdHYUQQjx48EBoamqK//77T24bLVq0EL6+vkKIjB9CAERQUJBcGUU/Pjds2CDs7e3ltpecnCz09PTE4cOHpeUsLS1FamqqVOarr74S3bt3F0IIsWPHDmFoaChevHiR5XuS+UM7LCxMABBnz56V5sfExAg9PT3xxx9/yNU/IiJCKrNs2TJRunRphetXFdm1jTNnzghDQ0ORlJQkN9/GxkasWrUqy3VWqVJF+Pv7S39bWloKT09PuTIfXrjJjbJly4r58+dLf6ekpIjy5ctn2TmQXRtISkoSxYsXly7+ZRowYIDo2bNnlnUYNmyY6Nq1qxBCiNjYWAFAuuj5oRYtWshdWBIio22XLVs22/0sqnJ6z65duyZkMpnUOZKWlibKlSsnVqxYIYQQuWpP06dPF9ra2tIP4dz4sA2npqaKEiVKiD59+kjToqKiBABx/vz5LNfz/mcrRMYxpnTp0tKFMiHetdvFixfLLfvhhZ6cPvvDhw8LLS0tuWPmwYMHVaZzQFNTU5QoUULo6OgIAEJDQ0Ns3749V9+rDh06CG9vb4Xr/vC44OvrK5ycnOTKTJo0Sa5zYMCAAWLgwIFyZc6cOSM0NDSk2MPS0lLuQml6erowNzeX2uaqVauEgYFBlp2Uef0uK/pct27dKoyNjaXvw5UrV4RMJhP37t0TQrzrHNiyZYu0TGxsrNDT05M6sXKz74Xl/dgvOjpaaGlpidOnT0vz69evL8Ucmb8VLl++LM0PDw/P9e+HgwcPCi0tLREVFSVNO3r0aJ47B+rXry969eqlcJu5iRNcXFyyvEiX11j3/XNbbmKuXbt2CX19falDND4+Xujq6oqDBw8KIXIfZ7FzIOcY+MPjS+b5Ztq0adK08+fPCwBSm2XngGq5cuWKACDu37//0TxF35OsYgULCwvxww8/yE2rXbu21FGcudyZM2dEixYtRMOGDcXz58+lsl9aXEmkbHzmAFE+CQkJgZaWFmrWrClNc3Bw4DBJNRMeHg4/Pz9cvHgRMTExSE9PBwBERkYiNDRUGg6fqU6dOnLLBwcH4+TJk9DX1/9o3Xfv3oWdnV3B7oCaqFq1qtzfZcuWRXR0NADAx8cHq1evxsSJE/HkyRMcPHgQJ06ckCtfr149ueH39evXx8KFC5GWloYbN24gLS3to88qOTkZJiYm0t/FihX7qB6KBAcHIyIiAgYGBnLTk5KS5FJWValSBZqamnL7dOPGDQBAy5YtYWlpiUqVKqF169Zo3bo1OnfujOLFi3+0vcxjWd26daVpJiYmsLe3R0hIiDStePHisLGxkdte5nuoyrJqG8HBwUhISJD7DAEgMTFR+hwSEhIwY8YM7N+/H1FRUUhNTUViYqI0/DlTrVq18lTH+Ph4REVFyX1GWlpaqFWr1kephTJl1wYiIiLw+vVrtGzZUm6ZN2/eoHr16tLfy5Ytw++//47IyEgkJibizZs3cHV1BZDxfIN+/frBw8MDLVu2hLu7O77++muULVsWQEY7Pnv2LH744QdpfWlpaUhKSsLr168VtsWiLKf3zNXVFY6OjggMDMTkyZPx119/ITo6Gl999RUA5Ko9AYClpSXMzMw+qW7vt2FNTU2YmJjAxcVFmla6dGkAkPu+ZvfZZnJxcVH4nIGc2nNOn31ISAgqVKgACwsLaX79+vVzt7NFQLNmzbBixQq8evUKixYtgpaWFrp27Ypbt27l+L0aMmQIunbtiqtXr6JVq1bw9PREgwYNFG4nJCRE7jsPfPw+BQcH4/r163JpLYUQSE9Px7179+Do6AhAvo3IZDKUKVNGag9BQUGoXr16ls8sKYjvsqenJ4YNG4Zdu3ahR48eWLt2LZo1ayalIVK0v6VKlZI7L+V23wtCdrGfs7MzWrVqhU2bNqFRo0a4d+8ezp8/j1WrVgEAQkNDoaWlhRo1akjrq1y5MoyNjXO17dDQUFSoUEHu+RQfxpafIygoCN9++63CebmJE0aOHIkhQ4bgyJEjcHd3R9euXaV2l5+xbm5irrZt20JbWxt79+5Fjx49sGPHDhgaGsLd3V2qT27iLMqQXQwMyB9fMs83WZ2D+FwV1VOtWjW0aNECLi4u8PDwQKtWrdCtW7ccj1nvxwovXrzAo0eP4ObmJlfGzc1NLr0iAPTs2RPly5fHiRMn5J5N8aXFlUTKxs4BIqJ81KFDB1haWmLNmjWwsLBAeno6nJ2dc/3Q0YSEBHTo0EHhgwwzL7JR3n34ECyZTCb9mO/bty8mT56M8+fP49y5c7C2tkajRo1yve6EhARoamriypUrchfrAcj9ENbT08vVQ4gTEhJQs2ZNhc8wef+iYXb7ZGBggKtXr+LUqVM4cuQI/Pz8MGPGDFy+fPmzOzAVbS+rC9OqJKv3MSEhAWXLlpV7dkSmzPdw/PjxOHr0KBYsWIDKlStDT08P3bp1++j7X6JEiYKqfpayawMJCQkAgP3796NcuXJyy+no6AAAtmzZgvHjx2PhwoWoX78+DAwM8NNPP8nlIQ8ICMDIkSNx6NAhbN26FVOnTsXRo0dRr149JCQkYObMmejSpctHdctNLv2iJjfvWa9evaTOgcDAQLRu3Vq6WJWb9gR8XltR1Ibfn5Z53Mk8PuTms82uLjnV8Uv77D9UokQJVK5cGQDw+++/o1q1avjtt9/g7OwMIPs20qZNGzx48AAHDhzA0aNH0aJFCwwbNgwLFiz4rLokJCRg0KBBGDly5EfzKlasKP0/u/NFTg8GLYjPs1ixYujbty8CAgLQpUsXBAYGfvTci5zkdt8LQk6xX69evTBy5Ej4+/sjMDAQLi4uchdLC5uGhsZH5+v3n1EE5NwOcuLj4wMPDw/s378fR44cwdy5c7Fw4UKMGDEiX2Pd3MRcxYoVQ7du3RAYGIgePXogMDAQ3bt3h5aWlrSO3MRZlDuKzjfZnYNItWhqauLo0aM4d+4cjhw5An9/f0yZMuWjmOFDnxv7tm3bFhs3bsT58+fRvHlzafqXHlsQFTZ2DhDlEwcHB6SmpuLKlSuoXbs2gIy7eZ4/f67cilGhiY2NRWhoKNasWSNdTP7f//4nzbe3t8fGjRuRnJwsXRi4fPmy3Dpq1KiBHTt2wMrKSvrRQoXLxMQEnp6eCAgIwPnz5+Ht7f1RmQ8D4AsXLsDW1haampqoXr060tLSEB0d/UmdCkDGD9jMO68y1ahRA1u3boW5uTkMDQ0/fYfe0tLSgru7O9zd3TF9+nQYGRnhxIkTHwXVjo6OSE1NxcWLF6U7WDPbtpOT02dvX9XVqFEDjx8/hpaW1kd3s2Y6e/Ys+vXrJz20MSEhIVcPNMy8G/vDzz4rJUuWRNmyZXHx4kU0btwYAKTzz/t3n34oqzbQsmVL6OjoIDIyEk2aNMly3xo0aIChQ4dK0xTdUVm9enVUr14dvr6+qF+/PgIDA1GvXj3UqFEDoaGh0kVUVZf5IPHs3rNvvvkGU6dOxZUrV7B9+3asXLlSmpeb9lRYcvvZfq6cPntHR0c8fPgQUVFR0oXBCxcu5Nv2C5OGhga+++47jB07FmFhYTm2ESDj4qOXlxe8vLzQqFEjTJgwQWHngKOjI/bu3Ss37cP3qUaNGrh9+3aevmdVq1bFr7/+imfPnikcPZDX77K2trbCY52Pjw+cnZ2xfPlypKamKrzgc+HCBelCf1xcHMLCwqQRAfmx758jp9gPADp16oSBAwfi0KFDCAwMRN++faV59vb2SE1NxbVr16TRxxEREYiLi8vV9u3t7fHw4UM8efJEuiP7w9jyQ2ZmZnj58iVevXolXbB7/8HXQEY7OH78uMIYKLdxQoUKFTB48GAMHjwYvr6+WLNmDUaMGJGvsW5uY65evXqhZcuWuHXrFk6cOIHZs2dL8/IrzlIX2cXApB5kMhnc3Nzg5uYGPz8/WFpaYteuXQp/xyhiaGgICwsLnD17Vu78ePbs2Y9GPg0ZMgTOzs7o2LEj9u/fL5X/0uJKImXTUHYFiL4U9vb2aN26NQYNGoSLFy/iypUr8PHxyfOdN6Q6jI2NYWJigtWrVyMiIgInTpzA2LFjpfnffPMN0tPTMXDgQISEhODw4cPSBYDMu2iGDRuGZ8+eoWfPnrh8+TLu3r2Lw4cPw9vbO9cXDinvfHx8sG7dOoSEhMDLy+uj+ZGRkRg7dixCQ0OxefNm+Pv7Y9SoUQAAOzs79OrVC3379sXOnTtx7949XLp0CXPnzsX+/fuz3a6VlRWuX7+O0NBQxMTEICUlBb169YKpqSk6deqEM2fO4N69ezh16hRGjhyJf//9N1f7s2/fPixZsgRBQUF48OAB1q9fj/T0dNjb239U1tbWFp06dcK3336L//3vfwgODkbv3r1Rrlw5dOrUKVfb+xK5u7ujfv368PT0xJEjR3D//n2cO3cOU6ZMwd9//w0g473buXMngoKCEBwcLH3nc2Jubg49PT0cOnQIT548QXx8fI7LjBo1Cj/++CN2796NO3fuYOjQodl2RmfXBgwMDDB+/HiMGTMG69atw927d3H16lX4+/tj3bp10r79/fffOHz4MMLCwjBt2jS5C1D37t2Dr68vzp8/jwcPHuDIkSMIDw+XLtz5+flh/fr1mDlzJm7duoWQkBBs2bIFU6dOzXFfi6LcvGdWVlZo0KABBgwYgLS0NHTs2FFaPjftqbDk9NnmVU6fvbu7O+zs7ODl5YXg4GCcOXMGU6ZMybftF7avvvoKmpqaWLVqVY5txM/PD3v27EFERARu3bqFffv2ZZn+ZvDgwQgPD8eECRMQGhqKwMBArF27Vq7MpEmTcO7cOQwfPhxBQUEIDw/Hnj17MHz48FzXv2fPnihTpgw8PT1x9uxZ/PPPP9ixYwfOnz8v1Tkv32UrKyscP34cjx8/lrsA7ujoiHr16mHSpEno2bOnwvj5+++/x/Hjx3Hz5k3069cPpqam8PT0zLd9/xw5xX5Axh2znp6emDZtGkJCQtCzZ09pnoODA9zd3TFw4EBcunQJ165dw8CBA3M9urBly5awsbGBl5cXrl+/jrNnz0qfRVbL161bF8WLF8d3332Hu3fvKmxL06dPx+bNmzF9+nSEhITgxo0b0p3+uYkTRo8ejcOHD+PevXu4evUqTp48KbXt/Ix1cxtzNW7cGGXKlEGvXr1gbW0tlxIpP+IsdZJdDExfvosXL2LOnDn4+++/ERkZiZ07d+Lp06dwdHRU+DsmKxMmTMC8efOwdetWhIaGYvLkyQgKClLYlkaMGIHZs2ejffv2UufrlxZXEikbOweI8lFAQAAsLCzQpEkTdOnSBQMHDoS5ubmyq0WFRENDA1u2bMGVK1fg7OyMMWPG4KeffpLmGxoa4s8//0RQUBBcXV0xZcoU+Pn5AXg3/DHzLoq0tDS0atUKLi4uGD16NIyMjKChwUN2YXF3d0fZsmXh4eEhlwc7U9++fZGYmIg6depg2LBhGDVqFAYOHCjNDwgIQN++fTFu3DjY29vD09MTly9fzjG1wbfffgt7e3vUqlULZmZmOHv2LIoXL47Tp0+jYsWK6NKlCxwdHTFgwAAkJSXl+g43IyMj7Ny5E82bN4ejoyNWrlyJzZs3o0qVKgrLBwQEoGbNmmjfvj3q168PIQQOHDjwUSoKdSKTyXDgwAE0btwY3t7esLOzQ48ePfDgwQPpbs2ff/4ZxsbGaNCgATp06AAPD49s7+TPpKWlhSVLlmDVqlWwsLDIVSfMuHHj0KdPH3h5eUmpYDJHLCiSUxuYNWsWpk2bhrlz58LR0RGtW7fG/v37YW1tDQAYNGgQunTpgu7du6Nu3bqIjY2Vu9O8ePHiuHPnDrp27Qo7OzsMHDgQw4YNw6BBgwAAHh4e2LdvH44cOYLatWujXr16WLRoESwtLXPc16Iqp/cMyLjoFBwcjM6dO8td7MxNeyosOX22eZXTZ6+hoYFdu3ZJx1QfHx+5HMKqRktLC8OHD8f8+fPh6+ubbRspVqwYfH19UbVqVTRu3BiamprYsmWLwvVWrFgRO3bswO7du1GtWjWsXLkSc+bMkStTtWpV/PXXXwgLC0OjRo1QvXp1+Pn5KTyPZaVYsWI4cuQIzM3N0bZtW7i4uODHH3+U7grO63d54cKFOHr0KCpUqCD3TBMAGDBgAN68eYP+/fsrXPbHH3/EqFGjULNmTTx+/Bh//vmnNPIqP/b9c+QU+2XKPBY0atToo1hg/fr1KF26NBo3bozOnTvj22+/hYGBQa5SY2hqamL37t1ISEhA7dq14ePjI3WuZbV8qVKlsHHjRhw4cAAuLi7YvHkzZsyYIVemadOm2LZtG/bu3QtXV1c0b94cly5dkubnFCekpaVh2LBhUru3s7PD8uXLAeR/rJubmEsmk6Fnz54IDg5Gr1695JbPjzhLneQUA9OXzdDQEKdPn0bbtm1hZ2eHqVOnYuHChWjTpo3C3zFZGTlyJMaOHYtx48bBxcUFhw4dwt69e2Fra6uw/OjRozFz5ky0bdsW586d+yLjSiJlkokvIUEwEZGK2rRpE7y9vREfH89RJkVIQkICypUrJ+U/fl/Tpk3h6uqKxYsXK6dyREREX6BZs2Zh27ZtuH79utz0U6dOoVmzZoiLi/vs5+Sokn///RcVKlTAsWPH0KJFi09e/uzZs2jYsCEiIiJgY2NTADUkIiKiLwkTWhMRFaL169ejUqVKKFeuHIKDgzFp0iR8/fXX7BgoItLT0xETE4OFCxfCyMhILg0IERER5b/M57MsXbpULhe8ujhx4gQSEhLg4uKCqKgoTJw4EVZWVtIzZXKya9cu6Ovrw9bWFhERERg1ahTc3NzYMUBERES5whwVRESF6PHjx+jduzccHR0xZswYfPXVV1i9erWyq0VvRUZGonTp0ggMDMTvv//Oh0KT0ujr62f5OnPmjLKrR0oQGRmZbbuIjIxUdhVJzW3atCnL9plVGjsAGD58OGrWrImmTZtmmVLoS5aSkoLvvvsOVapUQefOnWFmZoZTp05BW1s7V+/py5cvMWzYMDg4OKBfv36oXbs29uzZo+S9+jw8zhERERU+phUiIiIiKmIiIiKynFeuXDmONlJDqampuH//fpbzrays2KFJSvXy5Us8efJE4TxtbW3mgv4M6vae8jhHRERU+Ng5QERERERERERERESkZphWiIiIiIiIiIiIiIhIzbBzgIiIiIiIiIiIiIhIzbBzgIiIiIiIiIiIiIhIzbBzgIiIiIiIiIiIiIhIzbBzgIiIiIiIiIiIiIhIzbBzgIiIiIiIiIiIiIhIzbBzgIiIiIiIiIiIiIhIzbBzgIiIiIiIiIiIiIhIzfwf7MJ6gYdNT2gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.heatmap(data.corr(), cmap=\"BuPu\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7b50488a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_25292\\2636420491.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  data_corr = data.corr()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x2000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5Aqtk5TOHb3FSO/HX3n9tknxNUBD+//cPAEqWfok58TT+/pMAOJnxAT4OPsyMkV1h7x98n7NVZ9FFuKPytsNaY6YyPq9Z+xMIBIIbAaEACW56vv98ISqrmSq3QG4fP7rZ8k6fWozFYsTZORoX+57kzHkJhcVC94AwOrn0AmCvXRIFmmJm9piJp51nAxIvxSkuDtdJstKT+/LLBHo9hCRpKC2Np6RkL/eE30OkRyT6Wj0fJ36MpJBwGizXBarYeQar2dLsfQoEAkFbRihAgpuaPYeO45h1AIBbpkxrdssIk0nP6TNfAxAU+BjFCxdSm5WNysuL6FfeJMixKwApJfGEuXbi7k53X/Vc3i+8gNrfn9ozZ6j4bAV+fhMByD71P5QKJS/0eQGAH1N/5GTpSey7e6NwVGMpN1J9rLhZ+xQIBIK2jlCABDc1a5YsRoGVGt9w+vdvXuAzwJmc5ZhM5djbh+JiiKDo088A8H7pRWqO6VFKSopqctDmFBNVPeqSlPemoHR0wHfeGwAUf/UVvowAoLBwE1VVGcR4xzAsYBgWq4X3Dr6HpFLg0NsXgMo9Oc3bqEAgELRxhAIkuGk5cPgEzrlHABgz9eFmy7NYajl1ahEAQYGPcvb/3sVqMGDfty9Ot45CvycXgB2KHQAUbzmOwWRu1pyOgwbhOHQomExUvPs1Hh5DAastA+2Zns+glJRsPbWVw2cP49DHFyQwpJdRW1DVrLkFNxhWKxgrr/2jif24f/vtNwYOHIirqyseHh7cfvvtpKen287v2rWL7t27o9Pp6NWrF6tWrUKSJBISEmxjjhw5wujRo3F0dMTHx4cHH3yQwsLClnolBW0E0QxVcNOy+utlOACVPp2IienabHmFhZswGPJQqz1wKQgle8ProFDg+8rL1CQXY9HXUqQq4we/3dx2xpeA0hN8tzmBB2/t2ax5fV56kcodO6jcsQOvB5+liC3k5q6gQ+izhLqEMrbDWFalreKzw5/x0S0foevsTk1yMZV7cnG9o0Oz9y24Qaitgnf8r/28L+eAxqHRwysrK5k9ezZRUVHo9Xrmzp3LhAkTSEhIQK/XM3bsWMaMGcOyZcvIyspi1qxZda4vLS1l2LBhTJ8+nffee4/q6mpefPFFJk2axObNm1t4c4LrGWEBEtyUpGaeQZd5EIC4u+5pEZmnz8h1hPz9J1H43kcAuIwbhzYsjMp9cubVepddhHXpiSqoCwqs7P3pB8yWpt0B/xFNUBBukycDYPhwHY6OEVgsNZw5l4n2SOQjKCQFv5/+neSiZBz7yV9ylQfysRibZ4ESCK41d955JxMnTqRjx450796dhQsXkpSUxLFjx1i2bBmSJPH555/TpUsXRo8ezV//WjfL8qOPPiImJoZ33nmHzp07ExMTw8KFC9myZQsnTpxopV0JWgNhARLclHy/5Bu0WNC7tGfQoD7NlldZmU5JyW5AgVtuGAXx/0NSq/F6+ilMxTUYUksBWOe6i/nd/om7r5ZVb71MQOER1h9IY3TvsGbN7zHjUUq/+w7D0WN4lt+LXpFMTs5yggIfJdglmJHBI1mbsZbPkz5nweAFKN11mItrqE4qxKGnT7P3L7gBUNvL1pjWmLcJpKamMnfuXOLj4yksLMRikTMas7OzSUlJISoqCp1OZxvfp0/d/+/ExES2bNmCYz3lLtLT0+nUqdNVbELQFhEWIMFNR2FJOdLxXQD0uP3OZmd+AZw5swwAT8+h6D+R+3C53nsv6nbtbNafgw7J+LRrRy+fXnToFoXFMwAlFjau/KnZ86vc3XF7aAoA1v/sRal0pLo6+5xSBo92exSAjVkbOa0/jUMPbwCqDuY3e27BDYIkya6oa/1o4v/f2LFjKS4u5vPPPyc+Pp74+HgAjMbGtXk57yZLSEio80hNTWXw4MFNftkEbRehAAluOlYsX4XGYkSvc+e224Y1W57ZXE1u3g8AeFb3pWr/flCr8Zj+CFazFf1+WQFa67qD6ZHTbQpXv9vHAeCcuY+UnJJmr8Nj2jQUTk7UJp/EwyxntJ3JWQ5AmFsYA/wHYMXKsuPLsO8hW30MJ8swldY0e26B4FpQVFRESkoKr776KrfccgsRERGUlFz43wkPDycpKQmDwWA7tm/fvjoyevTowdGjRwkODqZjx451Hg4OjY9FErR9hAIkuKmwWCzkxcuBjj6xw1Aorz4N/TwFZ9dhMlWg0wVgXix/2LqMuwO1jw81qSVYK2opU1ZQ0E7PkIAhtuv6Dr8Fk9YBR3Mly79f0+x1KJ2dcbtXbrKq+eEsAGfPrsdglLNbJkfIcUKr0lZhcDKjCXEBK1QdLGj23ALBtcDNzQ0PDw8+++wz0tLS2Lx5M7Nnz7adv//++7FYLMyYMYPk5GTWrVvHu+++C2C78XjqqacoLi7mvvvuY9++faSnp7Nu3TqmTZuG2Sxi4m4mhAIkuKlYv3k3TtWF1Eoq7rpnfIvIzMv9EQAvzRAqt2wFScLj4UcAqDokKxdbnPdzd5dJKKQL/3IqtZrgAcMB0B/cSll1bbPX4vbgA0hqNZZNJ3BUhmG1msjNla1TA9oNINg5GH2tnlVpq3Doed4NVoC1ianIAkFroFAoWL58OQcOHCAyMpJnn32Wf/7zn7bzzs7O/PLLLyQkJNC9e3deeeUV5s6dC2CLC/L392fnzp2YzWZuvfVWunXrxqxZs3B1dUWhEF+JNxPiry24qdiz+hcAzCExeHq4NlteTU0uxSVyPJHmV7m6stOIEWhDQ7AYzFQelS0xezySGNth7CXXj7l7IhZJgW9NLj+sj2/2etTe3riMl11rDvFaAHJzf8BqtaKQFNwfcT8A3xz/Bm2kO5JagamwGmN2RbPnFgiuBcOHD+fYsWPU1NSQmJjIkCFDsFqtjB8/HoD+/fuTmJiIwWBg//79WCwW1Go1gYGBNhlhYWH8+OOPlJSUUFVVRXJyMu+9916LxAMK2g5CARLcNGSdysU+5xgAw++c0CIy8/J/Bqy4OMRQ88PvAHg8IhdVrD5WhMIEZ9QFdImMwUF9aXyBk7sH2g7RACRtWd8ia3Kf9jBIEtKyFBSShqqqdCoqkgAY12EcTmonssqziC/ah12k3IesOvFsi8wtELQ2S5cuZceOHWRkZLBq1SpbjR87O7vWXprgOkMoQIKbhp9/+AUlFsqd/IntFdVseVarldxz7i+ndB+stbXounXDLlpWaEr2ZwOwxWUf90bce1k5Q++4HQDP/KMczmq+IqINDcFx6FAUNRKOBe0AyM1bBYC92p7bO8jz/ZD6A3bRXgBUJZ3F2sx6RALB9UBeXh4PPPAAERERPPvss9x999189tlnrb0swXWIUIAENwUWi4XihJ0AtO8b1yIyKyqSqKpKQ6HQwtKjALjdL7uYzHojlpNyq4nijgZCXUIvK6dr716Y7FzQWQz8/FPLWIHcJsvrUP8iK1T5+b9gscgxRneG3QnAllNbqGpvQdKpsFTUYswsa5G5BYLW5IUXXiAzM5OamhoyMjJ47733sLdvWq0hwc1BqypA27ZtY+zYsfj7+yNJEqtWrbri+K1btyJJ0iWPvLy8OuP+85//EBwcjE6nIzY2lr179/6JuxC0BXbtScCxuohaScX4CWNaRGZu3koAXM1RWDLzUbq64jxmNACViQUorBIpukziokZcUY5CoSQgVs4OK0nYQU1t8zNRHPr1QxMUhOagAZXFkdraYoqL5R5k4e7hdPXoisli4tes1dh19QCg6rDohSQQCG4eWlUBqqysJDo6mv/85z9Nui4lJYXc3Fzbw9vb23bu22+/Zfbs2bz++uscPHiQ6OhoRo4cSUGBSPW9mdm+Rk4zN7TrglcLBD9bLCby838FQLelGgDXu+9CoZUDj8/uywRgl/thhgU2XGto1AQ5QNqv8hRrdh1t9vokhQLX++5FskjYJ54Lhj6nsAFMDJsIwI+pP2IXdS4OKKkQq1m4wQQCwc1BqypAo0eP5q233mLChKYFpHp7e+Pr62t7XJy6+K9//YtHH32UadOm0aVLFz755BPs7e1ZuHBhSy9f0EaoqqrBmn4IgO63XNka01hKS+OprS1GpXDG+mMKSBKu98hxPqaSGrR5VixYsOvmiU6la0AauPv6gV9HJGDvhpZxg7lOmICk06H5TXZtFRZuxGSqBGBMyBjsVHacLDvJcedMFPYqLJW1GDJKW2RugUAguN5pkzFA3bt3x8/PjxEjRrBz507bcaPRyIEDBxg+fLjtmEKhYPjw4ezevfuy8gwGA+Xl5XUeghuHX37ZgNZioFLtxOgRA1tEZkHBWgCcCwORLJLscmovBxyXJp4B4Kh9OiMjG+9ui7lFft+qMhMo1hsaGN0wShcXnMeMQX1KQl3liMVioKhoCwCOGkduDboVgJ8zf7mQDSbcYAKB4CahTSlAfn5+fPLJJ/zwww/88MMPBAQEEBcXx8GDclfvwsJCzGYzPj51mzv6+PhcEid0MfPnz8fFxcX2CAgI+FP3Ibi2HN8hf+nrOvdGo1Y3W57FYqLg7DoAVGvlIGOXiRNt5wsOZcrzemUT6RnZaLkDbhmGWVLiVlvKL1v2N3udAK533YWEhHa3HACdf05xA2zZYOsz16Pu6grIqfsiG0wgENwMtCkFKDw8nMcee4yePXvSv39/Fi5cSP/+/XnvvfeaJXfOnDmUlZXZHqdOnWqhFQtam7NFJdgVpAEw9LZRLSLT5v7CEeXuYhSOjjgNvwUAc4URx3xZyfLpEdqkwmpae3s0IbLClLRtS4us1S6mO5rgYHR75cDqoqKtmM1ydlpvn9542XlRbixnn+YIkk6JRV+L8ZQoiigQCG582pQCVB99+vQhLU3+gvP09ESpVJKfX7fDdX5+Pr6+vpeVodVqcXZ2rvMQ3Bis/mUDSixU2HnSq3tEi8g87/5yPO2FZJFwHjMGxbky+2cOpKJA4rhdBiO6jW6y7L4j5BglxzNJ5JRUNnutkiThMmEC6lMSqgotFksNhUVbAVAqlIwKkZXC1dmr0YW7A7IVSCC4XomLi2PWrFmtvYzrCvGaXB1tXgFKSEjAz88PAI1GQ8+ePdm0aZPtvMViYdOmTfTr16+1lihoRbL2yTFizl16t0iZ+4vdX8pfZUXbdeKFIP7iBLn44Wn/YrzsvZosv9fA/piUWpzMlfy0blez1wvgMn4ckkKJ7pwbrOAiN9htobcBsPXUVqROcqXqGqEACQRXxdSpU20tOa4lP/74I2+++eY1n7et06oKkF6vJyEhgYSEBAAyMjJISEggO1v+EpkzZw5TpkyxjX///ff56aefSEtL48iRI8yaNYvNmzfz1FNP2cbMnj2bzz//nCVLlpCcnMwTTzxBZWUl06ZNu6Z7E7Q+eXmF2BeeBGDo6JbN/lJa7NEcqUUTEoLuXOVnc2UtbnlywTXvHiFXJV+l0eAYHgNA6u7fW2TNah8fHPr3R3dQ/ncvLNyC2Syn7ndx70KwczAGs4Fd9odAKWE6W01tQVWLzC0Q3AyYzWYsFkurze/u7o6Tk1Orzd9WaVUFaP/+/cTExBATI3/gz549m5iYGFv33tzcXJsyBHKW13PPPUe3bt0YMmQIiYmJbNy4kVtuucU25p577uHdd99l7ty5dO/enYSEBH777bdLAqMFNz5rfl2HAivlDj7EdOvUIjILzsop6g5pTkgW2b103rKUfeA4ShSc1J1mQGTcVc8xeLScneVecJy0vNLmLhkAlwnjUWdLKEtVWCzVNjeYJEmMCZUz1X45vRptB1dAuMFuRqxWK1W1Vdf8YbU2PejeYrHwwgsv4O7ujq+vL2+88QYADz/8MLfffnudsbW1tXh7e/O///0PkN1FTz/9NE8//TQuLi54enry2muv1VmHwWDg+eefp127djg4OBAbG8vWrVtt5xcvXoyrqys///wzXbp0QavV8vDDD7NkyRJ++uknW5He89ecOnWKSZMm4erqiru7O+PGjSMzM9Mm77zl6N1338XPzw8PDw+eeuopamtrbWM+/vhjwsLC0Ol0+Pj4cNddd9nO/dEFVlJSwpQpU3Bzc8Pe3p7Ro0eTmpp6yfrXrVtHREQEjo6OjBo1itzc3Cb/LdoyqtacPC4u7opv/sWLF9d5/sILL/DCCy80KPf8m1twc3Nq/y4cAdfIPi0iz2q1Uli4EQDVxiJAgcvtt9nOFyZm4YcLZ/xLGKx1uep5uvbsyWqNA3bGSn5bv4Onp9ze8EUN4DRsGAp7B3T7yqkcAWcLfsPHW45Rui3kNj5O+Jg9uXswhz0PJ2Q3mHOcyIa8mag2VRO7LPaazxt/fzz26qa1qliyZAmzZ88mPj6e3bt3M3XqVAYMGMD06dMZPHgwubm5ttCIX3/9laqqKu6555461z/yyCPs3buX/fv3M2PGDAIDA3n00UcB+Tvk2LFjLF++HH9/f1auXMmoUaNISkoiLCwMgKqqKv7xj3/wxRdf4OHhgZ+fH9XV1ZSXl7No0SJAtszU1tYycuRI+vXrx/bt21GpVLz11luMGjWKw4cPo9FoANiyZQt+fn5s2bKFtLQ07rnnHrp3786jjz7K/v37mTlzJl9++SX9+/enuLiY7du3X/b1mTp1Kqmpqfz88884Ozvz4osvMmbMGI4dO4b6XCZsVVUV7777Ll9++SUKhYIHHniA559/nq+//rpJf4u2TJuPARII6uPM6TwcSrIAGH7brS0iU68/hsGQh8KiRntcwq57d9T+/gBYjCbcc+Vu014xwc2aR6FU4txZtopmHIxvliybTDs7nIYNwy5B/pcvKv7d1hss0DmQrh5dsVgt7HZMBMB4qgJzubFF5hYIWpqoqChef/11wsLCmDJlCr169WLTpk3079+f8PBwvvzyS9vYRYsWcffdd+Po6Gg7FhAQwHvvvUd4eDiTJ0/mL3/5iy2bODs7m0WLFvH9998zaNAgOnTowPPPP8/AgQNtig3IlqWPP/7YNqezszN2dnZotVpbkV6NRsO3336LxWLhiy++oFu3bkRERLBo0SKys7PrWJXc3Nz46KOP6Ny5M7fffju33XabLZ41OzsbBwcHbr/9doKCgoiJiWHmzJn1vjbnFZ8vvviCQYMGER0dzddff82ZM2fqtJuqra3lk08+oVevXvTo0YOnn366TvzszUCrWoAEgj+L39ZsQAJKHf3pFn518Th/5Gyh/OFgl+WIZKrEecyFIofpiUexs2goVJXQN7rh1hcN0f+WODYd3oFLwXFOF+tp7+7Y8EUN4HzbGMpW/4JCr8DkWEFp6V7c3QcAMDxoOEeLjrL27Hr6BzxJ7akKqo8X4djHr9nzCtoGdio74u9vGYW7qfM2laioqDrP/fz8bO2Opk+fzmeffcYLL7xAfn4+a9euZfPmzXXG9+3bt05SRL9+/ViwYAFms5mkpCTMZjOdOtV1mxsMBjw8PGzPNRrNJeuoj8TERNLS0i6J0ampqSE9Pd32vGvXriiVyjp7SkpKAmDEiBEEBQURGhrKqFGjGDVqFBMmTKi3yWtycjIqlYrY2AvWPA8PD8LDw0lOTrYds7e3p0OHDnXmu9laRgkFSHBDcjphH46AR9eeLSbzvPtLvb0cJBVOI0fazp1JSKMj3pzxK6G7pvnKSrdePVmn0uFgqmbtpj08evfwhi9qAMcBA1A6u6I7XERVf1mhO68AjQgawQcHP2Bf3j6kjvZwqoKalBKhAN1ESJLUZFdUa6H+Q0FTSZJsQchTpkzhpZdeYvfu3ezatYuQkBAGDRrUaNl6vR6lUsmBAwfqKCRAHSuSnZ1dozJL9Xo9PXv2rNe15OV1IVP0SntycnLi4MGDbN26lfXr1zN37lzeeOMN9u3bh6ura6P3djH1zXc18VhtGeECE9xwFBeXYleUCcCg4XEtIrOmJpeKiqNgBe0RBfa9e6P2kZvwWiwW3E/JDUddI/1bZD6lSo1dB7ko4on4lkmHlzQanG8dgS7xfDbYJtsHXpBzEGFuYZisJhKcUwAwpJViNbVeZotAcDV4eHgwfvx4Fi1axOLFi+vNAI6Pr2vp2rNnD2FhYSiVSmJiYjCbzRQUFNCxY8c6jyvVkwPZKmQ2m+sc69GjB6mpqXh7e18iz8Wl8bGCKpWK4cOH83//938cPnyYzMzMSyxbABEREZhMpjp7LCoqIiUlhS5dujR6vpsBoQAJbjjW/7YFJRbKdR70aKHsr8JzPbS0eQ4o9RLOYy4UOTyZehx3owsGyUj33s2vN1VdXU1SUhJ2/r5UBYZjr6nhw39/xMKFC1mzZg1Hjx6tkx3SFJzHjEFzXEKqhZqa0+grU2znRgTKpQJ+qVyPwlGN1WDGkCX64gnaHtOnT7eVQnnooYcuOZ+dnc3s2bNJSUnhm2++4d///jfPPPMMAJ06dWLy5MlMmTKFH3/8kYyMDPbu3cv8+fNZvXr1FecNDg7m8OHDpKSkUFhYSG1tLZMnT8bT05Nx48axfft2MjIy2Lp1KzNnzuT06dON2s+vv/7Khx9+SEJCAllZWSxduhSLxUJ4ePglY8PCwhg3bhyPPvooO3bsIDExkQceeIB27doxbty4Rs13syBcYIIbjrT9e7AD7MK6t0jxQ7jg/tLsqQGlBqdbLwRWZx44Rmd8yPY4Swf7q3d/FRcXs3PnThITEzGZTPJBByckoLiokOIi+YN77969aLVaoqOjGTRoUJPqf9j36YPaxQtNci6GKCuFZzfi5NgZkOOAPk78mF25u1B1fBxjQjE1KSXozqXGCwRtheHDh+Pn50fXrl3x97/UKjtlyhSqq6vp06cPSqWSZ555hhkzZtjOL1q0iLfeeovnnnuOM2fO4OnpSd++fS9Jsf8jjz76KFu3bqVXr17o9Xq2bNlCXFwc27Zt48UXX2TixIlUVFTQrl07brnllkZ3HXB1deXHH3/kjTfeoKamhrCwML755hu6du1a7/hFixbxzDPPcPvtt2M0Ghk8eDBr1qy5xO11syNZbzanXyMoLy/HxcWFsrIy0RajjVFTXc0H0+5DZTUR/fQbDB/Uq9kyTaZKtm3vhdVqxOtvalxDBxC48H+289vf/o6QCj8yB1QwcGzju79fkG9ix44dbN++3WY+9/LyIiwsjENbt0FOGqXtorh30m3k5OSQkpJCaWkpIPvxBw4cyMCBAy+JV7gceW++xemMpZQ9YMbZKYrevVcCcpr/HavuILM8k0/9/0XgJh0qH3t8n225OCrB9UFNTQ0ZGRmEhISgO9fG5UZCr9fTrl07Fi1axMSLGhWDXH6le/fuvP/++62zOEGzudL7tynf38ICJLih2Lx5JyqriUq1I0P6xbSIzOLiHVitRlRlWlR5Fpwev2D9yck/TVCFXGSzc2zTFYWSkhK+++47WwGy0NBQBg8eTFBQEJIkoTaaOfhlAorsZMIiXiA6OpqRI0eSkZHBli1bOH36NFu2bOHEiRPceeeduLu7Nzin0623onv6K8osZsorDlNjyEOn9UWSJIYHDeeLpC/41bqRJ6XbMeVXYSqtQeV6431JCm48LBYLhYWFLFiwAFdXV+64447WXpLgOkbEAAluKJJ2yr2/rIGRqFWNs4g0ROG59Hft/lokJByHDbWdO7ZvPwoU5DgW4undtGrjWVlZfPbZZ+Tm5mJvb89dd93Fgw8+SHBwsM111zduIBYk3I0lbN53HACFQkGHDh145JFHmDBhAlqtljNnzvD555+TkZHR4Lz2PXugUbmjzpTnKCq60HJjeKCcbbbx7BbUgbI7ryalpEn7Eghai+zsbHx8fFi2bBkLFy5EpRL3+ILLIxQgwQ2D2WzCnHUEgMj+A1pEptVqoahYVhB0RxTYRUej9va+MGdKBQCGkKYpW6mpqXz55ZdUV1fTrl07HnvsMSIjIy+JWbJzdMLiLdcxOrB9R51zkiQRHR3Nk08+Sbt27aiurubLL7/k6NGjV5xbUqlwHDYU3dFzRREvUoC6eHTB286balM1BX5yN/qa48VN2ptA0FoEBwdjtVo5depUnRZJF7N161bh/hIAQgES3EDs3ZuA1lSNQaHl1mH9W0SmXn8co7EQqVaJJl3CcfiFD9WSyhKCimRlKKRn49NL09PT+eabbzCZTISFhTF16tQrpsMGx8hxTBWph+ut0+Hi4sLUqVPp2rUrFouFFStWcOTIkSuuwWnECLTHZGWruHinrSq0JEkMDhgMwE67AwAY0kU6vEAguPEQCpDghiH+d9n9VePTESd7bYvILCraBoDmOEgmCafhFwoSHkzchaPFnkpVNe06dbiciDrk5OTYSuNHRERwzz33NJiZMWT4EAC89adJPJlf7xi1Ws2dd95J9+7dsVqt/PDDD5w4ceKyMh369UNb5IiiAsxmPWVlBy/M116eb2X5Gjkd3mgR6fACgeCGQyhAghuGstTDAARGNz/z6zxFxbICpDsKmg4d0IZcaKtReESu4VHqZ0BSNJxuX15eztdff43RaCQkJIQ777yzUTEKPgGBGO3dUGJhy+adlx2nUCi44447iI6Oxmq18v3331+2u7NCq8VpcBzaY5e6wWL9YtEqteRU5WAMlF17hrTSBtcpEAgEbQmhAAluCM6czsNRn48VGDa88WXvr4TJpKesTHYDaY8pcLoopsBgNuCZK7cN8OjSvkFZZrOZ77//nsrKSry9vbnnnnsaHaApSRLunbsDcCbpwBXHnleCQkNDqa2t5euvv6asrKzesU63jkB79NJAaDuVHbF+ch+hZGc5qLpGKEACgeAGQyhAghuCzZvkL/AyRz9CA65crr6xlJTsxmo1oTqrQFUo4XRR/M/BzH10rA4EIKh7RIOyNmzYwKlTp9Bqtdxzzz1Nrr0SO2QgAM6FaeSXVV1xrFKpZNKkSXh7e6PX6/n+++8vKc8P4DhoELp0HVhAX3mcGkOe7dx5N9ivVrnUfu3pCixVV1d9WiAQCK5HhAIkuCHISpAtI44du7WYzPPuL+1RUHl7o4uMtJ07mXgMJQpKHCtRu11ZmUlJSWHPnj0ATJgwoU5H6cYS2bMHJqUGB3MVG7YdbHC8Tqfj3nvvRavVcvr0aTZt2nTJGIWDA05R/VBnnQuGPhfvBDC4vRwIvb18F5KnFqxgOFm/JUkgEAjaIkIBErR5jAYjqrxUAGIGtEz2l9VqtQVAa48pcBw2FEmhsJ2TTtbIA0OvrPxUVVXxyy+/ANCvXz86d+58VetRqdWoAuS+P8fidzfqGnd3d8aPHw/Arl27OH78+CVjnOLibNlgRRcpQL4OvnR274wVK/k+suJTkyrqAQlan7i4OGbNmtXay7hqpk6davu/hLa/n7aMUIAEbZ6dO/eittRSpbRnSN/uLSKzqiqDmprTYALNCQmnoReKH2aVZRJeKru/AqIvbUZ4MWvXrkWv1+Pp6cmwYcOataausXKjVUv2MYyNTEuPiIigb9++APzyyy9UVlbWOe84ZMiFekDF27FYTLZz591gu7UJgAiEFtzcZGZmIkkSCQkJLSr3xx9/5M0332xRmYLGIRQgQZvn0E7ZIlLr1wmtpmUqv54vfqhJlVBKOuz79LGd239sDz4mD0wKM85h3pcTwfHjx0lKSkKSJMaPH9/sRoQD4uQ4IC/DWfYcbbji83mGDx+Ol5cXlZWVrFmzps45tb8/jnYRKPTn0uHLD9nOnVeAVhh+BQWYimowFdc0aw8CQVvEaDT+abLd3d2b1NBY0HIIBUjQ5qlMTwIgqHvvFpN5Ph5Gd0yBfd9YFHZ2tnMlyTkAlPsYUWjqrwBdW1vL2rVrAejfvz/t2zecKdYQTu7uGF3lztbxO/Y0+jqVSsX48eORJImjR49eUinaaUgc2mT5o6C4eLvteFfPrrjr3Cm0FGP0lc8LK9CNi9VqxVJVdc0fV9OP22Kx8MILL+Du7o6vry9vvPGG7VxpaSnTp0/Hy8sLZ2dnhg0bRmJiou18eno648aNw8fHB0dHR3r37s3GjRvryA8ODubNN99kypQpODs7M2PGDELOlcCIiYlBkiTi4uIaXKfZbGb27Nm4urri4eHBCy+8cMl+/+gC+/jjjwkLC0On0+Hj48Ndd91VZ9/z588nJCQEOzs7oqOjWbFiRZ35HnnkEdv58PBwPvjggzrzbd26lT59+uDg4ICrqysDBgwgKyvLdv6nn36iR48e6HQ6QkNDmTdvHiaTiRsR0ShF0KbJysjGvroYMwpuGd4y7S8sFgMlpfsA0B6XcHx4sO1cZW0lnrkOALh0vny22Y4dO2zdiIcMGdIi6wLwCu9GWXwOBcmHgcmNvq5du3YMGjSIbdu2sWbNGkJDQ7E7p9Q5xcWhff9jqntDcdEOOoTOBkAhKejn34/VJ1eT7nqGiBw/atJKcOjTMll2gusLa3U1KT2a3tC3uYQfPIBkb9+ka5YsWcLs2bOJj49n9+7dTJ06lQEDBjBixAjuvvtu7OzsWLt2LS4uLnz66afccsstnDhxAnd3d/R6PWPGjOHtt99Gq9WydOlSxo4dS0pKCoGBgbY53n33XebOncvrr78OwFNPPUWfPn3YuHEjXbt2RaPRNLjOBQsWsHjxYhYuXEhERAQLFixg5cqVl3WH79+/n5kzZ/Lll1/Sv39/iouL2b79wk3J/Pnz+eqrr/jkk08ICwtj27ZtPPDAA3h5eTFkyBAsFgvt27fn+++/x8PDg127djFjxgz8/PyYNGkSJpOJ8ePH8+ijj/LNN99gNBrZu3evrQXP9u3bmTJlCh9++CGDBg0iPT2dGTNmANhehxsJoQAJ2jS/b5H7Y5U5+RPs59kiMsvKErBYqlGUg+qMhOPgCwpQ/Ok9dKvsCIBfVP3Vn4uLi9mxQ17XyJEjG/VB2Vj6DOzHhvh1OBWfpLC8Gk9nu4YvOsfgwYM5duwYhYWFbN68mdtuuw0AXbdu2OW7U8pZyisOYzJVoFLJJvkB/gNYfXI1G5Q7ieAuDGmlWC3WRhV+FAj+LKKiomxfyGFhYXz00Uds2rQJOzs79u7dS0FBAVqtXA3+3XffZdWqVaxYsYIZM2YQHR1NdHS0Tdabb77JypUr+fnnn3n66adtx4cNG8Zzzz1ne65UytZeDw8PfH0bdxPw/vvvM2fOHCZOnAjAJ598wrp16y47Pjs7GwcHB26//XacnJwICgoiJiYGAIPBwDvvvMPGjRvp10+OBwwNDWXHjh18+umnDBkyBLVazbx582zyQkJC2L17N9999x2TJk2ivLycsrIybr/9djp0kD+/IiIulPGYN28eL730Eg899JBN/ptvvskLL7wgFCCB4HojOykBO8C5Y2SDYxtLcckuALQpCrShHdAEBNjOpR4+QidrD6q1RtS+9d+1btiwAbPZTEhICF26NL5HWGPoEhPNWoUaB3M1m3cnMmlk30Zfq1KpuO2221iyZAn79+8nJiYGf39/JKUS1+ihFOR/h9nHSknJHry8RgDQz1/+oF1fu5WZmklYqkzU5laiaefYovsStD6SnR3hB69caPPPmrepREVF1Xnu5+dHQUEBiYmJ6PX6S0pNVFdXk56eDoBer+eNN95g9erV5ObmYjKZqK6uJjs7u841vXo1r6J8WVkZubm5xMbG2o6pVCp69ep1WbffiBEjCAoKIjQ0lFGjRjFq1CgmTJiAvb09aWlpVFVVMWLEiDrXGI1Gm5IE8J///IeFCxeSnZ1NdXU1RqOR7t27A3K80dSpUxk5ciQjRoxg+PDhTJo0CT8/PwASExPZuXMnb7/9tk2e2WympqaGqqoq7JtoqbveEQqQoM1iNplQ5qUBENO3TwOjG0/JOQVIkyLheJH7ymq1YsmQs6jMwZpLOrcDnD59muTkZCRJYvTo0fWOaQ4qtRqFf0c4nczRvfugCQoQyHeEkZGRHDlyhNWrV/PII4+gUChwHDoU7c4VVPlYKC7eaVOAPO086ezemePFxynzNeCSrcZwslQoQDcgkiQ12RXVWvwxoUCSJCwWC3q9Hj8/P7Zu3XrJNa6urgA8//zzbNiwgXfffZeOHTtiZ2fHXXfddUmgs4ODw5+1/Mvi5OTEwYMH2bp1K+vXr2fu3Lm88cYb7Nu3D71eD8Dq1atp165dnevOW7uWL1/O888/z4IFC+jXrx9OTk7885//JD4+3jZ20aJFzJw5k99++41vv/2WV199lQ0bNtC3b1/0ej3z5s2zWawupqnFW9sCQgEStFn27ktAYzFSo9AyqH/3FpFpMlVQXiYHTGqPK3CcccH9lVqaSlipHMzsFxlyybVWq9UWTBkdHY239+UzxJpDaPceZJ5OpjrzmFyTqIlK1siRIzlx4gRnzpwhKSmJ6OhoHAb0R7tETVWcgaL8rXBRdn9///4cLz7OUft0+tMZQ3oZToOaH9QtELQ0PXr0IC8vD5VKRXBwcL1jdu7cydSpU5kwYQIgW4QyMzMblH3elV1fVfX6cHFxwc/Pj/j4eAafc6ObTCYOHDhAjx49LnudSqVi+PDhDB8+nNdffx1XV1c2b97MiBEj0Gq1ZGdnXzaucOfOnfTv358nn3zSduy85etiYmJiiImJYc6cOfTr149ly5bRt29fevToQUpKCh07dmzUHts6IgtM0GY5sEu+q6nx6oBdC8XZlJbuw4oZZQFoDI7YX/RBtTdzD2E1QQA4hF0ab5Senk5mZiZKpbJRGSJXS//BcrFHT/0ZjmafbfL1Tk5ODBok90vbvHkztbW1KB0dcXXsCRaoNp2ipibHNn6Avxxcvsa6BQBDRhlWS9MzdwSCP5vhw4fTr18/xo8fz/r168nMzGTXrl288sor7N+/H5Bjhn788UcSEhJITEzk/vvvx2JpuK6Wt7c3dnZ2/Pbbb+Tn51+2x97FPPPMM/z9739n1apVHD9+nCeffJLS0tLLjv/111/58MMPSUhIICsri6VLl2KxWAgPD8fJyYnnn3+eZ599liVLlpCens7Bgwf597//zZIlS2x7279/P+vWrePEiRO89tpr7Nu3zyY/IyODOXPmsHv3brKysli/fj2pqam2OKC5c+eydOlS5s2bx9GjR0lOTmb58uW8+uqrDe61LSIUIEGbpfiEnP7u0yWqgZFNkHlR/I9D//5IFylWecezUKKgyqkWlWtdc7DFYrG1m+jdu7fN3P5n4BsYiFHnghIL27btvSoZffv2xdnZmbKyMpt53CV26IW2GOdeB4AY7xjsVHYcko5g1UpYDWZqc/TN34hA0MJIksSaNWsYPHgw06ZNo1OnTtx7771kZWXh4+MDwL/+9S/c3Nzo378/Y8eOZeTIkVe0yJxHpVLx4Ycf8umnn+Lv78+4ceMavOa5557jwQcf5KGHHrK5pM5bnurD1dWVH3/8kWHDhhEREcEnn3zCN998Q9euXQE5YPu1115j/vz5REREMGrUKFavXm1L0X/ssceYOHEi99xzD7GxsRQVFdWxBtnb23P8+HHuvPNOOnXqxIwZM3jqqad47LHHANk6/Ouvv7J+/Xp69+5N3759ee+99wgKCmpwr20RyXo1RRhucMrLy3FxcbGlMQuuPyrKK/jk0ftRYCXutQ/oGVl/RlZTiY8fg74yBbfPVYRMfAu3u+8G5O7v//vX/3FHURym7nYE31s3QPLo0aN8//33aDQannnmmT89fuDff3sH49FdnA3sw9//OfeqZCQkJLBq1Sq0Wi3PPPMMilOnSfz8NvRjLPh43k5k1IX6IU9veprfT//OkvL/w/uMIy5jQnAaLNxgbZWamhoyMjIICQm5IWM7BDc2V3r/NuX7W1iABG2S7b/vRoGVco0rMV1CW0SmwViIvjIFkNtfOA64UFfoUMEhuuplJcsrIrDOdVar1Varo2/fvtckeDL6XNC3Ki+VmtrGxST8kaioKHx8fDAYDPz+++9oO4Vhny9nzxQXbq+TqdLfX3a7HdQdA8CQXtqM1QsEAkHrIxQgQZsk+Zw/n3bhKFqoJk1Jsez2UZ2SsPMORe3vbzu3P2MvHQxyOryug2ud69LS0sjLy0OtVtv6bv3Z9O4XixUJN2MJ2xNSmy6gtgbFqXhu7Shnj+zfF0/5vm9x84lBMkAtZVRWnrANH9BOVgbXWrcCYMgsx2oWxmPBzY2jo+NlHxcXMBRcn4gsMEGbpDorGTsgNDqmwbGNpaRE7immPS7h0L9uV/nSlFwghmp3M0rHugHX5z/oevXqdc3qZNg5OWHyCEBdlM3+HXsY0bsRXeZNRjj+Kxz6EjJ3gtlAKBDE3WRZ2rNjzXIGFe9Gk+aEoauV4sxvcYyU3WuBToG0c2xHmjULs9aK8lwckCZA9DAS3LxcqTHqH1PVBdcfQgEStDlOZ5/CrroEMwqGxPVrEZlWq5Xikp3AuQDopy+4v4qqi/DMl+veOP4h+ysrK4vs7GyUSqWtOuu1wiciiuId2ZSkHrnyQIsZDn8HW96GslMXjjv6IHmFE2dVsSQTDhJFv3ZH0KZIGLpaKTryOYGJSXDLXCTfbgzwH8B3J77jjFsRgXmecj0goQAJbmJulnTxGxXhAhO0ObZvlV1VpY7+BPt5NDC6cVRXZ1NTcwZMoMlU1+n+Hp8bT3RVJwBcw/3qruWc9ad79+7XPGC+9wDZ3eZUkklxxWW6tBelw6IxsOpxWflx9IHBL8BT++C5FHjoF0KmfkJwcDBmFOzq8y+cq2VXX6mbGkvaevh0CGycR39fudlsvPYwAIaTDacBCwQCwfWKsAAJ2hxZhw+iBuxDIhoc21jOu780GRIOXbqjdLwQyJyYfoAHjXFYsKINcbEdz8/PJy0tDUmSGDCgZRqxnqfWaCY3rZTctDLKC6up1teiVCnQOarxbOeIX0cXOnfrwmqFGntLDb/HJzFheO+6QpJWwM9/gdoq0DjB4Ocg9nFQX9p6IC4ujsWLF3PwUAKdu92BQv9vLI4SFZFDcUnaAjv+Ra+T3ZG0ElsUu7mbYRgyyrGaLUhKcR8lEAjaHkIBErQprBYL1hy5smnXZvbquZjSUrmejuZE3fgfq9VKVXoxALU+Egq7C/8y5+vnRERE4O7u3iLrKM6t5PCW06TE52EyXDm7y9nLDoVTIJSlc2T//gsKkMUCW96C7Qvk58GDYPzH4Bp4WVnBwcGEhISQkZHBUTc3PE9I1PSwUhwzHJcuj8DPf8ElJ4GI9u1I1uZg0lpQGcB4Ro82UJSKEAgEbQ+hAAnaFEePHEdjrsEoqYkb0HDxssZgtcoNQAG0qQocp1yw5pwsO0lIidz52bXThQ7QlZWVHD4su4JaIvOrRl9L/M8nObr9DOezzx3dtLTv7IabnwP2ThosZiv6khrOZldw+ngJ5Wer0Rj9MZGO+UQyFosVBVZY/SwcWCwLGTALbpkLCmWDaxg8eDAZGRkknTrF8BJnoIyi05sIGbwSfLvBtw8SW5nNMVdnTjmdJsQQiOFkmVCABAJBm0QoQII2RfxO2eqidwvEzanpXaTro7o6G4MxH2pBV+iCLvJCZ/ldZ3YRXSk3xrq4/cWBAwcwmUz4+fkRcFG3+KvhVHIxGxcdo6pcbsYYHOVJ9C0BtOvketk+X7UGM2kHCti9qpSSrO04VZ7mm7f3cFvoKlxTF4OkgLEfQo8HG72O4OBg/P39ycnJocA1FgfWU248hsViROEWDNPW0nf5eBaRzy5pFyEEyvWA4pq3f4FAIGgNhPNe0KbITZbbX7h37NJiMktLZaVKkyXh2LMvkvKCtSQ5PQkfkwcWhRVNsGzpMJvNtv46ffv2veqO71arlf1rM/n5wwSqyo24+doz/tkYbnsyivbhbleUq9Yqiejvx5R37sCs0AFGirNO8v2OgZw09IUJnzVJ+QG5jcDAgQMBOIofUgVYFSbKy2VLF1pHYu75ARUSOx1lN6Qxs1TUAxIIrgMWL17cIi14JEli1apVzZbTFhAKkKDNYDaZUJ3NBCCqd8vF/5ScV4BOSDhcFMxsNBshqxoAq78GhUZWjI4dO0ZFRQUODg62Hj1NxWK2sPWr48T/dBKs0GWQP3e/3Jt24W5NkqNSqZDay6m4DpYDGK0OrC15kcSzV+eW69y5M+7u7hjMFoy5ssJXlPe77bydzoVo7+5kaXMwSZVYa6E25SoKMQoEghblnnvu4cSJEw0PFNhoVQVo27ZtjB07Fn9//0ZpnT/++CMjRozAy8sLZ2dn+vXrx7p16+qMeeONN5Akqc6jc+dGFIkTXPccOJiE2mKkRqFlQGy3FpFptVopKb4Q/+Mw4EIA9OGzhwmvkJsAunTysR0/H/zcu3dvVKqme5EtFisbFydzbGcukgSD7+3E0MmdUWsajtOpj8j2agCcrHvpFi4HbO/4PpX4n0/S1FZ/CoXCltGWY5YVq+Izm+qMifXvh0Wykm2XCYDhl/+Bseqq1i4QXO8YjcbWXoKNy62ltrYWOzs7vL29r/GK2jatqgBVVlYSHR3Nf/7zn0aN37ZtGyNGjGDNmjUcOHCAoUOHMnbsWA4dOlRnXNeuXcnNzbU9duzY8WcsX3CNObhHbn9R7RGMvVbTwOjGUVNzGoMxD0xgbwpEc1E8z968vURWy0qALsQVgLy8PE6fPo1CoaBnz55Nns9qsbLly2RS9+WjUEiMmtGNbnHNaCpacJzehV8DkF3tiv+kofQdL/dG278mkwNrs5osMioqCkdHR87q5XVVWNKwWAy28339ZOvSHpdsAAxl7vDz0yD6KrdprFYrtQbzNX80VUm3WCzMnz+fkJAQ7OzsiI6OZsWKFVgsFtq3b89///vfOuMPHTqEQqEgK0v+XygtLWX69Om2G+lhw4aRmJhoG//GG2/QvXt3vvjii0Y3i42Li+Mvf/kLs2bNws3NDR8fHz7//HMqKyuZNm0aTk5OdOzYkbVr19quMZvNPPLII7Z9hIeH88EHH9SRO3XqVMaPH8/bb7+Nv78/4eHhZGZmIkkS3377LUOGDEGn0/H111/X6wL76aef6NGjBzqdjtDQUObNm4fJZLKdT01NZfDgweh0Orp06cKGDRsa/Xe4EWjVIOjRo0czevToRo9///336zx/5513+Omnn/jll1+IibnQEkGlUuHr64vgxuLsiSPYAx5hLRf/U1IiW3PUWRJOvevW8knJPModtd2wSlY05zKdDh48CEB4eDhOTk2vgrx3dQbHd+chKSRund6V0Bivq1+8QQ/fTcFDUYRGZcFoUrB99yGm3TUcpUrBzhVpxP98ErVWSfQtjQ9UVqvVxMbGsmnTRmprtKh1BsrKEnFzk4tDRnpGYqeyY7/mKPczGqOlC9akd5Da94G+j1/9fgStislo4bNnfm94YAsz44MhqLWNt37Onz+fr776ik8++YSwsDC2bdvGAw88wLp167jvvvtYtmwZTzzxhG38119/zYABAwgKkq25d999N3Z2dqxduxYXFxc+/fRTbrnlFk6cOGErZ5GWlsYPP/zAjz/+iFLZuLUtWbKEF154gb179/Ltt9/yxBNPsHLlSiZMmMDLL7/Me++9x4MPPkh2djb29vY2he3777/Hw8ODXbt2MWPGDPz8/Jg0aZJN7qZNm3B2dr5EOXnppZdYsGABMTEx6HS6S7wh27dvZ8qUKXz44YcMGjSI9PR0ZsyYAcDrr7+OxWJh4sSJ+Pj4EB8fT1lZGbNmzWr03+FGoE3HAFksFioqKi6pwZKamoq/vz+hoaFMnjyZ7OzsK8oxGAyUl5fXeQiuL0y1RjSF8h1c9z69GxjdeM4HQGtTJRz6XXB/VZuqkU6ds3r4alFolRiNRtud4tVYf07szWP/6kwA4iaH06FHM83Vvz4LhSlIzn5YAmSXYHqibA3tPjyQPmNDANkddvLQ2SaJ7tmzJyqVmtJy+Uai6PQFN5haoaanT09S7bIwKy1YcMVkbQ8b5kL+0ebtSSC4AgaDgXfeeYeFCxcycuRIQkNDmTp1Kg888ACffvopkydPZufOnbbPfIvFwvLly5k8eTIAO3bsYO/evXz//ff06tWLsLAw3n33XVxdXVmxYoVtHqPRyNKlS4mJiSEqKqpRa4uOjubVV18lLCyMOXPmoNPp8PT05NFHHyUsLIy5c+dSVFRkK5+hVquZN28evXr1IiQkhMmTJzNt2jS+++67OnIdHBz44osv6Nq1a52Yw1mzZjFx4kRCQkLw86tboR5g3rx5vPTSSzz00EOEhoYyYsQI3nzzTT799FMANm7cyPHjx1m6dCnR0dEMHjyYd955pwl/jbZPm06Df/fdd9Hr9XW05djYWBYvXkx4eDi5ubnMmzePQYMGceTIkcvesc+fP5958+Zdq2ULroL9+xJRWU1UK+3o16vlLEDFRecqQKcqsZ9zQbE6VHCIiErZleTUQU5/P3bsGAaDAVdXV0JDQ5s0T0FWOZuXHgcgZkQgXQb4N3BFAxz5AZK+A0kJdy0idG8eJzKOUnv6hFwPSCHRa0ww1RW1JG09zYbFx7jTqyee7R0bJd7e3p7o6GjO5BzDyzuLotwtdOw2x3a+r19fdpzZwWmnswSV+mDwmoS66F/ww3R4dAuoG3YbCK4vVBoFMz4Y0irzNpa0tDSqqqoYMWJEneNGo5GYmBi6d+9OREQEy5Yt46WXXuL333+noKCAu+++G4DExET0ej0eHnVb6FRXV5Oenm57HhQUhJdX06yzFytKSqUSDw8PunW7EKvo4yPHERYUFNiO/ec//2HhwoVkZ2dTXV2N0Wike/fudeR269YNjeZSl3+vBgrBJiYmsnPnTt5++23bMbPZTE1NDVVVVSQnJxMQEIC//4XPomvdz7C1abMK0LJly5g3bx4//fRTncCvi11qUVFRxMbGEhQUxHfffccjjzxSr6w5c+Ywe/Zs2/Py8vJm13YRtCyH4s/F/3iGoFO3zNu2uvo0htpcMIOTJhyV24UMrH15++hV1QHA1v5i/355DT179kShaPyHtrHaxLovjmI2WQiO8qTvhA7NW3hFPqx+Tv598PMQ1I9+mlxOrPgfHlX5HDt1lsggbzmt/e6OlORVcvp4CWs+PszdL/fCzrFx8VOxsbEsWrQFAL2UgdlsQKnUyuf8YgHYqz5MECMwuo+Hmi+h4BhsfhNGvn05sYLrFEmSmuSKag30ej0Aq1evvqTbulYrvzcnT55sU4CWLVvGqFGjbAqPXq/Hz8+PrVu3XiL74vgZBweHS843hFqtrvNckqQ6x86XtbBYLAAsX76c559/ngULFtCvXz+cnJz45z//aUuyaGgtDa1Rr9czb948Jk6ceMm5xsQ13Qy0SRfY8uXLmT59Ot999x3Dhw+/4lhXV1c6depEWlraZcdotVqcnZ3rPATXF0WpsmvFu9PVpZ3Xx3n3lzpLwimmf51zR7ITCTLKd0aaYBfy8/Ntwc9/vEO7Elarla3LUig/W42ju5ZbHopAobi6ukHnBMIvM6G6BHyjYNDzAHj6+WGwc0WJhR3b99qGK5QKRj4aiYuXHRXFNWxaktzooFNvb2+87YMxGOxAaaGs7KDtXCe3TrhqXUnQyVYtw2kDjDuXzLDnYzhz4Or3KBBchi5duqDVasnOzqZjx451HudvWu+//36OHDnCgQMHWLFihc39BdCjRw/y8vJQqVSXXO/p6Xm5af8Udu7cSf/+/XnyySeJiYmhY8eOdaxQzaVHjx6kpKRcss+OHTuiUCiIiIjg1KlT5Obm2q7Zs2dPi83fFmhzCtA333zDtGnT+Oabb7jtttsaHK/X60lPT6/XRypoGxhratAUnwIgpm/Lxf+UnOv/pU2VsO8bazuuN+pRnKmVn3iqUTqoOXBA/kJvavDzifg8Uvfly0HPj0Sic1A3fNGVSFgGJ34DpQYmfAKqC9Ych2C53EP20cN1LtE5qBn1WDeUKgVZSUUc3ny60dP1HTqMsjLZdJ+fttp2XCEp6O3bm2S7k1glK+YSAyafOOg2CawW+PkZMNc2Y6MCwaU4OTnx/PPP8+yzz7JkyRLS09M5ePAg//73v1myZAkgVzTv378/jzzyCGazmTvuuMN2/fDhw+nXrx/jx49n/fr1ZGZmsmvXLl555RWbhfdaERYWxv79+1m3bh0nTpzgtddesxVYbQnmzp3L0qVLmTdvHkePHiU5OZnly5fz6quvAvJr0alTJx566CESExPZvn07r7zySovN3xZoVQVIr9eTkJBAQkICABkZGSQkJNgC2ObMmcOUKVNs45ctW8aUKVNYsGABsbGx5OXlkZeXR1lZmW3M888/z++//257Y0+YMAGlUsl99913TfcmaDn2xR9CaTVTqXIgNjq8xeSWFO4EQJOuwv4if/rBgoN0ORf/4xDqQW1trS1wsSnBz5VlBrZ/JxcJ7HN7MH4dXBq4ogGqimG9/OFF3BzwqWsN63Q+EzInFYulrpXHs70jA+6SU/p3/ZhGQVbjAv3Du3ShpkiOhcjJ31bnXF+/vlQrDeQ4FAFgzCiDke+AnRvkJ8HuxpW3EAiawptvvslrr73G/PnziYiIYNSoUaxevZqQkBDbmMmTJ5OYmMiECROws7vQMkeSJNasWcPgwYOZNm0anTp14t577yUrK8sWo3OteOyxx5g4cSL33HMPsbGxFBUV8eSTT7aY/JEjR/Lrr7+yfv16evfuTd++fXnvvfds2XAKhYKVK1dSXV1Nnz59mD59ep14oZsBydrUIgwtyNatWxk6dOglxx966CEWL17M1KlTyczMtPlr4+Li+P33S9M0z48HuPfee9m2bRtFRUV4eXkxcOBA3n77bTp0aHzcRXl5OS4uLpSVlQl32HXAx//8kOr96yn268bb789vEZk1NTns3DUIzBC8JIYOX13IAHl337t0X+1N55oQ3O8JJ0Nzlu+//x5nZ2dmzZrV6PiftZ8mcfLQWbwCnbjrxZ4olM283/h5JhxcAt5d4bHfQVnXmlRSeJaFT03DgsTI+Z8TFVq3FITVauW3T49wMuEs7v4OTJrTG6W64TVt+uh16PIVFouCoXFJqFRy/MDJspOMWzWOJ/IncUdxHA6xvrhNCINDX8NPT4JKB0/uAfeQBmYQXGtqamrIyMhodJ0bgeB64krv36Z8f7dqEHRcXNwV4xHOKzXnqS9w7Y8sX768masSXG8UpydjB3iHRzY4trHY6v9kXxr/k3DmIHfVyPUyNCHOJK7ZCMhB9Y1VftIOFHDy0FkUColhUzo3X/k5tU9WfgBuW3CJ8gPg5umFwd4dbVUxe3btIyp0bJ3zkiQR90A4uemlFOdUsn9tJrF3NJzNFtP3DuLP/oBGW03aiXV07jIOgBDnEDx0HiTaneAO4jBknrMqdb8fDi+HjG2yxerer5u3d4FAIPgTaHMxQIKbi5rKSrQlcsxKr359WkxuyUX1fy6O/ykzlCHl1KJCCS4qalQmUlNlN1Z0dHSjZBtrTOz4Tu7J02NUEJ7tm14wsQ5mE6w+l6XYfTIEXT5V1T6wEwBZR4/Ue97OUcPge2U34sHfsjh7qqLB6d1iumPKlfeQmrzKdlySJHr59uKovZxgYMqvwlxZC5IEo/8pp+gf/xVOXvviegJBS5GdnY2jo+NlHw3VmRNcvwgFSHBdE7/nAAqslKud6dOtmenjF1FaeK7+T4Ya+x49bMf35+0n8lz6u32IG0eOHMFqtdKuXbtG1wU58FsWlWVGnD119Bwd1PzFHloKeYdB5woj/nbFoWHnlDRLbtplrasde3rTIcYLi8XK5qXJmM2WK8qUlEo8y+X4CCOpVFVd6PvV26c3ZSo9hfZyHJ7xfGyRd2fofa7sxLqXwWJuaJcCwXWJv7+/LVa1vsfFdXQEbQuhAAmua44ckCsbm7xDUTfXjXQOg+Es1bWnwQLODlEoLgqSjM+Lp2uVHCysCXGxBeg31vpTdraKhI3yHeGAu8JQqZtZV8VQAVvOxT0NfRkcrpyq27e/bCVzry4gOfvy1Z8H3xeO1kFF4Sl9o7LCAv3lAnlOzmc5dOhCtkwvXzl4/KD2mLzczAsJCcTNkZW2/CNwcGmDcwgE1yP1pcxf/LiahsiC6wOhAAmua0pOynVmfFuw/k9ZmZzSrsqRcO5et//XwZwDdK6Wg3bLnAzk5eWhUCjqlKC/EjtXpGExWQmIcCMkugXqiuz6N1QWgHso9JzW4HAPXx8MOhcUWNm16/JpvfbOGvpPlBW9fb9mUFlmuOxYAI9ed2CtVqBUmjh27DebdSnUJRR3nTuHdbLLz5hxUXaZvbusBAFsfgtqRIsZgUBw/SAUIMF1i6G6Gk3JGQB69u3RwOjGU1oq19rQpNWN/ymsLkTKN6KzasBOydHT8pd6p06dGlUZ9lRyMRmJhUgKiYF3d7JVfr1qynNlBQhg+Bt1av5cCV2gHOOTeTTpiuMi+vnhE+JMrcHMrh8uXygUQBsSiu7U+WyLNDIzMwE5DqinT0+OnIsDMp7RYzFe5O7q/Qh4dISqQthTt0u3QCAQtCZCARJct8TvPYQSC3qVI70jO7aY3JICuf6PNluD3UVVnffn7b/g/gp2ttX+aYz7y2q1snulXMU1ckg73P2bXkr/Era+A7VVEBALEXc0PP4cHc/1JDKduXwcEICkkBh8byeQ4MTefHJSSy4/VpJwIQwAF5f8OkXjevv2Jl9dRLm2EixWjNkXBVYr1TD0XHG1Xf+WaxkJBALBdYBQgATXLUn75NYLtV4hqFUt06PIZNKjN8jWCheHaBQXNRmMz4sn8pwClO+sR6/XY2dnR1hYWINyTx46y9nsCtRaJb3HBDd/oQXJcOgr+fcRb8qZVY3EFgdUlU/K6aIrjvUOcqbLQDmIc9vySwsoXoy7fxwAzi4FJCcfs/Vl6uXTCyRI1KUAFwVCn6fLePDtBsYK2Pl+o/chEAgEfyZCARJctxSfi//xCotoMZll5QkgWVEWgku3up2vD+Tup+u5DLCUSjmQOTIyssEgR4vFSvzPJwGIviUAO6fGuaquyJZ35JYSEWMhMLbh8Rfh5e+HQeuMEgu7djfck6vfuA5o7VUUndGTsifv8nJ7jEOqAbXaiE5XTGJiIgAdXDvgpnXjiE5WLA1/VIAUChj2mvx7/Geya08gEAhaGaEACa5Lao1GW/+v7r1bMP6n5Fz8T7oC+9gLdYUKqwuxnDXgZHHApLFyIlv+Mu/WrVuDMk/E51GSV4XWQUX3EYHNX2ReEiT/DEgX3EdNQJIktAGy1Soz6XADo0HnqKbnqGAA4n8+iclYf8q6pn0Q2hw5Y87FpYBDhw5htVpRSAp6+vTkqJ3sAjRmlWP9oyUp7FbZlWeqhu3vNnlPAsG1IDMzE0mSbNmf9bF161YkSaK0tPSarUvw5yAUIMF1yaGDh1FZzVQp7YiN6dxickty5X5W2mwtdpEXKksfyD9AZJWsNOR7VWE0GnF2dqZ9+/ZXlGc2Wdj7SwYAPUYGobVrgZTYrX+Xf3adAN5XZ/0K7SbHARnPpDaq+3u3oe1wdNdSWWogcfOpy45zssouQleXfAoLCzl9+lyRSt9eZOjOYFTWYjWYMRVU1b1QkuCWufLvBxZDSVbTNyUQAFOnTkWSJCRJQq1WExISwgsvvEBNTU2zZQcEBJCbm0tkZMtVnb8ekCSJVatWtfYyrjuEAiS4Lkk4F/9T7R6ETt0ydTYsllrKa+R6Nc52UUjqC+0kDuQfILJadn+lS7IbKDIyssHWFynxeVQU12DvoiEq7srKUqPIPSxXT0aCIS9etZh+A87FAVXmcTK3tMHxKrWSvuPk/R/8LYtqvbHece5+gwFwc84DrBw6JNdp6uXTC4tk4bguE+BCW4yLCR4IoXFgMcHOD5q0H4HgYkaNGkVubi4nT57kvffe49NPP+X1119vtlylUomvr6+o7XOTIBQgwXXJ2RPJAHh0bLn4nwr9MawKE5IeXMPrxv/IFaA7UouJk+dabzR0F2ixWDn4m2zJiBkRiErTAoHa560/kXfK1ZSvEp/27TFqHFFiYccV6gFdTKfePngGOGKsMbN/TWa9Y7xiJkItKHUGdLoKjhw5gtFoJMwtDBetC0fs5LYhlwRCn2fwX+Wfh74SsUDXGVarldqammv+uJp+3FqtFl9fXwICAhg/fjzDhw9nw4YNAFgsFubPn09ISAh2dnZER0ezYsWFZsclJSVMnjwZLy8vW5LDokWLgPpdYGvWrKFTp07Y2dkxdOhQWwmIi9mxYweDBg3Czs6OgIAAZs6cSWVlpe18cHAw77zzDg8//DBOTk4EBgby2Wef1ZFx+vRp7rvvPtzd3XFwcKBXr17Ex8fbzv/000/06NEDnU5HaGgo8+bNw2QyNfhaBQcHAzBhwgQkSSI4OJjMzEwUCkWdbE6A999/n6CgICwWi83Vt3r1aqKiotDpdPTt25cjR+q22Wlo79czQs0VXHdYzGaUhZkAdO0R02JyS0v2AqBNl3AYciH+p8xQRnlBMZ4mN9JVeZjMJtzd3fHz87uivPSDBZSdrUbroLJlUjWLnARIWQ2SolnWH5BN3ur2YXDyECeTDsNdIxq+RiHRf2JHfv4ggSO/nyH6lgCcPezqjNG2C0a7UYchoAZfzxIyTzuTnJxMdHQ0Pb17crRIjgO6JBD6PEEDILAfZO+G3R/ByLebtU9By2EyGPjwobuu+bwzl6xA3YyO9EeOHGHXrl0EBcltZ+bPn89XX33FJ598QlhYGNu2beOBBx7Ay8uLIUOG8Nprr3Hs2DHWrl2Lp6cnaWlpVFdX1yv71KlTTJw4kaeeeooZM2awf/9+nnvuuTpj0tPTGTVqFG+99RYLFy7k7NmzPP300zz99NM2xQpgwYIFvPnmm7z88susWLGCJ554giFDhhAeHo5er2fIkCG0a9eOn3/+GV9fXw4ePIjFIrep2b59O1OmTOHDDz9k0KBBpKenM2OG3LC5IcvXvn378Pb2ZtGiRYwaNQqlUomXlxfDhw9n0aJF9OrVyzZ20aJFTJ06tY7l+69//SsffPABvr6+vPzyy4wdO5YTJ06gVqsbvffrFWEBElx3HEk6htpSS41Cy4DeLeeLL86R43802RrsIi9Udpbjf+TYlgx7OW08MjLyioUMrVYrB9bK1p/oYQFodC1wL3E+OLjb3eDVqdniQiLlAG7DqcbFAQEERLjTvrMbFvOF/f0RJ7PsKvN3zAGwucF6+/bmuF0GVqyYi2swV9TjRpMkGPS8/Pv+hVB55TR9gaA+fv31VxwdHdHpdHTr1o2CggL++te/YjAYeOedd1i4cCEjR44kNDSUqVOn8sADD/Dpp58CcnPTmJgYevXqRXBwMMOHD2fs2LH1zvPf//6XDh06sGDBAsLDw5k8eTJTp06tM2b+/PlMnjyZWbNmERYWRv/+/fnwww9ZunRpnbikMWPG8OSTT9KxY0defPFFPD092bJlCwDLli3j7NmzrFq1ioEDB9KxY0cmTZpEv35y4+N58+bx0ksv8dBDDxEaGsqIESN48803bXu6Eud7GLq6uuLr62t7Pn36dL755hsMBrkK/MGDB0lKSmLatLoV519//XVGjBhBt27dWLJkCfn5+axcubJJe79eERYgwXXHwT1y6rbeNQAnuxZIKUdWWMorE0ABzsoIpIvq/xzIP0DX6g4YqCW7tgBo2P2VdaSIojN61Fol3Voi9qcwFZJ/lX8f9NyVxzaSvgP6kPnzUtyqcskqKCPYx7VR1/W5PYTTx0s4viuXnqOCcPasawVy8x1IIUfR6E4B/cjMzKS4uJhevr2oUtaQrcslqMYfY1Y5dpH1tAPpeAv4dYfcBIj/Lwx7tblbFbQAKq2WmUtWNDzwT5i3qQwdOpT//ve/VFZW8t5776FSqbjzzjs5evQoVVVVjBhR1+JpNBqJiZGtyU888QR33nknBw8e5NZbb2X8+PH079+/3nmSk5OJja1bhuK8UnKexMREDh8+zNdff207ZrVasVgsZGRkEBEhu/GjzhUoBdlC6+vrS0GB/HmTkJBATEwM7u7u9a4jMTGRnTt38vbbFyymZrOZmpoaqqqqsLe3v+LrVR/jx4/nqaeeYuXKldx7770sXryYoUOH2lxm9e3X3d2d8PBwkpOTm7T36xWhAAmuO3JTjqIBXELCW0xmVdVJTIoqJCO4Bv8h/id/P7Oq7iJLeRaL1YK3tzfe3t6XlSVbfzIBiBzcDp2D+rJjG83ODwArhI8Br5bZt39QEEa1A5raSrbvOkjwhGGNus6voysBXdw5dayY/WsyGTal7oeYV/e7SD32KWZnAx1D3Ug7WUJCQgJxQ+Nw1jhzRJdGUI0/hszLKECSBIOfh28fkOsC9f8L6FxaYMeC5iBJUrNcUdcSBwcHOnaUrbYLFy4kOjqa//3vf7Ybl9WrV9OuXbs612jPKVqjR48mKyuLNWvWsGHDBm655Raeeuop3n336soz6PV6HnvsMWbOnHnJucDAC2Ux1Oq6nxOSJNlcXHZ2dW8y6ptj3rx5TJw48ZJzuqv8m2k0GqZMmcKiRYuYOHEiy5Yt44MPmpac0Ni9X68IF5jgusJqsSDly0UFwy9qU9Fczvf/UmdKOPbuazuuN+rJLThNgNGXdEU+0LD1JzetjLyT5ShVCqKHBzR/ceU5kLhc/n3ArObLO4ckSajanctsO5zYpGv73C43hD2+J4/SP6S02/mHosmXv0xCneTXLCEhAazQw6cHx+zlv99lA6EBwm8DrwgwlMH+6z9WQHD9olAoePnll3n11Vfp0qULWq2W7OzsS7q2BwRc+F/18vLioYce4quvvuL999+/JCD5PBEREezdu7fOsT179tR53qNHD44dO1Zvp3iNpnEW7KioKBISEigurr9VTI8ePUhJSal3joYyVUFWvszmS+t7TZ8+nY0bN/Lxxx9jMpnqVbAu3m9JSQknTpywWXZaYu+tiVCABNcVaSmpqM0GjJKaQX27t5jc4jO/A6DNUGEXfcEUfajgEF0qQ6nBSI5S7oXVUOf3xE1ynZzwvr44uDTdfH8Jez4GSy0E9m9y1eeGCOoixwHVnEpt0nW+oS4EdvXAarHWmxHmaJIVJG3NYXQ6HeXl5Zw8eZJePr04dr4gYo4ea239RRVRKGTLD0D8p2CqP+1eIGgMd999N0qlkk8//ZTnn3+eZ599liVLlpCens7Bgwf597//zZIlSwCYO3cuP/30E2lpaRw9epRff/31sq6axx9/nNTUVP7617+SkpLCsmXLWLx4cZ0xL774Irt27eLpp58mISGB1NRUfvrpJ55++ulGr/++++7D19eX8ePHs3PnTk6ePMkPP/zA7t27bWteunQp8+bN4+jRoyQnJ7N8+XJefbVx7uPg4GA2bdpEXl4eJSUXev5FRETQt29fXnzxRe677756LVF/+9vf2LRpE0eOHGHq1Kl4enoyfvz4Ftt7ayIUIMF1xd7dclpmmXM7PJ2vbBZuCqVlslxHa0cUF5mMzwdAZykLsWLF19cXDw+Py8opO1vNycSzgBz83GyqS2H/Yvn3gbOaL+8P9O7XGwBXfQ55xfomXdtnrKzknIjPozS/rhXIzXsgAJWqk7Zq2YcPH6aXby/y1EWUqMrBbMV4+gpzdrsLHH2gIgeO/tiktQkEF6NSqXj66af5v//7P+bMmcNrr73G/PnziYiIYNSoUaxevZqQEPn9rNFomDNnDlFRUQwePBilUsny5cvrlRsYGMgPP/zAqlWriI6O5pNPPuGdd96pMyYqKorff/+dEydOMGjQIGJiYpg7dy7+/o3PDNVoNKxfvx5vb2/GjBlDt27d+Pvf/45SKZfWGDlyJL/++ivr16+nd+/e9O3bl/fee8+W+dYQCxYsYMOGDQQEBNhioc7zyCOPYDQaefjhh+u99u9//zvPPPMMPXv2JC8vj19++cVm3WmJvbcmkvVqijDc4JSXl+Pi4kJZWRnOzs6tvZybin/8dQ6q7CSqut3Ka69e6le+GgyGfHbs7A8W6HLsMfxmvmA798CaB3hk72jSzcWcVhYxbNgwBg8efFlZO75PJXHTKQK6uHPHzO7NX9z2BbDpb+DdBZ7Y1aSmp43BarHwjwcnoTbVEDh1DnePHtCk63/9KJGsI0V0GeDH0Acv3CVX5Zxg9/HRAIT4LGPp92tRq9U8+9yzDP1hKM9mTmZgRQzOo4JxjruConh+/z7d4PHtLb5/Qf3U1NSQkZFBSEjIVceQCG4M3nzzTb7//nsOH67bNmfr1q0MHTqUkpISXF1dW2dxl+FK79+mfH8LC5DgusFqtWLJld0nYd2jW0xuaals/VGflnDqcUEBqDZVczI/nXY1PuQoZN/7lbIWjNUmju2UU7+jb2kB609tDez5RP59wKw/5ctfUijA55wlJ7FpcUAAPUfJd5jH9+ShLzHYjtv7d0JdKN8FKnO34e7uTm1tLakpqcR4x1xwg10pDgig5zRQ20N+EmT83uT1CQSCq0Ov13PkyBE++ugj/vKXv7T2cloFoQAJrhtOZ2ahqa3CJCkZENtyBRCLz2wFQHNSgX1Md9vxxLOJhFUFkKMowSJZ8fT0tNXIqI/kXbnU1phx87UnMKL+dNUmcWQFVBaAc3uIvDT4sKXwD+8CQEXmiSZf69fRFf8wVyxmKwkbs+ucczTKylFJ/nZbim9iYuIlgdBXNDLbu0PMA/Lvu/7d5PUJBDc7X3/9NY6OjvU+rhTP+PTTT9OzZ0/i4uIu6/660REKkOC6IX6XnKlV4uBPgFfLuR5LiuRy8g7GIBQODrbj5+N/MpRyLY4uXbpcVobFYuXwFjn4OWpYAJKimdYaq/WC9afPo6BsgVT6y9CzT08AnMpOUVppaGB0PdefswId3X6mTo8wN0+5PkiF4qRNAcrIyKCLQxfSdacwSrVYqkyYCuuvsmuj7xOABGkboSC5yesTCG5m7rjjDhISEup9rFmz5rLXLV68GIPBwLfffmuLNbqYuLg4rFbrdef+akmEAiS4bsg8IvugtQEdW0ymyVRBtUJ2W7n51I1/2Z+3n4iqUE4r5GrEV3J/ZSUVUl5Yg9ZeRXisb/MXlrVTdvuo7KDHlObLuwKdu0VgUqjQWQzsOnCsydcHdHHHM8ARk9HC4S2nbce9ouS2CUbPahws1QQEBJxzY1qQlApSzjVGNdbXGPVi3EMh4nb5990fNXl9AsHNjJOTU71p6B07dmx0kPTNilCABNcFVquV2jNpAIRGRjUwuvGUlR0EyYqyAFxi4mzHjWYjx/KP4mB0wixZcHV2wdf38orNkW1nAIgY4I9a2wJNT/f8V/4Zfa/sBvoTUarUmD3komRHDhxq8vWSJNFzVDAASVtOY6yRGzA6+ndFWaYGJRQcWmGzAh1NOko3z242N9hl+4JdTL9zMQiHv4fKwiavUSAQCJqKUIAE1wWFubloDBWYUdCvX48Wk1t0vv/XSQX2PS7IPVJ4hJAqf06fD37u2uWyvb/KzlaTfUwe13VQC6R3lmRCyjnTdOzjzZfXCLzCZOtW8cnjV3V9aIwXrj72GKpMNmUQwNEgB4MX5/xO165dUSgU5OfnE20XTbJdIwoiniegD/jHgNkAB5dc1RoFAoGgKQgFSHBdsOdc/E+xvQ9h/i1nESnN3wGAQ6U/yotSIvfn76dLZUeyFbK14UrxP8d25IAVAiLccPVues+dS9j7OVgtEDoUvDs3X14jiO4lK392RVlUG01Nvl6hkOgxUrYiHd50CrNJLuHv6i4XbtRLqdjb29Opk9zE1a3YjWPnFCDT2WrMlbVXnkCSoI/c3Zp9C8Hc9DUKBAJBUxAKkOC64HyrBpV/hyt2YW8KFosBvTUDAFePuhWWD+QfIKiqPbWSGUet/SV9g85jNllI3iXHEEUOboGmpwY9HPxS/r3vE82X10i694zCggJHcyXxh9OuSkan3r7YO2uoLDOStl9ugeHVdTwANd5VGAvzbG6woowiKlXVZGvygEZagbpOBHsPKD99wUImEAgEfxJCARJcF1Sdkr+UA7pcuQ9XU6ioOIpVaUZRAa7dhtuO11pqScxPgFq5jk14h/DL9tM5eegs1RW1OLhoCIq6fIXoRpP4jdz/yr0DdBzR8PgWQqOzw+gmu+8S9h28KhlKtYJuQ2Ul8NDGU1itVpz9e6KoUoIGzh5cSadOndDpdOgr9PRQ9bjgBstuhAKk1kGPh+Tf99bfm0kgEAhaCqEACVqd8qIitFXFWIHYvj1bTG5R7rn4n3QJh169bcePFx3HR+9hK34Y2evyQde24OeB/iiVzfx3sVjkvlcAsY/J/bCuIa4hcpf5gtSrTzWPHNwOlUZB0Wk9p1NKkCQJh2rZelZ8egsqlcpWeyS0KpRj9nJBRENDmWDn6f0ISArI3A75Tc9YEwham8zMTCRJkhsE3wDExcUxa9asazrn1KlTbf3G/kyEAiRodfbukSs1F2s96RbSAinm5yg51wDVvswblZub7fj+/P10qwjHIJnQKTQEBgXWe31xbiU5qaVIEnQZ0ALBzxlboSgVNE7Q/f7my2siXc4VgVSezcBktlyVDJ2Dmoh+fgAkbJDrIrm6yMplhSUFwOYGIx+StbIL0nhaj9XUiDld2kPn2+Tf931+VWsUCASCxiAUIEGrk5xwrkWDTyjK5hYYPIfVaqHCJH8hu7jUtSodyD+AR7Vc8bmjd3C9RcBALvwHENTNEyf3FuiXtH+h/LP7faB1ar68JtLnnHXN1VjKoROnrlpO1C0BIEH20SKKcyrxihgLQI2vntqiQgIDA3F1dcVsMmM1qShT6sFkwZjTyGasfR6TfyYul5vFCgQCwZ+AUIAErc75Fg1+nS+fidVUKqvSMasNSAZw73yr7bjZYuZg7kGMZvl55y71Fz801ZpJ2SMH8EYOrj9AukmU58Dxc4G9vVqn7LyDszM1jt4A7N9z4KrluHrbExotK5CJm7JxadcPyajAag9F+39GkiRbh/hwY3jT0uEBggfKzWFrqyBh2VWvU9A0rFYrFqP5mj+a2o/7t99+Y+DAgbi6uuLh4cHtt99Oerrsau3fvz8vvvhinfFnz55FrVazbZvsEs/NzeW2227Dzs6OkJAQli1bRnBwMO+//36j5j9+/DgDBw5Ep9PRpUsXNm7ciCRJrFq1qt7xixcvvqSa8qpVqy5J9vjll1/o3bs3Op0OT09PJkyYYDtXUlLClClTcHNzw97entGjR5Oammo7n5WVxdixY3Fzc8PBwYGuXbvWqQJ95MgRRo8ejaOjIz4+Pjz44IMUFl5dvS2DwcDzzz9Pu3btcHBwIDY2lq1btwJyI1I7OzvWrl1b55qVK1fi5OREVVUVAKdOnWLSpEm4urri7u7OuHHjyMzMvKr1NAfVNZ9RILiIan0FmnJZ0TjfsqElKMmT09/VmRIOd17IAEstTcWn3J1qyYjSqqBTr/qVroyEQgxVJhzdtAR0aYG0/INfgtUMgf3B+/IVp/9sHII6YT5awJmUo8DV9x/rPjyAkwlnSYnPJ3ZcB+wrfanU5FB0ahO+PExkZCTbt2/HtcKVow7p9NVHyRWhBzVCuCTJ7UF+fVZ2g8U+fs3jpW5GrLUWcubuuubz+v+tP5Km8cVFKysrmT17NlFRUej1eubOncuECRNISEhg8uTJ/N///R9///vfbQrGt99+i7+/P4MGyW++KVOmUFhYyNatW1Gr1cyePZuCgoJGzW02mxk/fjyBgYHEx8dTUVHBc8891/RN/4HVq1czYcIEXnnlFZYuXYrRaKyjwEydOpXU1FR+/vlnnJ2defHFFxkzZgzHjh1DrVbz1FNPYTQa2bZtGw4ODhw7dgxHR0cASktLGTZsGNOnT+e9996jurqaF198kUmTJrF58+Ymr/Xpp5/m2LFjLF++HH9/f1auXMmoUaNISkoiLCyM22+/nWXLljF69GjbNV9//TXjx4/H3t6e2tpaRo4cSb9+/di+fTsqlYq33nqLUaNGcfjwYTQaTbNfz8YiFCBBq3Jg7yEkoFTtQs+I+mNxroairE0A2BW6ofb2th3fn7efzuVyrZr2Om809tp6r0/enQtA535+KJrrljOb4MBi+fdWsv6cp1NUFMlHd2DNzcBqtV51yQHfDi74hDiTn1FO0u+ncXXqQSU5VJjkwGUfHx+8vb0pKCggR1EKgCG7vPFzdpsEG96A4pOQvgnCrl3GnOD65s4776zzfOHChXh5eXHs2DEmTZrErFmz2LFjh03hWbZsGffddx+SJHH8+HE2btzIvn376NWrFwBffPEFYWFhjZp7w4YNpKens3XrVlvl+LfffpsRI5r3/nz77be59957mTdvnu1YdHQ0gE3x2blzJ/379wdkhSIgIIBVq1Zx9913k52dzZ133mmzvIaGhtrkfPTRR8TExPDOO+/Yji1cuJCAgABOnDhhq93VGLKzs1m0aBHZ2dn4+8txkc8//zy//fYbixYt4p133mHy5Mk8+OCDVFVVYW9vT3l5OatXr2blypWArJBaLBa++OIL22fBokWLcHV1ZevWrdx6662Xnb+lEQqQoFU5cjABgFrPYLSqFmgxcY5yw1HQgotj3QyvA/kH8DK4YcRCR/+Qeq+tKK7hVLKcIda5XwsEZaeug4ocucZNlzuaL68ZxPbvRfI34FpzluPZZ4kI8m74onqQJInuwwNZ9/kRjvx+htufGM2ZrF+p9i6ntrgYtbs7kZGRbN68GZXRjlpMqCvAXFyDysOu4Qm0jhAzGfZ8LMdOCQXoT0dSK/D/W/9WmbcppKamMnfuXOLj4yksLMRikYPrs7OziYyM5NZbb+Xrr79m0KBBZGRksHv3bj79VM6+TElJQaVS0eOiqvAdO3bE7aIkiSuRkpJCQEBAnbY5ffr0adL66yMhIYFHH3203nPJycmoVCpiYy9Ysj08PAgPDyc5Wc7onDlzJk888QTr169n+PDh3HnnnbZkhMTERLZs2WKzCF1Menp6kxSgpKQkzGbzJdcYDAY8POQyIWPGjEGtVvPzzz9z77338sMPP+Ds7Mzw4cNt60lLS8PJqW4cZE1Njc2Vea0QdmVBq1KcLrdm8OrYchWRDYYCarXlYAGPDiNtx61WK4fPHMZ47gMzPKp+V1TKnlywgn+YKy5eLVD5+Xzwc8wDoKrf4nSt8PD2pkbnigIre5oRBwQQ2t0TJw8dNfpaCnPDwQwWVyjZL/v/IyPlmk6eNZ4k6+RsMEN2ReMn6DlV/nniNyg7c8WhguYjSRIKjfKaP5pqhRw7dizFxcV8/vnnxMfHEx8fD4DRaARg8uTJrFixgtraWpYtW0a3bt1slpHWQKFQXBLnVFtbtzK6nV0jbgquwPTp0zl58iQPPvggSUlJ9OrVi3//+98A6PV6xo4de0mn+NTUVAYPHtykefR6PUqlkgMHDtSRlZyczAcffACARqPhrrvuYtkyOX5v2bJl3HPPPahUKpuMnj17XrKeEydOcP/91zY7VihAglaj1mhAXSJ3F4/q3XL9v0rydwKgOiPh1Gug7fjJspN4FLmBBB4WJzwjLk1tt1qsJO+S3V8RA/yav5jiDEiT3XH0nNZ8eS2ALqAjAFlHjzRLjkKpoNsQuTBi0paz2FXKgdFFGRsAcHd3p127dkhIJKmzADBmljV+Aq9wCBootw05uLRZaxXcGBQVFZGSksKrr77KLbfcQkREBCUlJXXGjBs3jpqaGn777TeWLVvG5MmTbefCw8MxmUwcOnShKXBaWtolMi5HeHg4p06dIj8/33Zs3759V7zGy8uLiooKKisrbcf+WCMoKiqKTZs21Xt9REQEJpPJpujBhdfh4hY+AQEBPP744/z4448899xzfP65XEaiR48eHD16lODg4Eu6xTs4ODRq3+eJiYnBbDZTUFBwiayLrWKTJ0/mt99+4+jRo2zevLnO36BHjx6kpqbi7e19iQwXF5cmrae5CAVI0GokHTyMwmpBr3SgX3TjzbANUXRyPQB2+Y6o/S8oOfvz9hNaKbu9gu39UOgu9QDnpJVSXliDWqekQ8zVuYfqcGAxYIUOt4B7/S63a01wV/lu2HD66lpiXEzEAD+5MOIZPSopBoByQ5Lt/Pk77zKrAQBjVhMsQAC9zimNB5eK/mAC3Nzc8PDw4LPPPiMtLY3Nmzcze/bsOmMcHBwYP348r732GsnJydx33322c507d2b48OHMmDGDvXv3cujQIWbMmIGdnV2jLFEjRoygQ4cOPPTQQxw+fJidO3fy6quvAlz2+tjYWOzt7Xn55ZdJT09n2bJlLF68uM6Y119/nW+++YbXX3+d5ORkkpKS+Mc//gFAWFgY48aN49FHH2XHjh0kJibywAMP0K5dO8aNGwfArFmzWLduHRkZGRw8eJAtW7YQESFbuJ966imKi4u577772LdvH+np6axbt45p06ZhNpsb98Kfo1OnTkyePJkpU6bw448/kpGRwd69e5k/fz6rV6+2jRs8eDC+vr5MnjyZkJCQOu67yZMn4+npybhx49i+fTsZGRls3bqVmTNncvr06Satp7m0qgK0bds2xo4di7+//xXTCC9m69at9OjRA61WS8eOHS95IwH85z//ITg4GJ1OR2xsLHv37m35xQuaTcJ+uSVDlVsgjjp1i8kt0ycA4KSum+F1IPcAdkb5jicsqEO91x4/Z/3p2NMbtbaZMUkmAxw61/er9yPNk9WCxPaTgz/dKnM5dbYJFpl60DmoCY+V7/yKi2UrXrVXKaZzd9Rdu3ZFkiQ0Jh0VUjW1+ZVYapqgyESMlWOnKnIgdX2z1ipo+ygUCpYvX86BAweIjIzk2Wef5Z///Ocl4yZPnkxiYiKDBg0iMLBucsXSpUvx8fFh8ODBTJgwgUcffRQnJyd0uoZrfSmVSlatWoVer6d3795Mnz6dV155BeCy17u7u/PVV1+xZs0aunXrxjfffMMbb7xRZ0xcXBzff/89P//8M927d2fYsGF1vrcWLVpEz549uf322+nXrx9Wq5U1a9agVsufm2azmaeeeoqIiAhGjRpFp06d+PjjjwHw9/dn586dmM1mbr31Vrp168asWbNwdXW9bAugK7Fo0SKmTJnCc889R3h4OOPHj2ffvn11XmdJkrjvvvtITEysY/0BsLe3Z9u2bQQGBjJx4kQiIiJ45JFHqKmpwfmihtXXAsna1CIMLcjatWvZuXMnPXv2ZOLEiaxcufKK5a8zMjKIjIzk8ccfZ/r06WzatIlZs2axevVqRo6UYz2+/fZbpkyZwieffEJsbCzvv/8+33//PSkpKXh7N+6Ovry8HBcXF8rKyq75H+RmYv7MWWjy06jtM56XnpveIjJNpkp+3xoFCuhW+jLeE2XFw2q1cueiO4nKjsLOquHpidNxiK77fjDWmFj0wg5MRgsT/9oTvw7NNMcmrYAfHgHndvDMYVBeHzkHVquVvz94L5raSto98Dz3jo1rlryiHD3L/7YXpaaKsHHPgATdTX/H49a7AViyZAkZGRmEW3wZZOyK58OR6Do1LugUgPWvwa4P5d5pD6xo1loFMjU1NWRkZBASEtKoL/4bmdOnTxMQEMDGjRu55ZZbmnz9zp07GThwIGlpaXToUP+NlaBludL7tynf361qARo9ejRvvfVWnYJPV+KTTz4hJCSEBQsWEBERwdNPP81dd93Fe++9Zxvzr3/9i0cffZRp06bRpUsXPvnkE+zt7Vm4cOGftQ3BVWAxm1GcleNCzrdoaAnKivaBApRF4NrzQgPUUxWncC6Wsw4CzJ7oQl0vuTbtQAEmowVXH3t8Q1tA8T2f+t5jynWj/MC5YFc/OU02NfFws+V5+DvSvrMbZqM91gofAArT19nOnw+GzlDKtVYMjS2IeJ7zwdBpG6Ekq9nrFdzcbN68mZ9//pmMjAx27drFvffeS3BwcKMDgleuXMmGDRvIzMxk48aNzJgxgwEDBgjlpw3SpmKAdu/ebUulO8/IkSPZvXs3IGcBHDhwoM4YhULB8OHDbWPqw2AwUF5eXuch+HM5cew4KkstNQotA3u3XAf4whNyBpI2xw71RSbZfXn7aF8lV3QOcfBD6XRpsa3z7q+I/n5XXR/HRvFJuaEnkpz9dZ3RPkJuWFqVfaJF5HWLk4Ohy8/KcQflVRcUqy5duoACjFYLJZK+8RWhz+PRAUKGAFYRDC1oNrW1tbz88st07dqVCRMm4OXlZSuK+PXXX+Po6Fjv43yT34qKCp566ik6d+7M1KlT6d27Nz/99FMr7+rqyM7Ovux+HR0dyc7Obu0l/qlcP7eljSAvLw8fH586x3x8fCgvL6e6upqSkhLMZnO9Y44fP35ZufPnz69TgErw57M/Xm6AWu7cHnenljPBl5bsA0dwkjrVUWIOZhxEbdGgtCoIuahI2HnKzlaTm16GJGGLaWkWh76Sf3a8RW7weZ3Ru28vflm3HOfyMxRVVOPh1Lw03OAoOSVeX9AJlw5bqfIsxlRSgsrNDTs7O/yD/MnJyCFdmY97tjNWsxVJ2QQls9fDkPG7HFMV9xIoWy5mTHBzMXLkSFvIxB+544476gTsXsz5eJspU6YwZcqUP2191xJ/f/8rdq3392+BJtDXMW1KAfqzmDNnTp1MgvLycgICAlpxRTc+Z44fQwU4BjWuAmtjsFhMVGrlLAI3v7rm7JyTOQQRhJ/FDacOnpdce2Kv3I6jfWc3HFybWavHbIJDX8u/xzzYPFl/Eh07h1Gr1KAxG9kRf5hxw+v/0G8sCoVEt7j27F0t/z1N7ayU79uG+61ylkpsTCwrM1aSpsyjpyGU2rxKNO0uLcx2WTrfBg7eoM+HlDXQZVyz1isQ1IeTk9MlBfpuZFQqFR07dmztZbQabcoF5uvrW6f+AkB+fj7Ozs7Y2dnh6emJUqmsd8zFNQr+iFarxdnZuc5D8OdhtVqx5MoNMsOiohoY3Xj0pUewqs1I1eDR/UIfmhx9Do6l8pdtoMUDbUjdv6/VauXEXvk906klrD9pG0GfJ2cvhY9pvrw/AYVCidU7GIDkhMQWkRnR3w8s7hj1XqCAohMXGiJGdI7AorCgl2oolCowZjfRDaZUQ49zyuR+Ec8nEAiaT5tSgPr163dJsagNGzbQr18/QK5A2bNnzzpjLBYLmzZtso0RtD6nMjLQmKqplVQM7Ne9xeSeTZGbB2pPadB2vFBXKD47HneD3NA0yM4fpXtdl9vZ7ApK86tQqRWEdvdq/kLOp75H3weqa9fYr6n4hMllAkozUlpEns5BTXhfP6rPyneUZfoLxeY0Gg0O/nIJgnRlHobMq4iz6/EQIMHJrVB0bUvmCwSCG49WVYD0er2tDDbIae4JCQm2wKs5c+bU8bU+/vjjnDx5khdeeIHjx4/z8ccf89133/Hss8/axsyePZvPP/+cJUuWkJyczBNPPEFlZSXTpl0fVXgFsHe3HP9T6uhHe4+Ws7aVFsidrB1NwXXifxKTE5GQcLM44hnqc0mAc0q87P4K6e6Fpp7iiE2iIl9u3QDXrfvrPDF95Lo9jsXZVBpqGxjdOLrFtaOqUFY+Kz3LbPWA4EI22EllATVNqQh9HrcgOaYK4OCSZq9VIBDc3LSqArR//35iYmKIiZEryM6ePZuYmBjmzp0LQG5ubp0o9JCQEFavXs2GDRuIjo5mwYIFfPHFF3UC2u655x7effdd5s6dS/fu3UlISOC33367JDBa0HpknmvBoG3fcr5nq9WKXim71Vw9/5+98w5vq77+/+tqSx7ynvGMVxw7dpazBxBICCPs0UKAllFa2vKj9EvpoAXaUlpWSym0lL0S9gpkELJ34sSJM7xX4r2Xtu7vj+s4uFm2dSHr83oePb6RdM89ShTr6Iz3mTzgsdYaZbFpvDcMY+JAbR+vx0vJtr7yV54K75GCd8DrhhETIUK9/WbfBlk5WXgkLWavjU35xx8SGAqhMf4EBihCi654D93bNvY/NnvsbFwaF72Sg9rOBtwdjqFfYMIPlJ8731SEJgUCgWCYnNIm6NmzZx+1JO6bHEvlefbs2QP2uByLe+65h3vuucdX9wTfEs6DpRiB5Cz1FhTaemtwWxzggdCsy/rvr++qx7+jr//HE4YhcWDGqeZAG7YuFyZ/PXGZIb45IctHyl/jTv8pEb3BgCskDm1LJbu35zNnijr/HllTJ1ByKBCduZPG3csJuugSAPyMfthD7eib9JRpGxhd1YluzBBLjqlzISBGUYY+8DlkXa2KzwKB4NzjjOoBEpz5NNbVYXR04kHDlMnqLUBtPvA5APpDWvxG5fTfv6ZwDQbZgEnWE2EMRh81cPlfcV/5K3VCJFqtj/8dqjdDSyno/WD04MQ9TzWhKUqWqrlUnQwQQOKYcJxtSnbvkH2gcGHMSGWstlLbiK2yfejGtbpvNEO/4oubgjOU2bNnc++996pq89VXXyUoKEhVm4LTn2H9xi8vL1fbD8E5wuH+nzZzOKmxoarZbTu0GgA/WyyS9sgOrwNFygd7nDcMc4IVSXOk/8dpd1O+qwmAtEkqlL8Oi/RlXQnGM2OUNmucUn42NFfidHtVsanRSISGKGWw3pheXK2t/Y/ljc7DK3mwSy7KS4f5e2TszSBpFKHJZt8XugoE119/PcXF6oiCCs4chhUApaSkcN555/Hmm29it9vV9klwFlO6W1EI1sWM9F1t+Rt0epRfXlbr+AH32+psAMR7QjEkDez/qShoxu30Yg03E5noYzO2vRP2fawcj7vFN1vfIRMm5uJFIsDdxfZ96n2xSZswDwB95CEqlx5Z6jguahz1FiXrVtxWidc5tG3UAATFKXvBAPJf9dVVgQCz2TzoXZGCs4dhBUD5+fmMGTOG++67j6ioKO666y6xcV0wKHqqSwAYkTFaNZtOZwfOgC4AwtIv6b+/vLYck8OEJEvEekMx/k8AdLj8lZZ39GTYkCn8AFy9EJauNECfIZj8/HBYowHI37JDNbuh4VnILiNavZ0DxUfKa356P5yxysRZlaaJ3sq245k4MYf3g+16WzRDq4Qsyzidzu/8Npx93G63m3vuuQer1UpYWBi/+93v+u0kJibyxz/+kYULF+Lv709CQgKffvopTU1NLFiwAH9/f8aMGcP27dv77YkS2LnJsJqgc3Nz+fvf/86TTz7Jp59+yquvvsr06dNJS0vjBz/4ATfffDPh4SroqQjOKrrb2zH1NAOQN2X8SZ49eFqKvlAWoDZqsE6f1n//ul3rAIj2BmPQGQYoD/d2OqnZr5Rm0vJUED88XP4adzOomNn6LrAmpuEsqKW+eJ9qNiVJi588kl720eXfQHebA/9gRWF7VMooXMV2nJKbkoIDjE2bPvQLpF4kmqFVxuVy8ec///k7v+6vf/1rDIah6WW99tpr/PCHP2Tr1q1s376dO++8k/j4eO644w4Ann76af785z/zu9/9jqeffpqbb76ZqVOn8oMf/IC//e1vPPDAAyxcuJC9e/eqmokWnFn41PWp0+m46qqreO+993j88ccpLS3l/vvvJy4ujoULF1JXV6eWn4KzgC2blQxDmyGEMSNjVbPbUrkCAEtnONI3fpFWlSkNuPHeUAxxAUi6I2/3km0NyDJEJgUSFGnxzYHGA1CbDxodjLnBN1ungIzcvqbxhgq83qF/Gz8ekfGzATCHl7JnxRHhwglRE3AalNLkvuGKMIpm6HOauLg4nn76adLT0/n+97/PT3/6U55++un+x+fPn89dd91FamoqDz30EJ2dnUycOJFrr72WtLQ0HnjgAfbv33/U1gDBuYVPY/Dbt2/n5ZdfZtGiRfj5+XH//ffzwx/+kIMHD/Lwww+zYMECURoT9LN/5y4AvJFJaDUq9v/YC8EPrJYjazVsNhueFg8SkqL/8z/rLw7v/lIl+1PwtvIzdS74n3mZz0lTJrD7NQh2tLKnvJacFHWC05CYWVTU/wtLeAn7ltWSd2UGWr2GcRHjeNn/36S2ZlLRewiH3YHRNIz9a2NvhrV/O9IMHXbu7jRSA71ez69//etTct2hMnny5AGZmylTpvDkk0/i8Sg9ZWO+sWLnsAZcdnb2Ufc1NjaecE2S4OxmWBmgp556iuzsbKZOnUptbS2vv/46VVVV/PGPfyQpKYkZM2bw6quvkp+fr7a/gjOYjr5v+1FpmarZ9Hgc2AOVUlZo8kX99xcWFSIh4S8bCZQtAwQQO5p6aazqQpIgZbyPjY9eD+x+VznOvdE3W6eIwOBg7H7KgtgtG7ef5NlDsBuYjeTVoDN14dE3UprfCECwKRj7CA/+shE3Xg7sKBzeBUQztKpIkoTBYPjOb99GCeqbQdVh+8e6z+tVZ/JRcGYyrADo+eef53vf+x5VVVV8/PHHXHrppWg0A01FRETw0ksvqeKk4MzHYevF0KGURMflqaf/01a2Clkvo+mG4LFHFqBuL1Q+yJM9USCBIeHIWHrpDuWDODY9GEugj7u6ylZBVx2YQ5QM0BmKJV7Z4l5zYK9qNjUaI34kKfbDS9i96mD/Y2NjxhIgKX/3hbv3DP8iohn6nGTLli0D/rx582ZSU1PRfkMCQyA4GcMKgFasWMEDDzxAdHT0gPtlWe5fXWEwGLjlljNnHFjw7bJj2y40yHTpApiYlaya3cMbx03NwWjNZgA8Hg+NVUqQE+8JRR/jj8Z4pNp7OADyOfsDR8pf2dec1otPT0ZqX8nAXVs2rKmc4xESNQMAS1gxjZWdNFQoS1AnRE6g3axk7sobq4cvp3G4Gbq3RWmGFpwTVFdXc99991FUVMQ777zDs88+y89//vNT7ZbgDGNYAdDIkSNpbm4+6v7W1laSkpJ8dkpw9rFnh7K+xBGWiFGn3re09r6N44H6Uf33HTx4ENklIwERshXjNzR+2ht6aa7pRtJIJI/1sV/H3gEHlijHud/zzdYpZtIUZXQ/xNZI8aEW1eyGRM8EwD9EySztWa1kgcZHjqfIr4wgrwWP7OXAgWEqUYtm6HOShQsXYrPZyMvL4yc/+Qk///nPufPOO0+1W4IzjGE1QR/vG2J3dzcmk8knhwRnJy2l+zECYSPVWxAqyzK9fkpZLST+/P779/aVcSLlQDRoBuj/lO5Qpj7iMoIx+/uYsdn7EbjtED4KonN9s3WKCY+OwmGyYrR3sHHjDtKvU6ecZ7WOA1lCa+1CZ26jZIfE1KtTCA8MpzvSRXJdJPmaCgoL9pCbmzu8i4hm6HOK1atX9x8///zzRz1eWVl51H3/+5mVmJg44L5bb72VW2+9VS0XBWcIQwqA7rvvPkBpIHvooYewWI6MD3s8HrZs2TL8X2KCsxa3y4WupQaA7AljVbPbWb0Nr58XXBA2YUH//YcDoFGuOIABC1BLtveVvyaoUP7a1Vf+yr3xjNP+ORbGESlQuoPKwt2gUgCk0wVgkUfQK9UQGrSNhrqL2Lf+EBPmJ5EZm41pj/L3Vl5ZQU9PD35+fiexeAwON0OXLFOaoS/6oyq+CwSCs5shlcB27tzJzp07kWWZPXv29P95586dHDhwgJycnGNucBec2+zdvRet7KFXY2bquFEnP2GQNO/7FABTkx/6QGWTe2trKz1tPcjIjPCGoouwoO3L9LTW9tBa24NGK5GU42P5q6UMarYoO6nGXO+brdOEpCylD8h5UN39WsERijhlsFERpixcW4vH42V85HiqLIcI9Qbglb3s379/+BcRzdACgWCIDCkDtGrVKgBuu+02/v73vxMY6OP+JME5Qf5WRQCxJzgOf9PQNT+OR3vbVogCfzm1/77DCw31Ggkj+gH6P/3lr8wQTH4++lHwjvJz5AUQcHboiEyeNpGKj18huKeOyoZ2EiODVLEbEjWDQ82L0MU2YKqT6Wl3ULGrmfHp4/mb+VPmdM2kRdNFYWEhEyZMGN5FhDK0QCAYIsNqgn7llVdE8CMYNPVFyooFa1K6qna7DUpZLSTmyCqFw3un4r3KpvnD/T+yLPdPf6X6rP3jhYJFyvEZqv1zLKLj4nAY/NHiZcMm9TS8rEFKUOOOlkn0U7JLu1fVEOMfQ3NoF8leRZSusrKSrq6u4V1ENEMLBIIhMugM0FVXXcWrr75KYGAgV1111Qmf++GHH/rsmODswOv1IDVWAjDq8MoFFeitL8Ed4gQgbIzyfrTb7VRVKusvxjqURtjDG+BbDvXQVt+LRieR6Gv5q3IddNSA0QrfWL56piNJErqYkVBZQNnuArji/JOfNAiMhjBMcgR2TSPBtk/QaH5JXWkHzQe7iItPxrPfTYQ3kEZNJ/v27WPSpEnDu5BohhYIBENg0Bkgq9Xar55ptVpPeBMIDlNeVIre48Ap6Zk+Sb0AqGnPRwDom42YwxOUa5WXI3tlXFoHwbI/2hATOquyYuFw+SthdChGs08bYI6Uv7KuBP3ZNfWYMFpZF2CrLlHVblDoZABcxhKSspTfEbtXHWRC9AT2mctJ9ihZoMLCYapCg1CGFggEQ2LQnwSvvPLKMY8FghOxtW8BamdgLGGBZtXstjVsgBjwdyX031dUpKzasEjK23pA+Uut6S9HN/Q1X5NzZmv/HItJU/I4uORNrN211LV1ER0ccPKTBkFI5HTqWz/FkSKT4tdCGVqKtzZwwYW5vGVZx43dl7BZX0JNTQ3t7e0EBQUN70Ljb1WmwXa9Def/DnTD2DEmEAjOCYbVA2Sz2ejt7e3/c1VVFc888wzLly9XzTHB2UHNPmXNgSU+TVW73ZpyAIJClXKJ1+ulpETJWqS6YwD6BRCba7rpaLKh1WtIzA7z7cL7PwVXD4SMhLg832ydhiSkJOHUmdHLbtZv2qWa3aAgRWjRlSBjrthEWJw/HpeXrgItdUGt+GEkiiAA9u71YR2HUIYWCASDZFgB0IIFC3j99dcBaG9vJy8vjyeffJIFCxYcU5hKcG4iyzLeujIAUnPUK3852xtxhCsBePjoKwE4dOgQvb29uCQX4x1KsHU4A3S4/JWYFYrB5GP56yzT/vlfJElCih4JQPGuAtXsmkxx6GUraKH90FqyZ48AoHDtIYLjo3BJLka6VCiDiWZogUAwSIYVAOXn5zNjhrLj5/333ycqKoqqqipef/11/vGPf6jqoODM5WBFJQZXL25Jy7TJ6gkgNu58H7Sg7dThP0LRrjk8/m4z9GCUDWgCDGhDTciy/A3xw0jfLtxWpTTYIsGYG3yzdRozYtRoALqrilSzKUkSQcFKtq7HUE1yqhGTn56uFjuZjsmUmGpI8kQgSRJ1dXXHXLUzaMberOgzHW6GFgiGSGVlJZIksWvXrlPtiuBbZFgBUG9vLwEBSm/A8uXLueqqq9BoNEyePJmqqipVHRScuWzZuA2ANr8Y4sLVk01oq10DgF9vbH9j/uEAKBilz8iYFIgkSTRWddHVYkdn0JCQFerbhXcvVn4mzVAabs9SJk5WylWBHQdp7bKpZjekTxDRkeLFWZBP5nRlmbJhfyT7zGWYMBDnp2gq+VQGE83Q5yS33norV1xxxal2Q3AGMawAKCUlhY8//piamhqWLVvGRRddBEBjY6PQBxL0U7V3NwDGuNSTPHNodHqVzESQVfmgbm9vp6GhARmZ0X1N0f3lr+195a8xYeiNPixhleVvlL++P3w7ZwCpo1JxaY0YZBfrt+xWzW5/H1CSTM+2LYyeGYskQUupnSajEmjFOxVF7z179vi2lV4oQwuOg8vlOtUuCE4ThhUAPfTQQ9x///0kJiYyadIkpkyZAijZoLFj1St1CM5cZFnuX6mQnD1GNbvu7g4c4YpYXnjG5cCR7E+LsYUxNiXYMiZZkb3fFD/0sfxVvRnaKsDgD6Mu883WaY5Go0WOTAJg/86dqtn180tFK1uQTdBeuY7AUDOJY5Sm9BCHUnZL7gxDq9XS3NxMY2Pj8C8mmqGHjCzLeDy93/ltqIHu+++/T3Z2NmazmdDQUObMmcMvf/lLXnvtNT755BOlj02SWL16dX8pa/HixcyaNQuTycRbb72F1+vlkUceYcSIERiNRnJzc1m6dOlxr+nxePjBD35ARkYG1dXVAHzyySeMGzcOk8lEcnIyDz/8MG6326d/A8F3y7A6Qq+55hqmT59OXV0dOd9obr3gggu48sorVXNOcObScPAgBmc3HjRMmTxONbvNuz5BNoCmR0NgkhJ4Hw6A3Ho7JtmAxqJDF2GhobKT7jYHeqOW+NEhvl24oC/7k7kADMNY2HmGEZWeSWvtAdor1OwD0mC1jqO1cz3d+go87e2MOW8EFQXNmGpGUBfUTLQrjOSoBEoOlVNYWEhk5DAD18PN0GseV5qhxWqMk+L12li9Jvs7v+7sWXvQai0nfyJQV1fHjTfeyF//+leuvPJKurq6WLduHQsXLqS6uprOzs5+mZaQkBBqa2sB+NWvfsWTTz7J2LFjMZlM/P3vf+fJJ5/k3//+N2PHjuXll1/m8ssvZ+/evaSmDsxYOxwObrzxRiorK1m3bh3h4eH91/zHP/7BjBkzKCsr48477wTg97//vYp/O4Jvk2FlgACioqIYO3YsGs0RE3l5eWRkZKjimODMZvPGrQC0+EWTGhOsmt2WqhUAWLqi0Gg0OJ1OKioqAIiSlbKXIdGKpJEo6St/JeWEoTP4UP5y9sLej5XjnLNn9cWJGD9JWV/h31ZNt82pmt2Q8KkAOEd66d2+ndj0YIKj/cCl4RBKZi/F0jchVljoWxlMNEOfddTV1eF2u7nqqqtITEwkOzubH//4x/j7+2M2mzEajURFRREVFYXBYOg/79577+Wqq64iKSmJ6OhonnjiCR544AFuuOEG0tPTefzxx8nNzeWZZ54ZcL3u7m4uueQSmpqaWLVqFeHhior8ww8/zK9+9StuueUWkpOTufDCC3n00Uf597///V3+dQh8ZFgZoJ6eHv7yl7+wcuVKGhsb8Xq9Ax4vLy9XxTnBmUv5bqV3RB+b0t+orAadLmWvWFCAklUqLy/H4/HQq+8lz54MKA3QslembIdK018HloCjE4LiIWGab7bOELLGjOZLjR6T18GGbXuYO3O8KnYP9wE5U2S6t24hYM4cxsyOZc07xbh7g0EPIe169Ho9bW1t1NbWEhsbO8yL9TVDlyxTmqEv+qMqr+FsRaMxM3vWnlNy3cGSk5PDBRdcQHZ2NnPnzuWiiy7immuuITj4xF+yvrlkt7Ozk9raWqZNG/h/edq0aRQUDJR+uPHGGxkxYgRff/01ZvMRPwsKCtiwYQN/+tOf+u/zeDzY7XZ6e3uxWAaX0RKcWoYVAN1+++2sWbOGm2++mejoaFU/4ARnPrIs46gpxgAkqdj/4+ntxhbWDkBY+qXAEfXnWlMtWS0XAGBMtFJX1kFPhxODWUf8KJXKXzk3gmbYSdMzCo1WizssAV1jKYU7dqoWAAUEZKGR9Xj9XXSUricaSJsUxaaPy+m1+YEeDLVe0nLS2LtvL4WFhcMPgEAoQw8BSZIGXYo6VWi1WlasWMHGjRtZvnw5zz77LL/5zW/YsmXLCc/z8xte2Xr+/Pm8+eabbNq0ifPPP7Ibr7u7m4cffviYezFNprNrPc7ZzLACoC+//JIlS5YcFUELBABNdbUYHF140DBt6oSTnzBIWvI/Q7aA5JAIGjl7gPqzV+/C32tBMmjQx/hT+q7SF5ScE4ZW70PQ0lkL5auV45yzV/vnWESkZdLZWEpr2QHVbGo0BgL8x9DRs4NubTme9nYMQUGMmhJNwdc1OPBg9GjJiBnJ3n172bt3LxdeeOGAUvuQONwM3VWrNEOLXqAzHkmSmDZtGtOmTeOhhx4iISGBjz76CIPBgMfjOen5gYGBxMTEsGHDBmbNmtV//4YNG8jLG6jufvfdd5OVlcXll1/OkiVL+p8/btw4ioqKSEkRC3fPZIb1WyU4OJiQEB+/VQvOWjZvUPR/Wi2RpMeq9z5prlBWrVi6ItBq9dTV1dHd3Y2slUl0K7V5Q0IgsgSlO5sAFcpfuxeD7IX4KRCS7JutM4yxeUrWx9xahc2p3nTL//YBAWTNVrI87S4lmxzrCcFoNNLZ2UlNTc3wLyaUoc8qtmzZwp///Ge2b99OdXU1H374IU1NTYwaNYrExER2795NUVERzc3NJxx3/+Uvf8njjz/O4sWLKSoq4le/+hW7du3i5z//+VHP/elPf8of//hHLr30UtavXw8ok9Cvv/46Dz/8MHv37mX//v0sWrSI3/72t9/aaxeoz7ACoEcffZSHHnpowD4wgeAwpbuVOrpO7f4fh7IiweqnSC0cnv5qNDeSZVO+iRmTrNSWtGPrdGK06BiR4UMDtizDrr7N7+dI8/M3yRmbjVvSYvbY2LJzv2p2+/uARnrp2ao0ywdFWIgbHUyLW2l67ipv7B+o8Gk1Bohm6LOIwMBA1q5dy/z580lLS+O3v/0tTz75JBdffDF33HEH6enpTJgwgfDwcDZs2HBcOz/72c+47777+MUvfkF2djZLly7l008/PWoC7DD33nsvDz/8MPPnz2fjxo3MnTuXzz//nOXLlzNx4kQmT57M008/TUJCwjHPF5yeDKsE9uSTT1JWVkZkZCSJiYno9foBj+fn56vinODMxF5djBFIHK3eSK2npwdbWCsAYanzgSMBUI2xhux2RZvHmGSldGMdAMljw9HqfCh/1eZDcxHoTDD63JN30BsMuELj0TVXULBtB7MnqfPvGRiYC7IGT6iXzk82ENV3f8558Ww8oDTPe6p6yZqeRUFBAfv27WPevHlotcOc5BPN0GcNo0aNOq5eT3h4+DEXch9rklCj0fD73//+uCPriYmJR5133333cd999/X/ee7cucydO3co7gtOM4YVAAm5ccHxaKlvwGjvwIukav9Pa/6XeAMBNwQnX0BnZyd1dUqgg85NsDsAdBK6aD/K+spfPosfHs7+ZFwKpnNT4Tx0ZAa9zRU0laiXAdLp/AiwZNBl20eXphRPezvaoCDiM0P4wt+BR9ZisGsJC4jCbDbT09NDZWUlI0eOHP5FRTO0QCD4H4YVAAmhJ8HxOKz/02qOICM+XDW7LWXLIB7MnWHodCaKi5WyiCvARYZDSTsbRgRQW96JvduFyV9PbHrQ8C/odkDh+8px7rlX/jpMzsTxbNryJYamShwuD0a9D3pK3yA4bApdNfv6+4AC5sxB0kjETQmgbb2bMJ2Eq6qLzMxMduzYQWFhoW8BkGiGFggE/8Ow6wPt7e3897//5cEHH6S1VSlN5Ofnc+jQIdWcE5x5FBfsAkAbo27/T4dd6SuyWhTl8cPlr0pDJTm9aQAYk62U7FDED0eODUej9aH8VbIcbG0QEA3J5/ng+ZnNhIk5eCQNfp4ethQUq2Y3KEjJDjpHyv19QACz54ylyaM0XNfl15CVlQXA/v37fVszIJqhBQLB/zCsT4jdu3eTlpbG448/zhNPPEF7ezsAH374IQ8++KCa/gnOMHqrlLH0hCz1+n+8PT3YQvr6f0ZejMvl6hfbrDRWktObDoA+IZBytaa/Dpe/xlwHGnWyHmciBpMZV7CizJy/Zbtqdq1WJQByx8h07d7Uf7+/v4XKUGV9ATV2EhIS8Pf3x263U1ZW5ttFv9kM3aheSU8gEJyZDCsAuu+++7j11lspKSkZIPo0f/581q5dq5pzgjOL1sYmTLZWvEhMmTJRNbttO77CEyaDF0JHXkhFRQVutxutWUuAbCLEbQWtRJPNg6PXjTnQQExq0PAv2NOs9IvAOTn99b+EpGQC0Fi8VzWbBkMIFqOycLVLLsbd1tb/mDTZg1eWsXg1dFR0Mnq0sijV52mwoDhIVxro2fof32ydJfi0akQgOEWo9b4dVgC0bds27rrrrqPuj42Npb6+3menBGcmmzcqaqxtpnAyEyJUs9tS+iUApq5gdDr/fvXnrqCu/u3vhvhASnf1ZX/GhqPR+FB+2/M+eN0QnQsRo3zy/WwgN0/J1piaKlTVAwoOU4RUnWleer+h5DthTC5Nkg2AimVV/WWwoqKiE2q7DIpJP1J+FiwCW7tvts5gDk/uCikTwZnI4fft/06gD5VhNUEfFij7X4qLi/uXxQnOPYp3KX06mpiRvgUg/0NHzy4AAk3ZyLLc3/+zV9rLjb3K+gtDYiDlS6sBFcpfBX3lr9zv+WbnLGF8Xi7rJC1+nh425R/g/MlZqtgNDp7MoUNv4kiT6dm4icB58wAYHTaat/zXEdmdS29ZO2HBo7BarXR0dFBSUkJmZubwL5o4HSIyoXEf7HwTpt6jyms509BqtQQFBdHYqOzLs1gsYqWR4LRHlmV6e3tpbGwkKCho+NIYfQwrALr88st55JFHePfddwFFmry6upoHHniAq68e+nTFc889x9/+9jfq6+vJycnh2WefPUqS/DCzZ89mzZo1R90/f/58lixZAsCtt97Ka6+9NuDxuXPnHlc/QqAOPVXFmID4TBX1f7p76A1pBiA0aS719fV0dXWh1Wk5qDvY3//TrpFw2tz4WQ1Ej7QO/4KN+6FuF2h0kHWNCq/gzMdgNOEKjUfbXMGuLdvUC4CCJgHgjpXpfH8d0X336zV6WpM7YDeEaiT2rqslKyuLDRs2UFhY6FsAJEmQdyd8fi9sexEm333O9nhFRSkKTIeDIIHgTCEoKKj//esLwxZCvOaaawgPD8dmszFr1izq6+uZMmXKgO24g2Hx4sXcd999vPDCC0yaNIlnnnmGuXPnUlRURETE0WWUDz/8EKfT2f/nlpYWcnJyuPbaawc8b968ebzyypFpD6NR6H58m7Q3N2PqbUEGpkxTT/+nM38N7mil3huWfBGbNinbqvXhemLc4QS7A0EnUVrVBcDI8RFIvmSfdvUtPk2dC36hPvl+NhGeNpqu5gqaS/epZtNgCMHPkkZPbzE9fgdx1tRgiItTrpc+AnZDgFZi+6oazvtpJhs2bKC4uBiHw+Hb/+cx18FXv4e2SihZAenz1HlBZxiSJBEdHU1ERITvpUWB4DtCr9f7nPk5zLACIKvVyooVK9iwYQMFBQV0d3czbtw45syZM2RbTz31FHfccQe33XYbAC+88AJLlizh5Zdf5le/+tVRz//fHWSLFi3CYrEcFQAZjUZVIkTB4Ni0QenhaDVFMDox+iTPHjzNxZ9DMhi6AzEYQvrLXw2WBnJalPF3Q1wA5XtaAEj1pfzl9cBuJat5Lmv/HItxkyayZuPnWJorsDlcmI2+1d4PExIylZ7eYqUMtmlTfwA0LmEilYZCEp0x+NncdFRBaGgoLS0tFBUVMWbMmOFf1OAH4xbCxmdh67/P2QDoMFqtVrUPFIHgTGLITdBer5eXX36ZSy+9lLvuuovnn3+e9evXU1tbO+TObKfTyY4dOwYEThqNhjlz5rBp06YTnHmEl156iRtuuAE/P78B969evZqIiAjS09O5++67aWlpOa4Nh8NBZ2fngJtgaBTl7wRAiklRtf+nvUexazVk09XV1a8ztd27neweJQDq9TPgcnjwDzYSmeiDYnP5KuiuB3OIkgES9JM7Phu3RofZa2f9Nh+nsb5BcPBkQGmE7vnG//mMkAyKAqoACNVKFHxVo940GMDE2wEJyr6G5hLf7QkEgjOOIQVAsixz+eWXc/vtt3Po0CGys7MZPXo0VVVV3HrrrVx55dD2JTU3N+PxeIiMHPitPTIyclDTZFu3bqWwsJDbb799wP3z5s3j9ddfZ+XKlTz++OOsWbOGiy++GI/Hc0w7jz32GFartf8W1/ctVDB4eqsOABCflaOaTU93N72hymRXWOI8SkqUD6qg8CDa5XZybUr/T02bHYAUn8tffc3P2deAzjB8O2chOr0Bd1giAAVb1dMDCgqaBEi4o6CrcAOy1wuAVqPF0ZdIDNVLtNX3EmJU9IhKS0t9n14KToT0i5VjMRIvEJyTDCkAevXVV1m7di0rV65k586dvPPOOyxatIiCggK++uorvv76a15//fVvy9ejeOmll8jOzj6qYfqGG27g8ssvJzs7myuuuILPP/+cbdu2sXr16mPaefDBB+no6Oi/1dTUfAfenz001dZitLXjQcOUqerp/3TuWI075nD/z7z+8XdPmId4ZxRWtz/oNRwo6QB8nP6ydygrEgBybvDJ77OVyHSl+bm1VD0RQb0+kAB/pam5N6odx4ED/Y+FpSsBT6BWQgdUbukmMjISr9fLgW88b9jk3an83PU22EXWVyA41xhSAPTOO+/w61//mvPOO3o1wPnnn8+vfvUr3nrrrUHbCwsLQ6vV0tDQMOD+hoaGk/bv9PT0sGjRIn74wx+e9DrJycmEhYVRWlp6zMeNRiOBgYEDboLBs/lw/48lkqwE9WQQmkuUqT5jlxVJCuhXfy7TlzGmr/zlCTbhdHoJDDMRkRAw/Ivt+wTcdghLh5hxPvt+NjJhqvJFw7+tki6bQzW7wSFTAHCmywPKYDkjx1Onb0aDRIhew6HidhJjUwCVymDJs5V/b2f3keZ3gUBwzjCkAGj37t3Mm3f8hsGLL76YgoKCQdszGAyMHz+elStX9t/n9XpZuXIlU6ZMOeG57733Hg6Hg5tuuumk1zl48CAtLS1ER6vXnCs4QsmuXQDoRqSp3P+TD4DVmE1FRQUul4uAwAC29mzt3//V7FJKJinjI3zTMTlc/sq9URmVFhzFmJzRuDQGjF4n6zbvVs1ucLDyf92R5qVn0+b++1ODUyn2V/qA4qKVJl1nrRLkVlRU0N3d7duFJQkm9WWBtv5baYIXCATnDEMKgFpbW4/q1/kmkZGRtH1D0n4w3Hfffbz44ou89tpr7N+/n7vvvpuenp7+qbCFCxcec7/YSy+9xBVXXEFo6MBR5e7ubn75y1+yefNmKisrWblyJQsWLCAlJYW5c0Vjq9rIsoyjWilNJY/JVc2up72d3jClcT1s5CX95a/gEcE4PU7G9PX/lNb2AJAy3ofyV2sFVG8EJBhzvU9+n81otFo8kcr6isLtKvYBWScAGjxh0Fm2FW+fzIVG0mCPVYJRi0ZRhj5U0EtkRBSyLLNvnwoj+Tk3gikIWsuh6Evf7QkEgjOGIQVAHo8Hne74k/NarXbIG5uvv/56nnjiCR566CFyc3PZtWsXS5cu7Q+0qqurqaurG3BOUVER69evP2b5S6vVsnv3bi6//HLS0tL44Q9/yPjx41m3bp3QAvoWqK2qwuDsxi1pmTVdPf2f9q1fKfo/MoQmzOkff28NaCXBEU2g2w9ZK9Hi8GINNxMW5z/8ixUsUn4mz4bAGN+dP4uJzlBELjvK1OsD0un8CQxUmucdCTZsO3f1PxaSpvx7BHYYSBwdgiyDn0cpj6tSBjP4wcS+3yMbn/XdnkAgOGMYkg6QLMvceuutxw0kHI7h9QXcc8893HPPsSXpj9W4nJ6eftyRe7PZzLJly4blh2DobFrX1//jF0NyVJBqdptLv4AMMPWE0Nxsp6urC71eT74rnzF95a9uvRYZSJ0YOfzylyyL1RdDIG/aJJatep+A9mo6euxY/UwnP2kQhARPprNzJ450mZ5NG/GbpPQb5aSOo1W7nxCPlTE5wVTubaWz2AShypejjo4OrFYflL9BaYbe+CzUbIaabRCnXiO/QCA4fRlSBuiWW24hIiJiwMj4N28REREsXLjw2/JVcBpSsWcXAOaEdFV3CXXYFLtWv7H95a+EpAQK2wr7A6CDHUrAnTLeh8Wr1ZugvQoMAZBxqU8+nwuMHp2OQ2tCL7tZuzFfNbvf7APq3rSx//6koGSKA5Qdb92ttcSkBiG5DARZlGb7vXtV2FAfEAXZ1ynHm0QWSCA4VxhSBuibqyUEAtnrxXOoBA2QPk69ySlXXR22aGW0PSzlEjYsVcpfUoSEdBBy+vp/mpwywdF+hMb6UP46PP0zegEYLD75fS4gaTTIUSPh0F72bt/BZRdOVcWu1ToOSdLjDXbRU78HT2cn2sBAJEmiN1aGdnCUtzPh4kl8WrILb2MQ+DdRWFjI1Kkq+DD1Htj1Juz/TOkJC0ny3aZAIDitGbIStEBwmIriUvRuO05Jz+yp6gVA7ZuX444CZND7j+3vASvXl5NsH4G/24xHgnaPTOoEH7I/Lhvs/Vg5zhGrLwbLiNFKH1BXhQpaPH1otWas1rEAOFI89G7d2v9YSIbSB2RtMhGbYiUiIQB9byggUVtbS2trq+8ORIyClDkge2Hz877bEwgEpz0iABIMm03rlZHl9sARRIf4kIX5H5orlgJgsodRXq5oRI0YMYKtrVsZ25MBQJPTi4yP5a8DS8DZBUHxEK9OJuNcYNoMpVwV2HmQhrYu1ewGBx1eiyHTs/EbekBp42nRtaOXdXRVNDP+4kQ0XgNGVxCgUjM0wNSfKj93vgG9KgRVAoHgtEYEQIJhU7NX0YIJSBqlmk1ZlulwKR9oQQET+6e/YpJiqOysJLdXCYAaXTKhI/wJjvI7rq2Tcrj8lXMjaMR/hcEyMjUZh94Pnexh1Tr1xuEH9AFt3NB//4jAOEoCDwJQXVhE0pgwQmL80PcqfUCqBUBJsyAyG1y9sEOU+wWCsx3xW18wLDxuN1J9GQCjJ4xXza6zrAxHnLLnKShxbr/6c3tgO3qvjmxbKgBNbq9v5a/OOmX5KYjVF0NEkiR0I5R/h+L8HarZtVpz0EhGvFbodVTg6lt8K0kSrjhFCNFV3oWkkRg/LwGjPRRkicbGRhobG313QJKOZIE2P6+USAUCwVmLCIAEw+LAnn3ovC7sGiOzp6i3ALV9y5H+n/auKDweD0FBQey27SbTlozeq8Pmlen2+ih+WPCO0u8RPwVCklXz/1xhZK7S8+WoOnBcSYqhotEYCQpSRtAdGTLd649kgSIzEwEIafXD6/SQMj6CoLBADI5gQMUsUNZVSkm0pwny31DHpkAgOC0RAZBgWGzdqOj/dAUnEOyvjhYMQHP1CgDMjkhKS2sBSEtLY3PdZnIP9/+4ZSISArCGm4d3EVmGnW8qx2NPvkpFcDQzZis9U0G9DRTXqJB96SMkZBoAjlFeetav679/bNpEGnWt6GQtDUVVaLQaJlyciNHeVwbbU6hOIKbVw7SfK8cb/g5up+82BQLBaYkIgATDon6/0v8TnDJaNZuy200XiuZPUNCk/v4f/1h/WuwtjO9VtoY3uby+bX6v2QKtZaD3g8wrfHX7nCQiKgqbJQwNMmtXbzz5CYMkJGQ6AM4Ume6tm5BdLgCCzcFUhyiB1sFCZalx+qRIwgNHgKyhta31KMX4YZN7E/hHQedB2L1YHZsCgeC0QwRAgiHjsPWia1aWVOZMzlPNrr2wEPtIRdxQEzSFnp4ejEYjlZpK/D1mUmxxADS7Zd+mv3b2lTZGXwlG9abXzjX8k5WAtLpPDFMVm/4Z6PWhyCawR3Rh++Zy5QQl0yhV2QHQaDVMuiQVgyMEgIJde9RxQm9SdIEA1j8llqQKBGcpIgASDJldW/LRyF46dYHMGJ+hmt3WHUvxhAFeiUN1ynRXSkoKWxq3MKYnHQmJTo9MUJKVgJBhlt0c3Ue0f8Z+XxW/z1Wy8pR+HW1dCW6PVxWbkqQ5UgbL8NK9bn3/Y3FZigJ4eHsgHruSGUqdGEmoaQQABbt2q9aPxPjbwBysLEnd+5E6NgUCwWmFCIAEQyZ/s6L/Y4tIxs+oV81uS50yleXvTqCoSMkwjUwdyfb67f36P81uLym+TH/t+wSc3Urjc/wUn30+l5k2PQ8vGgJcnWwpKFbN7pEASKZn/ZEAKDtlLPX6FrRoqdijLGPVaCRmXjwRyavF7uyhvKRSHSeM/jD5J8rxuifBq06AJxAITh9EACQYMm0lyv6l2Mxc1Wx6bTa6zRUAWIKm0NjYiCRJOIOd9Lp7mdCTBSgN0D6Vv3a9pfzM/b4y9iwYNmY/PxwhSlly24bNqtk93AfkSpDpLS/E3dICgEFroD68DYCG/VX9z8+YGEOAVukJW7NcPT/IuwOMgdC4D4q/VM+uQCA4LRABkGBItDU2YuxuwovE1JnqZVB6tm3Dnqr0WrS7FY2Z+Ph4trVuI9wVTJQrBK8sY0gIxM9qHN5FWsqgagNIGrH6QiXCMpS1GE1FKvXfACZjFBZLCmhQtsNvPNJkrU8KVH7WHOnLkTQSEycrWlQ1jWV0t6uk32MOgom3K8dr/qpMDwoEgrMGEQAJhsSGtcqKghZzBONSY1Sz27LzU2R/0Lh1lJa4ARg1ahQbD20kt0dZftrukUnKixr+RQ4rP488H6yxvrosAPKmKusr/Foq6bI5VLP7zXH47nVHxuGTc5TG68iuYGzdPf33T52Tiw4jssbNsvfVm0pjyk+UacG6XVD0hXp2BQLBKUcEQIIhUbRDWX2gi8tAq1GvhNTapoje+ZFBdbWy9iAqKYq9LXvJ61SEFps8MHJs+PAu4PUcCYCE9o9q5IzLwqk1YvQ6WLMhXzW7oX1lMMcoLz0bNiL39eCkxGVQZ2xGi4bigt39z9dqtYxKVyQZisv20dGkUhbILwwm/0g5/vpPohdIIDiLEAGQYNDIXi+uGmUDeNo49dZfuOrq6A1vUo5NuciyTFRUFHt79oIMY3uVXWNStB/mAMPwLlK+Crpqlcme9PlquX7Oo9XqkKNSACjcuk01u0FBeUiSDk8YOKRm7PuVpmdJkmiJVDI/rftrB5wz/fxJADgMraz/cJ9qvjD1p2C0QuNe2CcmwgSCswURAAkGTXlRMXqXDaek57yZE1Wz27l+FY6RSn/FwTpltcGoUaPYWLuRZEcsfrIBtywTNSV6+Bc5rPycfR3ohtlDJDgmcdm5AHSXqxd06HT+WK196zZGeen5xji8JS0UgMBDugHnREZGEh4aAZLM/qJ9NFR2quOMOfiILtCqx8DjVseuQCA4pYgASDBoNq5VeivarPHEhQaoZrf5wBdgAJ3Lj+Ji5UMrPSOdDYc2ML1V+Vbf7JEZOdzpr95WOLBEORbaP6ozY7bSrxPYVcuhxjbV7IYEH9ED+uY4fMbYsXjwEmkLobmhYcA54ycqQZPd3MCmD0vV0wWa9CMwh0BLCex5Vx2bAoHglCICIMGgOVioqPIGpWapZlN2u+lw7gJAI2Xi8XgJCQmhTddGi72FKV1jAfBE+GG0DFNzaM/74HFCVDZEq7e4VaCQmBSPzRSEFi8rV21Sze7hcXhHukxPQT6eTiU4jgiN4qC/shajaMeuAedkZ2ej0Whw67upKj9EVWGLOs6YAmH6vcrx6r+IHWECwVmACIAEg8Jpt6FtqgRgrIrrL2x79mBPUlYbtNmSAMjIyGBD7QYsbjNxHqUkFjbJl+mvvvJXrmh+/rYwJyp9WqU7t6tmMzAwG50uENkCrhFuejYc2Q7fG6dkduwlAzNOfn5+pKYqMgp2UwObPirDq5JKNRPvAP9IaK+C/NfUsSkQCE4ZIgASDIodW3aglT106fyZOTFTNbsdG1fiilc+zIpLtEBfAHRoA7NaZqCRJHq8MglTh9n/U78H6gpAa4Ax16nltuB/yJqkjMNrDh7A5VZnd5YkafuzQPbRXrpXr+5/LGJ0AgBRTVbc7oE9OTk5SpbPaWmipbabwrWHVPEHgwVm/lI5Xv0Y2DvUsSsQCE4JIgASDIr8jVsAcESkqLv+omY5aEDrDKO7W4e/vz8hUSHsatzF9Hal0doZakan1w7vAvmvKz/TLwZLiEpeC/6XmbOn4JGUtRibdh5QzW5o6CwAHKNlutesRfYowdWoMbnYNQ6sbn+K/keEMS0tDZPJhEfjwGVoY+tnFdi6VSpZjb8VQlOhtwXWP62OTYFAcEoQAZBgUHSUKJorMVljVbPpaW+ny1wJgN2jlC3S09PZWr8Vr1sm3a1kfYLGDbP52dkLBYuV43G3+Oqu4ASYLRacYUoJc+s69YQIQ0OUAMiVIOP0tGHr0/4xGIzUhirlr4O7Swaco9PpyM5WFKrl0FYcvW62fFKujkNaPVz0qHK86V/QXq2OXYFA8J0jAiDBSWmqrcXY04IXiemzpqpmt3vTRhyjlG/01YeUXp+MjAw21m5kQstk/DQavDJEzximavO+T8DRAUHxkHyeWm4LjsPh4LitqEA1m0ZjOAH+isChI3NgGUyX7A+AvurosfTDZbAeTSNeyc3e9bU0VXep41TaPEicAR4HrHxEHZsCgeA7RwRAgpOyfpWyiqDJL4acZB+akf+H1vwv8YQBXg319YEYjUaSkpJYf2g9s1r7RqADDehMuhMbOh47XlV+jlsIGvFW/7aZef4MAKzt1dQ2q9cf018Gy5QHBEDJY5VpxISOSFq6mgecExsbS2hoKB6Pm6AMB8iw7t1idcbiJQku+qNyvOc9OLTDd5sCgeA7R3wqCE5Kab6i8GtKHI1GpfUXsizT2qGUSjyOBLxePampqRzsOUhjRzOZzhEA+GeFDu8CjQegZjNIWjH99R0xMjUZm9GqjMOv3HDyEwbJ4QDInunFXlqE65DS1ByZMIJ2QzdG2cCenQNVqCVJYuxYJSPVa6xHZ9BQV9pB8daBukHDJiYXxtygHC/7rViUKhCcgYgASHBCXE4Hcm0pANlTJqtm11FSgi1OyRI0tSslrlGjRrH+0HpSW3IJ0ylNz6HDXX56uPk5bR4E+qAgLRg0kiRhSlLKVeqOw+ei01mR/cCVKNO1Zk3/9dqjlQWsHQeODmxycnKQJIm6+kOkzbICsOH9EuzdLnUcu+B3oDND9UZFa0ogEJxRiABIcEJ2bd2B1uumS+vHnKnqiQh2rVuJI1X51nzoUDBarZaUlBTWHFzDjJbp6CQJj0GLPspv6MZddijoW3w6/lbVfBacnOzJinK35uB+3Crp72g0uiPj8P/TBxSUoQTIIXVmvPLA6wUEBJCenq6cZ6knJMYPW5eLDR+WquIX1hEw8xfK8fLfgF2l1RsCgeA7QQRAghOyrW+ipzcilWA/9XZoNRUvAQPILn96e62kpKTgklwU1hwgy65ovBhTg5CkYZTcDnwOtjYIHAEpF6jms+DkzJw5BY+kxd/VxcYd6u0GCwudDYAjS6Z38xa8vb0ApIxTpr2SbDHsq95z1HmHy2B79uxhxg3K0tYDG+s4WKTSyo6pP4OQZOhuUBSiBQLBGYMIgAQnpL1YGTuOyxmnmk1PezudhmLFfmc8IJGZmcmmuk0kNmUT1af5E5g7zPH3/ubnm0EzTP0gwbAw+1lwhCUCsG2den1AIaEzAXDFy7hNDno2bwbAZPWjIbAdgPL8vUedl5KSQkBAAL29vXS46hk9Uym3rn7rAG6XCoKNOiNc/DfleMsL0HC0DwKB4PREBECC41JfU4OxtxUPGmb3LbxUg+71G/rH3+sawtBqtaSnp7OmZg3jmyfjr5WQJTClBg3deHMpVK4DSQNjRfPzqSAmWwmW2/qCZzUwGsIICFCmvhyjvHSvWt3/mCfZAIBUZjvqPK1WS25uLgD5+flMuSIZi9VAR6ON7V9UquNc6hwYdRnIHlhyv2iIFgjOEEQAJDgua75eC0CLfyxZicPMxhyD1q1f4I4CWZZob4tm5MiRGIwGdpYUkuWKB0CfEIhmOOPvh3c0pVyo9GgIvnNm9Y3DB7bXUNvUrprd0L4ymD1LGYeXvUrPT+JYZTVLSmssdV11R513uAxWVlZGr6ObmdenAbBzWbV62kBzHwO9RWmIFnvCBIIzAhEACY5L5U5F38QvJXt4vTjHQPZ4aGtbD4CtJwqPx0BmZiaFzYVEHkwnUqdcxy8rbOjG3U7Ydbj5WSg/nypGpiT1b4df8dV61eyGhSpilo5ML67WRuyFhQCEp4ygV2cnwOvHrp2bjzovJCSEpCRFpXrXrl0kjw1n5NhwvF6ZFa/sU6cUFhQH5/1GOV7+O+is9d2mQCD4VhEBkOCYOO02pPoyAMZOnaKaXVtBAbbkHgAamyLRaDT95a/RTXmE9gVA5lHD2NtVtAR6m8E/ClLnquazYGhIkoRlpNKcXLpjq2p2AwPHYDCEIZvBmSLTtWKFcj2tREessuurY1/9Mc8dN04py+3cuRNZlpn1vXTMgQba6nrUW5Mx+W6InQCOTvj8PlEKEwhOc0QAJDgmWzZs7tv+HsD5k7NUs9u5eiWOdOWDobUthpEjR2I2m9ldUEqyHIJGktCGmdGFmodu/HDz89ibQDtM9WiBKoyfoYytG2oPYHOoo7sjSRrCQs8HwJ7jpWvFV/3KzuFZfdvh6630unqPOjcjIwOz2UxnZydlZWWYAwycd1MGALtW1nCoWIWpMI0WFvwTNHoo/hIKP/DdpkAg+NYQAZDgmOzsm+Cxx4xSd/t72VJkE7hdZnq6Q8jMzKS+px5zeRRReuXtaM4cRvanuQTKVwOSsvpCcEqZNn0iLo0Bi6eXr9aqlwUKC58DgH2MjKOyAmeZkqUckass002xx7Gl5OhlrHq9njFjxgBKMzRA0pgwRk2LBhlWvrYfp+3onWJDJmIUzPylcvzl/0F3k+82BQLBt4IIgARH4fV66C1VJniSx+epZtdVW0tXkLI9u7klBo1GS0ZGBmvK15LckkvE4fJXxjACoG0vKT/T5kFwglouC4aJTm9AHjEKgIIN6m2HDwmehkZjwhMi446V6frqK+V6AUZagrsBqCkoPua5h8tgRUVFdHUpzc/Tr0klINREV4udtYtU2hU2/f9BZBb0tsCn94hSmEBwmiICIMFRlBbuQ+/qxaExcOH56vX/dK1ZjT1bmdxpbRlBcnKyUv7aWkmEZMKokZBMWgwJ1qEZdvYcaX7Ou101fwW+kT5JWZ3iLN+D16tOEKDVmo6oQo+R6VrxVf9jhjTlfWOpAo/36MbmyMhI4uLi8Hq9/Vkgg1nHnNsykTQSRVvqObDp6CmyIaMzwFX/Aa0RipfC9pd8tykQCFTntAiAnnvuORITEzGZTEyaNImtW4+fMn/11VeRJGnAzWQyDXiOLMs89NBDREdHYzabmTNnDiUlJd/2yzhrWLdyNQCtwSNJihhiMHICWnd8gScCvF4NbW3RjB49WunXKA4iSq9kf0zpIUjaIU6c7XkPHB2KIm/y+ar5K/CNORfOxIsGq6OVLQVFqtkND+srg+V4se/d278cNWGcknHK6hzJ7oaCY547ceJEALZv347HowRJMSlBTLpcmRJb+04xLYe6fXcycjTM+YNyvOy30KTe6xcIBOpwygOgxYsXc9999/H73/+e/Px8cnJymDt3Lo2Njcc9JzAwkLq6uv5bVVXVgMf/+te/8o9//IMXXniBLVu24Ofnx9y5c7Hb7d/2yzkraCxUvh1HZY9Xzaanu4c2tzJW394eBRiV6a/9G4nuSD7S/zPU6S9Zhm3/VY4n/BA0p/wtLegj0GrF3qcKvfHrNarZDQs7D5Bwxct4gmS6Vq4EwBwXhE3vxN9rYU/BsZexZmZmYrFY6Orqorj4SKls3EUJxGeG4HZ5WfZiIU67Cv1Ak34EyeeB2wYf/BDcDt9tCgQC1TjlnxZPPfUUd9xxB7fddhuZmZm88MILWCwWXn755eOeI0kSUVFR/bfIyMj+x2RZ5plnnuG3v/0tCxYsYMyYMbz++uvU1tby8ccffwev6MymvqYGQ3cTHjScd8FM1ez2rF+HPVOZBjpc/rJYLOzcUI5FoyFQK4EEprTgoRmu2Qr1e0BngtzvqeavQB1G5CoZl7YDO1WzaTCEYbUq4ob2bG9/GUzSSDgSlV9pruJjLybV6XT9vUDbtm3rv1/SSFxwayZ+VgNt9b2sfqvI934gjQaueB7MIcp7dPlvfbMnEAhU5ZQGQE6nkx07djBnzpz++zQaDXPmzGHTpk3HPa+7u5uEhATi4uJYsGABe/ce2b9TUVFBfX39AJtWq5VJkyYd16bD4aCzs3PA7Vxl9fJVADT7j2BsSrRqdtvXfIEzuW/8vTWWMWPG4PQ4kYqDiO7L/hgSA9FYhjhxdjj7k30NWIbRPC34VplzkSJeaO08RPnBBtXshh0ug43x0rtjB+7WVgCic0YCMKo5noqOimOeO368ktksLy+nubm5/35LoIGLbh+NRiNRsq2BncurfXc0MFoJggC2/gd2v+e7TYFAoAqnNABqbm7G4/EMyOCA0qxYX39sQbP09HRefvllPvnkE9588028Xi9Tp07l4MGDAP3nDcXmY489htVq7b/FxcX5+tLOWCp3Kt+KA9Jz0GhUUn92OmluWQMa6O4OxuMJUspf27cSaAsjqi/mMY8eovpzdxPs+1g5niian09H4hJG0OMfgQaZr5atUs1ueNgFADgywKv30P311wAEj47BI3mJd0azqXDtMc8NDg4mLU1Zh7F9+8BSWUxqMNOvU0bqN31cRuWe5qPOHzLp82DG/crxZz+Dhn2+2xQIBD5zyktgQ2XKlCksXLiQ3NxcZs2axYcffkh4eDj//ve/h23zwQcfpKOjo/9WU1OjosdnDj0dHWibKgGYPFvF8tfWbdhSFXG61tZY0tPTMRqN7NlQjUGCMF1f/09W6NAM73wdPE5FfTdmrGr+CtQlOEP5t6nZdey+nOFgsYzEbE4ErYwjU6Zz2XIANGYd3dFK/077nuOvozjcDL1r1y6cTueAx7JmxZI5IwZkWPHSXtrqe3x3+LxfQ/JscPXCuzeD/dzNMgsEpwunNAAKC1M2gTc0DEyNNzQ0EBUVNSgber2esWPHUlpaCtB/3lBsGo1GAgMDB9zORdasXIsGmVZjGLPGpatmt3PlUhyZR8bfs7OzcdicaMqCidJLSEjoY/3RBZlOYukbeD2w/RXlWGR/TmtmzJkNgF9TKU1t6iwflSSJiPCLALCN9dKzaRPuNkXNOSxHWaib3BDFwa6Dxzx/5MiRBAUFYbfbKezbKfZN2zOvTyM6xYrT7uHzfxbQ2+k8pp1Bo9HC1S9BYCy0lCpN0R4VGq0FAsGwOaUBkMFgYPz48azsm+IA8Hq9rFy5kilTBqc/4/F42LNnD9HRSr9KUlISUVFRA2x2dnayZcuWQds8V9m7WRGs0yRmYdCp89aQvV5aypYp+5ucJlyuEaSkpLB6zU70HiMRRuVDYMjZn+Jl0FGjNJiOvlIVXwXfDjm5WdgNAehlN18sVW8aLCLiYgAcY0CWXP27wUJzlBL2KFsya4q+Pua5Go2mPwu0bdu2oxqetToN8+7MJjDMRGeznSXPFfg+GeYXBte/oTTslyyH5b/xzZ5AIPCJU14Cu++++3jxxRd57bXX2L9/P3fffTc9PT3cdtttACxcuJAHH3yw//mPPPIIy5cvp7y8nPz8fG666Saqqqq4/XYlCyBJEvfeey9//OMf+fTTT9mzZw8LFy4kJiaGK6644lS8xDMCl92Ot2Y/AGOmTVPNrn33bnrilW/mra2xZGaORqfTUbyxER0QpTUAw+j/2dpX8hx3M+iHkDkSfOdIkoQlXZm8Kt2yQTW7AQHZmIwxyAYv9kyZzi+/BEAXZKI7xIkWDY27K497fm5uLjqdjrq6umOWvS2BBi77aS4mfz2NVV0s+08hHo/XN6djx8OVfe/dLS/Alv/4Zk8gEAybUx4AXX/99TzxxBM89NBD5ObmsmvXLpYuXdrfxFxdXU1d3RF11ra2Nu644w5GjRrF/Pnz6ezsZOPGjWRmZvY/5//+7//46U9/yp133snEiRPp7u5m6dKlRwkmCo6wae0GdF43nbpA5s5UT/+nc+VX2HIGlr9aDnWjafQjQi+jRYMu3Iw+wjJ4ow37lL1fkkaUv84Qps1RBCot9Qdo6VChp4a+MlhfFsg+1kvvlq24+6a6rNkxAMTWhlDfc+zhBz8/v/79YMebEA2KtHDJT8agM2io3tfKqtcPIPuqaj36Crjg98rx0gegaKlv9gQCwbA45QEQwD333ENVVRUOh4MtW7YwadKk/sdWr17Nq6++2v/np59+uv+59fX1LFmyhLFjBzbASpLEI488Qn19PXa7na+++qp/6kNwbPLXrAbAPmI0gWaDKjZlWaZl9xK8oeDxaHE600hISGDT10qmyeqnfBAOOfuz+V/Kz1GXQVC8Kr4Kvl0m5o3FbvDHILv4cpmaZbB5ANhzQdZ46Fy2DDhSBhvXM4pV5ccugwFMnqys6zhw4ABtbcfeCB+VZGXuHVn96zJWvaVCEDT9/8HYm0D2wnu3QOV63+wJBIIhc1oEQIJTi9vpxFGhaCllTFGv/OUsL6crUmlCbW2NZfToXGQPVO1oRwMkagKAIfb/9DTD7neV48k/Vs1XwbeLpNFgSlW+qBRvVu/DPjAwF6MxCtngxZFxpAymj/bD5ufGJBuoKjhw3PMjIiJITk5GluUTruBJzA7jwtsykSTYv6GO1e8U+RYESRJc+gykXQxuO7x9AxzaMXx7AoFgyIgASMD2DZvReZx0af259ILJqtntWr4cW67yIdHSHE92djblBU1g1xJg7sXg1aG1GtHH+g/B2VfA44CYcRA36eTPF5w2TLlAEUU01R2go9umik1J0hAePhdQpsFsO/JxNTQofUeZSmAdXuNHs+34ej6HhyPy8/NxOI6/riJ1YiQX3JoJEuxbV6tsj/clCNLq4dpXIWkmOLvgzauFRpBA8B0iAiABm79WBOp6YzMJD1CvT6p588d4omS8Xg2ynEVMTAw71pQBEOin6KCYR4ciSYMUXHQ7YduLyvHkHyvfogVnDJMnj8eu98PodfKFqmWwvmmwcRKyxkvXUqWnJrRvHH5y9xi+qlhx3PNHjhxJWFgYDoeDnTtPvLIjfVIUFywcBRIUrj3EV6/u860xWm+CG95RtKxsbfDapVC3e/j2BALBoBEB0DmOx+3CUab8wh2lYvnLUVZGZ7CyiqCtLZrs7Dy6Wuy0lNgBL5my0uRuGj2E8tfeD6G7AQKiIXOBar4Kvhs0Wi2GlFwADmxSrwwWZB2HwRCO1+jBkS7T+YVSBjMmWXEaPVg9/hTvPH5QodFo+vsOt2zZgtd74oAmY0o0c27NRKORKN7awBf/2oPL4Rn+CzD6w03vK2KevS1KEFSz7eTnCQQCnxAB0DnO9o1b0Xkc9GgtXDpnqmp2O5cupUeZfKalOZ6cnBz2rVeUeTXWOkweA5oAA8Yk6+AMyjJsek45nng76NRp1BZ8t+Sdp5TBjIf2091rV8WmJGmPlMEmeLEVFOA8eBBJK2EareyHi64JpLb7+MrQOTk5mM1m2traKCoqOuk10ydFMf/HY9DpNVTvbeGTZ3b6JpZoDoaFn0D8FLB3wBtXQMWxV3kIBAJ1EAHQOc7mlcqETE90JuGBZtXstm78FO8ID7Is4e8/jQD/QPasV7RWosyKoJwlOwxpsPvGqjdB/W5FRG7CD1TzU/DdMn36BOw6C0avgy+Wr1PNblTkZQDYx0nIepnOzz4DIGSsMg02tSuHpWVfHvf8w6KsABs2bBjUJviErFAuv3csRouOhopO3vvLNpoP+qB0bbLCTR8oKzOc3fDGVVCwePj2BALBCREB0DmMx+3CVloAQPpkFctfJSV0WJXyV0dHBGPGTKV8ZxPObi82fTtjXAkAmHPCB2/08Oh7zg1i6/sZjFarQz8yB4DC9eplOKzWcZhMscgGD/YsmY6PP0GWZYzJQbhMXqyeAEoK9pzQxqRJk9BqtRw8eJCqqqpBXTd6pJWr/2881ggz3a0OPvhbPuU7m4b/Qgx+cONipcTrdcFHd8Kqx5QMqEAgUBURAJ3DbFu/Gb3brpS/5qoXAHUuXUZ3nvLWam9LZtSoUexZo4zDe0IrMHr0aK1GDHEBgzPYWg4HlijHk+5WzU/BqWHqhXMAMB3aq6IooobIviyQbRI4q6qwFxQgaSUsfTpTybWRlLaVHtdGQEBAv6bY+vWD71EKjvLjmgcmMCIjGLfDw5f/3sPGD0qH3xytN8E1r8K0e5U/r/kLfHA7ONX5uxIIBAoiADqH2fzVVwB0x2QRETgEJeYTIMsyrRs+Q453IcsQFnYhXU1O6ko78EgeknVKmc2cM4Ty18Z/KoJxKXMgIkMVPwWnjqlTJ2AzBGD0OvlsycqTnzBIoiIvB8CR5cVrlmn/5BMArLmKKvTUrhy+LPviJL5NRZIkSktLByjQnwyTn57LfppDzvlKyW3nimo+eiKfzuZhjvtrNHDhw3DZ30HSQuH78OIF0FwyPHsCgeAoRAB0juKy23Eenv6aPks1u46SEtoiKgHo6IgkJ2cWe9YcAuBgcCETbKMAsIwZZPmruwl2vaUcH/5GLDij0Wi1+I9SFpGWbVJvHN7fPx0/vzRkjRd7rpfOL77E63RiTA7CbZKxegKo2L3/hP09ISEhjB49GlB6gYaCRqth+nWpzLsrq78vaPGftnFgc92geoqOyfhb4ZZPwT8SmvbDf2ZD4YfDsyUQCAYgAqBzlA1fr0HrddGhC2TBHPXEDzu//JLuyVoAeroziY6IpWiL8k3aaG1A79GhDTENXvxw678VpdzY8ZA4XTU/BaeWCy5VVlgENJZQU3d8kcKhcjgLZJumw9vRQffq1UhaCb8sJeDOaIxjT/OJe4GmT1feZ3v37qWlpWXIPowcG8F1v55IZFIgTpubla/uZ8lzu+luG+bUW+J0uGsdJExXmqPfvw0+vAts7cOzJxAIABEAnbNs/1opPbgScwjxN6piU5ZlWjZ8iBTlQJYlRoy4nOKtDbgdXlrN9eR6lOZnS0744MQPHd2wtU/4cNq9QvjwLCIrexQ9ljC0ePn802Wq2Y2MvBQAR5IDj1Wm45NPAQjIiQJgWlcun5d8fkIbUVFRpKamIssyGzduHJYfgWFmrrp/HJOvSEajk6gqbOGdh7ewe1UN3uH0BgVEKmPyM+5XlgDvXgTPT4Wy4+85EwgEJ0YEQOcgvZ2dyAeV/Ujjz7tANbu2nTtpSVK+zbe1RZObO72//FUevoUJ3ZkAmAdb/sp/DeztEJoCGZeo5qfg1CNJEuG5iu5U7Y6hlZpOhNkch9U6DiSwjffSvWYN7tZWjMlBeMxg9QRQu6cUh+f4Ky/gSBZo165ddHZ2DssXjVbD+HmJXP/rPCISA3HaPaxbXMLiP22j5kDr0A1qdXDB7+AHyyAkGToPwRtXwgd3QFfDsHwUCM5lRAB0DvLVlyvQyF5ajGFcMnOsanbbP/2EHqW1A0nKw9Yk0VbXg0vjINoko/Vq0Ef5YYj2O7kxt/OI8OHUn4JGq5qfgtODeQuUFRbWjmr2lVSrZjcqUlEJt8/WI7tddH72GZJWImBsNABTWrJZVbPqhDbi4+OJj4/H4/EMaSLsWITE+HH1/41n1vfSMfnpaa3t4dNndvHF87tpOdQ9dINxefCj9ZB3FyDBnnfhnxNg8/Pgcfvkq0BwLiECoHOQfeuVxlN96njMBnUCC6/TSfPOL9CF2PF6NWSk38DuVcroe3H4di7sUVYNWMZFDM5g4QfKN1z/SBhzgyo+Ck4vkhLj6A6ORwKWfnri6ayhEBl5GRqNAWeYHVecTNt77yHLMv7jlPUrk7vG8MWBE5fBJEnivD7V6h07dtDR0eGTTxqNRNbMWL7/yGSyzxuBpJGoKGhm0aNbWfZiIa21QxxxN/jB/L/CHV8ri4EdnbD0V/DCNNj/udANEggGgQiAzjFaGhrQNZYDMGPuHNXs9qxdS+MEZR9SZ0c8MWGjlM3vQFPoblJ74kECS+4gAiCvFzb8XTmefLeiiyI4K0maNAOAtt2bfVsq+g30eivhYRcBYJuhwVlahm3nTvSx/sihOoyyAX2xi4aeE5eNkpKSSExMxOPxsG6dOqrVJj89M69P44bf5ZEyQfm/ULqjkXce3cIXz++mtqR9aBNjsePg9q/g0meUdRpNB2Dx9+G/c8QqDYHgJIgA6Bxj6cdLkIBGv1jOm6Cepk77p5/iylF2IVmtc9i7thZkqAk6wATnSACMqcFoAwexw6voC2Xk1xAA429TzUfB6cflV87HI2kJsjezcn2+anajo68BwDZJRtbJtC9+F0mSsE6IBeC89gl8Vv7ZSe3Mnj0bgPz8fNra2lTzLyTaj7m3Z3HD7/IYOTYcZKgoaOajJ/N577HtFG2uw+0c5IJVjRYm3AY/26U0SestcGg7vHYZvHSRIiJ6kgWvAsG5iAiAziFkWaZmy2oArGOmoR2sEOFJ8HR0cKh+AwZ/G263jsyM77F/gzL6XhD1NfO6lIZSv8GUv2QZ1jyuHE+6C8xBqvgoOD0JCrLijlN0dzYtPf6urqESEjIVozEar96FPcdL59KleDo6sOQo78ExvWms3rvypNmWxMREkpOT8Xq9qmWBvklorD/z7srmxocmkTkjBq1eQ1N1F1+9up9XHtjA6rcO0FDRObiskDlIaZL+2S6YeAdoDVCzBRZ9D/41GfLfANcwhRkFgrMQEQCdQxTv3o2+pxWnpOPiy+aqZrdz2TJaZiq9RA57Fo0HJFwOD70BbVgNbkIdViSDFlNm6CCcXKYsPdX7wZSfqOaj4PRl0lylGVpfuYvWzmE0BR8DSdISHX0VAPYLTMgOBx2ffY4uxIQu0R8NGlJro9nVtOuktg5ngXbu3Elr6zCmtwZBSIwf530/g1sem8qky5MJCDXhtLnZu66W9x/fzjsPb2HLp+U0H+w6eTAUEAmXPAH37oHp/w+MVmgugk/vgScz4MtfQdPJN94LBGc7IgA6h1jxsdL42Rw+itzkQTYjD4KWzz5Cl6o0icbGXkPB18rW963hS5nTqYgsmrPD0Jys4fqb2Z+8O8TS03OE886bis0QiNHr4KMP1dMEio66GgBbQi+eYJn2d9/ta4ZWNIHO78jjw5KTqyrHx8eTkpKCLMusXr1aNf+OhdnfwIT5idz86BQW3JtL2qRItHoNbfW9bP+iksV/3MabD21m44el1Ja0nbhvKiAK5vwB/l8hXPgoBMUrshJbnofn8uDli2HnW2D3rcFbIDhTEQHQOYLLbqenaAcA6TPOH5wQ4SBwlJVREVSBTu/G4QjAzzOT7lYHsslFVchuZncrc/GDmv4qXQm1+UoPw5R7VPFPcPqj0Wqx5iiaQJWb1BP2s1gSCAqaBJJM7zQJR3Ex9t27sWSHI2shyRFL8b5COhwnDwAOT4Tt3r2b+vp61Xw8HpJGYkRGCBfeNprb/jqdObdlkpQThlavobPJxs7l1Xz05E5e+sU6vnh+N3tWH6SjqffY2SFTIEz7mVIa+/4HkHGpsl+seiN88mP4Wyosvgn2fQKuYapVCwRnICIAOkdYtewrdB4nHbpArpk/UzW77R98iG2y8i3UoJ/J3tVK78/eyA1M6c3C5DagDTJiTLKe2JAsK1uvASb8APwHKZYoOCu45JoFyEBQexUF+8pUsxsTfS0AtvN0yJJM29tvozHrsPStxji/ZSKfln16UjuxsbH9O8KWL1+umn+DwWjWkT4pivl3j+EHf5vORbePJnViJCZ/PS67h4qCZtYuKubN323mtQc3suy/hexedZCmmi683m8ERBotpM6BG95SskLn/xbC0sHjgP2fwbsL4W8pyub5wg/BPjwBSIHgTEF3qh0QfDfsXLkCHeBJmaDe6gunk4rNS7H8ROmLiAm+kaLyTtDK7Aj7iscafgyA38Sok29+L18NB7eBzgRTf6aKf4Izh+TEOHrCkvFvLmfpR5+Rk3mvKnYjIuZTXPJH3OZ2HFk6Or74koj778cvLwpbQRPndU7gV/ue5/ujvo9GOvH3wQsuuID9+/dTXl5OaWkpKSkpqvg4FAwmHakTIkmdEInslWmq6aJmfys1+1qpK+ugp91B6fZGSrc3AqA3aYlKCiQiMZCIhEAiEgLwCzIiBcbAzF8qU2MNhbDnfUV7q6MG9ryn3DR6SJoJGfMhfT4Exnznr1cg+DYRAdA5QGNtLbqGMmRg9iUXq2a3a/Vq6vK8BEoyLmcC5ZstgI2WuHJCZQvp3Qkggd+EyBMb+mbvz/jblCZOwTlH5qw5VH/wHxz7NtPrcGIxDkIy4SRotUZiYq6luvpFbJdaMO2x07b4XcJ+/GM0IUYsrZB4KIwtdVuYEjPlhLZCQkLIy8tj8+bNrFixguTkZDSaU5dElzRSX1ATyPh5ibicHhorO6kr7aCurJ36sg6cdg81+9uo2X9khN8SaCAiIYDwvoAoIiEdy4UPwwW/V76EFC2BA19ASwmUrVRuS34BMWMhbR6kzYXoXLGbT3DGIwKgc4DP3/sIgEb/eM5XUfun/sMP0M1vByAo4DJ27GkBCZYFvc317bMAMGWEoLWeJONUvhqqN4HWCNN+rpp/gjOLyy+fy5Mfv46fu5sPPl7Bzders/8tNuZGqqv/iy2uk4BwPW2LFhF25x3450XTubSSue3TWFy0+KQBEMDMmTPZuXMnDQ0NFBQUMHaseqtkfEVv0BKbFkxsWjAAXq9Ma2039eWdNFZ10ljZRWtdD72dTir3tFC558ime/9gY1+WKJKI+HsJn/o7TL0VR4Khg9ugdqdyW/0YBERD6kWQfjEkzQKD5VS9bIFg2IgA6CzH43bRuH0teiBy0nmqaf+46us5QDl+/p14vVo6DkwFepCTO7AZ25nXNQ0Av7yoExuSZVj5iHI88YcQGK2Kf4IzD6PJiCVrCp6CVRStWgoqBUAWSwKhoTNpaVmDba4R3ZvNdC5bhv+suXQsq2S0bST/LHmH+rx6ovxO/H61WCzMnDmTFStW8PXXX5OZmYnRqE5JWW00GomwEQGEjQgAFAFIl9NDc023EhBVddJU1UVbQy/dbQ6625oo39nUf35gmImIxAuJiL+aiAluwh2bMFR8AWWroKtOWVac/5pStk6aeSQ7ZB1xil6xQDA0RAB0lrNx5Rr0zh56tBZuuVI97Z/2Dz/EPsWFH6DTTGX/dmWX0deh7zG1KweLy4Q20IAp7SSj7Ac+75v88oPp96nmn+DM5LIbruGjglUEt1WwddcB8nLVyViOiL2JlpY19E5y479YpvX1Nwi89FLMo0Kx72vhovYpvF/8PveMPfn0YV5eHtu3b6etrY01a9Zw0UUXqeLjd4HeoCV6pJXokUeGEpw2N001XTRWdtFY3UljVRedTTY6m+10Ntv7+4kgmqDInxIR/ysi/BuIcG4hrPED9F1lULJcuS0BIrMhfZ4SEMWMg1NYJhQIToQIgM5yNi35FC3QkziehPBAVWzKHg+FG5YTdGstAN7Gy5Bl8EuWKdHu4c7OXwBgmRiFpD1Bxsnrga//qBxP+bGY/BIwMjkBW2QaloZiln/wEXm5D6piNzR0FiZTLHb7IWyTjEjr92AvKMAvLx77vhbmtE/h5/v/xh1j7sCoPXFGR6/Xc/HFF/P222+zefNmxo4dS3j4mfveNZh1A0pnAPYeF03VXX2ZIuVnd6uD9oZe2ht6KUYDTEGjnUpEjI7ogIPEuNYS3fEJxoY90LAH1v4N/MIhda6SGRp5HhgDTt0LFQj+BxEAncXUVlahbSjHi8T5CxaoZrd7zRrqxjmJ1HrwemIoXa/84syPXUFMbzhZ3SMH1/y85z1leaMpSOj+CPqZcPFl7Hv1STSl22jp6CLU6vuHpiRpiY39PmVlf8V2qQXz+m5aXnmV2KefRhtiJKAVchtT+Lzsc65Ou/qk9tLS0khLS6O4uJgvvviChQsXqqatdTpg8tMTNyqEuFFHMri9nc6BQVFlJ72dTuprXNQTyU6uBelaQkNcxJhLGeFYxoiuLRh2vQm73lRWcyROP1IqC048dS9QIEAEQGc1n737AQAN1mTmTExXzW7xu4sJuEQRg9P0XILXDUEJBla5l/CjNkV3xZQWjC74BFvc3U5Y9WflePq9YueXoJ+5F81k2zsv4edo5/3Fn3LXnd9XxW5M9LVUVPwDR2AbzjQdXcuX46yqxH9KLB1Lyrm87Tz+uvc1rky98qQj8QDz5s2jrKyMiooK9u3b168TdLZiCTSQkBVKQpay0kaWZbpa7NSWtFNb2k5tSTsdjTZaWvS0MIo9jEKjgajgNuI0G4l3fU146Sqksq/hy/+D8FGQdRVkXwshSaf41QnORURx9izFabfRtmsDAEnT56jW/Owor2C/tQmLXweyV0/5mjEA1KTswuwxMq9DWXzqPy32xIbyX4P2KvCPhLw7VfFNcHag0WqJyFOUlw9t/OrE6x6GgMEQ0r8l3n59EMgyrS+/rGQq9RoSHTEE1OlZd3BwS09DQkKYPl15vy9duhS7/dxSUZYkicAwMxlTojn/5lHc9MgUbn18GnPvyCJrVizWcDNeL9S2BLOl6RLea3mSV9rf5WvPI1Q6JuJuLIVVf4J/5Cpb67f9F3q/nV1rAsGxEAHQWcqKz5eh9zjo1AVw45UXqma36u230eYqqwO8tuk4bSbCEvz40PE6czomY/To0YWbMaYEHd+IvRNW96k+z/wlGPxU809wdnDt967GLemw2pr4bNl61ezGx90GSPREN+OKkmn/+BM8nS34jVfKtQtaZ/PavtcGbW/69OkEBwfT1dXFihUrVPPzTMXPaiRlfASzbkznpkencNOjU5h1YxpJOWHojVpsDh37m7JZ0vZrXm5dxDLX45TYZ+CsKlC0hp5Ig3e+pyxF9npO9csRnOWIAOgsRJZl9iz7TDnOmEZIwAlKUUPA093D9up9hIVXA3BoqzLq3ptbhd1t5+oOJdDynxpzYuXn9U9DbzOEpsD4W1XxTXB2ERoShJQ2AYDtSz5Sza7Fkkh4uPI+dVwfDC4Xra++hv8URX5hUvcYqqrL2Nu8d1D29Ho9l19+OQA7duygoqJCNV/PBqzhZrJmjWD+3WP44VMzuPzeXLJnxeIXZMTl0lDaksby9vt4pfUtljsepsqWhffAl/D2dfD3HKWRuuvb370mODcRAdBZyO6t29B3NuCUdFx+/ZWq2W34+CPsE3vRaLx4HAn0NsUTNdLK+72vMa5nFJH2ECSj9sSLT9trYPO/lOMLHwGtXjX/BGcXC276HjIQ3FzCxh2DC0gGQ3z87QB0pbfiCZRpW7wYjdGFMSUILRoWtJ3Hf/f8d9D2kpKSmDBBCdY+/fRTnE6nar6eTWi1GuIyQph5Yzq3/Hkq1zwwgXFz47GGm3G7NZS0jeHztod4tf0d1vX+mKZmnTIl+vRoWHwzVG5QdMMEApUQAdBZyFfvvgdAc3QOE9JO0oszSGRZZsvaVUSNKAWgcc/5gIRpcifV3dVc1T4HUCa/NMYT9NavfATcdkiYruwXEgiOQ3paMvaYTACWL35XNbtB1vFYA8ci48Z+bRByby8tr75KwAzl/8rFbdPZXLGR4rbiQducM2cOgYGBtLW18fXX6m20P1uRNBKRSYFMuTKF7z8ymWsemED27BGY/PXYHAZ2d17Iuy1P8V7Xc+zrnoVr7zJ4dT68eL6yqNXjPtUvQXAWIAKgs4zayio4uB8ZmHGFetmf1lWraMi0YzT24nUH0Fk5kdi0IN7tfpVYRwTjujJAUspfx+XQDtjT90E2949il5DgpJx3zXUAWKp2UlJZq5rdhMS7Aega14nHT6bt9TfQhoM+2g+zbOSy1ln8u+Dfg7ZnMpm47LLLANi8ebMohQ0BSVKCoZk3pHHr49O45CdjSBkfgUYn0dgTw6rOe3i19Q3Wdt1FS1UTvH8bPDsWNr8Azp5T7b7gDEYEQGcZH7/1DgD11mQum5Gjmt2NH39CZKKS/WktOg/Zqydkhpf8xnyuaVN6KkwZIehCzcc2IMuw7LfKcc6NymJFgeAkTJs2gW7rCLR4+eCNRarZDQs9H3//TLySA8f1IXh7e2l96SUCZilrHBa0zWZtxWpK20oHbTM1NZVx48YB8OGHH9Lb26uav+cKWq2GxOww5t6Rxa2PTWPKlSMJDDPhdOvZ0zOPRS3/4JP2P1LdEIL85QNKn9DGZ8Ep/q4FQ0cEQGcR3e3tdBduBiD9/EtUG33v2FVARYyNwMBmZK+WttJZxI0K5qPutwh2BXJhh7JEMmDmCXYA7fsYqjeCzgzn/04VvwTnBmMuVkQ8vfs2UN/UdpJnDw5JkkhKUsQ3Oyd04DXLtL31Nvpo0IaYsHoCuLB9Cv/Z/Z8h2Z03bx6hoaF0dXXx6aefIouelWFjDjAwbm4CNz0yhct+lkNybjiSBAfto/ms7fcsan+O/U1ZeJb9Af4+Bjb+UwRCgiEhAqCziA/fXITW66bFFM6NV5yvmt0Ni94hPLEMgI6qSXicgSRe5MfK6pVc0XYeWq8GQ0IghsTjrNpwdMOy3yjH034OVnX6kgTnBpdffiE95lCMXgdvvPyWanbDwy7E3y8dL3YcN4Yj2+20/Pe/BMxU3p9Xt8xhRcXyIWWBDAYD11xzDRqNhgMHDrB9+3bV/D1XkTQS8ZmhXPyjbG56dAo558ehM2ppdcTwdefPeL3lv+Q3TsO59FElI7T1RfC4TrXbgjOA0yIAeu6550hMTMRkMjFp0iS2bt163Oe++OKLzJgxg+DgYIKDg5kzZ85Rz7/11luRJGnAbd68ed/2yzil2Ht6qNv0FQBBU+bhb1Jnuqq7ooID5m7Cw6sAaCueQ3peFB+3L8LsMXJ5hyJYFzBrxPFXAaz9G3QegqAERfVZIBgCWq2O9IuvAsC1axWNLR2q2JUkDYlJPwWgc0I7XrNM+6JF6KPcaPz1RLpDuaB9En/P//uQ7EZHRzNnjjIUsHTpUg4dOqSKvwIIDDMz/bpUbn1sKlOuHImf1UCv28qmrlt4o+VF8hum4fz8t/CvybD/MzE1JjghpzwAWrx4Mffddx+///3vyc/PJycnh7lz59LY2HjM569evZobb7yRVatWsWnTJuLi4rjooouO+iUzb9486urq+m/vvPPOd/FyThmfLf4AndtOmz6YW268TDW7G15/g4iRZUiSTHfdaNw9CaRcFMgnpZ9wSdsMTG4DuggLpozjbH1vKoZNzynHFz8O+uP0CAkEJ+Dqqy+hxxSiehYoInwufn5peOjF8YMoZJeLpr8/TcCsOAC+1zyfDdXr2dGwY0h2J0+eTHp6Oh6Ph8WLF9Pd3a2azwIwWvSMm5vAzX+ayvkLMwgMN2P3+LOpeyFvNP+HHVVjcL5zO7w8D2q2nWp3BacppzwAeuqpp7jjjju47bbbyMzM5IUXXsBisfDyyy8f8/lvvfUWP/7xj8nNzSUjI4P//ve/eL1eVq5cOeB5RqORqKio/ltwcPAx7Z0NuJwOSlctAcA04UIirRZV7HYfPMgeTyuRUUoJoGX/fMacP4JFh94Et8y17XOBvuzPsfqNZBm+/CV4XcoCxPSLVfFLcO6h0+lInatMNdp3fk1zW6cqdiVJQ8rI/wOgfXQ9nmDo+nIpGnMD2kADEe4QLm6fzlPbnxpSP49Go+HKK68kNDSUzs5O3n//fTweoWysNlqdhlFTY/j+HyZxwa2jsIabsXsD2Nx9M280/4eCA2F4/jsP3rsNOkQmTjCQUxoAOZ1OduzY0Z8uBuUXx5w5c9i0adOgbPT29uJyuQgJGZiBWL16NREREaSnp3P33XfT0tJyXBsOh4POzs4BtzOJJe9/gsHZQ5fOn9tuVm/0ffUrrxA2shyNxktv80i8tlEkzPLn/eL3md8+nQCXBW2QEUtO+LEN7PsYyleD1gjz/qKaX4Jzk2uvvYweUzAmj53XX1IvCxQaOpugoDxkXNh/ojTyN/7tcQLOV7JAN7TMo6hxPyuqhrbqwmQyccMNN2AwGKisrOTLL78UTdHfEhqthozJ0XzvD5OYc+sogiIt2L0BrO/6IW81PUfR9hbkZ/MUFXq3EKoUKJzSAKi5uRmPx0NkZOSA+yMjI6mvH5z8+QMPPEBMTMyAIGrevHm8/vrrrFy5kscff5w1a9Zw8cUXH/cb2GOPPYbVau2/xcXFDf9Ffce4XS72L/sYACnnfGJDA1Sx21FVxR53J9HRihhc6/6Lybs0mTfLXgOXl++1XgJAwHlxSLpjvI3sHbD0QeV4+v8T254FPqPT6xh50RUA2Hd8xaH6ZlXsSpJEysgHAOiIqcSdZMResBt34060wUaC3YFc2jaLZ/KfwekZ2odneHg4V12l9C9t376d9evV22smOBqNVkP65GhufCiP827KwM9qoMsbwVcd97K4/hGqlnyC/K8pULry5MYEZz2nvATmC3/5y19YtGgRH330ESbTkX1XN9xwA5dffjnZ2dlcccUVfP7552zbto3Vq1cf086DDz5IR0dH/62mpuY7egW+88V7H2Kwd9Kt9WPhwutUs/vVG28QkVCGTufC3h6DUTOZqIkm3it+j/ltM5TsT7Cxf4nkUax4CLrqICRZND4LVOP66xfQbQnH6HXw+guvqGbXas0lPHwe4MV2dxgATU/8Ff/pUYCSBWpva+XVva8O2XZGRkb/EMbKlSvZvXu3Wm4LjoNGqyFzegzff3QKk69IxmDW0eJO4vO2h/ik9GZaXrlXWa/RqZ64puDM45QGQGFhYWi1WhoaGgbc39DQQFRU1AnPfeKJJ/jLX/7C8uXLGTNmzAmfm5ycTFhYGKWlxx5nNRqNBAYGDridCbgcdvZ9+SEAnjEXkBQVpIrdpooKDni7iY3dDyi9PzOuz+DV/a+AS+Z7bUr2J/D8+GNnfyrWwY5XlePLnxWNzwLV0Ol0jL/mJgA0B9ZzoKRaNdspI+9HkvR0+VfgOj8Ud2Mj3avfRB9lwd9j4abmS3hx94sc6h56L8nkyZOZMkXRy/r4448pKipSzW/B8dEbtIyfl8jNj04hd04cGq3EIecYFrc8xdotkdj/cT5sfxm83lPtquAUcEoDIIPBwPjx4wc0MB9uaD78y+JY/PWvf+XRRx9l6dKl/UsIT8TBgwdpaWkhOjpaFb9PFz566130zh46dYH84Ac3qGZ3+RtvEBN3AL3eib09lsjIS5BibCw6sIhL22bi7zKjDTEde+mpywaf/Uw5Hn8bJE5XzS+BAOCS+efTHZyATvaw+D+DX1p6MiyWJOLjfgBA5zVuZL1M+1tvYc7SKtdtn0lEbzCPb318WPYvvPBCsrKy8Hq9vPvuuxQXD37X2LmG7PLgPNhF545aDi4rpPjtjez/9xr2Pfs1+55eyf6/rWT/UyvZ9+zX7H9hNaVvbubQl4V0bjuEo7IDr2PgrjCTv55p16Ty/YcnM3JsODJa9vTO581Dj7N78XK8r1wKzSWn6NUKThUn2Fr53XDfffdxyy23MGHCBPLy8njmmWfo6enhtttuA2DhwoXExsby2GOPAfD444/z0EMP8fbbb5OYmNjfK+Tv74+/vz/d3d08/PDDXH311URFRVFWVsb//d//kZKSwty5c0/Z61Qbe08P5Ss/RQ/oJ84jMUKdrFXlzp1UaHqZ2Jf9aStawBU/SuOxXY9icOn4XquywDTw/Dgk7THi59WPQWs5BMTAhQ+r4pNA8E0kSeKChT9gy99/j6V6Jxu27mZa3omzwIMlMfEn1Dd8gsNRj+snGRieKaflhT8RcPkfsO9r5UcN1/Jrwz9YU7OGWXGzhmT78GSY1+tl3759LF68mBtuuIHU1FRVfD+TcbfZaS2qpXF/NZo6J5YeL25LMy5LAw5zA05DBx5LN15rL7JGETmUkNDKerQuPzQuP3qqA+goikDfG4GhNxKbSQfRBsJHxWFNi0IbaiIwzMy8u7I5WNTG+sXFtNTCuq472buzmunldxI3dz5M/TnoDKf4b0TwXXDKA6Drr7+epqYmHnroIerr68nNzWXp0qX9jdHV1dVoNEc+aJ9//nmcTifXXHPNADu///3v+cMf/oBWq2X37t289tprtLe3ExMTw0UXXcSjjz6K0Wj8Tl/bt8niV95A77bTbgjmpz+45uQnDAKv18uSjz5ixMh96HRu7G3xZIy9ilqq+azsM25rvgKL24QuwoJl7DF6fw7lK3t5AC59CkxWVfwSCP6X6VPHs+r9LCyHCln23xeYPP6faI8VkA8Rnc6P1JRfU7j3Z7SmVxCRYMFeWIj/nHzQJjG2J4NJ3dk8uvlRxkaOJdAwtC8eWq2Wq6++GlmW2b9/P++88w4LFiwgJ0e9vX1nArJXpre6jYqthXiLunBqD9IZvB9XYBVkH0Tn1+rzNbS9YVg6k3DuTqJtVSZuTyTa9EBG5GUQmxrEdb+ZyL4NdWz5uITW3ng+bfkdIz/YyPT8y/C/9jGIHafCKxWczkiymMs8is7OTqxWKx0dHadlP1B7UxP/+dkdaL1uOP8WfnHXtarY3frlUlbs/JqJEz9Gq/XQse+XLLj7Tn62+qccqCjkpfKH0Xm1hN46GvP/Ch86e+HfM6GlBLKuhmuOreMkEKhFWUUN7z/4U3Sym7DLfsgtN6kjASHLMjt33Uxb2yYCnCPxv7cajcFI+K9ewba7h1ZDB7cn/oEL0+byp+l/GtY13G43H3/8MYWFhQDMmTOHadOmHV9N/Syhu76NguWbaK88iDN4H97QIiwh1RgM9qOe63QasdsCsdn9cTrNeFwG3G4jskeLLEl4AY3Gg07nRKdzYjDYMJu7MJm6MRqP3gkmOf3wbxmNX3MO2o7RaEdHkDAjG6+fnm2fV7BndQ2yLKGXbOT5L2bM3HQ0s38pskFnGEP5/D7lGSDB0HnzuRfQet00WmL49cIFqti02+2s2riBhIwCtFoPtpZkps6/kfymHaw9uJZfNt2KzqvFmGzFlH4MUckVv1OCn4BomP+EKj4JBCdiZFIc5okX4dr6BdVfLqbl0gsIDfL9C4skSWSkP8qWrZfQZSjDfNsopFfK6PjgL5jz7iekzcptTVfwL+1i5sTP4bz484Z8DZ1Ox1VXXUVgYCAbN27kq6++orm5mfnz52MwnF0fuO3NrWz+ai2HysuQA/cTEFFKyNRDmDRHGo89bh225hDoCcckJxBkzSUgfjTNgXo6u3vp7uymq6Mdp9uO1+1B9nrwejxIeh1GoxEZDV5Zg1fW0dPSTXv7ITy6Q/j7txJobcRqrUdn6KEreitd0VvBo8OvJRvX25NwyRmkTcskY3Iea9/eS30VbOi6lQMfVzC74Baivv8biMo6hX+Dgm8LkQE6BqdzBqh0334+efiXAATf8Et+cOXQ+hCOx+evvMK+5l2MG/c5kgT6jqeYuuASrvv8Ory1dv5ZqWj6RNyTi2HE/2gNFS+Ht/uyUDd/BCPVW8QqEJyI3l47T/zoh/g5OnCNPo9fPfQL1WxXV79MSemf0GosRPzFjFTZRfBt9+NuS0NG5v6EJ2kI6eCjBR8RbBq+0vzmzZtZtmwZsiwTGRnJddddR2hoqGqv47vG6/VSV1dH4e49FO7ajVdfQ3R0MRERlWi1R5qT3W1mDM0jCA+YQlfgRPY3d1NXVoq9uR5ddwsWZycahvfx5NQYsFlC0YRFYw4OQic50OprsIYcIiysGovlG2K3LjNBtdMwNE7BPCYbpzmSjR8fwGFXSqqZlq+YcnEYpvN/BlqRMzjdGcrntwiAjsHpGgDJssxff/ZzdI3l1IZl8vg//oJOhb6HhkOHeOHfLzA6ZxXBwXX0NuQx98o3+KDyPR7b/BhPV/+S9N5EzLnhhN6QMfDknmb41xToaYTJP4Z5j/nsj0AwFD7+dDllb/0DDxpm3P8npkzMVsWuLHvYkX8jHR07CPRm4HdPGZJGS+g9/8ZZLVNvauHOhIeZFDeZ5y54Do00/P+L5eXlfPDBB/T09GAwGJg7dy7jxo07Y0piTqeTiooKioqKOLB/P3ZHF2HhVURHFxEY+A0V/nYD1uYkenQz2dUSQEt5Eeb2GgzeY29vd0taXHoLHqM/mPzQGE1oNFrQaEGSkN0uvE47XqcDydGLwd6Jwes4pi2PpKE7eAS60HD8A9sJDq8iIrIck6mn/zmG1lSCqi+EmIk0d/qxL19ZvGuSOpgWv5r0H96NFJFxTPuC0wMRAPnI6RoArf5yOTte/QduScvE+//K+RPSfbbp9Xr59+OP47SUk5X9NV6PjuTI9whJi+PSjy4lr3EUv6i7BcmgIfIXE9BZv9FILsuw6PtQtATCR8Gdq0FvOu61BIJvi0d+/kv86vfT6RfFr194HqNBr4rd3t4Ktmy9BK/XQURJLrqn96ENj8H/wkfx9nr4LHQt/4pYxM/H/Zzbs2/36VqHd4ZVVyvaRklJSVx22WVHrfk5Xejq6qKkpISioiLKyspwu93odA5iYoqIiTmA3tAXiHgkTJWhNHflUVBtRttYjskzsOfHLemwBUZhjkkkKjGRuIR40lKTiYmJGDAEMxg6O7uorKqltKSU6tJyOmtrkFoOYnEeyfrIgMscgCMsluDYdqJiSwkNPYgkKR+Hmu5IgqsuwhMwk7IDbpralcxPjGEfs+bpCZl3uxKECU47RADkI6djAGTv6eaZH92O3tlNc9psHnv0flXsbvz6a1asW8W4cUuw+HWg6bma8y77K49uepQl+z/jlfJHCXBbsF6c2L8hu59Nz8GyX4NGD3eugih1vnkLBEOl+mA9b/7yJxi9DrR5l3LvL36kmu2Dh96mqOh3SJKOqMWJSKursUy+DG3UZQD8YcTzbAvcy38v+i8Toyb6dC2v18vmzZv5+uuvcbvdaLVaJk6cyMyZM7FY1FlyPFxkWaahoYHi4mKKioo4dOiIIKTB2ENiTDER0UVIOiWbo+kwYKtOYndFHNQ3oJePlL9cGj3O8CQiM7KZMHkiuWMy0Oq+3fJSzcE6Nm3cTkVhIY6qffjZ25TXJWlwBwQhR1mJTqkiOroYXd9rwB5IcMV8HN5Z7Nkv4/Lq0eBiXMw2xt91A7rIlG/VZ8HQEQGQj5yOAdCLf32Szh2raNcHcdsT/yRZBdXn9vZ2/vn000TFF5KUtAuPM5AZM7+mpKeGm764ibvqr2FB23nows1E/nzcQNXn6i3w6nzwupWm57w7fPZHIPCF119/n6Ylr+KWtFz468cZN0adUoUsyxTu/RmNjV9g1EUS+jsXNHQTePVvkT3x2PRObk/4PR5/eHv+28QHxvt8zZaWFpYsWUJ5eTmgqNXn5eUxceLE7/R3ksvlorq6mqKiIoqKiujo6BjweLCfnbSoYgzRe6CvqVlq8qO6OJna/RJGz5HSVq8hEGPqWCbOnMH0qePRq5SlGy4HistZ89Va6grz8WupQIOMx2DCExFCWEYrsSMOYDTaAJBtwVgr5tHRnEdRvT8AgboGZs/xErfge3CGlCrPBUQA5COnWwBUsnc/Hz/yf2iQ0V/6Y35283yfbcqyzOvPPUdtdwXjx3+OVutmRMSjJI+6lus+vw6pzskzlQ+gQSLs9ixMKd9o8uxpgX/PgM5Dysj71S+JXwCCU47X6+WRn95HQHMpnf7RPPDcs1hM6pRk3e4utm67HJutGivZWO4pQkJH4LVPITtMVAbU8bOYx4gNGsGb89/EavRdA0uWZcrKylixYkX/uiCNRsOoUaMYM2YMI0eORKdy1sTr9VJbW0tFRQXl5eXU1NTgdh/J3Oh0OmLiY9B2HCTFvxh73FbQKEum5dpQinaHYq/SAMrvg159AJbMPGZedAHjx2UNuZz1XVFf38ySz5ZSs30DAe01eLU63CEhhGZ1EZewH8PhQKg3FL/ySzlYNpZGhx8AqWFFTL/7Eiyxvge+At8RAZCPnE4BkNfj4fGf3I2hrZa60FE89uzj6FVofN6xZQufffkFmaNXExp6EK0nh1lzPuA/u//DCzuf57nK3xBvj8KcE07ojd/4Ju31wNvXQelXEJqi9P0Y1dlALxD4SnnlQRY9+HOMXgeu0bP51UPqlIoBOrsK2bHjOrxeB+Fdk9A/sBPJLxz/i/8Ibok1ofn8Jfy/TIyeyL8u+BcmnTrBl9fr5cCBA2zZsoWqqqr++41GIykpKSQkJBAfH094eDha7eD7UlwuF62trTQ1NVFbW8uhQ4eora3F5RrYkOzv709aWhoRCRHUbltFLMX0JGxC1irP89ZGULTVH0eD0h/olrS44rKYcOFcLjx/GlrdmdUrU1pew5IPPqGzYD1mdy+esBBCs7sYkbC/v69JbkvAWLKAA1WjccoajJoeJs+QGX3dpcdWyBd8Z4gAyEdOpwDo3RdfpuarD7FrDJz3myeZmpXks83W1lb+9Y9/YA2tJnP0GmSvlkl5n9Mk67jm02u4ruEibmq+BI2fjsj/Nx6t/zd0SVY8BBv+Djoz3LESIkf77I9AoCbvf/AFVe/+C4DUhfdx+SXqyTI0NCyhcK+y6y66fBrSE9vQRY/BPOkeAF6K/pj3g5YzI3YGfz/v7+i16pZ56urq2LVrF/v27aOrq2vAYxqNhuDgYEJCQrBYLJjNZvR6PbIsI8sydrud3t5ebDYbbW1tR5WzDmMymUhMTCQpKYnk5GRki8y6914k1l5Kb/IWZJ0SBHgbw6nY7EdXnbLsuNsUQtSUOVxz/QJCg898FXi3y83nX3xNwVdLMTeVIocFETG2g9j4/Wi1StZLqs3Bse8SyluV38tRgY3MvnMaoSlxJzIt+BYRAZCPnC4BUEVxMe8/dD8a2UvP5Ot56P/d7LNNj8fDy//6Fw0dtYwf/zkGYy8xUXeQmnE/ty69lY6aZv5Z+SBaWUPIjRlYcsKPnFywCD66Szm++iXIVmcFh0CgNn/+zaMYS7dg01m4+S9/JyFOvUXIFRX/pLziaSS0xGwch/xmAYaMizFmXIkMPBH3Gl/7b+HChAt5fMbjqgdBoGSFDh48SEVFBVVVVdTU1ByVuRkMRqORsLAwoqOjiY2NJTY2lrCwMDQaDS22Fla++y/iu8qxpW7Dq+8rA7WEUbklgI4aIzISPRFpTLzkcuZdNPO0LXH5SnFpJR+98Q7e4m3oo83Ejm8gIroCAK9Hi6ZyOvV7FtDuDECDh5zxMhMXXoDeeGZlv84GRADkI6dDAORxu/jrj+/G0FFPXVAKf/j73/A3+f6LdPXKlaxet46M9PWER1Zg0CUzddrn/KfwZV7M/w/PVD1Asj0WU2YooTePOqJDcnA7vDIfPA6Y8Qu44CGffREIvi26e3p54ic/JsDWTEfgCH717DOq9QPJssy+/fdTX/8xGo2R6JWjkd8rxDj2ZgwJM5A1Y8TbAAAAKctJREFU8Pu4f7HNUsi02Gk8NespLPpvd4LL6/XS2dlJS0sL7e3t2Gw2bDZbf/+OJEkYjUYsFgsWiwWr1UpoaCgWi+UoraEWWwsr3nuWhNZKHBk78Ri6lQfaQ6jeaqW1wohb0uNNm8SVN11PRlryt/raTieaWtpY9Ma7tO9YjTUBRoyrxBrUCIDbFgj75lNRdj4eJAJMPcxcmEPiONEb9F0iAiAfOR0CoFf/8S9aNnyBTWPigt88wZSsRJ9tVlRU8NqrrxEWXsWozLUga5g48QMqHbDwy4XcVr+Aq1vnoLHoiLx3PNrAvtJXew389wLoboD0S+D6N+Es/aYnOHvYs7eEz//4AAavE1vSRH7959+plqHwep3sKbyH5uaVaDVmoleMxvvBHkx5d6CPmYBXB4/G/pvNlgJywnN49vxnfVKL/i5otjWzbNHTJDXX4BxdiMfUVyLrDKZ2ewiNJXqckgHt6Onc8IObiY+NOLUOn0JsdgfvLv6EytVLiBplZ0TWPkwmZf+YszkJ+87rqG1TRuRHpsOM26bhF3T2LOM+nREBkI+c6gBo56YtrHzmUSRAPm8h9//oOp9tdnV18a9/PIubDsaPW4LeaCMh/sfEJNzNtZ9dS1idH3+q+SkAoTdnYh7dJ8Xf2wovz4XmYojIhB8uF03PgjOGTz9fSfEbTyMBfrOu5Uc/vkU12x6Pg92776S1bT0ajZmYDTl43yzAPOludFFjkLXwRNzrfG3eTLRfNE+f9//bu/O4qqv88eOvz93ZLojsCAiIuCG4gZiJk2SalWUzajWlTlMzU1P2yzJtUsec76jpVN8xJ1u1+maOtqilaWXRYoijoqK4gSgoO8h64XKX8/uDvNPNFdnlPB8PHsr5nM+5533PXd58lnNeon/3jnfNXImphC/eX0Z4SQGW2KNYXRpXYldqPSna60f+UTVmxYA+Non7ZtxHgH/nXaajpVksVtav/5ST324ifFgZ/uFHUans2O0qGk4mUHhwKvVWV7QaK8Pv7MWAm8JRqeQds61JJkDN1J4JUEVZGa/O/BM6i4mzAbEs+ccidJrm/dVqs9lY88ab5BWeJWZACl7eZ3FzjWbYsE94dud8dp74jtdy5uNpdcdteCDd7vxpci9LHbw7EfLSwBjcmPx49miBKCWp7bzy8uuYUzdjR6HnlEf5zaRxLda2zVZHRsYjlJV/h6JoCD4xEvtLaRiG/h5t8BCEAv8O/ZJ3XD5Bp9Yxe9hsJkdP7hBLXGSfy+a7/3uRPmVVWIZkYXFrPJWj1HlQtj+QvEMq6hQX3Icmc9+Me/C5Di5sbi1Wq5UNH24hf8+HhCfk4Nm9AICGOnfMh8dx9uRYQMEvUMXoGUPwDZV/RLYWmQA1U3slQHa7jeX/bxbqwizK9d15cOnLRAQ2/7D5tq2fs2t3GqEhhwgLT0elGIiP38zmvN0s2bWEv+c+RqwpGo2/K/5/jkPRqsFmhfX3w7GtYPCE320Hv74tEKUktS273c7f5y7E5dRerIqauIdmc8uYG1qw/QYyj8ymqOhTAPxrR6F+bh/6flPRhY0EYH9wNvPd/xeLysrwwOEsHLGQIPegFuvD1RJCsPvU9xx85xVihIJlyBka3AsBUMxuVBzswakDUKt40G34WH47bQpeRvc272dnZbPa+PCjLVSfXEtQbCb6n9YZM5WGUrl3CpWVvVEQDPxVMPETe6EzyMVVW5pMgJqpvRKgd/53JaU/fk6DoqH/nxZwR9KgZre5d+9ePv30Uzw9i4gZ+CWKIujXdxnFmkimb5vO9II7uLs8GUWnwu/ROLT+bo1z/XzyR8hYDxoD3L8RwhKbH6AktZOGBgt/f2oOHkXHaFDpSHpiASMSYlusfSHsZGcv53TuawB4amNxX1aKxh6Hvv/dKIqKGs96nvX9X05oT2NQG3ig/wM8OODBVr9AGhovbP5i91rM67cQE9CNmsFZ/z3V1eBC9eEQTu5TqFY88R95K/fd/2vc3VxavV/XK6vVyvvvvoveupHuEUccp8WqTw6i6MBvsdvccTequPHe/kTE+V65QemqyQSomdojAfrq060c+L/GuUssN97LnD/f2+w2c3JyePedd9Hqaxgcux2toZbAgEn49pzN1C1T6VcQyjP5vwPA+76+uMb4gN0Omx6FA2tBpYHJ70KfCc3uiyS1t5paE8uefApjRS5mtYGRf36WUSMGt+hjFBV9RuaROdjtdWg0Xvin90f1kQnDkN+h0nsgFDs7QvfxT8O7WFRWvA3e3NPnHqZET2nxi6RrLbV8f3IHJzatJTKjCv+BRqoHHMOmb1wUVKl3p/pwKCcPWKlSdafH6Nu59967cDHortCydLXqzQ188K+/063H13h0b1w7zVzvTsXBUZTl3IFKUdMzpjuj7onGw1suJN0SZALUTG2dAGUeyOCzxc+hFjYKwkawePGcZs/2XFJSwuuvvo6NOuJiduDmWYy7WzT9Yt/l9189iuVMDctzZ6Gza/EYHYLnuJ6Nyc+nj0P6e6Co4ddvQ/87WyRGSeoISssreGX2bDyq82lQaRn60Gxuvqllj27W1BwnM/MpqmsOA+ClxOL2ZgMG7Tg0AY1HnaxqE+8Fb+dD16+wKwK9Wk9SjyRujbiVxMDEazoqZBd2siqy2JO3izPfbcMzNZt4twgaEs3UhBxyLFmhmDw5dzCY04dsVOl8iUyeyJTJt6Nr57W5rmdV1bV8+uYTdItOc5wWqyrtQcmem2moTESrU0iY2IuBv+qBSs4k3SwyAWqmtkyAivPzefOZJ9E31FDkFcmz/3gBL/fm3S5ZWVnJq6+sot5iom90Kj7+2Wg13Rg85COeTfsHR7IP8vLpZ/C0uqPv3Q2f6f1RhA02P9Z45EdRwd1vNq7zJUnXmXMV1bz8zByMFaexKhp6TfkTd991S4s+ht3eQM6pVzh9+nWEsKAoWryrYzFscMXV+y5ULo1He6y2SlI9drHK/wvKDY0TDWoUDQN8BhDjG0OkZyQhHiF4Gbww6owIIbAJG7WWWkrqSig2FXPyXDbFWRkoB47SN1swWNUL2zA9Nb1P0uDx3xXbKQuk5JAXZ48Lqgz+9Bk/id9MGo+mky1V0ZnlnTzBnh3P4B6e4TgtVnZyAIXpY9DY++IT7MrIKX3oEd2xp0zoyGQC1ExtlQBVlZez8qkn0NWWc07fnQf+Zzm9Q5p3Pri2tpZ//fM1as1V9Aw9REjPdBRFQ1zcu7x+4hs+zdjIi6efJrjBD22gG75/GIhKbYUPfwfHtjQe+blrFQxs/q33ktRR1dSaWPbMsxhLshCAbvjt/HnmQy0+k7HJlMPxE3+jrCwFAAU1nnX9cPkhEHfzbah0jZ8vwm6lzpRNljhEmiGDzG7FVLgJql3A+lN+orGB0dT441ch6FEmiKjwonddCK5BftT3t2IKO01dt+Og/PSxbtNgOR1G7gE11cVqKo09GHT7r5l4203X7azNnUHa9nWUVK7CxScPgPp6NwoPDKImexQaTTg9B/pww9298PJv/evDrjcyAWqmtkiA6mpqeXnW/0NXkU+1xoNfPfU8SYOimtWmyWRi1Yo3qKo7R3DgCSKidgHQt89iPi4q5t30Nfw993H61Iej9tLj90gsan0DfHAPnPoe1Hr4zRro0/zV5iWpo6s3N/Di8y+gz2p8n5hCBvLYvLl4ebb8Lcrnzu3m1Ol/UV7+vaNMq3jhmh+KS15/XMyD0Nb5ovy0irqwmrHXFGE3lSEstWA1N/5xolKDiwtWPzvmgHPU++RR53UCi1uR0+PZykIozdRTnKXC1qCmqnsEiXdNYeyYRJn4dBB2m50v352D8N+O1tA42/a5smDy0/pCxUi0umBiRocwdEJPDG7y9OTVkglQM7V2AmSuM/Hy7GfQFOdQpzIw6JF5TLixeXek1NTUsmrFG9SYK/DzPU103+8AiIh4kq+rDaza8yqLch9lQF0vFBcNfn+KRasrgw+mQtEh0HnAPR9A+I0tEaIkdRqvrXqPym82oMZOrd6L0Q8/waiRQ1vlsaqrM8kvWE9h4WasVufFSFU2F3Q1vqjN3mjMXihCjSJUCMWGTVuDTVeDxaUUi0vJf4/w/EQRGmw1EZw5pqL6mBVLTeMXZo1/H3415R5G3TCkVeKRmq+iOJ9dW2ahDtnjOC1WeKovxbtD0dlvwMUYTPzt4fQfFYxaXh90RTIBaqbWTIDMJhOvPjsHW8FJzIqOyGmzmDK+eXOSVJyr4rVX3qTOVoWP9xn69E1BUQtCekwntSGEFf9ZwfN5jzDQ1BvFoMb39zHoONZ45Ke2GNz84L71ENT82+4lqTPavmMnu1e/gqulGjsKqrgxPPzYw3i4t84pCLvdTEXFHkrLUqio2E1NzXGEaLjq/dWKG+4u/RDqKPbvqaYsLQ9tXeMq7RZFgzVyKBOmTmZQTO9W6b/U8jK/3URe8T/RdD8FgNnswtmj0VRmhKBXJ+IVEErC7eFEDfVHkbNJX5JMgJqptRIgs6mWj/4+n4ITx7Co9fhPfpwZdyY1q80zpwt4d817NAgTft1z6d33WxQVBATcxbfmcN5Nf4cFeX8kpi4KRf9T8nPuc9j058aFTf1jGo/8eIW0UJSS1DkVl1bwr7+/gMfZgwCYdEb63flb7rprXKufNrLbG6itzaK+/izmhhIaGkoRwgrCDooardYLrcYLgyEQtaYHn3+ezpGUL3EvO8n5r0KT1h3j4F8x5b5fy+UqOilrvYVdG/5GrfenaFwajxDW1niRd7AX9SeC0WmH4RMaQcIdEYTH+nSIGcU7GpkANVNrJUBb/rmMozu/xeDmzp3PLiK4V/Ou+TmQdpRNWz/CrlgI9D1FZJ/vURQICLibz6q7sTVjC3/L+zPh5mAUgxqf+3ujz/wb7F3d2ED0rTDpDdDLmV4lCRpnSl63YQtZm9/H1VINQLWxB0PvnMyt40e32/UzVquVb7/bzX+++QZb9n4MtjrHtspu4fROSubXk8Zj0Ms5fK4HpadzObR9CQ1hKai1jUf2ysuCOJMeij3PG50+noBefRl+RwQ9+naTidDPyASomVorAaouK+XTFxeT/NCj+PWMuOZ2hBBs//AHdh36BhQb4cGZ9IjcB4B/wGTeLqrn2IlDLMx7hACLDyoPLT6TvNB9+3sozAAUGPU0jJ7TeFGlJElOKqpqeH3FG4iMFDSicf6cavcAeo68mUl3T8DYBstD1NWb+fbbXez/8UesWftxsdb+d5vGDV2/4YyfdAcxfSNbvS9S2xN2wdGUbyg6vRpryC4UlR0hFEoKe5J/wB8KPNDqhhHUeyBDbw0nbEB3mQghE6Bma81rgIQQzXqRmqrMvPfaRxTUHkdRbPTtmUb3kGwAfALv53+yDmM8rWV2/gxc7QY03Q34DMtEs3MuWGrBtXvjUZ9eY1oqJEm6bp08nc+/334P5VgqWmEFwKJosYYOIHpYAjePHYWXZ8t8RjQ0WNibfpiD+/ZTmHkQfclJx2MCmFU6rD36M+DGJCaMS0IvJy7sEmwNFvZ8/H/U2jciAg4BIIRCUUEEBQf8oUCPTjcI356DGHprJJGD/br0ivMyAWqm9lwN/nKO7s3lk40fY1ZXoNPVMjAyBRffckCNLmA68w9+yc1nhvFA6e0A6EINdDe8iDp3a2MDYSNh0uvgGdxuMUhSZ5SbX8Qn6z6hYv8PuJsrHOU2VJiMgbgE9SQ4qjdh4WH0igwjwP/S12fU1NSSX1DMyVNnyDt5ivKzedQV5eFSWYDmZwkPgEnjhgjqTb/EGxg/bjRurnK5hK7KXGki/cM3qfX4HHyOA2C3qygqiKTwoD8i34JOMxDP4GHE39qX3vH+aHRd7wi/TICaqaMlQPW1Fja/l8KR/N0IlQUvYxH9e3+NytWKWu3OKZebeedgGrPypxFnigbALaIcr9KZKA3nGhc0Tf4rxP8B5BwgknTNbDY7X6bsIv277zHnHMLDfO6i9eyosKh12DR6BAqKsKEIOxprA1phuWT7ZpWe+m498I7sw7CRIxg+NEbe+iw5qS4s59DGt6nr/iWiexYAdrtCSXE4hYcDseXWoyUSvedQ4sbEMSApuEutMyYToGbqKAmQEILDP+ay9fPPMWkKUamsRAbvJyDsCKhA5xLJ2goj9iwXHi+8F0+bO2gE3Ywf4mZ6p7GRHsPgzlXg06vd4pCk65EQgkNHT7JvzwHOHD9GQ3Ee6tpzuFqqudIJCIuiwazzwGb0xcU3EP/QMGIHDyS2f5RcmkK6KuV5RRz77B3qfb/C7p3tKC8rCyb/eBjmbBsaswdafQwRQ0cweGwEgZGe1/11QjIBaqaOkAAVZFewdX0KZ0yHEWoLHu4l9I/4Dq2XqbGP+lheP17GjLO/YURN4ySKWtdSvK1/Qas6Cy7d4KZ5MGS6vNBZktpQVW0dBYWlVFRWUVlZgyLsqLUa1BoNXkZ3ggL96O5l7NLXaUgtpyK3mKNb11LvmYLVL4Pz+U11VXcKcsOpPKpDU1GPRtMHd/8hDL55EH2GB+JqvD7vGJQJUDO1ZwJUUWRix4a9HMn/D1ZdFRqNmcge6fj1OAEqQGVka3V3XLP6cX/JbbjaXUCx46H+CKP6fRTF1pj0jJkPrt5t2ndJkiSpfVQXlnN4y0fYtN9RF5SGomq8e9Fi0VFUEEnJcV/s+SbUDe5o9H0JjklgSHJfQvt7X1enWWUC1EztkQCVnqnmh88OceTUPsyGEhTFTqD/ccJD96EyNL6Qc0Ug+4+F8tuCXxNoaVw0Vac6SjfNCrSq0xB1C9z0HAQObJM+S5IkSR1LQ1Udh7Ztx3ouhaoe36O4VAAgBFRWBFB8tgdVJwyoyqvREIDatS9RCSMYMDKc4N7dOv2RSZkANVNbJUBCCAqyKvlhy0GyCw5hNhSjqOz4+WcTHnQArXvjZGfVwsjeUyH8Km8yvevDAFAox1PzPm7qL1DCRzae7gpNaLW+SpIkSZ2HsNg5kbqPcwc+p95/F1afo45tNpuastJQSvMCqMnRoKmsRqMEonaNInxoPIOSogns5YVa0/mODMkEqJnaIgGy2+2cOH6CTeu+wEQZanUD/v7ZhAYcdiQ+ZruOrPwIhmbfT6i58dZ1QR2emo9wV29C1XcMJP4ZQofDdX5hmyRJknRtqs6UcfTrz6HuP1QF7UJxK3Vss1h0lJf1oPysDzWn9Kgrq1HbvVAbeuHTayCDkmLpGePXaa4ZkglQM7X6avBmM6+9+irlFRW4uFQSFHSUAL9sVNrGU11mq5aqs4OIzb4PD6snAIIajOrPcHf5BnXcOEh8FLrLGWAlSZKkqyMsdnL2ZVK89yuE6wFqA/ag6EyO7TabmnPlwVSU+FKV64K9pAF1vRW1OgRtt0h6xg4iZng0gb080Rk07RjJpckEqJlaOwGq/GwLa7d8Rnm3bgyJ/RStb+MLsN7kgeuZ0UTm3obKrgdAKIV4qbfgFl6Bashk6DcRdG4t3idJkiSp67CZLJzcdYDyo99iczmMyX8/iqHSqY7J5EHFuUCqiryozdcjztWjMatRqQPReIYSEN2X2BEDCO7ljZunvp0icSYToGZqzQTILuwczfye2hlzqQuPpHKEwD2wgqBTt+Na3hcFBTtWDOpUjD7H0Q/ujzJgkjzaI0mSJLUKW72VnPTDlB7+DkUcpbb7EezGXBSVc3pQV+dBVaUvNWVGaopcaChWUNdZUNu7oej9MfiGENw7ipiEPviHerXLaTOZADVTayVA72W+x6Hv0hhVPIh+pnC0/HctHztWFM1BvH0KMcQGo46dAN7hLfbYkiRJknQlwi6ozC0hO303lvw92NxyqPM+juJefEFdm02NyeSFqcYLU4U7pjId5lIN9mqB2uqKStUdjYcv7v5BBPXqSZ+BkfiHemFw17bahIxN+f7uECfxVq5cybJlyygsLCQ2NpYVK1YQHx9/yfobNmxg3rx5nDp1iqioKJYuXcqtt97q2C6EYMGCBbzxxhtUVFRwww038OqrrxIVFdUW4VySVqXFrVZHrKk3APX6GvTaTLqH6TEM7oMq6iHQubZrHyVJkqSuS1EpePX0Y0jP24DbEFY75ScLOZ2ZjrlgH3ZtHmbP09g9c1FrGvDwKMPDowwC/9uGxaKjrs5IvckDc40b5io9xzIVDqWqsde4orK7ofT0466JY4kYNKz9Ym3vI0D//ve/eeCBB1i1ahUJCQm8/PLLbNiwgWPHjuHn53dB/R9//JFRo0axePFibrvtNtauXcvSpUvZt28fAwYMAGDp0qUsXryYd955h/DwcObNm0dGRgaZmZkYDFdeE6W1jgCV1pWSeyqbyHNBuPX2Qe2tR5Frc0mSJEmdSENFHWePZ1OSsw9L+XGs2gKsHkXY3fNRuVRedl+7XUV9vTsnd95FUJCO3zw2p0X71qlOgSUkJDBs2DBeeeUVoPH28JCQEB577DHmzLnwiZkyZQq1tbV89tlnjrLhw4cTFxfHqlWrEEIQFBTErFmzeOqppwCorKzE39+fNWvWMHXq1Cv2qSMshSFJkiRJnYWw2KnML6HgdDaVZzOwVJ7CqhRjd63A7laOMFSg0lejKAJzvStVPz5OXXQFM+5/ukX70WlOgTU0NLB3717mzp3rKFOpVCQnJ5OamnrRfVJTU3nyySedym655RY2btwIQE5ODoWFhSQnJzu2e3p6kpCQQGpq6kUTILPZjNlsdvxeVVXVnLAkSZIkqUtRtCq8wvzxCvMHRjhtE1Y7deU1lBadpTw/k7qSPIzdXTHcGNs+nf1JuyZApaWl2Gw2/P39ncr9/f05evToRfcpLCy8aP3CwkLH9vNll6rzS4sXL2bhwoXXFIMkSZIkSZemaFS4+hkJ9TMSGtO3vbvjIC9AAebOnUtlZaXjJy8vr727JEmSJElSK2rXBMjHxwe1Wk1RUZFTeVFREQEBARfdJyAg4LL1z//blDb1ej1Go9HpR5IkSZKk61e7JkA6nY4hQ4awY8cOR5ndbmfHjh0kJiZedJ/ExESn+gBffvmlo354eDgBAQFOdaqqqkhLS7tkm5IkSZIkdS3tPg/Qk08+ybRp0xg6dCjx8fG8/PLL1NbWMmPGDAAeeOABgoODWbx4MQAzZ84kKSmJf/zjH0yYMIF169axZ88eXn/9dQAUReGJJ57gb3/7G1FRUY7b4IOCgrjzzjvbK0xJkiRJkjqQdk+ApkyZQklJCfPnz6ewsJC4uDi2bdvmuIg5NzcX1c/myhkxYgRr167lueee49lnnyUqKoqNGzc65gACmD17NrW1tTz88MNUVFQwcuRItm3bdlVzAEmSJEmSdP1r93mAOiI5D5AkSZIkdT5N+f6Wd4FJkiRJktTlyARIkiRJkqQuRyZAkiRJkiR1OTIBkiRJkiSpy5EJkCRJkiRJXY5MgCRJkiRJ6nJkAiRJkiRJUpfT7hMhdkTnp0aqqqpq555IkiRJknS1zn9vX80UhzIBuojq6moAQkJC2rknkiRJkiQ1VXV1NZ6enpetI2eCvgi73U5+fj4eHh4oitIqj1FVVUVISAh5eXnX5WzTMr7OTcbXucn4OjcZ37UTQlBdXU1QUJDTMloXI48AXYRKpaJHjx5t8lhGo/G6fIGfJ+Pr3GR8nZuMr3OT8V2bKx35OU9eBC1JkiRJUpcjEyBJkiRJkrocmQC1E71ez4IFC9Dr9e3dlVYh4+vcZHydm4yvc5PxtQ15EbQkSZIkSV2OPAIkSZIkSVKXIxMgSZIkSZK6HJkASZIkSZLU5cgESJIkSZKkLkcmQJIkSZIkdTkyAWpF5eXl3HfffRiNRry8vHjwwQepqam57D6jR49GURSnnz/+8Y9OdXJzc5kwYQKurq74+fnx9NNPY7VaWzOUCzQ1tvLych577DGio6NxcXEhNDSUxx9/nMrKSqd6v4xdURTWrVvX2uEAsHLlSnr27InBYCAhIYHdu3dftv6GDRvo06cPBoOBmJgYtm7d6rRdCMH8+fMJDAzExcWF5ORkTpw40ZohXFZT4nvjjTe48cYb6datG926dSM5OfmC+tOnT79grMaNG9faYVxUU2Jbs2bNBf02GAxOdTrz2F3sM0RRFCZMmOCo05HG7rvvvuP2228nKCgIRVHYuHHjFfdJSUlh8ODB6PV6evXqxZo1ay6o09T3c2tpanwff/wxN998M76+vhiNRhITE9m+fbtTnb/+9a8XjF+fPn1aMYpLa2p8KSkpF319FhYWOtVrk/ETUqsZN26ciI2NFbt27RLff/+96NWrl7jnnnsuu09SUpJ46KGHREFBgeOnsrLSsd1qtYoBAwaI5ORkkZ6eLrZu3Sp8fHzE3LlzWzscJ02NLSMjQ0yaNEls3rxZZGVliR07doioqChx9913O9UDxOrVq53ir6ura+1wxLp164ROpxNvv/22OHz4sHjooYeEl5eXKCoqumj9nTt3CrVaLV544QWRmZkpnnvuOaHVakVGRoajzpIlS4Snp6fYuHGjOHDggLjjjjtEeHh4m8TzS02N79577xUrV64U6enp4siRI2L69OnC09NTnDlzxlFn2rRpYty4cU5jVV5e3lYhOTQ1ttWrVwuj0ejU78LCQqc6nXnsysrKnGI7dOiQUKvVYvXq1Y46HWXshBBi69at4i9/+Yv4+OOPBSA++eSTy9Y/efKkcHV1FU8++aTIzMwUK1asEGq1Wmzbts1Rp6nPWWtqanwzZ84US5cuFbt37xbHjx8Xc+fOFVqtVuzbt89RZ8GCBaJ///5O41dSUtLKkVxcU+P75ptvBCCOHTvm1H+bzeao01bjJxOgVpKZmSkA8Z///MdR9vnnnwtFUcTZs2cvuV9SUpKYOXPmJbdv3bpVqFQqpw/sV199VRiNRmE2m1uk71dyrbH90vr164VOpxMWi8VRdjVvoNYQHx8vHn30UcfvNptNBAUFicWLF1+0/uTJk8WECROcyhISEsQf/vAHIYQQdrtdBAQEiGXLljm2V1RUCL1eLz744INWiODymhrfL1mtVuHh4SHeeecdR9m0adPExIkTW7qrTdbU2FavXi08PT0v2d71NnYvvfSS8PDwEDU1NY6yjjJ2v3Q17//Zs2eL/v37O5VNmTJF3HLLLY7fm/uctZZr/Xzr16+fWLhwoeP3BQsWiNjY2JbrWAtpSgJ07ty5S9Zpq/GTp8BaSWpqKl5eXgwdOtRRlpycjEqlIi0t7bL7vv/++/j4+DBgwADmzp2LyWRyajcmJgZ/f39H2S233EJVVRWHDx9u+UAuojmx/VxlZSVGoxGNxnlN3kcffRQfHx/i4+N5++23Ea08V2dDQwN79+4lOTnZUaZSqUhOTiY1NfWi+6SmpjrVh8ZxOF8/JyeHwsJCpzqenp4kJCRcss3Wci3x/ZLJZMJiseDt7e1UnpKSgp+fH9HR0fzpT3+irKysRft+JdcaW01NDWFhYYSEhDBx4kSn9871NnZvvfUWU6dOxc3Nzam8vcfuWl3pvdcSz1lHYrfbqa6uvuC9d+LECYKCgoiIiOC+++4jNze3nXp4beLi4ggMDOTmm29m586djvK2HD+5GnwrKSwsxM/Pz6lMo9Hg7e19wbnOn7v33nsJCwsjKCiIgwcP8swzz3Ds2DE+/vhjR7s/T34Ax++Xa7clXWtsP1daWsqiRYt4+OGHncqff/55brrpJlxdXfniiy945JFHqKmp4fHHH2+x/l+sLzab7aLP69GjRy+6z6XG4Xz85/+9XJ22ci3x/dIzzzxDUFCQ04fSuHHjmDRpEuHh4WRnZ/Pss88yfvx4UlNTUavVLRrDpVxLbNHR0bz99tsMHDiQyspKli9fzogRIzh8+DA9evS4rsZu9+7dHDp0iLfeesupvCOM3bW61HuvqqqKuro6zp071+zXe0eyfPlyampqmDx5sqMsISGBNWvWEB0dTUFBAQsXLuTGG2/k0KFDeHh4tGNvrywwMJBVq1YxdOhQzGYzb775JqNHjyYtLY3Bgwe3yOfV1ZIJUBPNmTOHpUuXXrbOkSNHrrn9nycEMTExBAYGMmbMGLKzs4mMjLzmdq9Ga8d2XlVVFRMmTKBfv3789a9/ddo2b948x/8HDRpEbW0ty5Yta9UESLq8JUuWsG7dOlJSUpwuFp46darj/zExMQwcOJDIyEhSUlIYM2ZMe3T1qiQmJpKYmOj4fcSIEfTt25fXXnuNRYsWtWPPWt5bb71FTEwM8fHxTuWddey6mrVr17Jw4UI2bdrk9Efn+PHjHf8fOHAgCQkJhIWFsX79eh588MH26OpVi46OJjo62vH7iBEjyM7O5qWXXuK9995r077IBKiJZs2axfTp0y9bJyIigoCAAIqLi53KrVYr5eXlBAQEXPXjJSQkAJCVlUVkZCQBAQEXXA1fVFQE0KR2L6YtYquurmbcuHF4eHjwySefoNVqL1s/ISGBRYsWYTabW23hPB8fH9RqteN5PK+oqOiS8QQEBFy2/vl/i4qKCAwMdKoTFxfXgr2/smuJ77zly5ezZMkSvvrqKwYOHHjZuhEREfj4+JCVldVmX6LNie08rVbLoEGDyMrKAq6fsautrWXdunU8//zzV3yc9hi7a3Wp957RaMTFxQW1Wt3s10RHsG7dOn7/+9+zYcOGC075/ZKXlxe9e/d2vIY7m/j4eH744QegZd7TV0teA9REvr6+9OnT57I/Op2OxMREKioq2Lt3r2Pfr7/+Grvd7khqrsb+/fsBHB/EiYmJZGRkOCUgX375JUajkX79+nXo2Kqqqhg7diw6nY7NmzdfcOvxxezfv59u3bq16qrBOp2OIUOGsGPHDkeZ3W5nx44dTkcKfi4xMdGpPjSOw/n64eHhBAQEONWpqqoiLS3tkm22lmuJD+CFF15g0aJFbNu2zel6r0s5c+YMZWVlTklDa7vW2H7OZrORkZHh6Pf1MHbQOE2D2Wzmt7/97RUfpz3G7lpd6b3XEq+J9vbBBx8wY8YMPvjgA6fpCy6lpqaG7OzsTjF+F7N//35H39t0/Fr0kmrJybhx48SgQYNEWlqa+OGHH0RUVJTTreJnzpwR0dHRIi0tTQghRFZWlnj++efFnj17RE5Ojti0aZOIiIgQo0aNcuxz/jb4sWPHiv3794tt27YJX1/fdrkNvimxVVZWioSEBBETEyOysrKcbn+0Wq1CCCE2b94s3njjDZGRkSFOnDgh/vWvfwlXV1cxf/78Vo9n3bp1Qq/XizVr1ojMzEzx8MMPCy8vL8fddvfff7+YM2eOo/7OnTuFRqMRy5cvF0eOHBELFiy46G3wXl5eYtOmTeLgwYNi4sSJ7XordVPiW7JkidDpdOLDDz90Gqvq6mohhBDV1dXiqaeeEqmpqSInJ0d89dVXYvDgwSIqKkrU19d36NgWLlwotm/fLrKzs8XevXvF1KlThcFgEIcPH3aKv7OO3XkjR44UU6ZMuaC8I43d+f6kp6eL9PR0AYgXX3xRpKeni9OnTwshhJgzZ464//77HfXP3wb/9NNPiyNHjoiVK1de9Db4yz1nHTm+999/X2g0GrFy5Uqn915FRYWjzqxZs0RKSorIyckRO3fuFMnJycLHx0cUFxd3+PheeuklsXHjRnHixAmRkZEhZs6cKVQqlfjqq68cddpq/GQC1IrKysrEPffcI9zd3YXRaBQzZsxwfIEIIUROTo4AxDfffCOEECI3N1eMGjVKeHt7C71eL3r16iWefvppp3mAhBDi1KlTYvz48cLFxUX4+PiIWbNmOd1K3haaGtv5Wx8v9pOTkyOEaLyVPi4uTri7uws3NzcRGxsrVq1a5TQ/RGtasWKFCA0NFTqdTsTHx4tdu3Y5tiUlJYlp06Y51V+/fr3o3bu30Ol0on///mLLli1O2+12u5g3b57w9/cXer1ejBkzRhw7dqwtQrmopsQXFhZ20bFasGCBEEIIk8kkxo4dK3x9fYVWqxVhYWHioYceapcvGCGaFtsTTzzhqOvv7y9uvfVWpzlWhOjcYyeEEEePHhWA+OKLLy5oq6ON3aU+G87HNG3aNJGUlHTBPnFxcUKn04mIiAinOY7Ou9xz1paaGl9SUtJl6wvReNt/YGCg0Ol0Ijg4WEyZMkVkZWW1bWA/aWp8S5cuFZGRkcJgMAhvb28xevRo8fXXX1/QbluMnyJEK99jLEmSJEmS1MHIa4AkSZIkSepyZAIkSZIkSVKXIxMgSZIkSZK6HJkASZIkSZLU5cgESJIkSZKkLkcmQJIkSZIkdTkyAZIkSZIkqcuRCZAkSZIkSV2OTIAkSZIkSepyZAIkSZIkSVKXIxMgSZIkSZK6nP8PmY88EldNdHQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "data_corr = data.corr()\n",
    "data_corr.plot(kind='kde')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b80ba2",
   "metadata": {},
   "source": [
    "#### From the above heatmap, it is clear that age is the most significant factor affecting the occurance of stroke as it has the highest correlation coefficient."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a10e58",
   "metadata": {},
   "source": [
    "### Distribution of individuals encountering stroke with the help of smoking status and work type\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a7998f2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Individuals with stroke: Smoking status')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "plt.subplot(2,2,1)\n",
    "sns.countplot(x = data.work_type, hue = data.stroke, palette=\"rocket\")\n",
    "plt.title('Stroke vs Work type', fontsize=15)\n",
    "plt.xlabel('Work type', fontsize=15)\n",
    "plt.ylabel('Individuals with stroke: Work type', fontsize=15)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.countplot(x = data.smoking_status, hue = data.stroke, palette=\"crest\")\n",
    "plt.title('Stroke vs Smoking status', fontsize=14)\n",
    "plt.xlabel('Smoking status', fontsize=15)\n",
    "plt.ylabel('Individuals with stroke: Smoking status', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c3a94f",
   "metadata": {},
   "source": [
    "### Significance of Hypertension in Stroke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "65874b7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.heatmap(pd.crosstab(data['hypertension'],data['stroke']), cmap=\"coolwarm\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8eea9e9",
   "metadata": {},
   "source": [
    "## Autovisualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d6acddf2",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'autoviz'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mautoviz\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mautoviz\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mAutoViz_Class\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoViz_Class\n\u001b[0;32m      4\u001b[0m AV \u001b[38;5;241m=\u001b[39m AutoViz_Class()\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'autoviz'"
     ]
    }
   ],
   "source": [
    "from autoviz.AutoViz_Class import AutoViz_Class\n",
    "\n",
    "AV = AutoViz_Class()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9824e0bd",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'AV' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [14], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mAV\u001b[49m\u001b[38;5;241m.\u001b[39mAutoViz(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhealthcare-dataset-stroke-data.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      2\u001b[0m df \u001b[38;5;241m=\u001b[39m AV\u001b[38;5;241m.\u001b[39mAutoViz(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhealthcare-dataset-stroke-data.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, depVar\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstroke\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'AV' is not defined"
     ]
    }
   ],
   "source": [
    "df = AV.AutoViz(\"healthcare-dataset-stroke-data.csv\")\n",
    "df = AV.AutoViz(\"healthcare-dataset-stroke-data.csv\", depVar='stroke')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a1acebe",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8bdc42b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                     0\n",
       "gender                 0\n",
       "age                    0\n",
       "hypertension           0\n",
       "heart_disease          0\n",
       "ever_married           0\n",
       "work_type              0\n",
       "Residence_type         0\n",
       "avg_glucose_level      0\n",
       "bmi                  201\n",
       "smoking_status         0\n",
       "stroke                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17185ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(missing_values=np.nan, strategy='most_frequent')\n",
    "data = pd.DataFrame(imputer.fit_transform(data), columns=data.columns, index=data.id)\n",
    "data = data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d871e9bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "gender               0\n",
       "age                  0\n",
       "hypertension         0\n",
       "heart_disease        0\n",
       "ever_married         0\n",
       "work_type            0\n",
       "Residence_type       0\n",
       "avg_glucose_level    0\n",
       "bmi                  0\n",
       "smoking_status       0\n",
       "stroke               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228ac310",
   "metadata": {},
   "source": [
    "### One-Hot Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8f17f11",
   "metadata": {},
   "source": [
    "### One-hot encoding is essentially the representation of categorical variables as binary vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fca3d5b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>work_type_Govt_job</th>\n",
       "      <th>work_type_Never_worked</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>smoking_status_Unknown</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>gender_Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>202.21</td>\n",
       "      <td>28.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age hypertension heart_disease ever_married Residence_type  \\\n",
       "0  67.0            0             1            1              1   \n",
       "1  61.0            0             0            1              0   \n",
       "2  80.0            0             1            1              0   \n",
       "3  49.0            0             0            1              1   \n",
       "4  79.0            1             0            1              0   \n",
       "\n",
       "  avg_glucose_level   bmi stroke  work_type_Govt_job  work_type_Never_worked  \\\n",
       "0            228.69  36.6      1                   0                       0   \n",
       "1            202.21  28.7      1                   0                       0   \n",
       "2            105.92  32.5      1                   0                       0   \n",
       "3            171.23  34.4      1                   0                       0   \n",
       "4            174.12  24.0      1                   0                       0   \n",
       "\n",
       "   work_type_Private  work_type_Self-employed  work_type_children  \\\n",
       "0                  1                        0                   0   \n",
       "1                  0                        1                   0   \n",
       "2                  1                        0                   0   \n",
       "3                  1                        0                   0   \n",
       "4                  0                        1                   0   \n",
       "\n",
       "   smoking_status_Unknown  smoking_status_formerly smoked  \\\n",
       "0                       0                               1   \n",
       "1                       0                               0   \n",
       "2                       0                               0   \n",
       "3                       0                               0   \n",
       "4                       0                               0   \n",
       "\n",
       "   smoking_status_never smoked  smoking_status_smokes  gender_Female  \\\n",
       "0                            0                      0              0   \n",
       "1                            1                      0              1   \n",
       "2                            1                      0              0   \n",
       "3                            0                      1              1   \n",
       "4                            1                      0              1   \n",
       "\n",
       "   gender_Male  gender_Other  \n",
       "0            1             0  \n",
       "1            0             0  \n",
       "2            1             0  \n",
       "3            0             0  \n",
       "4            0             0  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= pd.get_dummies(data, columns=['work_type', 'smoking_status', 'gender'])\n",
    "data.drop(columns=['id'], inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47808ccd",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e405f394",
   "metadata": {},
   "source": [
    "### MinMaxScaler - Data is scaled in the range of 0 to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56ef7a5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.81689453 0.         1.         ... 0.         1.         0.        ]\n",
      " [0.74365234 0.         0.         ... 1.         0.         0.        ]\n",
      " [0.97558594 0.         1.         ... 0.         1.         0.        ]\n",
      " ...\n",
      " [0.42626953 0.         0.         ... 1.         0.         0.        ]\n",
      " [0.62158203 0.         0.         ... 0.         1.         0.        ]\n",
      " [0.53613281 0.         0.         ... 1.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "model = scaler.fit(data)\n",
    "scaled_data = model.transform(data)\n",
    "print(scaled_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79807c3",
   "metadata": {},
   "source": [
    "### StandardScaler - A feature is scaled by subtracting the mean from all the data points and dividing the resultant values by the standard deviation of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d8df733",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.05143428 -0.32860186  4.18503199 ... -1.18950991  1.18998977\n",
      "  -0.01399046]\n",
      " [ 0.78607007 -0.32860186 -0.2389468  ...  0.84068236 -0.84034336\n",
      "  -0.01399046]\n",
      " [ 1.62639008 -0.32860186  4.18503199 ... -1.18950991  1.18998977\n",
      "  -0.01399046]\n",
      " ...\n",
      " [-0.36384151 -0.32860186 -0.2389468  ...  0.84068236 -0.84034336\n",
      "  -0.01399046]\n",
      " [ 0.34379639 -0.32860186 -0.2389468  ... -1.18950991  1.18998977\n",
      "  -0.01399046]\n",
      " [ 0.03420481 -0.32860186 -0.2389468  ...  0.84068236 -0.84034336\n",
      "  -0.01399046]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "model = scaler.fit(data)\n",
    "scaled_data = model.transform(data)\n",
    "print(scaled_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee659b04",
   "metadata": {},
   "source": [
    "## Splitting data into Training and Testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e09e023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5110, 19), (5110,))"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_Data = data.pop('stroke').to_numpy()\n",
    "x_Data = data.to_numpy()\n",
    "\n",
    "x_Data = x_Data.astype(np.float32)\n",
    "y_Data = y_Data.astype(np.float32)\n",
    "x_Data.shape, y_Data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "331ed1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "split = int(0.80*len(x_Data))\n",
    "\n",
    "x_Train = x_Data[:split]\n",
    "y_Train = y_Data[:split]\n",
    "\n",
    "x_Test = x_Data[split:]\n",
    "y_Test = y_Data[split:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13f80748",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2439389d",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9edc337a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9393346379647749"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(x_Train, y_Train)\n",
    "lr.score(x_Train, y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59372ebd",
   "metadata": {},
   "source": [
    "### K-nearest neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5ef3e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9388454011741683"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(10)\n",
    "knn.fit(x_Train, y_Train)\n",
    "knn.score(x_Train, y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cb2f50f",
   "metadata": {},
   "source": [
    "### Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5bf6462",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40631115459882583"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "nb = GaussianNB()\n",
    "nb.fit(x_Train, y_Train)\n",
    "nb.score(x_Train, y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "030cfb10",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86b995f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9390900195694716"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "sv = SVC()\n",
    "sv.fit(x_Train, y_Train)\n",
    "sv.score(x_Train, y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fce8f08",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faed3d53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_Train, y_Train)\n",
    "rf.score(x_Train, y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acd78b7e",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "528b5a10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(x_Train, y_Train)\n",
    "dt.score(x_Train, y_Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "441afdc0",
   "metadata": {},
   "source": [
    "## Performance Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bfd4b05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xR4vpa9as0QMPPGC+I5SZmSlJ2rt3r/nv0tWuX2vXrs0xmGTJzMzUv//+q3///dc8rUKFClq2bJkmTJhww/dJV/dRenq6goODLe7UOTk5acCAAZL+d0fwVsTExOjXX3+Vn5+fxa9TFStWTL169brl9nKzZs0aXblyRSEhIRZjGwoVKmRe1/fff2/xHkdHR3OIvpN2JKlr164Wd7UaNGgg6Wrwux3WnjfXu9Vz2drj5k6OL2ts2bJF58+f18svv2xxDJpMJr355puSpFWrVmV73/X/HmadN1nd8bJ069ZNP/zwg5566qk7rhW423DnBDC4zZs3W1zkOjg4yM3NTU888YRefPFFi4uogwcPSroaaHbs2GHRTnx8vCRZ/J6/pGzdsG4k6321atXKcf7jjz+uAwcO6PDhwxZ3ZnJr//puV1kXJ9e/x9HRUdL/BhYXKVJErVq1kiSdOHFCx44d06lTp3Ts2DFz95v09PSbrs/e3l5FihQxtytdvTNRsmRJeXl5ZXv/td+kHjx4UIUKFdKyZcuyLffPP/9IuvqZ5fatZ82aNVWzZk1dvHhR0dHROnnypP755x/t379f58+f1wMPPHDTbZBkHmuSdXHj5uamgIAAZWZm6ujRozp+/LhOnTqlI0eOaPfu3ZKU668wOTs7q2nTplqzZo3+/PNPVa5cWadPn9bevXvVtm1bc3Bs27atli5dqhkzZmj58uXy9/eXv7+/nnjiiRy7jF1bb0BAgNasWaPnnntONWrUkL+/v/lnq28mt2PR29tbRYsW1eHDh2/azvWOHDkiSeZf2brWtXce8kLWuRoVFaW///7bYl7Wfrz+XPXy8soWNm+nncKFC6tkyZIW07KOoWvPhVth7XlzvVs5l609bu70+LJG1ud+6NAhhYWFZZuf1TXtWoUKFco2hqRFixaaPXu2VqxYoY0bN8rPz0/169eXv7+/RXdd4H5COAEM7trBszeTmJgoSZo/f/4Nl7n+G/OsC/+buXz5siRlG3SdJes/0pSUFIvp194Bud6NujwVLlz4pvX89ttvmjBhgvkioXDhwvL29laVKlVu+O1vTtt6/ZiICxcuWPUsg0uXLik9PV2zZs264TLX/1BBTm1MnDhRa9asMV9IlipVSrVr19bRo0dzDBC5bcO1dy9+/vlnTZ48WcePH5d09a5CpUqV9OijjyouLs5i2Zy0adNGa9as0Y8//qjKlStrzZo1yszMtPjxgIoVK+rLL7/U3LlztW3bNq1cuVIrV66Uo6OjnnvuOfXp0yfbxXSWYcOG6dFHH9WqVau0Z88e7dmzR1OmTFHFihU1ePBgi0HQ17vZsejh4WFxN8xaWedGTqHQxcUlT3+5LutcXb58+U3ryZLTuXQ77eR0fuV0DN0Ka8+bnNzKuWztcXMnx5c1Ll26JOlqV8cbuT7o5XTuenh4aM6cOQoPD9emTZv0448/6scff5S9vb2efPJJvfPOO7l2DQXuRYQT4B7i5OQke3t7bd682aJrRV7I+g8yNjY2x/lZ/1nn9o15Xjlz5oz69OmjQoUKafDgwapRo4bKly8vBwcH7dixI9cLhptxdnY2X/xeLzk52dxdztnZWU5OTjl23bDW8OHDtW3bNrVr105t2rSRt7e3+YK7devWunLlym21u2/fPr3zzjvy9PTUyJEjVa1aNZUpU0Z2dnZatmyZtm3bdtM26tatqxIlSmjDhg3q27evNmzYoJIlS2Ybs+Hj46NRo0YpLS1Nf/zxh3bs2KFVq1bp66+/VvHixdW1a9cc23dwcFCnTp3UqVMnxcTEKDIyUhs2bNC2bdvUv39/ff/99zccCHztsZjVpfFaly5duq3jsFixYpL+d8F/rf/++y/Hu3G3K2vbli1bdsOB4rZs505Ze95c71bPZWuPmzs5vqyR9d7JkyffsMuatUqXLq333ntPgwcP1p9//qmdO3dq1apVWrNmjYoUKaLBgwffUfvA3YYxJ8A9xMfHR+np6frzzz+zzTt06JAmTZqkXbt23VbbWb8w9Pvvv+c4/9dff5XJZNIjjzxyW+3fip9//lkpKSnq3bu3nnvuOVWoUMH8DX1W15bb/QbY29tbZ8+eVVxcXLZ5PXv21NNPP6309HT5+PgoNjY2x+UiIyM1depU7d+//4bruXTpkrZt26bHH39cQ4YMUY0aNczBJCEhQfHx8be9DT/99JMyMjI0dOhQBQQEqFy5cuaxPdZ+PnZ2dmrdurX+/fdfbdq0SUeOHFFAQIDFnaZVq1bpk08+UWZmphwcHFSrVi29/vrr+vzzzyVd/UY8J6dOndLUqVO1ZcsWSVe7K7Vt21YTJkxQ06ZNdfHiRR07duyGtWUdizm1f+LECcXFxalChQq5bt+N2jWZTDm2m9u+vB0+Pj6Srv6IxfXOnj2rCRMmaMOGDTZr505Ze95c71bOZWuPmzs9vqyRdQxm3e25VlJSksaPH68VK1bctJ1NmzZp7NixSkxMlJ2dnapWraru3bsrPDxc9vb2NzyHgHsZ4QS4h2R1/5owYYLFt78pKSkaO3asFixYoOTk5NtqO+tb84MHD5qfeJ1lxYoV+v3331WnTh2b/C5/VreUM2fOWEw/fvy45s6dK0kWg0tvRevWrZWZmalJkyZZtLFt2zZFR0erTp06sre3V9u2bZWZmamxY8eaH9omXQ0WH3/8sebMmZPr3atChQrJzs5O586ds3h/amqqxo4dq4yMjNvehqzuI9d/Pnv37jV3/7Gm7TZt2kiSeQDx9c/M+OOPP/TNN99k+3Y7a73Xj2vIUqhQIc2dO1czZsyw2Pb09HTzs15yGruQpXXr1rK3t9ecOXMsuv0kJydr3LhxOdZqDQ8PD/n7+ysqKkrr1q2zaPfaX33KC1nbMG3aNIu7kRkZGfrss8/01VdfKSYmxmbt5CQrJFy7j3Krw5rz5nq3ci5be9zc6fFljaZNm8rFxUXz58/PNtZn2rRp+vrrr60KQH///beWLl2abexaTEyM0tPTb3gOAfcyunUB95A6deqoU6dOWrRokYKCgtSwYUM5Ojpq8+bNOn36tJ566qmbPnsjN4MHD1ZISIjGjRunTZs2qWLFijpy5IgiIyPl6empIUOG5OHW3FijRo00depUzZ07V8ePH1e5cuV06tQpbdmyxTxe4GbjPW7k2WefVUREhNauXasjR46obt26On/+vDZs2CBPT0/17dtX0tUL961btyoiIkIdO3aUn5+fMjMzFRERofPnz6tLly7ZHhp5rSJFiqh58+Zav369unbtKj8/P125ckVbt27V2bNn5erqqgsXLiglJSXXcTs5adWqlRYtWqRPP/1Uv/76qzw9PfXXX39p+/btcnV11ZUrV6z6fB566CFVq1ZN+/fvV9WqVbONKXj11VcVERGh4cOHa/369SpfvrzOnj2riIgIFStWTJ07d86xXS8vL3Xo0EFfffWVOnTooAYNGsje3l6RkZE6cuSIgoKCch0MXKZMGfXr10+fffaZXnnlFfNzTrZv365Tp07pySefvK1wIkmDBg1ScHCw+SeXS5Ysqe3bt9/2XawbKVeunHkbOnbsqMaNG8vV1VW7du3SkSNHVKdOHT3//PM2aycnWRfG3377rS5duqSnn376hnekrD1vrncr5/KtHDd3cnxZw8XFRcOGDdOQIUPMx6CXl5f27t2rvXv3qkKFChY/w30jzz33nL777jtNnjxZUVFR8vHxUUJCgjZs2KDChQtb1QZwryGcAPeYfv36qXLlyvr222/1448/ymQyqVy5curcubPatWt3w4fiWaN8+fKaO3euZs+erW3btunXX3+Vh4eHgoKC1K1bNxUvXjwPt+TGPD09NWXKFE2ZMkVRUVHatWuXSpQooeeee07du3dXly5dFBkZqbS0tBsOyL4Re3t7jR8/XgsXLtTq1au1dOlSOTk5qWXLlnr99dfl4eEh6eoA4jFjxujbb7/VqlWr9N1338nR0VEPP/yw3nrrLT355JM3Xdd7770nLy8vbdy4Ud98842KFy+uihUrasSIEdq9e7dmzpyp7du3q3nz5re0DZUqVdLEiRMVFhamLVu2yGQyqVSpUurWrZuCgoLUtm1bbd++PdvD+XLSsmVL7d+/32IgfJYyZcpo1qxZmj17tn7//Xdt3bpVxYoVU4sWLRQSEpLrL7X16dNHDz30kFasWKHVq1crNTVVDz/8sAYPHqzAwMCb1hUUFKTy5ctr/vz52rRpkzIyMvTII4+oS5cuVr3/RsqUKaMvv/xS06dP186dOxUZGak6depo4MCBOT71/U5kbcPChQu1adMmpaWlqUyZMnr99dfVoUMHq3+sIq/auV7NmjXVoUMH/fDDD1q8eLEefvjhG4YTa8+b693quWztcXOnx5c1mjVrppkzZ2rOnDnatWuXUlJSVKJECb366qt65ZVXrBr35OrqqunTpys8PFy7du3Snj175OTkpHr16qlHjx569NFH86RW4G5iSkhIyNuvgwAA94z3339f69ev1w8//GDxHA0AAPIDY04AADn666+/FBERoWbNmhFMAAA2QbcuAICFBQsWaN26dTp27JjS09PVrVu3gi4JAHCfIJwAACyUKFFC//zzj4oXL64+ffrI29u7oEsCANwnGHMCAAAAwBAYcwIAAADAEAgnAAAAAAyBcAIAAADAEAgnAAAAAAyBcAIAAADAEAgnAAAAAAyBcAIAAADAEAgnAAAAAAyBcAIAAADAEAgnAAAAAAyBcAIAAADAEAgnAAAAAAyBcAIAAADAEAgnAAAAAAyBcAIAAADAEAgnAAAAAAyBcAIAAADAEAgnAAAAAAyBcAIAAADAEAoknCQmJqpDhw46ffp0tnnR0dHq0qWLXnjhBY0aNUppaWkFUCEAAAAAW7N5ONm3b59CQkL0zz//5Dh/+PDhGjhwoJYuXarMzEytWLHCtgUCAAAAKBA2DycrVqzQoEGD5OnpmW3emTNndOXKFT322GOSpLZt22rDhg22LhE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7d1ffvn2VlJSULwUCAAAAuD9YHU4mT56s06dPq3///uZpfn5++vzzz3Xs2DFNnz49XwoEAAAAcH+wOpxs375dvXv3VsOGDc3T7O3t5efnp9DQUG3cuDFfCgQAAABwf7A6nCQlJcnFxSXHeQ8++KASEhLyqiYAAAAA9yGrw0mlSpW0cuXKHOetXLlSPj4+eVYUAAAAgPuP1b/W1b17d/Xv31+dO3dWkyZNzHdLNm/erMOHD2v8+PH5WScAAACAe5zV4cTPz0/jx49XWFiYZs+erczMTJlMJlWqVEmffPKJ/P3987NOAAAAAPe4W3rOib+/v/z9/fXff/9p3759Onv2rKpUqaKHH344n8oDAAAAcL+4aTjZsmWLpk2bpnbt2umll16SJM2YMUMLFy40L/PCCy9o0KBB+VclAAAAgHtergPif//9d7399tuSpHLlykmSoqKitGDBAj322GMKDw/XBx98oFWrVumHH37I/2oBAAAA3LNyvXOyYMEC1a5dW59//rkcHK4uunTpUplMJg0bNkzly5dXlSpVdOzYMa1YsUJt2rSxSdEAAAAA7j253jn5448/9Nxzz5mDiSRFRkbqoYceUvny5c3T6tatqyNHjuRflQAAAADuebmGk0uXLsnd3d38+ujRo7p06ZJq1aplsZyDg4PS0tLyp0IAAAAA94Vcw4mbm5vOnTtnfh0VFSWTyaR69epZLHfs2DEVL148fyoEAAAAcF/INZzUqVNHK1asUEZGhtLS0vT999/L0dFRfn5+5mVSUlL0zTffqGbNmvldKwAAAIB7WK4D4rt166bu3burXbt2yszMVExMjEJCQvTAAw9IklasWKElS5bo1KlTGj16tE0KBgAAAHBvyjWcPPLII5o9e7YWLlyo8+fPq2vXrnrhhRfM82fOnCl7e3uNGzdOFStWzPdiAQAAANy7bvoQxgoVKmjYsGE5zpszZ47c3d1lZ5dr7zAAAAAAuKmbhpPceHp65lUdAAAAAO5z3PIAAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGQDgBAAAAYAiEEwAAAACGYPNwsnbtWgUFBen555/XN998c8Pltm7dqsDAQBtWBgAAAKAgOdhyZTExMZo2bZrmzZunwoULq0ePHqpTp44qVKhgsdy5c+c0adIkW5YGAAAAoIDZ9M5JZGSk6tatK1dXVzk5OalFixaKiIjIttzo0aMVHBxsy9IAAAAAFDCbhpO4uDh5eHiYX7u7uysmJsZimcWLF6ty5cqqXr26LUsDAAAAUMBs2q0rMzMz2zSTyWT++9GjRxUREaEpU6ZkCy3WOHz4sFXLJScl3XLbyD/JSUlW77s7cSUpOd/XAeslJyXbZL/j/lI8mfPcSJKTk3Uqn8/zEpkp+do+bl1yUpL+zuf9XrpoYdtexCJXycnJOh1j3T738fHJdb5N96unp6d+++038+tz587J09PT/HrDhg2Ki4tTly5dlJaWptjYWPXs2VMzZ860qv2bbWyWywn852UkTs7OVu+7O3H00p/5vg5Yz8nZSd422O+4vyTt5zw3Eicnp3z/9z315FEl5usacKucnJ3lU9Y7X9eRcSFGqfm6BtwKJycn+ZR8KE/asmm3Ll9fX+3evVvx8fFKSUlRRESE/P39zfNDQkK0dOlSLVy4UBMmTJCnp6fVwQQAAADA3c2m4cTLy0uhoaEKDQ3Vyy+/rKeeekrVqlVTv379dODAAVuWAgAAAMBgbN5dLyAgQAEBARbTJk6cmG250qVLa+XKlTaqCgAAAEBB4wnxAAAAAAyBcAIAAADAEAgnAAAAAAyBcAIAAADAEAgnAAAAAAyBcAIAAADAEAgnAAAAAAyBcAIAAADAEGz+EEYAsJXEU7FKOhtf0GXg/zmXeFAuZTwLugwAgIERTgDcs5LOxmvTm5MKugz8v6aT+xBOAAC5olsXAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAAAAAwBAdbr3Dt2rUKDw9XamqqOnbsqPbt21vM//nnnxUWFqbMzEyVLl1aw4cPV7FixWxdJgAAAAAbs+mdk5iYGE2bNk1hYWFauHChli9frmPHjpnnJyYmauzYsZowYYIWLVokHx8fzZw505YlAgAAACggNg0nkZGRqlu3rlxdXeXk5KQWLVooIiLCPD8tLU1vv/22vLy8JEkVK1bUv//+a8sSAQAAABQQm4aTuLg4eXh4mF+7u7srJibG/NrNzU1NmzaVJKWkpGju3Llq0qSJLUsEAAAAUEBsOuYkMzMz2zSTyZRtWmJiogYNGiQfHx+1bdvW6vYPHz5s1XLJSUlWt4n8l5yUZPW+uxNXkpLzfR2wXnJScr7v98LJ7HMjSU7O/31enH1uKMnJyTqVz/u8RGZKvraPW5eclKS/83m/ly5a2PYDp3FDycnJOh1j3T738fHJdb5N96unp6d+++038+tz587J09PTYpm4uDj16dNHdevWVf/+/W+p/ZttbJbLCfznZSROzs5W77s7cfTSn/m+DljPydlJ3vm832MuRedr+7g1Tk5Oeiif93nSfs5zI3Fycsr3f99TTx5VYr6uAbfKydlZPmW983UdGRdilJqva8CtcHJykk/Jh/KkLZt26/L19dXu3bsVHx+vlJQURUREyN/f3zw/PT1db731llq0aKG33norx7sqAAAAAO5NNr1z4uXlpdDQUIWGhio1NVWBgYGqVq2a+vXrp5CQEJ09e1aHDh1Senq6eaB8lSpVNHToUFuWCQAAAKAA2Ly7XkBAgAICAiymTZw4UZJUtWpV7dq1y9YlAQAAADAAnhAPAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMgXACAAAAwBAIJwAAAAAMwebhZO3atQoKCtLzzz+vb775Jtv86OhodenSRS+88IJGjRqltLQ0W5cIAAAAoADYNJzExMRo2rRpCgsL08KFC7V8+XIdO3bMYpnhw4dr4MCBWrp0qTIzM7VixQpblggAAACggNg0nERGRqpu3bpydXWVk5OTWrRooYiICPP8M2fO6MqVK3rsscckSW3bttWGDRtsWSIAAACAAuJgy5XFxcXJw8PD/Nrd3V0HDhwwv46NjbWY7+HhoZiYmDyvo2a9x7XhjzV53i6Mzbt2ZY3bMbugy4ANedWupJe2Ty7oMmBDztUqy+frWQVdBmyoUFlvPdj3k4IuAzZm5+olR78XC7oM5AOb3jnJzMzMNs1kMuX6Hjs7xuwDAAAA9wObXvl7enrq3Llz5tfnzp2Tp6fnDedff6cFAAAAwL3LpuHE19dXu3fvVnx8vFJSUhQRESF/f3/z/FKlSqlw4cL6/fffJUmrV69W/fr1bVkiAAAAgAJiSkhIyN7XKh+tXbtWc+bMUWpqqgIDA/Xqq6+qX79+CgkJUdWqVRUdHa0xY8YoKSlJlSpV0vDhw1W4cGFblggAAACgANg8nAAAAABAThhtDgAAAMAQCCcAAAAADMGmzzkxsqioKM2cOVPTp0+/o3ZefvllLVy48IbzQ0NDNW3aNKuWDQwMVJEiReTgcHU3JSYmqkqVKnr//ffl5OR0R3XmhdjYWI0ePVoTJ04s6FLuetcff5cvX1bv3r31+OOPa+PGjXrqqaf0+uuvm5cfMWKE6tSpo7Zt2yowMDDX+bg7nD59Wu3atdMXX3yhJ554wjw9MDBQPXv21CeffKKyZcsqMzNTaWlpCggIUPfu3QuwYtyODRs2aO7cuUpLS1NmZqaefvppFStWTOvXr9cXX3xhsezIkSP16KOPKigoSMePH9ekSZN0+vRpSZK3t7cGDhwoNze3AtgK3InTp0/rxRdf1COPPCLp6mMWLl++rDZt2qht27YW87KMHz9eJUqUKIhy73nX748rV66oYsWKGjRokNzd3W+prRkzZqhKlSpq3LhxjvNHjRql559/XlWrVr2ldr///nt9/fXXkqS//vpL5cqVk4ODg2rUqKG33377ltq6GxBO8lhuYUO6ehFq7bKSNGHCBJUuXVqSlJqaqp49e+qHH37Qiy8W/IOHPD09CSb5ICkpSX379lXt2rX15ptvauPGjfr666/VrFkzValSJcf33Gw+7g4ODg4aM2aMFi1apAceeMBiXpUqVczhNSkpSS+99JKaNm2qChUqFESpuA0xMTH6/PPPNW/ePLm5uSkpKUm9evVSjx49tG/fPp0/f17FixeXJKWkpGjr1q3q06ePYmNjFRoaqsGDB6tRo0bKzMzUnDlzNGjQIM2cObOAtwq3w9PT0+IaIDY2Vi+88IJatWqVbR7y37WfeWZmpqZOnap33333ls+v1157Ldf5Q4cOva36nnnmGT3zzDOSrn5hde214b2IcHIT4eHhWrt2rezs7PTEE0+od+/esre31+LFi7VkyRK5uLjo4YcfVpkyZRQSEiJfX19FRkYqMjJSkydffTJ1sWLFNGrUKM2adfWpxd26dVN4eLh52QsXLmjUqFE6fvy4ChUqpH79+qlevXrZarl06ZISExPl6uoqSdqxY4dmzJihtLQ0lS5dWkOGDJGbm5uioqL06aefyt7eXo899pj++usvTZ8+Xb169VKxYsV07NgxjRkzRufOncvx/Z9//rl27dole3t7NW7cWD179sxxe5KSkhQaGqqVK1fq3LlzGjVqlM6ePSt7e3u9/vrr8vf3V1hYmGJjY3XixAmdOXNGgYGBfNubi+TkZPXr109169ZVr169zNO7du2qkSNHat68eSpUqFC2991sPu4Onp6e8vX11eeff64hQ4bccLmUlBTZ29vLxcXFhtXhTiUkJCgtLU1XrlyRJDk7O+v9999X4cKF1bRpU61bt05BQUGSpE2bNqlu3bpyc3PT9OnT9cQTT6hRo0aSrj68+NVXX1Xp0qWVlpZmvruOu1dcXJwyMzN14cKFgi7lvmcymRQSEqKAgAAdPnxY27dv1/r165Weni4/Pz/17t1bJpNJixYt0rJly2Rvb6+GDRuqd+/e5l4LTZs21bBhw8zP7gsODlbjxo3Vq1cv9ezZU3Xq1Mnx+vLs2bN6++235e3trUOHDql48eL66KOPzNd9ORkxYoQuXLigkydPqnfv3nJ3d9eECROUkpIiNzc3vfvuuypTpoxOnDihsWPH6sKFCypSpIgGDhyoRx991FYf6y1hzEkutm3bpi1btmju3LmaP3++Tp48qWXLlunw4cP65ptvNHfuXIWFhenEiRPZ3hseHq53331X8+bNU6NGjfTnn39q4MCB5nnXmjFjhsqVK6clS5ZoxIgR5m5fktS/f3916tRJAQEB6tevn9q3b6+WLVsqPj5ekydP1qRJk7RgwQL5+flp8uTJSktL0/vvv6+RI0dqwYIF2f7Tqlixor799lt5enrm+P4zZ85o+/btWrRokWbNmqUTJ07oypUrOW7PtT799FPVrVtXixYt0scff6wPP/zQfFIeOXJEX3zxhcLDwzVv3jxdunQpT/bPvSYlJUVvvfWWjh07po4dO1rMCwgIUJkyZW74Lc7N5uPu0bdvX+3cuVO7du2ymH7w4EG9/PLL6tSpkwIDA1W7dm2Lh9jC+CpVqqTGjRurXbt26tq1q7744gulp6erXLlyeuaZZ/Tjjz+al129erX5m9JDhw6pWrVqFm3Z29vrqaeeIpjcpWJjY/Xyyy+rffv2atWqlaZPn65x48bJy8vLPC/rz/z58wu63PtOoUKFVK5cOUVHR+vgwYOaM2eOFixYoNjYWK1du1b79+/X0qVLNWfOHC1cuFB//vmnDh48aH7/pk2bVKpUKc2bN08jRozQb7/9ZtH+ja4vJenw4cPq1KmTvv76axUtWlRr1669ab2urq5asmSJ/Pz8NGrUKH344YeaP3++OnXqpDFjxki6GmJ69+6t+fPna/DgwXrvvffy7gPLY/yrlotffvlFTz75pIoUKSLp6m21H374Qf/9958aNmxo/tbyySef1MWLFy3e26hRI7399ttq0qSJGjdubNGH/Hp79uzRhx9+KOlqePjyyy/N87Ju3UVERGjChAlq3LixTCaT9u3bp7Nnzyo0NFSSlJGRoWLFiunIkSMqXry4fHx8zDWPHz/e3F716tUl6Ybv9/T0lKOjo4KDg9WwYUP16tVLjo6OOW5PVt/nrM8q60AvU6aMqlWrpv3790uS6tSpo0KFCql48eIqVqyYEhMTVbRo0VvdHfe8AwcO6LXXXtNDDz2k0aNHa9y4cRbz3333XXXu3FnNmjXL8f03m4+7g4uLi4YMGWLu3pXl+m5db731lubOnauuXbsWUKW4He+++666d++uXbt2aefOnerRo4dGjhyppk2b6sKFCzp16pQcHR31zz//mP/fsLPje8R7TVY3ooyMDE2cOFFHjhxRvXr1FBMTQ7cugzCZTFq8eLHi4+P16quvSro6HqVkyZI6d+6cxXXglClTLN77+OOPa9q0aYqJiVHDhg2z9Ri50fVlgwYN9OCDD5rvaHh7e2e7vsxJ1pcX//zzj06dOqUBAwaY512+fFlJSUk6cOCARo4caZ6enJyshIQEQ45bI5zkIiMjw+J1Zmam0tPTZW9vn23e9Tp16qRGjRpp69at+uKLL7R///4bdme6/puvv//+W+XLl7eY1rx5c+3atUtjxozRF198oYyMDNWoUUOfffaZpKsnTFJSkmJjY3OtzdHR0bxtOb3fwcFB4eHh2rNnj7Zv364ePXpo+vTpOW5PQECAxWeT02clyeIhmiaTKduyuKp69erq0aOHUlJS9PLLL2vp0qV64YUXzPM9PDzUr18/jRw5Ut7e3tnef7P5uHv4+fmZu3flxNnZWU2bNs12dwXGtnXrViUnJ6tVq1bmPuQrVqzQd999p2bNmunpp5/Wjz/+KEdHR7Vu3docSqpUqWLxrax09d/wd999V++8884tD9qFcdjZ2alPnz7q3LmzFixYoCeffLKgS4KujvE9fvy46tSpo6eeekovv/yypKvd6+3t7fXdd99ZLB8bG2sOGpJUvnx5LVmyRDt27NCWLVu0cOFCLVmyxDz/RteX0v+u066ddzNZ605PT1eZMmXM4TY9PV3nz59XRkaGHB0dLULv2bNnc+0uVpD4OiYXdevW1U8//aSUlBSlpaVp1apVqlOnjurVq6ft27crMTFRqampioiIkMlksnhvt27dlJSUpI4dO6pjx446dOiQpKu34tPS0iyWrVWrltatWyfpajDp27dvtvakqwOt9u3bpy1btqhatWr6448/dPz4cUnS7NmzNWnSJD3yyCO6dOmSjhw5Ikn68ccfc2zrRu8/dOiQevXqpVq1aqlv37565JFH9M8//9xwe679rFauXClJOnXqlPbu3avHHnvslj/z+1lWiCtSpIhGjBihyZMn69ixYxbLZHXf2rhxY45t3Gw+7h5Z3btiY2OzzUtPT1dUVJQqV65cAJXhdhUpUkRTpkwx33XOzMzUsWPHVKlSJUlS27ZttWnTJm3YsMHil/batWunbdu2adu2beb3zZ49W/Hx8QSTe4CDg4P69u2rOXPmmLtDo+BkZGQoLCxM1atX17PPPqs1a9YoKSlJaWlpGjRokCIiIlSzZk3t2LHDPH3o0KEWXyAsWbJEYWFhatmypd555x3Fx8crMTHRPP9G15d36uGHH9bFixf166+/Srr6K1/Dhg2Ti4uLypUrpzVr1kiSdu3addPB+wWJOyfX+O2339SkSRPz64CAADVo0EBdunQxD4R66aWX5ODgoKCgIPXo0UPOzs5yc3PLlnRDQ0M1YsQIOTg4yNHRUe+++64kqXHjxnr55Zc1b94887IhISEaPXq0OnXqJAcHB33wwQc5BorixYvrlVde0RdffKFFixZp2LBhGjJkiDIyMuTl5aURI0aoUKFCGjFihD744APZ2dmpfPny2WqTrn7LntP73dzc9Nhjj6ljx44qUqSIKlWqJH9/fzk6Oua4PVkGDBigMWPGaNWqVTKZTHrvvffk4eFxR/vjfla9enV17NhRQ4cONQ+ezfLuu++qQ4cON3zvzebj7pDVvatPnz6S/jfmRLo6Pqlq1armrga4O9StW1fBwcF66623zF9S+fn5KTg4WJJUokQJubm5KSMjQ2XKlDG/z8PDQxMnTtSkSZM0efJkZWRk6NFHH9Unn3xSINuBvOfv76/q1avf8eMMcHuyxvlIV8NJpUqV9OGHH8rV1VXR0dHq1q2bMjIy5O/vrzZt2shkMql9+/bq0aOHMjIy1KxZM/n6+pov/p9++mkNGzZMHTt2lIODg3r27GnRnb1Ro0aKjo7Odn0ZExNzR9tRuHBhjRkzRuPHj9d///2nBx54QO+//76kqz9N/vHHH2v+/PnmX4bM6VrTCEwJCQn0sblFx48f17Zt29SpUydJ0sCBAxUYGGj+JZWClJGRocmTJ6tnz55ycnLSwoULFRsbq379+hV0aQAAAECuuHNyG0qVKqUDBw6oQ4cOMplM8vPzU8OGDQu6LElX+6+6urqqa9eucnBwUKlSpW77d7UBAAAAW+LOCQAAAABDYEA8AAAAAEMgnAAAAAAwBMIJAAAAAEMgnAAA7livXr3k6+ub6xPr33vvPfn6+mr48OF3tK7IyEj5+voqKirqlt7n6+uradOm3dG6AQD5i3ACAMgTJpNJBw4cMD9k8FrJycnasmVLAVQFALibEE4AAHmiUqVKcnJy0oYNG7LN27Jli5ycnOTl5VUAlQEA7haEEwBAnihSpIgaNmyYYzhZt26dmjVrJnt7e/O0K1euaPbs2Wrfvr0aNmyo559/XnPnzlVGRobFe7/99lu98MILatSokUJDQ/Xvv/9ma//s2bMaNmyYWrZsqUaNGum1117TH3/8kfcbCQDIV4QTAECeadmyZbauXYmJidqxY4eefPJJ87TMzEwNGDBA8+bN0zPPPKPPPvtMLVu21PTp0zV69GjzcosXL9a4ceNUv359ffLJJ6patarGjh1rsc6EhAQFBwfrjz/+0IABAzR69GgVLlxYr7/+ug4ePJj/Gw0AyDM8IR4AkGfq16+vBx54QBs2bNArr7wiSdq0aZPc3NxUq1Yt83I7duxQZGSkRowYodatW0uSnnjiCTk6OmrGjBnq0KGDKlasqPDwcDVv3lwDBgyQJPn5+SkxMVHLly83t/XVV1/p/PnzWrx4scqWLStJatCggTp37qypU6fqiy++sNXmAwDuEHdOAAB5xtHRUY0aNbLo2rVu3Tq1bNlSJpPJPC0qKkp2dnZq2bKlxfuzgsqePXt0/PhxnT9/Xg0bNrRYpkWLFhavd+/eLW9vb5UsWVJpaWlKS0tTZmamGjRooD179ig1NTWvNxMAkE+4cwIAyFMtW7bUwIEDdfr0aTk7OysyMlKvvfaaxTIXL15UsWLFVKhQIYvp7u7ukqRLly7p4sWLkiQ3N7ccl8ly4cIFnThxQvXr18+xnoSEBHl6et7JJgEAbIRwAgDIU35+fnJxcVFERIRcXFxUqlQpVa1a1WKZYsWK6eLFi0pNTbUIKHFxcZKuBhJXV1dJ0vnz5y3ee+HCBYvXLi4uqlGjhvr3759jPdeHGwCAcdGtCwCQpwoXLqzGjRsrIiJCGzZsUKtWrbItU7t2bWVkZGj9+vUW09euXStJqlGjhsqXL68SJUpk+/Wv65+XUrt2bf3zzz8qX768qlatav6zceNGLVmyRA4OfA8HAHcLwgkAIM+1atVK+/fv1y+//GLxK11Z6tevrzp16ujjjz/W/PnzFRkZqenTp2vWrFl66qmn5OPjI5PJpDfeeEM7duzQqFGjtHPnToWFhWnFihUWbXXq1Ekmk0mvv/661q1bp927d+vTTz/V3LlzVa5cOYuxLgAAY+PrJABAnnviiSdUtGhReXh4yNvbO9t8k8mkCRMmaMaMGVq8eLHi4+NVqlQp9erVS507dzYvFxAQIEkKDw/X2rVrVbFiRb3zzjsaPny4eRlPT0/Nnj1bU6ZM0dixY3XlyhWVLVtWb7/9tl588cX831gAQJ4xJSQkZBZ0EQAAAABAty4AAAAAhkA4AQAAAGAIhBMAAAAAhkA4AQAAAGAIhBMAAAAAhkA4AQAAAGAIhBMAAAAAhkA4AQAAAGAIhBMAAAAAhvB/etECpVsJSd4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataPerf = pd.DataFrame(data={'Model': ['LogisticRegression', 'KNN', 'NB', 'SVC','RF',  'DecisionTree'], 'Score': [lr.score(x_Test, y_Test), knn.score(x_Test, y_Test), nb.score(x_Test, y_Test),sv.score(x_Test, y_Test), rf.score(x_Test, y_Test), dt.score(x_Test, y_Test)]})\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(x=\"Model\", y=\"Score\", data=dataPerf, palette=\"rocket\")\n",
    "plt.title('Performance analysis of different classifiers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a72904b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.997065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.369863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RF</td>\n",
       "      <td>0.996086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>0.940313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model     Score\n",
       "0  LogisticRegression  1.000000\n",
       "1                 KNN  0.997065\n",
       "2                  NB  0.369863\n",
       "3                 SVC  1.000000\n",
       "4                  RF  0.996086\n",
       "5        DecisionTree  0.940313"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataPerf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a6243c0",
   "metadata": {},
   "source": [
    "## Confusion Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ee4e8bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_lr = lr.predict(x_Test)\n",
    "pred_knn = knn.predict(x_Test)\n",
    "pred_nb = nb.predict(x_Test)\n",
    "pred_svc = sv.predict(x_Test)\n",
    "pred_rf = rf.predict(x_Test)\n",
    "pred_dt = dt.predict(x_Test)\n",
    "pred_Vals = pd.DataFrame(data={'truth': y_Test, 'regression': pred_lr, 'knn': pred_knn, 'naive-bayes': pred_nb, 'svm': pred_svc, 'random-forest': pred_rf, 'decision-tree': pred_dt})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fb0a2ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x675 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 9.375))\n",
    "plt.subplot(2, 3, 1)\n",
    "sns.heatmap(sklearn.metrics.confusion_matrix(y_Test, pred_lr), annot=True).set(title='Logistic Regression')\n",
    "plt.subplot(2, 3, 2)\n",
    "sns.heatmap(sklearn.metrics.confusion_matrix(y_Test, pred_knn), annot=True).set(title='KNN')\n",
    "plt.subplot(2, 3, 3)\n",
    "sns.heatmap(sklearn.metrics.confusion_matrix(y_Test, pred_nb), annot=True).set(title='Naive-Bayes')\n",
    "plt.subplot(2, 3, 4)\n",
    "sns.heatmap(sklearn.metrics.confusion_matrix(y_Test, pred_svc), annot=True).set(title='SVM')\n",
    "plt.subplot(2, 3, 5)\n",
    "sns.heatmap(sklearn.metrics.confusion_matrix(y_Test, pred_rf), annot=True).set(title='Random Forest')\n",
    "plt.subplot(2, 3, 6)\n",
    "sns.heatmap(sklearn.metrics.confusion_matrix(y_Test, pred_dt), annot=True).set(title='Decision Tree')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f944c5",
   "metadata": {},
   "source": [
    "#### From the above graphs and scores, we can conclude that the Logistic Regression and the Support Vector classifier are the best suited models for this classification followed closely by the K-nearest neighbours and the Random Forest Classifier models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d25c3ae4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac4f0419",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "b98f472bb8ba48098397e3b897b5be76f7bf0e62d98845cdb0e8066dc5677259"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}