HD2_Logistic,KNN,SVM-Reg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hd = pd.read_csv(\"heart disease classification dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>120.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>298</td>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>299</td>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>300</td>\n",
       "      <td>68</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>301</td>\n",
       "      <td>57</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>302</td>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  age     sex  cp  trestbps   chol  fbs  restecg  thalach  \\\n",
       "0             0   63    male   3     145.0  233.0    1        0    150.0   \n",
       "1             1   37    male   2     130.0  250.0    0        1    187.0   \n",
       "2             2   41  female   1     130.0  204.0    0        0    172.0   \n",
       "3             3   56    male   1     120.0  236.0    0        1    178.0   \n",
       "4             4   57  female   0       NaN  354.0    0        1    163.0   \n",
       "..          ...  ...     ...  ..       ...    ...  ...      ...      ...   \n",
       "298         298   57  female   0     140.0  241.0    0        1    123.0   \n",
       "299         299   45    male   3     110.0  264.0    0        1    132.0   \n",
       "300         300   68    male   0     144.0  193.0    1        1    141.0   \n",
       "301         301   57    male   0       NaN  131.0    0        1    115.0   \n",
       "302         302   57  female   1     130.0  236.0    0        0    174.0   \n",
       "\n",
       "     exang  oldpeak  slope  ca  thal target  \n",
       "0        0      2.3      0   0     1    yes  \n",
       "1        0      3.5      0   0     2    yes  \n",
       "2        0      1.4      2   0     2    yes  \n",
       "3        0      0.8      2   0     2    yes  \n",
       "4        1      0.6      2   0     2    yes  \n",
       "..     ...      ...    ...  ..   ...    ...  \n",
       "298      1      0.2      1   0     3     no  \n",
       "299      0      1.2      1   0     3     no  \n",
       "300      0      3.4      1   2     3     no  \n",
       "301      1      1.2      1   1     3     no  \n",
       "302      0      0.0      1   1     2     no  \n",
       "\n",
       "[303 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Unnamed: 0</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>120.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  age     sex  cp  trestbps   chol  fbs  restecg  thalach  exang  \\\n",
       "0           0   63    male   3     145.0  233.0    1        0    150.0      0   \n",
       "1           1   37    male   2     130.0  250.0    0        1    187.0      0   \n",
       "2           2   41  female   1     130.0  204.0    0        0    172.0      0   \n",
       "3           3   56    male   1     120.0  236.0    0        1    178.0      0   \n",
       "4           4   57  female   0       NaN  354.0    0        1    163.0      1   \n",
       "\n",
       "   oldpeak  slope  ca  thal target  \n",
       "0      2.3      0   0     1    yes  \n",
       "1      3.5      0   0     2    yes  \n",
       "2      1.4      2   0     2    yes  \n",
       "3      0.8      2   0     2    yes  \n",
       "4      0.6      2   0     2    yes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Unnamed: 0</th>\n",
       "      <th>age</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>299.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>151.000000</td>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.712375</td>\n",
       "      <td>246.317881</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.865772</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>87.612784</td>\n",
       "      <td>9.082101</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.629032</td>\n",
       "      <td>51.908285</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.563687</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>134.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>151.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>226.500000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>302.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0         age          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  299.000000  302.000000  303.000000   \n",
       "mean   151.000000   54.366337    0.966997  131.712375  246.317881    0.148515   \n",
       "std     87.612784    9.082101    1.032052   17.629032   51.908285    0.356198   \n",
       "min      0.000000   29.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     75.500000   47.500000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%    151.000000   55.000000    1.000000  130.000000  240.500000    0.000000   \n",
       "75%    226.500000   61.000000    2.000000  140.000000  274.750000    0.000000   \n",
       "max    302.000000   77.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  298.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.865772    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.563687    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  134.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  152.500000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal  \n",
       "count  303.000000  \n",
       "mean     2.313531  \n",
       "std      0.612277  \n",
       "min      0.000000  \n",
       "25%      2.000000  \n",
       "50%      2.000000  \n",
       "75%      3.000000  \n",
       "max      3.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 15 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  303 non-null    int64  \n",
      " 1   age         303 non-null    int64  \n",
      " 2   sex         303 non-null    object \n",
      " 3   cp          303 non-null    int64  \n",
      " 4   trestbps    299 non-null    float64\n",
      " 5   chol        302 non-null    float64\n",
      " 6   fbs         303 non-null    int64  \n",
      " 7   restecg     303 non-null    int64  \n",
      " 8   thalach     298 non-null    float64\n",
      " 9   exang       303 non-null    int64  \n",
      " 10  oldpeak     303 non-null    float64\n",
      " 11  slope       303 non-null    int64  \n",
      " 12  ca          303 non-null    int64  \n",
      " 13  thal        303 non-null    int64  \n",
      " 14  target      303 non-null    object \n",
      "dtypes: float64(4), int64(9), object(2)\n",
      "memory usage: 35.6+ KB\n"
     ]
    }
   ],
   "source": [
    "hd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(hd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Unnamed: 0</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>120.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>298</td>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>299</td>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>300</td>\n",
       "      <td>68</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>301</td>\n",
       "      <td>57</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>302</td>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  age     sex  cp  trestbps   chol  fbs  restecg  thalach  \\\n",
       "0             0   63    male   3     145.0  233.0    1        0    150.0   \n",
       "1             1   37    male   2     130.0  250.0    0        1    187.0   \n",
       "2             2   41  female   1     130.0  204.0    0        0    172.0   \n",
       "3             3   56    male   1     120.0  236.0    0        1    178.0   \n",
       "4             4   57  female   0       NaN  354.0    0        1    163.0   \n",
       "..          ...  ...     ...  ..       ...    ...  ...      ...      ...   \n",
       "298         298   57  female   0     140.0  241.0    0        1    123.0   \n",
       "299         299   45    male   3     110.0  264.0    0        1    132.0   \n",
       "300         300   68    male   0     144.0  193.0    1        1    141.0   \n",
       "301         301   57    male   0       NaN  131.0    0        1    115.0   \n",
       "302         302   57  female   1     130.0  236.0    0        0    174.0   \n",
       "\n",
       "     exang  oldpeak  slope  ca  thal target  \n",
       "0        0      2.3      0   0     1    yes  \n",
       "1        0      3.5      0   0     2    yes  \n",
       "2        0      1.4      2   0     2    yes  \n",
       "3        0      0.8      2   0     2    yes  \n",
       "4        1      0.6      2   0     2    yes  \n",
       "..     ...      ...    ...  ..   ...    ...  \n",
       "298      1      0.2      1   0     3     no  \n",
       "299      0      1.2      1   0     3     no  \n",
       "300      0      3.4      1   2     3     no  \n",
       "301      1      1.2      1   1     3     no  \n",
       "302      0      0.0      1   1     2     no  \n",
       "\n",
       "[303 rows x 15 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DATA VISUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sex', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sex',hue='target',data=df,palette='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='target', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGwCAYAAABPSaTdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAwrklEQVR4nO3dfVQV9aL/8c9G5EEREFIeTpBYpmIomkpUq0w5IRZXjuZDcc3M8N7UzMg0VklpGmmWT1GYJzWWenu4XjH1XpUwsaOIT0dPJhcfwmBpQKVA+IAI+/dH1/mdnVqJG/Zmer/WmrXY35n58hlcyseZ2bMtVqvVKgAAAJNycXQAAACAxkTZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApkbZAQAApubq6ADOoL6+XqdOnVKbNm1ksVgcHQcAAPwOVqtVP/30k4KDg+Xicu3zN5QdSadOnVJISIijYwAAgAYoKSnRzTfffM31lB1Jbdq0kfTzD8vb29vBaQAAwO9RVVWlkJAQ4/f4tVB2JOPSlbe3N2UHAIBm5rduQeEGZQAAYGqUHQAAYGqUHQAAYGrcswMAgIPV1dWptrbW0TGcTsuWLdWiRYsbnoeyAwCAg1itVpWWlqqiosLRUZyWr6+vAgMDb+g5eJQdAAAc5HLRad++vVq1asWDbf+J1WrVuXPnVF5eLkkKCgpq8FyUHQAAHKCurs4oOv7+/o6O45Q8PT0lSeXl5Wrfvn2DL2lxgzIAAA5w+R6dVq1aOTiJc7v887mRe5ooOwAAOBCXrn6dPX4+lB0AAGBqlB0AAGBqlB0AAGBqlB0AAJqxfv36afLkyY6OYXC2PBJlBwCAP7yLFy86OkKjouwAANBMPfHEE8rNzdXChQtlsVhksVh0/PhxjR07VmFhYfL09FTnzp21cOHCK/ZLSEjQ7NmzFRwcrM6dO0uSdu7cqcjISHl4eKh3797KysqSxWLRgQMHjH0PHTqkuLg4eXl5KSAgQKNGjdIPP/xwzTwnTpxoqh/HNfFQwSa0t3dfR0fA/+m9d7ejIwDADVu4cKGOHDmiO+64QzNnzpQktW3bVjfffLM+/fRT+fv7a+fOnRo3bpyCgoI0fPhwY9+cnBx5e3srOztbklRVVaX4+HgNGjRIq1ev1rfffnvF5aiKigr1799fTz31lObPn6/z589r2rRpGj58uLZu3XrVPO3atWuaH8avoOwAANBM+fj4yM3NTa1atVJgYKAxPmPGDOPrsLAw5eXl6ZNPPrEpO61bt9Zf//pXubm5SZIyMjJksVi0dOlSeXh4KDw8XCdPnlRSUpKxzzvvvKOePXvq9ddfN8aWLVumkJAQHTlyRLfffvtV8zgaZQcAAJNJT0/XsmXLVFxcrPPnz+vixYuKjIy02SYiIsIoOpJUWFio7t27y8PDwxjr29f2isTBgwf1xRdfyMvL64rvefz4cd1+++32PRA7oewAAGAiH330kaZMmaK33npL0dHRatOmjd58803l5+fbbNe6devrnru6ulrx8fGaM2fOFetu5IM6GxtlBwCAZszNzU11dXXG6x07dujuu+/W+PHjjbHjx4//5jydO3fWypUrVVNTI3d3d0nSnj17bLbp1auX1qxZow4dOsjV9eoV4pd5nAHvxgIAoBnr0KGD8vPzdeLECf3www/q1KmT9u7dq82bN+vIkSOaPn36FaXlah577DHV19dr3LhxKigo0ObNmzVv3jxJ///zqSZMmKDTp0/r0Ucf1Z49e3T8+HFt3rxZY8aMMQrOL/PU19c33sH/TpQdAACasSlTpqhFixYKDw9Xu3btFBsbqyFDhmjEiBGKiorSjz/+aHOW51q8vb21fv16HThwQJGRkXrppZeUmpoqScZ9PMHBwdqxY4fq6ur04IMPKiIiQpMnT5avr69cXFyumqe4uLjxDv53slitVqujvvn27dv15ptvat++ffruu++0du1aJSQk2GxTUFCgadOmKTc3V5cuXVJ4eLjWrFmj0NBQSdKFCxf0/PPP66OPPlJNTY1iY2P17rvvKiAg4HfnqKqqko+PjyorK+Xt7W3PQ7TBW8+dB289B+BoFy5cUFFRkcLCwmxuCnYmq1at0pgxY1RZWSlPT0+HZPi1n9Pv/f3t0DM7Z8+eVY8ePZSenn7V9cePH9e9996rLl26aNu2bfrHP/6h6dOn2xzsc889p/Xr1+vTTz9Vbm6uTp06pSFDhjTVIQAAYBqZmZn629/+pqKiImVlZRnP0HFU0bEXh96gHBcXp7i4uGuuf+mllzRo0CDNnTvXGLv11luNrysrK/XBBx9o9erV6t+/vyRp+fLl6tq1q3bt2qW77rrrqvPW1NSopqbGeF1VVXWjhwIAQLNXWlqq1NRUlZaWKigoSMOGDdPs2bMdHeuGOe09O/X19dq4caNuv/12xcbGqn379oqKilJWVpaxzb59+1RbW6uYmBhjrEuXLgoNDVVeXt41505LS5OPj4+xhISENOahAADQLEydOlUnTpwwLh3Nnz9frVq1cnSsG+a0Zae8vFzV1dV64403NHDgQG3ZskV/+ctfNGTIEOXm5kr6uYG6ubnJ19fXZt+AgACVlpZec+6UlBRVVlYaS0lJSWMeCgAAcCCnfc7O5beqDR48WM8995wkKTIyUjt37lRGRobuv//+Bs/t7u5uPEMAAACYm9Oe2bnpppvk6uqq8PBwm/GuXbsab2MLDAzUxYsXVVFRYbNNWVmZU30mBwAAcBynLTtubm7q06ePCgsLbcaPHDmiW265RZJ05513qmXLlsrJyTHWFxYWqri4WNHR0U2aFwAAOCeHXsaqrq7WsWPHjNdFRUU6cOCA/Pz8FBoaqhdeeEEjRozQfffdpwceeECbNm3S+vXrtW3bNkk/f9rr2LFjlZycLD8/P3l7e+uZZ55RdHT0Nd+JBQAA/lgcWnb27t2rBx54wHidnJwsSRo9erRWrFihv/zlL8rIyFBaWpomTZqkzp07a82aNbr33nuNfebPny8XFxcNHTrU5qGCAAAAkoOfoOwseILyHw9PUAbgaL/2ZOCm/H3h7P8eNvsnKAMAADQ2yg4AAPjdMjMz5e/vb/NJBJKUkJCgUaNGSZLWrVunXr16ycPDQx07dtSMGTN06dIlSZLVatWrr76q0NBQubu7Kzg4WJMmTWrUzJQdAADwuw0bNkx1dXX67LPPjLHy8nJt3LhRTz75pL788ks9/vjjevbZZ3X48GEtWbJEK1asMD52Ys2aNZo/f76WLFmio0ePKisrSxEREY2ambIDAAB+N09PTz322GNavny5MbZy5UqFhoaqX79+mjFjhl588UWNHj1aHTt21J///Ge99tprWrJkiSSpuLhYgYGBiomJUWhoqPr27aukpKRGzUzZAQAA1yUpKUlbtmzRyZMnJUkrVqzQE088IYvFooMHD2rmzJny8vIylqSkJH333Xc6d+6chg0bpvPnz6tjx45KSkrS2rVrjUtcjcVpPy4CAAA4p549e6pHjx7KzMzUgw8+qK+//lobN26U9PMz9GbMmKEhQ4ZcsZ+Hh4dCQkJUWFiozz//XNnZ2Ro/frzefPNN5ebmqmXLlo2Sl7IDAACu21NPPaUFCxbo5MmTiomJUUhIiCSpV69eKiws1G233XbNfT09PRUfH6/4+HhNmDBBXbp00VdffaVevXo1SlbKDgAAuG6PPfaYpkyZoqVLlyozM9MYT01N1cMPP6zQ0FA98sgjcnFx0cGDB3Xo0CHNmjVLK1asUF1dnaKiotSqVSutXLlSnp6exkdBNQbu2QEAANfNx8dHQ4cOlZeXlxISEozx2NhYbdiwQVu2bFGfPn101113af78+UaZ8fX11dKlS3XPPfeoe/fu+vzzz7V+/Xr5+/s3WlbO7AAA4GSc/anGl508eVKJiYlyd3e3GY+NjVVsbOxV90lISLApR02BsgMAAK7LmTNntG3bNm3btq1ZfB4lZQcAAFyXnj176syZM5ozZ446d+7s6Di/ibIDAACuy4kTJxwd4bpwgzIAADA1yg4AADA1yg4AADA1yg4AADA1yg4AADA1yg4AADA1yg4AADA1nrMDAICTGTj94yb7XpteG9Fk38tROLMDAABMjbIDAACuS79+/TRp0iRNnTpVfn5+CgwM1KuvvmqsLy4u1uDBg+Xl5SVvb28NHz5cZWVlDstL2QEAANftww8/VOvWrZWfn6+5c+dq5syZys7OVn19vQYPHqzTp08rNzdX2dnZ+uabbzRihOMul3HPDgAAuG7du3fXK6+8Iknq1KmT3nnnHeXk5EiSvvrqKxUVFSkkJESSlJmZqW7dumnPnj3q06dPk2flzA4AALhu3bt3t3kdFBSk8vJyFRQUKCQkxCg6khQeHi5fX18VFBQ0dUxJlB0AANAALVu2tHltsVhUX1/voDS/jrIDAADspmvXriopKVFJSYkxdvjwYVVUVCg8PNwhmSg7AADAbmJiYhQREaHExETt379fu3fv1uOPP677779fvXv3dkgmyg4AALAbi8WidevWqW3btrrvvvsUExOjjh076uOPm+5Bib/Eu7EAAHAyzv5U423btl0xlpWVZXwdGhqqdevWNV2g3+DQMzvbt29XfHy8goODZbFYbH5Qv/Tv//7vslgsWrBggc346dOnlZiYKG9vb/n6+mrs2LGqrq5u3OAAAKDZcGjZOXv2rHr06KH09PRf3W7t2rXatWuXgoODr1iXmJior7/+WtnZ2dqwYYO2b9+ucePGNVZkAADQzDj0MlZcXJzi4uJ+dZuTJ0/qmWee0ebNm/XQQw/ZrCsoKNCmTZu0Z88e46anxYsXa9CgQZo3b95VyxEAAPhjceoblOvr6zVq1Ci98MIL6tat2xXr8/Ly5Ovra3N3d0xMjFxcXJSfn3/NeWtqalRVVWWzAAAAc3LqsjNnzhy5urpq0qRJV11fWlqq9u3b24y5urrKz89PpaWl15w3LS1NPj4+xvLPT3kEAKApWa1WR0dwavb4+Tht2dm3b58WLlyoFStWyGKx2HXulJQUVVZWGss/P/gIAICmcPkJxOfOnXNwEud2+efzyyc2Xw+nfev5l19+qfLycoWGhhpjdXV1ev7557VgwQKdOHFCgYGBKi8vt9nv0qVLOn36tAIDA685t7u7u9zd3RstOwAAv6VFixby9fU1fo+1atXK7v+5b86sVqvOnTun8vJy+fr6qkWLFg2ey2nLzqhRoxQTE2MzFhsbq1GjRmnMmDGSpOjoaFVUVGjfvn268847JUlbt25VfX29oqKimjwzAADX4/J/zH/5H3f8f76+vr96AuP3cGjZqa6u1rFjx4zXRUVFOnDggPz8/BQaGip/f3+b7Vu2bKnAwEB17txZ0s+fvzFw4EAlJSUpIyNDtbW1mjhxokaOHMk7sQDgD2rgdMc9qbeh3FwtauPuKrOd2Pnrs4NuaP+WLVve0Bmdyxxadvbu3asHHnjAeJ2cnCxJGj16tFasWPG75li1apUmTpyoAQMGyMXFRUOHDtWiRYsaIy4AAI3i4iWrfrxU6+gYdufh4eHoCJIcXHb69et3XXdZnzhx4ooxPz8/rV692o6pAACAmTjtu7EAAADsgbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMjbIDAABMzaFlZ/v27YqPj1dwcLAsFouysrKMdbW1tZo2bZoiIiLUunVrBQcH6/HHH9epU6ds5jh9+rQSExPl7e0tX19fjR07VtXV1U18JAAAwFk5tOycPXtWPXr0UHp6+hXrzp07p/3792v69Onav3+//uu//kuFhYX6l3/5F5vtEhMT9fXXXys7O1sbNmzQ9u3bNW7cuKY6BAAA4ORcHfnN4+LiFBcXd9V1Pj4+ys7Othl755131LdvXxUXFys0NFQFBQXatGmT9uzZo969e0uSFi9erEGDBmnevHkKDg5u9GMAAADOrVnds1NZWSmLxSJfX19JUl5ennx9fY2iI0kxMTFycXFRfn7+NeepqalRVVWVzQIAAMyp2ZSdCxcuaNq0aXr00Ufl7e0tSSotLVX79u1ttnN1dZWfn59KS0uvOVdaWpp8fHyMJSQkpFGzAwAAx2kWZae2tlbDhw+X1WrVe++9d8PzpaSkqLKy0lhKSkrskBIAADgjh96z83tcLjrffvuttm7dapzVkaTAwECVl5fbbH/p0iWdPn1agYGB15zT3d1d7u7ujZYZAAA4D6c+s3O56Bw9elSff/65/P39bdZHR0eroqJC+/btM8a2bt2q+vp6RUVFNXVcAADghBx6Zqe6ulrHjh0zXhcVFenAgQPy8/NTUFCQHnnkEe3fv18bNmxQXV2dcR+On5+f3Nzc1LVrVw0cOFBJSUnKyMhQbW2tJk6cqJEjR/JOLAAAIMnBZWfv3r164IEHjNfJycmSpNGjR+vVV1/VZ599JkmKjIy02e+LL75Qv379JEmrVq3SxIkTNWDAALm4uGjo0KFatGhRk+QHAADOz6Flp1+/frJarddc/2vrLvPz89Pq1avtGQsAAJiIU9+zAwAAcKMoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQoOwAAwNQcWna2b9+u+Ph4BQcHy2KxKCsry2a91WpVamqqgoKC5OnpqZiYGB09etRmm9OnTysxMVHe3t7y9fXV2LFjVV1d3YRHAQAAnJlDy87Zs2fVo0cPpaenX3X93LlztWjRImVkZCg/P1+tW7dWbGysLly4YGyTmJior7/+WtnZ2dqwYYO2b9+ucePGNdUhAAAAJ+fqyG8eFxenuLi4q66zWq1asGCBXn75ZQ0ePFiSlJmZqYCAAGVlZWnkyJEqKCjQpk2btGfPHvXu3VuStHjxYg0aNEjz5s1TcHDwVeeuqalRTU2N8bqqqsrORwYAAJyF096zU1RUpNLSUsXExBhjPj4+ioqKUl5eniQpLy9Pvr6+RtGRpJiYGLm4uCg/P/+ac6elpcnHx8dYQkJCGu9AAACAQzlt2SktLZUkBQQE2IwHBAQY60pLS9W+fXub9a6urvLz8zO2uZqUlBRVVlYaS0lJiZ3TAwAAZ+HQy1iO4u7uLnd3d0fHAAAATcBpz+wEBgZKksrKymzGy8rKjHWBgYEqLy+3WX/p0iWdPn3a2AYAAPyxOW3ZCQsLU2BgoHJycoyxqqoq5efnKzo6WpIUHR2tiooK7du3z9hm69atqq+vV1RUVJNnBgAAzsehl7Gqq6t17Ngx43VRUZEOHDggPz8/hYaGavLkyZo1a5Y6deqksLAwTZ8+XcHBwUpISJAkde3aVQMHDlRSUpIyMjJUW1uriRMnauTIkdd8JxYAAPhjcWjZ2bt3rx544AHjdXJysiRp9OjRWrFihaZOnaqzZ89q3Lhxqqio0L333qtNmzbJw8PD2GfVqlWaOHGiBgwYIBcXFw0dOlSLFi1q8mMBAADOyWK1Wq2ODuFoVVVV8vHxUWVlpby9vRvt++zt3bfR5sb16b13t6MjAGgkA6d/7OgI+D+bXhvRqPP/3t/fTnvPDgAAgD1QdgAAgKlRdgAAgKlRdgAAgKk1qOz0799fFRUVV4xXVVWpf//+N5oJAADAbhpUdrZt26aLFy9eMX7hwgV9+eWXNxwKAADAXq7rOTv/+Mc/jK8PHz5s82GbdXV12rRpk/70pz/ZLx0AAMANuq6yExkZKYvFIovFctXLVZ6enlq8eLHdwgEAANyo6yo7RUVFslqt6tixo3bv3q127doZ69zc3NS+fXu1aNHC7iEBAAAa6rrKzi233CJJqq+vb5QwAAAA9tbgz8Y6evSovvjiC5WXl19RflJTU284GAAAgD00qOwsXbpUTz/9tG666SYFBgbKYrEY6ywWC2UHAAA4jQaVnVmzZmn27NmaNm2avfMAAADYVYOes3PmzBkNGzbM3lkAAADsrkFlZ9iwYdqyZYu9swAAANhdgy5j3XbbbZo+fbp27dqliIgItWzZ0mb9pEmT7BIOAADgRjWo7Lz//vvy8vJSbm6ucnNzbdZZLBbKDgAAcBoNKjtFRUX2zgEAANAoGnTPDgAAQHPRoDM7Tz755K+uX7ZsWYPCAAAA2FuDys6ZM2dsXtfW1urQoUOqqKi46geEAgAAOEqDys7atWuvGKuvr9fTTz+tW2+99YZDAQAA2Ivd7tlxcXFRcnKy5s+fb68pAQAAbphdb1A+fvy4Ll26ZM8pAQAAbkiDLmMlJyfbvLZarfruu++0ceNGjR492i7BAAAA7KFBZefvf/+7zWsXFxe1a9dOb7311m++UwsAAKApNajsfPHFF/bOAQAA0CgaVHYu+/7771VYWChJ6ty5s9q1a2eXUAAAAPbSoBuUz549qyeffFJBQUG67777dN999yk4OFhjx47VuXPn7J0RAACgwRpUdpKTk5Wbm6v169eroqJCFRUVWrdunXJzc/X888/bOyMAAECDNegy1po1a/Sf//mf6tevnzE2aNAgeXp6avjw4XrvvffslQ8AAOCGNOjMzrlz5xQQEHDFePv27e16Gauurk7Tp09XWFiYPD09deutt+q1116T1Wo1trFarUpNTVVQUJA8PT0VExOjo0eP2i0DAABo3hpUdqKjo/XKK6/owoULxtj58+c1Y8YMRUdH2y3cnDlz9N577+mdd95RQUGB5syZo7lz52rx4sXGNnPnztWiRYuUkZGh/Px8tW7dWrGxsTbZAADAH1eDLmMtWLBAAwcO1M0336wePXpIkg4ePCh3d3dt2bLFbuF27typwYMH66GHHpIkdejQQf/xH/+h3bt3S/r5rM6CBQv08ssva/DgwZKkzMxMBQQEKCsrSyNHjrRbFgAA0Dw16MxORESEjh49qrS0NEVGRioyMlJvvPGGjh07pm7dutkt3N13362cnBwdOXJE0s+F6m9/+5vi4uIkSUVFRSotLVVMTIyxj4+Pj6KiopSXl3fNeWtqalRVVWWzAAAAc2rQmZ20tDQFBAQoKSnJZnzZsmX6/vvvNW3aNLuEe/HFF1VVVaUuXbqoRYsWqqur0+zZs5WYmChJKi0tlaQr7h8KCAgw1l0r/4wZM+ySEQAAOLcGndlZsmSJunTpcsV4t27dlJGRccOhLvvkk0+0atUqrV69Wvv379eHH36oefPm6cMPP7yheVNSUlRZWWksJSUldkoMAACcTYPO7JSWliooKOiK8Xbt2um777674VCXvfDCC3rxxReNe28iIiL07bffKi0tTaNHj1ZgYKAkqayszCZPWVmZIiMjrzmvu7u73N3d7ZYTAAA4rwad2QkJCdGOHTuuGN+xY4eCg4NvONRl586dk4uLbcQWLVqovr5ekhQWFqbAwEDl5OQY66uqqpSfn2/Xd4UBAIDmq0FndpKSkjR58mTV1taqf//+kqScnBxNnTrVrk9Qjo+P1+zZsxUaGqpu3brp73//u95++23jk9UtFosmT56sWbNmqVOnTgoLC9P06dMVHByshIQEu+UAAADNV4PKzgsvvKAff/xR48eP18WLFyVJHh4emjZtmlJSUuwWbvHixZo+fbrGjx+v8vJyBQcH69/+7d+UmppqbDN16lSdPXtW48aNU0VFhe69915t2rRJHh4edssBAACaL4v1nx9HfJ2qq6tVUFAgT09PderUqdneB1NVVSUfHx9VVlbK29u70b7P3t59G21uXJ/ee3c7OgKARjJw+seOjoD/s+m1EY06/+/9/d2gMzuXeXl5qU+fPjcyBQAAQKNq0A3KAAAAzQVlBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmBplBwAAmJrTl52TJ0/qX//1X+Xv7y9PT09FRERo7969xnqr1arU1FQFBQXJ09NTMTExOnr0qAMTAwAAZ+LUZefMmTO655571LJlS/3P//yPDh8+rLfeektt27Y1tpk7d64WLVqkjIwM5efnq3Xr1oqNjdWFCxccmBwAADgLV0cH+DVz5sxRSEiIli9fboyFhYUZX1utVi1YsEAvv/yyBg8eLEnKzMxUQECAsrKyNHLkyCbPDAAAnItTn9n57LPP1Lt3bw0bNkzt27dXz549tXTpUmN9UVGRSktLFRMTY4z5+PgoKipKeXl515y3pqZGVVVVNgsAADAnpy4733zzjd577z116tRJmzdv1tNPP61Jkybpww8/lCSVlpZKkgICAmz2CwgIMNZdTVpamnx8fIwlJCSk8Q4CAAA4lFOXnfr6evXq1Uuvv/66evbsqXHjxikpKUkZGRk3NG9KSooqKyuNpaSkxE6JAQCAs3HqshMUFKTw8HCbsa5du6q4uFiSFBgYKEkqKyuz2aasrMxYdzXu7u7y9va2WQAAgDk5ddm55557VFhYaDN25MgR3XLLLZJ+vlk5MDBQOTk5xvqqqirl5+crOjq6SbMCAADn5NTvxnruued099136/XXX9fw4cO1e/duvf/++3r//fclSRaLRZMnT9asWbPUqVMnhYWFafr06QoODlZCQoJjwwMAAKfg1GWnT58+Wrt2rVJSUjRz5kyFhYVpwYIFSkxMNLaZOnWqzp49q3HjxqmiokL33nuvNm3aJA8PDwcmBwAAzsKpy44kPfzww3r44Yevud5isWjmzJmaOXNmE6YCAADNhVPfswMAAHCjKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUKDsAAMDUmlXZeeONN2SxWDR58mRj7MKFC5owYYL8/f3l5eWloUOHqqyszHEhAQCAU2k2ZWfPnj1asmSJunfvbjP+3HPPaf369fr000+Vm5urU6dOaciQIQ5KCQAAnE2zKDvV1dVKTEzU0qVL1bZtW2O8srJSH3zwgd5++231799fd955p5YvX66dO3dq165d15yvpqZGVVVVNgsAADCnZlF2JkyYoIceekgxMTE24/v27VNtba3NeJcuXRQaGqq8vLxrzpeWliYfHx9jCQkJabTsAADAsZy+7Hz00Ufav3+/0tLSrlhXWloqNzc3+fr62owHBASotLT0mnOmpKSosrLSWEpKSuwdGwAAOAlXRwf4NSUlJXr22WeVnZ0tDw8Pu83r7u4ud3d3u80HAACcl1Of2dm3b5/Ky8vVq1cvubq6ytXVVbm5uVq0aJFcXV0VEBCgixcvqqKiwma/srIyBQYGOiY0AABwKk59ZmfAgAH66quvbMbGjBmjLl26aNq0aQoJCVHLli2Vk5OjoUOHSpIKCwtVXFys6OhoR0QGAABOxqnLTps2bXTHHXfYjLVu3Vr+/v7G+NixY5WcnCw/Pz95e3vrmWeeUXR0tO666y5HRAYAAE7GqcvO7zF//ny5uLho6NChqqmpUWxsrN59911HxwIAAE6i2ZWdbdu22bz28PBQenq60tPTHRMIAAA4Nae+QRkAAOBGUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpOX3ZSUtLU58+fdSmTRu1b99eCQkJKiwstNnmwoULmjBhgvz9/eXl5aWhQ4eqrKzMQYkBAIAzcfqyk5ubqwkTJmjXrl3Kzs5WbW2tHnzwQZ09e9bY5rnnntP69ev16aefKjc3V6dOndKQIUMcmBoAADgLV0cH+C2bNm2yeb1ixQq1b99e+/bt03333afKykp98MEHWr16tfr37y9JWr58ubp27apdu3bprrvuckRsAADgJJz+zM4vVVZWSpL8/PwkSfv27VNtba1iYmKMbbp06aLQ0FDl5eVddY6amhpVVVXZLAAAwJyaVdmpr6/X5MmTdc899+iOO+6QJJWWlsrNzU2+vr422wYEBKi0tPSq86SlpcnHx8dYQkJCGjs6AABwkGZVdiZMmKBDhw7po48+uqF5UlJSVFlZaSwlJSV2SggAAJyN09+zc9nEiRO1YcMGbd++XTfffLMxHhgYqIsXL6qiosLm7E5ZWZkCAwOvOpe7u7vc3d0bOzIAAHACTn9mx2q1auLEiVq7dq22bt2qsLAwm/V33nmnWrZsqZycHGOssLBQxcXFio6Obuq4AADAyTj9mZ0JEyZo9erVWrdundq0aWPch+Pj4yNPT0/5+Pho7NixSk5Olp+fn7y9vfXMM88oOjqad2IBAADnLzvvvfeeJKlfv34248uXL9cTTzwhSZo/f75cXFw0dOhQ1dTUKDY2Vu+++24TJwUAAM7I6cuO1Wr9zW08PDyUnp6u9PT0JkgEAACaE6e/ZwcAAOBGUHYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpUXYAAICpmabspKenq0OHDvLw8FBUVJR2797t6EgAAMAJmKLsfPzxx0pOTtYrr7yi/fv3q0ePHoqNjVV5ebmjowEAAAczRdl5++23lZSUpDFjxig8PFwZGRlq1aqVli1b5uhoAADAwVwdHeBGXbx4Ufv27VNKSoox5uLiopiYGOXl5V11n5qaGtXU1BivKysrJUlVVVWNmrW6rq5R58fv19h/1gAc51LNOUdHwP9p7H9rL89vtVp/dbtmX3Z++OEH1dXVKSAgwGY8ICBA//u//3vVfdLS0jRjxowrxkNCQholI5yQj4+jEwCA6fm8+WSTfJ+ffvpJPr/y73qzLzsNkZKSouTkZON1fX29Tp8+LX9/f1ksFgcmQ1OoqqpSSEiISkpK5O3t7eg4AOyIv99/LFarVT/99JOCg4N/dbtmX3ZuuukmtWjRQmVlZTbjZWVlCgwMvOo+7u7ucnd3txnz9fVtrIhwUt7e3vxjCJgUf7//OH7tjM5lzf4GZTc3N915553Kyckxxurr65WTk6Po6GgHJgMAAM6g2Z/ZkaTk5GSNHj1avXv3Vt++fbVgwQKdPXtWY8aMcXQ0AADgYKYoOyNGjND333+v1NRUlZaWKjIyUps2bbripmVA+vky5iuvvHLFpUwAzR9/v3E1FutvvV8LAACgGWv29+wAAAD8GsoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOAAAwNcoOTCEzM1P+/v42n2YvSQkJCRo1apQkad26derVq5c8PDzUsWNHzZgxQ5cuXZL08+ervPrqqwoNDZW7u7uCg4M1adKkJj8OAL+tX79+mjRpkqZOnSo/Pz8FBgbq1VdfNdYXFxdr8ODB8vLykre3t4YPH37FRwrhj4WyA1MYNmyY6urq9Nlnnxlj5eXl2rhxo5588kl9+eWXevzxx/Xss8/q8OHDWrJkiVasWKHZs2dLktasWaP58+dryZIlOnr0qLKyshQREeGowwHwGz788EO1bt1a+fn5mjt3rmbOnKns7GzV19dr8ODBOn36tHJzc5Wdna1vvvlGI0aMcHRkOBAPFYRpjB8/XidOnNB///d/S5Lefvttpaen69ixY/rzn/+sAQMGKCUlxdh+5cqVmjp1qk6dOqW3335bS5Ys0aFDh9SyZUtHHQKA36Ffv36qq6vTl19+aYz17dtX/fv314ABAxQXF6eioiKFhIRIkg4fPqxu3bpp9+7d6tOnj6Niw4E4swPTSEpK0pYtW3Ty5ElJ0ooVK/TEE0/IYrHo4MGDmjlzpry8vIwlKSlJ3333nc6dO6dhw4bp/Pnz6tixo5KSkrR27VrjEhcA59O9e3eb10FBQSovL1dBQYFCQkKMoiNJ4eHh8vX1VUFBQVPHhJMwxWdjAZLUs2dP9ejRQ5mZmXrwwQf19ddfa+PGjZKk6upqzZgxQ0OGDLliPw8PD4WEhKiwsFCff/65srOzNX78eL355pvKzc3lTA/ghH7599Jisai+vt5BaeDsKDswlaeeekoLFizQyZMnFRMTY/zvrlevXiosLNRtt912zX09PT0VHx+v+Ph4TZgwQV26dNFXX32lXr16NVV8ADeoa9euKikpUUlJic1lrIqKCoWHhzs4HRyFsgNTeeyxxzRlyhQtXbpUmZmZxnhqaqoefvhhhYaG6pFHHpGLi4sOHjyoQ4cOadasWVqxYoXq6uoUFRWlVq1aaeXKlfL09NQtt9ziwKMBcL1iYmIUERGhxMRELViwQJcuXdL48eN1//33q3fv3o6OBwfhnh2Yio+Pj4YOHSovLy8lJCQY47GxsdqwYYO2bNmiPn366K677tL8+fONMuPr66ulS5fqnnvuUffu3fX5559r/fr18vf3d9CRAGgIi8WidevWqW3btrrvvvsUExOjjh076uOPP3Z0NDgQ78aC6QwYMEDdunXTokWLHB0FAOAEKDswjTNnzmjbtm165JFHdPjwYXXu3NnRkQAAToB7dmAaPXv21JkzZzRnzhyKDgDAwJkdAABgatygDAAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yAwAATI2yA8Dp9OvXT5MnT3Z0DIOz5QFwfSg7AEzp4sWLjo4AwElQdgA4lSeeeEK5ublauHChLBaLLBaLjh8/rrFjxyosLEyenp7q3LmzFi5ceMV+CQkJmj17toKDg42naO/cuVORkZHy8PBQ7969lZWVJYvFogMHDhj7Hjp0SHFxcfLy8lJAQIBGjRqlH3744Zp5Tpw40VQ/DgB2wMdFAHAqCxcu1JEjR3THHXdo5syZkqS2bdvq5ptv1qeffip/f3/t3LlT48aNU1BQkIYPH27sm5OTI29vb2VnZ0uSqqqqFB8fr0GDBmn16tX69ttvr7gcVVFRof79++upp57S/Pnzdf78eU2bNk3Dhw/X1q1br5qnXbt2TfPDAGAXlB0ATsXHx0dubm5q1aqVAgMDjfEZM2YYX4eFhSkvL0+ffPKJTdlp3bq1/vrXv8rNzU2SlJGRIYvFoqVLl8rDw0Ph4eE6efKkkpKSjH3eeecd9ezZU6+//roxtmzZMoWEhOjIkSO6/fbbr5oHQPNB2QHQLKSnp2vZsmUqLi7W+fPndfHiRUVGRtpsExERYRQdSSosLFT37t3l4eFhjPXt29dmn4MHD+qLL76Ql5fXFd/z+PHjuv322+17IACaHGUHgNP76KOPNGXKFL311luKjo5WmzZt9Oabbyo/P99mu9atW1/33NXV1YqPj9ecOXOuWBcUFNTgzACcB2UHgNNxc3NTXV2d8XrHjh26++67NX78eGPs+PHjvzlP586dtXLlStXU1Mjd3V2StGfPHpttevXqpTVr1qhDhw5ydb36P4m/zAOgeeHdWACcTocOHZSfn68TJ07ohx9+UKdOnbR3715t3rxZR44c0fTp068oLVfz2GOPqb6+XuPGjVNBQYE2b96sefPmSZIsFoskacKECTp9+rQeffRR7dmzR8ePH9fmzZs1ZswYo+D8Mk99fX3jHTwAu6PsAHA6U6ZMUYsWLRQeHq527dopNjZWQ4YM0YgRIxQVFaUff/zR5izPtXh7e2v9+vU6cOCAIiMj9dJLLyk1NVWSjPt4goODtWPHDtXV1enBBx9URESEJk+eLF9fX7m4uFw1T3FxceMdPAC7s1itVqujQwBAU1m1apXGjBmjyspKeXp6OjoOgCbAPTsATC0zM1MdO3bUn/70Jx08eNB4hg5FB/jjoOwAMLXS0lKlpqaqtLRUQUFBGjZsmGbPnu3oWACaEJexAACAqXGDMgAAMDXKDgAAMDXKDgAAMDXKDgAAMDXKDgAAMDXKDgAAMDXKDgAAMDXKDgAAMLX/B4Un7ALOC7T1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='target',hue='target',data=df,palette='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1b0e072cf50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAHpCAYAAABz6m0rAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC+mklEQVR4nOz9eZxcVZ34/7/OXWrr7upOd5JOAlkJkISAQAKKLIKiqIgiuCPgzDgqBuYDihK/flxHJYzLuMWPu4igfD7+HNCMDgpokE2EKEskEDoJSUjSnaS36u7a7r3n/P64VdVdvSRdSfWWvJ+PRz+6u+rWvefequ561znn/T7KGGMQQgghhJhirIlugBBCCCHEoZAgRgghhBBTkgQxQgghhJiSJIgRQgghxJQkQYwQQgghpiQJYoQQQggxJUkQI4QQQogpSYIYwBhDKpVCSuYIIYQQU4cEMUBPTw/19fX09PRMdFOEEEIIMUoSxAghhBBiSpIgRgghhBBTkgQxQgghhJiSJIgRQgghxJQkQYwQQgghpiQJYoQQQggxJUkQI4QQQogpSYIYIYQQQkxJEsQIIYQQYkqSIEYIIYQQU5IEMUIIIYSYkiSIEUIIIcSUJEGMEEIIIaYkZ6IbIISYPLQ2bG5N0ZX2aEi4nDAriWWpiW6WEEIMS4IYIQQAT2xt57YHt7G9vQ8/0Di2xfymGq46dyErFzVNdPOEEGKICR1OuvnmmznjjDOoq6tj5syZXHrppTz//PNl22SzWVatWkVTUxO1tbVcfvnltLW1lW2zY8cOLr74YhKJBDNnzuRjH/sYvu+P56kIMaU9sbWdNeuepaWth3jEpqk2Sjxi09LWw5p1z/LE1vaJbqIQQgwxoUHMAw88wKpVq/jLX/7Cvffei+d5vO51r6Ovr6+0zQ033MC6dev45S9/yQMPPMDu3bu57LLLSvcHQcDFF19MPp/nkUce4ac//Sm33norn/70pyfilISYcrQ23PbgNtI5nxnJKDHXxrIUMddmRjJKOudz24Pb0NpMdFOFEKKMMsZMmv9M+/btY+bMmTzwwAOcd955dHd3M2PGDH7+85/ztre9DYDnnnuOpUuX8uijj/KKV7yC//mf/+FNb3oTu3fvprm5GYDvfve73HTTTezbt49IJDLkOLlcjlwuV/o9lUoxd+5curu7SSaT43OyQkwSz+3u5qY7nyQesYm59pD7s15AJh9wy7tOZcmc+glooRBCDG9SZSd1d3cD0NjYCMCGDRvwPI8LL7ywtM2SJUuYN28ejz76KACPPvooJ598cimAAbjoootIpVL84x//GPY4N998M/X19aWvuXPnjtUpCTHpdaU9/EATsYf/dxCxLfxA05X2xrllQghxYJMmiNFac/3113P22WezfPlyAFpbW4lEIjQ0NJRt29zcTGtra2mbgQFM8f7ifcP5xCc+QXd3d+lr586dVT4bIaaOhoSLY1vkAz3s/fnCJN+GhDvOLRNCiAObNNlJq1atYuPGjTz00ENjfqxoNEo0Gh3z4wgxFZwwK8n8phpa2nqIJi2U6k+pNsaQSnssbq7jhFky1CqEmFwmRU/Mtddey3//93/zpz/9iWOPPbZ0+6xZs8jn83R1dZVt39bWxqxZs0rbDM5WKv5e3EYIMTLLUlx17kISUYd9qRxZL0BrQ9YL2JfKURN1uOrchVIvRggx6UxoEGOM4dprr+Wuu+7ij3/8IwsXLiy7f8WKFbiuy/3331+67fnnn2fHjh2cddZZAJx11lk888wz7N27t7TNvffeSzKZZNmyZeNzIkJMcSsXNbH6kmUsbq4jkw9o782RyQcsbq7jpkuWSZ0YIcSkNKHZSR/+8If5+c9/zq9//WtOPPHE0u319fXE43EArrnmGn73u99x6623kkwmue666wB45JFHgDDF+tRTT2XOnDn8x3/8B62trVx55ZW8//3v50tf+tKo2pFKpaivr5fsJHHUk4q9QoipZEKDmIFj7wP95Cc/4X3vex8QFrv76Ec/yi9+8QtyuRwXXXQR3/nOd8qGirZv384111zD+vXrqamp4eqrr2bNmjU4zuim/EgQI4QQQkw9k6pOzESRIEYIIYSYeibFxF4hhBBCiEpJECOEEEKIKUmCGCGEEEJMSRLECCGEEGJKkiBGCCGEEFOSBDFCCCGEmJIkiBFCCCHElCRBjBBCCCGmJAlihBBCCDElSRAjhBBCiClJghghhBBCTEkSxAghhBBiSpIgRgghhBBTkgQxQgghhJiSJIgRQgghxJQkQYwQQgghpiQJYoQQQggxJTkT3QAhhBCimrQ2bG5N0ZX2aEi4nDAriWWpiW6WGAMSxAghhDhiPLG1ndse3Mb29j78QOPYFvObarjq3IWsXNQ00c0TVSbDSUIIIY4IT2xtZ826Z2lp6yEesWmqjRKP2LS09bBm3bM8sbV9opsoqkyCGCGEEFOe1obbHtxGOuczIxkl5tpYliLm2sxIRknnfG57cBtam4luqqgiCWKEEEJMeZtbU2xv7yOZcFGqfP6LUopkwmV7ex+bW1MT1EIxFiSIEUIIMeV1pT38QBOxh39bi9gWfqDpSnvj3DIxliSIEUIIMeU1JFwc2yIf6GHvzxcm+TYk3HFumRhLEsQIIYSY8k6YlWR+Uw2ptIcx5fNejDGk0h7zm2o4YVZyglooxoIEMUIIIaY8y1Jcde5CElGHfakcWS9Aa0PWC9iXylETdbjq3IVSL+YIo8zgkPUolEqlqK+vp7u7m2RSonQhhJiqpE7M0UWCGCSIEUKII4lU7D16SMVeIYQQRxTLUiyZUz/RzRDjQObECCGEEGJKkiBGCCGEEFOSBDFCCCGEmJIkiBFCCCHElCRBjBBCCCGmJAlihBBCCDElSRAjhBBCiClJghghhBBCTEkSxAghhBBiSpIgRgghhBBTkgQxQgghhJiSJIgRQgghxJQkQYwQQgghpiQJYoQQQggxJUkQI4QQQogpSYIYIYQQQkxJEsQIIYQQYkqSIEYIIYQQU5IEMUIIIYSYkiSIEUIIIcSUJEGMEEIIIaYkCWKEEEIIMSVJECOEEEKIKUmCGCGEEEJMSRLECCGEEGJKkiBGCCGEEFOSBDFCCCGEmJIkiBFCCCHElCRBjBBCCCGmJAlihBBCCDElSRAjhBBCiClJghghhBBCTEnORDdAiCOR1obNrSm60h4NCZcTZiWxLDXRzToqVHLtjdZ4GzeiOzqwGhtxly9HWfLZToipYkKDmD//+c98+ctfZsOGDezZs4e77rqLSy+9tHR/b28vq1ev5u6776a9vZ2FCxfyb//2b3zoQx8qbZPNZvnoRz/KnXfeSS6X46KLLuI73/kOzc3NE3BGQsATW9u57cFtbG/vww80jm0xv6mGq85dyMpFTRPdvCNaJdc+99DD9Kxdi9+yBeN5KNfFWXwcdatWET3n7Ak6AyFEJSb0I0dfXx8ve9nLWLt27bD3f+QjH+Gee+7h9ttvZ9OmTVx//fVce+21/OY3vyltc8MNN7Bu3Tp++ctf8sADD7B7924uu+yy8ToFIco8sbWdNeuepaWth3jEpqk2Sjxi09LWw5p1z/LE1vaJbuIRq5Jrn3voYTpXr8bbtAlVU4PVPBNVU4O36Tk6V68m99DDE3gmQojRUsYYM9GNAFBKDemJWb58Oe985zv51Kc+VbptxYoVvOENb+ALX/gC3d3dzJgxg5///Oe87W1vA+C5555j6dKlPProo7ziFa8Y1bFTqRT19fV0d3eTTCarel7i6KG14fqfbaClrYcZyShK9Q9hGGPYl8qxuLmOr1+5QoaWqqySa68wtF/xXrxNm7BmzRqyrW5tw126hKY7bpehJSEmuUn9F/rKV76S3/zmN+zatQtjDH/605/YvHkzr3vd6wDYsGEDnudx4YUXlh6zZMkS5s2bx6OPPjrifnO5HKlUquxLiMO1uTXF9vY+kgm37I0RwiA9mXDZ3t7H5lZ5vVVbJdfe27gRv2ULVsO0Ybe1GurxW7bgbdw4nqcghDgEkzqI+da3vsWyZcs49thjiUQivP71r2ft2rWcd955ALS2thKJRGhoaCh7XHNzM62trSPu9+abb6a+vr70NXfu3LE8DXGU6Ep7+IEmYg//ZxWxLfxA05X2xrllR75Krr3u6MB4HkQjw+8sGsX44XZCiMlt0gcxf/nLX/jNb37Dhg0b+OpXv8qqVau47777Dmu/n/jEJ+ju7i597dy5s0otFkezhoSLY1vkAz3s/fnCRNOGhDvOLTvyVXLtrcZGlOtCLj/8znI5lBNuJ4SY3CZtinUmk+H/+//+P+666y4uvvhiAE455RSefPJJvvKVr3DhhRcya9Ys8vk8XV1dZb0xbW1tzJo1a8R9R6NRotHoWJ+COMqcMCvJ/KYaWtp6iCatIXMtUmmPxc11nDBL5l2NRiXpz5VcezVrOc7i4/A2PYcVax46J6arG3fpEtzly8f8HCUVX4jDM2mDGM/z8DwPa9A/Ldu20Tr8tLVixQpc1+X+++/n8ssvB+D5559nx44dnHXWWePeZnF0syzFVecuZM26Z9mXypFMuEQKvQOptEdN1OGqcxfKm9QoVJr+XNm1V9StWkXn6tXo1jashnqIRiGXQ3d1o2prqVu1aswn9UoqvhCHb0Kzk3p7e2lpaQHgtNNO42tf+xoXXHABjY2NzJs3j/PPP5/9+/fz7W9/m/nz5/PAAw9wzTXX8LWvfY1rrrkGgGuuuYbf/e533HrrrSSTSa677joAHnnkkVG3Q7KTRDXJm9PhKaY/m95erIZp4dyVXB7d1YWqrWHamjUj1nE55Doxvodyxq9OTDEdPJ3zhwRciajD6kuWyWtFiFGY0CBm/fr1XHDBBUNuv/rqq7n11ltpbW3lE5/4BH/4wx/o6Ohg/vz5fOADH+CGG24odQEXi9394he/KCt2d6DhpMEkiBHVJsMEh8Zofdjpz5O9Yq+k4gtRPZOmTsxEkiBGiMkh//TTdPzLv6JqalDx2JD7TSaD6UvT+KMfEDnllAlo4eF7bnc3N935JPGITcy1h9yf9QIy+YBb3nUqS+bUT0ALhZg6JnV2khDi6HI0pD9LKr4Q1SNBjBBi0jga0p8lFV+I6pEgRggxabjLw/Rn3dXF4JHuYvqzs/i4cUl/HivFdPBU2hv2HFNpj/lNNZKKL8QoSBAjhJg0lGWF6c21NejWtnAOjNaYTAbd2jZu6c9jqZgOnog67EvlyHoBWhuyXsC+VE5S8YWogEzsRSb2CjHZTGT683iRVHwhDp8EMUgQI8RkNBHpz+NNUvGFODwSxCBBjBBCCDEVHVkfa4QQQghx1JAgRgghhBBTkgQxQgghhJiSJu0q1kKIo4NMbhVCHCoJYoQQE0bSjIUQh0OGk4QQE+KJre2sWfcsLW09xCM2TbVR4hGblrYe1qx7lie2tk90E4UQk5wEMUKIcae14bYHt5HO+cxIRom5NpaliLk2M5JR0jmf2x7chtZHfQUIIcQBSBAjhBh3m1tTbG/vI5lwUap8/otSimTCZXt7H5tbUxPUQiHEVCBBjBBi3HWlPfxAE7GH/xcUsS38QNOV9sa5ZUKIqUSCGCHEuGtIuDi2RT7Qw96fL0zybUi449wyIcRUIkGMEGLcnTAryfymGlJpj8ErnxhjSKU95jfVcMKsqbsMiNGa/NNPk12/nvzTT2P08AGbEOLQSYq1EGLcWZbiqnMXsmbds+xL5UgmXCKFnplU2qMm6nDVuQunbL2YslW4PQ/lHnmrcAsxGcgCkMgCkEJMlCOxTkzuoYfpXL0a09uL1TANohHI5dFdXajaGqatWSOBjBBVIkEMEsQIMZGOpIq9Rmvar3gv3qZNWLNmlWVeGWPQrW24S5fQdMftKEtG84U4XDKcJISYUJalWDKnfqKbURXexo34LVuwGqYNmzpuNdTjt2zB27iRyCmnTFArhThyyEcBIYSoEt3RgfG8cAhpONEoxvfQHR3j2zAhjlASxAghRJVYjY0o14VcfvgNcjmU42I1No5vw4Q4QslwkhBizFRrvst4zJsxWuNt3Iju6MBqbMRdvvyA81aGa5O7fDnO4uPwNj2HFWseOiemqxt36RLc5cur2nYhjlYSxAghxkS1Mo/GI4Op0pToA7Xp5FWr6Fy9Gt3ahtVQD9Eo5HLorm5UbS11q1bJpF4hqkSyk5DsJCGqrbhCdTrnD6kBk4g6rL5k2agCkGrt50AqTYkeTZtO3v1cf1DkeyhH6sQIMRakJ0YIUVWDV6guDqnELJto0mJfKsdtD27j9AWNBxwSqtZ+DsRoTc/atWEAMzAlOh7DijWjW9voWbuWyCvPQlnWqNv09StfSdMrz6poeEoIUTn5ixJCVFW1Vqgej5WuK0mJrrRNyrKInHIKsfPPJ3LKKRLACDEG5K9KCFFV1VqhejxWuq40JVpW3xZicpEgRghRVdVaoXo8VrquNCVaVt8WYnKRIEYIUVXVWqF6PFa6LqZE666uYY+hu7pxFh9XSok+GlbfFmIqkSBGCFFVxRWqE1GHfakcWS9Aa0PWC9iXyo16hepq7edAlGWFKc+1NejWNkwmg9Eak8mgW9uGpESPR5uEEKMnKdZIirUQY2HK1okZRUr0kbj6thBTkQQxSBAjxFg52ir2Sg+MEONLghgkiBFCCCGmIpkTI4QQQogpSYIYIYQQQkxJEsQIIYQQYkqSIEYIIYQQU5IEMUIIIYSYkiSIEUIIIcSU5Ex0A4QQByc1SYQQYigJYoSY5KQ6rBBCDE+Gk4SYxJ7Y2s6adc/S0tZDPGLTVBslHrFpaethzbpneWJr+0Q3UQghJowEMUJMUlobbntwG+mcz4xklJhrY1mKmGszIxklnfO57cFtaH3UF90WQhylJIgRYpLa3Jpie3sfyYSLUuXzX5RSJBMu29v72NyamqAWCiHExJIgRohJqivt4QeaiD38n2nEtvADTVfaG+eWCSHE5CBBjBCTVEPCxbEt8oEe9v58YZJvQ8Id55YJIcTkIEGMEJPUCbOSzG+qIZX2GLzYvDGGVNpjflMNJ8ySldeFEEcnCWKEmKQsS3HVuQtJRB32pXJkvQCtDVkvYF8qR03U4apzF0q9GCHEUUuZwR/xjkKpVIr6+nq6u7tJJuVTrZhcpE6MEEIMT4IYJIgRk59U7BVCiKGkYq8QU4BlKZbMqZ/oZgghxKQic2KEEEIIMSVJECOEEEKIKUmCGCGEEEJMSRLECCGEEGJKkiBGCCGEEFOSBDFCCCGEmJIkiBFCCCHElCR1YsRRp5LCcWNZZO5Q912tNhmt8TZuRHd0YDU24i5fjrIO/XNNtffnB5r7Nu5hT3eW2fUxLlw+G2eEFb3Hq02HemzV0AAoTFfnuLdDiCPZhFbs/fOf/8yXv/xlNmzYwJ49e7jrrru49NJLy7bZtGkTN910Ew888AC+77Ns2TJ+9atfMW/ePACy2Swf/ehHufPOO8nlclx00UV85zvfobm5edTtkIq9R49KSviPZbn/Q913tdqUe+hhetauxW/ZgvE8lOviLD6OulWriJ5zdsXnU+393fHwNn764DZ6sx4GUEBtzOXqcxdyxdkLJ6RNlRh4bN3Xh8lkQClUPIaVqBm3dghxpJvQjwJ9fX287GUvY+3atcPev2XLFs455xyWLFnC+vXrefrpp/nUpz5FLBYrbXPDDTewbt06fvnLX/LAAw+we/duLrvssvE6BTGFPLG1nTXrnqWlrYd4xKapNko8YtPS1sOadc/yxNb2Q9p2LNtRjccNlnvoYTpXr8bbtAlVU4PVPBNVU4O36Tk6V68m99DDFZ1Ptfd3x8Pb+M59L9CT8bAshWspLEvRk/H4zn0vcMfD28a9TZUYeGyjVBjABAH4PqYvDZY1Lu0Q4mgwadZOUkoN6Yl517veheu6/OxnPxv2Md3d3cyYMYOf//znvO1tbwPgueeeY+nSpTz66KO84hWvGNWxpSfmyKe14fqfbaClrYcZyShK9Q+/GGPYl8qxuLmOr1+5AmDU21Y6jFNJOwbu+1AfN5jRmvYr3ou3aRPWrFlD9qNb23CXLqHpjttHNdxR7f35geaNX15PT8bDdRTWgP1pY/B8Q13c5XcfO3/EoaVqt6kSA4+tmpvRL24PgxjXDbuTPB8Vi2EvWIBuG7t2CHG0mLR/OVprfvvb33LCCSdw0UUXMXPmTF7+8pdz9913l7bZsGEDnudx4YUXlm5bsmQJ8+bN49FHHx1x37lcjlQqVfYljmybW1Nsb+8jmXDL3tQgDKCTCZft7X1sbk1VtO1YtqMajxvM27gRv2ULVsO0YfdjNdTjt2zB27hxVOdT7f3dt3EPvVkP2y4PYAAspbBtRW/W476Ne8atTZUoO3Y2h8nlwHFQSqFQYNvhbbnsmLZDiKPFpA1i9u7dS29vL2vWrOH1r389f/jDH3jrW9/KZZddxgMPPABAa2srkUiEhoaGssc2NzfT2to64r5vvvlm6uvrS19z584dy1MRk0BX2sMPNJERPr1HbAs/0HSlvYq2Hct2VONxg+mODoznQTQy/AbRKMb30B0dBz+ZMdjfnu4shpH/MVmAKWw3Xm2qxMBjm8AHY2BgIKUUGIPxgzFthxBHi0kbxGitAXjLW97CDTfcwKmnnsrq1at505vexHe/+93D2vcnPvEJuru7S187d+6sRpPFJNaQcHFsi3ygh70/X5gk25BwK9p2LNtRjccNZjU2olwXcvnhN8jlUI6L1dh48JMZg/3Nro+hgOHPMrxdFbYbrzZVYuCxle2UgpaSQlCjHHtM2yHE0WLSBjHTp0/HcRyWLVtWdvvSpUvZsWMHALNmzSKfz9PV1VW2TVtbG7NmzRpx39FolGQyWfYljmwnzEoyv6mGVNpj8DQwYwyptMf8phpOmJWsaNuxbEc1HjeYu3w5zuLj0F1dw+5Hd3XjLD4Od/nyUZ1Ptfd34fLZ1MZcgsCgB+1PG0MQGGpjLhcunz1ubapE2bFjUVQ0Gk7oNQaDgSAIb4vGxrQdQhwtJm0QE4lEOOOMM3j++efLbt+8eTPz588HYMWKFbiuy/3331+6//nnn2fHjh2cddZZ49peMblZluKqcxeSiDrsS+XIegFaG7JewL5Ujpqow1XnLsQqZMKMdtuxbEc1HjeYsizqVq1C1dagW9swmQxGa0wmg25tQ9XWhvePcqJptffn2BZXF87D8w2+Nmgdfvd8g20prj534QHrxVS7TZUYeGzTthdVnwTbBs+DvAdKYdXXo9vGth1CHC0mNDupt7eXlpYWAE477TS+9rWvccEFF9DY2Mi8efO46667eOc738natWu54IILuOeee7j++utZv34955xzDgDXXHMNv/vd77j11ltJJpNcd911ADzyyCOjbodkJx09pE5MqKyGiu+hnCrWianC/qpeJ6YKbapEWZ2YdB8mXawTE8dKJKROjBBVMqFBzPr167nggguG3H711Vdz6623AvDjH/+Ym2++mZdeeokTTzyRz33uc7zlLW8pbVssdveLX/yirNjdgYaTBpMg5ugiFXtDUrF3bEnFXiHG3qSpEzORJIgRQgghph75KCCEEEKIKUmCGCGEEEJMSbKKtRBiQk3kvBUhxNQmQYwQYsJM5ErTQoipTz7uCCEmxESuNC2EODJIECOEGHdGa3rWrsX09oYrTcdjKMtCxWNYs5oxvX3h/XqkBQiEEEKCGCHEBKjGStNGa/JPP012/XryTz8tAY8QRyGZEyOEGHfF1Z7VgVaa7u4ecYVnmUsjhADpiRFCTIDDWWla5tIIIYokiBFCjLtDXWla5tIIIQaSIEYIMe4OdaXpasylmWhaG57b3c1fWvbz3O5utD7qV34R4pDJnBghxISInnM209as6Z/b0t2NclzcpUtGnNtyuHNpJtpYrowuxNFIghghxISJnnM2kVeeNeqKvWVzaeKxoRscYC7NRHtiaztr1j1LOueTTLhEbJd8oGlp62HNumdZfckyCWSEqJAEMUKICaUsi8gpp4xq2+JcGm/Tc1ix5rIhpeJcGnfpkiFzaSaa1obbHtxGOuczIxkttTtm2USTFvtSOW57cBunL2jEstRB9iaEKJI5MUKIKeNQ59JMtM2tKba395FMuMPO5UkmXLa397G5NTVBLRRiajrsv/RUKsXdd9/Npk2bqtEeIYQ4oOJcGnfpEkxfGr13L6Yvjbt0CdPW3Dwp68R0pT38QBOxh/+XG7Et/EDTlfbGuWVCTG0VDye94x3v4LzzzuPaa68lk8mwcuVKXnzxRYwx3HnnnVx++eVj0U4hhCipdC7NRGtIuDi2RT7QxCx7yP35wiTfhoQ7Aa0TYuqqOIj585//zCc/+UkA7rrrLowxdHV18dOf/pQvfOELEsRMYVobNrem6Ep7NCRcTpiVlPF5MWmNNJdmMr6OT5iVZH5TDS1tPUST1pC5PKm0x+LmOk6YlTys4xitDzuwG80+JuM1FkenioOY7u5uGgsz/++55x4uv/xyEokEF198MR/72Meq3kAxPiT1UxwJJuvr2LIUV527kDXrnmVfKlfITgp7ZlJpj5qow1XnLjysQKAaSzGMZh+T9RqLo1PFfa9z587l0Ucfpa+vj3vuuYfXve51AHR2dhKLDZPyKCa9YupnS1sP8YhNU22UeMQupX4+sbV9opsoxEFN9tfxykVNrL5kGYub68jkA9p7c2TyAYub67jpMNOrq7EUw2j2MdmvsTj6VNwTc/3113PFFVdQW1vL/PnzOf/884FwmOnkk0+udvvEGJPUT3EkmCqv45WLmjh9QWNVh2KGLMVQHKqKx7BizejWNnrWriXyyrNGHFoazT66167lttffMOmvsTi6VBzEfPjDH+bMM89k586dvPa1r8Uq/FEsWrSIL3zhC1VvoBhblaR+LplTP0GtFOLAptLr2LJUVdtQyVIMI9XjGc0+Nu9N8+KeLpK18Ul/jcXR45CK3a1cuZKVK1dijMEYg1KKiy++uNptE+OgP/Vz+KyIiG3RE3iS+ikmtaP5dVyNpRhGs4+UiR40TfxIvcZi8jqkfMQf/ehHLF++nFgsRiwWY/ny5fzwhz+sdtvEOBiY+jkcSf0UU8HR/DouW4phOKNYimE0+0gGuaP2GovJq+KemE9/+tN87Wtf47rrruOss84C4NFHH+WGG25gx44dfP7zn696I8XYGa/Uz6lkYPpoMuaCMqQyftn8hSAIePahJ+nc38206fUsO+dUbHto/Y+JMl4psNVI6a3ESOd1qK9jP9Dct3EPe7qzzK6PceHy2TiDehoO9bkezbWpxvWzlyxB1dXh79yJ1diI1dCAshQY0Jk0et9+nEUL0b5Pdv36YY8zmuUcTlg6mwWzG2hp65X/FWLSUMaYitaBnzFjBt/85jd597vfXXb7L37xC6677jr2799f1QaOh1QqRX19Pd3d3SSTR98f4NCF6cpTPw83c2IqGZg+msn7ZPPhp86YaxGPOsxvqmGlneKRJ1/kJasGX1k4RnOs7uPq847jrDe/aoLPYPxSYKuR0luJg51Xpa/jOx7exk8f3EZv1sMACqiNuVx97kKuOHshAI/+5gF++uctFT/Xo7k21bh+Pd/9Hj3f+jYmlQJd6CGxLKivh2wWcrnwNtsGpVDxOFZNzbDHKWYnmd4+rIZ6iEYhl0N3daNqa5m25maembNE/leISaXiIKahoYHHH3+c448/vuz2zZs3c+aZZ9LV1VXN9o2Loz2IAan9AOXBnOtYtPfk0IU/D9tSNNZGSfdlSecDYn6OaV4frtF4yqLXjhHXeT5+7jETGsgc6I08EXWqtlJy/xteL1bDNIhGIJdHd3WhamuYtmZNVQOZ0Z7XaF/Hdzy8je/c9wJaG2xbYQEaCAKDZSk+fOHxLGrfwX88uIu0FaEuyI76uR7NtQEO+/r1fPd7pG5eA0EATqFT3fdh4L/0aDS8rci2sWbMgHx+2OOUBVa+h3KkToyY3CoOYq677jpc1+VrX/ta2e033ngjmUyGtWvXVrWB40GCmNDRXIVTa8P1P9tAS1sPM+qi7OxIk/M1jq3AGHxtiDo2OpMhZzlEA49jcl0Ur4420OEkWBCkWHvL1RMytFR2DgNSYCHs7t+XyrG4uY6vX7nisFN62694L96mTeXpuIXj6NY23KVLaLrj9qoMLVV6Xgd7HfuB5o1fXk9PxsN1FNaA/Wlj8HxDXdxhwf7tbLeTNPlpBl6uAz3Xo7k2zpITAYX/3KFfP+37tL7sNEx3N0QjKGWVHk82G25kWRCLQT4PrgMG8LywN2bBfEzb3mGPIxV7xVRySNlJP/rRj/jDH/7AK17xCgAee+wxduzYwVVXXcVHPvKR0naDAx0xuVU79XMqGZiim/M1+UBjWyoMUpTCtiDnBaAsbB3gWw45yyGmw0+5loLaIMtLVg3PPvQkJ79qxYSew1imwFYjpbcSlZ7XwV7H923cQ2/WC3tgBu3PUgrbht6Mz1angYZBAUy4zcjP9aiuzabnQKnDun6ZX/8a09MDjlMKYICwF0aFgTdah8NJjoNCgQLjOJhcDpXNoUY4zkjLOZRdg6P4f4WYXCoOYjZu3Mjpp58OwJYtWwCYPn0606dPZ+PGjaXtBv9xCjGZDUzRzeSD0ntBkaLYS6+wMOHQgyr/dOoaTa+y6NzfPY4t7zdeacbVSOmtRLXPa093FsPIqZkWEACeZeMaDcP8KxvpuR7VtfEK7TyM6xfs2hUGKc6gf+HGlA8nDXkhhwGOCXxUTU1VnychJkLFQcyf/vSnsWiHEBNqYIqubanwfz3971+G4nuBKd1um/JUU68w8XPa9In5hDpeKyWXpePGh1lqZBQpvZWo9nnNro+hCOfADBfIaMLn19UBnrKwGZpSPNJzPapr47rhi+kwrp99zDHhcJHW4fcipfp7Yoq/DwxkCj8r26n68yTERDisAeudO3eyc+fOarVFiAlTTNFNpT2ijkXEtgh0GLAYYwi0IeqGn8wDy8bRPlHdP2FSG+i1Yxyr+1h2zqkTfg6Dp7oVU2DnN9UcdgpsMR1Xd3UNexzd1Y2z+Djc5csP6zhF1T6vC5fPpjbmEgSmNHG7SBtDEBhq4w6L/C567Rh60KzBAz3Xo7o2S5fgLFlyWNcv/pa3oOrqwPcxA4PpgWNfxTkxQYAhLEyK76OiUUwsWvXnSYiJUHEQ4/s+n/rUp6ivr2fBggUsWLCA+vp6/vf//t94nlRqFFNTcZXhRNRhX0+OuriLAjxf4wUGSynq4i5uNIJlDLYOyGERGMhi0eEkiGuPq887bsLqxZSdQypH1gvQ2pD1AvalclVZKRnCORN1q1ahamvQrW2YTAajNSaTQbe2oWprw/urVC+m2ufl2BZXF7b3/HDSttbhd8832Jbi6nMX8U/nHUdc5+lwEmRH+Vwry6Lmwx9my+zj2BAkecFOEgy6NslrryV57eFdP8txqLvu2jB1OpfH+D5Ga/CD/o3iceyGhrAXJu+B54Fto+qTmLa9Ix7HaE3+6afJrl9P/umnw/0KMUlVnJ10zTXX8F//9V98/vOfLyt299nPfpZLL72U//N//s+YNHQsSXaSKJI6MaM3mnTcaqr2eY1FnZhiG1/c3YHX24ft5TmmZy+Xb3uY06dZI9eJOcTrV6oT09PTP1RUV0f8TRcT7NiB37IFnU5jMhkwBpWIYyWGrxMzpE3jUPtHiMNVcRBTX1/PnXfeyRve8Iay23/3u9/x7ne/m+7uiZnUeDgkiBEDScXe0ZssFXsPVTUr9g6tZaPI92VJZX0SEZubLj+VM46bXvaYalw/7ftkfv1rgl27sI85hvhb3oLlOGX7Vg3TAIPp6hrxOONd+0eIaqg4iJk5cyYPPPAAS5cuLbt906ZNnHfeeezbt6+qDRwPEsQIIQ7HeNXoGSvjXftHiGqp+NV47bXX8u///u/kiuWsgVwuxxe/+EWuvfbaqjZOCCGmgkpq2UxGldT+EZPP+eefz/XXXz/RzSgZz/aMKsX6sssuK/v9vvvu49hjj+VlL3sZAE899RT5fJ7XvOY11W+hEEJMcuNVo2esjHftHzH55PN5IpERnv9JbFQ9MfX19WVfl19+OW9605uYO3cuc+fO5U1vehOXXXYZ9fVSwVEIcfQZWMtmONWq0TNWyurbDEdqykxa73vf+3jggQf4xje+gVIKpRRbtmzhX/7lX1i4cCHxeJwTTzyRb3zjG0Med+mll/LFL36ROXPmcOKJJwLwyCOPcOqppxKLxVi5ciV33303SimefPLJ0mM3btzIG97wBmpra2lububKK68sLf48XHtefPHFMTv/UfXE/OQnPxmzBghRTbKmixjJWE5CLtayaWnrIZq0hswpSaU9FjfXHXaNnrFSrG/jbXoOK9Y8dE5MVzfu0iVSU2YS+sY3vsHmzZtZvnw5n//85wGYNm0axx57LL/85S9pamrikUce4QMf+ACzZ8/mHe94R+mx999/P8lkknvvvRcI54decsklvPGNb+TnP/8527dvHzIs1NXVxatf/Wre//7385//+Z9kMhluuukm3vGOd/DHP/5x2PbMmDFjzM6/4oq9r371q/mv//ovGhoaym5PpVJceuml/PGPf6xW24SoiKyuK0Yy1qnDxVo2a9Y9y75UbshK29Wq0TNWirV/OlevRre2YTXUhytg53Loru6q1/4R1VNfX08kEiGRSDBr1qzS7Z/73OdKPy9cuJBHH32U//f//l9ZEFNTU8MPf/jD0jDSd7/7XZRS/OAHPyAWi7Fs2TJ27drFv/7rv5Ye8+1vf5vTTjuNL33pS6XbfvzjHzN37lw2b97MCSecMGx7xkrFr8j169eTzw/tcsxmszz44INVaZQQlSqmt7a09RCP2DTVRolHbFraeliz7lme2No+0U0UE6SYOuxt2oSqqcFqnomqqcHb9Bydq1eTe+jhqhxn5aImVl+yjMXNdWTyAe29OTL5gMXNddx0ybJJH0hHzzmbaWvW4C5dgulLo/fuxfSlcZcuYdqamyW9eopZu3YtK1asYMaMGdTW1vL973+fHTt2lG1z8sknl82Def755znllFOIxfqXwzjzzDPLHvPUU0/xpz/9idra2tLXkiVLgP71FMfTqHtinn766dLPzz77LK2traXfgyDgnnvu4Zhjjqlu64QYBa0Ntz24jXTOL0tvjVk20aTFvlSO2x7cxukLGiftJ2ExNozW9KxdG9Y+GZg6HI9hxZrRrW30rF1L5JVnVaWXYeWiJk5f0DhlhzSj55xN5JVnjWvtH1F9d955JzfeeCNf/epXOeuss6irq+PLX/4yjz32WNl2NTU1Fe+7t7eXSy65hFtuuWXIfbNnzz7kNh+qUQcxp556ammSzqtf/eoh98fjcb71rW9VtXFCjEYl6a1L5kz9yefVnvczEfOIKj1mJdsPnPsStLePOnU4csopVTk3y1JT+nWmLKtq10KMj0gkQhD0Lznx8MMP88pXvpIPf/jDpdtG00ty4okncvvtt5PL5YhGowA8/vjjZducfvrp/OpXv2LBggU4g1dRH6E9Y2nUQcy2bdswxrBo0SL++te/lk3UiUQizJw5c1JVLBVHj6me3lqJas/7mYh5RJUes5LtB899IQjQPT3YkQhquBWjJXVYHAEWLFjAY489xosvvkhtbS3HH388t912G7///e9ZuHAhP/vZz3j88cdZuHDhAffznve8h09+8pN84AMfYPXq1ezYsYOvfOUrAKUPAatWreIHP/gB7373u/n4xz9OY2MjLS0t3Hnnnfzwhz/Etu0h7WlsbMQao968Ue91/vz5LFiwAK01K1euZP78+aWv2bNnSwAjJsxUT28drWrP+5mIeUSVHrOS7Uea+0IQELz0Eqa3b2iDJHX4iOEHmnTOpzudZ18qy+7OzEQ3adzceOON2LbNsmXLmDFjBhdddBGXXXYZ73znO3n5y19Oe3t7Wa/MSJLJJOvWrePJJ5/k1FNP5ZOf/CSf/vSnAUrzZObMmcPDDz9MEAS87nWv4+STT+b666+noaGhFKgMbs/guTjVVPGyAz/96U+ZPn06F198MQAf//jH+f73v8+yZcv4xS9+wfz588ekoWNJlh2Y2qZ6yffRGOkcjQlXc27vyTN/eg3f+5czh6z9U8n+ivsci2vmB5oP/uivbN/fR1NdhJhrl53H4GNW0kaFGbZsvjEGf9s26EtDIoG7aFG40iNSTn8qC7Qh7wfkfU3O1+R9jdaD3soUzGuqfM6HKHfHHXfwT//0T3R3dxOPxye6OUNU/Ff7pS99qXQijz76KN/+9rf5j//4D6ZPn84NN9xQ9QYKcTDF9NZE1GFfKkfWC9A6fHPfl8pN+vTW0Rhu3k9fzmdne5pdnRky+YBNu7v54I/+OqoelPEuk//E1nY++KO/sml3N5l8wK7ODDvb0/Tl/BGPWUkbRyqbr5TCnjkTHAcyGXR3F0ZrTCaDbm2T1OEpIu9rerMe7T05dndm2NWRZl8qR3faI5sPhgYw4pDddtttPPTQQ2zbto277767VANmMgYwcAh1Ynbu3MnixYsBuPvuu3nb297GBz7wAc4++2zOP//8ardPiFEpprcW5070BB6ObbG4ue6IqBMzeN5PX86ntSuDNgbbUmCBr2FHex9r1j3L6oOk9I7nPKLikFB3OizNYFuAgpwf0NqVYVZDnJqoM+SYlbTxQGXzrdpazDFz0Lt2Y/rSmGwW5bi4S5dUrU6MqJ5iL0vO04WeloDKxgvE4WhtbeXTn/40ra2tzJ49m7e//e188YtfnOhmjajiIKa2tpb29nbmzZvHH/7wBz7ykY8A4XhZJnP0jEGOxA80xoDryCe78TbV01sPZOC8n6iyaO/JoY3BscKMQQ1YFjTWROjJ+gdNKR+4v5g1dD5bteYRDUx/b6yJkPECUApLgbLA14b2nhyJiD3kmJW0saxs/jATeC3XhenTSX7iJuymJkkdnkS8QqCS88KhIX+EuW1ifHz84x/n4x//+EQ3Y9QqDmJe+9rX8v73v5/TTjuNzZs388Y3vhGAf/zjHyxYsKDa7Zty0vmArr48KHBtq/ClcG2LiGONar6COHRTPb11JAPL2tfFHfKBxi4EMIbw02vUsYhHHJSlDppSPl5l8gcOCcUcm4htkfM1yg7bblthMJL1AnoyftkxK2mjmjW6svmJt75VApcJ5geaTD4g5+vS0K8Qh6riv+a1a9dy1llnsW/fPn71q1/R1BR2WW/YsIF3v/vdVW/glGXCTxjhbHmP/YWx3J3tfbR1Z+jozdGb9ch5ARXOrRZHoYHzftp78hgNGNAG/MBgKUVTXRRUOMziB/qAQ0HjNY+of0jIAgVNdVEspfADgzbhORgD7T35IcespI3Fsvmqtgbd2obJZGTuyyRhjCGbD+jsy7O7M8PuzgydfXnSOV8CGHHYKs5OOhJVMzsplfHCnphKFHptIraF6/R/t4+AYRBRXU9sbef/3PcCm3Z3A+EbfcS2aKqLUhMNO1azXkAmH3DLu049aK/UWNeJeW53Nzfd+STxiE3MDYeE+nI+7T058kF/RsnSOfVcc+Hx1a0T43sop7prJInR8Qu9a5l8QNaboDktkp10VDikIObBBx/ke9/7Hlu3buWXv/wlxxxzDD/72c9YuHAh55xzzli0c0xNeBAzAttSpaAm4shwlAgVU5V3tPfRWBMhHnHK0oYrTY8ey4q9I6ZJG8jkfTr68sxrOnhq+KFW7JW5L+PD8zX5QJPzArLeJJnXIkHMUaHiOTG/+tWvuPLKK7niiiv429/+Ri6XA6C7u5svfelL/O53v6t6I49WgTYE+YAs/eWblYKIY5Xm2EQcG7cwv0BUx2R/E3Rsi2suPJ41656lJ+ujCr0xh7pi8ljOIzrQ6s49WZ+GRIRrLjz+oMF5sY3FYOavW9tHDGYmomz+ZH/NVJMf9NdmKdZqkf58MVEq7ok57bTTuOGGG7jqqquoq6vjqaeeYtGiRfz973/nDW94Q9nCkFPFZO2JGTUZjqqawWXrlTt5hyMmYsmAQ1WNtk7W851Kr5lKFQMWrxi0BMMUlZuspCfmqFBxEJNIJHj22WdZsGBBWRCzdetWli1bRjabHau2jpkpH8SMwLZUaRhKsqMOrli23vT2YjVMg2gEcnl0VxeqtoZpa9ZMujeliVi88VAdTluLtWbSOb+sNyeV9khEnYPWxRkrU/E1M5LikFC+ELRM+fosEsQcFSoeTpo1axYtLS1D0qkfeughFi1aVK12iSoItCGTDyfXFQ0ejip+P9qHo4zW9KxdG74ZDShbTzyGFWtGt7bRs3YtkVeeNamGCaZSSvmhtnVgrZmB82pilk00abEvlTtoXZyxMFVfM0BZz4oMCU19R9Nw5mAVBzH/+q//yv/6X/+LH//4xyil2L17N48++ig33ngjn/rUp8aijaKKjCEsKuUNmHgnw1Ejlq2HsHS91VCP37IFb+PGcZ9vcbSrZPmB8QzopsJrxhiDF/SvM1QMXJCA5YhxJA9njkbFodrq1at5z3vew2te8xp6e3s577zzeP/7388HP/hBrrvuurFooxhrhZo2fTmfrr48e1NZdnWkeakjTWtXhvaeHN3pPD0Zj76cT9YLJkf2QRUVy9YzTNl6AKJRjB+Wtxfjq6zWzDBGUxdnLEy214wxhpwX0JPxaO/N0dqVYWfhb7ijN09v1ifvSwBzJBlp5XZv03N0rl5N7qGHx+S4t912G01NTaXEnqJLL72UK6+8EoBf//rXnH766cRiMRYtWsTnPvc5fD9cK80Yw2c/+1nmzZtHNBplzpw5/Nu//dshtaWinpggCHj44YdZtWoVH/vYx2hpaaG3t5dly5ZRW1t7SA0Qk5fWhrw24T++YRRrlEQKvTaWpbCVwrHVlJt7c7Cy9eRyKCcsby/G13gtkVCpiX7N5P0wpbm4kvOR9sFCHNhEDme+/e1v59/+7d/4zW9+w9vf/nYA9u7dy29/+1v+8Ic/8OCDD3LVVVfxzW9+k3PPPZctW7bwgQ98AIDPfOYz/OpXv+I///M/ufPOOznppJNobW3lqaeeOqS2VBTE2LbN6173OjZt2kRDQwPLli07pIOKI4PWhqwOi1kNMcWWXXCX95etJ9bMtppmUm6MpJdlYV8bFMrWu8uXH3A/lYxNjzTRdeDtybgDRpHKjs/E3cMZW69GLZfh9nHCrCTzmhJs3pOiLhYGNGHhPINOZ0ilfRZNi3B889APUiMdZ7jbAwP3bdzDnu4ss+tjXLh8No5toX2fzK9/TbBrF/YxxxB/y1uwHKf8NRNtZqtbT8qKkdRZFnnd6K4UO089i22xWdS/1A3KkMr41MccFrZvh85OVMM0wGC6ug56vcMaLIVy/fmArXt7SGV9kjGHBTNqsQYNaWljeHFf7wG3GfZ5DAKy99+HbmvDmjETZ/48TE8Pqq4+bGtPCqu+AeeE41HKwhiNv/kFdHdX2e3DPu8VbCsObCKHM+PxOO95z3v4yU9+Ugpibr/9dubNm8f555/Pa1/7WlavXs3VV18NwKJFi/j3f/93Pv7xj/OZz3yGHTt2MGvWLC688EJc12XevHmceeaZh9SWirOTVq5cyS233MJrXvOaQzrgQH/+85/58pe/zIYNG9izZw933XUXl1566bDbfuhDH+J73/se//mf/8n1119fur2jo4PrrruOdevWYVkWl19+Od/4xjcq6hk6UrOTJiPLUkSd8E0o6tpEJtFCmbmHHuaBW77Lr449k13JZgLLwdY+x6TauPylv/Kqmz50wDHmSsamR0oXfvniJh5raWd7ex+ZnE+2MHcpFgnXRRrLlOLDGVs/5Kq6A47zwrs/yC86a4a9Jvc+08oLbT1obbAsiAC1uR68AOJ+jmv+sY7Tp1llbR3pONFXvYrcAw+U3f6blZfwyzln0BeEoy0KqI25vMtp4/U/+RKmpwe0BstC1dVRd9211H3og+Qeepj1n/8m/zXvFeyqb8a3HBztk8z0gOvQO2suWWWTzYfPY1RpYplejulq5a3P3svyHRvBGFQ8jlVTU7re9lln4QW61MuSG1D19pmdXdz9xEvs7koT6HAV8zkNCS5deSwnz20Y9TbD6bvzTvp+djumrw+KPTtKQSQSTqgzBhWLohIJnHnziJx5Bvm/Po6/YwfG81GugzNvHjVXXEFkxell+85v+Bt9d9wxqm2r4gjPTsquX0/n9R8Jh5CGCXyN1ui9e5n2n18jdv75VT/+3//+d8444wy2b9/OMcccwymnnMLb3/52PvWpTzFjxgx6e3ux7f6e0yAIyGaz9PX10d7eztlnn40xhte//vW88Y1v5JJLLsFxKp6mW3kQc8899/CJT3yCf//3f2fFihXU1JS/SCoJAv7nf/6Hhx9+mBUrVnDZZZeNGMTcddddfO5zn2Pfvn187GMfKwti3vCGN7Bnzx6+973v4Xke//RP/8QZZ5zBz3/+81G3Q4KYCTRgUrFjKyylUCr8JDHeQ1NPbG3n5v+3gb6eNLWZFG7g49kOvfE6aupq+MQ7VowYPFSSajtSunB7T450PiARsUlEHTp6cwSFmhzFtZE8X49JSvHhpApXkv480nGedBr57mmXkW1oor6+ZsRr0pMJ1xvTWqOMYWHvXt730sOcsm9LWVuBYY8T7N2L6etD1SSwZzZDNMLdM17GbQvPQysL27LC3jAgCDRWEHDFE//FW577U7hMuNbg+2DbJD+xmo1zlvAfD7xE2napy/bhBj490QQdNdMAqI/a9AQKbQwYsAKfhmwK33aJ5dN86JE7WL7nefKRGH7zbPKBwatLUnfjx4Z9Y39mZxff/1ML2XxAbczBtS28QNOb9YlFbD5wwWKAg24zXCDTd+ed9H7v+2HwYqn+IKZIqfB2y0Y1NkK6D5PJhAFYUxNEXMh76FQPKhGn/sYbS+eQ3/A3ur/yFUw6jZVMHnDbqjnCg5j800/T8S//iqqpQQ0znGkyGUxfmsYf/WDMJpavWLGCt73tbbzuda/jzDPP5MUXX2Tu3LnE43E+97nPcdlllw15zKJFi7Asi0wmw3333ce9997LL3/5SxYuXMgDDzyA61Y2LFxx2FNctfrNb37zkJVilVIEwTBDCyN4wxvewBve8IYDbrNr1y6uu+46fv/733PxxReX3bdp0ybuueceHn/8cVauXAnAt771Ld74xjfyla98hTlz5oy6LWKCFCYVeyPMu4Hwf2dpaMrpH56qZnBTTOPNWC7Nx86AbBIT+ERsh5pYlH09+RHTeCsZmzao4dOFlY1fKCTmB5qejIc2BtdWUFgwsSfjMbcxwb6e6qYUH87YeiXpzwoz7HF0PMZdJ7yOtBVhemo/blMdKFV2TQJtaEi4NMQd+rbvxPd8eqO11Jo8J/fuQg1oa+rb3wbU0POJxTC+D0GACQKIRQmU4v837yy0snACH8solBPFAlTOw7cc/utlb+RNLQ/jEPbEGNuCXJ7ub63lp2+4nrSTpMlPoxyFsR364nUowh6d7rxG2TaubWHyOQJl0RutoTm1l85YPf/3tEtozPWifA+VC1DHzMHs76DvjjtwTz+1bKhFG8PdT7xENh/QWBuheFpRyyLiROjozXP3EzsBDrLNS5x0bH3Z0JIOAvp+dnsYuLhOGKgNeaEYcCLge9Dbg9EaAo3RAUQj4XWORbGiEfS+9tI5APTdcUcYwMyYPuD5GLqtDC2N3sDhzAOt3H6wIfDD8f73v5+vf/3r7Nq1iwsvvJC5c+cCcPrpp/P888+zePHiER8bj8e55JJLuOSSS1i1ahVLlizhmWee4fTTKwtmKw5ifvKTnzB37tyybiIArTU7duyodHcHpLXmyiuv5GMf+xgnnXTSkPsfffRRGhoaSgEMwIUXXohlWTz22GO89a1vHXa/uVyubFZ1KpWqartFdRlDKT2UAZPhB04sdm0L21Y41qH13JSl8VoWJOIMDA8OlMZbydj01unzh00XznoBnjbYtgpTYFHYVv9yErYV3p7zddVTig9nbL2S9OdF+7cPe5ytiZnsik+jzs+Cn8dksqhEfMg1yXmaqJ8nmk0TtSxcL83u2DS2JmayOL23v62bngOlhhzHZDOQz4PjhJNxM1kePPZU+uwottZYCtAGow1ojWUMtg5IR+I8vOB0XvXiE4XzsjCOw1a3npdIUBdkw8cqi5zl4FkOltFopQiwcExxnwalNJ5lk7WjxP0MrXUzeKlhNnM7d2PyeaxcHpWsxd+xA3/zC7gnnlhq/4v7etndlaY25jDoUqMU1MYcdnSkAQ64ze6uNC/u62XRzLrSfdn77wuHkBwbUP0ZTEpRVkBGB2A7mOL/T8eGvAfZXGlys1IKa8A5APg7dmAlk8O/vkY4X3FgxZXbO1evRre2YTXUQzQKuRy6q3tcVm5/z3vew4033sgPfvADbrvtttLtn/70p3nTm97EvHnzeNvb3oZlWTz11FNs3LiRL3zhC9x6660EQcDLX/5yEokEt99+O/F4nPnz51fchoqDmH/+539mz549zJw5s+z29vZ2LrzwwtJEnmq45ZZbcBxnxNSr1tbWIe1wHIfGxsYDLn9w880387nPfa5q7RQTY8SJxQocq7/Hxi0MVbm2NWLPRX8a7/BdmRHboifwhk3jLabaqgOl2nZ3ozs66ErMGfY4gTYYA7YC3wAY1IAwShG+lwTaEI/YI7blUFTS/sEquW4jHSflxvCVTa3RYAwm8FGUX5PAgK8NkcAPL4SycI1Pr4qRcgd0pUejYdozDEl9Nn4QPta2C70xPvuidRgFyhRnwpjS3A8AhcZgs68wPFRiWaSitfiWhROEAYtGkVc2Gij7iGcGxASARqEti6jvkY7Y9EbiYbCgDSYIUIk4pqcX3d1Vfp2yPoE2uCME6a5t4Rd6NN3EyNv05XxS2fKeFt3WBtqAbZWd/xDGhENKxW0cF3TYs1X2lxWJlJ2D8XxU/QjDBIO2FaMXPedspq1Z0z/3q7sb5bi4S5eMS52Y+vp6Lr/8cn7729+WTQW56KKL+O///m8+//nPc8stt+C6LkuWLOH9738/AA0NDaxZs4aPfOQjBEHAySefzLp162hqqnyIvOIgpjhsNFhvby+x2DBphodow4YNfOMb3+Bvf/tb1avJfuITn+AjH/lI6fdUKlXqBhNHABOu+eIHkKE8wLEtVRbUFH8+nDTeSlJtRzpO2OsCGgqfoFVpgmnhlFCqv0emminFh5MqXMl1G+k4SS+LYwI8ZRFVCmWH/5YGXxPHKtynFBiNZ0VwTEDSG7DUSS4XHkOpIcdRjl0IFjQUjjMj14MyYcgYHqlwsML/HIOFwjCjr7O0H19ZZG0HhUEZyFgu0cJjVWFPZSGAon+V8cI2ltZ4toWjA2rzmUJgplC2Dfk8ynGw6hvKrmUy5mBbCi/QRIf5dO0FGqcwUf5A29iWIhkr/9dvNTcXghMNyhraA1M6F1Vqa/F5KLV7oEHnoFwn7LGJRYfuc4TzFaMTPedsIq88a8Iq9u7atYsrrriCaLT8ub3ooou46KKLhn3MpZdeOmIST6VGHcQU3/SVUnzqU58ikUiU7guCgMcee4xTTz21Ko0CePDBB9m7dy/z5s0rO85HP/pRvv71r/Piiy8ya9Ys9u7dW/Y43/fp6Ohg1qxZI+47Go0OueDiyDNciikaAh3AoE6MRMRhVn2M7fv7mF4XLZtgbIwhlfZY3FzHCbOGTvyuZGz6BBTzm2poaeshmuxf7iHm2riWIutpYm54e84PUBagFIE2RB2LqGOxryfH7IY4HX15ntvdfdhp14cztn7CrOSw51N87MDrpmYNf5xF6b0ck+nkxVgjUVuVJikOvCbxiE3UtVBuDBWNorNZeqIxFmT2syi9t6ytztIlgMJ/rvw42i1k2Pg+uA5BxOXcjuf54fxX0etEUYHGshTKUhhlEyiFb9nU5NMsa93M/ngDvm0ToCDvMYM8zWTZZdfg+lmU0bg6wHECPMsuBSyacJI6lkJj4eoA1/h0R+o4pmcvx3TtgcBHxWKYaASzvwP3uEU4Jxxfdq3nT08wO2LYmcrgxmyseKw/ODLQm/WZ1xT+X97RniZiu5ieHowXnm8+niCV9pndEGfe9PIJr7HXXEjvN76F6ekFV5U6pYYEMpYNvhe2VetwGCkWQefy0NeHclxMXQ2kesvOwZk3D2/LVqzi3JmBr69B24rKTcTK7Z2dnaxfv57169fzne98Z1yPPdCoQ7W///3v/P3vf8cYwzPPPFP6/e9//zvPPfccL3vZy7j11lur1rArr7ySp59+mieffLL0NWfOHD72sY/x+9//HoCzzjqLrq4uNmzYUHrcH//4R7TWvPzlL69aW8TU88zOLr549z/48m838Z17N/Pl327ii3f/g2d2do34mDeffixR12ZfKkdfzifnhRNsW7uyxFyLy844lowXDJmEXBybVrU16Na2MCtAa0wmg25tKxubtizFVecuJBF12JfKkfWCcFjMD3AKw12ObVEXd7GUwgsMnq9RQF3cZXdXhr6cz+6uNF+8eyM33fkk1/9sA09sbT/ka1VJ+wcb8Xy8gH2pHDVRh6vOXVgIDoY/jspkeOs//kBce3Qmp5P19ZBrYluKnK/RBrzGGXTEG4jnM1z+4iOoQW1NXnstyWvLj+Pt3Il+9tn+Cauej3l2E8HOl7h052Moo/FsB99yCTyNn8/j2+Hcltc+9wB5DTllhdlieQ9si7orr+CtZ8wl5ufptqLkAwP5gES2t9QTU+daKMAPDL7loowhkU/THU8S83Nc+NwDWL4XBge1tZj9HahEgporriib5Jrf8DdSN36cV//pTiLdHbS3d5PetZugt4+cp+nozROP2Fy6ci6XrpxLNJdm/669ZPbup687xe5enz0dGdJ5n709WW7+9bNlfwuWbVNz5XvD4STPL/VElb9QVDipt9BWVUyHzeQwra2Y/e3o1lZMy1YI/NI5KGWFPyfi6H3tmGw2fH1ls+h97cOer5j8TjvtNN73vvdxyy23cOIEzmWqOMX6n/7pn/jGN75x2KnIEA5BtbS0AOEF+drXvsYFF1xAY2NjWQ9M0YIFC7j++uuHpFi3tbXx3e9+t5RivXLlSkmxPoqNJg11pFoZo66vMaiYn2NZ+I/9hfR3vwstL4SfVp3q1omxLYt0zifiWDTVRau+knNZXZWDtH+wQ64TM+A4B6oTU7wmxdvn2h5vfep3nPTMwyO2tXic3BMbIB1OePUsh7ztkndcPNvFtxyIx1j/yrdwz8xTSAdhQTZlDAk/z2tf/CuvefrecL4IgKVQtbXUXPleat71LvIb/sZja2/j3jmn0lbbVKoTU5PrA8chPX0WOWWTLzyPEWWIZPto7tnPa1se4YTdmwt1YqKoeGLYuimD05Ofb5zH76efRFu0nsC2cGprOWZmQ+k12nfnnWz41b3cd8I57GyYTToSx6BwfY/6XA9uQ5J0pGbYv4WyOjHa9A8dDawTU2grbgS9e1f/tRnItqj90Aepede7ys6jVCfG91GO1IkRh6/iIKaa1q9fzwUXXDDk9quvvnrYXp3hgpiOjg6uvfbasmJ33/zmN6XY3VFKG8MX7/4HO9r7ylJMIfwf3NGbZ15TDZ+89KQRq5ceaqXT8BhhRVLT3Y0zrZ7YkiXYdrgsQ/HLUuESDRbQsreHVMY/aMXernSeH63fwu7ONDPrY0O65PelcixuruPrV644rKGlyVixd/A1Kd6uMAdsqzGGdG+anaefSR6bvONiBn7aL/7rsy2afn8PxnG477PfZF8qQ5NjOCP1IjZgtEGnejDdXdizZzPthz/Adl2M0XR99GN4W7bA9Om85NTRa0Wp1TmO9Xsw+9vZs/Q0zIdWUReLgDL05gLqIjbHdu2GVPeIVXD7m9h/jIHpyRrYGW2kpy9HfXMjJ3/pk9iWjQ4C9r/5LZieXvxIhK+f+0+01TaRzPYSCbxwArNl4yw+jo4+b9i/hdFU7KW2lu6P3VQYfnL7r2dxP56Hqqtl+m9+jTVgvsy4VuyVIOaoUHl5vCo6//zzqSSGevHFF4fc1tjYWFGviziyjSYNdbgU04EspUa872CUssrSRPOBgQPUTqqNutTFXGzLYl9PFscKA55jGxMsmN6/JtULrSn29+aorymfUxAes3orOR/O2LplqVEfe6TjjLSP4W43BtSy5RityQWGdMYnMAatDdqEa36lfvJT0k68cNBhXhBhyheZX9xJ7KxXsHLTI6h4HDVgAqqyFHZDEhOLYPr60Fu3Yp94Iv7mF/pThy3FfN0LurfQYIVJ1nJMyzM05PbjLhjU3T7M3KrhlB1jQPstYH6uA+NnMS270C+0YJ94Ylmq9J6G2aRiddRne8OsruIEGq0xPT3UJmqH/VuwbJvE64afkFmU/sPvS8dRpUB1QGDt2Ji+PrL331e2r8F/H0IcrgkNYoSottGkoQ6XYjqRzIBsqhx62G1eaO0h54WVa4sL/RXf1JQC11Kk/GDcV3IeK0Gh6J9fKHRXLHgXmML3wm0H3c/uXaM73u5d6O6uilKBK93+UFTcpgGp0r2ReDgxeWAQXcjVN55/WH8LZSnZw1EWmCDcTogxJEGMOKKMJg11uBTTya6ucF55XxN1C+c1oBcz52mUUnh+wN7ubP+q4gO+DrUQ4KEK67wUAg5jClMshn43BgJjSrf5gR6xTEml7DnHjHo7q76holTgSrc/FBW3aUCqdG0+g6MDPNsu9MRQyP1WKNc5rL+FspRshqbWF1OvrebmivctRCWm1n9yIQ5iwYxa5jQk2NHeR8QZOicmTEOtCdOtp5DRntfcpprhVxUvUmArNWSOTnF/pd4dCqVAikMEA7Jui8M1xeJ7gTb4hYAlzMo1VQtCDlfiiveQ/ulPoVjobvCFA3BsEle8B8uxK0oFdk44fsxThys9xsBU6WO69tDc286u5EzcTE+h8lA4J0bV1dFbmBNzKH8LA49jrKGp9fgBqq6W2GsuPORzF2I0JKdNHFEspbh05bHEIjYdvXlynkZrBqWhHjvqibqTxcDzau/J0ZPx6M369GQ82ntyoz+vQuCR9zWZfEBv1ieV8ehOh19dfXm6+vJ09uXp6M3T3psLv3py7O8Jv3f25elOe6QyHn05n6wX4Ae6NMQzWQIYANt1iV0crvemUexomM2zM49jR8NsdCFAi138RmzXrTgVeDxSh8uOsXc/ujtF0NOL7k6h9+4fcoyBqdJWPs+Fzz9I1M/THasjrxwMFn5TEx193mH9LZSlZOc9TBBgChWHiynoNVe+t2xSrxBjYUKzkyYLyU468ow6VXqK+c3fXuKuJ14infNLFX0TUYe3rjyWN59+7EQ3b9L6y1e/z+86I4U0aBtHBzT3tvPGaXle8dEPlG1baSrweKQOl6U+F6v71tSUUr0PtP3z0xdy35LzaKubga6pwalJVO1vodJ2jSvJTjoqSBCDBDFHqsNJlZ6MBta/iTpWaXwn5+uD1r85mvVfN59Etg/byxO4EdKxGmIRZ9jrVmkq8FimDpfqxPSlUfEoRlkoozHZPCoRp/7GG4cNlgamSquZzex52Svozeuq/y2UpWQ3NxN7zYWTowfmKApiKilvcKSROTHiiHU4qdKTjTaGu594iWw+GFL/pqZQ/+buJ3YSj1j05oKK3qjGtXbHOCu/blFUXf/k2Fjpur3EScfWl12rSlOBB26vg4DMvfdW5U3dGE3fHXeEhe5m9teJwYB2s5iODnq+/32mfWctlm0PeS7jr31t6blcfEgtOLjRpGSLsVNJockjkQQxYlyZXJaw3K2DGmbBQDG8g9W/cWzFc7tT3PLfm1Aw6uGzsqEQz0e5Y1xFdZxVo25QJYZUvLUUvd/41iEPrwxXJ8b0ZdAd7Zh8HrTGf/55Oj+8itgF55P/6+NH7HMphnpiaztr1j1LOueTTLhEbJd8oGlp62HNumcPu4r3SM4//3xOOeUUYrEYP/zhD4lEInzoQx/is5/9LAA7duzguuuu4/7778eyLF7/+tfzrW99i+YxyFaTIEaMq6CrO/znSyHzxbVRlgOODbYdrsfi2GGA49hHTI/A4TpQ/Zt0PqCrL09gwLGgLhbBCzQ72vv4/p9aRhxmGlzOXtW7kPfwtmyl+ytfGXGYYioZz7pBfXfeSe/3vg+BLryeLTAa09Mb3g4VBzKD68SYvgxBa2uYwmwVjhH4+Nu20bt5Myoex2pqmtTPpTFhNpsXaPwg/O75OvwehPWByn8ublvZz8UU8jeeegxvOm10qfZTidaG2x7cRjrnMyMZ7V9M1rKJJi32pXLc9uA2Tl/QOCZDSz/96U/5yEc+wmOPPcajjz7K+973Ps4++2xe85rX8Ja3vIXa2loeeOABfN9n1apVvPOd72T9+vVVb4cEMWLCGEy4EB8+5IbfRoXdDIVAxwLbQTkOyrHBcVGTYex9HIxY/8ZAV18+rDumIOY6WBZELYuIExlxuKRsmGJAOXtiUaxoBL2vnb477sA9/dQpHUiOV90gHQT0/ez2MICJuANSjm2MFWbw9P3sduJvf3tFQ0tldWKiUXRHexjAOE44JUobsKxS5WETBKhoNJwvVXgu/X3tdP78F9SctBxfM+hNv/8Nf9QBhR4+cCht6w+zzwE/+6MoUlhNJ8+dVvFjwiyrPMbzMJ4Hvo/xfew5c4ZUzJ4om1tTbG/vI5lwh7SpmlW8R3LKKafwmc98BoDjjz+eb3/729x///0APPPMM2zbto25c+cCcNttt3HSSSfx+OOPc8YZZ1S1HRLEiEnNmAMHOoN7c5RjFwKd8Du2PWn+6RyOkerE5HxNvrCqtutYRJz+N+oDDZeMVM4+fJzCStbi79iBv/mFCSsTf6gTswc+rjZqM6chzo72dFXqBo00f2hguX+jLDzbJqccdjTMJhWtIeblaOppZ+9//xHnjDNH6EUw5Eu3FW73XfpOvZhcTx9BNIbXmMO3HXwr/PIsC9928ZWNb9sElo3vRvEtG1/ZeJaNLgahP/jroTwNU54XDF8FG+gPUooBS94D38P4B6i1NEl0pT38QBOxh6/mHLEtegJvzKp4nzJo2ZDZs2ezd+9eNm3axNy5c0sBDMCyZctoaGhg06ZNEsQIMdCoenPs4lBVIcCxrbArvjhcZdswqGDXZFOsE/P9P7XQ0Zsvrc6dzQfowjBSQ01kyLyPkYZLxqNk/uEYbYp8sSJw8U3/mZ1d3PPUblpTWQJtsBTURBwCo2nrzhJzbSyl8LUm6wW4tsXM+ij/9fjOIb0GAwMKPzDkurrJte3Dy/v4ysK39xK4L+InavD9GN5bPoVvOegDLZi5A9jxTGUXo34xVP+D9KSiAKewIrxrWzi2RcRRhdsKK8XbFq5l4RS2Kd02+Genfz/N9TEWTU+gMxnwPIzvhwGL74f1baZwcm5DwsUprGQfG2Z+Yb4wybchMcLf+GFy3fL9KqXQeuSAcaxIECOOeCYIIAgwB8l8V5YVzjFQNsopBDqWFQY9thMudDeBc3VOntvABy5YXHpz7yvUinGtsOs4ERn6j2yk4ZJDLZk/7HyGQT+HwxGHPp+hvTfHi/v7wuAlLBuM1obOvi7+sauLeMTGGErHONjbUCrTH8Cl8+WfsLOe5v5/VLC+T6wJYoNu8wDC18VUYSlKwcLAwKEsiBgQGPQHDiP9rArBQ2E/1nD7HHkftqUO+0NE2GvrYQq9Kngec+Mexusi2N1VnQs3iZwwK8n8phpa2nqIJodWTU6lPRY313HCKBcbrZalS5eyc+dOdu7cWeqNefbZZ+nq6mLZsmVVP54EMUIUGK1Ba8A/eMCDCgMeyypMQg6DHmWpwjCWhbKdww54tCkshFh4o5/dEOOfz1/E9n29dGd9IpbFH57ZQ1sqXC8JGFD+H/qyPvWJCI9v7eDRF9r75zP4ir4VbyXfl8WPxcNhCGXhKZtAWXjaEERj6Ee6yf/5LxM2nyEYJkTpy03+rv4hjCmU/VfYRtPYkBjyJl98Qx/au1AIAizwf/tbrM523Fg0vA2NowOsfXtxMhkcxyI6ownXBDgmwA4C7Pb9xOYdS9PnP0vEsXFta8rXEDE6CBMESvNW/EKPbP/rRQEmGpm4Ro4xy1Jcde5C1qx7ln2pXCE7KeyZSaU9aqIOV527cNyf6wsvvJCTTz6ZK664gq9//ev4vs+HP/xhXvWqV7Fy5cqqH0+CGCEG0MaEn/C1Ia8NfgBeYW2gsu/D3R4Uf2fAdgbPgGfANwrfFB/HoO2Gz8gIKggaBvcyFGVTWX71+M6hd9TOg4NNBemduoUbC504ZZ9Qm5MxamLOkN6H/sChvOfAsSysjv14v/1vXMcO7zMBjgmDB8do7FwGJ5MhseJUdj7yd3556htxPY+ozqMKwxUKwHHJOy55y+G6ZdM58YyTKj6nfOSVhYyyDFayFiIRyOfR7R2YTCbMTrL7+m9P9aISCerf9TYi8an5hm58vzBfZUDQEkzBQHYMrFzUxOpLlpXqxPQEHo5tsbi5bsLqxCil+PWvf811113HeeedV5ZiPRYkiBETxhhDYAa94Zfe4PsDA6/sfoYJKMofVwwm+rcbur/+oKM8GBnnjoYjwnDzGQYOL1Qyn2FfT5a/tOwnEQmzrJRSAxajDI/Wm/V4xyvms/zYhtL+ntuT4tYHttCQiJQeN5DW0JXO875XLeLUeZVlq+T+upvu7X/Dmt4YDjkOYrRGd3VQ//Kr6MwZtLaJBX2FnpfCFSpk1LlGk1YWqc6eQ7jSEFlxOvU33thf26enF+U4uEuWEDnzjP46McXbj1s0JerEGB0UhoD8wvCvj/EC8PJTet7KeFi5qInTFzSOa8Xe4VKl77777tLP8+bN49e//vWYHX8gCWKOIsVJkPkDzGcYOeWysvoMw+/T4Hl+uG0hGJF/T5WzVDgPxrZUYd6CKgUIurBQc8SxScYdXMfGKQwhREaYl+DYYO3fj5PL4NYkiB0zh4hjj+t8hqKte3t4ZmcXMdcm6g4NGHKeJuranDCrjmMbE6XbZ9fHcGwLXxuiw9SEOZxU6krmDzW96myc9dvxIlEiOghX/7bsYgSGpywco0lOO/TCepEVp+OefuqwWVKJd75j0lZfLs1Z8f3+DCA/wARTIxtoMrMsNSZp1FOBBDFjyBSHJgI9INPhwJMiD/Tz0H0cuDhUsd7DRM1nOFLYirC3wFK4VvE74fchtyscGyKFIKN4e8Qe3ePKfi98d+3yxx3KmjelDC1lhVGQpUCFc3qMghenRUjlNfVxlwUz67CKBQeHMVzq8+GEMNVIiR4pBf1gjxsN54TjcebNw9uyFSsa6a+cawwmm8N0dGDPnYu9eDGLFTT/8Tl2uTW4fpaBH4a1gbQT4Zigj8Urlgw5zkjp2yPdPlzqe9nyB8awbRzXDjPGhPVUdBicEPhlgQq+Rj62iGqTIKZKsvmAt3/zwVLdjkrnM4h+TvENe0gAoHCswhu9PSgAsKAnp9meytOX1xgTpuUmozanzIwxrz6CMygwKAYVwwYUlsK1w3ZM5UUji0oZWoNu/8feDOteSLG7x8PXBsdSzKlzueT4JMtnJsCxULZbqqj8j7Y+fv3MXnZ35/pTn6cluHTl3ENafHK4VOr6uItlUZZK7gWa3qxPPGJz6cpjhzwnI6WgH+xxo6GURc0VV9D9la+g97VjJWvRvo9pb4dcHpQi2LOH7o99nJorruCtZ8zlh0+00e3ESAR5XKPxlEXaiRDTPm89Yy72oABxpOUfyoaIKlhKYCxXcTe+jwn8sFelOKHWl3kqYmLIKtZUZxVrP9Cc8/l7q9yysTfifIZCvQXHGjqcMGJ9hhHrNoQTJF1bobq7cLQ/KJBQAwKMofMZRuMfezP86KkOsp6mNhK21w8MvXlNzLX4l5c1ctLMePUv4BRW6TU72PbvXzGT5bPrwq4rZZVXVXYcsFTZ0MbAVbkHBx2WBdMSEbozXkVvwmP55l0MNLyWFkyqGwwQiWBNn45yHHSqp7Sq9D+6fe56fCdtVhy/MITUrDO89Yy5nPbqM4fsd+DyD0TCJQN0e3v/ZN2mpv7bBxxnuEDmQNd1NKudmyAIe1SCoH/YJwjC3wdlAE1mCpibrO5kZmfRwkldT+poJD0xVRLOCwi7rg9kNPUZBs9DGDHIqCCgGI/5DKPhu7nS2knVYoxh3Qspsp5mWtwuDSVEHMU026YzE7DuhRTLZsTkH1BBpddsNNv/ZlMHS6c5B7zGinAoy1iKux/ZQzabZ1oi7HkBQ9RWRBIOHWmfmqjN+89fTG9+9MMhJ89t4KRj6w+p0u/BRFacjnPqy+j88CoC30c1NmLFYqX5LgOXazj1q1/mlPNX0LLhOVKdPSSn1bF4xZIhPTAjLv8QjRZ6z3Q4PFMcxjrIshDDrnZuIOpYhevqcffj21na6GIZU+qhww8w2gcvmDJBihAgQUzVKKX43j+fSc7XZPPBiEHEVK/PMBm92JVnd49HbcQadqXimojF7h6PF7vyLJw2zMTMo1Cl16xa19hgQBu2tefYncpT61ooozGDSsPX2Ibd+/vQ+/ayvDEG2kLvzWBsq38+j1Kgwg8PYUpSWKxQKcXChihYcajy31vQ0oJubw8XWRw0yXe45RoOlkY90vIPJpcFzwuH8fIeZHMQj5WOo+pq8bZvx/vHJtzjjwvnowSarW097O7opdZVYS/KoE9VNTbsbk+zdcse+VsQRwQJYqrolHnTSGU8uvqmbm2NqagnH05adobJSoFwzkufp+nJj39J7Mmq0mtW7Ws86v3lgvIihKPa+1CKQo621R8ElSY5K8qCIiDsUjUGKHwvHNjfuTPsSaypCSevFvO/C4zjYrwegv37cBYsOECLwv6OYO/ecH+1teH+CsfWuXx4zpYNOkDn8wMWjjThcfM5/O3bUPX9Q+DdHRl8X+O49rDdwvK3II40EsSIKa8uEs7d8QNDxBn6ydsLDI5S1EUmR6rpZFDpNav2NR7v58xQCESCAIqxwqHsyI2EgVAuB8NVg83lQCmMr/H37j14u4qrUGezZftTlsIoFa5YrYpDvgNa7HnhUhi15XP45G9BHG3klSymvAUNEebUufTm9ZAPn8ZAX14zp85lQcPUrFg6Fiq9ZtW+xlP1OXMWzseeMxvT2ztkqMYYg+ntw54zG2fh/MPan4pEw8nQfgCuUxbgHOg4U/W6CnGopCdGTEnGGF7sytOT19RFLC5enOQnT3fQmQmoiYTzkLzA0FfInLnk+OSQCaeD97GgITI5Jv4ajb9tO6Y3hapNhm9UVS5YppTiksV1/PCJvbSnfGxLYWwLSykCbYhH7LJrppTikuOT/OipQddYG/pymrhrcdmSemKOBcbgb90GPd2oZD2RRQtBWWhjMAY0YaHDhdMibOvK0dbn0WAZXBPgK5tuYxGxFCtnD8gmK1wT3ZNih5MkPb2Zuog97HNmdMCWTTtIpdIkkwmOWzovrHlzsOs6muuuLBKXvJmeH/8Y09EJtTXgumG6cW8fxBMkLnlz+LiB+6upBRSmr6d83wfYH7ZdWJDUwvT2ElgOu2oaSakIyRlJlr7pTcMcp4431sFPOqGjN6DWBscEeMomVbyus+L4W1+EvlG8voKA3KOPovfvx2pswpozG9J91Xldjub6TLBS7ZtcFm/Tc+T+tgGneRbRc84etnqzGH+SYk11UqxL+5I5MQfkt7UddnbSSLVNTp4Z55m9mfB2E3abF2ueDE6vPlB9lIlMxfY3/oP0ut8Q7N4Dvg+Ogz1nNolL3oyzvPK1dg52nNtrl/HonOXoYtaMAgvFaxfV8r/OmBlmr1GYSoLiybY0P9/Yyc6Uh2cMrlLMq49wxfJprJidwNuwgd477iDYsRPjeSjXxZ43l9orrsBdsQKAX/yjk9uf6aDX0+jCfBNlDK72MSocRonZFomYy9z6CG+P7Gfxujt4MuPy64XnsLtuBoHr4sTjzG2q4e1LGzhlZhyN4W+PbOSXm7rYY9eWUptnB728uSHH4qcfxtvTSuAHGNctu66VXvey7QMfbGfE/ZlsFoqv+UgEFYsN2fdI+7NnziT3l7/yfO0s7jvxXNrqpuPbLk4swrEzkrzR7WDB/XcPOc7zzYu577izaKttIutG8OwIKEXEgmg+S3PPfl679VFOTO0e8Twzv/0tmbt/A5k0ZetxxKKomprDel1Wen0wBnwvnOScz6HyecjnCr/nUflcuI/CV/H3BiuAXJgRafL5/p9zeUwu3MaUbiv8Xvwq/B7Ox+qnamqIrDidulWriJ5zdsXnLqpLghgkiBlPhxvEHKxWyT+f0khNxDpg78pkrSnjb/wHPT/+MWQyqNracBjB8zG9vRCPU/fP/1zRG4YirBw8sDCgrUA/+XdSX/sav5lzOrcvvxijFJbWUFhlWds2llJ88PTpvPukoesMaWN4oSNHVzagIWZzfGMUSym8DRvo/upXMX2D6p2kUqiaBPUf/Sj/v9givve3/WgDNgble2gUgR12CkcCjxl9HUTQ6BnNpAJFLNXJ67Y8zB+OO5uME6XWS+N6eTzHpa9hBjWJKDe+Yib+5s189dksadulzs/iFnogeu0YsXyGD234JacGnRBxCfI+Xm8fQW0dzhvfSOoP95PPeVBXN/rrPkLPzcDnEcfBpFLhXBwIVz1vaAh7Wgbve9D+TF8fvT/5Cc/HZ3DnSReRsSPUeFncwMezHTK19UTTvbzrH/dwYs8eTFdX+IZbWMJcK8XDi87gnqXn4zsudbk+XM/Dcx3S0Rpigce7W9Zzwu7nh7Ql89vfkrnz/0Kgw96gYvZY4e3CStaijEFFo9RceinO/HkDgoviVw5VCDr6g4s8es9uvI0bUZ4XzonOeyijw/0ByrJKv1vRSHjt8v2LaU40lUyiolFUbQ3T1qyRQGaCSRCDBDHj6XCCGGMMtzyylx3d+bJaJeF90JkJmFcf4aZXzhxxWKga+xgTRpNacwvBjp2oxmnl6bbGYDo6sefNJbn6plI3u63AUWGtn2KAEq4ooEpLFAx3nM6P3kh2y4v8yyWfodeN4eigMDku7BUxSuHZLnURi1+/YyHOaLrNC/v1t2wtr3dSaL/etx+OW8T7zlxFT16HRQ09D4zBKMhbDqCwjGZB316U76OiUQyw34qhLQcLTVOut1CWJezmJxqls3E2ixpc/M0vsC1ST5PXF2YjFY+fz9MRTzK/ew+feeE32AOWDQj27QffD9c+mjEd33LwLBtDOOwVtHfCwoXEP/VpfKMOPhl4wPPItAZM295wsm8xsygIwHGx5szGdA59Tgfvx9vxEt95+bvZlWikPp8unbvyPewgIONGmdvXztV/+b/YXi5Mv/aDcEKwgXuXnU9PJEEy20sk8HADD1f7OGi0gYZ8L8s7t6OyOaxYFGfeXFQ+j9/SggqCMKgwphBgmMNaXuJIoaZNwz72GHRrG+7SJTTdcbsMLU0gmRMjpoxq1CqZrDVl/G3bCXbvQdXWlgUAyoBjNE7cxd6+leRL24mfeHwYtBxCDRRv8wsEO3byyAmvoM+JYZcCGAhXKwzfsGwMvZ7m/hd7uWjRwQP74n4H1zuBYv2UOtYHjfTmg8IQVSF1WVF4awyzb7Sy6HVi1JlM2J0PRKMO+yO1TM91D3gTVWFgkM9Tp3y27PcxkQbq/ExZAEPhE31tLs2uuplsdRs53u/sb1c0gu7uRjWHQatrAtwB5fNNzMZs3Uz97hdxTjihtNK5NhDocK2yIJdD53IE2Ty5lhbUi9tw3QiqqxPSPaANKqf7AwFjUC0pVMRFbezA+vItWPFY2RCJ6e6iYft2PNth1b3fJeIXApDAww18XO2P6vl+94a7R7UdAJle6GwHYCpM+zWOG/b2RaKYaDScSxSNQiSCcSMQjZJIxMIhqmgk7D2JhLerSCT8ikYhEh3wc/H2CMHu3fR869uoeBwVj4X/IKJRrELAYjXU47dswdu4kcgpp0zw1Th6SRAjpoxq1CqZjDVlbAWqL0U804cbs3HzHrbR2EbjmLAdxmh0uptEXxcR59A/9ZnuLoznsa+mERPGLMOygMBAW69X0X4H1iwpE4mwN5rEFPZd3qVRHvT4ymFg+WsLCm0d1NjCNq4OwmksgU99NkU0CHADDyfwcXwPN5fB1gE5J4LV243OdYdRiDHobBa0Rqd6MJls2IOhi7VhTFijxsuT+uxnwXbCXsSBcya8/utjA4nC16ikC9///McRN4kANfSMdo8TwqAgFoNEvBQ84EYg4mIiYWBQ/Ap6e/E2PY+JxzCBxmQyGGVhLIswxCtMpm2YBrEIpreP+DvfgbP85HBf0Uhh34VU9wNQQP1hLDtgelJgDCoR7+9pGRigR6OY7m50R8chH0McPglixJRRjRoYE1VHQ0FpfSinsCp2aZ6KpfCmJ+nWWVRaDakEC4RzAhwXVd9weO2ob0C5LjP6OlCGEYdHNOH/6+Zat6L9mlw+fKPRur9gnDaYXJbjzYuctnMjEeMT83NEcjlc7eH4HrYOiAQekcCjPtdLwsvi+mEgogqBXMLLEB3QI+EEfvhdBzh69IsPDhue9vUdcKgo2LV71PufKIZwKLA48JN3XPbWTce3bHzLCYfK7PDLdyLknAhZN8opqZ0ks73oQBN97WvRvb1k/7Q+DCyUCpc+KO5XqdJxmN4UDvnl8tTdcAPOooUHbaO/dRs9//mfqFgsDCL37i0UGSz8vZnwdWMVhvVNQhFbfCJmzjFjd+FGUHxNk/dguL/JXA7luFiNjePeNtFPghgxZRRrYOzozjPNHjqfpS+vmVfvcmzHbrydw6ePjm4fkcrqaAyYkOnWJYkvXhguOWGVByoH4p5wPPa8ueGckugwc0pSPTjHLcI94fjRNcn3y7MxCj0HQT7PU8eehOnu5qytj6PQxLw80SAfBhGFIME2mhrj8fKXonR7XnnmRi4/NKsjl8NkMwesIHcKbZzSsmFU7Z+yClV/AxSBbUOgcbU/dC6JZRUq8lpELzgfe9ZsjOsSuOFQSOA49Pzuf8h1dPPfSy5gT800IkYTFAMRZZFXNh0105ie6+UDj9yO5Xvhfv0gDBBQ/ODs97Ir2Ux9tidsg9Fhyrbr0B2p4ZhMB6du/h96O8CeN5foe6/E0obs356Gvr5wknNhf+XtV6jaOkxXF/a8uUPr4oww6blYF6c4ZwjXLcwZKlRO1jrsZXEjpTlDo625U20H/Zvs6sZdugR3+fIJaZ8ISRAjpowRa5UU6sFEjcern7iX3pYnR0yTPdg+RqopU9YO+rN+ePopcr/8v7jbd2Dns1gDUoqd008PV//N5NDDpXLmcmGqZyHYcI87Dr+lhWDnS+HYvWWFNSry+XD+R6Dp+uSnhk8LLfxeDDQIRu6ZOLnw/bznHjroNc9vrOAJmkS0UuTtCJ7tkLddfMuhPtdLTBWybYrVex0HZ/48/J0vhRN8o1Ha6qbz1LQF7I8myTlRdG0t06bVcc5x0zh+VhIVieBv2ULm9/cQtLZhfL+USv7CpVfzi/YY2/f34mPhBB7HdLXy1qf+h5P3PBc2zi4sCeC6EIsSf8PriZz58iHnUL9wHt1f/Sov29/CY/PfRtqJUuNncPIenmXTF00Q83Jc8Ox6LF0INAY875aCC5/7M3ee/ma6E0kS+TSu5+M5DulImJ30uh1PQEdneX0bG+KXvjnMTvL8QnZSeRCj6uqgq6v8cQUHS1cv1sWhswuVSGB8P0wrhzB7K5HAdHYOu+9xpSxqr7iC7q9+Fb1vP1ayLixAmM+ju7pRtbXUrVolk3onmGQnIdlJ46nqdWIK9WBmqRyvfuw3nLjnhaHpybEYdVdfjbP4OPDyqFyOltYeHtrSQWcqg+3liWmfma7mzOk2x8YopYNaXuErn0d5/b+bXB69fz/+tm3hG0fxH1lpnZ8C+fMKWVZpDkt3rI79tY1kbZe87eLZLlpZTMv2kIrV0pWoJx+NE0Tj1NbGOSWWo3nj47zouTw6ezmttU3konFMopbp0+t5w7LpLD8mydNdmlv+spf92sG3bIqThRVQb2mub7mH5c/+BeN7KKe8fk2xvs3f0y7fPeUtpJ0YSZMn2tCAH4uRymkSrsWNr5jJKbs3DZtK/lR0Bt9d8XayyWkkbYPVsQ/PC+iN1hDzcnzooZ9xctvm8FpEIuEkbqD+i1/APfHEYS/bwHb913Hnsat2RqH+TcAx2U4u2fIIJ2x9Ej8w4ZCR64bp6rZdCmien3089x1/Dm110wkiEex8PqwTs+1RTuyubp2Y0ZYJGLlOTBQVi1alNpIC5h7GnJiistpHRmO5EZzFx0mdmElCghgkiBk3xuC/tDP8h1YIEsprSuTDiZK53Ig1Jvp/z5HuyxFkc7hBnsiObZDJomwLpXVYZ0IblNZhhspEn/sE8y270CsRIe+4A76HQUTedsF1ydkR4jUxXrFoGioSLWV0hFkdhQwP1w0zOaKDsjqi0UK5fIuumz5BsGsXVlMjVixemrs7MN36b6s+Q1ufT3ONw3n5VuyeLkyygRenz6Mrp8tq0GB0mAHV1cUWt4He5mNoiDml+7Ux3HjfbrZ05miKWeSyeYJAY9sW0ViE9qzmuGkRbp7bh0p1oeobwqG5AZ/ytQ74yG+2sLXPMD2qsGKxsnbvTwccNy3CJ//na+hBqeQa+NxJb2dboonpOotz7FzA4O/YEaZ4J6axoHcvn3riZ9iWhYlGMPvbcY5bxLSvfuXAvQ2Fc/c7Onnml7+jszdDQyLCcX17S5OkdTaD7ujEmXssNf/2b/i9vQS1SQKj8HtS+DX1bJs2h46sps5VHNu5p/oVeystEzDGFXurFcQApedAxWPYTU24y5dLD8wkIcNJYlyou36F9dlPYh90y9EbNtwcXTLNxCkEAKqQrVFM5wx/jg5NA42GtxONDAgmBgYXA++LDtpX+PO9u3J86S/7SzVk8kF5vY/ip5hZtQ5RW5H1DPMvmM2JTbFDOkXv+efDuRLTpw+ZpFxMtzY7dvJqvxX3lGIPRH1pm2H7JJSFe+KJuMBwn81f6MixoztPMmph2RbxmvK210VhR7fHi6fP58Qlw/d6vNDp8ZLvUl+rsAZlgCmlqItabG9P09IdsHhQKvnW2mZeSjRS52fBz2Ny2bDy7PQZBG2t1Ob7eKlmOttmzGdxx07M/nZUTQ21V1xx8Dfrwrnz/PMs2rEpTPk1A66rAiseRzUpdHsHluNQ8/Khw1OzCYMJTxvycxbjBeHPnjaDR4v62TbRc845cPsKRioTULx+1NYQ7N6Dv217OAlYWaOaDDwpFJ4DZ9HCybE0iSiRIEaMD3d0WS7jZnAwMfjnUjARKa8pUeiJCNpayd73R1RdbbgujworzalCpoXBoLu7Sf7bdUTOfHl/b8UEfHpry2QwJpwjcaB+Vz8w1BWqHXdlR5/tM9ho0q1NTw+mu+uQjzFYVzbA04bkCKnzEVsd9LxGtY/A0G1HwyGkAVJOnEDZuKZ8boqqSWA3zyLS0U6vUnRnfEwmi3PcorKlGEajGtdVKUXEVkQGfZoItME3Bj8weJow0An0yMHNcO3rTYVzYNwR3lZcN8wC602NfqdCHIQEMWJcmOjw3bom0l/3wUQG1JiIFgpWDagxUV5zIhru041ATwr/97/HcR1c18ZSoJRF4QdMPsyuSX7qfxM56aQwgDnMYMJ7/nnyf/krKhYfPiU6m8WKxrDnzceqrx96/zhqrnVRKhzyOFCSlGOHvTSuUjTEDr3P7KCpqVVKFx+oIWbjWuEE7egwqfOjOa/R7MOxFfVBbsi5Jf0MdmGZg6gK+iv0EgYyQSRGJJtnznvfQX1z3ZChrNEYy+tqWwobRXTQ5fG1IR8YfB1+eYXvwwU3qjYZTnz1/LJVt0s8D2wn3E6IKpEgRoyPV55D8Nt78VM9GAVEouE/vAqCCQvClGVbEbGKnyjD6q+dLc+FqZANQ1MhTXcK57hFRE87tWqZDtVOiR5Lr1lQyzf/uo+evMayDJYK52mG015DloJaBzqymuOmRTm+MTriGkkHMxHX5vjGKPPqI2zpzDHdVkOO2ZPrP69R7cNS5LQpLR0UtVS4j6YEi+tt9JZU2bkt6m3j2HQHLyaaiFqgov3DWcYYevKahTFYHK2sh2vgebgnnjDu17VY22iwUlAzYEjKXrSAdDF9OjLMnJjevglNmRZHJglixPioqQ2/2tr6MxFG4Khw3R9HUbZ44XD/TENqaCpkJBKmQqZ6Rj/3oBLDpV+O9TEPkWNZvPfkRr73t/14ur83ZuCH6WTUoiNrSLgWVyyfxt9bM9yxsZMd3Xk8bXCt8tWqD2gCro2lFFcsn8ZX/rKX/emAuqhFpNCz1FPILLpi+bQDBmHFfXzhoVa2dudLtfoKpV9oiNpcsbyR5PQr6f7yV9D727Hqk+EwYT7PW/9xL989/XI66meQJAy084Eh1ZMj3tvNm//2K3pan0e5Ls5xi6j74AeJnv3KwjUrtMuySl/Dzb2o/9jH6LjpJnRHJ1ZDfdhzmcuFKb/19SQ/+lGcRYsGFBrUpcKDBspvL/4+UPH3YsXiwvmXLoIxGG2IGE2k1D5Vav70f3oP7V/4Av6enehkkiAaI/ADvL4MQaJmYlOmxRFJspOQ7KTxNDDF2imMz7uFxQrDYIVDnjhXlgo5TBrtWJiIYx6qX/yjk9uf6aDX0+F7FGFvTMxRJCN2KUgB+Mpf9pL2NMlofx2dgWnGBw1kmJhrs2FPuj/4MuEQ0miCL6UUODYbWrP8+/qddGX8AUFeuBpTQ8LlM5edzBnHTSf30MP0rF2L37KldG7O4uN44d0f5BedNWxv78MPNLbvMWdXC2997o+c6neEwyy5PLqr65BXQR7p2JMl5fdA7bNe8Qp8PyDwfPJ5Hy/v4ed9PN/HBEFYHVhr0AH4GnPwJTdHVNXspAKZ2Dv5SBCDBDHjxbYU1v69RH2PmGMNv8ry4SqkQpru4dNox8REHPMQ+Tpc1LGt12NmjcPcpEtv3pSGi4BSqvL0hD1kSCBMM47ylQvnjGpoaSKuzXDDYLbrgOOiXKd/Ardth3NXLAtlWWhtuP5nG2hp62F6XYS8Hw6ZOJYi4ij29+RZ3FzH169cgWUpjNZ4GzeiOzqwGhtLabdaGza3pujszaG+9HnmPvkIzqzmocM/h7EK8kjHniwqbZ8xprCgpsHXOhymCjSeF+DnPdABRhd7lgJMMCDQMbp/zaugP/CRIOboIMNJYky4jkXEsYg5djgkZFtYlsLPuJjsGC6uWExHHU/jfczDCAwcyyqtSl18s4f+eRoDU5WHS5Oti1rs6M7zQkdudCnYA66NNobnDzLHRuuATU+20NXVR0NDDUtPXYxlHXiSsXJscByU44TVYR2HZbOdMEgp3s7B31g3t6bY3t5HMuFiWRaxQe9/yYTL9vY+NremWDKnHmVZw65ebFmKJXPqyT71FE/sfImnF55GnRVe4143RtLLsii9d8gqyAcLirrSHg0JlxNmJbEGHFtrw/ND7p/YN9qRrs2I26twrptjA4MKMWgdBjR+EGZMeYHG8zWBHv7ztwmCMEtKB9h1blgR2Pf7vwfjt7irGHsSxIjDphREHZuoaxF1bCKONeH/RI9UZUM0nlcqd1/pEE3ZsMuAOS+nz4ofdqpyJccbOMzz2ANPcvszHbzk1BWq0qY49pEHeO/pzZx14cqw18S2w6DEdfu/j+KTcdkQR+G6DR6C6Up7+IEmYg9fDiBiW/QEHl3pgxcjemJrO7f+qY0XT383WTdKznbBQFR7xLXHMdlOLt/1OMu7N6A7OkZs3+DhKce2mN9Uw1XnLmTloiae2NrObQ9uG/H+I4FlKaKWTdSFmgG360IauOfrsu+lHjYFVkPNkP2ZYgq874dDV74fLg9S+lljAj+sNCwDFZOeDCchw0kVUxArBi2uTdQZfhLicPxduzDZ3Bg38MjkbdgwbLl7nUqhahLUf/SjowpkNuxJjzjnxbEUnjbURy2iztBAJutrsp7hixUUwxvxeHlNwrX5+AXz8Da/wFee6CBtR6jzs7iE6cq9doy4zvPxc4/hrDe/quJrBmEA07l6Naa3F6th2ojzUp7b3c1Ndz5JPGITc4f2/mS9gEw+4JZ3ncqSOSOnzT+xtZ01656lry+L09lOZ7QOXfj7sDBMy/fhWQ5xP8c1z/yas99zMT3f//6Q9j3pNPLd0y4j29BEfX0NEdsiH2hSaY9E1OGtK4/lrideIp3zSSbcIfevvmTZERPIVKIYzPiBpj5xeMNJJgjA80q9OKq+XoaTJpnJM4gqJi8FUdciGXeZmYwxtzHBzPoY9YkIMdeWP+rxYDS9d9wRBjAzwkq4yrJQsSjWjOmYvjS9d9wRzg04AG0Md2zsJO1ppifCANRSiqhjMT1h4wVhN30qp4d8Ci2mKs+rjxwwVRkIl3+IRiAR547nUqQDw4xklFg8ih2NEkvEmdGQIB3Az55u52dP7CZtR2jy08SUxlaKGJpGP03GivDTP28hOMCiliNfNk3P2rVhgDBrFioeC69bPIY1qxnT2xferzUnzEoyv6mGVNob9txTaY/5TTWcMGvkDzpaG257cBvpnM+MaQl6ozUYpXCMxjEag6LXiTEt30vGcvmv5a+j93e/G9I+E49x10mvI21FaEztJ1bo3Yy5NjOSUdI5n58+uI2+rBdeV9cecv9tD25DjzDkciRzHYuaqHPYAQyAsm1ULIZVW4vV0CD/6yYhCWJEOVX4JxBzaKyNMKshztzGBM31cRpqIsQiR07Qoo3h+fYsj+3q4/n2LHoSd0p6m18g2LETKzl0he1iKf9gx068zS8ccD8Hm/OSjNnYKqwPsj8dkPU12hiyvmZ/OiDhWrz7pAZe6Mjx191pnu/2IB7DmtaA3TwT55g5OAsX4CxYgHPssWwxCXakPJI1UbBtsr6hNxeQ9cKAJJlwadndxYt2krogO6QYn6WgNsjyklXDsw89Wfl127gRv2ULVsO04a/bgHkplqW46tyFJKIO+1I5sl6A1oasF7AvlaMm6nDVuQsPOFQ6cF5N3jd4TgTLaJQxKGOwjMazbDwDtX6W3fWzeGFfBqthGkYpWhIz+Vv9PB5oPJGX4tOoC7KQy2Ey2bJ2x1yb3qw37N+jUqps/o4QRzKZE3OUsy1F1A3nsUQLk3GPlCDlQEYzR2MyqVYp/9GU1leW4q0n1vO31gw7uvP05DWuZXHcjAQvX9DAL1p62NGRCTN3bIv5TT2FORi1Q49XmGfi+4q93VnygS7VHonYFo01EbzAYCwb19cMt1KnazS9yqJzf/fBLtMQuqMjvG4jVIwmGsV0d6M7OgBYuaiJ1ZcsK80z6Qk8HNticXPdqOaZDJxXk8kHGKWwbTuca1EIZLRlE2CRaEiStmxSVpSnG4/jV8ecwa7YNHxlo4E+J8p07RM1BhP4ZZdGqTBFfqQMsUrm7wgxlUkQcxRxbEXEsXFtRcSxcG0LZ4Q3syPZ4DkaSdvCCwxbOnN85S97R10HZTxVq+T8kNL6YSWzMHhQCs83uA68/JS5XHVRks37s3TnAhoSLqmMx3/896YhczBa2npYs+7ZYedgNCRctDG0dmcxGGxLld6Ac35Aa3eWmA2uDvCURfj2Xc5TFo7RTJte+fINVmNjeN1yeYgPM4cnl0M5LlZjY+mmlYuaOH1B4zAZQQcP7hsSLk7hutiWCoOWIAi7vJXCqPD8baPJdXXjNDTRVjed3yw4h4wTDecDmQx9VpSUG2d/JIkVaOrs8n/VplBxeaTew3xhkm9DYpKtWSZElUkQc4RSCmKuXeplidjjnzE0XGroRBs8J6TY6xR1FNPtcAjljo2dnDYrPro6KOPkcEr5K0tBJIqKuCxpnMa8jT1s2d/HjHgkvG/AflJ9ORY317FkwUwsS7F0bhygVEMlnfOZkYyWjh+zbKJJi32pHLc9uI3TFzSWvc4WN9cR6HCBQddRpWuqACzwfIMVcZgfpNhuJ4n46bIhJW2g14mxIEix7JxTK79uy5fjLD4Ob9NzWLFharV0deMuXYK7fHnZ44pp0pUqzqsp1ppx/Tx5ZaMKwZlWFhHt41rQ6cSZ393KY4tWkLZcmvK9pXOv1TlSgUfWdumIJ6mLly9jkPUCamMu2XxAMm6GnFcq7bG4uW5S/M0JMZaOvo/hRyilIBaxaagpzGNpqmFGMkYy7pYm/Y2nJ7a2c/3PNnDTnU/y73c9w013Psn1P9vAhl2949qOwSqpgzKpFEr5q5oEet9+TDaL0RqTzaL37Q9L+V91FVZNTTg/ZcZ07DmzcebPw1m4EOeYOdgzZuBMm8bV5y8mEXXZ1zP6eR8D53pUMgejpa0nXFzQUgSFemTGlOqShffZFq9ZsZC4ztPhJMhiERjIYtHhJIhrj6vPOy4clqn4slnUrVqFqq1Bt7ZhMpnwumUy6NY2VG1teH+VCsUNnFezvzNNba4PZQy+svCVhcJQ62fpjNQS1x5nbd3A7vpZ1PkZlO+FbTOA1jRkU1hGk7Mj9GT8Ic/T1ecupCbmHvL8HSGOBBLETFHFoKU+4dJcH2NuUw0zC0FLZJjU2PFUTDFtaeshHrFpqo0Sj9i0tPXw5Qd3s2FPesLaVpwTMlK14Iit8IypuA7KeHBXrAjTqBcfh8nm0J2dmFwed9lSpn35P0hcfhn27NnYjY1YySRWPF4q9DZQcd7H4uY6MvmA9t4cmXzA4uY6bhohLbd/rsdIc2ks/EAPmYPRlfawFDQ3xIg6FtqEVXC1MUQdi+aGGJaCeWeczMfPPYYFQYqs5dLpxMlaLguCFB8/d84hp1cDRM85m2lr1uAuXYLpS6P37sX0pXGXLmHampurXqq/eH0XJQwGRUx72BhsY4gFHijFgsx+PvziH5nZux/fsonPakbFYmFFWt8DralxFLMSDo6tyOT9Ic/TFWcvrPh5FOJII8NJU8WA2iyxwhDRZJyAW5ZiOtywQ1d6QodrhswJGSQfhOvtNMQq/9Rfbcp1UI4Lkf7Cbs5bLyX+tssPu+R8pfM+Bs71iA1TQXekORjFx7m2xbymGrJeQKDDuTEx1ybrB/i2oSHhsuTNr+LMi8/h2YeepHN/N9Om17PsnFMPqQdmsOg5ZxN55VnjVqp/5aImll/QzBMf+Q49yaZhK/aqTIYX7Hoc28KLx4ktXIjJZMNJvLaDisdwfc20nM+HLzyehprokOfpcObvCHEkkCBmMipkbriFjCHXnjpZQwcbdqiL2pWVra+y4xujzKuPhGsD2WrIXIKenOa4adGD1kEZyXDr9owUrCmlwHXCiacDK9C6blgu/wDPdyUl3UdSybyPgXM9oklr1HMwBj8uFrEP+Djbtjn5VWOzOGSlpfAPV/Tkk1kyM4G36e9Yw62d1NXN8UtmMa/WYWtXH5GEg5WIl7YbeH1ed/KcEQOT0TyPk2WtpcnSDnHkkCBmEnBsVSrbP5UCluEcvHS7omcCh2sspbhi+TS+8pe97E8H1EUtIrYiH4QBTMK1uGL5tEPqJRopbfu9L2ti5fywEmspYCkGLVNEca7HmnXPsi+VG1IhdqQ5GIf6uCNBcT5O5+rV6NY2rIZ6iEYhl0N3dYdLKHR18+b//h7/56RL2NsTpU7tI9bUiBdPVO36jGbJhfEwWdohjiyy7ADjv+yAY1vEBpTsP5LSnA9Wuj2TzpLN64rK1o+FsoDDhENIh1MnppS27evSvKR8YEhlgyOqBPyhrtVzNKzxM5KyN2/fC1O6m5oI9u2DwMdqmMbTM47jV7NW8FJsGoFt49YnWTCn8bCvz2iXXBhrk6Ud4sgjQQxjHMQoiDr9CyNGXRv7CPzUWVRMxW1p6ymbEwNh9/i+rjTHNUT5yoVzJjyFuZKhn4GUUuE8FddFRSLoSISP/PIftOztG/6cU2Ha8tevXHFE9DgMv6rywc/rUB93JBg4jKIaGkit+Q/85zaFSw0UXi8a2BKfQXdPlsZjZnDGj75xWPOBjNa0X/FevE3lx4HCcFZrG+7SJTTdcfuYDulMlnaII9PU6c+eIuz/f3t3Hh5VleYP/HvuvbWlKpUNSBHWCCI7qNgYRIGWEdAGGujH1kYal0fHEWbEbte2sfvpnmcAu2VUGqF7pp8Wt+7f9LSoOLYjyo7ISBCjGNnDlg2yVSq13eX8/qhUUZVUJVWVSqpu8n6ehwfNvTn3nHMrqZdb530PY8gySX2uAm5QZx8fZBnEpD+uSXlfGev0aRCTRDCjETCZQkELjMaIe3q8sgln6z1xpR8nU3sk0yRbQyXZ7+sNwtfj+MvKoJ5qvxWCAOBqzyVw2QN+tALq0aMQu7CGJ5EtF7pzrVCm9IP0ThTEpJjVLMFq7tvT2lHp9qVjsnFdQeZVEWWMBdasGI1XAhWjESyOfwl3vg6ISsCTKxLdCiHTr6OXfpDeqW+/25JuEyv1U6uqBPemt5AcM0itgUqgim0ocElSsunHpG9KZiuETL6OXvpBeicKYlKoL3/mH034xweqquLo3sOoO30OuTYzxkweCaHNG34ia1TiOZeJAmAwggWfsBgMgY+FkvzcPdb9vbrQhiEmDacbvKE0WURJk22bfhzeXo5ZQnHdWaChIeWpp+HXsVskgDM4vd33Gg2/XrZJhHLqFJrqnHHVfVFUDR9/XYWqJi8G5pgxe/zAiIXviabopiKlN542or02uCLDtfEVqGfPQhw2DLYVj0A0GpPeCkFTFHjefRfqxYsQBw6EpmngNTUQBw2CZeFCCJIUcQ7rXwjYbFAuXoSQlwcxLy+0wWZH10l0zlRV7bC2T7LjTRSlb/dNtLAXqVnY25ezLzpz4L3d2LLnFC4IVihMgKRpGKw0454J+Zg6YzKAxHaVDp3r9EPWAIPIMDTPjGVTB2PKiH5X6q2kcN1NrPt7d14Lrv7z73G4QcOmcfMDm/gxpV2abNsKquHtyR4fxJZmDGqsxqLjOzGp6XzKUk/Dr+PxKfDKgT18zEYBFqOU8tdo+PXcLV54/YHdm02qDLPqx2CtBctvGRG1Au+b+89gy94zcHllcATeb21mA5bfXIylNxUnnKKbipTeeNqI9tooqq/Ewu1bMOHC0SuNSRKy7voh8tatDcvWaWmXes1stnaVhJs3/x7NG34H3twcqOob/mtbFMGys2Gccj38h0oD56hRShgIAtiAARCzbTGvk+ictfvZ5lrUe5zoeBNF6dt9V1qDmD179uA3v/kNSktLUVVVha1bt+L73/8+AECWZfz85z/HBx98gNOnTyMnJwezZ8/G2rVrUVRUFGqjvr4e//zP/4xt27ZBEAQsWbIEL730Emw2W9z96GoQEyyz33Z3X6db7lXptck48N5uPL/3ItyCEdmqFwZNgcxEuCQzLKqMn441Qxo1KmJXaYMYqKjr9GnIMop4csZgXF+cDyaKOFTZguc/OgW3X+2xuY55f5taYGqsw8NfvI3JSn3cabIR7XEZrLoSMhhcpsB+Oo8c/xDjK8q6nHoafh2DJKDe5YOqBX7cBcZQkG2CrGgpm7eI6yl+1LkVaK2BpACOPJ8LMhNh0fx48uZBEW9yb+4/g1c+PgFN4xBFBgGBbB1V5RAEhn8cIeG2zb+IO0U3FSm98bTxVdHodq8Nb2UVnF4FZtmHh/e9jglV30a0m3XP0lAg0zb1Otobb/Pm38O5Zm0gMBGE9gGKIAQCm1j/3waz22GcPKnddRKds3Y/21yDzAS4RHPUexzveBNF6dt9W1o/TmppacGkSZNw//33Y/HixRHH3G43Dh8+jNWrV2PSpEloaGjAo48+igULFuDQoUOh85YuXYqqqips374dsizjvvvuw0MPPYS33nqrR8bQaZn9GLv79gWqqmLLnlNwi3YUtO5OzMFg4hqMcgvqDVa88VU9JF9dYFdpqwFMFMCYANHIYMoCLjl9eONoE6ZcNxIA8Ma7J+H2qz021zHvLxMgOi+jTjBi67jbMOn4O5jkuogJJy9Gpsk+GZkmG9FethFqRSW4psIkGWBSWlBnsOFvw6dhvLcGvLoGzRs3wjitJOHH4uHX6ZdtxIV6DzTeumcUY1BUjmaPjCH5WbjU3PV5i7iezYDzlS5wJkLiGhgAhQlwGSwY6G1EvZSFLXtO4Tt3TIcoilBUDVv2noHWZqdrAYDAAjtdv17ejFkuN4zhKboWMwRzIbQ288Q1Dc0bNwbe1OI4P5p42mjauBGvzX0s4rXBNQ5DfR3yOUe9NRdbJ83DuOrjEMBDT0/cf/l/sP/6V3FthaApCpo3/C4QuBiNgC/KerK2AUusAMZgAOTAJpN5W16FGLYOLNE5i/azDQaI0GBU3O3uMdA9Wz+k4l4TfUvrXZ03bx7+9V//FYsWLWp3LCcnB9u3b8edd96Ja665BjfeeCN+97vfobS0FOfOnQMAlJeX48MPP8R//ud/YurUqZg+fTo2bNiAv/zlL6isrIx5XZ/PB6fTGfEnWcnu7tsXfLPvCC4IVmSrXrR9b2RgsCk+VBhzcarBB3u2GYLJGPgoSBQAgbWbv3TMdaxrco8X8PlgU324aMnD6awBAK6kyV4v12L40c+hHj0asz14feA+X+DjABb4+CRb8eKiOQ9nrIURqadd6bdf4fCrGsTWOWUI7B7tVzX4FC0l8xZ+PV+LDzITILQGMAAgcA5ZkOAXJdhULy4IVnyz7wgA4OOvq+DyyoEnMG3uq8AYRAa0CAbsH1XSaYoukFhKbyzxtHG81o2KqsaI14Z2qRbgPPBRmLcFF3MdOFMwJPiNgb8VBa6NrwS+1Jp6bZ45E8aJE9u90XrefTfw8ZAkdfh0JW4GA+DxwLttW8LjDZ+zjn62BYZ29zjUVifjTVQq7jXRN12Fpk1NTWCMITc3FwBw4MAB5ObmYsqUKaFzZs+eDUEQcPDgwZjtrFmzBjk5OaE/Q4YMSbpPye7u2xc0XG6CwgQYeNgvXxZ4EsAEBgM0KIIIWeVxzV865jrWNbkaWOth4BoUJsJpaJN1YTKBK3K7tNHw9oJtgF1p28CVK+3FaCPRfisaD1wm7DhD4NKqxlMybxHXUxVwsDbX4+AA1NbXg8IENFxuAgBUNXnBEfuXkQCAM4ZL1rzoJ7SZp2BKLzpK6e1kXuNpwyma2r02uO9KoUuDqkARRDjN2e2+XT17Nua1I867eDEQvAhC5DqYZHAeake9eDHiUKJzFvVnO0zbe9xdUnGvib7pJojxer146qmncPfdd4fWrVRXV2PAgAER50mShPz8fFRXV8ds65lnnkFTU1Poz/nz55PuV3h6bTR9Ob02r18OpNbPyYNaYxgAgMwESJoKg8jimr90zHWsazJRAhgLjIGrsMveyG+MkTYa3l6wDYS9EchMutJeF1JPw68jCSxwmbDjHIFLB5/IdHXeIq4nSqGg5cr1Wp8Atb4eJK4hr18gc21gjhkMgTUw0WgAGOfo39IQ/YQ28xSR0hvH+dHE04Zd9bV7bYTXQpFFCZKmwu5tbvft4rBhMa8dcd6gQVfWuHR1oTpjoXbEQYMiDiU6Z9F+tsO1vcfdJRX3muibLoIYWZZx5513gnOOTZs2dbk9k8kEu90e8SdZwV16nW4ZbddIB9NrhxVY26XX9gVjp0/GYK0FLtEMrc0/IjUOuEQzhqtOjCzKjWv+0jHXsa7JLIEnJS7RjEGeBlzlro3oi9bYBGnkiHZpo+HtwWwCM5kAVQXngcCiWTJjkLcBxS01MdsIp2kc31Y24bOTl/FtZRO01okOv45RYjCKAlSNg/PWJyKtT2BMkpCSeQu/nslqgoFr0JgADa0LdBmDqKkwqApcohmDtRaMnT4ZADB7/EDYzAaoKofW5r5qnEPlgFWTcdPxz6Led62xCeKIq8A1Du+uXeAahzjiKmiNjYE0ZLcHWnNz4G9Ni2teg2nBWmNjzGuOGpCF4QMjX7tC/wEAY+AAXGYrBjVWo7jufPAbA39LEmwrHolrXi0LF4JlZwOKEghmukpRwLKyYJ4/P+LL8Yw3fM7i+dkOv8fJ4poGf1kZvLt2wV9WBq5pEV8Lv9fx9Jv0PhkfxAQDmLNnz2L79u0RAYfD4UBtbW3E+YqioL6+Hg6Ho0f6Fyyzn2WScMnpg1dWoWkcXlnFJaevV+/S2xlRFLH8lhGwaH7US1nwQoDKAS8E1EtZsGgylt8yAstvGRHX/KVjrmNeU9HQYO8Hi+bHoqMfgXk8gV+wHg+06howmw3ZK1a0+8w/or1mP+T8/tAEET4NqJOssKh+LKn4FOigjaBDp+uw6vVSPPWXI/j11q/w1F+OYNXrpTh0ui7iOpeb/ci2GCCwQNaXrATWqmRbDLjUnJp5i7ieS0Z2lhGcA7IgQRYkaEyALIg4bymAAI7lt4wILfiURAHLW68vKxyKxqFpgb9lhUMUGJaNyYbBZoFWXQPeZq4hiuCNTWh48CE0rPoJGh58CLyxCVxRoB47DqWiAur5C4G/jx0HRLHDeQWu7EDNbNao12Q2G3JWrMCPb74q4rXBAcj5Bai35sIi+7Doy79D4JEp0Vl3/TBiUW2H8ypJyP7nlYAoAn5/4O/2k9/x/wfJcuvHlwwNy++Fb9/+hMYbPmfx/mx3Ze8n3779qFt6D+ofeBANq36C+gcexKXbv4dLt38v9LXgvYYoxNVv0vtkTJ0YxlhEijVwJYA5ceIEdu7cif79+0d8T3l5OcaOHYtDhw7h+uuvBwB89NFHmDt3Li5cuBCRit0RqhPTveKpJZHI/KVjrjurE5No2mjMOjEndmJSY+d1YuJN609XnZgTNU40tgSfUHAwziG0fqiUa5Hwy7uub3fNhOrExNgNOpheq9bWgrtcgYYZC715gzEIBQXIf+nFuNJu40kLTqZOTKJSVSdG6N8fgt3eYWp6ImnQ8daJSVS0tGmtoRFaVVVgyEVFYLk5oVRqiCLE/v2h1dWlNH2bZL60BjEulwsnT54EAFx77bVYv349Zs2ahfz8fAwcOBA/+MEPcPjwYbz//vsoLCwMfV9+fj6Mrf+SmTdvHmpqarB58+ZQivWUKVMSSrFO1S7WVLE3ts6qegKJzV865jrWNZOtFJpsxd5Odwpvs2t2T1fsVVQN//jH/8O5uhbkWQ2A3w9V0SBJIkxWMy43+2Pu6p1Ixd5Yu0GDA/Lp04DbDWRlQRzoAFQNTBIBkxlaTWK7Jqe6Ym+ykqnY6/7DH6BcuAAhPx9Cbi6YcKWSdKzdo1NdsTdR0Xa95pxDPVMB7vEAAJjFAqm4GGBXxiKNHg3700+CNzZSxd4+JK1BzK5duzBr1qx2X1++fDl++ctfori4OOr37dy5EzNnzgQQKHa3cuXKiGJ3L7/8co8WuyOkJ31b2YSn/nIEFqMIs6H9m4VXVuHxq1h31+S07BrdU/3zl5Wh/oEHwazWwBqlVtzjgVJxJftHGjYMLMsScZy3uJH/x/9I2a7JmfgPmFjzE9Qd85AK0frN3R4oZ89e+ahM0yANHwZmCdzXTB0L6X5pLXY3c+bMdouxwsUTX+Xn5/dYYTtCMkGm75rdU/2LtTsyV9TAxy2iGFg0rSoR6d6p3jU5Uz9K1uvu0dH6faUcQWjzJ3BFvXJfM3QspPvRszZCdCbT0/p7qn+x0muZJEakEzOxzb/VUph2G1ybdLKmGRajiAKbCRajiJM1zVi77RscOl3X5WskS6/px9H6faUcAQ8FM0wKe8qXoWMh3Y+CGEJ0JtPT+nuqf7HSgpnZEijRryiBhb7hHzWlMO227ZYUZoMIQWAwG0T0t5vg9il4rXU7hXRING06U0Ttt8UcKEegKIE0cZMpcJ+R2WMh3Y+CGEJ0JtPT+nuqfzHTgr2e1u0rxMC/4L3ebkm7zfQtRxJNm84U0foNzsFy7KGsLCEnB5xn/lhI98uYFOt0ooW9RI8ydS1GT/cvVlqwacYM+HbvTvmuyUGfnbyMX2/9CgU2U9SATNM46lw+rF40ATeO7Nfl6yWru3aP7m6xUuoBUCo1CaEgBhTEEP3KxKyYcD3Vv1hpwcmmv8cj07PEwnXnPHSnaP0GoMuxkO5BQQwoiCGEJC6iXk+2MbAruaoEPsIym3Cpg3o4epdIUKTXAIroQ1pTrAkhRK+Ca3/W/Fcpai40wuZphkFVIIsSXJZs2LKtvXLLkYiPeWQZzBD7I51EziUkGRQOE0JIkiZUfot/3P86htVfgNdgRmNWDrwGM4bVX8BD+1/HhMpv093FlApuByCXl4NZrRAKB4BZrZDLv0XD009H7MeUyLmEJIs+TgJ9nEQISVx4eXw4HDhjLYTTYIZd9qK4pQaIUdZfr6JtBxA61mYbAwBxn9sb5oakD32cRAjpNpm+8Lgr5K+/hnLyFITcPDDGMNJde+UgY+C5OVBOnoL89de9ohR+2/GGY4xBCBsvgLjP7Q1zQ9KHghhCSLfI9BTwrtJrWf9kJTrevjQ3JH3oOR4hJOUyuRx/qui1rH+yEhlvX5sbkj4UxBBCUirTy/Gnil7L+icrkfH2tbkh6UNBDCEkpTK9HH+q6LWsf7ISGW9fmxuSPvQKIoSkVKNbhqJqMIrRf70YRQGKqqHRLfdwz1LPNP0m5K1dC8OY0eAtbmi1teAtbhjGjEbe2jW9rhZKIuPta3ND0oMW9hJCUio3ywBJFOBXNZiF9uX4/a2LfHOzDGnoXeqZpt8E47SSPlOVNpHx9rW5IT2PghhCdCbVZdxTlQYd7NfQujoMMQGnWzSY7EK7GiFOt4yRhdkY5bB3y3jikerUbw6G0/2GoTGrKNAeGOJpLeX9SPFcxmqPCULcqdGJnEtIoiiIIURHUl3GPVVp0G37tcAxCpuvXYJLsh32HCuMrU9mnG4ZVpMUKsefjrL0qU79Tra9VPcj1XNJWwYQPaCKvaCKvUQfgmXcucsFITcPMBkBnx9aYyOYzYq8tWsTenMJpkG7fQrsWYaIQCPLJOHp+WPjejON1a8jUj62jv4uKgeNhCoZ2r1Jp3o8PTnmrraX6n6kei7TcW8ISQZ9MEmIDnBNQ/PGjYE3FYcDzGIOPNa3mCE4CsFdLYHjmhZXe6lKg+6oX5OlFqze90f87Nh7+PnCcVh312S8uOx6TLmqIOXj6ckxd7W9VPcj1XOZjntDSLIoiCFEBxIt+d6ZVKVBd9YvKdeO4Uc/x/Xeaowuygmt90j1eOKR6tTvZNtLdT9SPZfpuDeEJIuCGEJ0IFjyHR2VcVfkuMu4pyoNOtl+pXo88Uh16ney7aW6H6mey3TcG0KSRUEMITqQ6jLu4WnQ0cSbBp1sv9JRlj5VY+5qe6nuR6rnkrYMIHpCQQwhOpDqMu6jHHYMK7DC6Zajtud0yxhWYA2lQae6X+koS5+qMXe1vVT3I9VzSVsGED2hIIb0OZrG8W1lEz47eRnfVjYltYdPIm105Xpc0+AvK4Nvzx6Y580Ds0aWcVfdHhx3cXxROArnvjsfWifJhsH2/Ht24+5BHFkmEbVNXjS5/Wj2yGhy+1Hb5I1Ig+5IsuXlORgqlz2MI0Mn4JhHxHHRjtLsITgh2CFfuAgIAszz5sU9T/EQBIYf31yMLJOES04fPM1uKE4nPM1uXHL64hqzrCh4f9tB/MerH+N/3v8MdzpUWDQ/LtW3wOtXoGkcXr+CS/UtsGh+3D2IgyHynrTth1dWA98nq1H70dnrJxUl/sOvcay6GdZHHqEtA4guUIo1KMW6L0lFbY5E2ujK9aLV6RAKAt+j1dXhS2sR3r56Bi7aC6GIEiRoGKy1YPktI1CyYEZc7b03ZT7+WnQDWlSAA2AAbGYDlt9cjKU3Fcc1H+3aVmQwKXZNkfA5cbd44fUrAOcwKX6YZR8GOaux+PhuTHJXdUtdkgPv7caWPadwQbBCYQIk3vG8Bb32xg68Xt6MFsEADgYGjiy/B9MulqHWPgAXcx1QTWaIPi8GNVZj0fGdmNR0Pq55iPXaSOT1k8g9iKcfd+e14Oo//z7h9gjpSRTEgIKYviIVtTkSaaMr1+uoTgesWTg2ezFechXCLRmRrfpg4BpkJsAlmmHR/Hjy5kERb8jR2iuzOPDKiH+A22BGVq4NgtkCzgGvrCZVqySearHhc2KQBNS7fFA1DnAOQVWQ53VClkywaDIeOf4hxleUpbQuSXAeVFcLKgZfA2eWHXa3E8MvHIdoy4p5ndfe2IHfH/NCYwyipoJpGjgToAoiBK7hnuOfYPyFb9DEReQwFSONfghx1FbpqGJvMq+fRCv2dnaNp743BpNaKmnLAJKx6NVI+oRU1OZIpI2uXK+zOh2qy40/V0twSyYUKB6YoUFkgBka8hU3PIIRW/acgqqqMdvjgoC/DZ8Gj9GCAk8jrA2XkW2WYM8yJFWrBLhSXt48cyaMEye2e7MLn5N+2UY0e2RonMMgMhg0BZwJcJlsyFda4BGN+NvwaUAK65KEz4PkKMTVahOubz6Pq9UmSI4BMa8jKwpeL2+GxhgkTYXINQgARGiQNBkaE/C3q6Zj6OVzuPb8V7jqcgVEc3y1VQSBYXRRDm4c2S8iBT3Z109n9yDW/Yh1jdf3VUAaPyGu9ghJB3pFkj4hFbU5EmmjK9frrE5HReFwXLT1Q7bsQdvlGwIDbKoXFwQrvtl3JGZ7p7MG4KI5D9mKF0wUwX0+cI83oflIVPic+BUOv6pBFFjgcyyNQ+AaZEGCX5CQrXhx0ZyHM9bClNUlSbb+yf/+vRQtggGipkIAv/K5GwK/QEVNhdtgxv5BEwBJAvx+cK8nrrZjSXUtmXRdg5DuRkEM6RNSUZsjkTa6cr3O6nQ4TTYoggQDV6MeN3ANChPQcLkpZntOgxkKE2HgCsAEgHNwVYmrf8kKnxNF4+C8NRZo/USbIRAfqEyAgStQmAinwZyyuiTJ1j+pqmsGZ7E3dGTg4GC4lJUHCK1zqbS5N2mq45PuaxDS3SiIIX1CKmpzJNJGV67XWZ0Ou88FSVMgMzHqcbl1sWpev5yY7dllLySuQmYSwDWAMTDxyn6widYqiUf4nEgCA2t9CIPWpwDBBxwi1yAzCRJXYZe9KatLkmz9k4EF2WCcI9YHa8FFvv3dDYDWOpdSm3uTpjo+6b4GId2NghjSJ6SiNkcibXTlep3V6RheU4FBrstwGSxou2RF44BLNGOw1oKx0yfHbO8qdy0GeRvQLJnBVRXMZAKzmBOaj0SFz4lRYjCKQmBRLwMgMGhMgEFTYNQUNEtmDPI2oLilJqIuSTBF3LtrF/xlZQmtkxHHjUPFuBtQauiPE5b+CP/OtvVPwq8za5ABVk2GKojQwK48MgKgAVAFEVmyFzdd/ApQFMBoBDNbYrYdj6sLbRhi0uBsbIHW4g483eEcXr8Kl1dGfbMPQ/Ozkro/wbENPXY4cI0U1ashJB2kzk8hRP+CtTnWbvsGl5y+dpkY8dQISbSNZK8XrPvR8PTT0KprIOTmACYT4PMF3gyzbVg6Ngf/fsGPeikLNtV7JTtJMsOiyVh+ywiIohizPWYyYUnFp3hlxD+g3pKLnLxsCBzwK2rc89GVe3C52Y9siwGyS4OsckAwQFAV2PwtqJessKh+LKn4FAirS+L/9EC7FPF4U36DacQV1yyA39EMSVUw2NuAJdWlmHjpJLTGpg6v84Nxs/HakGlQBBGiBjCugeNKdtIPTu+FQRLARRFMkgJrYsLuWSK1VYKp0gsaNGwaNx+1zSYYhAa0WLLh4wwa5xAYQ7NXxuGK+oR3uw4f2wLHKGy+dgkuyXbYc6wJ/0wQkm6UYg1Kse5LdFsnJkqdjkTrnURr7+iEm7B10u04rxqSno9Ehc+Jx6fAKweeiZiYBrPHFaixcmInJjVeqbECIGbKeWcp2G3TiA0eN7x19WjmEiyKD/90dBuuyxM6vc47I2/G26NmoUU0RtSJWXJsBxZdPARp5AiYZsyAb/fupGurtE2FL+s/Aq8OvglnbAPAGYPABBgNAuxZRsiKllAqfKy0/SNSPraO/i4qB42EKhl65DVASKpQEAMKYvqajmpzdEcbXbleZ3U/VFXFN/uOoOFyE/L65WDs9MmhJzDxtsfBujwfiQqfE7tFAjiD0ysjxyyhuO4s0NAQ6h8A1C29B3J5eSBFPCyThnMOrboGhjGjUfDmG1HTule9XoqTNc3obzdd+V7Oobk9uOxWcFWeES89fAsExjq9DkZfg0M/ehTV9S448q347mAzRGdTxL1JtFZL6Bqa1u76GoBfXvN9nMwaAJuvBQaDhKzhQ8AYA+ccl5w+jCzMxovLru/wnkVrO3xsSnUtzk8uAf/Zc8izmXrkNUBIKtDHSaTPCdbm6Kk2unK9YN2PWERRxIQZ13epPQZ0eT4S1eGcDM6N+F9/WVncqdFtxxYzjZgxCNYs2I0qzvtUnKhx4arLZzu9Dj91GnOGZ8G44MaYY+vsnsXSUSp8nuyGkcuA1wd4vECWpV0adEf3sLP0cinXjuFHP0e+txrGUYn3nZB0oYW9hJCMlmxqNJBYGnFXrpMK3ZkKn+6xEdJdKIghhGS0ZFOjgcTSiLtynVTozlT4dI+NkO5CQQwhpMclkirdWcp5R+nLiaS6d+U6qdCdqfDpHhsh3YWCGEJIj/Lt24+6pfeg/oEH0bDqJ6h/4EHULb0Hvn37o54fTBFnNiu06hpwjwdc08A9HmhhKdjRFs8G07qzTBIuOX3wyio0jcMrq7jk9EWkEXflOqkQ7fpM07Ck4lNY/B7UW3Ih5/eH1rpJZ9v+J9p2T46NkO5C2Umg7CRCekpHu3N3lirdWcp5RxJJde/KdVKhO1Ph0z02QlKNghhQEENIT+gszbejVOnwNpJJXwYSS3XvynVSoTtT4dM9NkJSiYIYUBBDSE/wl5Wh/oEHwazW0LqOcNzjAW9xI/+P/5FUinIy9PCGnkgf9TAeQlKJ6sQQQnpEMM2XdZTm29TUY2m+bUvwJ7KNQU9JpI96GA8hqUYhOiGkR2RSmm9wbY5cXg5mtUIoHABmtUIu/xYNTz8dc5FxT0qkj3oYDyHdgYIYQkiPyJQ0X65paN64MbC42OEAs5jBBAHMYobgKAR3tQSOJ7BDdjr7qIfxENJdKIghhPSIVKf5JlJrJlxnJfjDtzFIl0T6qIfxENJdaE0MIaTHmKbfhLy1a6+s3WhqApMMMIwZndDaja6s/8i0tTnRJNrHTB8PId2FghhCSI8yTb8JxmklSWfRtK01w1przQTXf3RUawZoszYnSpZUJpTgT7SPmT4eQroLfZxECOlxwZ2ezTNnwjhxYkIfIXV1/UemrM3pSCJ91MN4COkuFMQQQnQjFes/9FCCP5E+6mE8hHQXelUTQnQjuFYEHa3/UORO138E1+YYxowGb3FDq60Fb3HDMGY08tauyYi6Kon0UQ/jIaQ70JoYQohupHI9S1fX5vSERPqoh/EQkmoUxBCSYah0fGzB9R9y+bcQzIXt919qbIJhzOi4138E1+Z0VXfes0T6mKrxEKIXFMQQkkGodHzHgus/Gp5+Glp1DYTcHMBkAnw+aI1NaVn/QfeMkPShDSBBG0CSzNA2dRitqcNaYyOYzdpp6nBfEhE4KDKYlJ7Age4ZIelFQQwoiCHpxzUNdUvvgVxeHkgdbvsxSXUNDGNGo+DNN+ijpVbp/tiN7hkh6UcfJxGSARJJHaY1DwHduf4jngCJ7hkh6UdBDCEZQA+l8PuKeNe40D0jJP3oGSchGSAidTgaKh3fI4JrXOTycjCrFULhADCrNbSlgW/f/tC5dM8IST8KYgjJAOGl4zVNA3d7oDU3B/7WtIjS8ZrG8W1lEz47eRnfVjZB0/S9rC3Z3ai7ox/BLQ3gcOBUwVB8kTccpwqGAlG2NKBy/4SkH32cREgGCKYO1z/6KNRjx4HwN0XGIBQUIHvFCpRWNOC1vWdwtq4FiqpBEgUMK7DixzcXY8pVBekbQJIyKT05uMbl6+GT8Lfh03DRnAeFiZC4ikHeBiwxf4oJJ8tDa1wyMd2bkL4mrT9de/bswfz581FUVATGGN55552I45xzPPfccxg4cCAsFgtmz56NEydORJxTX1+PpUuXwm63Izc3Fw888ABcLlcPjoKQVApbINrmX/eH6xWs3fYNTtY0w2IUUWAzwWIUcbKmGWu3fYNDp+t6uK9dk8hHNz1Bq6/HlzlD8Mqouaiw9INZlZEnu2BWZVRY+uGVUXPxZe6QiDUuVO6fkPRK65OYlpYWTJo0Cffffz8WL17c7vjzzz+Pl19+GVu2bEFxcTFWr16NOXPm4JtvvoHZHCg5vnTpUlRVVWH79u2QZRn33XcfHnroIbz11ls9PRxCkhb8KAOqAumaawCfF1xRwSQRMJmh1NRiy55TcA8oRn+7KZQNYxZEmOwCLjl9eG3vGVw3PB+CwDq5Wvq12406mN1jMUMwF0KrrkHzxo0wTivpuScZeXnYOmoWPIIBBbIrFE6auAKj7EKdZMXWq2dhRl5exLdRuX9C0ietQcy8efMwb968qMc453jxxRfx85//HAsXLgQAvPbaaygsLMQ777yDu+66C+Xl5fjwww/x+eefY8qUKQCADRs24Pbbb8dvf/tbFBUVRW3b5/PB5/OF/t/pdKZ4ZIQkJiJdV2CAxRL+TAYVg6/GBcEKu6BFTee1Zxlwtq4Fx6udGF2U07OdT0ImpiefKRiGi7knYfO4AIFFPBQDB2y+FlzMdeBMwTCMafO9VO6fkPTI2H8qnDlzBtXV1Zg9e3boazk5OZg6dSoOHDgAADhw4AByc3NDAQwAzJ49G4Ig4ODBgzHbXrNmDXJyckJ/hgwZ0n0DISQOne3O7MzKgcIESFyJetwoClBUDY1uuTu7mTKp2o06lZq8ClRrNgzggCKDaxo4Dzw1giLDAA2qNRtN3uj3gBDS8zI2iKmurgYAFBYWRny9sLAwdKy6uhoDBgyIOC5JEvLz80PnRPPMM8+gqakp9Of8+fMp7j0hieksXdfuboLENSgs+sNTf+si39wsQ3d2M2UyMT05N8sAg8UE7igCM5uB1uAFmgZmNkNzFMFgMelmjgnpC/pkdpLJZILJZEp3NwgJ6Wx35uEXTmDw6Bac0wbAxHm74063jJGF2Rjl0Me2GanejToVRjnsGFZgxckaDf2HDwe8PnBVARMlwGxCfbMfIwusupljQvqCjH0S43A4AAA1NTURX6+pqQkdczgcqK2tjTiuKArq6+tD5xCiB8F0XWazQquuAfd4Ah9neDzQqmsg2qxYfssIZJkkXHL64JVVaBqHV1ZxyemD1SThxzcX62JRL9D5eNORniwIDD++uTgwx81++AxGwGqDz2DEpWa/7uaYkL4gY4OY4uJiOBwOfPLJJ6GvOZ1OHDx4ECUlJQCAkpISNDY2orS0NHTOjh07oGkapk6d2uN9JqQrOkvXLVkwA0/PH4uRhdnw+FXUuXzw+FWMLMzGU/PH6q5OTCamJ0+5qqBXzTEhvV1ad7F2uVw4efIkAODaa6/F+vXrMWvWLOTn52Po0KFYt24d1q5dG5FiXVZWFpFiPW/ePNTU1GDz5s2hFOspU6YklGJNu1iTTNLZ5oOaxnG82olGt4zcLANGOey6fjqQ7t2oo+ltc0xIb5XWIGbXrl2YNWtWu68vX74cr776Kjjn+MUvfoE//OEPaGxsxPTp0/HKK69g1KhRoXPr6+uxcuVKbNu2DYIgYMmSJXj55Zdhs9ni7gcFMYQQQoj+pDWIyRQUxBBCCCH6k7FrYgghhBBCOkJBDCGEEEJ0iYIYQgghhOgSBTGEEEII0SUKYgghhBCiSxTEEEIIIUSXKIghhBBCiC5REEMIIYQQXaIghhBCCCG6JKW7A4QQki6ZuG9TJqH5IZmOghhCSJ/k27cfzRs3Qjl5ClyWwQwGSCNHIHvFirTsoJ1paH6IHtDeSaC9kwjpa3z79qPh6afBXS4IuXmAyQj4/NAaG8FsVuStXdun36hpfohe0HNBQkifwjUNzRs3Bt6gHQ4wixlMEMAsZgiOQnBXS+C4pqW7q2lB80P0hIIYQkifIn/9NZSTpyDk5oExFnGMMQYhNwfKyVOQv/46TT1ML5ofoicUxBBC+hStvh5clgMfkURjMoErMrT6+p7tWIag+SF6QkEMIaRPEfLzwQwGwOePfoLPByYZIOTn92zHMgTND9ETCmIIIX2KYfx4SCNHQGtsRNu8Bs45tMYmSCNHwDB+fJp6mF40P0RPKIghhPQpTBCQvWIFmM0KrboG3OMB1zRwjwdadQ2YzRY43kfrodD8ED2hFGtQijUhfVFEHRRFBpOoDko4mh+iBxTEgIIYQvoqqkjbMZofkukoiAEFMYQQQogeUUhNCCGEEF2iIIYQQgghukRBDCGEEEJ0iYIYQgghhOgSBTGEEEII0SUKYgghhBCiSxTEEEIIIUSXKIghhBBCiC5REEMIIYQQXaIghhBCCCG6REEMIYQQQnSJghhCCCGE6JKU7g5kguAemE6nM809IYQQ0ldkZ2eDMZbubugaBTEAmpubAQBDhgxJc08IIYT0FU1NTbDb7enuhq4xHnwM0YdpmobKykrdRMVOpxNDhgzB+fPne/UPQF8YJ42x9+gL46QxppZe3nMyGT2JASAIAgYPHpzubiTMbrf32l8k4frCOGmMvUdfGCeNkWQKWthLCCGEEF2iIIYQQgghukRBjA6ZTCb84he/gMlkSndXulVfGCeNsffoC+OkMZJMQwt7CSGEEKJL9CSGEEIIIbpEQQwhhBBCdImCGEIIIYToEgUxhBBCCNElCmIymKqqWL16NYqLi2GxWDBixAj8+te/RvhabM45nnvuOQwcOBAWiwWzZ8/GiRMn0tjrju3Zswfz589HUVERGGN45513Io7HM576+nosXboUdrsdubm5eOCBB+ByuXpwFB3raIyyLOOpp57ChAkTYLVaUVRUhB//+MeorKyMaCPTxwh0fi/DPfzww2CM4cUXX4z4eqaPM54xlpeXY8GCBcjJyYHVasUNN9yAc+fOhY57vV6sWLECBQUFsNlsWLJkCWpqanpwFB3rbIwulwsrV67E4MGDYbFYMHbsWGzevDninEwf45o1a3DDDTcgOzsbAwYMwPe//30cO3Ys4px4xnDu3DnccccdyMrKwoABA/DEE09AUZSeHAppg4KYDLZu3Tps2rQJv/vd71BeXo5169bh+eefx4YNG0LnPP/883j55ZexefNmHDx4EFarFXPmzIHX601jz2NraWnBpEmTsHHjxqjH4xnP0qVLcfToUWzfvh3vv/8+9uzZg4ceeqinhtCpjsbodrtx+PBhrF69GocPH8bbb7+NY8eOYcGCBRHnZfoYgc7vZdDWrVvx2WefoaioqN2xTB9nZ2M8deoUpk+fjtGjR2PXrl0oKyvD6tWrYTabQ+c89thj2LZtG/76179i9+7dqKysxOLFi3tqCJ3qbIw/+clP8OGHH+KNN95AeXk5Vq1ahZUrV+K9994LnZPpY9y9ezdWrFiBzz77DNu3b4csy7jtttvQ0tISOqezMaiqijvuuAN+vx+ffvoptmzZgldffRXPPfdcOoZEgjjJWHfccQe///77I762ePFivnTpUs4555qmcYfDwX/zm9+Ejjc2NnKTycT//Oc/92hfkwGAb926NfT/8Yznm2++4QD4559/Hjrn73//O2eM8YsXL/ZY3+PVdozR/N///R8HwM+ePcs5198YOY89zgsXLvBBgwbxr7/+mg8bNoz/+7//e+iY3sYZbYw//OEP+T333BPzexobG7nBYOB//etfQ18rLy/nAPiBAwe6q6tJizbGcePG8V/96lcRX7vuuuv4s88+yznX3xg557y2tpYD4Lt37+acxzeGDz74gAuCwKurq0PnbNq0idvtdu7z+Xp2ACSEnsRksGnTpuGTTz7B8ePHAQBffvkl9u3bh3nz5gEAzpw5g+rqasyePTv0PTk5OZg6dSoOHDiQlj53RTzjOXDgAHJzczFlypTQObNnz4YgCDh48GCP9zkVmpqawBhDbm4ugN4zRk3TsGzZMjzxxBMYN25cu+N6H6emafif//kfjBo1CnPmzMGAAQMwderUiI9jSktLIctyxGt69OjRGDp0qG5+RqdNm4b33nsPFy9eBOccO3fuxPHjx3HbbbcB0OcYm5qaAAD5+fkA4hvDgQMHMGHCBBQWFobOmTNnDpxOJ44ePdqDvSfhKIjJYE8//TTuuusujB49GgaDAddeey1WrVqFpUuXAgCqq6sBIOKHKvj/wWN6Es94qqurMWDAgIjjkiQhPz9fl2P2er146qmncPfdd4c2m+stY1y3bh0kScK//Mu/RD2u93HW1tbC5XJh7dq1mDt3Lj766CMsWrQIixcvxu7duwEExmg0GkMBapCefkY3bNiAsWPHYvDgwTAajZg7dy42btyIW265BYD+xqhpGlatWoWbbroJ48ePBxDfGKqrq6P+bgoeI+lBu1hnsP/6r//Cm2++ibfeegvjxo3DkSNHsGrVKhQVFWH58uXp7h7pIlmWceedd4Jzjk2bNqW7OylVWlqKl156CYcPHwZjLN3d6RaapgEAFi5ciMceewwAMHnyZHz66afYvHkzZsyYkc7upcyGDRvw2Wef4b333sOwYcOwZ88erFixAkVFRRFPLvRixYoV+Prrr7Fv3750d4WkAD2JyWBPPPFE6GnMhAkTsGzZMjz22GNYs2YNAMDhcABAuxX0NTU1oWN6Es94HA4HamtrI44rioL6+npdjTkYwJw9exbbt28PPYUBescY9+7di9raWgwdOhSSJEGSJJw9exY//elPMXz4cAD6H2e/fv0gSRLGjh0b8fUxY8aEspMcDgf8fj8aGxsjztHLz6jH48HPfvYzrF+/HvPnz8fEiROxcuVK/PCHP8Rvf/tbAPoa48qVK/H+++9j586dGDx4cOjr8YzB4XBE/d0UPEbSg4KYDOZ2uyEIkbdIFMXQvwCLi4vhcDjwySefhI47nU4cPHgQJSUlPdrXVIhnPCUlJWhsbERpaWnonB07dkDTNEydOrXH+5yMYABz4sQJfPzxxygoKIg43hvGuGzZMpSVleHIkSOhP0VFRXjiiSfwv//7vwD0P06j0YgbbrihXaru8ePHMWzYMADA9ddfD4PBEPGaPnbsGM6dO6eLn1FZliHLcoe/h/QwRs45Vq5cia1bt2LHjh0oLi6OOB7PGEpKSvDVV19FBN7Bf4C0DWRJD0rzwmLSgeXLl/NBgwbx999/n585c4a//fbbvF+/fvzJJ58MnbN27Vqem5vL3333XV5WVsYXLlzIi4uLucfjSWPPY2tubuZffPEF/+KLLzgAvn79ev7FF1+EMnPiGc/cuXP5tddeyw8ePMj37dvHr776an733Xena0jtdDRGv9/PFyxYwAcPHsyPHDnCq6qqQn/CMxwyfYycd34v22qbncR55o+zszG+/fbb3GAw8D/84Q/8xIkTfMOGDVwURb53795QGw8//DAfOnQo37FjBz906BAvKSnhJSUl6RpSO52NccaMGXzcuHF8586d/PTp0/xPf/oTN5vN/JVXXgm1kelj/Kd/+ieek5PDd+3aFfEz53a7Q+d0NgZFUfj48eP5bbfdxo8cOcI//PBD3r9/f/7MM8+kY0ikFQUxGczpdPJHH32UDx06lJvNZn7VVVfxZ599NuLNTtM0vnr1al5YWMhNJhO/9dZb+bFjx9LY647t3LmTA2j3Z/ny5Zzz+MZTV1fH7777bm6z2bjdbuf33Xcfb25uTsNooutojGfOnIl6DADfuXNnqI1MHyPnnd/LtqIFMZk+znjG+Mc//pGPHDmSm81mPmnSJP7OO+9EtOHxePgjjzzC8/LyeFZWFl+0aBGvqqrq4ZHE1tkYq6qq+L333suLioq42Wzm11xzDX/hhRe4pmmhNjJ9jLF+5v70pz+FzolnDBUVFXzevHncYrHwfv368Z/+9KdcluUeHg0JxzgPK/9KCCGEEKITtCaGEEIIIbpEQQwhhBBCdImCGEIIIYToEgUxhBBCCNElCmIIIYQQoksUxBBCCCFElyiIIYQQQoguURBDCCGEEF2iIIaQPmTXrl1gjLXb6C5Rw4cPx4svvpiSPgHAzJkzsWrVqpS1RwjpGyiIIaQXo+CAENKbURBDCCGEEF2iIIaQXuree+/F7t278dJLL4ExBsYYKioqAAClpaWYMmUKsrKyMG3aNBw7diz0fadOncLChQtRWFgIm82GG264AR9//HGH11q/fj0mTJgAq9WKIUOG4JFHHoHL5Yo4Z//+/Zg5cyaysrKQl5eHOXPmoKGhIXRc0zQ8+eSTyM/Ph8PhwC9/+cuUzQUhpHeiIIaQXuqll15CSUkJHnzwQVRVVaGqqgpDhgwBADz77LN44YUXcOjQIUiShPvvvz/0fS6XC7fffjs++eQTfPHFF5g7dy7mz5+Pc+fOxbyWIAh4+eWXcfToUWzZsgU7duzAk08+GTp+5MgR3HrrrRg7diwOHDiAffv2Yf78+VBVNXTOli1bYLVacfDgQTz//PP41a9+he3bt3fDzBBCegvaxZqQXmzmzJmYPHlyaBHurl27MGvWLHz88ce49dZbAQAffPAB7rjjDng8HpjN5qjtjB8/Hg8//DBWrlwJILCwd9WqVTHX2/z3f/83Hn74YVy+fBkA8KMf/Qjnzp3Dvn37YvZTVVXs3bs39LXvfOc7+O53v4u1a9cmM3RCSB9AT2II6YMmTpwY+u+BAwcCAGprawEEnsQ8/vjjGDNmDHJzc2Gz2VBeXt7hk5hgUDRo0CBkZ2dj2bJlqKurg9vtBnDlSUy8fQr2K9gnQgiJhoIYQvogg8EQ+m/GGIDAmhQAePzxx7F161b827/9G/bu3YsjR45gwoQJ8Pv9UduqqKjA9773PUycOBF/+9vfUFpaio0bNwJA6HssFktCfQr2K9gnQgiJhoIYQnoxo9EYse4kHvv378e9996LRYsWYcKECXA4HKEFwdGUlpZC0zS88MILuPHGGzFq1ChUVlZGnDNx4kR88sknyQyBEEJioiCGkF5s+PDhOHjwICoqKnD58uW4nmxcffXVePvtt3HkyBF8+eWX+NGPftTh940cORKyLGPDhg04ffo0Xn/9dWzevDninGeeeQaff/45HnnkEZSVleHbb7/Fpk2bQmtmCCEkGRTEENKLPf744xBFEWPHjkX//v07XNcStH79euTl5WHatGmYP38+5syZg+uuuy7m+ZMmTcL69euxbt06jB8/Hm+++SbWrFkTcc6oUaPw0Ucf4csvv8R3vvMdlJSU4N1334UkSV0eIyGk76LsJEIIIYToEj2JIYQQQoguURBDCCGEEF2iIIYQQgghukRBDCGEEEJ0iYIYQgghhOgSBTGEEEII0SUKYgghhBCiSxTEEEIIIUSXKIghhBBCiC5REEMIIYQQXaIghhBCCCG69P8BpHzJT7AtDFEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 573.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='thalach', y='trestbps',hue='target',data=df,palette='Set1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1b0e00e6150>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 573.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='thalach',y='chol',hue='target',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Unnamed: 0'}>,\n",
       "        <Axes: title={'center': 'age'}>, <Axes: title={'center': 'cp'}>,\n",
       "        <Axes: title={'center': 'trestbps'}>],\n",
       "       [<Axes: title={'center': 'chol'}>,\n",
       "        <Axes: title={'center': 'fbs'}>,\n",
       "        <Axes: title={'center': 'restecg'}>,\n",
       "        <Axes: title={'center': 'thalach'}>],\n",
       "       [<Axes: title={'center': 'exang'}>,\n",
       "        <Axes: title={'center': 'oldpeak'}>,\n",
       "        <Axes: title={'center': 'slope'}>,\n",
       "        <Axes: title={'center': 'ca'}>],\n",
       "       [<Axes: title={'center': 'thal'}>, <Axes: >, <Axes: >, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x1100 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hd.hist(figsize=(11,11))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data cleaning and data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies=pd.get_dummies(hd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "hd=dummies.drop('Unnamed: 0',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>target_no</th>\n",
       "      <th>target_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>120.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0     63   3     145.0  233.0    1        0    150.0      0      2.3      0   \n",
       "1     37   2     130.0  250.0    0        1    187.0      0      3.5      0   \n",
       "2     41   1     130.0  204.0    0        0    172.0      0      1.4      2   \n",
       "3     56   1     120.0  236.0    0        1    178.0      0      0.8      2   \n",
       "4     57   0       NaN  354.0    0        1    163.0      1      0.6      2   \n",
       "..   ...  ..       ...    ...  ...      ...      ...    ...      ...    ...   \n",
       "298   57   0     140.0  241.0    0        1    123.0      1      0.2      1   \n",
       "299   45   3     110.0  264.0    0        1    132.0      0      1.2      1   \n",
       "300   68   0     144.0  193.0    1        1    141.0      0      3.4      1   \n",
       "301   57   0       NaN  131.0    0        1    115.0      1      1.2      1   \n",
       "302   57   1     130.0  236.0    0        0    174.0      0      0.0      1   \n",
       "\n",
       "     ca  thal  sex_female  sex_male  target_no  target_yes  \n",
       "0     0     1       False      True      False        True  \n",
       "1     0     2       False      True      False        True  \n",
       "2     0     2        True     False      False        True  \n",
       "3     0     2       False      True      False        True  \n",
       "4     0     2        True     False      False        True  \n",
       "..   ..   ...         ...       ...        ...         ...  \n",
       "298   0     3        True     False       True       False  \n",
       "299   0     3       False      True       True       False  \n",
       "300   2     3       False      True       True       False  \n",
       "301   1     3       False      True       True       False  \n",
       "302   1     2        True     False       True       False  \n",
       "\n",
       "[303 rows x 16 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "hd=dummies.drop('target_no',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Unnamed: 0</th>\n",
       "      <th>age</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>target_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>120.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>298</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>299</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>300</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>301</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>302</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  age  cp  trestbps   chol  fbs  restecg  thalach  exang  \\\n",
       "0             0   63   3     145.0  233.0    1        0    150.0      0   \n",
       "1             1   37   2     130.0  250.0    0        1    187.0      0   \n",
       "2             2   41   1     130.0  204.0    0        0    172.0      0   \n",
       "3             3   56   1     120.0  236.0    0        1    178.0      0   \n",
       "4             4   57   0       NaN  354.0    0        1    163.0      1   \n",
       "..          ...  ...  ..       ...    ...  ...      ...      ...    ...   \n",
       "298         298   57   0     140.0  241.0    0        1    123.0      1   \n",
       "299         299   45   3     110.0  264.0    0        1    132.0      0   \n",
       "300         300   68   0     144.0  193.0    1        1    141.0      0   \n",
       "301         301   57   0       NaN  131.0    0        1    115.0      1   \n",
       "302         302   57   1     130.0  236.0    0        0    174.0      0   \n",
       "\n",
       "     oldpeak  slope  ca  thal  sex_female  sex_male  target_yes  \n",
       "0        2.3      0   0     1       False      True        True  \n",
       "1        3.5      0   0     2       False      True        True  \n",
       "2        1.4      2   0     2        True     False        True  \n",
       "3        0.8      2   0     2       False      True        True  \n",
       "4        0.6      2   0     2        True     False        True  \n",
       "..       ...    ...  ..   ...         ...       ...         ...  \n",
       "298      0.2      1   0     3        True     False       False  \n",
       "299      1.2      1   0     3       False      True       False  \n",
       "300      3.4      1   2     3       False      True       False  \n",
       "301      1.2      1   1     3       False      True       False  \n",
       "302      0.0      1   1     2        True     False       False  \n",
       "\n",
       "[303 rows x 16 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(hd['age'].dropna(),kde=False, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGdCAYAAAAIbpn/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAib0lEQVR4nO3deXDU9f3H8ddCyHIl4cxVwiGnHEEFixnAHxIEAnW4ZFBgOKRaMCoQUUmrItUahAGBUYNtMZFRRGkBRQsIAWJVQBONiNZwCATMAQWTkChLZL+/Pxx3unIlmw3f/YTnY2Zn3O/3u9+8+XYLz/nuN991WJZlCQAAwEB17B4AAADAV4QMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMF2T1ATXO73crPz1dISIgcDofd4wAAgEqwLEtnzpxRdHS06tS59HmXWh8y+fn5iomJsXsMAADgg2PHjqlVq1aXXF/rQyYkJETSzwciNDTU5mkAAEBllJaWKiYmxvPv+KXU+pD55eOk0NBQQgYAAMNc6bIQLvYFAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxguweAACuNW3nvufza48sGO7HSQDzcUYGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxbA2Z1NRUxcbGKjQ0VKGhoYqLi9OmTZs868+ePavExEQ1b95cjRs31pgxY1RUVGTjxAAAIJDYGjKtWrXSggULlJ2draysLA0cOFAjRozQV199JUmaPXu2Nm7cqLVr1yozM1P5+fkaPXq0nSMDAIAA4rAsy7J7iP/VrFkzLVq0SHfeeadatmyp1atX684775QkffPNN7r++uu1a9cu3XLLLZXaX2lpqcLCwlRSUqLQ0NCaHB0AKqXt3Pd8fu2RBcP9OAkQuCr773fAXCNz/vx5rVmzRuXl5YqLi1N2drYqKio0aNAgzzZdunRR69attWvXrkvux+VyqbS01OsBAABqJ9tD5ssvv1Tjxo3ldDo1ffp0rV+/Xl27dlVhYaGCg4PVpEkTr+0jIiJUWFh4yf2lpKQoLCzM84iJianhPwEAALCL7SHTuXNn5eTkaM+ePZoxY4YmT56sr7/+2uf9JScnq6SkxPM4duyYH6cFAACBJMjuAYKDg9WhQwdJUq9evfTpp59q2bJlGjdunM6dO6fi4mKvszJFRUWKjIy85P6cTqecTmdNjw0AAAKA7Wdkfs3tdsvlcqlXr16qV6+eMjIyPOtyc3OVl5enuLg4GycEAACBwtYzMsnJyUpISFDr1q115swZrV69Wjt37tSWLVsUFhamadOmKSkpSc2aNVNoaKgefPBBxcXFVfo3lgAAQO1ma8icOHFCkyZNUkFBgcLCwhQbG6stW7bo9ttvlyQ9//zzqlOnjsaMGSOXy6UhQ4bopZdesnNkAAAQQALuPjL+xn1kAAQa7iMDXJlx95EBAACoKkIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABjL1pBJSUnRzTffrJCQEIWHh2vkyJHKzc312mbAgAFyOBxej+nTp9s0MQAACCS2hkxmZqYSExO1e/dubd26VRUVFRo8eLDKy8u9trv33ntVUFDgeSxcuNCmiQEAQCAJsvOHb9682et5enq6wsPDlZ2drVtvvdWzvGHDhoqMjLza4wEAgAAXUNfIlJSUSJKaNWvmtfz1119XixYt1L17dyUnJ+uHH36wYzwAABBgbD0j87/cbrdmzZqlvn37qnv37p7l48ePV5s2bRQdHa29e/fqscceU25urtatW3fR/bhcLrlcLs/z0tLSGp8dAADYI2BCJjExUfv27dOHH37otfy+++7z/HePHj0UFRWl+Ph4HTp0SO3bt79gPykpKZo/f36NzwsAAOwXEB8tPfDAA3r33Xe1Y8cOtWrV6rLb9unTR5J08ODBi65PTk5WSUmJ53Hs2DG/zwsAAAKDrWdkLMvSgw8+qPXr12vnzp1q167dFV+Tk5MjSYqKirroeqfTKafT6c8xAQBAgLI1ZBITE7V69Wq9/fbbCgkJUWFhoSQpLCxMDRo00KFDh7R69WoNGzZMzZs31969ezV79mzdeuutio2NtXN0AAAQAGwNmdTUVEk/3/Tuf6WlpWnKlCkKDg7Wtm3btHTpUpWXlysmJkZjxozR448/bsO0AAAg0Nj+0dLlxMTEKDMz8ypNAwAATBMQF/sCAAD4gpABAADGImQAAICxCBkAAGCsgLmzLwD4ou3c93x+7ZEFw/04CQA7cEYGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADG8ilkvv32W7/88JSUFN18880KCQlReHi4Ro4cqdzcXK9tzp49q8TERDVv3lyNGzfWmDFjVFRU5JefDwAAzOZTyHTo0EG33XabXnvtNZ09e9bnH56ZmanExETt3r1bW7duVUVFhQYPHqzy8nLPNrNnz9bGjRu1du1aZWZmKj8/X6NHj/b5ZwIAgNrDp5D57LPPFBsbq6SkJEVGRuoPf/iDPvnkkyrvZ/PmzZoyZYq6deumnj17Kj09XXl5ecrOzpYklZSUaOXKlVqyZIkGDhyoXr16KS0tTR9//LF2797ty+gAAKAW8SlkbrjhBi1btkz5+fl65ZVXVFBQoH79+ql79+5asmSJTp486dMwJSUlkqRmzZpJkrKzs1VRUaFBgwZ5tunSpYtat26tXbt2XXQfLpdLpaWlXg8AAFA7Veti36CgII0ePVpr167Vc889p4MHD2rOnDmKiYnRpEmTVFBQUOl9ud1uzZo1S3379lX37t0lSYWFhQoODlaTJk28to2IiFBhYeFF95OSkqKwsDDPIyYmxuc/HwAACGzVCpmsrCzdf//9ioqK0pIlSzRnzhwdOnRIW7duVX5+vkaMGFHpfSUmJmrfvn1as2ZNdUZScnKySkpKPI9jx45Va38AACBwBfnyoiVLligtLU25ubkaNmyYVq1apWHDhqlOnZ+7qF27dkpPT1fbtm0rtb8HHnhA7777rj744AO1atXKszwyMlLnzp1TcXGx11mZoqIiRUZGXnRfTqdTTqfTlz8WAAAwjE9nZFJTUzV+/HgdPXpUGzZs0O9+9ztPxPwiPDxcK1euvOx+LMvSAw88oPXr12v79u1q166d1/pevXqpXr16ysjI8CzLzc1VXl6e4uLifBkdAADUIj6dkTlw4MAVtwkODtbkyZMvu01iYqJWr16tt99+WyEhIZ7rXsLCwtSgQQOFhYVp2rRpSkpKUrNmzRQaGqoHH3xQcXFxuuWWW3wZHQAA1CI+hUxaWpoaN26ssWPHei1fu3atfvjhhysGzC9SU1MlSQMGDLhg/1OmTJEkPf/886pTp47GjBkjl8ulIUOG6KWXXvJlbAAAUMv49NFSSkqKWrRoccHy8PBwPfvss5Xej2VZF338EjGSVL9+fb344os6ffq0ysvLtW7dukteHwMAAK4tPoVMXl7eBdezSFKbNm2Ul5dX7aEAAAAqw6eQCQ8P1969ey9Y/sUXX6h58+bVHgoAAKAyfAqZu+++Ww899JB27Nih8+fP6/z589q+fbtmzpypu+66y98zAgAAXJRPF/s+/fTTOnLkiOLj4xUU9PMu3G63Jk2aVKVrZAAAAKrDp5AJDg7Wm2++qaefflpffPGFGjRooB49eqhNmzb+ng+AIdrOfc/n1x5ZMNyPk1SeiTNXR3X+vNVh4rGCOXwKmV906tRJnTp18tcsAAAAVeJTyJw/f17p6enKyMjQiRMn5Ha7vdZv377dL8MBAABcjk8hM3PmTKWnp2v48OHq3r27HA6Hv+cCAAC4Ip9CZs2aNXrrrbc0bNgwf88DAABQaT79+nVwcLA6dOjg71kAAACqxKeQefjhh7Vs2TJZluXveQAAACrNp4+WPvzwQ+3YsUObNm1St27dVK9ePa/169at88twAAAAl+NTyDRp0kSjRo3y9ywAAABV4lPIpKWl+XsOAACAKvPpGhlJ+umnn7Rt2za9/PLLOnPmjCQpPz9fZWVlfhsOAADgcnw6I3P06FENHTpUeXl5crlcuv322xUSEqLnnntOLpdLK1as8PecAAAAF/DpjMzMmTPVu3dvff/992rQoIFn+ahRo5SRkeG34QAAAC7HpzMy//73v/Xxxx8rODjYa3nbtm313Xff+WUwAACAK/HpjIzb7db58+cvWH78+HGFhIRUeygAAIDK8ClkBg8erKVLl3qeOxwOlZWVad68eXxtAQAAuGp8+mhp8eLFGjJkiLp27aqzZ89q/PjxOnDggFq0aKE33njD3zMCAABclE8h06pVK33xxRdas2aN9u7dq7KyMk2bNk0TJkzwuvgXAACgJvkUMpIUFBSkiRMn+nMWAACAKvEpZFatWnXZ9ZMmTfJpGAAAgKrwKWRmzpzp9byiokI//PCDgoOD1bBhQ0IGAABcFT6FzPfff3/BsgMHDmjGjBl65JFHqj0UAAS6tnPfs3sEAKrGdy39WseOHbVgwYILztYAAADUFL+FjPTzBcD5+fn+3CUAAMAl+fTR0jvvvOP13LIsFRQU6IUXXlDfvn39MhgAAMCV+BQyI0eO9HrucDjUsmVLDRw4UIsXL/bHXAAAAFfkU8i43W5/zwEAAFBlfr1GBgAA4Gry6YxMUlJSpbddsmSJLz8CAADginwKmc8//1yff/65Kioq1LlzZ0nS/v37VbduXd10002e7RwOh3+mBAAAuAifQuaOO+5QSEiIXn31VTVt2lTSzzfJmzp1qvr376+HH37Yr0MCAABcjE/XyCxevFgpKSmeiJGkpk2b6plnnuG3lgAAwFXj0xmZ0tJSnTx58oLlJ0+e1JkzZ6o9FBAoqnMb+iMLhvtxEuDaxP8HcSU+nZEZNWqUpk6dqnXr1un48eM6fvy4/vnPf2ratGkaPXq0v2cEAAC4KJ/OyKxYsUJz5szR+PHjVVFR8fOOgoI0bdo0LVq0yK8DAgAAXIpPIdOwYUO99NJLWrRokQ4dOiRJat++vRo1auTX4QAAAC6nWjfEKygoUEFBgTp27KhGjRrJsix/zQUAAHBFPoXMqVOnFB8fr06dOmnYsGEqKCiQJE2bNo1fvQYAAFeNTyEze/Zs1atXT3l5eWrYsKFn+bhx47R58+ZK7+eDDz7QHXfcoejoaDkcDm3YsMFr/ZQpU+RwOLweQ4cO9WVkAABQC/l0jcz777+vLVu2qFWrVl7LO3bsqKNHj1Z6P+Xl5erZs6fuueeeS/6209ChQ5WWluZ57nQ6fRkZAADUQj6FTHl5udeZmF+cPn26SqGRkJCghISEy27jdDoVGRlZ5RkBAEDt59NHS/3799eqVas8zx0Oh9xutxYuXKjbbrvNb8NJ0s6dOxUeHq7OnTtrxowZOnXq1GW3d7lcKi0t9XoAAIDayaczMgsXLlR8fLyysrJ07tw5Pfroo/rqq690+vRpffTRR34bbujQoRo9erTatWunQ4cO6Y9//KMSEhK0a9cu1a1b96KvSUlJ0fz58/02A2AH7mYKAJXjU8h0795d+/fv1wsvvKCQkBCVlZVp9OjRSkxMVFRUlN+Gu+uuuzz/3aNHD8XGxqp9+/bauXOn4uPjL/qa5ORkJSUleZ6XlpYqJibGbzMBAIDAUeWQqaio0NChQ7VixQr96U9/qomZLum6665TixYtdPDgwUuGjNPp5IJgAACuEVW+RqZevXrau3dvTcxyRcePH9epU6f8etYHAACYy6eLfSdOnKiVK1dW+4eXlZUpJydHOTk5kqTDhw8rJydHeXl5Kisr0yOPPKLdu3fryJEjysjI0IgRI9ShQwcNGTKk2j8bAACYz6drZH766Se98sor2rZtm3r16nXBdywtWbKkUvvJysry+i2nX65tmTx5slJTU7V37169+uqrKi4uVnR0tAYPHqynn36aj44AAICkKobMt99+q7Zt22rfvn266aabJEn79+/32sbhcFR6fwMGDLjs9zNt2bKlKuMBAIBrTJVCpmPHjiooKNCOHTsk/fyVBMuXL1dERESNDAcAAHA5VbpG5tdnTzZt2qTy8nK/DgQAAFBZPl3s+4vLfSwEAABQ06oUMr98A/WvlwEAANihStfIWJalKVOmeH5r6OzZs5o+ffoFv7W0bt06/00IAABwCVUKmcmTJ3s9nzhxol+HAQAAqIoqhUxaWlpNzQEAAFBl1brYFwAAwE6EDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjVemGeABQE9rOfc/uEQAYijMyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFjc2ReoZapzl9wjC4b7cRIAqHmckQEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICx+IoCoIZU56sCgEvhfQV444wMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADCWrSHzwQcf6I477lB0dLQcDoc2bNjgtd6yLD355JOKiopSgwYNNGjQIB04cMCeYQEAQMCxNWTKy8vVs2dPvfjiixddv3DhQi1fvlwrVqzQnj171KhRIw0ZMkRnz569ypMCAIBAZOt9ZBISEpSQkHDRdZZlaenSpXr88cc1YsQISdKqVasUERGhDRs26K677rqaowIAgAAUsNfIHD58WIWFhRo0aJBnWVhYmPr06aNdu3bZOBkAAAgUAXtn38LCQklSRESE1/KIiAjPuotxuVxyuVye56WlpTUzIAAAsF3AhoyvUlJSNH/+fLvHQADhlu4AUHsF7EdLkZGRkqSioiKv5UVFRZ51F5OcnKySkhLP49ixYzU6JwAAsE/Ahky7du0UGRmpjIwMz7LS0lLt2bNHcXFxl3yd0+lUaGio1wMAANROtn60VFZWpoMHD3qeHz58WDk5OWrWrJlat26tWbNm6ZlnnlHHjh3Vrl07PfHEE4qOjtbIkSPtGxoAAAQMW0MmKytLt912m+d5UlKSJGny5MlKT0/Xo48+qvLyct13330qLi5Wv379tHnzZtWvX9+ukQEAQACxNWQGDBggy7Iuud7hcOjPf/6z/vznP1/FqQAAgCkC9hoZAACAKyFkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgrCC7BwAA1G5t575n9wioxTgjAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGPxFQUwArc4B3A1VefvnCMLhvtxElwJZ2QAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsbiz7zWIO1YCAGoLzsgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFgBHTJPPfWUHA6H16NLly52jwUAAAJEwN9Hplu3btq2bZvneVBQwI8MAACukoCvgqCgIEVGRto9BgAACEAB/dGSJB04cEDR0dG67rrrNGHCBOXl5V12e5fLpdLSUq8HAAConQL6jEyfPn2Unp6uzp07q6CgQPPnz1f//v21b98+hYSEXPQ1KSkpmj9//lWeFJVRna9GwNXB/0YATBPQZ2QSEhI0duxYxcbGasiQIfrXv/6l4uJivfXWW5d8TXJyskpKSjyPY8eOXcWJAQDA1RTQZ2R+rUmTJurUqZMOHjx4yW2cTqecTudVnAoAANgloM/I/FpZWZkOHTqkqKgou0cBAAABIKBDZs6cOcrMzNSRI0f08ccfa9SoUapbt67uvvtuu0cDAAABIKA/Wjp+/LjuvvtunTp1Si1btlS/fv20e/dutWzZ0u7RAABAAAjokFmzZo3dIwAAgAAW0B8tAQAAXA4hAwAAjEXIAAAAYxEyAADAWAF9sS8AAL7iKzeuDZyRAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMbizr4AAPhRde4ofGTBcD9Ocm3gjAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMxVcUAABwjTP5axU4IwMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABj8RUF1WDyLZ0BALVLdf5NMhlnZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsYwImRdffFFt27ZV/fr11adPH33yySd2jwQAAAJAwIfMm2++qaSkJM2bN0+fffaZevbsqSFDhujEiRN2jwYAAGwW8CGzZMkS3XvvvZo6daq6du2qFStWqGHDhnrllVfsHg0AANgsoG+Id+7cOWVnZys5OdmzrE6dOho0aJB27dp10de4XC65XC7P85KSEklSaWmp3+dzu37w+bU1MU9l2TV3dX4uAFwLTPw7tqb+Pftlv5ZlXXa7gA6Z//73vzp//rwiIiK8lkdEROibb7656GtSUlI0f/78C5bHxMTUyIy+Cltq9wS+MXVuADCBiX/H1vTMZ86cUVhY2CXXB3TI+CI5OVlJSUme5263W6dPn1bz5s3lcDiu+jylpaWKiYnRsWPHFBoaetV//rWK424Pjrs9OO724LjXLMuydObMGUVHR192u4AOmRYtWqhu3boqKiryWl5UVKTIyMiLvsbpdMrpdHota9KkSU2NWGmhoaG80W3AcbcHx90eHHd7cNxrzuXOxPwioC/2DQ4OVq9evZSRkeFZ5na7lZGRobi4OBsnAwAAgSCgz8hIUlJSkiZPnqzevXvrt7/9rZYuXary8nJNnTrV7tEAAIDNAj5kxo0bp5MnT+rJJ59UYWGhbrjhBm3evPmCC4ADldPp1Lx58y74uAs1i+NuD467PTju9uC4BwaHdaXfawIAAAhQAX2NDAAAwOUQMgAAwFiEDAAAMBYhAwAAjEXI+EFqaqpiY2M9N0WKi4vTpk2bPOvPnj2rxMRENW/eXI0bN9aYMWMuuMkfqm/BggVyOByaNWuWZxnH3v+eeuopORwOr0eXLl086znmNee7777TxIkT1bx5czVo0EA9evRQVlaWZ71lWXryyScVFRWlBg0aaNCgQTpw4ICNE5uvbdu2F7zfHQ6HEhMTJfF+DwSEjB+0atVKCxYsUHZ2trKysjRw4ECNGDFCX331lSRp9uzZ2rhxo9auXavMzEzl5+dr9OjRNk9du3z66ad6+eWXFRsb67WcY18zunXrpoKCAs/jww8/9KzjmNeM77//Xn379lW9evW0adMmff3111q8eLGaNm3q2WbhwoVavny5VqxYoT179qhRo0YaMmSIzp49a+PkZvv000+93utbt26VJI0dO1YS7/eAYKFGNG3a1Pr73/9uFRcXW/Xq1bPWrl3rWfef//zHkmTt2rXLxglrjzNnzlgdO3a0tm7dav3f//2fNXPmTMuyLI59DZk3b57Vs2fPi67jmNecxx57zOrXr98l17vdbisyMtJatGiRZ1lxcbHldDqtN95442qMeE2YOXOm1b59e8vtdvN+DxCckfGz8+fPa82aNSovL1dcXJyys7NVUVGhQYMGebbp0qWLWrdurV27dtk4ae2RmJio4cOHex1jSRz7GnTgwAFFR0fruuuu04QJE5SXlyeJY16T3nnnHfXu3Vtjx45VeHi4brzxRv3tb3/zrD98+LAKCwu9jn1YWJj69OnDsfeTc+fO6bXXXtM999wjh8PB+z1AEDJ+8uWXX6px48ZyOp2aPn261q9fr65du6qwsFDBwcEXfHFlRESECgsL7Rm2FlmzZo0+++wzpaSkXLCOY18z+vTpo/T0dG3evFmpqak6fPiw+vfvrzNnznDMa9C3336r1NRUdezYUVu2bNGMGTP00EMP6dVXX5Ukz/H99V3POfb+s2HDBhUXF2vKlCmS+DsmUAT8VxSYonPnzsrJyVFJSYn+8Y9/aPLkycrMzLR7rFrt2LFjmjlzprZu3ar69evbPc41IyEhwfPfsbGx6tOnj9q0aaO33npLDRo0sHGy2s3tdqt379569tlnJUk33nij9u3bpxUrVmjy5Mk2T3dtWLlypRISEhQdHW33KPgfnJHxk+DgYHXo0EG9evVSSkqKevbsqWXLlikyMlLnzp1TcXGx1/ZFRUWKjIy0Z9haIjs7WydOnNBNN92koKAgBQUFKTMzU8uXL1dQUJAiIiI49ldBkyZN1KlTJx08eJD3ew2KiopS165dvZZdf/31no/1fjm+v/6NGY69fxw9elTbtm3T73//e88y3u+BgZCpIW63Wy6XS7169VK9evWUkZHhWZebm6u8vDzFxcXZOKH54uPj9eWXXyonJ8fz6N27tyZMmOD5b459zSsrK9OhQ4cUFRXF+70G9e3bV7m5uV7L9u/frzZt2kiS2rVrp8jISK9jX1paqj179nDs/SAtLU3h4eEaPny4Zxnv9wBh99XGtcHcuXOtzMxM6/Dhw9bevXutuXPnWg6Hw3r//fcty7Ks6dOnW61bt7a2b99uZWVlWXFxcVZcXJzNU9dO//tbS5bFsa8JDz/8sLVz507r8OHD1kcffWQNGjTIatGihXXixAnLsjjmNeWTTz6xgoKCrL/85S/WgQMHrNdff91q2LCh9dprr3m2WbBggdWkSRPr7bfftvbu3WuNGDHCateunfXjjz/aOLn5zp8/b7Vu3dp67LHHLljH+91+hIwf3HPPPVabNm2s4OBgq2XLllZ8fLwnYizLsn788Ufr/vvvt5o2bWo1bNjQGjVqlFVQUGDjxLXXr0OGY+9/48aNs6Kioqzg4GDrN7/5jTVu3Djr4MGDnvUc85qzceNGq3v37pbT6bS6dOli/fWvf/Va73a7rSeeeMKKiIiwnE6nFR8fb+Xm5to0be2xZcsWS9JFjyXvd/s5LMuy7D4rBAAA4AuukQEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABjr/wGgeiv+FpeuUwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hd['age'].plot.hist(bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.24.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(hd.isnull(),yticklabels=False, cbar=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "hd.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(hd.isnull(),yticklabels=False, cbar=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'age', 'cp', 'trestbps', 'chol', 'fbs', 'restecg',\n",
       "       'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'sex_female',\n",
       "       'sex_male', 'target_yes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Unnamed: 0</th>\n",
       "      <th>age</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "      <th>target_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.179975</td>\n",
       "      <td>-0.398856</td>\n",
       "      <td>0.098182</td>\n",
       "      <td>0.051948</td>\n",
       "      <td>-0.008088</td>\n",
       "      <td>-0.019919</td>\n",
       "      <td>-0.391267</td>\n",
       "      <td>0.378961</td>\n",
       "      <td>0.307510</td>\n",
       "      <td>-0.274223</td>\n",
       "      <td>0.380823</td>\n",
       "      <td>0.264371</td>\n",
       "      <td>-0.181503</td>\n",
       "      <td>0.181503</td>\n",
       "      <td>-0.862056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.179975</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.070586</td>\n",
       "      <td>0.276586</td>\n",
       "      <td>0.216155</td>\n",
       "      <td>0.127349</td>\n",
       "      <td>-0.119509</td>\n",
       "      <td>-0.396884</td>\n",
       "      <td>0.095919</td>\n",
       "      <td>0.211278</td>\n",
       "      <td>-0.167542</td>\n",
       "      <td>0.280493</td>\n",
       "      <td>0.067060</td>\n",
       "      <td>0.104863</td>\n",
       "      <td>-0.104863</td>\n",
       "      <td>-0.215905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.398856</td>\n",
       "      <td>-0.070586</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051210</td>\n",
       "      <td>-0.091003</td>\n",
       "      <td>0.097889</td>\n",
       "      <td>0.045198</td>\n",
       "      <td>0.302782</td>\n",
       "      <td>-0.391609</td>\n",
       "      <td>-0.153591</td>\n",
       "      <td>0.122779</td>\n",
       "      <td>-0.182492</td>\n",
       "      <td>-0.171826</td>\n",
       "      <td>0.043666</td>\n",
       "      <td>-0.043666</td>\n",
       "      <td>0.441747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.098182</td>\n",
       "      <td>0.276586</td>\n",
       "      <td>0.051210</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.135529</td>\n",
       "      <td>0.168406</td>\n",
       "      <td>-0.102731</td>\n",
       "      <td>-0.028102</td>\n",
       "      <td>0.072459</td>\n",
       "      <td>0.193254</td>\n",
       "      <td>-0.115657</td>\n",
       "      <td>0.096166</td>\n",
       "      <td>0.073411</td>\n",
       "      <td>0.068282</td>\n",
       "      <td>-0.068282</td>\n",
       "      <td>-0.139090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.051948</td>\n",
       "      <td>0.216155</td>\n",
       "      <td>-0.091003</td>\n",
       "      <td>0.135529</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026193</td>\n",
       "      <td>-0.166307</td>\n",
       "      <td>-0.038738</td>\n",
       "      <td>0.071904</td>\n",
       "      <td>0.066434</td>\n",
       "      <td>-0.026953</td>\n",
       "      <td>0.089595</td>\n",
       "      <td>0.106809</td>\n",
       "      <td>0.189984</td>\n",
       "      <td>-0.189984</td>\n",
       "      <td>-0.103311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>-0.008088</td>\n",
       "      <td>0.127349</td>\n",
       "      <td>0.097889</td>\n",
       "      <td>0.168406</td>\n",
       "      <td>0.026193</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.078938</td>\n",
       "      <td>0.001339</td>\n",
       "      <td>0.036164</td>\n",
       "      <td>0.009607</td>\n",
       "      <td>-0.060520</td>\n",
       "      <td>0.118146</td>\n",
       "      <td>-0.012527</td>\n",
       "      <td>-0.028242</td>\n",
       "      <td>0.028242</td>\n",
       "      <td>-0.028695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.019919</td>\n",
       "      <td>-0.119509</td>\n",
       "      <td>0.045198</td>\n",
       "      <td>-0.102731</td>\n",
       "      <td>-0.166307</td>\n",
       "      <td>-0.078938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038211</td>\n",
       "      <td>-0.073327</td>\n",
       "      <td>-0.051767</td>\n",
       "      <td>0.087550</td>\n",
       "      <td>-0.069496</td>\n",
       "      <td>-0.018690</td>\n",
       "      <td>0.053143</td>\n",
       "      <td>-0.053143</td>\n",
       "      <td>0.143242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.391267</td>\n",
       "      <td>-0.396884</td>\n",
       "      <td>0.302782</td>\n",
       "      <td>-0.028102</td>\n",
       "      <td>-0.038738</td>\n",
       "      <td>0.001339</td>\n",
       "      <td>0.038211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.395491</td>\n",
       "      <td>-0.347278</td>\n",
       "      <td>0.379258</td>\n",
       "      <td>-0.207472</td>\n",
       "      <td>-0.099342</td>\n",
       "      <td>0.045322</td>\n",
       "      <td>-0.045322</td>\n",
       "      <td>0.411950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.378961</td>\n",
       "      <td>0.095919</td>\n",
       "      <td>-0.391609</td>\n",
       "      <td>0.072459</td>\n",
       "      <td>0.071904</td>\n",
       "      <td>0.036164</td>\n",
       "      <td>-0.073327</td>\n",
       "      <td>-0.395491</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.286938</td>\n",
       "      <td>-0.259066</td>\n",
       "      <td>0.121005</td>\n",
       "      <td>0.199949</td>\n",
       "      <td>-0.143038</td>\n",
       "      <td>0.143038</td>\n",
       "      <td>-0.451127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.307510</td>\n",
       "      <td>0.211278</td>\n",
       "      <td>-0.153591</td>\n",
       "      <td>0.193254</td>\n",
       "      <td>0.066434</td>\n",
       "      <td>0.009607</td>\n",
       "      <td>-0.051767</td>\n",
       "      <td>-0.347278</td>\n",
       "      <td>0.286938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.572282</td>\n",
       "      <td>0.225590</td>\n",
       "      <td>0.206558</td>\n",
       "      <td>-0.099115</td>\n",
       "      <td>0.099115</td>\n",
       "      <td>-0.440061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.274223</td>\n",
       "      <td>-0.167542</td>\n",
       "      <td>0.122779</td>\n",
       "      <td>-0.115657</td>\n",
       "      <td>-0.026953</td>\n",
       "      <td>-0.060520</td>\n",
       "      <td>0.087550</td>\n",
       "      <td>0.379258</td>\n",
       "      <td>-0.259066</td>\n",
       "      <td>-0.572282</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.075180</td>\n",
       "      <td>-0.098280</td>\n",
       "      <td>0.027255</td>\n",
       "      <td>-0.027255</td>\n",
       "      <td>0.346160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.380823</td>\n",
       "      <td>0.280493</td>\n",
       "      <td>-0.182492</td>\n",
       "      <td>0.096166</td>\n",
       "      <td>0.089595</td>\n",
       "      <td>0.118146</td>\n",
       "      <td>-0.069496</td>\n",
       "      <td>-0.207472</td>\n",
       "      <td>0.121005</td>\n",
       "      <td>0.225590</td>\n",
       "      <td>-0.075180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.159101</td>\n",
       "      <td>-0.103767</td>\n",
       "      <td>0.103767</td>\n",
       "      <td>-0.392463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.264371</td>\n",
       "      <td>0.067060</td>\n",
       "      <td>-0.171826</td>\n",
       "      <td>0.073411</td>\n",
       "      <td>0.106809</td>\n",
       "      <td>-0.012527</td>\n",
       "      <td>-0.018690</td>\n",
       "      <td>-0.099342</td>\n",
       "      <td>0.199949</td>\n",
       "      <td>0.206558</td>\n",
       "      <td>-0.098280</td>\n",
       "      <td>0.159101</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.219176</td>\n",
       "      <td>0.219176</td>\n",
       "      <td>-0.350782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex_female</th>\n",
       "      <td>-0.181503</td>\n",
       "      <td>0.104863</td>\n",
       "      <td>0.043666</td>\n",
       "      <td>0.068282</td>\n",
       "      <td>0.189984</td>\n",
       "      <td>-0.028242</td>\n",
       "      <td>0.053143</td>\n",
       "      <td>0.045322</td>\n",
       "      <td>-0.143038</td>\n",
       "      <td>-0.099115</td>\n",
       "      <td>0.027255</td>\n",
       "      <td>-0.103767</td>\n",
       "      <td>-0.219176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.271211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex_male</th>\n",
       "      <td>0.181503</td>\n",
       "      <td>-0.104863</td>\n",
       "      <td>-0.043666</td>\n",
       "      <td>-0.068282</td>\n",
       "      <td>-0.189984</td>\n",
       "      <td>0.028242</td>\n",
       "      <td>-0.053143</td>\n",
       "      <td>-0.045322</td>\n",
       "      <td>0.143038</td>\n",
       "      <td>0.099115</td>\n",
       "      <td>-0.027255</td>\n",
       "      <td>0.103767</td>\n",
       "      <td>0.219176</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.271211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target_yes</th>\n",
       "      <td>-0.862056</td>\n",
       "      <td>-0.215905</td>\n",
       "      <td>0.441747</td>\n",
       "      <td>-0.139090</td>\n",
       "      <td>-0.103311</td>\n",
       "      <td>-0.028695</td>\n",
       "      <td>0.143242</td>\n",
       "      <td>0.411950</td>\n",
       "      <td>-0.451127</td>\n",
       "      <td>-0.440061</td>\n",
       "      <td>0.346160</td>\n",
       "      <td>-0.392463</td>\n",
       "      <td>-0.350782</td>\n",
       "      <td>0.271211</td>\n",
       "      <td>-0.271211</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0       age        cp  trestbps      chol       fbs  \\\n",
       "Unnamed: 0    1.000000  0.179975 -0.398856  0.098182  0.051948 -0.008088   \n",
       "age           0.179975  1.000000 -0.070586  0.276586  0.216155  0.127349   \n",
       "cp           -0.398856 -0.070586  1.000000  0.051210 -0.091003  0.097889   \n",
       "trestbps      0.098182  0.276586  0.051210  1.000000  0.135529  0.168406   \n",
       "chol          0.051948  0.216155 -0.091003  0.135529  1.000000  0.026193   \n",
       "fbs          -0.008088  0.127349  0.097889  0.168406  0.026193  1.000000   \n",
       "restecg      -0.019919 -0.119509  0.045198 -0.102731 -0.166307 -0.078938   \n",
       "thalach      -0.391267 -0.396884  0.302782 -0.028102 -0.038738  0.001339   \n",
       "exang         0.378961  0.095919 -0.391609  0.072459  0.071904  0.036164   \n",
       "oldpeak       0.307510  0.211278 -0.153591  0.193254  0.066434  0.009607   \n",
       "slope        -0.274223 -0.167542  0.122779 -0.115657 -0.026953 -0.060520   \n",
       "ca            0.380823  0.280493 -0.182492  0.096166  0.089595  0.118146   \n",
       "thal          0.264371  0.067060 -0.171826  0.073411  0.106809 -0.012527   \n",
       "sex_female   -0.181503  0.104863  0.043666  0.068282  0.189984 -0.028242   \n",
       "sex_male      0.181503 -0.104863 -0.043666 -0.068282 -0.189984  0.028242   \n",
       "target_yes   -0.862056 -0.215905  0.441747 -0.139090 -0.103311 -0.028695   \n",
       "\n",
       "             restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "Unnamed: 0 -0.019919 -0.391267  0.378961  0.307510 -0.274223  0.380823   \n",
       "age        -0.119509 -0.396884  0.095919  0.211278 -0.167542  0.280493   \n",
       "cp          0.045198  0.302782 -0.391609 -0.153591  0.122779 -0.182492   \n",
       "trestbps   -0.102731 -0.028102  0.072459  0.193254 -0.115657  0.096166   \n",
       "chol       -0.166307 -0.038738  0.071904  0.066434 -0.026953  0.089595   \n",
       "fbs        -0.078938  0.001339  0.036164  0.009607 -0.060520  0.118146   \n",
       "restecg     1.000000  0.038211 -0.073327 -0.051767  0.087550 -0.069496   \n",
       "thalach     0.038211  1.000000 -0.395491 -0.347278  0.379258 -0.207472   \n",
       "exang      -0.073327 -0.395491  1.000000  0.286938 -0.259066  0.121005   \n",
       "oldpeak    -0.051767 -0.347278  0.286938  1.000000 -0.572282  0.225590   \n",
       "slope       0.087550  0.379258 -0.259066 -0.572282  1.000000 -0.075180   \n",
       "ca         -0.069496 -0.207472  0.121005  0.225590 -0.075180  1.000000   \n",
       "thal       -0.018690 -0.099342  0.199949  0.206558 -0.098280  0.159101   \n",
       "sex_female  0.053143  0.045322 -0.143038 -0.099115  0.027255 -0.103767   \n",
       "sex_male   -0.053143 -0.045322  0.143038  0.099115 -0.027255  0.103767   \n",
       "target_yes  0.143242  0.411950 -0.451127 -0.440061  0.346160 -0.392463   \n",
       "\n",
       "                thal  sex_female  sex_male  target_yes  \n",
       "Unnamed: 0  0.264371   -0.181503  0.181503   -0.862056  \n",
       "age         0.067060    0.104863 -0.104863   -0.215905  \n",
       "cp         -0.171826    0.043666 -0.043666    0.441747  \n",
       "trestbps    0.073411    0.068282 -0.068282   -0.139090  \n",
       "chol        0.106809    0.189984 -0.189984   -0.103311  \n",
       "fbs        -0.012527   -0.028242  0.028242   -0.028695  \n",
       "restecg    -0.018690    0.053143 -0.053143    0.143242  \n",
       "thalach    -0.099342    0.045322 -0.045322    0.411950  \n",
       "exang       0.199949   -0.143038  0.143038   -0.451127  \n",
       "oldpeak     0.206558   -0.099115  0.099115   -0.440061  \n",
       "slope      -0.098280    0.027255 -0.027255    0.346160  \n",
       "ca          0.159101   -0.103767  0.103767   -0.392463  \n",
       "thal        1.000000   -0.219176  0.219176   -0.350782  \n",
       "sex_female -0.219176    1.000000 -1.000000    0.271211  \n",
       "sex_male    0.219176   -1.000000  1.000000   -0.271211  \n",
       "target_yes -0.350782    0.271211 -0.271211    1.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=hd.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAHpCAYAAABTBjLWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACj10lEQVR4nOzdeVyN6f8/8Ndp30tps7UoCSW7ZJsRhTG2sY1dMjM0lqxNWjBkjChjhrEzw2DM8B3DZIksSbIUQ5YimVFZk6JU5/794ed85qg4Oedu0ev5edyPj65znfd13cdUb9d13dclEQRBABERERGJTq2yO0BERERUUzDxIiIiIqogTLyIiIiIKggTLyIiIqIKwsSLiIiIqIIw8SIiIiKqIEy8iIiIiCoIEy8iIiKiCsLEi4iIiKiCMPEiIiIiqiBMvIiIiKjKO378OPr06YM6depAIpFgz549b31PTEwMWrZsCW1tbTg4OGDTpk0l6nz//fewtbWFjo4O2rVrhzNnzqi+8//BxIuIiIiqvLy8PDRv3hzff/+9QvVv3bqF3r1744MPPkBiYiKmTp2K8ePH48CBA7I6O3bsgL+/P0JCQnD+/Hk0b94cXl5euHfvnli3AQkPySYiIqLqRCKRYPfu3ejXr1+ZdWbPno19+/bh77//lpUNHToU2dnZiIqKAgC0a9cObdq0wcqVKwEAUqkU9evXx5dffok5c+aI0neOeBEREVGlKCgoQE5OjtxVUFCgkthxcXHw9PSUK/Py8kJcXBwA4MWLFzh37pxcHTU1NXh6esrqiEFDtMikMsXSGFHj3+r1q6jxAeCbSxaitzHBIUf0Np4Vif8t81TkNgRBImp8ANj7r6bobUT0TxG9jYJs8f9tuuy0g6jxA7yvixofAJ5maonexvZkG9Hb6F0/S/Q23I4fFDV+7rMbosYHVPs7KSwsBvPmzZMrCwkJQWhoqNKxMzMzYWlpKVdmaWmJnJwcPH/+HI8fP0ZxcXGpda5evap0+2Vh4kVERESKk0pVFiogIAD+/v5yZdra2iqLXxUx8SIiIqJKoa2tLVqiZWVlhaws+VHMrKwsGBkZQVdXF+rq6lBXVy+1jpWVlSh9ArjGi4iIiMpDKlXdJSJ3d3dER0fLlR06dAju7u4AAC0tLbRq1UqujlQqRXR0tKyOGDjiRURERIoTOWEqS25uLlJS/re289atW0hMTISpqSkaNGiAgIAA/Pvvv9iyZQsA4PPPP8fKlSsxa9YsjBs3DkeOHMHOnTuxb98+WQx/f3+MHj0arVu3Rtu2bREREYG8vDyMHTtWtPtg4qWg0NBQ7NmzB4mJiZXdFSIiohrn7Nmz+OCDD2Rfv1obNnr0aGzatAkZGRlIT0+XvW5nZ4d9+/Zh2rRpiIyMRL169bBu3Tp4eXnJ6gwZMgT3799HcHAwMjMz4ebmhqioqBIL7lWpXFONXbt2xdSpU0uUb9q0CSYmJirq0vvl119/RePGjaGjowMXFxfs37+/srtERET07gRBdVc5dO3aFYIglLhe7Ua/adMmxMTElHjPhQsXUFBQgNTUVIwZM6ZEXD8/P9y+fRsFBQWIj49Hu3bt3vGDUQzXeIno1KlTGDZsGHx8fHDhwgX069cP/fr1k9vMjYiIqFqpJmu8qipREq8xY8agX79+WLp0KaytrWFmZoZJkyahsLBQVsfW1haLFi3CuHHjYGhoiAYNGmDNmjVycWbPno1GjRpBT08P9vb2CAoKkosRGhoKNzc3bNiwAQ0aNICBgQEmTpyI4uJiLFmyBFZWVrCwsMDChQvl4mZnZ2P8+PEwNzeHkZERPvzwQyQlJcnVWbx4MSwtLWFoaAgfHx/k5+eX+3OIjIyEt7c3Zs6cCWdnZyxYsAAtW7aU7ZBLRERU7TDxUopoI15Hjx5Famoqjh49is2bN2PTpk0lDqcMDw9H69atceHCBUycOBFffPEFrl27Jnvd0NAQmzZtwpUrVxAZGYm1a9di+fLlcjFSU1Px119/ISoqCr/88gvWr1+P3r17459//sGxY8fwzTffYO7cuYiPj5e9Z9CgQbh37x7++usvnDt3Di1btkS3bt3w6NEjAMDOnTsRGhqKRYsW4ezZs7C2tsYPP/wg125MTAwkEgnS0tLK/AzetmsuERER1SyiLa6vVasWVq5cCXV1dTRu3Bi9e/dGdHQ0fH19ZXV69eqFiRMnAng5urV8+XIcPXoUTk5OAIC5c+fK6tra2mLGjBnYvn07Zs2aJSuXSqXYsGEDDA0N0aRJE3zwwQe4du0a9u/fDzU1NTg5OeGbb77B0aNH0a5dO5w8eRJnzpzBvXv3ZHuHLF26FHv27MGuXbswYcIEREREwMfHBz4+PgCAr7/+GocPH5Yb9dLT04OTkxM0NcveobusXXMzMzPLfE9BQUGJ4xI0NF9AW1v8naGJiIjeqoaOVKmKaCNeTZs2hbq6uuxra2vrEqd9u7q6yv4skUhgZWUlV2fHjh3w8PCAlZUVDAwMMHfuXLknFoCXCZmhoaHsa0tLSzRp0gRqampyZa/iJiUlITc3F2ZmZjAwMJBdt27dQmpqKgAgOTm5xOK61/f0aNu2La5evYq6deuW63N5m7CwMBgbG8tdixdvU2kbRERE74xTjUop14iXkZERnjx5UqI8OzsbxsbGcmWvjwRJJBJIX/uQ31QnLi4Ow4cPx7x58+Dl5QVjY2Ns374d4eHhb43xpri5ubmwtrYu8eQDAJU/mVnWrrlv2hG3tOMTNDRPq7RfREREVDnKlXg5OTnh4MGSB3yeP38ejRo1UlmngJdPBNrY2CAwMFBWdvv2baXjtmzZEpmZmdDQ0ICtrW2pdZydnREfH49Ro0bJyk6fLn/y82rX3P9uwfHfXXNLU9rxCcVSTjMSEVEVUUNHqlSlXFONX3zxBa5fv47Jkyfj4sWLuHbtGpYtW4ZffvkF06dPV2nHHB0dkZ6eju3btyM1NRUrVqzA7t27lY7r6ekJd3d39OvXDwcPHkRaWhpOnTqFwMBAnD17FgAwZcoUbNiwARs3bsT169cREhKCy5cvy8U5c+YMGjdujH///bfMtqZMmYKoqCiEh4fj6tWrCA0NxdmzZ+Hn56f0fRAREVUGiSBV2VUTlSvxsre3x/Hjx3H16lV4enqiXbt22LlzJ3799Vd4e3urtGMff/wxpk2bBj8/P7i5ueHUqVMICgpSOq5EIsH+/fvRuXNnjB07Fo0aNcLQoUNx+/Zt2UL4IUOGICgoCLNmzUKrVq1w+/ZtfPHFF3Jxnj17hmvXrsltb/G6Dh06YNu2bVizZg2aN2+OXbt2Yc+ePWjWrJnS90FERETVj0QQyrl1LFW4YmmMqPFv9fpV1PgA8M0lC9HbmOCQI3obz4rEP2XrqchtCIJE1PgAsPffsp/2VZWI/ilvr6Skgmzx95hedtpB1PgB3tdFjQ8ATzPFXw6xPdlG9DZ61896eyUluR0vuVxHlXKf3RA1PgBI76nud4aaxSCVxaoueFYjERERKU7K8Rpl8MggIiIiogrCES8iIiJSHJ9qVAoTLyIiIlIcEy+lMPEiIiIixdXQbSBUhWu8iIiIiCoIR7yqAbG3e7DbL/7jvB+2PiJ6G7ZWj0Vv49kz8R+bz87TETX+2YcmosavKJcrYIsSC6M80duw1BF39CDvvvhbe6RkmYneRkfzbNHbiM00F72NYJuRorchOk41KoWJFxERESmO20kohVONRERERBWEI15ERESkOE41KoWJFxERESmOiZdSONVIREREVEE44kVEREQKk3DESylMvIiIiEhxAp9qVAanGt8gKioKHTt2hImJCczMzPDRRx8hNTVV9vqpU6fg5uYGHR0dtG7dGnv27IFEIkFiYqKszt9//42ePXvCwMAAlpaWGDlyJB48eFAJd0NERESVjYnXG+Tl5cHf3x9nz55FdHQ01NTU0L9/f0ilUuTk5KBPnz5wcXHB+fPnsWDBAsyePVvu/dnZ2fjwww/RokULnD17FlFRUcjKysLgwYMr6Y6IiIiUJJWq7qqBONX4BgMHDpT7esOGDTA3N8eVK1dw8uRJSCQSrF27Fjo6OmjSpAn+/fdf+Pr6yuqvXLkSLVq0wKJFi+Ri1K9fH9evX0ejRo1KtFlQUICCggL5MmkxtNXUVXx3RERE76CGJkyqwhGvN7hx4waGDRsGe3t7GBkZwdbWFgCQnp6Oa9euwdXVFTo6/zvepW3btnLvT0pKwtGjR2FgYCC7GjduDAByU5b/FRYWBmNjY7nrx5vnxLlBIiKi8pIKqrtqII54vUGfPn1gY2ODtWvXok6dOpBKpWjWrBlevHih0Ptzc3PRp08ffPPNNyVes7a2LvU9AQEB8Pf3lyu788ms8neeiIiIqhwmXmV4+PAhrl27hrVr16JTp04AgJMnT8ped3Jyws8//4yCggJoa2sDABISEuRitGzZEr/99htsbW2hoaHYR62trS2LJyvjNCMREVUVnGpUCqcay1CrVi2YmZlhzZo1SElJwZEjR+RGoj799FNIpVJMmDABycnJOHDgAJYuXQoAkEgkAIBJkybh0aNHGDZsGBISEpCamooDBw5g7NixKC4urpT7IiIiUgoX1yuFiVcZ1NTUsH37dpw7dw7NmjXDtGnT8O2338peNzIywt69e5GYmAg3NzcEBgYiODgYAGTrvurUqYPY2FgUFxejR48ecHFxwdSpU2FiYgI1NX70RERENQ2nGt/A09MTV65ckSsT/rNxXIcOHZCUlCT7euvWrdDU1ESDBg1kZY6Ojvj999/F7ywREVFF4AaqSmHipYQtW7bA3t4edevWRVJSEmbPno3BgwdDV1e3srtGREQkjho6RagqTLyUkJmZieDgYGRmZsLa2hqDBg3CwoULK7tbREREVEVxoZESZs2ahbS0NOTn5+PWrVtYvnw59PT0KrtbRERE4qnEfby+//572NraQkdHB+3atcOZM2fKrNu1a1dIJJISV+/evWV1xowZU+J1b2/vd/pYFMURLyIiIlJcJU017tixA/7+/li9ejXatWuHiIgIeHl54dq1a7CwsChR//fff5fbd/Phw4do3rw5Bg0aJFfP29sbGzdulH39+pZOqsYRLyIiIqryli1bBl9fX4wdOxZNmjTB6tWroaenhw0bNpRa39TUFFZWVrLr0KFD0NPTK5F4aWtry9WrVauWqPfBxIuIiIgUp8J9vAoKCpCTkyN3vX5eMQC8ePEC586dg6enp6xMTU0Nnp6eiIuLU6jb69evx9ChQ6Gvry9XHhMTAwsLCzg5OeGLL77Aw4cPlft83oJTjdXAN5dKDqGq0oetj4gaHwAGn/1Q9DZOdvpL9DbuPNN5eyUlORnlihq/oUG+qPEBoH+zTNHbmBNjJ3obLUyNRG+jo3m2qPG3/W0ranwAUJeIv71AM+PnordhqCn+xtbDet0UvQ3RqfCMxbCwMMybN0+uLCQkBKGhoXJlDx48QHFxMSwtLeXKLS0tcfXq1be2c+bMGfz9999Yv369XLm3tzcGDBgAOzs7pKam4quvvkLPnj0RFxcHdXVxTo1h4kVERESKE1S3xqu084nFWGO1fv16uLi4oG3btnLlQ4cOlf3ZxcUFrq6uaNiwIWJiYtCtWzeV9wPgVCMRERFVEm1tbRgZGcldpSVetWvXhrq6OrKysuTKs7KyYGVl9cY28vLysH37dvj4+Ly1P/b29qhduzZSUlLKdyPlwMSLiIiIFFcJ20loaWmhVatWiI6O/l83pFJER0fD3d39je/99ddfUVBQgBEjRry1nX/++QcPHz6EtbW1wn0rLyZeREREpLhKOiTb398fa9euxebNm5GcnIwvvvgCeXl5GDt2LABg1KhRCAgIKPG+9evXo1+/fjAzM5Mrz83NxcyZM3H69GmkpaUhOjoaffv2hYODA7y8vN7983kLrvEiIiKiKm/IkCG4f/++7MQYNzc3REVFyRbcp6enQ01Nfjzp2rVrOHnyJA4ePFginrq6Oi5evIjNmzcjOzsbderUQY8ePbBgwQJR9/Ji4kVERESKU+FTjeXl5+cHPz+/Ul+LiYkpUebk5AShjEO9dXV1ceDAAVV2TyFMvIiIiEhxPCRbKVzjpSSpVIolS5bAwcEB2traaNCgARYuXIi0tDRIJBJs374dHTp0gI6ODpo1a4Zjx45VdpeJiIiokjDxUlJAQAAWL16MoKAgXLlyBdu2bZPb4G3mzJmYPn06Lly4AHd3d/Tp00f0XXGJiIhEU4mHZL8PmHgp4enTp4iMjMSSJUswevRoNGzYEB07dsT48eNldfz8/DBw4EA4Oztj1apVMDY2LrFzLhERUbUhSFV31UBc46WE5ORkFBQUvHF32//uL6KhoYHWrVsjOTm5zPoFBQUlzqkqFoqgLuFfFRERUXXHES8l6OrqqjxmWFgYjI2N5a7EpydU3g4REdE74VSjUph4KcHR0RG6urpyO+m+7vTp07I/FxUV4dy5c3B2di6zfkBAAJ48eSJ3uRl2Umm/iYiI3hkTL6Vw/koJOjo6mD17NmbNmgUtLS14eHjg/v37uHz5smz68fvvv4ejoyOcnZ2xfPlyPH78GOPGjSszpra2domN2zjNSEREVQa3k1AKf6MrKSgoCBoaGggODsbdu3dhbW2Nzz//XPb64sWLsXjxYiQmJsLBwQF//PEHateuXYk9JiIiosrCxEtJampqCAwMRGBgoFx5WloaAMDZ2Rnx8fGV0DMiIiIR1NApQlVh4kVERESKY+KlFC6uJyIiIqogHPESia2tbZkHcxIREVVbXFyvFCZeREREpDgOKiiFU41EREREFYQjXkRERKQ4Lq5XChOvamCCQ46o8W2tHosaHwBOdvpL9DY6nugpehtXuu8RvQ0t9WJR4195YihqfAB4cqOe6G2Msc8VvQ1LgzzR2zieYSFq/A61n4gaHwAKpeJPngiQiN5GoVT8Njb92VDU+BMWiRr+JSZeSuFUIxEREVEF4YgXERERKY5PNSqFiRcREREpjlONSmHiRURERIpj4qUUrvEqw5gxY9CvX7/K7gYRERG9R6p04tW1a1dMnTpV1DbS0tIgkUiQmJgoajtERETvBamguqsGqtZTjYIgoLi4GBoa1fo2iIiIqg1BhQmT+Bt4VD1VdsRrzJgxOHbsGCIjIyGRSCCRSLBp0yZIJBL89ddfaNWqFbS1tXHy5ElIpVKEhYXBzs4Ourq6aN68OXbt2iWL9fjxYwwfPhzm5ubQ1dWFo6MjNm7cCACws7MDALRo0QISiQRdu3aV68e8efNgbm4OIyMjfP7553jx4oXsta5du8LPzw9+fn4wNjZG7dq1ERQUJHdG4w8//ABHR0fo6OjA0tISn3zyiYifGhEREVVlVXaoKDIyEtevX0ezZs0wf/58AMDly5cBAHPmzMHSpUthb2+PWrVqISwsDD///DNWr14NR0dHHD9+HCNGjIC5uTm6dOmCoKAgXLlyBX/99Rdq166NlJQUPH/+HABw5swZtG3bFocPH0bTpk2hpaUl60N0dDR0dHQQExODtLQ0jB07FmZmZli4cKGszubNm+Hj44MzZ87g7NmzmDBhAho0aABfX1+cPXsWkydPxk8//YQOHTrg0aNHOHHiRAV+ikRERCrGsxqVUmUTL2NjY2hpaUFPTw9WVlYAgKtXrwIA5s+fj+7duwMACgoKsGjRIhw+fBju7u4AAHt7e5w8eRI//vgjunTpgvT0dLRo0QKtW7cGANja2sraMTc3BwCYmZnJ2nlFS0sLGzZsgJ6eHpo2bYr58+dj5syZWLBgAdTUXg4W1q9fH8uXL4dEIoGTkxMuXbqE5cuXw9fXF+np6dDX18dHH30EQ0ND2NjYoEWLFuJ9aERERGKroWuzVKXKTjW+yasECgBSUlLw7NkzdO/eHQYGBrJry5YtSE1NBQB88cUX2L59O9zc3DBr1iycOnVKoXaaN28OPT092dfu7u7Izc3FnTt3ZGXt27eHRCKRq3Pjxg0UFxeje/fusLGxgb29PUaOHImtW7fi2bNnb2yzoKAAOTk5ctcLaZFC/SUiIqKqrVomXvr6+rI/5+a+PK9t3759SExMlF1XrlyRrfPq2bMnbt++jWnTpuHu3bvo1q0bZsyYIXo/DQ0Ncf78efzyyy+wtrZGcHAwmjdvjuzs7DLfExYWBmNjY7lrU/oZ0ftKRESkED7VqJQqnXhpaWmhuPjNBwY3adIE2traSE9Ph4ODg9xVv359WT1zc3OMHj0aP//8MyIiIrBmzRpZGwBKbScpKUm2FgwATp8+DQMDA7m48fHxcu85ffo0HB0doa6uDgDQ0NCAp6cnlixZgosXLyItLQ1Hjhwp834CAgLw5MkTuWtMg7Zv/AyIiIgqDBMvpVTZNV7Ay7VY8fHxSEtLg4GBAaSlnA9laGiIGTNmYNq0aZBKpejYsSOePHmC2NhYGBkZYfTo0QgODkarVq3QtGlTFBQU4M8//4SzszMAwMLCArq6uoiKikK9evWgo6MDY2NjAMCLFy/g4+ODuXPnIi0tDSEhIfDz85Ot7wKA9PR0+Pv747PPPsP58+fx3XffITw8HADw559/4ubNm+jcuTNq1aqF/fv3QyqVwsnJqcx71tbWhra2tlyZllqV/msiIiIiBVXp3+gzZszA6NGj0aRJEzx//ly2BcTrFixYAHNzc4SFheHmzZswMTFBy5Yt8dVXXwF4OaoVEBCAtLQ06OrqolOnTti+fTuAlyNSK1aswPz58xEcHIxOnTohJiYGANCtWzc4Ojqic+fOKCgowLBhwxAaGirX9qhRo/D8+XO0bdsW6urqmDJlCiZMmAAAMDExwe+//47Q0FDk5+fD0dERv/zyC5o2bSrOB0ZERCS2GjpSpSoSQeBzoe+qa9eucHNzQ0REhKjtJHSZLmp8W6vHosYHgCv/mIveRscTPUVv40r3PaK3oaX+5ul1ZZ2+ZyZqfAAw0RL/gRBz7QLR27A0yBO9jeMZFqLGb2b8VNT4AFAoFX/VilABW23+80xH9DaeFKqLGn/CxRBR4wNAceQElcVSn7JGZbGqiyo94kVERERVDEe8lFKlF9cTERERvU+YeCkhJiZG9GlGIiKiKqUSn2r8/vvvYWtrCx0dHbRr1w5nzpS93dKrYwb/e+noyE8nC4KA4OBgWFtbQ1dXF56enrhx40a5+1UeTLyIiIhIcZWUeO3YsQP+/v4ICQnB+fPn0bx5c3h5eeHevXtlvsfIyAgZGRmy6/bt23KvL1myBCtWrMDq1asRHx8PfX19eHl5IT8//50+GkUw8SIiIqIqb9myZfD19cXYsWPRpEkTrF69Gnp6etiwYUOZ75FIJLCyspJdlpaWstcEQUBERATmzp2Lvn37wtXVFVu2bMHdu3exZ88e0e6DiRcREREpThBUdpV2TF5BQcknll+8eIFz587B09NTVqampgZPT0/ExcWV2dXc3FzY2Nigfv366Nu3Ly5fvix77datW8jMzJSLaWxsjHbt2r0xprL4VGM18KxI3L+mZ8+0RI0PAHcq4DHtitjqocmhfqK3EdPxgKjxnxaJ/1i+kaboTVSIzFwD0dswFXnrDWkFbMOgLfIWKABgoive1M8rZx5VwN+3ZsmNwKsbQYW3EBYWhnnz5smVhYSElNgz88GDByguLpYbsQIAS0tLXL16tdTYTk5O2LBhA1xdXfHkyRMsXboUHTp0wOXLl1GvXj1kZmbKYrwe89VrYmDiRURERJUiICAA/v7+cmWvn97yrtzd3eHu7i77ukOHDnB2dsaPP/6IBQsWqKSNd8HEi4iIiBSnwn28SjsmrzS1a9eGuro6srKy5MqzsrJgZWWlUFuamppo0aIFUlJSAED2vqysLFhbW8vFdHNzU/AOyo9rvIiIiEhxlfBUo5aWFlq1aoXo6Oj/dUMqRXR0tNyo1psUFxfj0qVLsiTLzs4OVlZWcjFzcnIQHx+vcMx3wREvIiIiqvL8/f0xevRotG7dGm3btkVERATy8vIwduxYAC/PTq5bty7CwsIAAPPnz0f79u3h4OCA7OxsfPvtt7h9+zbGjx8P4OUTj1OnTsXXX38NR0dH2NnZISgoCHXq1EG/fv1Euw8mXgpKS0uDnZ0dLly4oNQQpK2tLaZOnYqpU6eqrG9EREQVRZWL68tjyJAhuH//PoKDg5GZmQk3NzdERUXJFsenp6dDTe1/E3mPHz+Gr68vMjMzUatWLbRq1QqnTp1CkyZNZHVmzZqFvLw8TJgwAdnZ2ejYsSOioqJKbLSqSky8iIiISHGVeFajn58f/Pz8Sn0tJiZG7uvly5dj+fLlb4wnkUgwf/58zJ8/X1VdfCsmXkRERKS46r8jRqXi4vrXSKVSLFmyBA4ODtDW1kaDBg2wcOFC2es3b97EBx98AD09PTRv3rzEJmu//fYbmjZtCm1tbdja2iI8PLyib4GIiIiqKCZerwkICMDixYsRFBSEK1euYNu2bXKbqwUGBmLGjBlITExEo0aNMGzYMBQVvdwA8dy5cxg8eDCGDh2KS5cuITQ0FEFBQdi0aVMl3Q0REZFqCVJBZVdNxKnG/3j69CkiIyOxcuVKjB49GgDQsGFDdOzYEWlpaQCAGTNmoHfv3gCAefPmoWnTpkhJSUHjxo2xbNkydOvWDUFBQQCARo0a4cqVK/j2228xZsyYyrglIiIi1eJUo1I44vUfycnJKCgoQLdu3cqs4+rqKvvzq71AXp2MnpycDA8PD7n6Hh4euHHjBoqLFTtSo7Rzq15IxT1ShIiIiCoGE6//0NXVfWsdTc3/HUInkbw8A00qVV36HxYWBmNjY7lr6z+nVRafiIhIKYIKrxqIidd/ODo6QldXV24X2/JwdnZGbGysXFlsbCwaNWoEdXV1hWIEBATgyZMnctfweu3fqT9ERESqxjVeyuEar//Q0dHB7NmzMWvWLGhpacHDwwP379/H5cuX3zj9+Mr06dPRpk0bLFiwAEOGDEFcXBxWrlyJH374QeE+lHZulZYa/5qIiIjeB/yN/pqgoCBoaGggODgYd+/ehbW1NT7//HOF3tuyZUvs3LkTwcHBWLBgAaytrTF//nwurCciovcHF9crhYnXa9TU1BAYGIjAwMASrwmC/LCoiYlJibKBAwdi4MCBZcZ/9XQkERFRdVRZRwa9L7jGi4iIiKiCcMSLiIiIFMcRL6Uw8SIiIiKFcapROUy8iIiISHFMvJTCNV5EREREFYQjXkRERKQwoWbue6oyTLyqgadF4v41ZefpiBofAJyMckVvQ0tdsfMwlRHT8YDobXQ96SVq/KzWR0WNDwC66uLPReQXK3YahDIKpeJPCrwQuQ1tNfG/L/55pvdetOFq/Fz0NnKLxP/vVmxc46UcTjUSERERVRCOeBEREZHiOOKlFCZeREREpDBONSqHU41EREREFYQjXkRERKQwPtWoHI54lYMgCJgwYQJMTU0hkUhgYmKCqVOnVna3iIiIKo5UorqrBmLiVQ5RUVHYtGkT/vzzT2RkZKBZs2aV3SUiIiKqRjjVWA6pqamwtrZGhw4dAAAaGvz4iIioZuHieuVwxEtBY8aMwZdffon09HRIJBLY2toCAIqKiuDn5wdjY2PUrl0bQUFBEP4zAf7DDz/A0dEROjo6sLS0xCeffFJJd0BERKQ8QZCo7KqJmHgpKDIyEvPnz0e9evWQkZGBhIQEAMDmzZuhoaGBM2fOIDIyEsuWLcO6desAAGfPnsXkyZMxf/58XLt2DVFRUejcuXNl3gYREZFSBKnqrpqIc2UKMjY2hqGhIdTV1WFlZSUrr1+/PpYvXw6JRAInJydcunQJy5cvh6+vL9LT06Gvr4+PPvoIhoaGsLGxQYsWLd7YTkFBAQoKCuTKCqVF0FTjXxUREVF1xxEvJbVv3x4Syf+GS93d3XHjxg0UFxeje/fusLGxgb29PUaOHImtW7fi2bNnb4wXFhYGY2NjuevXu6fEvg0iIiKFcMRLOUy8RGRoaIjz58/jl19+gbW1NYKDg9G8eXNkZ2eX+Z6AgAA8efJE7hpUp0PFdZqIiOgNuMZLOUy8lBQfHy/39enTp+Ho6Ah19Zcn0GtoaMDT0xNLlizBxYsXkZaWhiNHjpQZT1tbG0ZGRnIXpxmJiIjeD/yNrqT09HT4+/vjs88+w/nz5/Hdd98hPDwcAPDnn3/i5s2b6Ny5M2rVqoX9+/dDKpXCycmpkntNRET0boQauvGpqjDxUtKoUaPw/PlztG3bFurq6pgyZQomTJgAADAxMcHvv/+O0NBQ5Ofnw9HREb/88guaNm1ayb0mIiJ6NzwySDlMvMph6tSpckcExcTEyP68atWqEvU7duwoV4eIiIhqNiZeREREpLCauiheVZh4ERERkcK4xks5fKqRiIiIqoXvv/8etra20NHRQbt27XDmzJky665duxadOnVCrVq1UKtWLXh6epaoP2bMGEgkErnL29tb1Htg4kVEREQKEwTVXeWxY8cO+Pv7IyQkBOfPn0fz5s3h5eWFe/fulVo/JiYGw4YNw9GjRxEXF4f69eujR48e+Pfff+XqeXt7IyMjQ3b98ssv7/rRKIRTjURERKQwVa7xKu2YPG1tbWhra5eou2zZMvj6+mLs2LEAgNWrV2Pfvn3YsGED5syZU6L+1q1b5b5et24dfvvtN0RHR2PUqFFy7f33KECxMfGqBsReyHj2oYmo8QGgoUG+6G1ceWIoehtPi8Rf25DV+qio8Yec/UDU+ABwrGOU6G1k5WuJ3oammvjPzTfQf/MxYsq6liP+94Weuvhnv5hoForexv0C8f+bamySI3obYpOqcI1XWFgY5s2bJ1cWEhKC0NBQubIXL17g3LlzCAgIkJWpqanB09MTcXFxCrX17NkzFBYWwtTUVK48JiYGFhYWqFWrFj788EN8/fXXMDMze7cbUgATLyIiIqoUAQEB8Pf3lysrbbTrwYMHKC4uhqWlpVy5paUlrl69qlBbs2fPRp06deDp6Skr8/b2xoABA2BnZ4fU1FR89dVX6NmzJ+Li4mQn0KgaEy8iIiJSmCo3UC1rWlHVFi9ejO3btyMmJgY6Ojqy8qFDh8r+7OLiAldXVzRs2BAxMTHo1q2bKH3h4noiIiJSWGUckl27dm2oq6sjKytLrjwrK+ut67OWLl2KxYsX4+DBg3B1dX1jXXt7e9SuXRspKSkK9628mHgRERFRlaalpYVWrVohOjpaViaVShEdHQ13d/cy37dkyRIsWLAAUVFRaN269Vvb+eeff/Dw4UNYW1urpN+l4VQjERERKayydq739/fH6NGj0bp1a7Rt2xYRERHIy8uTPeU4atQo1K1bF2FhYQCAb775BsHBwdi2bRtsbW2RmZkJADAwMICBgQFyc3Mxb948DBw4EFZWVkhNTcWsWbPg4OAALy8v0e6DiRcREREpTFpJideQIUNw//59BAcHIzMzE25uboiKipItuE9PT4ea2v8m8latWoUXL17gk08+kYvz6qlJdXV1XLx4EZs3b0Z2djbq1KmDHj16YMGCBaKuO6vxideYMWOQnZ2NPXv2VHZXiIiI6A38/Pzg5+dX6msxMTFyX6elpb0xlq6uLg4cOKCinimu2ideL168gJaW+HuvEBEREc9qVFa1W1zftWtX+Pn5YerUqahduza8vLzw999/o2fPnjAwMIClpSVGjhyJBw8eyN6za9cuuLi4QFdXF2ZmZvD09EReXh5CQ0OxefNm/N///Z/sjKZXGfOdO3cwePBgmJiYwNTUFH379i2RPW/YsAFNmzaFtrY2rK2t5bLwq1evomPHjtDR0UGTJk1w+PBhSCQSjqwREVG1VllHBr0vql3iBQCbN2+GlpYWYmNjsXjxYnz44Ydo0aIFzp49i6ioKGRlZWHw4MEAgIyMDAwbNgzjxo1DcnIyYmJiMGDAAAiCgBkzZmDw4MFy5zR16NABhYWF8PLygqGhIU6cOIHY2FgYGBjA29sbL168APBy7njSpEmYMGECLl26hD/++AMODg4AgOLiYvTr1w96enqIj4/HmjVrEBgYWGmfFxEREVUN1XKq0dHREUuWLAEAfP3112jRogUWLVoke33Dhg2oX78+rl+/jtzcXBQVFWHAgAGwsbEB8HKTtFd0dXVRUFAgtw/Izz//DKlUinXr1kEieTmkunHjRpiYmCAmJgY9evTA119/jenTp2PKlCmy97Vp0wYAcOjQIaSmpiImJkYWd+HChejevftb7620c6sKpUXQVKuWf1VERPSeqazF9e+Lajni1apVK9mfk5KScPToUdnjoQYGBmjcuDEAIDU1Fc2bN0e3bt3g4uKCQYMGYe3atXj8+PEb4yclJSElJQWGhoaymKampsjPz0dqairu3buHu3fvlrmr7bVr11C/fn25ZK5t27YK3VtYWBiMjY3lrl8zTin0XiIiIrFVxgaq75NqOYyir68v+3Nubi769OmDb775pkQ9a2trqKur49ChQzh16hQOHjyI7777DoGBgYiPj4ednV2p8XNzc9GqVasSJ5sDgLm5udzjqqpW2rlVhz9cVEZtIiKiisURL+VUy8Trv1q2bInffvsNtra20NAo/XYkEgk8PDzg4eGB4OBg2NjYYPfu3fD394eWlhaKi4tLxNyxYwcsLCxgZGRUakxbW1tER0fjgw8+KPGak5MT7ty5g6ysLNn+IgkJCQrdT2nnVnGakYiI6P1QLaca/2vSpEl49OgRhg0bhoSEBKSmpuLAgQMYO3YsiouLER8fj0WLFuHs2bNIT0/H77//jvv378PZ2RnAywTq4sWLuHbtGh48eIDCwkIMHz4ctWvXRt++fXHixAncunULMTExmDx5Mv755x8AQGhoKMLDw7FixQrcuHED58+fx3fffQcA6N69Oxo2bIjRo0fj4sWLiI2Nxdy5cwFAtmaMiIioOuJUo3KqfeJVp04dxMbGori4GD169ICLiwumTp0KExMTqKmpwcjICMePH0evXr3QqFEjzJ07F+Hh4ejZsycAwNfXF05OTmjdujXMzc0RGxsLPT09HD9+HA0aNMCAAQPg7OwMHx8f5Ofny0bARo8ejYiICPzwww9o2rQpPvroI9y4cQMAoK6ujj179iA3Nxdt2rTB+PHjZU81/vdUdCIioupGqsKrJpIIQk3dSaNixcbGomPHjkhJSUHDhg3L9d69beeK1KuX7hWIP5XZ0CBf9DZu54mf1D4tEv9faKZa4v44GnK25PS4qh3rGCV6G1n54m+crKkm/o/H+vrPRY2flqsnanwA0FMX/1eokWah6G3cLxD/v6nGJjmixm92OFLU+ACQ3ONLlcVyPvidymJVF1w8JJLdu3fDwMAAjo6OSElJwZQpU+Dh4VHupIuIiKgqqalThKrCxEskT58+xezZs5Geno7atWvD09MT4eHhld0tIiIipfCpRuUw8RLJqFGjMGrUqMruBhEREVUhTLyIiIhIYZxqVA4TLyIiIlKYlI/kKaXabydBREREVF1wxKsa2PuvZmV3QWn9m2WK3saTG/VEb8OoAv4qdEV+NL8itnroctJb9Da+qH9M9DZ0NcSfUglrnyZq/BNHHEWNDwAW2sVvr6QkQ80i0dtQq4AZNIfmj8RvRGScalQOEy8iIiJSmBRMvJTBxIuIiIgUxm3XlcM1XkREREQVhCNeREREpDBuoKqc92LEKyYmBhKJBNnZ2UrFsbW1RUREhEr6BABdu3bF1KlTVRaPiIioskkhUdlVE1XLxIsJDREREVVHnGokIiIihXFxvXKq3YjXmDFjcOzYMURGRkIikUAikSAtLQ0AcO7cObRu3Rp6enro0KEDrl27Jntfamoq+vbtC0tLSxgYGKBNmzY4fPjwG9tatmwZXFxcoK+vj/r162PixInIzc2VqxMbG4uuXbtCT08PtWrVgpeXFx4/fix7XSqVYtasWTA1NYWVlRVCQ0NV9lkQERFVNKkgUdlVE1W7xCsyMhLu7u7w9fVFRkYGMjIyUL9+fQBAYGAgwsPDcfbsWWhoaGDcuHGy9+Xm5qJXr16Ijo7GhQsX4O3tjT59+iA9Pb3MttTU1LBixQpcvnwZmzdvxpEjRzBr1izZ64mJiejWrRuaNGmCuLg4nDx5En369EFx8f82E9y8eTP09fURHx+PJUuWYP78+Th06JAInwwRERFVddVuqtHY2BhaWlrQ09ODlZUVAODq1asAgIULF6JLly4AgDlz5qB3797Iz8+Hjo4OmjdvjubNm8viLFiwALt378Yff/wBPz+/Utv67zoyW1tbfP311/j888/xww8/AACWLFmC1q1by74GgKZNm8rFcHV1RUhICADA0dERK1euRHR0NLp3715qmwUFBSgoKJArKxaKoC6pdn9VRET0HhJq6KJ4Val2I15v4urqKvuztbU1AODevXsAXo54zZgxA87OzjAxMYGBgQGSk5PfOOJ1+PBhdOvWDXXr1oWhoSFGjhyJhw8f4tmzZwD+N+KlaJ9e9etVn0oTFhYGY2NjuSvx6Yk33zgREVEFkQqqu2qi9yrx0tT830F6EsnLjFwqfXnu3YwZM7B7924sWrQIJ06cQGJiIlxcXPDixYtSY6WlpeGjjz6Cq6srfvvtN5w7dw7ff/89AMjeo6urW64+verXqz6VJiAgAE+ePJG73Aw7vbUdIiKiisA1XsqplvNXWlpacuuoFBEbG4sxY8agf//+AF6OgL1alF+ac+fOQSqVIjw8HGpqL/PTnTt3ytVxdXVFdHQ05s2bV74beANtbW1oa2vLlXGakYiI6P1QLUe8bG1tER8fj7S0NDx48OCNI0ivODo64vfff0diYiKSkpLw6aefvvF9Dg4OKCwsxHfffYebN2/ip59+wurVq+XqBAQEICEhARMnTsTFixdx9epVrFq1Cg8ePFD6HomIiKoiARKVXTVRtUy8ZsyYAXV1dTRp0gTm5uZvXKf1yrJly1CrVi106NABffr0gZeXF1q2bFlm/ebNm2PZsmX45ptv0KxZM2zduhVhYWFydRo1aoSDBw8iKSkJbdu2hbu7O/7v//4PGhocoSIiovcT13gpRyII3AqtqptQV3VTmZVlcddbordx4kY90duoiB8UuupvH8FVhpaauPEBoMtJb9Hb+KL+MdHb0NUQ/1/kYX1uiBr/hyOOosYHAAvt8i39eBcN9AreXklJj15ovr2Sknp1ThM1vk74T6LGB4AjHeaoLNaHpxarLFZ1waEZIiIiUlhNnSJUFSZeREREpLCaOkWoKtVyjRcRERHVPN9//z1sbW2ho6ODdu3a4cyZM2+s/+uvv6Jx48bQ0dGBi4sL9u/fL/e6IAgIDg6GtbU1dHV14enpiRs3xJ3+Z+JFRERECqusfbx27NgBf39/hISE4Pz582jevDm8vLzK3JT81KlTGDZsGHx8fHDhwgX069cP/fr1w99//y2rs2TJEqxYsQKrV69GfHw89PX14eXlhfz8fKU+ozdh4kVEREQKE1R4lceyZcvg6+uLsWPHokmTJli9ejX09PSwYcOGUutHRkbC29sbM2fOhLOzMxYsWICWLVti5cqVL+9DEBAREYG5c+eib9++cHV1xZYtW3D37l3s2bOnnL1THBMvIiIiqhQFBQXIycmRu14/rxh4eWLMuXPn4OnpKStTU1ODp6cn4uLiSo0dFxcnVx8AvLy8ZPVv3bqFzMxMuTrGxsZo165dmTFVgYvrq4GI/imixr98yULU+AAwJ8ZO9DbG2OeK3kZFyC9WFzV+Vr6WqPGBitnqYdWdLqK38fjTzaK38e3BRqLG71v3kajxAcBYV7xpmVcO/mMlehseFuJ/Vj4/24saf2u4qOEBQKVH/YSFhZU4/SUkJAShoaFyZQ8ePEBxcTEsLS3lyi0tLXH16tVSY2dmZpZaPzMzU/b6q7Ky6oiBiRcREREpTJU7AQYEBMDf31+u7PVj8943TLyIiIhIYYIKR7xKO5+4NLVr14a6ujqysrLkyrOysmBlVfpoqJWV1Rvrv/r/rKwsWFtby9Vxc3Mrz22UC9d4ERERUZWmpaWFVq1aITo6WlYmlUoRHR0Nd3f3Ut/j7u4uVx8ADh06JKtvZ2cHKysruTo5OTmIj48vM6YqcMSLiIiIFCb+oWOl8/f3x+jRo9G6dWu0bdsWERERyMvLw9ixYwEAo0aNQt26dWXnKk+ZMgVdunRBeHg4evfuje3bt+Ps2bNYs2YNAEAikWDq1Kn4+uuv4ejoCDs7OwQFBaFOnTro16+faPfBxIuIiIgUVlk71w8ZMgT3799HcHAwMjMz4ebmhqioKNni+PT0dKip/W8ir0OHDti2bRvmzp2Lr776Co6OjtizZw+aNWsmqzNr1izk5eVhwoQJyM7ORseOHREVFQUdHR3R7oOJFxEREVULfn5+8PPzK/W1mJiYEmWDBg3CoEGDyownkUgwf/58zJ8/X1VdfCsmXkRERKQwHpKtnPd2cb1UKkVYWBjs7Oygq6uL5s2bY9euXRAEAZ6envDy8oIgvBwvffToEerVq4fg4GAAQHFxMXx8fGTvdXJyQmRkpFz8MWPGoF+/fli6dCmsra1hZmaGSZMmobCwUFYnIyMDvXv3hq6uLuzs7LBt2zbY2toiIiKiwj4HIiIiVZIKqrtqovd2xCssLAw///wzVq9eDUdHRxw/fhwjRoyAubk5Nm/eDBcXF6xYsQJTpkzB559/jrp168oSL6lUinr16uHXX3+FmZkZTp06hQkTJsDa2hqDBw+WtXH06FFYW1vj6NGjSElJwZAhQ+Dm5gZfX18ALxf6PXjwADExMdDU1IS/v3+ZZ0oRERHR+++9TLwKCgqwaNEiHD58WPZIqL29PU6ePIkff/wR27Ztw48//ohRo0YhMzMT+/fvx4ULF6Ch8fLj0NTUlNtJ187ODnFxcdi5c6dc4lWrVi2sXLkS6urqaNy4MXr37o3o6Gj4+vri6tWrOHz4MBISEtC6dWsAwLp16+Do6PjWvr9+XEJxcTG01cXdzZyIiEgRnGpUzns51ZiSkoJnz56he/fuMDAwkF1btmxBamoqgJcL7vr374/Fixdj6dKlJRKi77//Hq1atYK5uTkMDAywZs0apKeny9Vp2rQp1P+TEFlbW8tGtK5duwYNDQ20bNlS9rqDgwNq1ar1xr6HhYXB2NhY7lp67u83voeIiKiicKpROe/liFdu7ssz+/bt24e6devKvfZqh9xnz57h3LlzUFdXx40bN+TqbN++HTNmzEB4eDjc3d1haGiIb7/9FvHx8XL1NDU15b6WSCSQSpXb4aS04xOKZ3+uVEwiIiKqGt7LxKtJkybQ1tZGeno6unQp/SDd6dOnQ01NDX/99Rd69eqF3r1748MPPwQAxMbGokOHDpg4caKs/quRMkU5OTmhqKgIFy5cQKtWrQC8HIl7/PjxG99X2vEJzzjNSEREVURNHalSlfcy8TI0NMSMGTMwbdo0SKVSdOzYEU+ePEFsbCyMjIxQu3ZtbNiwAXFxcWjZsiVmzpyJ0aNH4+LFi6hVqxYcHR2xZcsWHDhwAHZ2dvjpp5+QkJAAOzs7hfvQuHFjeHp6YsKECVi1ahU0NTUxffp06OrqQiLh/DgREVVPXOOlnPdyjRcALFiwAEFBQQgLC4OzszO8vb2xb98+2NrawsfHB6GhobL1V/PmzYOlpSU+//zllN5nn32GAQMGYMiQIWjXrh0ePnwoN/qlqC1btsDS0hKdO3dG//794evrC0NDQ1F3xCUiIhIT13gp570c8QJerreaMmUKpkyZUuK1zMxMua81NTVx9uxZ2dfa2trYuHEjNm7cKFfv1flPALBp06YScV/fn8va2hr79++Xff3PP//g3r17cHBwKM+tEBER0XvivU28qoIjR44gNzcXLi4uyMjIwKxZs2Bra4vOnTtXdteIiIjeSWUdkv2+YOIlosLCQnz11Ve4efMmDA0N0aFDB2zdurXE05BERETVhSBwjZcymHiJyMvLC15eXpXdDSIiIqoimHgRERGRwjjVqBwmXkRERKSwmvo0oqq8t9tJEBEREVU1HPGqBgqyxc2PLYzyRI0PAC1MjURvw9JA/PvIzDUQvY1Cqbh/35pq4v9zVVdD/MW3jz/dLHobtbaNFr2NGNN9osYf1/iZqPEBoLhY/H/D66qLP8H1vFD8X4lq78HmoxzwUg4TLyIiIlKYlE81KoVTjUREREQVhCNeREREpDBONSqHiRcREREpjE81KqfaTzXGxMRAIpEgOzu7zDqbNm2CiYlJhfXpldDQULi5uVV4u0RERGKRqvCqiap94kVERERUXXCqkYiIiBQmcKpRKdVixKugoACTJ0+GhYUFdHR00LFjRyQkJJRZf9OmTWjQoAH09PTQv39/PHz4UO71V1OAP/74I+rXrw89PT0MHjwYT548kau3bt06ODs7Q0dHB40bN8YPP/wg9/rs2bPRqFEj6Onpwd7eHkFBQSgsLCyzX6mpqbC3t4efnx8E/pdLRETVkBQSlV01UbVIvGbNmoXffvsNmzdvxvnz5+Hg4AAvLy88evSoRN34+Hj4+PjAz88PiYmJ+OCDD/D111+XqJeSkoKdO3di7969iIqKwoULFzBx4kTZ61u3bkVwcDAWLlyI5ORkLFq0CEFBQdi8+X+bNhoaGmLTpk24cuUKIiMjsXbtWixfvrzUe7h48SI6duyITz/9FCtXroREUjP/gyMiIqrJqvxUY15eHlatWoVNmzahZ8+eAIC1a9fi0KFDWL9+Pdq0aSNXPzIyEt7e3pg1axYAoFGjRjh16hSioqLk6uXn52PLli2oW7cuAOC7775D7969ER4eDisrK4SEhCA8PBwDBgwAANjZ2eHKlSv48ccfMXr0y92s586dK4tna2uLGTNmYPv27bK2Xzl16hQ++ugjBAYGYvr06W+834KCAhQUFMiXFRdDW11doc+LiIhITJywUU6VH/FKTU1FYWEhPDw8ZGWamppo27YtkpOTS9RPTk5Gu3bt5Mrc3d1L1GvQoIEs6XpVRyqV4tq1a8jLy0Nqaip8fHxgYGAgu77++mukpqbK3rNjxw54eHjAysoKBgYGmDt3LtLT0+XaSU9PR/fu3REcHPzWpAsAwsLCYGxsLHctv3zpre8jIiKqCHyqUTlVPvGqDLm5uQBejqwlJibKrr///hunT58GAMTFxWH48OHo1asX/vzzT1y4cAGBgYF48eKFXCxzc3O0bdsWv/zyC3Jyct7adkBAAJ48eSJ3TWvqovqbJCIiogpX5ROvhg0bQktLC7GxsbKywsJCJCQkoEmTJiXqOzs7Iz4+Xq7sVbL0X+np6bh7965cHTU1NTg5OcHS0hJ16tTBzZs34eDgIHfZ2dkBeDl9aGNjg8DAQLRu3RqOjo64fft2iXZ0dXXx559/QkdHB15eXnj69Okb71dbWxtGRkZyF6cZiYioqpAKqrtqoiq/xktfXx9ffPEFZs6cCVNTUzRo0ABLlizBs2fP4OPjg6SkJLn6kydPhoeHB5YuXYq+ffviwIEDJdZ3AYCOjg5Gjx6NpUuXIicnB5MnT8bgwYNhZWUFAJg3bx4mT54MY2NjeHt7o6CgAGfPnsXjx4/h7+8PR0dHpKenY/v27WjTpg327duH3bt3l3kP+/btQ8+ePdGzZ09ERUXBwMBA9R8WERGRyGpovqQyVX7ECwAWL16MgQMHYuTIkWjZsiVSUlJw4MAB1KpVq0Td9u3bY+3atYiMjETz5s1x8OBBuUXwrzg4OGDAgAHo1asXevToAVdXV7ntIsaPH49169Zh48aNcHFxQZcuXbBp0ybZiNfHH3+MadOmwc/PD25ubjh16hSCgoLKvAcDAwP89ddfEAQBvXv3Rl5engo+GSIiIqpOJEIN3FAqNDQUe/bsQWJiYmV3RSGPR4wWNX7OE11R4wPA/jRr0dvoUS9L9DYyc8UfqcwtFHcg+mmR+FPXJ+5rit5GkPst0duotU3c7z0A8DTdJ2r8LZ3fvrZUWcXF4v8b/vhdC9HbcDbKFb2NFVcNRY3/U1aoqPEBYInzfJXFmpUcrLJY1UWVn2okIiKiqqPmDdeoFhMvIiIiUlhN3QZCVarFGi9VCw0NrTbTjERERFQ+jx49wvDhw2FkZAQTExP4+PjItooqq/6XX34JJycn6OrqokGDBpg8eXKJowQlEkmJa/v27eXqG0e8iIiISGHVYRuI4cOHIyMjA4cOHUJhYSHGjh2LCRMmYNu2baXWv3v3Lu7evYulS5eiSZMmuH37Nj7//HPcvXsXu3btkqu7ceNGeHt7y742MTEpV9+YeBEREZHCqnrelZycjKioKCQkJKB169YAXh4L2KtXLyxduhR16tQp8Z5mzZrht99+k33dsGFDLFy4ECNGjEBRURE0NP6XLpmYmMi2nnoXNXKqkYiIiCpfQUEBcnJy5K7Xzysur7i4OJiYmMiSLgDw9PSEmppaiQ3W3+TJkycwMjKSS7oAYNKkSahduzbatm2LDRs2oLybQ3DEqxpYdtpB1PiWOuIvlexoni16G8czxH/c3FSrSPQ2XkjF/fdQA/1nosYHgLD2aaK38e3BRqK3ESPyVg8AcPhRb1Hj72x9RNT4AFAoSERvo67ui7dXUtLd5+JvrfPDx6lvr1TFqXKqMSwsDPPmzZMrCwkJQWho6DvHzMzMhIWF/O8DDQ0NmJqaIjMzU6EYDx48wIIFCzBhwgS58vnz5+PDDz+Enp4eDh48iIkTJyI3NxeTJ09WuH9MvIiIiEhhAlSXaAcEBMDf31+uTFtbu9S6c+bMwTfffPPGeMnJyUr3KScnB71790aTJk1KJID/3Si9RYsWyMvLw7fffsvEi4iIiKo+bW3tMhOt102fPh1jxox5Yx17e3tYWVnh3r17cuVFRUV49OjRW9dmPX36FN7e3jA0NMTu3buhqfnmzaDbtWuHBQsWoKCgQOH7YOJFRERECquspxrNzc1hbm7+1nru7u7Izs7GuXPn0KpVKwDAkSNHIJVK0a5duzLfl5OTAy8vL2hra+OPP/6Ajo7OW9tKTExErVq1FE66ACZeREREVA5VfTsJZ2dneHt7w9fXF6tXr0ZhYSH8/PwwdOhQ2RON//77L7p164YtW7agbdu2yMnJQY8ePfDs2TP8/PPPsoX+wMuET11dHXv37kVWVhbat28PHR0dHDp0CIsWLcKMGTPK1T8mXv/fmDFjkJ2djT179lR2V4iIiEgJW7duhZ+fH7p16wY1NTUMHDgQK1askL1eWFiIa9eu4dmzlw8bnT9/XvbEo4OD/ANtt27dgq2tLTQ1NfH9999j2rRpEAQBDg4OWLZsGXx9fcvVNyZeREREpLAqPuAFADA1NS1zs1QAsLW1ldsGomvXrm/dFsLb21tu49R3xcSLiIiIFFbVpxqruhq3gequXbvg4uICXV1dmJmZwdPTE3l5eSXqFRQUYPLkybCwsICOjg46duyIhIQE2esxMTGQSCTYt28fXF1doaOjg/bt2+Pvv/+Wi3Py5El06tQJurq6qF+/PiZPnlxqe0RERNWBoML/1UQ1KvHKyMjAsGHDMG7cOCQnJyMmJgYDBgwodXhx1qxZ+O2337B582acP38eDg4O8PLywqNHj+TqzZw5E+Hh4UhISIC5uTn69OmDwsJCAEBqaiq8vb0xcOBAXLx4ETt27MDJkyfh5+dXIfdLREREVUuNmmrMyMhAUVERBgwYABsbGwCAi4tLiXp5eXlYtWoVNm3ahJ49ewIA1q5di0OHDmH9+vWYOXOmrG5ISAi6d+8OANi8eTPq1auH3bt3Y/DgwQgLC8Pw4cMxdepUAICjoyNWrFiBLl26YNWqVaU+qlpQUFDiuIQioQgakhr1V0VERFUUpxqVU6NGvJo3b45u3brBxcUFgwYNwtq1a/H48eMS9VJTU1FYWAgPDw9ZmaamJtq2bVtiV1x3d3fZn01NTeHk5CSrk5SUhE2bNsHAwEB2eXl5QSqV4tatW6X2MSwsDMbGxnJX7OPjqrh9IiIipQkqvGqiGpV4qaur49ChQ/jrr7/QpEkTfPfdd3ByciozCVJWbm4uPvvsMyQmJsqupKQk3LhxAw0bNiz1PQEBAXjy5Inc5VGrsyj9IyIioopV4+avJBIJPDw84OHhgeDgYNjY2GD37t1ydRo2bAgtLS3ExsbKpiQLCwuRkJAgmzZ85fTp02jQoAEA4PHjx7h+/TqcnZ0BAC1btsSVK1dK7AnyJqUdn8BpRiIiqio41aicGvUbPT4+HtHR0ejRowcsLCwQHx+P+/fvw9nZGRcvXpTV09fXxxdffIGZM2fC1NQUDRo0wJIlS/Ds2TP4+PjIxZw/fz7MzMxgaWmJwMBA1K5dG/369QMAzJ49G+3bt4efnx/Gjx8PfX19XLlyBYcOHcLKlSsr8taJiIhU4i3bXdFb1KjEy8jICMePH0dERARycnJgY2OD8PBw9OzZEzt27JCru3jxYkilUowcORJPnz5F69atceDAAdSqVatEvSlTpuDGjRtwc3PD3r17oaWlBQBwdXXFsWPHEBgYiE6dOkEQBDRs2BBDhgypsHsmIiKiqqNGJV7Ozs6Iiooq9bVNmzbJfa2jo4MVK1bIHTFQmo4dO5bYu+u/2rRpg4MHD5a7r0RERFWRtLI7UM3VqMSLiIiIlMM1XsqpUU81EhEREVUmjni9I0UO1CQiInrf8Fefcph4ERERkcK4xks5TLyIiIhIYRzxUg7XeBERERFVEI54VQMB3tdFjZ93X1PU+ACw7W9b0dvoUPuJ6G1IIRG9DW21YlHjX8sxFDU+AJw44ih6G33rPhK9jXGNn4nexs7WR0SNP/jsh6LGB4Dr3r+J3saZe2ait9HMJEf0NqbvV/wkk3exRtToL3GqUTlMvIiIiEhhfLBMOZxqJCIiIqogHPEiIiIihXEDVeUw8SIiIiKFMe9SDqcaiYiIiCoIR7yIiIhIYZxqVA4TLyIiIlIYEy/lcKpRSVKpFEuWLIGDgwO0tbXRoEEDLFy4EAAwe/ZsNGrUCHp6erC3t0dQUBAKCwsrucdERERUWTjipaSAgACsXbsWy5cvR8eOHZGRkYGrV68CAAwNDbFp0ybUqVMHly5dgq+vLwwNDTFr1qxK7jUREdG7Ebi8XilMvJTw9OlTREZGYuXKlRg9ejQAoGHDhujYsSMAYO7cubK6tra2mDFjBrZv3/7GxKugoAAFBQVyZcXFxdBWVxfhDoiIiMqHU43K4VSjEpKTk1FQUIBu3bqV+vqOHTvg4eEBKysrGBgYYO7cuUhPT39jzLCwMBgbG8tdS8/9LUb3iYiIyk0QVHfVREy8lKCrq1vma3FxcRg+fDh69eqFP//8ExcuXEBgYCBevHjxxpgBAQF48uSJ3DWjVTNVd52IiIgqAacaleDo6AhdXV1ER0dj/Pjxcq+dOnUKNjY2CAwMlJXdvn37rTG1tbWhra0tV/aM04xERFRFSLnGSylMvJSgo6OD2bNnY9asWdDS0oKHhwfu37+Py5cvw9HREenp6di+fTvatGmDffv2Yffu3ZXdZSIiIqXU1ClCVeFUo5KCgoIwffp0BAcHw9nZGUOGDMG9e/fw8ccfY9q0afDz84ObmxtOnTqFoKCgyu4uERERVSKOeClJTU0NgYGBclOKryxZsgRLliyRK5s6dWoF9YyIiEj1pJXdgWqOiRcREREpTOBco1I41UhERERUQZh4ERERkcKkguousTx69AjDhw+HkZERTExM4OPjg9zc3De+p2vXrpBIJHLX559/LlcnPT0dvXv3hp6eHiwsLDBz5kwUFRWVq2+caiQiIiKFVYftJIYPH46MjAwcOnQIhYWFGDt2LCZMmIBt27a98X2+vr6YP3++7Gs9PT3Zn4uLi9G7d29YWVnh1KlTyMjIwKhRo6CpqYlFixYp3DcmXkRERPTeSE5ORlRUFBISEtC6dWsAwHfffYdevXph6dKlqFOnTpnv1dPTg5WVVamvHTx4EFeuXMHhw4dhaWkJNzc3LFiwALNnz0ZoaCi0tLQU6h+nGomIiEhhqjwyqKCgADk5OXLX6+cVl1dcXBxMTExkSRcAeHp6Qk1NDfHx8W9879atW1G7dm00a9YMAQEBePbsmVxcFxcXWFpaysq8vLyQk5ODy5cvK9w/jnhVA08zFcui31VKlpmo8QFAXSL+0HShVPx/R2irF4vexj/P9N5eSQl66uI/DG6hLf7nZKybL3obxcXi/zdVKEhEjX/d+zdR4wNAo6iBorfxR5PjorfxoVGe6G3oapiI3obYVDnVGBYWhnnz5smVhYSEIDQ09J1jZmZmwsLCQq5MQ0MDpqamyMzMLPN9n376KWxsbFCnTh1cvHgRs2fPxrVr1/D777/L4v436QIg+/pNcV/HxIuIiIgUpsrdJAICAuDv7y9X9vqxea/MmTMH33zzzRvjJScnv3NfJkyYIPuzi4sLrK2t0a1bN6SmpqJhw4bvHPd1TLyIiIioUpR2PnFZpk+fjjFjxryxjr29PaysrHDv3j258qKiIjx69KjM9VuladeuHQAgJSUFDRs2hJWVFc6cOSNXJysrCwDKFZeJFxERESmssp5qNDc3h7m5+Vvrubu7Izs7G+fOnUOrVq0AAEeOHIFUKpUlU4pITEwEAFhbW8viLly4EPfu3ZNNZR46dAhGRkZo0qSJwnG5uJ6IiIgUJhUElV1icHZ2hre3N3x9fXHmzBnExsbCz88PQ4cOlT3R+O+//6Jx48ayEazU1FQsWLAA586dQ1paGv744w+MGjUKnTt3hqurKwCgR48eaNKkCUaOHImkpCQcOHAAc+fOxaRJkxQetQOYeCkkJiYGEokE2dnZSsWxtbVFRESESvpEREREpdu6dSsaN26Mbt26oVevXujYsSPWrFkje72wsBDXrl2TPbWopaWFw4cPo0ePHmjcuDGmT5+OgQMHYu/evbL3qKur488//4S6ujrc3d0xYsQIjBo1Sm7fL0VwqrEUXbt2hZubG5MkIiKi1wjVYANVU1PTN26WamtrK3fmZP369XHs2LG3xrWxscH+/fuV6hsTLyIiIlKY+BvSvN841fiaMWPG4NixY4iMjJSd1ZSWlgYAOHfuHFq3bg09PT106NAB165dk70vNTUVffv2haWlJQwMDNCmTRscPny4ku6CiIiIqiImXq+JjIyEu7s7fH19kZGRgYyMDNSvXx8AEBgYiPDwcJw9exYaGhoYN26c7H25ubno1asXoqOjceHCBXh7e6NPnz5IT0+vrFshIiJSOSkElV01EacaX2NsbAwtLS2585quXr0KAFi4cCG6dOkC4OVGbr1790Z+fj50dHTQvHlzNG/eXBZnwYIF2L17N/744w/4+flV/I0QERGJQBDpacSagiNe5fDqkVLgf/t6vNqkLTc3FzNmzICzszNMTExgYGCA5OTkco94lXpuVbH4x68QERGR+Jh4lYOmpqbszxLJy/PVpNKXywxnzJiB3bt3Y9GiRThx4gQSExPh4uKCFy9elKuNsLAwGBsby10rriWp7iaIiIiUwKlG5XCqsRRaWlooLucoU2xsLMaMGYP+/fsDeDkC9mpRfnmUdm5V9ujJ5Y5DREQkhpqaMKkKE69S2NraIj4+HmlpaTAwMJCNar2Jo6Mjfv/9d/Tp0wcSiQRBQUEKve91pZ1b9VxdvdxxiIiIxCBwQwmlcKqxFDNmzIC6ujqaNGkCc3NzhdZpLVu2DLVq1UKHDh3Qp08feHl5oWXLlhXQWyIiIqouOOJVikaNGiEuLk6u7PUT0d3c3OSe7LC1tcWRI0fk6kyaNEnu63eZeiQiIqpKONWoHCZeREREpDAmXsrhVCMRERFRBeGIFxERESlMysX1SmHiRURERAoTJEy8lMGpRiIiIqIKwhEvIiIiUhgX1yuHiVc1sD3ZRtT4Hc2zRY0PAM2Mn4vehgCJ6G2Y6OaL3sY/z/REjW+iWShqfAAw1CwSvY2D/1iJ3oauuvhTKnV1y3esWHmduWcmanwA+KPJcdHbmHGls+htaKh3F72NrE9Gid6G2LjGSzmcaiQiIiKqIBzxIiIiIoXxyCDlMPEiIiIihUn5VKNSmHgRERGRwrjGSznVfo3X1atX0b59e+jo6MDNza1S+xIaGlrpfSAiIqKqq9qPeIWEhEBfXx/Xrl2DgYFBZXeHiIjovcYRL+VU+8QrNTUVvXv3ho2NuFsuEBERERfXK0tlU427du2Ci4sLdHV1YWZmBk9PT+Tl5QEA1q1bB2dnZ+jo6KBx48b44YcfZO8bN24cXF1dUVBQAAB48eIFWrRogVGj3r7XiUQiwblz5zB//nxIJBKEhoYCAO7cuYPBgwfDxMQEpqam6Nu3L9LS0mTvGzNmDPr164dFixbB0tISJiYmmD9/PoqKijBz5kyYmpqiXr162Lhxo1x7s2fPRqNGjaCnpwd7e3sEBQWhsPDNeyK96d6JiIioZlHJiFdGRgaGDRuGJUuWoH///nj69ClOnDgBQRCwdetWBAcHY+XKlWjRogUuXLgAX19f6OvrY/To0VixYgWaN2+OOXPmYPny5QgMDER2djZWrlypULuenp7w9vbGjBkzYGBggMLCQnh5ecHd3R0nTpyAhoYGvv76a3h7e+PixYvQ0tICABw5cgT16tXD8ePHERsbCx8fH5w6dQqdO3dGfHw8duzYgc8++wzdu3dHvXr1AACGhobYtGkT6tSpg0uXLsHX1xeGhoaYNWtWqf17270TERFVN1IUV3YXqjWVJV5FRUUYMGCAbMrPxcUFwMs1WOHh4RgwYAAAwM7ODleuXMGPP/6I0aNHw8DAAD///DO6dOkCQ0NDRERE4OjRozAyMnpru1ZWVtDQ0ICBgQGsrF7uYv3zzz9DKpVi3bp1kEhe7mS+ceNGmJiYICYmBj169AAAmJqaYsWKFVBTU4OTkxOWLFmCZ8+e4auvvgIABAQEYPHixTh58iSGDh0KAJg7d66sbVtbW8yYMQPbt28vM/F6270TERFVN5xqVI5KEq/mzZujW7ducHFxgZeXF3r06IFPPvkEWlpaSE1NhY+PD3x9fWX1i4qKYGxsLPva3d0dM2bMwIIFCzB79mx07NjxnfuSlJSElJQUGBoaypXn5+cjNTVV9nXTpk2hpva/mVZLS0s0a9ZM9rW6ujrMzMxw7949WdmOHTuwYsUKpKamIjc3F0VFRWUmiHl5eQrd++sKCgpk066y90iLoKFW7ZfjERER1Xgq+W2urq6OQ4cO4dSpUzh48CC+++47BAYGYu/evQCAtWvXol27diXe84pUKkVsbCzU1dWRkpKiVF9yc3PRqlUrbN26tcRr5ubmsj9ramrKvSaRSEotk0pfZvZxcXEYPnw45s2bBy8vLxgbG2P79u0IDw8vsx/A2+/9dWFhYZg3b55cmVftruhp8UGZ7yEiIqoo3EBVOSobRpFIJPDw8ICHhweCg4NhY2OD2NhY1KlTBzdv3sTw4cPLfO+3336Lq1ev4tixY/Dy8sLGjRsxduzYd+pHy5YtsWPHDlhYWCg0XamoU6dOwcbGBoGBgbKy27dvl1nf0tJSoXt/XUBAAPz9/eXK1rZbWv4OExERiYBrvJSjksQrPj4e0dHR6NGjBywsLBAfH4/79+/D2dkZ8+bNw+TJk2FsbAxvb28UFBTg7NmzePz4Mfz9/XHhwgUEBwdj165d8PDwwLJlyzBlyhR06dIF9vb25e7L8OHD8e2336Jv376YP38+6tWrh9u3b+P333/HrFmzZAvly8vR0RHp6enYvn072rRpg3379mH37t1vfM/b7r002tra0NbWlivjNCMREdH7QSW/0Y2MjHD8+HFEREQgJycHNjY2CA8PR8+ePQEAenp6+PbbbzFz5kzo6+vDxcUFU6dORX5+PkaMGIExY8agT58+AIAJEyZg3759GDlyJI4fP/7GabnS6Onp4fjx45g9ezYGDBiAp0+fom7duujWrZtSI2Aff/wxpk2bBj8/PxQUFKB3794ICgqSbWFRmvHjx5d570RERNURF9crRyIIglDZnaA3i2w6X9T4Hc2zRY0PAE8LtURvQ1NN/B8GVga5orfx90NTUeObaL557zlVECARvY1rT3VFb0NXXfz/purqvhA1/j/PtN9eSUmZ+eKfPjfjSmfR29BQ7y56G1mfvH2PSmVY/Lpe1PgA0NC0v8pipT5688zR+4hzWERERKQwjngpp0ofkr1o0SIYGBiUer2axiQiIiKqLqp04vX5558jMTGx1GvdunWV3T0iIqIaR0Cxyi6xPHr0CMOHD4eRkRFMTEzg4+Mj2+apNGlpaZBIJKVev/76q6xeaa9v3769XH2r0lONpqamMDUVd70LERERKU5aDaYahw8fjoyMDBw6dAiFhYUYO3YsJkyYgG3btpVav379+sjIyJArW7NmDb799tsSM2wbN26Et7e37GsTE5Ny9a1KJ15ERERE5ZGcnIyoqCgkJCSgdevWAIDvvvsOvXr1wtKlS1GnTp0S71FXV5cdPfjK7t27MXjwYBgYGMiVm5iYlKhbHlV6qpGIiIiqFgFSlV0FBQXIycmRu14/Nq+84uLiYGJiIku6AMDT0xNqamqIj49XKMa5c+eQmJgIHx+fEq9NmjQJtWvXRtu2bbFhwwaUd3MIjnhVA73rZ4kaPzbT/O2VlGSoKf5Ox4VS8bcwOPPI4O2VlORq/FzU+PcLxN/aQ038vwp4WDwSvY3nheL/iLz7XNxtMZqZ5IgaHwA+NMoTvY2K2OqhqPiQ6G3Mdzohavx5b6+iNEFQ3c/z0o7JCwkJeeMemW+TmZkJCwsLuTINDQ2YmpoiMzNToRjr16+Hs7MzOnToIFc+f/58fPjhh9DT08PBgwcxceJE5ObmYvLkyQr3j4kXERERVYrSjsl7/fSWV+bMmYNvvvnmjfGSk5OV7tPz58+xbds2BAUFlXjtv2UtWrRAXl4evv32WyZeREREJA5VLq4v7Zi8skyfPh1jxox5Yx17e3tYWVnh3r17cuVFRUV49OiRQmuzdu3ahWfPnmHUqLdvdtuuXTssWLAABQUFCt8HEy8iIiJSmJjbQLyJubk5zM3fvjTG3d0d2dnZOHfuHFq1agUAOHLkCKRSKdq1a/fW969fvx4ff/yxQm0lJiaiVq1aCiddABMvIiIieo84OzvD29sbvr6+WL16NQoLC+Hn54ehQ4fKnmj8999/0a1bN2zZsgVt27aVvTclJQXHjx/H/v37S8Tdu3cvsrKy0L59e+jo6ODQoUNYtGgRZsyYUa7+MfEiIiIihQlC1d/Ha+vWrfDz80O3bt2gpqaGgQMHYsWKFbLXCwsLce3aNTx79kzufRs2bEC9evXQo0ePEjE1NTXx/fffY9q0aRAEAQ4ODli2bBl8fX3L1TcmXiokkUiwe/du9OvXr7K7QkREJIrqsIGqqalpmZulAoCtrW2p20AsWrQIixYtKvU93t7echunvismXkRERKQwVW4nURNxA1UiIiKiClKpideuXbvg4uICXV1dmJmZwdPTE3l5LzfiW7duHZydnaGjo4PGjRvjhx9+kL1v3LhxcHV1le1u++LFC7Ro0UKhRz9fHYS5c+dOdOrUCbq6umjTpg2uX78uO17AwMAAPXv2xP3792XvS0hIQPfu3VG7dm0YGxujS5cuOH/+/BvbunPnDgYPHgwTExOYmpqib9++SEtLe4dPioiIqGpQ5c71NVGlJV4ZGRkYNmwYxo0bh+TkZMTExGDAgAEQBAFbt25FcHAwFi5ciOTkZCxatAhBQUHYvHkzAGDFihXIy8vDnDlzAACBgYHIzs7GypUrFW4/JCQEc+fOxfnz56GhoYFPP/0Us2bNQmRkJE6cOIGUlBQEBwfL6j99+hSjR4/GyZMncfr0aTg6OqJXr154+vRpqfELCwvh5eUFQ0NDnDhxArGxsTAwMIC3tzdevHihxCdHRERUeQRBqrKrJqq0NV4ZGRkoKirCgAEDYGNjAwBwcXEB8DIpCg8Px4ABAwAAdnZ2uHLlCn788UeMHj0aBgYG+Pnnn9GlSxcYGhoiIiICR48ehZGRkcLtz5gxA15eXgCAKVOmYNiwYYiOjoaHhwcAwMfHB5s2bZLV//DDD+Xev2bNGpiYmODYsWP46KOPSsTfsWMHpFIp1q1bB4nk5fkpGzduhImJCWJiYkp9YgIACgoKSpxTVSAthraausL3RkRERFVTpY14NW/eHN26dYOLiwsGDRqEtWvX4vHjx8jLy0Nqaip8fHxgYGAgu77++mukpqbK3u/u7o4ZM2ZgwYIFmD59Ojp27Fiu9l1dXWV/trS0BPC/xO9V2X93vs3KyoKvry8cHR1hbGwMIyMj5ObmIj09vdT4SUlJSElJgaGhoeweTE1NkZ+fL3cfrwsLC4OxsbHc9ePNc+W6NyIiIrEIKFbZVRNV2oiXuro6Dh06hFOnTuHgwYP47rvvEBgYiL179wIA1q5dW2KHWXX1/436SKVSxMbGQl1dHSkpKeVuX1NTU/bnVyNSr5dJpf8bBh09ejQePnyIyMhI2NjYQFtbG+7u7mVOG+bm5qJVq1bYunVridfetBtuaedW3flklmI3RUREJLKaOkWoKpW6nYREIoGHhwc8PDwQHBwMGxsbxMbGok6dOrh58yaGDx9e5nu//fZbXL16FceOHYOXlxc2btyIsWPHitbX2NhY/PDDD+jVqxeAlwvnHzx4UGb9li1bYseOHbCwsCjXFGhp51ZxmpGIiOj9UGlTjfHx8Vi0aBHOnj2L9PR0/P7777h//z6cnZ0xb948hIWFYcWKFbh+/TouXbqEjRs3YtmyZQCACxcuIDg4GOvWrYOHhweWLVuGKVOm4ObNm6L119HRET/99BOSk5MRHx+P4cOHQ1dXt8z6w4cPR+3atdG3b1+cOHECt27dQkxMDCZPnox//vlHtH4SERGJiYvrlVNpiZeRkRGOHz+OXr16oVGjRpg7dy7Cw8PRs2dPjB8/HuvWrcPGjRvh4uKCLl26YNOmTbCzs0N+fj5GjBiBMWPGoE+fPgCACRMm4IMPPsDIkSNRXCzOnPH69evx+PFjtGzZEiNHjsTkyZNhYWFRZn09PT0cP34cDRo0wIABA+Ds7AwfHx/k5+eXawSMiIioKpGq8H81kUQobc98qlJSvCeJGj828+0nsCvLUFP8RZSFUonobdzNF3923tX4uajxH78Q/x7UxP+rQCPjHNHbeF4o/md193nZI+eqUE/v2dsrKcnCKE/0Nhrs3SB6G0XFh0RvY77TCVHjz7sRJGp8ADA2aKayWE9y/1ZZrOqCRwYRERGRwmrqFKGqvHdHBi1atEhuG4r/Xj179qzs7hEREVVrglCssqsmeu9GvD7//HMMHjy41NfetBieiIiI3q6mHvWjKu9d4mVqagpTU9PK7gYRERFRCe9d4kVERETi4Rov5TDxqgbcjh8UNX6wzUhR4wPAsF7i7bH2yqY/G4rehqmm+D9wcovE3TC3sYn4TwM6NH8kehs+P9uL3oYaxH8884ePyz5CTBWm73cQNT4A6GqYiN5G1iejRG9D7CcOASD4WifR2xAbEy/lvHeL64mIiIiqKo54ERERkcK4uF45TLyIiIhIYZxqVA6nGomIiIgqCEe8iIiISGEc8VIOEy8iIiIqByZeyhB1qrFr166YOnWqmE2US1XrDxEREdUsVX7E68WLF9DS0qrsbhARERE41ags0Ua8xowZg2PHjiEyMhISiQQSiQSpqanw8fGBnZ0ddHV14eTkhMjIyBLv69evHxYuXIg6derAyckJAHDq1Cm4ublBR0cHrVu3xp49eyCRSJCYmCh7799//42ePXvCwMAAlpaWGDlyJB48eFBmf9LS0srsvyAIcHBwwNKlS+XKExMTIZFIkJKSAgDIzs7G+PHjYW5uDiMjI3z44YdISkqS1U9KSsIHH3wAQ0NDGBkZoVWrVjh79qwyHy0REVGlESBV2VUTiZZ4RUZGwt3dHb6+vsjIyEBGRgbq1auHevXq4ddff8WVK1cQHByMr776Cjt37pR7b3R0NK5du4ZDhw7hzz//RE5ODvr06QMXFxecP38eCxYswOzZs+Xek52djQ8//BAtWrTA2bNnERUVhaysLNmB2aX1p379+mX2XyKRYNy4cdi4caNc+caNG9G5c2c4OLzcDXrQoEG4d+8e/vrrL5w7dw4tW7ZEt27d8OjRy527hw8fjnr16iEhIQHnzp3DnDlzoKmpqfTnS0REVBkEQaqyqyYSbarR2NgYWlpa0NPTg5WVlax83rx5sj/b2dkhLi4OO3fulCVIAKCvr49169bJphhXr14NiUSCtWvXQkdHB02aNMG///4LX19f2XtWrlyJFi1aYNGiRbKyDRs2oH79+rh+/ToaNWpUan/eZMyYMQgODsaZM2fQtm1bFBYWYtu2bbJRsJMnT+LMmTO4d+8etLW1AQBLly7Fnj17sGvXLkyYMAHp6emYOXMmGjduDABwdHR8Y5sFBQUoKCiQKxMEARKJ+EeXEBERkbgqfB+v77//Hq1atYK5uTkMDAywZs0apKeny9VxcXGRW9d17do1uLq6QkdHR1bWtm1bufckJSXh6NGjMDAwkF2vkp3U1Hc7C61OnTro3bs3NmzYAADYu3cvCgoKMGjQIFmbubm5MDMzk2v31q1bsjb9/f0xfvx4eHp6YvHixW/tS1hYGIyNjeWuwiLxz70jIiJSTLEKr5qnQhOv7du3Y8aMGfDx8cHBgweRmJiIsWPH4sWLF3L19PX1yx07NzcXffr0QWJiotx148YNdO7c+Z37PH78eGzfvh3Pnz/Hxo0bMWTIEOjp6cnatLa2LtHmtWvXMHPmTABAaGgoLl++jN69e+PIkSNo0qQJdu/eXWZ7AQEBePLkidylqWH6zv0nIiJSJU41KkfUpxq1tLRQXPy/jDY2NhYdOnTAxIkTZWWKjEY5OTnh559/RkFBgWxKLyEhQa5Oy5Yt8dtvv8HW1hYaGqXf1uv9UUSvXr2gr6+PVatWISoqCsePH5drMzMzExoaGrC1tS0zRqNGjdCoUSNMmzYNw4YNw8aNG9G/f/9S62pra8vu8RVOMxIREb0fRB3xsrW1RXx8PNLS0vDgwQM4Ojri7NmzOHDgAK5fv46goKASCVRpPv30U0ilUkyYMAHJyck4cOCAbJ3Vq6Rk0qRJePToEYYNG4aEhASkpqbiwIEDGDt2rCzZer0/Uunbs211dXWMGTMGAQEBcHR0hLu7u+w1T09PuLu7o1+/fjh48CDS0tJw6tQpBAYG4uzZs3j+/Dn8/PwQExOD27dvIzY2FgkJCXB2dn6Xj5OIiKgKkKrwqnlETbxmzJgBdXV1NGnSBObm5vDy8sKAAQMwZMgQtGvXDg8fPpQb/SqLkZER9u7di8TERLi5uSEwMBDBwcEAIFv3VadOHcTGxqK4uBg9evSAi4sLpk6dChMTE6ipqZXan9fXlpXFx8cHL168wNixY+XKJRIJ9u/fj86dO2Ps2LFo1KgRhg4ditu3b8PS0hLq6up4+PAhRo0ahUaNGmHw4MHo2bOn3AMGRERE1YogVd1VA0kEQRAquxPvYuvWrRg7diyePHkCXV1dUds6ceIEunXrhjt37sDS0lLUtkpjoPfmJyGVFWwzUtT4ADC1f4robWz6s6HobWirif/tYqJVJGr8hkZPRY0PAA7NxX8gxOdne9HbUIP40/w/fPxuD/8oavp+B1HjA4CuhvifU2Db26K38X2irehtBF/rJGp8dbWuosZ/2YaeymIVS5+pLFZ1UeV3rn9ly5YtsLe3R926dZGUlITZs2dj8ODBoiZdBQUFuH//PkJDQzFo0KBKSbqIiIiqEgHVcrymyqg2iVdmZiaCg4ORmZkJa2trDBo0CAsXLlQq5ueff46ff/651NdGjBiB9u3bw8fHB25ubtiyZYtSbREREb0fauYUoapUm8Rr1qxZmDVrlkpjzp8/HzNmzCj1NSMjI1hYWGDMmDEqbZOIiIjEtXDhQuzbtw+JiYnQ0tJCdnb2W98jCAJCQkKwdu1aZGdnw8PDA6tWrZLb+PzRo0f48ssvsXfvXqipqWHgwIGIjIyEgYGBwn2rNomXGCwsLGBhYVHZ3SAiIqo+qsHS8BcvXmDQoEFwd3fH+vXrFXrPkiVLsGLFCmzevBl2dnYICgqCl5cXrly5InuQb/jw4cjIyMChQ4dQWFiIsWPHYsKECdi2bZvCfavRiRcRERGVjyrXeJV2TF5p+1mW16vdAzZt2qRQfUEQEBERgblz56Jv374AXq4tt7S0xJ49ezB06FAkJycjKioKCQkJaN26NQDgu+++Q69evbB06VLUqVNHsc4J9F7Jz88XQkJChPz8fLZRifHZRtVq4324B7ZRdeK/T21UtpCQEAGA3BUSEqKy+Bs3bhSMjY3fWi81NVUAIFy4cEGuvHPnzsLkyZMFQRCE9evXCyYmJnKvFxYWCurq6sLvv/+ucJ+YeL1nnjx5IgAQnjx5wjYqMT7bqFptvA/3wDaqTvz3qY3Klp+fLzx58kTuUmWiqWjiFRsbKwAQ7t69K1c+aNAgYfDgwYIgCMLChQuFRo0alXivubm58MMPPyjcpwo/JJuIiIgIeDmtaGRkJHeVNc04Z84cSCSSN15Xr16t4DsoP67xIiIioipv+vTpb91pwN7+3TZWtrKyAgBkZWXB2tpaVp6VlQU3NzdZnXv37sm9r6ioCI8ePZK9XxFMvIiIiKjKMzc3h7m5uSix7ezsYGVlhejoaFmilZOTg/j4eHzxxRcAAHd3d2RnZ+PcuXNo1aoVAODIkSOQSqVo166dwm1xqvE9o62tjZCQEKWfCHnf23gf7oFtVJ34bKNqtfE+3ENFtfG+Sk9PR2JiItLT01FcXIzExEQkJiYiNzdXVqdx48bYvXs3gJdnL0+dOhVff/01/vjjD1y6dAmjRo1CnTp10K9fPwCAs7MzvL294evrizNnziA2NhZ+fn4YOnSo4k80ohqf1UhERERUmjFjxmDz5s0lyo8ePYquXbsCeJlsbdy4UTZ9Kfz/DVTXrFmD7OxsdOzYET/88AMaNWoke/+jR4/g5+cnt4HqihUryrWBKhMvIiIiogrCqUYiIiKiCsLEi4iIiKiCMPEiIiIiqiBMvIiIiIgqCBMvIiIRPH/+vLK7QERVEBOvauzKlSuYOHEiWrRoAWtra1hbW6NFixaYOHEirly5IkqbKSkpOHDggOyXiiofin38+DGWLl0KHx8f+Pj4YOnSpXj06JHK4r8uJycHe/bsQXJysmhtiO19uIfqbPLkyaWW5+XloVevXipt68WLF7h27RqKiopUGvd98tNPP8HDwwN16tTB7du3AQARERH4v//7P5W1UVRUhMOHD+PHH3/E06dPAQB3796V2x/qXZ0/fx6XLl2Sff1///d/6NevH7766iu8ePFC6fhUNXA7iWrqr7/+Qr9+/dCyZUt4eXnB0tISwMvjDQ4dOoRz587h//7v/+Dl5aWS9h4+fIghQ4bgyJEjkEgkuHHjBuzt7TFu3DjUqlUL4eHhSsU/fvw4Pv74YxgZGaF169YAgHPnziE7Oxt79+5F586dlb6HwYMHo3PnzvDz88Pz58/RvHlzpKWlQRAEbN++HQMHDnynuCtWrFC4blm/qBUl1j287o8//ii1XCKRQEdHBw4ODrCzs1OqjejoaERHR+PevXuQSqVyr23YsEGp2EDZfy//vYfOnTtDXV39ndto2LAhRowYgXnz5snK8vLy4O3tDQA4ceLEO8d+5dmzZ/jyyy9lexJdv34d9vb2+PLLL1G3bl3MmTNH6TaKi4uxfPly7Ny5E+np6SV+yb/LP4Aq8vsCAFatWoXg4GBMnToVCxcuxN9//w17e3ts2rQJmzdvxtGjR5Vu4/bt2/D29kZ6ejoKCgpkfxdTpkxBQUEBVq9erVT8Nm3aYM6cORg4cCBu3ryJpk2bon///khISEDv3r0RERGh9D1QFaDwcdpUpbi6ugpBQUFlvh4SEiK4uLiorL2RI0cKXl5ewp07dwQDAwMhNTVVEARBiIqKEpo0aaJ0/GbNmgm+vr5CUVGRrKyoqEiYMGGC0KxZM6XjC4IgWFpaComJiYIgCMLWrVsFBwcHIS8vT/jhhx8ENze3d45ra2ur0GVnZ1dl7+F1EolEUFNTEyQSidz1qkxNTU3o3Lmz8OjRo3eKHxoaKqipqQlt27YV+vbtK/Tr10/uUgVbW1tBX19fkEgkgqmpqWBqaipIJBJBX19fsLS0FCQSidCwYUMhPT39ndtISUkRrK2theXLlwuCIAg5OTmCu7u70KlTJyE3N1cl9zF58mShVatWwokTJwR9fX3Z996ePXtU9nceFBQkWFtbC0uXLhV0dHSEBQsWCD4+PoKZmZkQGRn5TjEr8vtCEATB2dlZ2L17tyAIgtzPqEuXLglmZmYqaaNv377CiBEjhIKCArk2jh49Kjg4OCgd38jISEhJSREEQRAWL14s9OjRQxAEQTh58qRQr149peNT1cDEq5rS0dERrl69WubrV69eFXR0dFTW3n9/4f/3B05qaqqgr6+vdPyy7keV96GjoyP7JTty5Ehh9uzZgiAIwu3bt1VyDxWhou7h8OHDQrt27YTDhw8LOTk5Qk5OjnD48GHB3d1d2Ldvn3Dy5EmhadOmwrhx494pvpWVlbBlyxaV9bc027ZtE7p27Sr7RSYIgnDjxg3hww8/FLZv3y7cuXNH8PDwEAYOHKhUO0lJSYKpqakQGRkptG/fXujSpYvKki5BEIQGDRoIcXFxgiDIf+/duHFDMDQ0VEkb9vb2wp9//ilr49VnFhkZKQwbNkwlbYhNR0dHSEtLEwRB/nO6fv26yn6GmJqayn5O/beNW7duCbq6ukrHNzQ0FK5fvy4IgiB4enoKERERgiC8/P5W5c9zqlxc41VN2draYt++fWW+vm/fPtjY2Kisvby8POjp6ZUof/TokUrOEWvZsmWp65SSk5PRvHlzpeMDQP369REXF4e8vDxERUWhR48eAF6uLdPR0VFJG/8lvPyHjUpjVtQ9TJkyBcuWLUO3bt1gaGgIQ0NDdOvWDd9++y1mzpwJDw8PRERE4NChQ+8U/8WLF+jQoYPK+luauXPnYvny5WjYsKGszMHBAUuXLkVAQADq1auHJUuWIDY2Vql2XF1d8eeff+Krr76Cnp4e/vrrL+jr6yvbfZn79+/DwsKiRHleXh4kEolK2sjMzISLiwsAwMDAAE+ePAEAfPTRR2/8OVOV2NnZITExsUR5VFQUnJ2dVdKGVCpFcXFxifJ//vkHhoaGSsdv3bo1vv76a/z00084duwYevfuDQC4deuWbDkJVX8ald0Bejfz58/Hp59+ipiYGHh6esqt8YqOjkZUVBS2bdumsvY6deqELVu2YMGCBQBerpORSqVYsmQJPvjgA6XjT548GVOmTEFKSgrat28PADh9+jS+//57LF68GBcvXpTVdXV1fac2pk6diuHDh8PAwAA2Njay87qOHz8u+6WjClu2bMG3336LGzduAAAaNWqEmTNnYuTIkUrHrqh7SE1NhZGRUYlyIyMj3Lx5EwDg6OiIBw8evFP88ePHY9u2bQgKClKqn2+SkZFR6kL0oqIiZGZmAgDq1KkjWyCtqBYtWpSa8Ghra+Pu3bvw8PCQlZ0/f76cvS6pdevW2LdvH7788ksAkLW9bt06uLu7Kx0fAOrVq4eMjAw0aNAADRs2xMGDB9GyZUskJCSo7IDmf/75B3/88Uepa8iWLVumdHx/f39MmjQJ+fn5EAQBZ86cwS+//IKwsDCsW7dO6fgA0KNHD0RERGDNmjUAXv5d5ObmIiQkRCUPU0RERGD48OHYs2cPAgMD4eDgAADYtWuX6P9QoYrDxfXV2KlTp7BixQrExcXJfpFYWVnB3d0dU6ZMUdkPZQD4+++/0a1bN7Rs2RJHjhzBxx9/jMuXL+PRo0eIjY2VG1V4F2pqbx58lUgkEAQBEomk1H9xKurs2bO4c+cOunfvLjvUdN++fTAxMZH7hfmuli1bhqCgIPj5+cninTx5Et9//z2+/vprTJs2Tek2xL4HAOjYsSMMDQ2xZcsWmJubA3g58jJq1Cjk5eXh+PHjOHz4MCZNmoRr164pFNPf31/2Z6lUis2bN8PV1RWurq7Q1NSUq6uKX8S9e/dGZmYm1q1bhxYtWgAALly4AF9fX1hZWeHPP//E3r178dVXX8k9SfY2/11I/zYhISHl7vfrTp48iZ49e2LEiBHYtGkTPvvsM1y5cgWnTp3CsWPH0KpVK6XbmDNnDoyMjPDVV19hx44dGDFiBGxtbZGeno5p06Zh8eLFSsWPjo7Gxx9/DHt7e1y9ehXNmjWTPRTy6meKKmzduhWhoaFITU0F8DKxnjdvHnx8fFQS/59//oGXlxcEQcCNGzfQunVr3LhxA7Vr18bx48dLHZlUhfz8fKirq5f4PqHqiYkXKezJkydYuXIlkpKSkJubi5YtW2LSpEmwtrZWOvarR78VoYop1Ff/2atqquYVOzs7zJs3D6NGjZIr37x5M0JDQ3Hr1i2VtSXWPQDAtWvX0LdvX9y6dQv169cHANy5cwf29vb4v//7PzRq1Ah79uzB06dPFR7JU3RkVCKRqOQXcWZmJkaOHIno6GjZL6yioiJ069YNP/30EywtLXH06FEUFhbKpmyrqtTUVCxevFjue2/27NkqHeX8r9OnT+PUqVNwdHREnz59lI7Xtm1b9OzZE/PmzYOhoSGSkpJgYWGB4cOHw9vbG1988YUKev0/z549Q25uriiJUFFREbZv346LFy/K/i6GDx8OXV1dlcTPzs7Grl27kJqaipkzZ8LU1BTnz5+HpaUl6tatq5I2qHIx8aIqISwsDJaWlhg3bpxc+YYNG3D//n3Mnj1bJe2sX78ey5cvl00DOjo6YurUqRg/frxK4uvo6ODvv/+WTRG8cuPGDbi4uCA/P1/pNsS+h1ekUikOHjyI69evAwCcnJzQvXv3t45OVjVXr16VuwcnJ6dK7lHVI/b3n6GhIRITE9GwYUPUqlULJ0+eRNOmTZGUlIS+ffsiLS1Nqfjvi4sXL6Jbt24wMTFBWloarl27Bnt7e8ydOxfp6enYsmVLZXeRVIBrvEgh/11j9V+v9kRq0KCBUmtBfvzxx1LXpDVt2hRDhw5VSeIVHByMZcuW4csvv5RNw8bFxWHatGlIT0/H/PnzlW7DwcEBO3fuxFdffSVXvmPHDjg6OiodvyLu4RU1NTV4e3vL9qRSpSdPnqC4uBimpqZy5Y8ePYKGhkap68veVePGjdG4cWOVxfsvMfa/Kqud3bt3yx5AadKkCfr27QsNDdX8CBf7+09fX1/22VhbWyM1NRVNmzYFgHdeJwiUvd6uNO+63q6sPe1K8/HHH79TG6/4+/tj7NixWLJkidxi/V69euHTTz9VKjZVHUy8SCFubm6yH3ClTXFpampiyJAh+PHHH9/p6brMzMxSpyzNzc2RkZHxjr2Wt2rVKqxduxbDhg2TlX388cdwdXXFl19+qZKkZd68eRgyZAiOHz8uW28VGxuL6Oho7Ny5U+n4FXEPwMuHHRwcHEpsbLly5UqkpKQovZHj0KFD0adPH0ycOFGufOfOnfjjjz+wf/9+peIDL5OVTZs2lblJqyqmM+fNm4d169Zh+vTpmDt3LgIDA5GWloY9e/YgODhY6fgAcPnyZXz88cfIzMyUjdZ98803MDc3x969e9GsWTOl2xD7+699+/Y4efIknJ2d0atXL0yfPh2XLl3C77//LnuY5l3069dP6b6pqg1l158CQEJCAn788ccS5XXr1pWt46X3QCVsYUHV0J49ewQnJydh3bp1wsWLF4WLFy8K69atE5ydnYXt27cLP//8s1CvXj1h+vTp7xTfwcFB+Omnn0qUb9myRWUbLBobG8v2yPmva9euCcbGxippQxAE4ezZs8Lw4cOFli1bCi1bthSGDx8unD9/XiWxK+oe6tSpI5w9e7ZE+blz54S6desqHb9WrVrClStXSpQnJycLpqamSscXBEGYNGmSoK+vLwwePFiYMmWKMHXqVLlLFSpi/6v27dsLffr0kdus9tGjR8LHH38suLu7q6QNsb//UlNThaSkJEEQBCE3N1f47LPPBBcXF2HAgAGyvbdIEMzNzWU/K/67T9jBgwe5gep7hIkXKaRNmzZCVFRUifKoqCihTZs2giAIwu7duwV7e/t3iv/NN98IZmZmwoYNG4S0tDQhLS1NWL9+vWBmZiYsWrRIqb6/4ufnJ0ybNq1E+fTp04WJEyeqpA2xVdQ9aGtrCzdu3ChRfuPGDUFbW1vp+Hp6esLFixdLlF+8eFElG1EKgiCYmZkJ+/btU0mssujp6Qm3b98WBOHlprDnzp0TBOFlomFkZKSSNnR0dIS///67RPmlS5dUtqlmRXz/0dv5+PgI/fr1E168eCEYGBgIN2/eFG7fvi20aNFCmDJlSmV3j1SEU43vgfT0dGhqaspNFWRkZKCwsBANGjRQSRuXLl0q9WlCGxsb2aP4bm5u7zwtMXPmTDx8+BATJ06UrQXR0dHB7NmzERAQ8O4df8369etx8OBB2fRGfHw80tPTMWrUKLntDpTZzkAqlSIlJaXU6a13OXPyv/0CXu7fVNY9qIqDgwOioqLg5+cnV/7XX3/B3t5e6fht27bFmjVr8N1338mVr169WiXbIwCAlpZWiYccVK0i9r9q1KgRsrKyZGuiXrl3757K7q+ivv9evHhR6veFKn5OVdR6u7y8PBw7dqzUNpQ9czI8PByffPIJLCws8Pz5c3Tp0gWZmZlwd3fHwoULlYpNVQefanwPqKmpoXHjxrhy5YqszNnZGdevX1d6zcErLVq0QPPmzbFmzRpoaWkBAAoLC+Hr64ukpCRcuHABsbGxGDFihFJbJuTm5iI5ORm6urpwdHRU2S8voGK2Mzh9+jQ+/fRT3L59u8Su9e+6BqRWrVpo1qwZNDQ0ZPuZqbrfr9uwYQP8/Pwwc+ZMfPjhhwBe7sUUHh6OiIgI+Pr6KhU/NjYWnp6eaNOmDbp16yaLn5CQgIMHD6JTp05K30N4eDhu3ryJlStXirLlBiD+/lcAsH//fsyaNQuhoaFymwvPnz8fixcvRseOHWV1lX0oQazvv+vXr8PHxwenTp2SKxdUsDffK8HBwW9cb6eKg7gvXLiAXr164dmzZ8jLy4OpqSkePHgAPT09WFhYyDYXVtbJkyfltqvw9PRUSVyqGph4vQeOHTsGPT09tGnTRlaWkJCAZ8+eoUuXLipp49SpU/j444+hpqYm2zn+0qVLKC4uxp9//on27dvjp59+QmZmJmbOnKmSNqsjNzc3NGrUCPPmzYO1tXWJX/jGxsbljqmmpobMzExYWFjA3t4eCQkJMDMzU1WXy7Rq1SosXLgQd+/eBfDymKrQ0FCVjawlJibi22+/RWJiInR1deHq6oqAgACVPP0JAP3798fRo0dhamqKpk2blth88vfff1dJO/8VFxeHuLg4le1/BchvLlzWAy6qTGDE4OHhAQ0NDcyZM6fU7wtVHAvWsGFDrFixAr1795bbvmLFihU4ffq0Sk7y6Nq1Kxo1aoTVq1fD2NgYSUlJ0NTUxIgRIzBlyhQMGDBAqfg3b95UyYgyVW1MvEhhT58+xdatW+X2RPr0009VckZZRbtz5w4AyDYHVRV9fX0kJSWpdIrLzMwM+/fvR7t27aCmpoasrCzZbvIV4f79+9DV1ZXtkl9djB079o2vb9y4sYJ6opxjx44pXFdV/9BSNX19fZw7d060bT1etZGcnIwGDRrA2toa+/btQ8uWLXHz5k20aNFCdv6kMkxMTBAfHw8nJyeYmJggLi4Ozs7OiI+Px+jRo3H16lWl4qupqaFLly7w8fHBJ598IsoZslT5uMaLFGZoaIjOnTvD1tZWtrbh6NGjAJTfv6YiFBUVYd68eVixYgVyc3MBvDwQ+Msvv0RISIhKjuNo164dUlJSVJp4DRw4EJ07d0adOnUgkUjQunVrqKurl1pXVVMdwMvPKyYmBqmpqbI9hO7evQsjIyOVJmH5+fkl1sqoYh+vikqsfvrpJ6xevRq3bt1CXFwcbGxsEBERATs7O/Tt21fp+FU1mSqPJk2aKLVflyIqYr2dpqambATSwsIC6enpcHZ2hrGxsewfc8o4f/48Nm7cCH9/f/j5+WHIkCEYN24c2rVrp3RsqjqYeFVDFbFp4Otu3ryJ/v3749KlS3LnJr5SVac4/uvLL7/E77//jiVLlshtPhoaGoqHDx9i1apV7xT3v5vLfvnll5g+fToyMzPh4uJSIpl7lwO+16xZgwEDBiAlJQWTJ0+Gr6+v6KOMt2/fhre3N9LT01FQUIDu3bvD0NAQ33zzDQoKCrB69Wql4j979gyzZs3Czp078fDhwxKvV4f/noCX07HBwcGYOnUqFi5cKOu3iYkJIiIiVJJ4AS+PkVm/fr1sA9WmTZti3Lhx7zR1XVFycnJkf/7mm28wa9YsLFq0qNTvC1Uk2v3790d0dDTatWuHL7/8EiNGjMD69etl6+1UoUWLFkhISICjoyO6dOmC4OBgPHjwAD/99JNK9lNzc3NDZGQkwsPD8ccff2DTpk3o1KkTGjVqhHHjxmHkyJEVOtpNIqmchylJGaGhobJrzpw5gpGRkdC+fXth2rRpwrRp0wR3d3fByMhImDNnjsra/Oijj4S+ffsK9+/fFwwMDITLly8LJ06cENq2bSscP35cZe2IycjISNi/f3+J8n379in16L9EIhHU1NQEiURS6vXqNTU1NWW6LwiCIIwZM0bIyclROs7b9O3bVxgxYoRQUFAgt5/Q0aNHBQcHB6XjT5w4UXB2dhZ27dol6OrqChs2bBAWLFgg1KtXT/j555+Vjv/Kr7/+KgwaNEho166d0KJFC7lLFZydnYXdu3cLgiC/79KlS5cEMzMzlbSRkJAgmJqaCnXr1hX69+8v9O/fX6hXr55gZmYm276iKnr13/yr6/WvVfl9UZpTp04J4eHhwh9//KGymAkJCcKRI0cEQRCErKwswcvLSzA0NBRatmwpJCYmqqydV/Lz84Vly5YJ2tragkQiEbS1tYWRI0cKd+/eVXlbVHG4xquaGz9+PKytrbFgwQK58pCQENy5cwcbNmxQSTu1a9fGkSNH4OrqCmNjY5w5cwZOTk44cuQIpk+fjgsXLqikHTFZWFjg2LFjcHZ2litPTk5G586dcf/+/XeKW9EHfFcEMzMznDp1Ck5OTrJDje3t7ZGWloYmTZrg2bNnSsVv0KABtmzZgq5du8LIyAjnz5+Hg4MDfvrpJ/zyyy8q2bl+xYoVCAwMxJgxY7BmzRqMHTsWqampSEhIwKRJk1TyeL6uri6uXr0KGxsbuc/pxo0bcHV1xfPnz5Vuo1OnTnBwcMDatWtlRwQVFRVh/PjxuHnzJo4fP650G2L479q0tLQ01K9fv8QUuVQqRXp6OkaPHl3R3avSzp49iw0bNmD79u3Q19fH6NGj4ePjg3/++Qfz5s1DTk4Ozpw5U9ndpHdV2ZkfKcfIyKjUncyvX7+usg0cBUEQTExMhJs3bwqC8HK37lf/6ktJSVHZhpdimzdvnjBs2DAhPz9fVpafny8MHz5cCA0NVUkbixYtEtavX1+ifP369cLixYtV0kZFMDExES5fviwIgvxIzokTJwQLCwul4+vr68s2Hq1bt64QHx8vCIIg3Lx5U9DX11c6viAIgpOTk7Bt2zZBEOTvISgoSJg0aZJK2nB2dhb27NlToo0VK1aobFRNR0dHSE5OLlF++fLlavO9p6amJmRlZZUof/DggUpHvP79919hx44dwnfffSdERkbKXdVBeHi40KxZM0FTU1Po27evsHfvXqG4uFiuzp07dwR1dfVK6iGpAtd4VXO6urqIjY0t8Qh+bGysSp+IadasGZKSkmBnZ4d27dphyZIl0NLSwpo1a6r048+vP959+PBh1KtXT/b4elJSEl68eCHbS0pZFXHYd0Xo0aMHIiIisGbNGgAvty3Izc1FSEgIevXqpXR8e3t73Lp1Cw0aNEDjxo2xc+dOtG3bFnv37oWJiYnS8YGXGwt36NABwMvvk6dPnwIARo4cifbt22PlypVKt+Hv749JkyYhPz8fgiDgzJkz+OWXXxAWFoZ169YpHR94uf4pPT29xBOBd+7cqTZPFAuvrQl9JTc3V2U/pzZt2oTPPvsMWlpaMDMzk2tPIpGoZB+vhw8fIjg4GEePHi11I1hlN2ldtWoVxo0bhzFjxpR6dibwcuR+/fr1SrVDlYuJVzU3depUfPHFFzh//jzatm0L4OVO5hs2bEBQUJDK2pk7dy7y8vIAAPPnz8dHH32ETp06wczMDDt27FBZO6r2+uLjgQMHyn2t6u0kKuKw74oQHh4OLy8vNGnSBPn5+fj0009x48YN1K5dG7/88ovS8ceOHYukpCR06dIFc+bMQZ8+fbBy5UoUFhYqdWrAf1lZWeHRo0ewsbFBgwYNcPr0aTRv3hy3bt0qcxPa8ho/fjx0dXUxd+5cPHv2DJ9++inq1KmDyMhIDB06VCVtDBkyBD4+Pli6dKkskYyNjcXMmTPlDkuvil6duiCRSBAUFAQ9PT3Za8XFxYiPj4ebm5tK2goKCkJwcDACAgLk9j5TpZEjRyIlJQU+Pj6wtLRU+ca8N27ceGsdLS0txMfHo3fv3qhdu7ZK26eKwTVe74GdO3ciMjJS9sSTs7Mzpvy/9u48KKor+wP4t0FAEFRobHAgCAIjixuGKEZcicLIRIXoYEATFywljhKgUaIGpLRAmYIISGIiDgE1LiOEpLRkUXFhGWEAwR0BoTVCLGHQIIgs7/cHP3rsNBqlX/frbs+nqqvMa7z3NEH69r3nnRMYiL/97W9ynbe5uRmGhoZyqwquimxtbREREYFly5ZJXD948CAiIiJYLfcgb11dXTh27BgqKirEFbT9/Pygq6vL+lz19fUoLS2FjY3NgO787I+/vz/eeecdREREICkpCaGhoZg2bRr+85//wNvbm/Vdg7a2NrS2tkIgELA67vPnzxEaGop9+/ahq6sLQG9Zg4CAAOzatYvV7g5s6+sWceHCBUydOlXc9QLoXUBYWlpCKBSyUjSXz+ejuLgY1tbWMo/1MgYGBsjPz2el4Ksshg4diitXrij1aQN5OVp4kbfGnDlzkJGRIXWU9eTJEyxatIiVdjsxMTGIiYnBP/7xD4lWO5s2bUJISAirfe/k6eLFi3j//ffFydx9urq6UFhYOKCek4rW09ODnp4e8Ws4evQoCgsLYWtrKz6SYsvDhw9x+/ZtAICdnZ1cbvlva2tDTU0NgN4q7S/uHim7lStXIj4+npWyES+zadMmGBkZISwsTG5zvPfee0hMTBS3buLKizdyENVDCy810NLSghMnTqC2thZCoRBGRkYoKyuDiYkJzMzMuA5PabzYeudFDx8+hJmZGTo7O2Weg2EYhIWFISEhQarZcHh4uMzjK4qmpiYaGhqkvldNTU0QCAQDqrOVkJDw2l/LRj6OIvz222/47LPPcOTIEXG+j6amJnx8fJCUlKTUdbbUTXd3N/7617+ivb2931phbBxhl5SUICwsDOHh4Rg7dqxc6pG9Dlp4qTbK8VJxlZWV+OCDDzBs2DDU1dXB398fRkZGyMjIgEgkQlpaGtchcu7FAqc3btxAY2Oj+L+7u7uRlZXF2gKVx+Nh9+7d+PLLL+XW7FsRXpYM3dTUhCFDhgxozK+++uq1vo6tROjt27cjPDxcKt/n8ePHWLduHSu5av7+/igvL8epU6ckivIGBgZi7dq1OHr06IDGfZOef/LoOamKoqOjkZ2djTFjxgCAVHI9G4YPH44nT56Id7P7MEreK5MoF1p4qbjg4GCsWLECMTExEnc4zZ8/X9zm5W03ceJE8Hg88Hg8qV+YQO8db4mJiazOqa+vL9G0XFX0veHzeDysWLFCYsHY3d2NyspKcYL3m7p79y4rMb6uAwcOICcnB4cOHRLvDJw/fx6ffPIJTE1NWZnj5MmTyM7Ohqurq/iau7s79u/fDw8PjwGPSztlby42Nhb//Oc/sWLFCrnN4efnBy0tLfzwww9ySa4nbwdaeKm4kpISfPvtt1LXzczMJHZ23mZ9d7GNHj0axcXFEvk32traEAgEL+19+Lbpe8NnGAYGBgYSifTa2tpwcXHBmjVruArvjVRWVmLt2rWYOHEiYmNjUVVVhfj4eISGhiIyMpKVOfh8fr+LpGHDhsHQ0HDA477YZ7K9vR09PT3inca6ujpkZmbC3t4e7u7uA55D3ejo6GDatGlynePatWsoLy8X76oRMhC08FJxOjo6Ej3R+lRVVVFPr//XVy3+9zV3iLS+N/y+u80Geqz4Ou7fv4+ff/4ZIpFIqkk2G/k4hoaGOH78OLZs2YK1a9di0KBBOH36NGs124DeMivBwcE4ePCgeBetsbERoaGhrJVzWbhwIby9vbFu3Tq0tLTAxcUFWlpaePToEeLi4hAQEMDKPKouMDAQiYmJb5RL+KacnZ1x7949uS28RCIR3nnnHamdNIZhcO/ePVhYWAAAli1bprB8MsI+Sq5Xcf7+/mhqasLx48dhZGSEyspKaGpqYtGiRZgxYwb27NnDdYhKIzU1FcbGxvD09ATQexfUd999BwcHBxw5ckRl2vkoQnt7OxiGEd85V19fjx9//BEODg6YN2+ezOOfPXsWCxYswOjRo3Hr1i2MHTsWdXV1YBgGkyZNYuUOUwBITExEWFgYFi1ahNLSUmhqauKHH35grRyAk5MTqqur0dHRIX5TFIlE0NHRkSqRMNCG9cbGxrhw4QIcHR2RnJyMxMRElJeXIz09HeHh4eIyMm87Ly8vnDt3Dnw+H46OjlKJ72zkwv3rX//C9u3bERoa2m8Cv6ylUORxUwtRPrTjpeJiY2OxePFiCAQCtLe3Y+bMmWhsbMTUqVNZ6UWnTqKiovDNN98A6E2A3rt3L/bs2YOTJ08iKCiIkpRf8PtdlsmTJ0NbW5u1XZYvvvgCQqEQkZGRMDAwQHp6OgQCAfz8/GTKjXqRh4cHSkpKkJqaisWLF6O9vR3BwcFwcXFBZGQkNm3aJPMcixYtkj3QP9DW1ibO38zJyYG3tzc0NDTg4uLyRn1C1d3w4cPf6KaEgfDx8QEArFq1SnyNx+OxllyviAr/hHu046Um8vPzUVlZKS50+cEHH3AdktLR09PDrVu3YGFhgc2bN6OhoQFpaWm4fv06Zs2aNeAm2epI3rssBgYGuHLlCqytrWFoaIj8/Hw4OjqioqICCxcuRF1dncyvYe7cuUhNTcWf/vQnieunTp2Cv7+/ynQSGD9+PPz9/eHl5YWxY8ciKysLU6dORWlpKTw9PSmXU4H+aKE70F3zvgr/8fHxWLNmTb8V/jU1NVFQUDCg8YlyoR0vNeHq6ipxZxWRpq+vj6amJlhYWCAnJ0f8y27w4MFob2/nODrlIu9dliFDhojzukaOHImamho4OjoCAB49eiTz+ACQm5uLS5cuYdOmTaipqcGJEydgZmaG5uZmHD9+nJU5FCE8PBy+vr4ICgqCm5ubuGxFTk4OnJycOI5OuXR1deH8+fOoqamBr68vDAwM8ODBAwwdOhT6+voyj/+6CytPT08kJye/tN/i75WXlwPo3fG6evWqVIX/CRMmQCgUvnnARCnRwksNlJSUvLRpK1t979TB3Llz4e/vDycnJ1RVVYmbPV+/fh2WlpbcBqdkbGxskJmZCS8vL2RnZyMoKAhAb7FZNpJ6XVxckJ+fD3t7e8yfPx8hISG4evUqMjIyWKsKnp6ejuXLl8PPzw/l5eXo6OgA0FvHKzo6GtOnTx/QuG/SJkvWpskAsHjxYri6uqKhoUEiN83NzQ1eXl4yj68u6uvr4eHhAZFIhI6ODsydOxcGBgbYvXs3Ojo6sG/fPoXFcvHixTf6MJeXlwdAMRX+Cfdo4aXioqKisG3bNowZM0aqrgzVmJGUlJSEbdu24d69e0hPTwefzwcAlJaWKn2zYUV7cZdlzpw5rO+yxMXFobW1FQAQGRmJ1tZWHDt2DLa2tqx9WNi5cyf27duHTz75RKKQ6bRp07Bz584Bj/viDStNTU3YuXMn3N3dJQqoZmdns9qk3tTUVKr22OTJk1kbXx0EBgbC2dkZFRUV4n/bQG/SvaqUQOm7q7i6uho1NTWYMWMGdHV1X5r7RVQT5XipOBMTE+zevVuuRQPJ26mxsVG8y9JX/b24uBhDhw6FnZ0dx9H9MT09Pdy4cQOWlpYSLVZqa2vh4OCAZ8+eyTzHRx99hNmzZ+Pvf/+7xPW9e/fizJkzyMzMlHkO8nr4fD4KCwsxZswYif/fdXV1cHBwQFtbm8JiGWhLn+bmZixZsgR5eXng8Xi4c+cORo8ejVWrVsHQ0BCxsbFyipgoksYffwlRZhoaGnIvGqhOLl26hGXLluH999/HL7/8AgA4ePAg8vPzOY5M+ZiamsLAwAC5ubniY5P33nuP1UXX8+fPcf/+fYhEIokHG0xNTVFdXS11PT8/n7Ued9nZ2f3ehenh4YEzZ86wMgd5PT09Pf3eVXj//n2Jrh7K7PPPP4eWlhZEIpFEgr2Pjw+ysrI4jIywiRZeKi4oKAhJSUlch6ES0tPT4e7uDl1dXZSVlUnk/ERFRXEcnXJpamqCm5sb/vznP2P+/PniOwBXr16NkJAQmcevqqrC9OnToauri1GjRsHKygpWVlawtLSElZWVzOMDwJo1axAYGIjLly+Dx+PhwYMHOHz4MIRCIWtFR/l8Pn766Sep6z/99JPEcReRv3nz5kkcA/N4PLS2tiIiIkKcz6nscnJysHv3bpibm0tct7W1pdIhaoRyvFScUCiEp6cnrK2t4eDgIJeigepCXjk/6igoKEj8ydve3l583cfHB8HBwTIfeaxcuRKDBg3CyZMnMXLkSLnkr4SFhaGnpwdubm5oa2vDjBkzoKOjA6FQiA0bNrAyR2RkJPz9/XH+/HlMmTIFAHD58mVkZWVh//79rMxBXk9sbCzc3d3Fx8i+vr64c+cOjI2NWWmIrghPnz6V2Onq09zcLNE3lag2WnipuI0bNyIvLw+zZ88Gn8+nBMxXuH37NmbMmCF1fdiwYWhpaVF8QEosJycH2dnZcvvkfeXKFZSWlso1V4zH42Hr1q0IDQ1FdXU1Wltb4eDgwEpZgT4rVqyAvb09EhISxB9y7O3tkZ+fL16IEcUwNzdHRUUFjh07hoqKCrS2tmL16tXw8/OT6DkqLy8mwG/ZsgVGRkZvPMb06dORlpaGHTt2AOj9Ge7p6UFMTAxmz57NaryEQwxRafr6+szJkye5DkMlWFlZMbm5uQzD9H7fampqGIZhmNTUVMbe3p7L0JSOvr4+U1VVJf5z3/eqpKSEMTIyknl8Z2dn5tKlSzKPQ95uTk5OTHNzM8MwDBMZGck8ffpUrvN9+umnTGtrq9T1u3fvMq6urjKPf/XqVUYgEDAeHh6MtrY2s3jxYsbe3p4xMTFhqqurZR6fKAfK8VJxRkZGsLa25joMlaCInB910ffJuw8bn7yfPHkifuzevRubNm3C+fPn0dTUJPFcf03flcnvY33Vg8jXzZs38fTpUwD/K0siTxUVFRg/fjyKiorE11JTUzFhwgQYGxvLPP7YsWNRVVUFV1dXLFy4EE+fPoW3tzfKy8vp97waoXISKi4lJQVZWVlISUnpNzeA/A/DMIiKikJ0dLT41vK+nJ++rX3S6/r165gzZ464YfWCBQtw/fp1NDc3o6CgYEBvAhoaGhJH4Uw/tYkYlnreydPvX0d/VOF1qIOpU6dCX18frq6uiIyMhFAofOlRcnh4uMzzdXZ2YsuWLUhISEBISAiqq6tx+vRpxMXFqUytMMI9WnipOCcnJ9TU1IBhGFhaWkol15eVlXEUmXLp7u5GQUEBxo8fDz09Pbnl/KiDzs5OeHh4IDo6Grm5ueJ8mUmTJmH9+vWv3Qbl9y5cuCD+c11dHd555x1oampKfE1PTw9EIhE+/fRTmV6DPL34Ov7IzJkz5RgJuX37NiIiIlBTU4OysjI4ODhg0CDp1GUej8fq78KIiAjs2LEDgwYNwoULF8TFc2VVWVnZ73Uej4fBgwfDwsKCkuzVAC28VFxkZOQrn4+IiFBQJMpv8ODBuHnzJmvlCtTZiBEjUFhYCFtbW7mMr6mpiYaGBggEAonrTU1NEAgEKrVT1NLSggMHDogbhzs4OGD16tUYNmwYx5G9XTQ0NNDY2Cj1M8Wmzs5OhIWFISkpCSEhIcjPz0dVVRUOHDjASsmKF3dT+96aX9xd1dLSgo+PD7799lsMHjxY5vkIR7hJLSNE8d59913mzJkzXIehEj7//HNm8+bNchufx+MxDx8+lLpeV1fH6OnpyW1etpWUlDB8Pp8xMzNjvLy8GC8vL8bc3Jzh8/lMaWkp1+GRfsyfP5958ODBgP7u+PHjGRsbG6aoqIhhGIbp6elhdu3axejo6DABAQEyx5aZmcmMGTOGSU5OZiorK5nKykomOTmZsbe3Z44ePcocOnSIMTc3Z0JCQmSei3CHdrzIWyMrKwtffPEFduzYgXfffRdDhgyReJ4a0/7Phg0bkJaWBltb236/VwPtpxgcHAwAiI+Px5o1ayTyEru7u3H58mVoamqioKBg4MEr0PTp02FjY4P9+/eLj7i6urrg7++P2tpaXLx4keMIye8NtJ0P0FtAOCEhQerfQ3l5OZYvX45r167JFNvkyZOxY8cOuLu7S1zv6/1ZXFyMzMxMhISEoKamRqa5CHdo4aXiuru78dVXX+H48eMQiUR4/vy5xPPNzc0cRaZ8+voNAug3yVuVjrfk7VV3LvJ4PJw7d06mcfvyYrS1tcXPaWtrw9LSEkKhUG5HnGzT1dVFeXm5VD2yGzduwNnZWaH9AcnrkWXh9SodHR0y51+97Ofp1q1bcHJyQnt7Oye9Jwm7qICqiouMjERycjJCQkKwbds2bN26FXV1dcjMzGTlLh51kpKS8sqEbvI/eXl5ch135cqViI+PV/ldxqFDh0IkEkm9Ud67d09l+gOSN3Pw4EHs27cPd+/eRVFREUaNGoU9e/bAysoKCxculGlsOzs77Nq1C9999534Q0lnZyd27dol/hn75ZdfYGJiIvPrIBzi9KCTyGz06NHiAqr6+vriInvx8fHMxx9/zGVoSkdDQ4P59ddfpa4/evSI0dDQ4CAiouo2bNjAmJubM0ePHmVEIhEjEomYI0eOMObm5kxgYCDX4ZF+vFgQ+E19/fXXjLGxMbNz505GV1dXPE5KSgoza9YsmWMrKChg+Hw+M2LECMbNzY1xc3NjBAIBw+fzxXllaWlpTExMjMxzEe7QUaOKGzJkCG7evAkLCwuMHDkSp06dwqRJk1BbWwsnJyc8fvyY6xCVhoaGBn799VeMGDFC4np9fT0cHBzEhRgJeV3Pnz9HaGgo9u3bh66uLgC9d54FBARg165ddOu/EpLlqNHBwQFRUVFYtGiRxDjXrl3DrFmz8OjRI5nj++2333D48GFUVVUBAMaMGQNfX1/aQVUjdNSo4szNzdHQ0AALCwtYW1sjJycHkyZNQklJCf3S/399Cd08Hg9ffvllvwndEydO5Cg6osq0tbURHx+P6OhocbKztbU1FTNWU3fv3oWTk5PUdR0dHZk/uHV2dsLOzg4nT57EunXrZBqLKDdaeKk4Ly8vnD17FlOmTMGGDRuwbNkyHDhwACKRCEFBQVyHpxTKy8sB9CbRX716VSqhe8KECRAKhVyFR9SAnp4exo0bx3UY5CUYFhpYA4CVlRWuXLmCUaNGSVzPysqCvb29TDFqaWnh2bNnMo1BVAMdNaqZoqIiFBUVwdbWFh9++CHX4SgVdUnoJoRIW7FiBZKSkqRKPdTV1WH58uW4dOmSzHMkJydj+/btiI2NxerVq5GcnIyamhpER0cjOTkZS5culWn8qKgoVFVVITk5ud8K/EQ90MKLEEKIynNycsKTJ09w6NAhcQuf1NRUbNy4EXPmzMGPP/7IyjyHDx/G9u3bxUfLZmZm2L59O1avXi3z2H0nGPr6+hg3bpzUIjIjI0PmOQj3aEmtBu7cuYO8vDw8fPgQPT09Es9RSQlCyNuguLgYW7ZswaxZs+TWwLq9vR1eXl7w8/NDW1sbrl27hoKCApibm7My/vDhw/HRRx+xMhZRXrTjpeL279+PgIAAGBsbw9TUVKIwKNuNYQkhRNnJq4E1AMybNw/e3t5Yt24dWlpaYGdnBy0tLTx69AhxcXEICAhgbS6ivmjhpeJGjRqFzz77DJs3b+Y6FEII4Yy8G1gDgLGxMS5cuABHR0ckJycjMTER5eXlSE9PR3h4uLhROiGvQkeNKu6///0vlixZwnUYhBDCqb4WTefPn4eLiwsYhkFMTAy8vb2xatUqfP311zLP0dbWJq6nlZOTA29vb2hoaMDFxQX19fUyjw8AJ06ceGkLODrBUA8af/wlRJktWbIEOTk5XIdBCCGccnZ2xpUrV+Di4gKgN9Vi8+bNKCoqYq1ZuY2NDTIzM3Hv3j1kZ2dj3rx5AICHDx+ycrd0QkICVq5cCRMTE5SXl2Py5Mng8/mora3FX/7yF5nHJ8qBjhpVXHR0NOLi4uDp6Ylx48ZBS0tL4vmNGzdyFBkhhCgHNhpYA727Ub6+vuju7oabm5v4Q290dDQuXryI06dPyzS+nZ0dIiIi8PHHH0tUxg8PD0dzczP27t0r82sg3KOFl4qzsrJ66XM8Hg+1tbUKjIYQQrgjzwbWfRobG9HQ0IAJEyZAQ6P30Ki4uBhDhw6Vapb+pvT09HDz5k2MGjUKAoEAubm5mDBhAu7cuQMXFxc0NTWx8RIIx+ioUcXdvXv3pQ9adBFC3hbffPMNgoODMX/+fLS0tKC7uxtAb4mGPXv2sDaPqakpnJycxIsuAJg8ebLMi66+sZubmwEAFhYW+Pe//w2g9/c87ZGoD1p4EUIIUXmJiYnYv38/tm7dCk1NTfF1Z2dnXL16lcPIXt+cOXPw888/A+jttBEUFIS5c+fCx8cHXl5eHEdH2EJ3Naq47u5ufP/99zh79my/BVTPnTvHUWSEEKI48mxgrShbt26FmZkZAGD9+vXg8/koLCzEggUL4OHhwXF0hC208FJxgYGB+P777+Hp6YmxY8dKFFAlhJC3hTwbWCuKjY0NGhoaIBAIAABLly7F0qVL0dTUBIFAID4+JaqNFl4q7ujRozh+/DhrBQIJIUQVBQcHY/369Xj27BkYhkFxcTGOHDkibmCtCl6Wx9Xa2orBgwcrOBoiL7TwUnHa2tqwsbHhOgxCCOGUv78/dHV1sW3bNrS1tcHX1xdmZmaIj4/H0qVLuQ7vlYKDgwH03okeHh4OPT098XPd3d24fPkyJk6cyFF0hG1UTkLFxcbGora2Fnv37qVjRkLIW6u9vR0Mw0BPT0+igbWDgwPc3d25Du+VZs+eDQDi3pLa2tri57S1tWFpaQmhUAhbW1uuQiQsooWXivPy8kJeXh6MjIzg6OgoVUA1IyODo8gIIURx1KGB9cqVKxEfH89KFXyivKichIobPnw4vLy8MHPmTBgbG2PYsGESD0IIeRuUlZVh+vTpAHorzJuYmKC+vh5paWlISEjgOLrXk5KSQouutwDleKm4lJQUrkMghBDOKaKBNSFsoB0vQgghKk/eDawJYQstvFSUoaEhjIyMpB5WVlZwd3dHbm4u1yESQojChIeHQygUwtLSElOmTMHUqVMB9O5+9VdYlRCuUHK9ikpNTe33ektLC0pLS3Hs2DGcOHECH374oYIjI4QQbsizgTUhbKGFl5qKi4vDiRMnUFhYyHUohBBCCPl/tPBSU1VVVXBxcRF3uieEEEII9yjHS011dHRIFOEjhBBCCPdo4aWmDhw4QC0mCCGEECVDdbxUVF9vr997/PgxysrKUFVVhYsXLyo4KkIIIYS8Ci28VFR5eXm/14cOHYq5c+ciIyMDVlZWCo6KEEIIIa9CyfWEEEIIIQpCOV6EEEIIIQpCCy9CCCGEEAWhhRchhBBCiILQwosQQgghREFo4UUIIYQQoiC08CKEEEIIURBaeBFCCCGEKAgtvAghhBBCFOT/APUqpcjJ6uBUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr, cmap='magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(hd.drop('target_yes',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       True\n",
       "1       True\n",
       "2       True\n",
       "3       True\n",
       "5       True\n",
       "       ...  \n",
       "296    False\n",
       "298    False\n",
       "299    False\n",
       "300    False\n",
       "302    False\n",
       "Name: target_yes, Length: 293, dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hd['target_yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = hd['target_yes'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = hd\n",
    "y = np.ravel(arr)\n",
    "x_test, x_train, y_test, y_train = train_test_split(x,y, test_size=0.3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       1.00      0.87      0.93        95\n",
      "        True       0.90      1.00      0.95       110\n",
      "\n",
      "    accuracy                           0.94       205\n",
      "   macro avg       0.95      0.94      0.94       205\n",
      "weighted avg       0.95      0.94      0.94       205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 83,  12],\n",
       "       [  0, 110]], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9414634146341463"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_accuracy = accuracy_score(y_test, pred)\n",
    "svm_accuracy"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KMEANS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function sklearn.datasets._samples_generator.make_blobs(n_samples=100, n_features=2, *, centers=None, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None, return_centers=False)>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = make_blobs(n_samples=100, n_features=2, centers=None, cluster_std=1.0,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 2.63185834,  0.6893649 ],\n",
       "        [ 0.08080352,  4.69068983],\n",
       "        [ 3.00251949,  0.74265357],\n",
       "        [-0.63762777,  4.09104705],\n",
       "        [-0.07228289,  2.88376939],\n",
       "        [ 0.62835793,  4.4601363 ],\n",
       "        [-2.67437267,  2.48006222],\n",
       "        [-0.57748321,  3.0054335 ],\n",
       "        [ 2.72756228,  1.3051255 ],\n",
       "        [ 0.34194798,  3.94104616],\n",
       "        [ 1.70536064,  4.43277024],\n",
       "        [ 2.20656076,  5.50616718],\n",
       "        [ 2.52092996, -0.63858003],\n",
       "        [ 2.50904929,  5.7731461 ],\n",
       "        [-2.27165884,  2.09144372],\n",
       "        [ 3.92282648,  1.80370832],\n",
       "        [-1.62535654,  2.25440397],\n",
       "        [ 0.1631238 ,  2.57750473],\n",
       "        [-1.59514562,  4.63122498],\n",
       "        [-2.63128735,  2.97004734],\n",
       "        [-2.17052242,  0.69447911],\n",
       "        [-1.56618683,  1.74978876],\n",
       "        [-0.88677249,  1.30092622],\n",
       "        [ 0.08848433,  2.32299086],\n",
       "        [ 0.9845149 ,  1.95211539],\n",
       "        [ 2.18217961,  1.29965302],\n",
       "        [ 1.28535145,  1.43691285],\n",
       "        [ 0.89011768,  1.79849015],\n",
       "        [-1.89608585,  2.67850308],\n",
       "        [-0.75511346,  3.74138642],\n",
       "        [ 1.12031365,  5.75806083],\n",
       "        [ 3.54351972,  2.79355284],\n",
       "        [ 1.64164854,  0.15020885],\n",
       "        [ 2.47034915,  4.09862906],\n",
       "        [-1.98243652,  2.93536142],\n",
       "        [ 0.85624076,  3.86236175],\n",
       "        [ 0.87305123,  4.71438583],\n",
       "        [ 1.38093486,  0.92949422],\n",
       "        [ 2.41163392,  1.60423683],\n",
       "        [-2.26646701,  4.46089686],\n",
       "        [-0.40026809,  1.83795075],\n",
       "        [ 2.45760916,  0.21285357],\n",
       "        [ 2.3535057 ,  2.22404956],\n",
       "        [-0.73000011,  6.25456272],\n",
       "        [ 1.1312175 ,  4.68194985],\n",
       "        [ 0.4666179 ,  3.86571303],\n",
       "        [ 1.18447037,  0.318814  ],\n",
       "        [ 1.36069966,  0.74802912],\n",
       "        [-2.43972624,  4.03489855],\n",
       "        [-1.00362735,  2.74633593],\n",
       "        [ 0.63633194,  4.25441021],\n",
       "        [ 1.41942144,  1.57409695],\n",
       "        [ 0.06897171,  4.35573272],\n",
       "        [ 2.2635425 ,  1.8743027 ],\n",
       "        [-2.02493646,  4.84741432],\n",
       "        [-0.6700734 ,  2.26685667],\n",
       "        [ 3.23404709,  0.71773882],\n",
       "        [ 2.06576754,  2.68353415],\n",
       "        [ 1.0220286 ,  4.11660348],\n",
       "        [ 3.93841822, -0.4500954 ],\n",
       "        [ 0.78478252,  1.86706037],\n",
       "        [-2.81976092,  3.18493313],\n",
       "        [-2.33031368,  2.22833248],\n",
       "        [-1.36023052,  3.5529137 ],\n",
       "        [-3.01816161,  3.35727396],\n",
       "        [ 1.65209057,  2.12010873],\n",
       "        [ 1.7373078 ,  4.42546234],\n",
       "        [-2.52711936,  1.37311116],\n",
       "        [ 1.74371499,  0.953829  ],\n",
       "        [ 1.9263585 ,  4.15243012],\n",
       "        [-0.60604519,  3.23660991],\n",
       "        [ 3.2460247 ,  2.84942165],\n",
       "        [-1.57671974,  4.95740592],\n",
       "        [ 3.97820955,  2.37817845],\n",
       "        [ 1.19404184,  2.80772861],\n",
       "        [ 2.11567076,  3.06896151],\n",
       "        [ 1.15369622,  3.90200639],\n",
       "        [ 0.30380963,  3.94423417],\n",
       "        [-1.88089792,  1.54293097],\n",
       "        [ 2.43169305, -0.20173713],\n",
       "        [-0.27652528,  5.08127768],\n",
       "        [ 1.0427873 ,  4.60625923],\n",
       "        [ 1.78726415,  1.70012006],\n",
       "        [-0.65392827,  4.76656958],\n",
       "        [ 0.88214412,  2.84128485],\n",
       "        [ 1.42013331,  4.63746165],\n",
       "        [ 0.94808785,  4.7321192 ],\n",
       "        [ 0.46546494,  3.12315514],\n",
       "        [ 2.66934689,  1.81987033],\n",
       "        [ 0.58894326,  4.00148458],\n",
       "        [ 1.62011397,  2.74692739],\n",
       "        [ 2.45127423, -0.19539785],\n",
       "        [-0.42724442,  3.57314599],\n",
       "        [-2.56114686,  3.59947678],\n",
       "        [-2.84281142,  2.45629766],\n",
       "        [-0.33887422,  3.23482487],\n",
       "        [ 1.28933778,  3.44969159],\n",
       "        [ 1.84070628,  3.56162231],\n",
       "        [-0.90167256,  1.31582461],\n",
       "        [-2.75233953,  3.76224524]]),\n",
       " array([1, 0, 1, 0, 0, 0, 2, 2, 1, 0, 0, 0, 1, 0, 2, 1, 2, 0, 2, 2, 2, 2,\n",
       "        2, 0, 1, 1, 1, 1, 2, 2, 0, 1, 1, 0, 2, 2, 0, 1, 1, 2, 2, 1, 1, 0,\n",
       "        0, 0, 1, 1, 2, 2, 2, 1, 0, 1, 2, 2, 1, 1, 0, 1, 1, 2, 2, 2, 2, 1,\n",
       "        0, 2, 1, 0, 2, 0, 0, 1, 1, 0, 0, 0, 2, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "        1, 0, 1, 1, 2, 2, 2, 2, 0, 0, 2, 2]))"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b0eb9ba210>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(k[0][:,0],k[0][:,1], c=k[1], cmap='magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jrjub\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning:\n",
      "\n",
      "The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(k[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.9801637 ,  4.30837857],\n",
       "       [-1.5510878 ,  2.88827923],\n",
       "       [ 2.2427373 ,  1.30652003]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 2, 1, 1, 0, 1, 1, 2, 0, 0, 0, 2, 0, 1, 2, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 2, 2, 2, 2, 1, 1, 0, 2, 2, 0, 1, 0, 0, 2, 2, 1, 1, 2, 2, 0,\n",
       "       0, 0, 2, 2, 1, 1, 0, 2, 0, 2, 1, 1, 2, 2, 0, 2, 2, 1, 1, 1, 1, 2,\n",
       "       0, 1, 2, 0, 1, 2, 1, 2, 0, 0, 0, 0, 1, 2, 0, 0, 2, 0, 0, 0, 0, 0,\n",
       "       2, 0, 2, 2, 1, 1, 1, 1, 0, 0, 1, 1])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "kn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, False,  True,  True,  True, False,  True,\n",
       "       False,  True, False,  True, False, False, False, False,  True,\n",
       "        True, False,  True,  True, False, False, False, False,  True,\n",
       "       False,  True,  True,  True, False,  True, False,  True,  True,\n",
       "        True,  True,  True, False, False,  True, False, False, False,\n",
       "       False,  True,  True, False, False,  True,  True, False, False,\n",
       "        True,  True,  True,  True, False,  True,  True,  True, False,\n",
       "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "       False, False,  True,  True,  True, False, False, False,  True,\n",
       "        True, False, False,  True, False,  True, False])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = kn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 87,   8],\n",
       "       [  2, 108]], dtype=int64)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.98      0.92      0.95        95\n",
      "        True       0.93      0.98      0.96       110\n",
      "\n",
      "    accuracy                           0.95       205\n",
      "   macro avg       0.95      0.95      0.95       205\n",
      "weighted avg       0.95      0.95      0.95       205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9512195121951219"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_accuracy = accuracy_score(y_test, predict)\n",
    "kmeans_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 89   6]\n",
      " [  0 110]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       1.00      0.94      0.97        95\n",
      "        True       0.95      1.00      0.97       110\n",
      "\n",
      "    accuracy                           0.97       205\n",
      "   macro avg       0.97      0.97      0.97       205\n",
      "weighted avg       0.97      0.97      0.97       205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "kn_n = KNeighborsClassifier(n_neighbors=17)\n",
    "kn_n.fit(x_train,y_train)\n",
    "pd=kn_n.predict(x_test)\n",
    "print(confusion_matrix(y_test,pd))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,pd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9707317073170731"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_accuracy = accuracy_score(y_test,pd)\n",
    "knn_accuracy"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jrjub\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = lm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True,  True,  True,  True, False, False,  True,\n",
       "       False, False, False,  True,  True,  True,  True,  True,  True,\n",
       "       False,  True, False, False,  True, False,  True,  True,  True,\n",
       "       False,  True,  True, False, False,  True,  True,  True,  True,\n",
       "       False,  True, False,  True,  True, False, False,  True,  True,\n",
       "        True, False,  True,  True,  True,  True, False,  True, False,\n",
       "        True,  True,  True, False, False, False,  True, False, False,\n",
       "        True,  True, False,  True, False,  True,  True, False, False,\n",
       "       False,  True, False, False, False,  True,  True,  True,  True,\n",
       "        True,  True, False,  True, False, False,  True,  True, False,\n",
       "        True, False,  True,  True,  True, False,  True,  True, False,\n",
       "        True, False,  True,  True,  True,  True,  True, False,  True,\n",
       "        True, False, False, False,  True, False, False, False,  True,\n",
       "       False,  True, False,  True,  True,  True,  True,  True,  True,\n",
       "        True, False,  True,  True, False, False, False,  True, False,\n",
       "       False,  True, False, False,  True, False, False, False,  True,\n",
       "       False,  True, False, False, False,  True, False, False,  True,\n",
       "        True,  True, False,  True,  True, False, False,  True,  True,\n",
       "        True,  True,  True, False,  True,  True, False,  True, False,\n",
       "       False,  True, False, False, False,  True, False,  True,  True,\n",
       "       False,  True, False,  True,  True, False,  True, False, False,\n",
       "        True, False, False, False, False, False,  True, False, False,\n",
       "       False,  True, False, False,  True,  True,  True])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.metrics as metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.99      0.97      0.98        95\n",
      "        True       0.97      0.99      0.98       110\n",
      "\n",
      "    accuracy                           0.98       205\n",
      "   macro avg       0.98      0.98      0.98       205\n",
      "weighted avg       0.98      0.98      0.98       205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 92,   3],\n",
       "       [  1, 109]], dtype=int64)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acurracy 0.9804878048780488\n",
      "Precision 0.9732142857142857\n",
      "Acurracy 0.990909090909091\n"
     ]
    }
   ],
   "source": [
    "print(\"Acurracy\",metrics.accuracy_score(y_test,prediction))\n",
    "print(\"Precision\",metrics.precision_score(y_test,prediction))\n",
    "print(\"Acurracy\",metrics.recall_score(y_test,prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = ['SVM Model', 'KNN Model','Logistic Regression']\n",
    "acc=[0.9414634146341463,0.9512195121951219,0.9804878048780488]\n",
    "plt.bar(model,acc)\n",
    "plt.title(\"Accuracy Rates of three models\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}