Approach 3 - Shreyash Deshmukh.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d432f6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42637882",
   "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>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('heart.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "590e6181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age               0\n",
       "Sex               0\n",
       "ChestPainType     0\n",
       "RestingBP         0\n",
       "Cholesterol       0\n",
       "FastingBS         0\n",
       "RestingECG        0\n",
       "MaxHR             0\n",
       "ExerciseAngina    0\n",
       "Oldpeak           0\n",
       "ST_Slope          0\n",
       "HeartDisease      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6fc6c46b",
   "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>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.510893</td>\n",
       "      <td>132.396514</td>\n",
       "      <td>198.799564</td>\n",
       "      <td>0.233115</td>\n",
       "      <td>136.809368</td>\n",
       "      <td>0.887364</td>\n",
       "      <td>0.553377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.432617</td>\n",
       "      <td>18.514154</td>\n",
       "      <td>109.384145</td>\n",
       "      <td>0.423046</td>\n",
       "      <td>25.460334</td>\n",
       "      <td>1.066570</td>\n",
       "      <td>0.497414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
       "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
       "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
       "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
       "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
       "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
       "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
       "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
       "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
       "\n",
       "          Oldpeak  HeartDisease  \n",
       "count  918.000000    918.000000  \n",
       "mean     0.887364      0.553377  \n",
       "std      1.066570      0.497414  \n",
       "min     -2.600000      0.000000  \n",
       "25%      0.000000      0.000000  \n",
       "50%      0.600000      1.000000  \n",
       "75%      1.500000      1.000000  \n",
       "max      6.200000      1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9914ee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                 int64\n",
       "Sex                object\n",
       "ChestPainType      object\n",
       "RestingBP           int64\n",
       "Cholesterol         int64\n",
       "FastingBS           int64\n",
       "RestingECG         object\n",
       "MaxHR               int64\n",
       "ExerciseAngina     object\n",
       "Oldpeak           float64\n",
       "ST_Slope           object\n",
       "HeartDisease        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "49abab3c",
   "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>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>ST_Slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>Normal</td>\n",
       "      <td>N</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>Normal</td>\n",
       "      <td>N</td>\n",
       "      <td>Flat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>ST</td>\n",
       "      <td>N</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Y</td>\n",
       "      <td>Flat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>Normal</td>\n",
       "      <td>N</td>\n",
       "      <td>Up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex ChestPainType RestingECG ExerciseAngina ST_Slope\n",
       "0   M           ATA     Normal              N       Up\n",
       "1   F           NAP     Normal              N     Flat\n",
       "2   M           ATA         ST              N       Up\n",
       "3   F           ASY     Normal              Y     Flat\n",
       "4   M           NAP     Normal              N       Up"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df = df.select_dtypes(include=['object']).copy()\n",
    "obj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2161e51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    725\n",
       "F    193\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df[\"Sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e4b77e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ASY    496\n",
       "NAP    203\n",
       "ATA    173\n",
       "TA      46\n",
       "Name: ChestPainType, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df[\"ChestPainType\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07ae03a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Normal    552\n",
       "LVH       188\n",
       "ST        178\n",
       "Name: RestingECG, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df[\"RestingECG\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "685902a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N    547\n",
       "Y    371\n",
       "Name: ExerciseAngina, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df[\"ExerciseAngina\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a040f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Flat    460\n",
       "Up      395\n",
       "Down     63\n",
       "Name: ST_Slope, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df[\"ST_Slope\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f045cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanup = {\"Sex\":  {\"M\": 0, \"F\": 1},\n",
    "          \"ChestPainType\" : {\"ASY\" : 0, \"NAP\" : 1, \"ATA\": 2, \"TA\" : 3},\n",
    "          \"RestingECG\": {\"Normal\" : 0, \"LVH\": 1, \"ST\": 2},\n",
    "          \"ExerciseAngina\": {\"N\": 0, \"Y\": 1},\n",
    "          \"ST_Slope\":{\"Flat\": 0, \"Up\":1, \"Down\":2}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "04090b44",
   "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>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>ST_Slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex  ChestPainType  RestingECG  ExerciseAngina  ST_Slope\n",
       "0    0              2           0               0         1\n",
       "1    1              1           0               0         0\n",
       "2    0              2           2               0         1\n",
       "3    1              0           0               1         0\n",
       "4    0              1           0               0         1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df = obj_df.replace(cleanup)\n",
    "obj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ad62c13",
   "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>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  RestingECG  \\\n",
       "0   40    0              2        140          289          0           0   \n",
       "1   49    1              1        160          180          0           0   \n",
       "2   37    0              2        130          283          0           2   \n",
       "3   48    1              0        138          214          0           0   \n",
       "4   54    0              1        150          195          0           0   \n",
       "\n",
       "   MaxHR  ExerciseAngina  Oldpeak  ST_Slope  HeartDisease  \n",
       "0    172               0      0.0         1             0  \n",
       "1    156               0      1.0         0             1  \n",
       "2     98               0      0.0         1             0  \n",
       "3    108               1      1.5         0             1  \n",
       "4    122               0      0.0         1             0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.replace(cleanup)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6fb9e88c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1bf2096d",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b1a7dbf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df.drop('HeartDisease',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "485ee7f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_features = scaler.transform(df.drop('HeartDisease',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "193e4c3c",
   "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>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.433140</td>\n",
       "      <td>-0.515952</td>\n",
       "      <td>1.345086</td>\n",
       "      <td>0.410909</td>\n",
       "      <td>0.825070</td>\n",
       "      <td>-0.551341</td>\n",
       "      <td>-0.747055</td>\n",
       "      <td>1.382928</td>\n",
       "      <td>-0.823556</td>\n",
       "      <td>-0.832432</td>\n",
       "      <td>0.699073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.478484</td>\n",
       "      <td>1.938163</td>\n",
       "      <td>0.270422</td>\n",
       "      <td>1.491752</td>\n",
       "      <td>-0.171961</td>\n",
       "      <td>-0.551341</td>\n",
       "      <td>-0.747055</td>\n",
       "      <td>0.754157</td>\n",
       "      <td>-0.823556</td>\n",
       "      <td>0.105664</td>\n",
       "      <td>-0.917423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.751359</td>\n",
       "      <td>-0.515952</td>\n",
       "      <td>1.345086</td>\n",
       "      <td>-0.129513</td>\n",
       "      <td>0.770188</td>\n",
       "      <td>-0.551341</td>\n",
       "      <td>1.774257</td>\n",
       "      <td>-1.525138</td>\n",
       "      <td>-0.823556</td>\n",
       "      <td>-0.832432</td>\n",
       "      <td>0.699073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.584556</td>\n",
       "      <td>1.938163</td>\n",
       "      <td>-0.804242</td>\n",
       "      <td>0.302825</td>\n",
       "      <td>0.139040</td>\n",
       "      <td>-0.551341</td>\n",
       "      <td>-0.747055</td>\n",
       "      <td>-1.132156</td>\n",
       "      <td>1.214246</td>\n",
       "      <td>0.574711</td>\n",
       "      <td>-0.917423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.051881</td>\n",
       "      <td>-0.515952</td>\n",
       "      <td>0.270422</td>\n",
       "      <td>0.951331</td>\n",
       "      <td>-0.034755</td>\n",
       "      <td>-0.551341</td>\n",
       "      <td>-0.747055</td>\n",
       "      <td>-0.581981</td>\n",
       "      <td>-0.823556</td>\n",
       "      <td>-0.832432</td>\n",
       "      <td>0.699073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age       Sex  ChestPainType  RestingBP  Cholesterol  FastingBS  \\\n",
       "0 -1.433140 -0.515952       1.345086   0.410909     0.825070  -0.551341   \n",
       "1 -0.478484  1.938163       0.270422   1.491752    -0.171961  -0.551341   \n",
       "2 -1.751359 -0.515952       1.345086  -0.129513     0.770188  -0.551341   \n",
       "3 -0.584556  1.938163      -0.804242   0.302825     0.139040  -0.551341   \n",
       "4  0.051881 -0.515952       0.270422   0.951331    -0.034755  -0.551341   \n",
       "\n",
       "   RestingECG     MaxHR  ExerciseAngina   Oldpeak  ST_Slope  \n",
       "0   -0.747055  1.382928       -0.823556 -0.832432  0.699073  \n",
       "1   -0.747055  0.754157       -0.823556  0.105664 -0.917423  \n",
       "2    1.774257 -1.525138       -0.823556 -0.832432  0.699073  \n",
       "3   -0.747055 -1.132156        1.214246  0.574711 -0.917423  \n",
       "4   -0.747055 -0.581981       -0.823556 -0.832432  0.699073  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])\n",
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "804f6f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(scaled_features,df['HeartDisease'],\n",
    "                                                    test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "92e64d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f25da96c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "33793f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "99e32e71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "63902ac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_rate = []\n",
    "\n",
    "for i in range(1,40):\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    score=cross_val_score(knn,df_feat,df['HeartDisease'],cv=10)\n",
    "    accuracy_rate.append(score.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3d4e7daf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8027830864787386,\n",
       " 0.7919015766841854,\n",
       " 0.8408743430482561,\n",
       " 0.8201624462494028,\n",
       " 0.8485308170090778,\n",
       " 0.8332895365504062,\n",
       " 0.8419612995699952,\n",
       " 0.8354395604395604,\n",
       " 0.8474080267558529,\n",
       " 0.8375895843287147,\n",
       " 0.8517558528428093,\n",
       " 0.8408504538939322,\n",
       " 0.8517797419971332,\n",
       " 0.8473960821786909,\n",
       " 0.848506927854754,\n",
       " 0.8441471571906355,\n",
       " 0.84848303870043,\n",
       " 0.8386884854276158,\n",
       " 0.8463091256569516,\n",
       " 0.8408743430482561,\n",
       " 0.8517797419971332,\n",
       " 0.848494983277592,\n",
       " 0.8528547539417104,\n",
       " 0.8430363115145724,\n",
       " 0.8506808408982321,\n",
       " 0.84089823220258,\n",
       " 0.8408862876254182,\n",
       " 0.8365145723841376,\n",
       " 0.8430482560917344,\n",
       " 0.838676540850454,\n",
       " 0.84848303870043,\n",
       " 0.8430363115145724,\n",
       " 0.8495699952221691,\n",
       " 0.8419493549928333,\n",
       " 0.8452341137123746,\n",
       " 0.8441232680363114,\n",
       " 0.8473960821786909,\n",
       " 0.8430124223602485,\n",
       " 0.8451982799808887]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2750d5ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(accuracy_rate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "14aea945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8528547539417104"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_rate[22]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2610343f",
   "metadata": {},
   "source": [
    "## k=23 has Highest Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f7930c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy Rate')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAHwCAYAAADjOch3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAByuElEQVR4nO3dd5yU5dX/8c9ZWDqoCBJUUNnYETEioiQx1ojdRGPXWB9FDUGNij5PejRq1PwMliiW2GNswYI1GjWCgooUGywqIqIC0nZh2XJ+f1wz2WWZmZ1edr7v12tfs3PXw703s2ev+7rOZe6OiIiIiBSHikIHICIiIiLNlJyJiIiIFBElZyIiIiJFRMmZiIiISBFRciYiIiJSRJSciYiIiBQRJWciIrIOM9vSzNzMOhY6FpFypORMpEyZ2ctm9o2ZdS50LLlgZj8wsyYzW2VmK83sQzM7NYX9XzazM3IZY6rM7C4z+32L9zua2RdmdmGMbT8ws9NiLB9jZtNyHauIpE/JmUgZMrMtge8BDhyW53PnszVmobv3AHoBY4HbzGzbPJ4/Z8xsKPAS8Ad3vzbGJn8DTo6x/KTIOhEpUkrORMrTycAU4C7glJYrzGyAmT1qZl+b2RIzG99i3Zlm9n6kJeo9M/tOZLmb2bdbbPffFp5IC9YCM7vEzBYBd5rZRmb2ZOQc30S+37zF/r3N7E4zWxhZ/3hk+SwzO7TFdpVmtjiSqMTlwdPAUmBIZN+4MZjZHwjJ6/hIy9v4yPLtzOx5M1saaYn7SazzmdmxrVunzGysmU2MfH9Q5PqtNLPPzeyiRPHHOP5w4AXgMncfH2eze4DvmtkWLfbbPvLvf8DMDjazd8xshZl9Zma/TnC+T8xsvxbvf21m97Z4P8LMXjezZWb2rpn9IJV/j4isS8mZSHk6Gbgv8vVDM+sHYGYdgCeBT4Etgc2AByPrjgZ+Hdm3F6HFbUmS5/sW0BvYAjiL8NlzZ+T9QGA10DLJuAfoBuwIbAJcH1l+N3Bii+0OAr5w9+mJTm5mFWZ2GNAHmBtZHDcGd78ceBU4z917uPt5ZtYdeB64PxLTccBNZrZjjFNOBLY1s61bLDs+si/A7cD/uHtPYDDwr0TxtzIceAYY6+4T4m3k7gsILWsntVh8MvC0uy8GaiLvNwQOBs4xsyNSiAMAM9sMeAr4PeFnfBHwiJn1TfVYIhIoORMpM2b2XUJC8pC7vwVUExIHCL/4NwV+4e417r7G3V+LrDsDuNrdp0Zaoua6+6dJnrYJ+JW717n7andf4u6PuHutu68E/gDsFYmvPzAKONvdv3H3enf/d+Q49wIHmVmvyPuTCIlcPJua2TJC4vUYcIG7vwOQKIY4DgE+cfc73b3B3d8GHgGOar2hu9cC/yQkcESStO0ISRtAPbCDmfWK/BvfTnDe1kYAy4FJSWz7NyLJmZlVACdEluHuL7v7THdvcvcZwAMk/vfHcyIh4Xs6cqzngWmExFlE0qDkTKT8nAI8F2k9gdCaE320OQD41N0bYuw3gJDIpeNrd18TfWNm3czsr2b2qZmtAF4BNoy03A0Alrr7N60P4u4Lgf8APzazDQlJ3H0JzrvQ3TcktPTdAOyTZAyxbAHsHnl0tyyS9J1AaBWM5X4iyRkh+X08krQB/JiQvHxqZv82sz0S/BtauxGYCjxvZhu1se2jQH8zGwH8gNAa+RSAme1uZi9FHusuB84mtCymagvg6FbX5btA/zSOJSKAhkmLlBEz6wr8BOgQ6f8F0JmQlOwMfAYMNLOOMRK0z4CqOIeuJfzij/oWsKDFe2+1/YXAtsDu7r4o0mfsHcAi5+ltZhu6+7IY5/oboRWvIzDZ3T+P9+/978nd68zsEuBDMzvC3R9vI4ZYMX8G/Nvd92/rfBHPAX0ixz2OMCAhGs9U4HAzqwTOAx4iJKXJaCQkhQ8Dz5rZfu6+ItaG7l5rZg8THl92BR5097WR1fcTHuOOcvc1ZvZn4idnNaz/8436DLjH3c9MMn4RaYNazkTKyxGEX+47AEMjX9sT+ledDLwJfAH80cy6m1kXMxsZ2XcCcJGZ7WrBt1t0Np8OHG9mHczsQNp+PNaT8KhxmZn1Bn4VXeHuXxAe2d0U6bRfaWbfb7Hv48B3gDGEPmhJiSQl1wK/bCuGiC+BQS3ePwlsY2YnRWKqNLPdIp3sY52vgZBAXUPoi/U8gJl1MrMTzGwDd68HVhB+JkmL7Hc0sBh4OtIfLp6/AccQWutajtLsSWihXBMZYHB8rJ0jpgPHRv7Nw1j3Ue69wKFm9sPIz7+LhUEgm8c8koi0ScmZSHk5BbjT3ee7+6LoF6EF5QRCq9GhwLeB+YTWr2MA3P0fhH5Z9wMrCUlS78hxx0T2WxY5zuNtxPFnQkvOYsKo0WdarT+J0C/rA+Ar4OfRFe6+mtDXayvCY7tU3EFoGTw0iRj+H3CUhZGcN0T6pR0AHAssBBYBVxFaHuO5H9gP+EerlsiTgE8ij1PPJjLIwcwGWhgdOrCtf0gk2fwRsAZ4ItIqGssrhD5qn0da7KJGA781s5WEhPWhBKf7P0Kr6TfAb2ge2IC7fwYcDlwGfE1oSfsF+v0ikjZzb91yLyJS3Mzsl8A27n5imxuLiJQY9TkTkZISeQR5OuuWiBARaTfU7CwiJcPMziQ8Npvk7q8UOh4RkVzQY00RERGRIqKWMxEREZEiouRMREREpIi0qwEBffr08S233LLQYYiIiIi06a233lrs7uvNQ9uukrMtt9ySadOmFToMERERkTaZWcz5ifVYU0RERKSIKDkTERERKSJKzkRERESKiJIzERERkSKi5ExERESkiCg5ExERESkiSs5EREREioiSMxEREZEiouRMREREpIgoORMREREpIkrORERERIqIkjMRERGRIqLkTERERKSIKDkTESlB1dUwdnQd/XqtpkNFE/16rWbs6DqqqwsdmYhkSsmZiEiJmTQJRgypoeuEG3h95WDqvBOvrxxM1wk3MGJIDZMmFTpCEcmEuXuhY8iaYcOG+bRp0wodhohIzlRXh8RsYu1+7MGU9dZPZgSHdXuBKTO6U1VVgABFJGlm9pa7D2u9XC1nIiIlZPy1dZxZf1PMxAxgD6ZwRv3N3Hh9XZ4jE5FsUXImIlJC7r+3idPrb0m4zRn1N3P/PY15ikhEsk3JmYhICVm8qjNb8GnCbQYyn8WruuQpIhHJNiVnIiIlpE+POj5li4TbzGcgfXqsyVNEIpJtSs5E0pDPMgYqmVA68vGzOv7ECm6vPDvhNhMqz+H4kzpk76QikldKzkRSlM8yBiqZUDry9bM678LO3FY5msmMiLl+MiOYUHkO547tnJ0TikjeqZSGSAryWcZAJRNKR75/VtdcA7++uIbzOt7MWQ03M5D5zGcgEyrPYULlOdz9cHdGjcr8PCKSWyqlIZIF+SxjoJIJpSPfP6tf/AKmzu5O/f+cz8heM+lCHd+pnEndWeczZYYSM5FSp5YzkRT067Wa11cOpop5cbepZhAje81k0fJuJXMuyUw+f1bz5sEWW0CHFl3KDjwQvvgC3n03o0OLSJ7FazlTciaSgg4VTdR5JzoSv4ZUPR3pWlFHQ2NmDdP5PJdkJl8/q/p6+Pa34fvfh3vuaV7+0kth3QEHpH1oESkAPdYUyYJ8ljFQyYTSka+f1f33w/z5cNxx6y7fe28lZiLtiZIzkRTks4yBSiaUjnz8rJqa4KqrYMgQ1utT5g4vvABvvJH24eNSKReR/FNyJpKCfJYxUMmE0pGPn9U//wnvvw+XXgpm664zg9NPhz//Oe3Dx6RSLiIF4u7t5mvXXXd1kVx7+mn3Pt1W+aWV1/hcBvlaOvpcBvklldd4n26r/Omnc3+uC7jGe3fJ7rkkM9Gf1cUd1v1ZXcg13t1W+cSJmR3/6KPdBw1yr6+Pvf7HPw7rs2Xu3PDveZ0R7qFxbp2v1xnhfbqt8rlzs3dOkXIDTPMY+YxazkRSNGoUTJnRnS9/cj47V4QyBjsxk+pR2S9jED3XilNCyYSuFXWM7DWT2zqfz857qGRCMYn+rOrPXvdnVX3Q+dR4d2bPzuz4DzwAzz8PHTvGXj98eBjJuXhxZueJUikXkcJRciaShqoqOPKYztQ0dWPikxWsphvDv9s5J8VgBw2CBx/tzHGndaOhsYJFy7vxi8s789JLMHNm9s8n6evbF84Y3ZkFS5p/Vo891Zkf/Qj+9jdoaEjvuHV1oXTGoEHxtxk+PLxOnZreOVq7/94mTq+/JeE2Z9TfzP33xB+hKiLpyWlyZmYHmtmHZjbXzC6NsX4DM3vCzN41s9lmdmqLdZ+Y2Uwzm25mqo8hRWfOnPA6YgQMGADTp+fmPAsWwNKloYRC1LnnQvfucO21uTmnpOeFF2DHHeGdd9ZdfsstIWmK1+qVyLRp4f6aErsB67923TX0PctWcrZ4VWe24NOE2wxkPotXdcnOCUXkv9L4qEiOmXUAbgT2BxYAU81soru/12Kzc4H33P1QM+sLfGhm97n72sj6vd09S430Itk1Zw707g0bbwxDh+YuOYsed+jQ5mW9e8M//hF+IUvxmD49tHANHrzu8r59w+uaNfDhh7Dzzskf88orQw2zHXZIvF3PnjBjBmyzTUohx9WnRx2frtwiYWHd5vIgKoIskk25bDkbDsx193mRZOtB4PBW2zjQ08wM6AEsBdJs+BfJr48+gq23Dt/vsgt88AGsXp3980yfHlpEhgxZd/moUbDJJtk/n6TvnXdgu+2ga9fY6087LdQjW7o0ueN98AE89lhoKe3Vq+3tBw+GTp2SjzcRlXIpPSp70n7kMjnbDPisxfsFkWUtjQe2BxYCM4Ex7t4UWefAc2b2lpmdlcM4RdKyyy5w8MHh+/POgy+/jP9LORPTp4dHmj17rr/u7bdh333hq6+yf15J3fTp67ZwtnbxxbBkSSiHkYyrroIuXWDMmOS2/+gj+PnPw6PwTKmUS2lR2ZN2JtYQzmx8AUcDE1q8Pwn4S6ttjgKuBwz4NvAx0CuybtPI6ybAu8D345znLGAaMG3gwIE5GOgqUliPPup+552x133wgbuZ++WX5zUkieHrr0OViT/9KfF2F10UtnvllcTbLVzo3rGj+/nnJx/D1Knh2A89lPw+ify3PEjH3JeNkfSp7EnpIk4pjZzNrWlmewC/dvcfRt6PiySDV7bY5ingj+7+auT9v4BL3f3NVsf6NbDK3f+U6JyaW1PypbERKirWLQb65z+HTvpnnpnfWI46Cl58ET79NLlHX5Ibq1fDv/4F22+feFRlTU0YNNCtW2hpi/cY0h1eeSUca8CA5GJYuza0sP7sZ3DNNSn/E2J67z0YNqSOTh0aWVXfhU6+hlEHd+Dq/5eb0cmSurGj6+g64QauqL847jbjKq+h7qzzuW68WjqLSSHm1pwKbG1mW5lZJ+BYYGKrbeYD+0YC7AdsC8wzs+5m1jOyvDtwADArh7GKpGTiRNhgg/CLK+rxx+GOO7J7nkWL4N13E5dguOQSWLYMbr01u+eW1HTtGh5zJ0rMICTwN90Ukqivv46/nRnstVfyiRmERG/o0OyN2ARYuBBWN3bmvke7sWxFBXUV3RiymxKzYqKyJ+1PzpIzd28AzgOeBd4HHnL32WZ2tplFe5n+DtjTzGYCLwKXeBid2Q94zczeBd4EnnL3Z3IVq0iqPvoIVq6EzTdvXjZ0aBgt15jFz7+HHgrHTfRLfLfdQr+z664L9bCkMJ56Cl5/PbltDzoIJk+GzVr3wo248srQ+tXUFHt9IsOHh/Ib2boPn38eKitDotijRxg1+uabbe8n+aOyJ+1PzkppALj708DTrZbd0uL7hYRWsdb7zQNSGGwukl9z5oSRki0fIw4dCrW1Yd1222XnPNOnh/N861uJt/v1r3NXykOSc/HFoTjxxNbPB+KoqAjV/O+4A37xi+ZH5CtXwtVXw957h21Stdtu8PDDocUrlVa3eJ5/HvbYIyRmEJK/iRPDY9fWc3xKYajsSfujGQLKlIZcZ2bOnOYyGlG77BJes5kkRUf/tfVL8LvfDSNGO2fYnSSf90V7ugdXrw5lLxKN1IzlkUfCY+mD92++DgP7rqZ2WR0nnpheLCeckL3E7OuvQ3mQ/fdvXjZ8eEgqP/kk8+OXg3zc5yp70v4oOStDGnKduVjJ2fbbh5a0bJW1WLsWZs9uTvra0tAAt90Gz6TZASCf90V7uwdnzQqPIJP9WUVtvjn0qKhhxxebr8O0usH8zG7gf05K7zp06JC9Fq3evcMjzFNOaV52xBFhtoJ4j2SlWb7uc5U9aYdiDeEs1a9dd901gwGt5UFDrjPX1BRKVzz66PrrGhuzd5533w0/lgceSG77hgb3bbZx32WXEGMq8nlftMd78NZbQ/jz5iW/Ty6vwxVXuB95ZOr7Sfbk+z5/+mn3jbuu8gtYt+zJpSp7UtSIU0pDLWdlZvy1dZxZfxN7EHuivj2Ywhn1N3Pj9epZHo8Z/P73cOSR669Lp49QPIMGwbPPwj77JLd9hw6h39M774R+QqnI533RHu/B6dNDq+mWWya/Ty6vw4oV8MQTYbqodLnDuHHwxhvrr3vpJbjxxvSPXQ7yfZ+PGgVjxnXnL5zPHj1m0rWijpG9ZlJ31vlMmdGdUaOychrJl1gZW6l+qeWsbZv0rPW5DIr5l1z0ay6DvF+vmkKHWrS++cZ92bLY6156yX3PPd2/+CKfETVbs8Z9s83c9947tf3yeV+0x3tw9Wr3Dz9MbZ9cXodHHw2HmDw55V3/64MPwjFuvnn9dWPGuHfr5l5fn/7x27tC3OcHHOC+9dbNLecXXBB+VlK8UMuZgIZcZ8ONN8KGG4Zioq1VVIRyCu+8k/l57r03lFtIRefOcMEFoWUjVotHPPm8L9rjPdilS+oTjufyOgwfHl4zqXcWbX1tORig5fFra9et8yfryvd9vmxZKIJ85JHNfQ6/+gruvz+9kixSWErOykyfHnV8yhYJt2keci2xzJkTOkN3777+up0jBWAyHbHpDuefD3ffnfq+Z54J++2XuHBtS0uX5ve+aG/34KefwoUXkvLou1xeh802g003zawe2fPPw1ZbEbPYbDT5U72z+PJ9n0+fDh07hgEbUfvvH0bczpiRlVNIHik5KzPtfch1Poatf/TR+iM1ozbYIPQVy7Tl7NNPw1/CqZZmgFB5/pZb4OH7El+HJUtCUrHZZrD/gfm7L9rbPThlSigAvHJlavvl+jocd1zsxCoZ9fWh9TVWqxmE4260kZKzRPJ9n//gB6HEye67Ny/bb7/w+txzWTlFwbSnsjtJi/Wss1S/1Oesbe1xpFxUdJLmcZVX+1wGeT0dfC6DfFzl1VkdrdSnj/uZZ8Zf/6MfhX4fmXjssfAjmTIl9X2j1+HSONfh0UfDaL5evdwrKtxPO839tdc0WjNdl17qXlnpXleX2n7FfB3mzHHv39/9H/+Iv80BB7jvtVfeQio5xfLzHTzYfb/9cnuOXMrX53qhEKfPWcETqmx+KTlLTvMv7/Yz5DpfH4TffBMOefXV8be58Ub3I44IpS3S9atfhcSpJsW+wslch+62ysH9sMPcZ81q3jfefXGRZf++OPts966s8os7lv49+MMfug8dmt6+uf6/2NjoXlub3r5NTYnv4eXLUy/ZUm6iP9+LO6z7872Q7N7nTzzhvuuu7h9/vP66a65xv+ii7Jwn34olwc0lJWeyjrlz3ceeu8a7V9R4BY2+Sc8aH3vumpK9yX9+zhofV3l1zP/A0a9LK6/xseeuyeg8K1aE0WszZmQp8DiOPNJ9++1T3y+Z6/CLDtf4MUfGvg7R+6JfrxrvUNHovbvWeCVrYo7YS9fate4DB7p/5zvrnqtfr9K8B/v1c//pT9Pfv/U1z9Z1qKlx33BD9z/8IbPjSGbmznUfudsa70qNd7Dm/1O33569c5x2mvsGG6Teelvs8vW5XkhKziSmTTYJd0FFRfilWaqKsTxDJi1na9e6f/ZZ6vtl+zrU17tvtZX77rtnr5XkrrtCKE891bzs4IPdTzopO8fPp2++ce/b1/3Pfy50JLFts4374Yents/y5e6DBrk/8kji7erq3E8+2f3uu9MOr2w89JD7+eeH7+vqQhHrVAoWJ1JfH7paHH98/G0aGtwXLMjO+fKpGD/Xsy1ecqYBAWXMPYzU698/DLX+NPGo76KWr2HrH3yQXPmA4cPhnHPSP09lZZjaJ1XZvg4dO4ZJud94A/7979Tjaa2pCf74xzCqtWVRzC5dQgmSUrPhhvDll3DuuYWOJLbddku9nMbLL8O8eWHqpkQ6dQrbPv10utGVj6OPhhtuCN936hSKWG+1VXaO/Z//hIEALUdptnbEEXDwwdk5Xz61x7I7yVJyVsZWrgzlFnbbLbwv5ZEv+Rq2/vvfk1Sl7Q02gLffTu8cb78NP/tZmLw6Vbm4DqeeCv36wZVXph5Pa59/Hl7HjVt3/sfhw8P9t2RJ5ufIN7OQxBaj4cPDfRS97sl4/nno1g322CO542vEZmI1NaGcRUvuYfaPVGfyiOXxx0N9wwMPjL/NHnvAu++GPyRKSXsru5MKJWdlrFMn+Mc/YMyY8H7evMLGk4l8DVv/6KPkio0OHRomw66vT/0cL78Mf/lLmI4pVbm4Dl26hNau444Lv1QyMWBAmMz96KPXXR79AyGToqnx5HIY/kUXhXIkxSqd6/r887DXXuEXfjLHnzcvtNxIbE89BZtsEpKjKDO45BK47LLMj7/nnuGPnZ49428TLYny4ouZny+fjj+xggntqOxOSmI96yzVL/U5S09jo3uXLu4XXljoSNKXj1E9TU2hg/U557S97b33hlPPnJn6eU46KZQxSEcxj26aN8995crY65Yvdzdz/+1vs3vOXA/D33rr4p5gvLY29G9qOSo3kfnzw61y7bXJbf/SS2H7Uhpdm28XXujeufP6nfWvvz79z4hUNTS4b7RRZgNXCmHuXPcNKovz8yxbUJ8zaW3RInjhhTANyzXXwCGHFDqi9FVVwd0Pd+ewbi9waeU1VDOIejpSzSDGVV7DYd1e4O6Hu6ddlBPCI7dly+IXoG1pl13CazozBUyfnl7xWVj3OozL8nWoqYFrrw2th+k4/fTwV36s1rdeveDnP4eddkrv2LFUV8PJR9UwsXY/rqi/mCrm0ZFGqpjHFfUXM7F2P04+qibtFrSVK2Hu3OafdTHq2jU8it9xx+S2b2yE//kfOOig5Lbfdddw7LrSmaM+7958M9wjnTqtu/yEE8Lj8LvuSv/Y06bBF1+0vV2HDrDvvqFVNNPW73xavRpWNnbnwI7rf55d3CE7n+tFK1bGVqpfajlLzQMPhD9AZs8udCTZ07IsQQWN3qsye+UZ/vOfcL2efLLtbevrQwvb66+ndo7Vq907dnS/7LL0YozKRXmGL78MLaynn576vq+/Hq7dddelf/5U5XoYfvR+mDgxy4Fn2apV7v/+d2ghl/yqrw8TxP/sZ7HXH3lkGDGf7kj5IUPcv//95LadPNn92WdL6z644w73TTd1f/PNdT/PenSo8T691vhHHxU6wsyhUhrS2vjx4Q5YtMh92bJQjb7Ui0p++mlIit5/P3zwffvb2Tv2N9+EYo+LF2fvmK3Nneu++eZh6H0xOvfcUA0/1TIfhx7q3rt3/MeaUZ9/3vY2ycr1MPwbbwyHSafkST7dcUeI84MPEm/X2Oj+zjvp/fJuair9z45cmDEjXPt77429/p//dN9ss/B5larq6vz/wVMIsQpxv/GG+/Tp+Y8lF+IlZ3qsWcaWLg2vvXvD3/4GI0asP6qo1Lz1Ftx8M6xaFUaSzZ3b/O/M1IYbhke/G2+c3PaNjaH0hqfwGKGqCj77DI46Kq0Qc+6ii0I5jOuuS36fmTPhiSfCwJMePeJv9/bbYZ7PSZMyjxNyPwy/W7fQcX6zzdLaPW+igwLaGlU5Y0Z4/PbAA6kd/8knoU+f0i7Fkyv9+8OECbD33rHXH3xwuG7bbZf6sR97LLwmKqHR2tSpcPfdqZ8r3774orlES7du668fPjyU42nPlJyVsSVLwgifysrmCZJLecQmhBGSZrD99uE/MGRvBOAzz8Crrya//W23hTg++yz1c7UsM1FMttwSjj8ebr01+bIXEyeGpOy88xJvN3hw6JeTrZ9Xrofh//SnYWRtsf6sorbfHrp3b/u6Rss6xEsk4unfP/wBpJIa6+vTJ/S13HTT2Os7dAhfDQ2wJsXb8PHHQ4KSSr20O+8MNfnWrk3tXPk2Zgz86EeJ+9MtWQInnpi9P+aKjZKzMrZkSXMr0KBB4bWUa51BSM4GDQq/jHbfHV57Db7//ewc+7LL4Iorkt8++pddKoMCTjgB/u//Ugor7y65JLTGJJucXX45vP9+ckVNhw7N3i/5XJZXiT4XLQUdOsCwYW1f1+efD5374yUS8ey0Uyi7oeRsfY8/Dh9/nHibJUtg4EC45Zbkj7t0KUyeDEcemVo8++8fnipMmZLafrHkqkTNU0+FEk//+78h8Y+nZ0945ZXs1F+MymXZnVQpOStj48aFx5nQ/NdXe2g5Gzw4fN+9O4wcGUasZcod5sxJbqRm1JAhoVUl2eSsqSk8/vvmm7RCzJsdd4SXXkqu3tuqVeE12dkOhg8PI9AaG9OPL+q8CztzW+VoJjMi5vrJjGBC5TmcOzaJgl6tzJ4dks3nnss0yvzYbTd45534LSZr1oRW4Wg9rFR06hQehyo5W9fq1aF7woQJibfbeOPwaPzOO5NP+Hv3hk8+gbMT/+2xnr33hoqKzIvfTpoEI4bU0HXCDby+cjB13onXVw6m64QbGDGkJu3WrFWrYPRo2GEHuPjixNt26hRqDL76apglIVO5+jelLVZHtFL90oCAzGy2mfsppxQ6ivQ1NIQBAP/7v83Lpk51/81vMu+s/MUXoa3khhtS22/bbd2POCK5befODee47bbU4yuEzz8PowDjWbjQvVev+J2hY7n7bs9q7adonbOL7BqfyyBfS0efyyC/yK7JqM7ZPfeEOJOtH1ZoH30URszGm+/1+ed9vflOU/Gzn4VRifX16cfY3kRH8z7+eNvbRgeXvP127uPaY48wV266cllL8YILwmFefTW57Vetct944zDgKBOFrA+JRmtKa4884v7WW83vn3giPx8OudYyEYuOSJ0/P7Nj/vvf4TjPPJPafscc477llslt+/DD4RzTpqUeXyEccEBI6NfEqURx0UXuFRWe0gfa55+HxGfp0uzE6B4SqC4Va3yDTmEY/gadaryzrfE33kj/mBdcEMqKtJdkZNWqUCJm1ar09n/+efeLL3ZfsSK7cZWyaJHZhQvb3nbJEvdOnZonR09k+fIwmf2UKenF9ctfum+wQShQnI5clqi5665wH6XiN7/J/A+6XJfdSUTJmaynd+9QGqE9e/PNcJc//HBmx5kwIRynujq1/V57LQyXT6bl7n//171Dh1DrrBQ8+2y4JhMmrL9uyRL3Hj3cjz8+/3G1tmKF+zXX+H+TsZkz3X/4w7ZLSySyzz7uu+2Wnfjy5amn3P/+90JHUT6OPz6UxUnWT34SPpPj/bET9eCDnlLrUmsrVqRfV8099yVqUrV4cZiFIZkkOJ5C/pviJWfqc1amGhtD36aWnbQXLoSHH0591FCx+POfQx8Pb9FvY8iQMBo10/4wxx8f+o5tkXjw33pGjoTDDktuRN+3vhXmnOySXmWHvNt/f/jOd+Cqq9bvIzZ+fOg/cumlqR/3o4/CfZgtPXuGEiDR0buDB4eRt9tum97x3DObxaFQbroJfvvb9ZcvXgy/+Q3Mn5/Z8Vevbrvzezl5883mMibJuPRSePDB8HmVyGOPQd++yU1MH0t0hH66clGi5tZbw/3Z8rM7WRtvDH/6U+LBA23JddmddCg5K1PLloX/CC1rdr38ckgOSnXE5ksvhbpiLROhzp3DL9FMyzN07RpGX6Y6Gbl7iOvtt9ve9txzU68xVUhmYVDJnDnw6KPNy+vr4cYb4dBD05uO6W9/C5OsZ+uPhGeeiV3r7vPPw1eq1q4NUxwdfnjmseXT8OHw3nth2qmWXngBfv3r5KYBSuTII0P5Awleey1Mi5esXXYJf/BUJPitXFcX6n8dfnjqn0Ut3XVX+tP1ZbtEzfz5cMEFoV5eJv71r1DjMh25LruTDiVnZSr6y6plclbqtc5ajtRsabfdQsKZzl9lUddem97IPLPQ6nbDDYm3a2wMozVLzZFHwre/DX++qnn4+eYbr+bg/es4//z0jrnbbqHuUzrzkrb21VcwatT6ZQpqa8No06uuSv2YnTuHkioHH5x5fPm0227h/8Bbb627/PnnQ4HlYcMyO/6wYaHgcG1tZsdpL/r1I+U5Hz//PLTyxkuU//WvkFynUng2lpUrQ8mKdD7rkylRcxPnsFGfDsydu+7yWKUq9v9+HY2NoeUsk5qB994bkryvvkp931yW3UmXkrMyFa1R1fKxZinXOqupCR80sZKzq68Ow87T/Y/f1BRq7jz7bHr7Dx0ayhgk8tJLYfLvadPSO0ehPPccLFtYw14z1h1+/q2HbuD4I9Ibfh59/JiN0gwvvBBeDzhg3eXduoWWvfvuS33S7oULw/1WaqKP2Fq2IruH5GyffTJriYHwc2tsbPteLwePPhoetaX6B2FNTfhD8J57Yq9vagpdJfbdN7P4oiVT0impcd6Fnflrh8Qlam6vPIdPv+i8TgHueKUqDv30Bjo31vD++2n8Q1q4+OLwf/kvf0l939NHd2Z8U27K7qQtVke0Uv3SgIDkrVoVRgV+803zsqYm9549kxsxVGyiHf8feyz7x54/Pxz75pvT23/cuDCZeaKOvldHBgrlct7ObMvl8PPNNnM/4YTMY/zpT0Mn61glJJ55JoT6j3+kdswjjgglUkrRoEHuJ5/c/P6DDzK7t1tauNDLYq7HZBx6qPt226W37557hn1zOVdpU5P7gAHuP/5x6vvW14f7qDur/JLKdUvUXFrZXKKm5Yjr0aPdN6jMfamKH/3IfcMNw4jWZNXVuR94YAhlo86r/NIE/6ZcQAMCpKXu3WHXXcPjjCiz0Axfii1na9fCnnuGAQCxjBkDf/xjeseeMye8plKAtqWhQ8Njuvfei7/N9OkwYEDy83YWg/HX1nFm/U3sQexy43swhTPqb+bG61NsmiK0wmTachZtFdp339itQvvtF4rj3nlnasedPr105/V7443Q3yjqgw9Cf8p0is+21r9/uIfLvRite7gG0RbgVJ16avi5tL6OS5dm75GxWfiZ/+tfqRd8rqgIs35ce0t31p51PiN7zaRrRR0je82k7qzzmTKjO6NGwUYbhe3d4dUX6jgjR58VLV1ySehPfeutyW3f2BimgHrmmbDP1NndqUvwb8qrWBlbqX6p5Sx5U6e6//Wv6w+pfucd908+KUhIObX33u7DhqW37803e0a10j76KOx/xx3xt9lhB/fDDkvv+IWSy+HnH38cynFk4v33Qxi33hp/m8suC/Wlkq2r9s034ZhXXplZbMWkrdINqXj88dCKXc6iLe3jx6e3//Ll7l27uv/P/6y7/KKLQqtQtn5e//yn+9FH56e1Pp+lKk44IfH/+ajGRvfTTgun/9OfMj5t2lDLmbQ0cWKY+qP1yKChQ1MvF1EMvI2+HcOHw7vvpt6/CMLE5V26hClW0lFVFVosjj029vrVq8NfyqVWmiGXw8+33LLtuTjbsu228OGH8OMfx99mzJjQHzH6V35b3n03vJbazyrqyy/DhO2vvNK8rHMWu9Ecfnhq5SPao2iLV7rXoVcvOOWUdctduIcSGiNGZO/nddhh8NBDybfWu4dWpttvT/1c+SxVce+9cOaZbW/X1BSeaPzyl2EaqGKj5KxMLV0afiG1ftzzySdw/fXJT2pdLLbZBn71q/jrhw8PJR6iv1xT8Yc/wKJFiYe4J1JREc4fb47PtWvDgIMDD0zv+IWSy+Hn7vD734dfHukyC/dFoiRvk01Sq48U7ey+yy7px1VIPXqEzuYvvhgmzt511zDKOVvWrIF//jMkxeVqwYJwnTN59H3zzet2bJ89O3Q3yXSUZixffpncdo88EgbQLF+e+jnyXaqivj5Mnh7vke2qVdCxY+jS8OtfZ+WUWafkrEwtWRL7l9acOWE4cjY/sHNt6VKYOzcUV4wn1ki1VGywQXr7RU2dGmqCxSqXscEGoQhoukUlCyWXw8/Nwl/A992XXmxr14a+O1Nid3FZR3V16H8zeXLb2x54YCjL0a9fenEV2qJF0G/DOq77w2q+u2cT7729mhuvr8taP9P6+lBe5e9/z87xcilWWYexozO/FmPGhM+kTFu4qqvhtBNDfDvv1EQXVvP25Oz9rCD0wx0wICQriSxfDj/7Wfij5Gc/S/08+S5V8dRT8JOfrFt/Mer660PiHP2DO5PyHTkV61lnqX6pz1ny9t8/9uS30cm3b789/zGlKzrv5aRJ8bdpanIfOdL9lltSO3ZDQ5iG5bnnMovxtttCjLFGI82Z475sWWbHL4RcTxZ80knu/fqlN2otek88+mjb265YESbtPvPM1M9TSqKTwP/Crva5DPJ6OvhcBvm4yquzOhptxx3dDz44O8fKlei1GFeZ22uRSXwbdlrlF5Lb+KIT3j/5ZOLtRo8O8+ROnZreefI9sXhDg/sWW7hv1meNb9Kz1ius0TfpWesH/GCNQxilWizz4qK5NaWlXXd1P+ig9ZevXRvmd7z88vzHlK4bbwx38mefZf/Y8+aFY992W2bHmTrV487xOWKE+/e+l9nxCyX6Sy4Xw8//8hdPeyBGdJ7SlqViEjn55FBGpiZBf+Q1a8LP76uvUo+n0PL5y/HUU9379s1tKYhM5PJavP9++L+cbhKT6/haW73avUsX9zFjEsdj5v6zn2V2rlx+VsQ6V6zk9kKu9h4Vq/yf/8zeuTKl5EzWsWiR+4IFsdcNGuR+7LH5jScT55zjvsEGyf0yaGoKo3SSFZ3c++WX0w7P3cOHYKykt6EhtNpk+sFXSHPnuo89d43361XjHSoavV+vGh977pqMf3m88Ua49o88kvq+w4e777FH8tu/9FI41z33xN/m7bfDNqU4efjPz1nj4yqvjvnLPvp1aeU1PvbczIcC3nRTOOTHH2cedy7k8lrceWc4xHvvFWd8sey/fxgtnsiLL4YW5kzl6rOi9Tny2UqXKSVnkrT99w+/3ErF3/7m/n//1/Z2b70V/qJ/8cXkjz1+fPhf8vnn6ccXNXjw+o97okVAE5XZKFdr1oSkO9WSBEuXhkcwv/xl8vs0NrpvtZX7PvvE3+aOO8LP6sMPU4unGOSzlMG0aeGQDz2UhcBzIJfXYvTo0AKbyh+A+YwvlmgB7Fh/rCdbYqaY5Du5zVS85KxjYXu8SSE0NISRcAcdFLtQ4j33hOHcpeLkk5Pbbsst4euvw1D3ffZJbp85c0LB3lRG9MUzdOj6o0Wj80eW6ui/XOrcOQxcSXVaofnzQ8HgVAqrVlTAZZfBihXh0ztWJ+F33gn3wre/nVo8xSCfpQyGDAmlYdIt2pxrubwWb74Z5hhNd2Q35PdnBXDUUbDpput/5n/8cfjMuvHGUEKjVNx/bxOv19+ScJsz6m9m5D2juW58noJKg5KzMrR0aRgd2KdP7OSslEairVkT5qNLplZP797hF2sqIzbr68MHVDZG9Nx6a6iX1tL06aGe0Q47ZH789iid+R533jkkB+6p7XfGGYnXR2cGyOQXb6H06VHHpyu3oIr4M103lzLoltG5KitDjblilatrsWZN+OPrgguKM754ttoqfLXkDqNHh9Hle+2V8SnyKt/Jba6U4MeMZCpawyxeQjNvXviAydaQ7VwNWYdQTLNPH3j11eS2T3VaoBtvhNdeSy+21rp2XT/JO/nk0FLZqVN2ztHevPlmKLz5wQfJ7xOtbZROQl1TE8pAtC550tQUkrNSLT6b71IGr78O55wTWumLTa6uxTffwAEHZJ7M5PtnVV0NZ5xUR++uzZ/Ph/2wjmeeCTUeBwzIymnyJt811XIm1rPOUv1Sn7PkvPZaePT+7LOx17/zjscdWZiqXA9Zv/baEGuyI+iuvz57fchSVV8fpha56678n7tUvfde+Hkle82qq8MUN22VBojn3nvD+V56ad3lTU2h5Mm8eekdt9Dy3Uk6eh1nzMjO8bKp2DuM5zO+6OfzxR3W/Xy+KDKq8YknMj9HvrWXPmcFT6iy+aXkLDn//Gf4yccb7r18eVh/1VWZnScfHzKnnhpqYSVrxgz3Cy6IP1K1pepq9+9+NySz2TJggPtxx4Xvly51f+AB96+/zt7x25vGxtDBevTo5La/5ZZwe73/fnrnq6lx79UrlNZob/JZyiA6n+yECdk7ZjZFSy1cwLrX4qKK9K9FNucozcfPqtiT1HSV2r9LyZn8V3S4d6JWgD593M86K7Pz5OMvmN12c99338zijOfJJ0OY//lP9o556KHu228fvp80KRw/0zId7d0++yQ/af2Pf+y++eaZ1dg666xQ3qRl6YDHHku9gHExykcpA/dw/TfcMPPPkFy67DL3zTdpvha9OtZ498o1/s476R1v++3dzz47e/Hl+mdVai1MqcjnHyKZUnIm/9XYGFptGhribzN8eOZJT66HhDc2hl+iiQooxrJmTShh0ZboI9BsFh395S9DmYeaGvcrrwzHT7ZQarm69FL3yspQKy6RhoaQEJx2WmbnmzzZ12v1OfjgUPlekrf//u5DhxY6isRaJvHROnZXXpn6cZYtC/v+/vfZiy3X8l2yI9/y9YdIpuIlZxoQUIYqKmJPet5SVRUsW5bZeXI9aqahAW64AY45JrX9xowJncxjzXPZ0pw5Yd7LPn3SCi+moUPDeWfNCh3Mt9wSNtwwe8dvj/beGw49tO37cdq0sE0qJTRi2X33MNrwP/9pXjZ9usqdpGr48DCCsRgHBdTUrF8yZZdd4K9/Tb40T0vTpoXX6By+paC9jGqMp6oKrhvfmUXLu9HQWMGi5d24bnxnqqoKHVlylJyVoXvvhSuuSLzN3Xc3f+CkK9ejZjp1gtNPT33C8N12C7/E585NvN2cOaFWUzYnxv3Od2CnncJEw/qFn5wDDoBHHoFvfSvxdn36wC9+Afvum9n5zEJidscd4f3XX8Pnn5fuSM1C+e1v4f33oWMRFmw6/XT47nfXX37WWaHmV6qiI8CHDcssrnxqN6Ma2yklZ0Uml2Unov75z5CgJZKND9RcDwl/7z2YOTP1/aJ/3bZV72yLLeAHP0j9+G0dc8aM0Drz0Uf6hZ+K2trE66uq4OqroW/fzM+18cbh/+LPz6lj+y1XYzRx5S+z/3+xPfv449Q/y/Lx+VdXB08/Hb+24OTJcMIJqbX4vflm+EOud+/sxJgP+S7ZISmK9ayzVL9Kvc9ZrstORO29t/vIkYm3+fhj96OOCv1v0pXrUTPHHee+xRap71dfX7j5LOfODR1xN+lZ6xXW6H171PrPzym+fhDF5owz3LfdNv76VatC+Yu6uuycLzqa7yJy+3+xvYp+lv3Ckr9++fr8e/rp8BH01FOx1z/+eFh/333JH/Pee0tvwEipjWpsr9CAgOKWz/8oO+8cRg0m8tln4dQ335zZuaIfuBfZuqNmLumY+aiZnXZaf67KZH3ve+4jRqR/7nT8t6ZQhX7hp+r3v/eEgyeiI2tfeCHzc+mXVmbSuX75vOZnnuneo0f80heNjWEi8J12ymzUbykopVGN7ZWSsyKXz2HNAwa4//SnibdpbHTv3Nn9oosyPp1/8IF7945rfINONd7BGn2jLjV+9mmZtRatXRtG8F1ySXr7v/ii+7//HX/9E0+EVrlkRnUmQ7/wM/Pcc+FSPf987PVjxrh36dL2iM5ktOcSA/mQzvXL1zVvaHDfZBP3n/wk8XZ/+1s4bTLFjD/7LBQoLtVErlRGNbZXSs6KXD6HNW+8cSjE2pbttnM/8siMT+fTpoV/wgMPZH6sqNmzwzHvuSd7x2zpmmvC8Zcuzc7x9As/M0uXhst0xRWx1++wg/sBB2TnXO29xECuJXv9Nuxc43/4QyhdsWHn/Fzz+nr3hx5qu3bh2rXuAwe23f3D3X3cOPeOHd1razMKTcpUvOSsCMfRlKd8DmtevLh5/sFEqqrCPJuZis57+b3vhdeVK8OghB/+MP3O27NmhdfBg9Pbv6kpdAru1y/28Pc5c8Lov402Su/4rd1/bxOv19+ScJsz6m9m5D2juW58ds7Znmy0UehwHWte1M8/D4NDTj01O+dq7yUGci3Z67e8rguXXx7eG/m55h07wtFHt71dZSX8/vdhpG5jY+KyQ1OnhhHYXbtmFJrIOjRas0jke1hzog+bqJ13zk4NriOPDKU5NtssvJ83D046CR5/PP1j7rcfTJoE22+f3v5m8NOfwi1x8qWPPgrJQLboF37mLr0Ujj9+/eUvvBBeM61vFqUSA5lJ9vpt0msNa9bA6tXQt2fur7k7jB8fRpEm46ST4IILEn9WNjWF5Gz48LTDEokpp8mZmR1oZh+a2VwzuzTG+g3M7Akze9fMZpvZqa3WdzCzd8zsyVzGWQzyNaz5k09CkcV332172z/8AV5+OaPTAaF8xEknNb8fMgS22goeeyz9Y/buDQceCJ07p7e/WfhAjdUSA801zrJFv/Azd9ppsVs9jj8eXnsttF5kg0oMZCaV69e5M3Tpkp9rPmsWnH8+PP988vvU18NddzW31Lc2Zw4sX67kTHIg1rPObHwBHYBqYBDQCXgX2KHVNpcBV0W+7wssBTq1WH8BcD/wZDLnLOU+Z/nqMP7SS+GQL76YlbDbtGBBmMuzdd+tCy5w79QpTLKejltucX/rrcxi+9WvwlRKK1euu7yxMcyRd++9mR2/JfU5y1xjo/usWe6ffJLb82jwRmaKdbTmb37jbua+aFHy+yxdGkZ2Hndc7PX33BNCnDkz/bikvJHvAQHAHsCzLd6PA8a12mYccBNgwFbAXKAism5z4EVgn3JIztxblFrokLthzY88En7q06e3ve3Che677+7+6KPpn2/ChHC+999fd/mrr4blDz6Y+jFra8OH7K9/nX5c7s3lFxKN2swW/cLP3OrVoeP1pZc2L3v/ffexY8OIuWxSiYHMpHP9cn3Nhw5NroN/axddFP6Iq65ef91XX4XP1ETzFIskUojk7ChgQov3JwHjW23TE3gJ+AJYBRzcYt3DwK7AD8olOXMPv8QP/eEa70qNV5D9Yc233hp+6vPnt71tbW3Y9re/Tf98p5zi3rfv+sPMo0Pax45N/ZhvvRXievjh9ONyd//yy3CcP/1p3eW1tbn5sNUv/Mztuqv7Pvs0v4+Oql2wIPvnUomBzKRz/eLt8847mcXy8cfhPrnmmtT3/fzz0Mp/9tmZxSASSyGSs6NjJGd/abXNUcD1kZazbwMfA72AQ4CbItskTM6As4BpwLSBAwfm8BLmz513hp/MvHnZP/aVV4Zj1yQ5In2zzdquiZbIoEHxy3F8/XV6x4zWIMpGDbL3318/EbviivBhnOw1SoV+4WfmnHPce/UKjzjd3fffP5TRkPbruONC0ehMPPxwqIuY7v+zs84KdR+/+KJ5WV2d+/XXx25RE0lWvOQslwMCFgADWrzfHFjYaptTgehDs7mR5Gw7YCRwmJl9AjwI7GNmMWeDdPdb3X2Yuw/rm41J9YpAdA7B7t2zf2z3UD4i2WHfgwaR9rx2CxeGkZmxJhiGUKoiHbNmhUnPq6rS27+l7bZbfzTWnDlhwEG3bpkfv7WqKrhufGcWLe9GQ2MFi5Z347rxnbPybykHu+0GK1aE0bRr1oQyLdkapSnFacSI8HP+z3/SP8aPfxxKCKX7/+wXvwgjw7/4onnZzJkwdixMm5Z+XCLx5DI5mwpsbWZbmVkn4FhgYqtt5gP7AphZP2BbYJ67j3P3zd19y8h+/3L3E3MYa1HZeWf4zndgm23gnXeye+xx42DRojBaMRmZ1Dp7663wGq1vFsv//A9ceGFqx509O3xQZmNy9pkz4eyzwwd3VLZHakr2REfFvflmGKG5Zo2Ss/bu9NPDRPRXXpnZcXr1Sn/fb38b3n4bdtmleVl0pLdGakou5Cw5c/cG4DzgWeB94CF3n21mZ5tZdMz074A9zWwmofP/Je6+OPYRy8fIkXDTTWGI9sLWbY15tuee4cOnqSn1fQ89NMTf8gOtteXL4d57kyuKG/Xoo/BkloqrLF0Kf/3ruiU1lJwVr+22gyeegEMOgS+/hP79Ya+9Ch2V5FL37jBmDDz1FMyYkfr+d98dWu+XLMksDrPweTV5cnj/5puhiPYWiSvkiKQlp3XO3P1pd9/G3avc/Q+RZbe4+y2R7xe6+wHuvpO7D3b39R5duvvL7n5ILuMsNitXNj/SzHZyduGF8JvfJL/9mWeGZKgizTulf//ELVxHHglffdX8gZeMzp1h883Ti6e1XXcN/7ZocrZiRfilr+SsOH3yCbz4dB3bb7mak09qonHVav7v4rq0H71LaTj3XOjRA/7yl9T3ffhhWLAgdFXI1E9+AqP2qaNfr9X87a4mVn+zmgvO1f0n2acZAorQpZeGFitYt49DNjz7bHIFaFsLYy+St3x56OcxZUri7UaNCv3Hki1IO2sW/Pzn8NlnqcUTT48esMMOzclZU1OYtmW//bJzfMmeSZNgxJAautx2A6+vHEydd+L1lYPpOuEGRgypYdKkQkcoudK7d5hu7c9/Tm2/VavguefgiCOS78oRz6RJMO2VGs5YE+6/tXRieoPuP8kNJWdFqKYmzCXYt2/2W86WLAn9N5K1fHmYdml8ivM9vv56aHGLDm6Ip1evkAg99lhyCeB//gP/7/+l9hi0LdGZAtzDdFWXXw7DhmXv+JK56mo4+agaJtbux5UNF1PFPDrSSBXzuKL+YibW7sfJR9WoBaMd+973whOFVP5QfPZZqKsLyVkmovffk2v240/o/pPcU3JWhGpqwofQSSeFgQHZ4h76WKWSnPXqFR71zZ2b2rlefTU8zhwxou1tTzsNjjoqfIi2Zdas0No1cGBq8SSy227Qs2dIXD/7LAyYkOIy/to6zqy/iT2I3RS7B1M4o/5mbrw+iZtIStarr8LQoaHrQTIeeyx83sUbMZ4s3X+Sb+apPq8qYsOGDfNp7WBc80EHhX5Y2f6nrFoVkpCrroKLL05+v6FDQx+vVDrhf+97YV66th5rpmrvvcNEydk8rnvzI4+TTw7zic6fn73jS+b69VrN6ysHU0X8ocPVDGJkr5ksWp6DGihSFObMgW23DV0/rrii7e3vuAO++Sb1EeGt6f6TXDGzt9x9vWc1ajkrQtGWMwgJTjaPu/32MGBA29u2lGqtszVrwmPCRCU0WquvD49CE3EPpS8GD07+uMlo2Rflo480GKAYLV7VmS34NOE2A5nP4lVd8hSRFMLWW4dW9htvDF0u2nLaaZknZqD7T/JPyVkROuOMMEryD38IhVCz1b+qXz947z047rjU9quqgo8/Tr6cxqJFoVZbKiUObrwxlBD5+OP426xYEQYP7LRT8sdN1uWXh5FYKqNRnPr0qONTEtcsmM9A+vRYk6eIpFAuvTR8FtxyS+LtpkzJvHxGlO4/yTclZ0XopJPgxBND5/SGhnULpBbC3nuHZHFNkp87W24ZWs4OSaEAymGHhddEozY32CAMkDj//OSPm4zqanj2iTqe+MdqvlnaxN/vWs3Y0RoeX0yOP7GC2yvPTrjNhMpzOP6kDgm3kdL3ne/AAQfA9deHLg6xNDXBj34UCkxng+4/yTclZ0Xok09CP4n+/cP7bI3YfPbZ8Kgx1f5UBx0U6gslO51ROgVrBw2CIUPg8cfb3jbdmmuxRMsz7PveDcwiDI+fVqfh8cXmvAs7c1vlaCYTe4TJZEYwofIczh3bOc+RSSH87nfhM6lTp9jr33wzlCHKdJRmlO4/yTclZ0Vo2DD43/+FTTcN77NV6+zjj8OUN5WVqe9bXx8GFLSlsTEMHrj22tTPccQRIb6vvoq9/te/hrPOSv248bQsz3BVo4bHF7OqKrj74e4c1u0FxlVeQzWDqKcj1QxiXOU1HNbtBe5+uLvmKC0Tw4fD0UevPy9u1OOPh9HiBx2UnfPp/pN8U3JWhGprQytVtOUsW8lZtP9FqpWyGxpC+Yqrrmp721mzQrz9+qUe35FHhk7/Tz0Ve/2zz4YO+9mi4fGlZdQomDKjO3Vnnc/IXjPpWlHHyF4zqTvrfKbM6M6oUYWOUPKpri4UjG7d2u4eukfsvXeoF5ktuv8kn7IwdbRkU1NT6EfRvTt861thTrntt8/OsZcsCcftnGLLe8eOoRUvmRakV18Nr6mM1IzaeefQcrb77uuvcw+J309/mvpx47n/3iZer0/cq/iM+psZec9orkuxCK/kRlUVXDe+c4ufh8oWlKvKSnjggfD5dPjhzaOuq6vDH3FjxmT/nLr/JF+UnBWZaEX9aBKV6nQliSxdmv78clVVySdnAwakNxmwWRixGcv8+eGxajbLaGh4vEjpqqiASy6BU04JfUejjzC//e1QNDsbc2mKFIoeaxaZlskZhBGS8fpgpWrAgPjJT1uqqmBe/PqLQGjdeu219FrNolauhAsuYL2O+LNmhddsJmcaHi9S2o47LswWcuWV6y6vqsruI02RfFNyVmS6dQs1v77//fD+8MPh0EOzc+zf/S48BkjHoEGhpMeKFfG3aWgIQ9ePPz69c0D49997L9x99/rrdt0Vdtwx/WO3puHxIqWtsjK0nL35Wh19uq+mQ0UTvSpXc8qxKoUjpU3JWZHp0QNGj25uIerfP/uTn6djv/3CgIBEs31VVsL//R8cfHD65+nQISSkTz217lybBx8cprPacMP0j92ahseLlLZJk+Dma2sYYzfwRu1g6rwT7zQMZtNHVApHSpuSsyKzciW8+26YaglCR/xFi9KrHdba8OHwxz+mt++uu4b5ODfYIP42s2YlN6VKW444IlyHf/2reVkupoDV8HiR0tWyFM7Vvm4pnCsbVApHSpuSsyIzdWqYaHzq1PC+f//wuDDTaUiamuCtt5KrVRbPp5/CZ5/FX3/UUWF2g0ztu29oQYzOFtDQEEau/uUvmR+7NQ2PFylNKoUj7ZmSsyLTekBAtmYJWLEiJGiZjGDaddcw32csX30FH36Y2WCAqC5d4IQTmq9BdXU4fq9emR87lujw+EXLu9HQWMGi5d24bnxntZiJFLH7723i9CRK4dx/T5YmJxbJI5XSKDLRx5nRxGTXXeFPf4JNNsnsuEuXhteNN07/GIlGbL72Wnj97nfTP35LLSc1zsVITREpbSqFI+2ZWs6KTOvkbKut4MILm1vQ0pXu7AAtDRoUv9bZq69C164hmcwW9zBCdNasUAMtW8V4RaT0qRSOtGdKzopMNDlrOcl4dXXivl7J6NIlFGlMpzhsVFVV6HfW0LD+uldfDZX9401EnKrqathlhzq22GQ1v/l1E11ZzeUXaXi8iAQqhSPtmZKzIrP//nDXXeuOitxtt+TmtUxkp51CeYohQ9I/xqBBYWLz+fPXX3fHHemPBG1t0iQYMaSGH865gRk+mLV0YoYPpusEDY8XkUClcKQ9M89FjYICGTZsmE+bNq3QYWTdjjvCttvCo48WNo5PPoHJk0MLXKKSGpmorg6J2cTa/WKOwprMCA7r9gJTZqjEhUi5mzQplNM4o/5mzqi/mYHMZz4DmVB5DhMqz+HuhzXiWoqbmb3l7sNaL1fLWZH56CN44411l226aeajNf/4xzB9U2MGA5e23DJMl9I6MXvySfj73zMK7780PF5EkqVSONJeqeWsyJxzTmgh+/LL5mUnnwz//nfo75Wun/0sTIm0bFlm8U2ZEvqVfec7zcv23jsUjc3Gpe/XazWvrxxMFfEn8qxmECN7zWTR8m5xtxERESl2ajkrETU16w4GgNBy9sUXmVXJX7IkszIaUT/96bq1ztauDS192ahvBhoeLyIiouSsyNTUNJfRiDrmGLjnnsweSS5dmp3krHWts7ffhtWrs1ffTMPjRUSk3Ck5KzKxkrNddgkJWscMSgYvWZJZjbOoqqrQaT/aivfqq+E1W8mZhseLiEi5U3JWZGpr13+sWVsLL78cHm2m64AD4MADMwoNCOU0Vq4MxWEB3nsPttkG+vXL/Nig4fEiIiKavqnIXHnl+n3LFi0Kne7vvDP0+UrH73+fcWgA/y1fMW8e9O0bYsp0kEHr49/9cHcOO+qFhMPjVUZDRETaK7WcFZmRI9d/RBiduindljP3MOl5NowcGR5l7rhj87INN8zOsaM0PF5ERMqZkrMi88wzMHv2usu6dg0JULq1zpYsgcpK+OtfMw6P3r1D8tijBzzwABx7bPOUU9lUVQXXje/MouXdaGisYNHyblw3vrNazEREpN1TclZkjj8ebr55/eX9+6ffcrZ0aWg569Ejs9iinngiFJ598kl45ZX1+8iJiIhI+tTnrMjEGq0Jmc0SsGRJeM3GaE2Aq6+Gigr4+ONQ38wsO8cVERERJWdFpaEhFHWNlZxdcUVIiNIRTc6yUeesuhqWf1XHnI+aqKMzz31Vx9jRFZx3oR45ioiIZIMeaxaR2trwGis5Gz4chq03wUNyli4Nr5m2nE2aFCYlHzX3BmYxmLV0YlrdYLpOuIERQ2qYNCmz44uIiIiSs6IS7VgfKzmbPx/uvTe9zvfbbAPnn59ZLbLqajj5qBom1u7HVU0XU8U8OtJIFfO4ov5iJtbux8lH1VBdnf45RERERMlZUendOxSbPfTQ9ddNngwnnRT6eaVqxAi44Qbo2TP92MZfW8eZ9TexB1Nirt+DKZxRfzM3Xl+X/klEREREyVkx6dwZ9toLNtts/XWZ1DpbuTL0ZcvE/fc2cXr9LQm3OaP+Zu6/J4MJQEVERETJWTH58ku4777w2tqmm4bXdEZsnnkmDB6cWWyLV3VmCz5NuM1A5rN4VZfMTiQiIlLmlJwVkVmz4MQT4aOP1l+XScvZkiWZj9Ts06OOT9ki4TbzGUifHmsyO5GIiEiZU3JWRBINCOjeHXr1Si85W7o08+Ts+BMruL3y7ITbTKg8h+NP6pDZiURERMqckrMiEk3O4lXcf/lluOyy1I+7ZEnmZTTOu7Azt1WOZjIjYq6fzAgmVJ7DuWM7Z3YiERGRMqfkrIgkajkD2GWX9MphZKPlrKoK7n64O4d1e4FxlddQzSDq6Ug1gxhXeQ2HdXuBux/urkK0IiIiGVJyVkTaajl79VW46abUjukeWtsOPjiz2ABGjYIpM7pTd9b5jOw1k64VdYzsNZO6s85nyozujBqV+TlERETKnbl7oWPImmHDhvm0adMKHUbaFi+GTz4JLWQdYnTd+sUvYPz4MJOA5rMUEREpbWb2lruvN/+P5tYsIn36hK94+veHNWtg2TLYaKPkjrl6dSjNsemm0KlTVsIUERGRHNJjzSLy73/DXXfFXx+tdZbKiM1p02CrreCVVzIKTURERPJEyVkRue8+GDcu/vp0ap0tWRJeMx2tKSIiIvmh5KyI1NTEHwwAzclZKrMELF0aXjMdrSkiIiL5oT5nRaSmJn4ZDYBBg+Czz1IrpxFNztRyJiIiUhqUnBWRtpKzjh1h881TO+aSJWG/Hj0yi01ERETyQ8lZEamtTZycAfz1r1BZCaedltwxDzsMtthCpTdERERKhZKzIvLww9DYmHibBx4I2ySbnO2xR/gSERGR0qABAUWkf/+2H1v275/aaM3Zs2H+/MziEhERkfxRclZE/t//g+efT7zNppuG0ZrJTuxw3HHws59lHpuIiIjkR06TMzM70Mw+NLO5ZnZpjPUbmNkTZvaumc02s1Mjy7uY2Zstlv8ml3EWi1/9Cp58MvE2/fuHqv8rViR3zGxMei4iIiL5k7PkzMw6ADcCo4AdgOPMbIdWm50LvOfuOwM/AK41s05AHbBPZPlQ4EAzG5GrWItFW6M1IbScVVSEKZmSsWSJymiIiIiUkly2nA0H5rr7PHdfCzwIHN5qGwd6mpkBPYClQIMHqyLbVEa+2s8M7THU10NDQ+IitABHHw1r18I227R9zNWrw1ycajkTEREpHblMzjYDPmvxfkFkWUvjge2BhcBMYIy7N0FoeTOz6cBXwPPu/kYOYy24mprw2lbLWWUldOiQ3DE1dZOIiEjpyWVyFquyVuvWrx8C04FNCY8vx5tZLwB3b3T3ocDmwHAzGxzzJGZnmdk0M5v29ddfZyn0/Es2Oauvh3POgccfb/uYG24IDz4I++6baXQiIiKSL7lMzhYAA1q835zQQtbSqcCjkceYc4GPge1abuDuy4CXgQNjncTdb3X3Ye4+rG/fvlkKPf/694fFi+HEExNv17Ej3HMPvPJK28fs0QOOOQaqqrITo4iIiOReLpOzqcDWZrZVpJP/scDEVtvMB/YFMLN+wLbAPDPra2YbRpZ3BfYDPshhrAVXURH6hrXV58ws+Vpnn38OL70U+p6JiIhIachZcubuDcB5wLPA+8BD7j7bzM42s7Mjm/0O2NPMZgIvApe4+2KgP/CSmc0gJHnPu3sbRSZK27x5MG5ceG1LtNZZW556CvbZp7nvmYiIiBS/nE7f5O5PA0+3WnZLi+8XAgfE2G8GsEsuYys2c+fCH/8IhxwCgwYl3rZ/f3j77baPGU3KNFpTRESkdGiGgCKR7IAAgIEDk5vIfOlS6NIFunbNLDYRERHJHyVnRaK2Nry21ecM4Oqr4cMP295uyRK1momIiJQaJWdFIpWWs2Rp6iYREZHSo+SsSERbzpJJzj74AA49FKZNS7zdb34DN96YeWwiIiKSP0rOisSYMaHkxQYbtL2te5gg/aOPEm+3887w3e9mJz4RERHJDyVnRcIsdN5PpqN///7hta1aZ488ArNmZR6biIiI5I+SsyLxwAOhzlkyNtggjMBMVOvMHY47Du67LzvxiYiISH4oOSsSL74Id9+d3LbJzBKwalWYh1OTnouIiJQWJWdFoqYmtZGa3/lOmNg8nqVLw6tGa4qIiJSWpGcIMLPu7l6Ty2DKWarJ2T/+kXh9dHYAtZyJiIiUljZbzsxsTzN7jzA/Jma2s5ndlPPIykxtbXIFaJOlqZtERERKUzKPNa8HfggsAXD3d4Hv5zKocuQOvXolv/0//gFDh4a+ZbGMGAFvvBG2ERERkdKR1GNNd//M1q3x0JibcMrXiy+mtv2aNfDuu2FQwNZbr7++Z08YPjw7sYmIiEj+JNNy9pmZ7Qm4mXUys4uIPOKUwonWOotXTmPyZLj99tAiJyIiIqUjmeTsbOBcYDNgATAUGJ3DmMrS2WcnX0oDYNNNw2u8chp//zuMHZtcUVsREREpHskkZ9u6+wnu3s/dN3H3E4Htcx1Yubn/fnjnneS3b2uWAE16LiIiUpqSSc7+kuQySZN76qU0NtwQ9torfgK2ZInKaIiIiJSiuAMCzGwPYE+gr5ld0GJVL6BDrgMrJ3V10NSUWnJmBi+/HH+9Ws5ERERKU6KWs05AD0IC17PF1wrgqNyHVj5qa8NrtuucqeVMRESk9MRtOXP3fwP/NrO73P3TPMZUdurq4Fvfgo02Sm2/Cy8MozJff339dS+/rJGaIiIipSiZOme1ZnYNsCPQJbrQ3ffJWVRlpq1JzOOpr4fZs2Ovi47mFBERkdKSzICA+4APgK2A3wCfAFNzGJMkqX9/WLEiDCZoadUq+N3vQpFaERERKS3JJGcbu/vtQL27/9vdTwNG5DiusjJzJhx6aHhNRbxaZ4sWwS9/qeRMRESkFCWTnNVHXr8ws4PNbBdg8xzGVHY+/xyefDL+PJnxxKt1tnRpeNVoTRERkdKTTJ+z35vZBsCFhPpmvYCf5zKochN9LJnqaM2qKjjmGOjRY93lS5aEV43WFBERKT1tJmfu/mTk2+XA3gBmNjKXQZWbaHKWSp0zCMnZgw+uvzyanKnlTEREpPQkKkLbAfgJYU7NZ9x9lpkdAlwGdAV2yU+I7V+6yVlUUxNUtHhArceaIiIipStRn7PbgTOAjYEbzOxO4E/A1e6uxCyLunULrWCtH08mY+hQ+OlP1102ejR8/XXqddNERESk8BI91hwGDHH3JjPrAiwGvu3ui/ITWvk45ZTwlY5u3WDhwnWXdewIffpkHpeIiIjkX6KWs7Xu3gTg7muAj5SYFZ9YBWzvuAOuv74w8YiIiEhmEiVn25nZjMjXzBbvZ5rZjHwFWA6uvRaOPDK9fTfddP2Ws4ceij1QQERERIpfosea2+ctijI3ezZMm5bevv37w7JlsHo1dO0ali1ZoseaIiIipSrRxOea7DxPamrSH6n53e+GCdDr65uTs6VLYdttsxefiIiI5E8yRWglxzJJzr7//fDV0pIlKkArIiJSqpKZvklyrLY2/eQMQnIXrZXW2BgecarGmYiISGlqMzkzs0PMTElcDlVVwU47pbfv4sWhPtqECeF9hw6wZg1cfnn24hMREZH8Seax5rHA/zOzR4A73f39HMdUdm67Lf19e/eGysp1y2mYhVpnIiIiUnrabBFz9xMJUzVVA3ea2WQzO8vMeuY8OmlTRQV861vNydn778Npp8FHHxU2LhEREUlPUo8r3X0F8AjwINAfOBJ428zOz2FsZWOvveCPf0x//5a1zubOhTvvhBUrshObiIiI5Fcyfc4ONbPHgH8BlcBwdx8F7AxclOP4ysJbb4W5MNO16abNLWdLloRXjdYUEREpTcn0TDoauN7dX2m50N1rzey03IRVPtwzH6158snw1Vfh+6VLw6tGa4qIiJSmZJKzXwH/7W5uZl2Bfu7+ibu/mLPIysTq1SFByyQ5O+KI5u+XLAkjNnv1yjg0ERERKYBk+pz9A2hq8b4xskyyIFqfrFu39I+xZg18+GFzojdwYBixKSIiIqUnmeSso7uvjb6JfN8pdyGVn333ha22Sn//l1+G7baDd96BK66AefOyFpqIiIjkWTKPNb82s8PcfSKAmR0OLM5tWOWjb1944YXMjtG/f3htWetMRERESlMyydnZwH1mNh4w4DPg5JxGJSmJJmcLF8K554ZWuIs0jlZERKQkJVOEttrdRwA7ADu4+57uPjf3oZWH11+HQYPgzTfTP0afPmFGgC++gCeegFmzshefiIiI5FdSk/yY2cHAjkAXi/Q0d/ff5jCusvHNN/Dxx5kdo+UsAUuXqoyGiIhIKUumCO0twDHA+YTHmkcDW+Q4rrJRWxteMxmtCfCnP8EJJ4TRnypAKyIiUrqSGa25p7ufDHzj7r8B9gAG5Das8hEtpZFJnTOAY46BHXYI36vlTEREpHQlk5ytibzWmtmmQD2QQeEHaSlbydnChfDMM7DNNmE6JxERESlNyfQ5e8LMNgSuAd4GHLgtl0GVky22gMMPh549MzvO7bfDL38ZCtJ27pyd2ERERCT/EiZnZlYBvOjuy4BHzOxJoIu7L89HcOXgkEPCV6ai5TQWLQoJn4iIiJSmhI813b0JuLbF+zolZsUp+ihz221hsUoEi4iIlKxk+pw9Z2Y/NtNsjblw4YXNHfnTVV0N999VRxdWs7auiR0HrWbs6Dqqq7MTo4iIiORPMsnZBYSJzuvMbIWZrTSzFTmOq2wsXtw8KCAdkybBiCE1DHjsBmYxmLV04vWVg+k64QZGDKlh0qTsxSoiIiK5l8wMAT3dvcLdO7l7r8j7Xskc3MwONLMPzWyumV0aY/0GZvaEmb1rZrPN7NTI8gFm9pKZvR9ZPib1f1ppqK1Nf6RmdTWcfFQNE2v348qGi6liHh1ppIp5XFF/MRNr9+Pko2rUgiYiIlJCkilC+/1YX0ns1wG4ERhFmPrpODNr/QDvXOA9d98Z+AFwrZl1AhqAC919e2AEcG6MfduFmpr0k7Px19ZxZv1N7MGUmOv3YApn1N/MjdfXZRChiIiI5FMypTR+0eL7LsBw4C1gnzb2Gw7Mdfd5AGb2IHA48F6LbRzoGenP1gNYCjS4+xfAFwDuvtLM3gc2a7Vvu5BJcnb/vU28Xn9Lwm3OqL+ZkfeM5rrx6Z1DRERE8qvN5MzdD2353swGAFcncezNgM9avF8A7N5qm/HARGAh0BM4JjJCtOX5tgR2Ad5I4pwl54ADoLIyvX0Xr+rMFnyacJuBzGfxqi7pnUBERETyLqmJz1tZAAxOYrtYozu91fsfAtMJrXBVwPNm9qq7rwAwsx7AI8DPo8vWO4nZWcBZAAMHDkwm/qJy+eXp79unRx2frtyCKubF3WY+A+nTYw2Q4eSdIiIikhfJ9Dn7i5ndEPkaD7wKvJvEsRew7hycmxNayFo6FXjUg7nAx8B2kfNWEhKz+9z90Xgncfdb3X2Yuw/r27dvEmEVF2+drqbg+BMruL3y7ITbTKg8h+NP6pD+SURERCSvkimlMY3Qx+wtYDJwibufmMR+U4GtzWyrSCf/YwmPMFuaD+wLYGb9gG2BeZE+aLcD77v7dUn9S0pUv37w85+nt+95F3bmtsrRTGZEzPWTGcGEynM4d6zmcxIRESkVyTzWfBhY4+6NEEZhmlk3d69NtJO7N5jZecCzQAfgDnefbWZnR9bfAvwOuMvMZhIeg17i7ovN7LvAScBMM5seOeRl7v50Gv/GorZqFXTqlN6+VVVw98PdOeyoFzij/mbOqL+ZgcxnPgOZUHkOEyrP4e6Hu1NVld2YRUREJHeSSc5eBPYDVkXedwWeA/Zsa8dIMvV0q2W3tPh+IXBAjP1eI3aftXalqQlWr4ZuGXQHGzUKpszozo3Xn8/Ie0azeFUX+vRYw/EndWDK2M5KzEREREpMMslZF3ePJma4+yozU+/yLKiNtD2mW0ojqqoKrhvfuUW5DP14RERESlUyfc5qzOw70TdmtiuwOnchlY/otE2ZJmciIiLSfiTTcvZz4B9mFh1p2R84JmcRlZFOnWDMGBg6tNCRiIiISLFIpgjtVDPbjjCS0oAP3L0+55GVgY02gj//udBRiIiISDFJps7ZuUB3d5/l7jOBHmY2OvehtX8NDaHfWSa1zkRERKR9SabP2Znuviz6xt2/Ac7MWURl5JVXQn+zV14pdCQiIiJSLJJJzioiRWGBUOcMSLMyl7QUHRCQSSkNERERaV+SGRDwLPCQmd1CmBvzbOCZnEZVJjRaU0RERFpLJjm7hDCx+DmEAQHPAbflMqhyoeRMREREWmvzsaa7N7n7Le5+lLv/GJgN/CX3obV/2SpCKyIiIu1HMi1nmNlQ4DhCfbOPgUdzGFPZGDYMLr8cevYsdCQiIiJSLOImZ2a2DXAsISlbAvwdMHffO0+xtXt77BG+RERERKIStZx9ALwKHOrucwHMbGxeoioT33wDjY3Qp0+hIxEREZFikajP2Y+BRcBLZnabme1LGBAgWTJuHOy4Y6GjEBERkWISNzlz98fc/RhgO+BlYCzQz8xuNrMD8hRfu1Zbq8EAIiIisq5kRmvWuPt97n4IsDkwHbg014GVg5oaFaAVERGRdSUzQ8B/uftSd/+ru++Tq4DKSU2NWs5ERERkXSklZ5JdSs5ERESktaTqnElujB4NHToUOgoREREpJkrOCui44wodgYiIiBQbPdYsoI8+gq+/LnQUIiIiUkyUnBXQiBHw298WOgoREREpJkrOCkgDAkRERKQ1JWcF0tAAa9eqzpmIiIisS8lZgdTWhle1nImIiEhLSs4KpKYmvCo5ExERkZZUSqNAevaE226DkSMLHYmIiIgUEyVnBdKjB5xxRqGjEBERkWKjx5oFsnw5vPkmrFxZ6EhERESkmCg5K5CpU2H33WH69EJHIiIiIsVEyVmBaECAiIiIxKLkrECUnImIiEgsSs4KJFrnTEVoRUREpCUlZwWiljMRERGJRaU0CuSgg2CTTaBXr0JHIiIiIsVEyVmBbL11+BIRERFpSY81C+S99+DVVwsdhYiIiBQbJWcF8uc/w09+UugoREREpNgoOSuQmhoNBhAREZH1KTkrECVnIiIiEouSswKprVWNMxEREVmfkrMCUcuZiIiIxKJSGgVy/fWFjkBERESKkZKzAhk+vNARiIiISDHSY80CmTgR3n230FGIiIhIsVFyViAnnwx33FHoKERERKTYKDkrkJoajdYUERGR9Sk5K4C1a6GhQaM1RUREZH1Kzgqgpia8KjkTERGR1pScFUBtbXhVciYiIiKtqZRGAfTpA5Mnw5ZbFjoSERERKTZKzgqgc2cYMaLQUYiIiEgx0mPNAli4MJTRWLSo0JGIiIhIsVFyVgCzZsHpp0N1daEjERERkWKj5KwANFpTRERE4lFyVgBKzkRERCSenCZnZnagmX1oZnPN7NIY6zcwsyfM7F0zm21mp7ZYd4eZfWVms3IZYyGolIaIiIjEk7PkzMw6ADcCo4AdgOPMbIdWm50LvOfuOwM/AK41s06RdXcBB+YqvkJSy5mIiIjEk8uWs+HAXHef5+5rgQeBw1tt40BPMzOgB7AUaABw91ci79udU06BGTOgZ89CRyIiIiLFJpd1zjYDPmvxfgGwe6ttxgMTgYVAT+AYd2/KYUxFoXfv8CUiIiLSWi5bzizGMm/1/ofAdGBTYCgw3sx6pXQSs7PMbJqZTfv666/TiTPvXngBJkwodBQiIiJSjHKZnC0ABrR4vzmhhaylU4FHPZgLfAxsl8pJ3P1Wdx/m7sP69u2bUcD58uCD8KtfFToKERERKUa5TM6mAlub2VaRTv7HEh5htjQf2BfAzPoB2wLzchhTUaip0WAAERERiS1nyZm7NwDnAc8C7wMPuftsMzvbzM6ObPY7YE8zmwm8CFzi7osBzOwBYDKwrZktMLPTcxVrvik5ExERkXhyOvG5uz8NPN1q2S0tvl8IHBBn3+NyGVsh1dRAt26FjkJERESKkWYIKAC1nImIiEg8OW05k9iefBIaGwsdhYiIiBQjJWcF0KdPoSMQERGRYqXHmgVw1VXwzDOFjkJERESKkZKzAvj97+HZZwsdhYiIiBQjJWd55g61tRoQICIiIrEpOcuzujpoalJyJiIiIrEpOcuzmprwquRMREREYlFylme1teFVyZmIiIjEolIaebb55rBiBVRWFjoSERERKUZKzvLMDHr2LHQUIiIiUqz0WDPP5syBCy6A6upCRyIiIiLFSMlZns2dC9dfD199VehIREREpBgpOcszDQgQERGRRJSc5ZlKaYiIiEgiSs7yTMmZiIiIJKLkLM/WrAmv3boVNg4REREpTkrO8mzsWGhsVDkNERERiU11zgqgQimxiIiIxKE0Ic/uuQcuuqjQUYiIiEixUnKWZy+9BA89VOgoREREpFgpOcuzmhoNBhAREZH4lJzlWU2NymiIiIhIfErO8kzJmYiIiCSi0Zp51qkTdO1a6ChERESkWCk5y7Nnny10BCIiIlLM9FhTREREpIgoOcuzn/4U7ryz0FGIiIhIsdJjzTz7xz9g440LHYWIiIgUK7Wc5VFTE9TWarSmiIiIxKfkLI/WrAmvSs5EREQkHiVneVRTE16VnImIiEg8Ss7yaO1aGDhQfc5EREQkPg0IyKPNNoNPPy10FCIiIlLM1HImIiIiUkSUnOXRO+/AD38Is2YVOhIREREpVkrOklBdDWNH19Gv12o6VDTRr9dqxo6uo7o6teN88QU891wopyEiIiISi5KzNkyaBCOG1NB1wg28vnIwdd6J11cOpuuEGxgxpIZJk5I/VnS0ZrduuYlVRERESp8GBCRQXQ0nH1XDxNr92IMp/11exTyuqL+YQ+sf5bCjXmDKjO5UVbV9PJXSEBERkbao5SyB8dfWcWb9TeskZi3twRTOqL+ZG6+vS+p4Ss5ERESkLUrOErj/3iZOr78l4TZn1N/M/fc0JnW8nj1hhx2UnImIiEh85u6FjiFrhg0b5tOmTcva8TpUNFHnnehI/OSrno50raijoVF5roiIiCTPzN5y92GtlyujSKBPjzo+ZYuE28xnIH16rMlTRCIiItLeKTlL4PgTK7i98uyE20yoPIfjT+qQ1PH++Ec49NBsRCYiIiLtlZKzBM67sDO3VY5mMiNirp/MCCZUnsO5Yzsndbz334eZM7MZoYiIiLQ3Ss4SqKqCux/uzmHdXmBc5TVUM4h6OlLNIMZVXsNh3V7g7oeTK6MBofisapyJiIhIIkrO2jBqFEyZ0Z26s85nZK+ZdKGOXTvNpO6s85kyozujRiV/rJoajdQUERGRxFSENglVVXDd+M5cNx6amqCiIr3mLyVnIiIi0hYlZymqyKCtcfvtoWvX7MUiIiIi7Y8ea6Zo9mzYay9Ip5zaLbfA9ddnPyYRERFpP5ScpahnT3jlFZg6tdCRiIiISHuk5CxFAwZA797wzjup77v77nDlldmPSURERNoPJWcpMoOhQ2H69NT3nTEDvvkm2xGJiIhIe6LkLA1Dh4Zisg0Nye/T2Ahr1mi0poiIiCSm0Zpp+N73QrX/ZcugT5/k9lm9OryqCK2IiIgkouQsDUccEb5SUVMTXtVyJiIiIonk9LGmmR1oZh+a2VwzuzTG+g3M7Akze9fMZpvZqcnuWwwaG5Pf1gwOOoikp3oSERGR8pSzljMz6wDcCOwPLACmmtlEd3+vxWbnAu+5+6Fm1hf40MzuAxqT2LegfvxjWLkSnnsuue032QSeeiq3MYmIiEjpy2XL2XBgrrvPc/e1wIPA4a22caCnmRnQA1gKNCS5b0H17g1vvw3uhY5ERERE2pNcJmebAZ+1eL8gsqyl8cD2wEJgJjDG3ZuS3LegdtkFliyBBQuS2/7VV2GzzeDNN3Mbl4iIiJS2XCZnFmNZ63amHwLTgU2BocB4M+uV5L7hJGZnmdk0M5v29ddfpx9tioYODa/J1jtbtgwWLsxsbk4RERFp/3KZKiwABrR4vzmhhaylU4FHPZgLfAxsl+S+ALj7re4+zN2H9e3bN2vBt2XIkNDJP9mZAjRaU0RERJKRy+RsKrC1mW1lZp2AY4GJrbaZD+wLYGb9gG2BeUnuW1A9esAll8BuuyW3fW1teFWdMxEREUkkZ6M13b3BzM4DngU6AHe4+2wzOzuy/hbgd8BdZjaT8CjzEndfDBBr31zFmq5U5slUy5mIiIgkI6dFaN39aeDpVstuafH9QuCAZPctNk1N8PHHsOmm0LVr4m2rquAnPwktbiIiIiLxqHt6Bp5/Hr79bXjjjba3Pegg+PvfoUuX3MclIiIipUvJWQZSHbEpIiIi0hYlZxno1w++9a3kRmyefz5ss03uYxIREZHSpuQsQ0OHJtdytmwZNDTkOBgREREpeUrOMrTLLvDee1BXl3i7mhqN1BQREZG25XS0Zjk47jgYNqzt7ZSciYiISDKUnGVop53CV1tqa1WAVkRERNqm5CwL3ngD1qyBvfaKv80hh0DnzvmLSUREREqTkrMsuPDCMM/mq6/G3+aSS/IXj4iIiJQuDQjIgl12CSM2m5rib6ORmiIiIpIMJWdZMHQorFoF8+bF36ZvXxgzJm8hiYiISIlScpYFycwUUFvb9vybIiIiIkrOsmDHHaFjx/gzBTQ0wNq1KqUhIiIibdOAgCzo0gXefBO23jr2+tra8KrkTERERNqi5CxLdtkl/rqamvCq5ExERETaoseaWfLhh3DZZbB48frrunSBiy9OnMCJiIiIgJKzrFm4EK68Et5+e/11G20EV10Fw4fnPy4REREpLUrOsiQ6YjPWoIC1a+Gbb6CxMa8hiYiISAlScpYlG20EW2wRu5zGK69A797w+ut5D0tERERKjJKzLNpll9gtZxoQICIiIslScpZFQ4fC119DXd26y6PJWbdueQ9JRERESoySsyy69NIwWrNz53WXq+VMREREkqU6Z1nUOimLUnImIiIiyVLLWZaNHQtXXLHushEj4De/gZ49CxOTiIiIlA61nGXZu++GlrLLLmteNmJE+BIRERFpi1rOsmyXXWDGjDDZedTXX4citSIiIiJtUXKWZUOHwpo18NFHzcsuuwx23bVgIYmIiEgJUXKWZdH5M1vWO6ut1WAAERERSY6SsyzbdlsYMmTdZTU1Ss5EREQkORoQkGWVlWFQQEtKzkRERCRZajnLEffwBXqsKSIiIslTcpYDEyfCt74FCxaE92PHwrnnFjYmERERKQ16rJkDffrAV1+FQQEDBsBRRxU6IhERESkVajnLgSFDwAymTw/vZ86ERYsKGpKIiIiUCCVnOdCjB2y9dXNy9t3vwpVXFjQkERERKRFKznJk6NDmWmcaECAiIiLJUp+zHDnySNh88zBbQEODkjMRERFJjpKzHDn22PD1zTfhvZIzERERSYYea+ZQXR3Mnx++79atsLGIiIhIaVDLWQ5tsw3sthvcfTeMGFHoaERERKQUKDnLoR13hDlz4KSTCh2JiIiIlAo91syhoUNhxgx44QVYsaLQ0YiIiEgpUHKWI9XVMPOtOrqwmgP2b2JQ/9WMHV1HdXWhIxMREZFipuQsByZNghFDahj8rxuYxWDW0ok3agfTdcINjBhSw6RJhY5QREREipW5e6FjyJphw4b5tGnTChpDdXVIzCbW7sceTFlv/WRGcFi3F5gyoztVVQUIUERERIqCmb3l7sNaL1fLWZaNv7aOM+tvipmYAezBFM6ov5kbr6/Lc2QiIiJSCpScZdn99zZxev0tCbc5o/5m7r+nMU8RiYiISClRcpZli1d1Zgs+TbjNQOazeFWXPEUkIiIipUTJWZb16VHHp2yRcJv5DKRPjzV5ikhERERKiZKzLDv+xApurzw74TYTKs/h+JM65CkiERERKSVKzrLsvAs7c1vlaCYTe76myYxgQuU5nDu2c54jExERkVKg5CzLqqrg7oe7c1i3FxhXeQ3VDKKejlQziHGV13BYtxe4+2GV0RAREZHYlJzlwKhRMGVGd+rOOp+RvWbStaKOkb1mUnfW+UyZ0Z1RowodoYiIiBQrFaEVERERKQAVoRUREREpAUrORERERIpITpMzMzvQzD40s7lmdmmM9b8ws+mRr1lm1mhmvSPrxkSWzTazn+cyThEREZFikbPkzMw6ADcCo4AdgOPMbIeW27j7Ne4+1N2HAuOAf7v7UjMbDJwJDAd2Bg4xs61zFauIiIhIschly9lwYK67z3P3tcCDwOEJtj8OeCDy/fbAFHevdfcG4N/AkTmMVURERKQo5DI52wz4rMX7BZFl6zGzbsCBwCORRbOA75vZxpF1BwED4ux7lplNM7NpX3/9ddaCFxERESmEXCZnFmNZvLodhwL/cfelAO7+PnAV8DzwDPAu0BBrR3e/1d2Hufuwvn37Zh61iIiISAHlMjlbwLqtXZsDC+NseyzNjzQBcPfb3f077v59YCkwJydRioiIiBSRXCZnU4GtzWwrM+tESMAmtt7IzDYA9gL+2Wr5JpHXgcCPaJW8iYiIiLRHHXN1YHdvMLPzgGeBDsAd7j7bzM6OrL8lsumRwHPuXtPqEI+Y2cZAPXCuu3+Tq1hFREREioWmbxIREREpAE3fJCIiIlIClJyJiIiIFJF29VjTzL4GPk1hlz7A4hyFU0p0HZrpWjTTtWimaxHoOjTTtWima9Es1WuxhbuvVwesXSVnqTKzabGe9ZYbXYdmuhbNdC2a6VoEug7NdC2a6Vo0y9a10GNNERERkSKi5ExERESkiJR7cnZroQMoEroOzXQtmulaNNO1CHQdmulaNNO1aJaVa1HWfc5EREREik25t5yJiIiIFJWyTM7M7EAz+9DM5prZpYWOp5DM7BMzm2lm082srKZXMLM7zOwrM5vVYllvM3vezOZEXjcqZIz5Euda/NrMPo/cG9PN7KBCxpgPZjbAzF4ys/fNbLaZjYksL7v7IsG1KKv7wsy6mNmbZvZu5Dr8JrK8HO+JeNeirO6Jlsysg5m9Y2ZPRt5n5b4ou8eaZtYB+AjYH1hAmKD9OHd/r6CBFYiZfQIMc/eyq1FjZt8HVgF3u/vgyLKrgaXu/sdI4r6Ru19SyDjzIc61+DWwyt3/VMjY8snM+gP93f1tM+sJvAUcAfyUMrsvElyLn1BG94WZGdDd3VeZWSXwGjAG+BHld0/EuxYHUkb3REtmdgEwDOjl7odk63dIObacDQfmuvs8d18LPAgcXuCYpADc/RVgaavFhwN/i3z/N8Ivo3YvzrUoO+7+hbu/Hfl+JfA+sBlleF8kuBZlxYNVkbeVkS+nPO+JeNeiLJnZ5sDBwIQWi7NyX5RjcrYZ8FmL9wsoww+cFhx4zszeMrOzCh1MEejn7l9A+OUEbFLgeArtPDObEXns2e4f27RkZlsCuwBvUOb3RatrAWV2X0QeXU0HvgKed/eyvSfiXAsos3si4s/AxUBTi2VZuS/KMTmzGMvKNvMHRrr7d4BRwLmRx1siADcDVcBQ4Avg2oJGk0dm1gN4BPi5u68odDyFFONalN194e6N7j4U2BwYbmaDCxxSwcS5FmV3T5jZIcBX7v5WLo5fjsnZAmBAi/ebAwsLFEvBufvCyOtXwGOEx77l7MtIX5ton5uvChxPwbj7l5EP4ibgNsrk3oj0pXkEuM/dH40sLsv7Ita1KNf7AsDdlwEvE/pYleU9EdXyWpTpPTESOCzSb/tBYB8zu5cs3RflmJxNBbY2s63MrBNwLDCxwDEVhJl1j3T0xcy6AwcAsxLv1e5NBE6JfH8K8M8CxlJQ0Q+YiCMpg3sj0uH5duB9d7+uxaqyuy/iXYtyuy/MrK+ZbRj5viuwH/AB5XlPxLwW5XZPALj7OHff3N23JOQR/3L3E8nSfdExK1GWEHdvMLPzgGeBDsAd7j67wGEVSj/gsfAZTEfgfnd/prAh5Y+ZPQD8AOhjZguAXwF/BB4ys9OB+cDRhYswf+Jcix+Y2VDCY/9PgP8pVHx5NBI4CZgZ6VcDcBnleV/EuxbHldl90R/4W2SkfwXwkLs/aWaTKb97It61uKfM7olEsvJZUXalNERERESKWTk+1hQREREpWkrORERERIqIkjMRERGRIqLkTERERKSIKDkTERERKSJKzkREYjCzVS2+P8jM5pjZwELGJCLloezqnImIpMLM9gX+Ahzg7vMLHY+ItH9KzkRE4jCz7xGmoznI3asLHY+IlAcVoRURicHM6oGVwA/cfUah4xGR8qE+ZyIisdUDrwOnFzoQESkvSs5ERGJrAn4C7GZmlxU6GBEpH+pzJiISh7vXmtkhwKtm9qW7317omESk/VNyJiKSgLsvNbMDgVfMbLG7/7PQMYlI+6YBASIiIiJFRH3ORERERIqIkjMRERGRIqLkTERERKSIKDkTERERKSJKzkRERESKiJIzERERkSKi5ExERESkiCg5ExERESki/x/vlmToGa4gHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "plt.plot(range(1,40),accuracy_rate,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "plt.title('Accuracy Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Accuracy Rate')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}