Eksperiment modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "13136208",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as py\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565b48d1",
   "metadata": {},
   "source": [
    "# Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "58660456",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Heart Attack.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e9fd48c",
   "metadata": {},
   "source": [
    "# Call DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba81dde0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>impluse</th>\n",
       "      <th>pressurehight</th>\n",
       "      <th>pressurelow</th>\n",
       "      <th>glucose</th>\n",
       "      <th>kcm</th>\n",
       "      <th>troponin</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>160</td>\n",
       "      <td>83</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.012</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>98</td>\n",
       "      <td>46</td>\n",
       "      <td>296.0</td>\n",
       "      <td>6.75</td>\n",
       "      <td>1.060</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>160</td>\n",
       "      <td>77</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.003</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>120</td>\n",
       "      <td>55</td>\n",
       "      <td>270.0</td>\n",
       "      <td>13.87</td>\n",
       "      <td>0.122</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>112</td>\n",
       "      <td>65</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.003</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>1314</th>\n",
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       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>122</td>\n",
       "      <td>67</td>\n",
       "      <td>204.0</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.006</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>125</td>\n",
       "      <td>55</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.172</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>168</td>\n",
       "      <td>104</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.24</td>\n",
       "      <td>4.250</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>117</td>\n",
       "      <td>68</td>\n",
       "      <td>443.0</td>\n",
       "      <td>5.80</td>\n",
       "      <td>0.359</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>157</td>\n",
       "      <td>79</td>\n",
       "      <td>134.0</td>\n",
       "      <td>50.89</td>\n",
       "      <td>1.770</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1319 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender  impluse  pressurehight  pressurelow  glucose    kcm  \\\n",
       "0      64       1       66            160           83    160.0   1.80   \n",
       "1      21       1       94             98           46    296.0   6.75   \n",
       "2      55       1       64            160           77    270.0   1.99   \n",
       "3      64       1       70            120           55    270.0  13.87   \n",
       "4      55       1       64            112           65    300.0   1.08   \n",
       "...   ...     ...      ...            ...          ...      ...    ...   \n",
       "1314   44       1       94            122           67    204.0   1.63   \n",
       "1315   66       1       84            125           55    149.0   1.33   \n",
       "1316   45       1       85            168          104     96.0   1.24   \n",
       "1317   54       1       58            117           68    443.0   5.80   \n",
       "1318   51       1       94            157           79    134.0  50.89   \n",
       "\n",
       "      troponin     class  \n",
       "0        0.012  negative  \n",
       "1        1.060  positive  \n",
       "2        0.003  negative  \n",
       "3        0.122  positive  \n",
       "4        0.003  negative  \n",
       "...        ...       ...  \n",
       "1314     0.006  negative  \n",
       "1315     0.172  positive  \n",
       "1316     4.250  positive  \n",
       "1317     0.359  positive  \n",
       "1318     1.770  positive  \n",
       "\n",
       "[1319 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d13370",
   "metadata": {},
   "source": [
    "# Info DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e881601e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1319 entries, 0 to 1318\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   age            1319 non-null   int64  \n",
      " 1   gender         1319 non-null   int64  \n",
      " 2   impluse        1319 non-null   int64  \n",
      " 3   pressurehight  1319 non-null   int64  \n",
      " 4   pressurelow    1319 non-null   int64  \n",
      " 5   glucose        1319 non-null   float64\n",
      " 6   kcm            1319 non-null   float64\n",
      " 7   troponin       1319 non-null   float64\n",
      " 8   class          1319 non-null   object \n",
      "dtypes: float64(3), int64(5), object(1)\n",
      "memory usage: 92.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdae79c5",
   "metadata": {},
   "source": [
    "# Checking Missing Value and Duplicated Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2152d771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age              0\n",
       "gender           0\n",
       "impluse          0\n",
       "pressurehight    0\n",
       "pressurelow      0\n",
       "glucose          0\n",
       "kcm              0\n",
       "troponin         0\n",
       "class            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c941209c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4d7cb01",
   "metadata": {},
   "source": [
    "# Change class to numeric 1 = Positive, 0 Negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce2f81a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "46f05875",
   "metadata": {},
   "outputs": [],
   "source": [
    "#untuk mengubah jadi 1 0 pake label encoder aja jadi import sklearn from preprocessing\n",
    "label_encoder = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68505b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "#kalau mau ngubah nomornya, panggil kkolomnya lalu pakai fit transform\n",
    "df[\"class\"] = label_encoder.fit_transform(df[\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "08ed3b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>impluse</th>\n",
       "      <th>pressurehight</th>\n",
       "      <th>pressurelow</th>\n",
       "      <th>glucose</th>\n",
       "      <th>kcm</th>\n",
       "      <th>troponin</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
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       "      <td>160</td>\n",
       "      <td>83</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>98</td>\n",
       "      <td>46</td>\n",
       "      <td>296.0</td>\n",
       "      <td>6.75</td>\n",
       "      <td>1.060</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>160</td>\n",
       "      <td>77</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>120</td>\n",
       "      <td>55</td>\n",
       "      <td>270.0</td>\n",
       "      <td>13.87</td>\n",
       "      <td>0.122</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>112</td>\n",
       "      <td>65</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
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       "      <th>1314</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>122</td>\n",
       "      <td>67</td>\n",
       "      <td>204.0</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>125</td>\n",
       "      <td>55</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.172</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>168</td>\n",
       "      <td>104</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.24</td>\n",
       "      <td>4.250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>117</td>\n",
       "      <td>68</td>\n",
       "      <td>443.0</td>\n",
       "      <td>5.80</td>\n",
       "      <td>0.359</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>157</td>\n",
       "      <td>79</td>\n",
       "      <td>134.0</td>\n",
       "      <td>50.89</td>\n",
       "      <td>1.770</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1319 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender  impluse  pressurehight  pressurelow  glucose    kcm  \\\n",
       "0      64       1       66            160           83    160.0   1.80   \n",
       "1      21       1       94             98           46    296.0   6.75   \n",
       "2      55       1       64            160           77    270.0   1.99   \n",
       "3      64       1       70            120           55    270.0  13.87   \n",
       "4      55       1       64            112           65    300.0   1.08   \n",
       "...   ...     ...      ...            ...          ...      ...    ...   \n",
       "1314   44       1       94            122           67    204.0   1.63   \n",
       "1315   66       1       84            125           55    149.0   1.33   \n",
       "1316   45       1       85            168          104     96.0   1.24   \n",
       "1317   54       1       58            117           68    443.0   5.80   \n",
       "1318   51       1       94            157           79    134.0  50.89   \n",
       "\n",
       "      troponin  class  \n",
       "0        0.012      0  \n",
       "1        1.060      1  \n",
       "2        0.003      0  \n",
       "3        0.122      1  \n",
       "4        0.003      0  \n",
       "...        ...    ...  \n",
       "1314     0.006      0  \n",
       "1315     0.172      1  \n",
       "1316     4.250      1  \n",
       "1317     0.359      1  \n",
       "1318     1.770      1  \n",
       "\n",
       "[1319 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac6c2b0",
   "metadata": {},
   "source": [
    "# Looking distribution Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3041036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age              -0.239219\n",
       "gender           -0.674363\n",
       "impluse          18.247720\n",
       "pressurehight     0.685246\n",
       "pressurelow       0.489202\n",
       "glucose           1.933475\n",
       "kcm               4.978095\n",
       "troponin          5.799107\n",
       "class            -0.469310\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a441fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1400 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(15,14), layout=(3,3), bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b22c4231",
   "metadata": {},
   "source": [
    "# Cheking outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cd537b",
   "metadata": {},
   "source": [
    "## all feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "63997ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'All outlier'}>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,14))\n",
    "plt.title(\"All outlier\")\n",
    "df.boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e4d979e",
   "metadata": {},
   "source": [
    "## Remove outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e999281c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age              18.0000\n",
      "gender            1.0000\n",
      "impluse          21.0000\n",
      "pressurehight    33.0000\n",
      "pressurelow      19.0000\n",
      "glucose          71.5000\n",
      "kcm               4.1500\n",
      "troponin          0.0795\n",
      "class             1.0000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "q1 = df.quantile(0.25)\n",
    "q3 = df.quantile(0.75)\n",
    "iqr = q3-q1\n",
    "print(iqr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7a380795",
   "metadata": {},
   "outputs": [],
   "source": [
    "lowerbound = q1 - iqr *1.5\n",
    "upperbound = q3+iqr *1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "838c9868",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age              20.00000\n",
      "gender           -1.50000\n",
      "impluse          32.50000\n",
      "pressurehight    60.50000\n",
      "pressurelow      33.50000\n",
      "glucose          -9.25000\n",
      "kcm              -4.57000\n",
      "troponin         -0.11325\n",
      "class            -1.50000\n",
      "dtype: float64\n",
      "age               92.00000\n",
      "gender             2.50000\n",
      "impluse          116.50000\n",
      "pressurehight    192.50000\n",
      "pressurelow      109.50000\n",
      "glucose          276.75000\n",
      "kcm               12.03000\n",
      "troponin           0.20475\n",
      "class              2.50000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(lowerbound)\n",
    "print(upperbound)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e71791b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_outlier = df[~((df>upperbound)|(df<lowerbound)).any(axis=1)]\n",
    "\n",
    "#kalau any itu ketika salah satu tidak memenuhi syarata langsung di kick\n",
    "#kalau all ya kalau satu ga memenuhi syarat brati salah"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b3ee777b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(789, 9)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_outlier.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cbe70987",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'No outlier'}>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hAABQRaZNm5ZTTjkla9asyU9+8pN+1wh7+OGH8+CDD2b8+PGZNm1apUcFgCFHCAMAgCpSW1ubpUuX5q677spll12WhoaGnHXWWWloaMhll12Wu+66K0uWLEltbW2lRwWAIcfF8gEAoMrMnTs3y5cvz4IFCzJ9+vS++8ePH5/ly5dn7ty5FZwOAIYuIQwAAKrQ3Llzc+mll+bee+/NN77xjVx00UU5//zznQkGAC/CWyMBAAAAKIIQBgAAVWjFihWZMGFCWlpasmzZsrS0tGTChAlZsWJFpUcDgCFLCAMAgCqzYsWKzJs3L83NzWlra8vtt9+etra2NDc3Z968eWIYALwAIQwAAKrI7t27s2DBgsyePTsrV67MlClT0tjYmClTpmTlypWZPXt2Fi5cmN27d1d6VAAYcoQwAACoIm1tbXnsscdy7bXXZtiw/t/ODxs2LIsWLcqmTZvS1tZWoQkBYOgSwgAAoIps3rw5STJp0qT9bt97/979AIB9hDAAAKgi48aNS5KsX79+v9v33r93PwBgHyEMAACqyLRp03LKKadk8eLF2bNnT79te/bsyQ033JDx48dn2rRpFZoQAIYuIQwAAKpIbW1tli5dmrvuuitz5szJ2rVr09nZmbVr12bOnDm56667smTJktTW1lZ6VAAYcuoqPQAAAHBw5s6dm+XLl2fBggWZPn163/3jx4/P8uXLM3fu3ApOBwBDlxAGAABVaO7cuZk9e3ZuuummfOc738nrXve6XHXVVRk+fHilRwOAIctbIwEAoAqtWLEip59+ehYuXJi77747CxcuzOmnn54VK1ZUejQAGLKEMAAAqDIrVqzIvHnz0tzcnLa2ttx+++1pa2tLc3Nz5s2bJ4YBwAsQwgAAoIrs3r07CxYsyOzZs7Ny5cpMmTIljY2NmTJlSlauXJnZs2dn4cKF2b17d6VHBYAhRwgDAIAq0tbWlsceeyzXXnttent7s2rVqqxevTqrVq1Kb29vFi1alE2bNqWtra3SowLAkCOEAQBAFdm8eXOSZOPGjZkwYUJaWlqybNmytLS0ZMKECfnpT3/abz8AYB8hDAAAqsi4ceOSJG9729v2e42wt73tbf32AwD2qav0AAAAwIGbOnVq6urqctxxx2XFihXp7e3NU089lSlTpmTFihU58cQT89RTT2Xq1KmVHhUAhhxnhAEAQBVZs2ZNenp60tHRkblz52bt2rXp7OzM2rVrM3fu3HR0dKSnpydr1qyp9KgAMOQIYQAAUEX2Xvvr1ltvzbp16zJ9+vRceeWVmT59etavX59bb721334AwD5CGAAAVJG91/469dRT8+ijj6a1tTXz589Pa2trHnnkkfzJn/xJv/0AgH2EMAAAqCLTpk3LKaecksWLF6empiYzZszI9OnTM2PGjNTU1OSGG27I+PHjM23atEqPCgBDjhAGAABVpLa2NkuXLs1dd92VOXPm9LtG2Jw5c3LXXXdlyZIlqa2trfSoADDk+KmRAABQZebOnZvly5dnwYIFmT59et/948ePz/LlyzN37twKTgcAQ5cQBgAAVWju3Lm59NJLc++99+Yb3/hGLrroopx//vnOBAOAF+GtkQAAAAAUQQgDAIAqtGLFikyYMCEtLS1ZtmxZWlpaMmHChKxYsaLSowHAkCWEAQBAlVmxYkXmzZuX5ubmtLW15fbbb09bW1uam5szb948MQwAXoAQBgAAVWT37t1ZsGBBZs+enZUrV2bKlClpbGzMlClTsnLlysyePTsLFy7M7t27Kz0qAAw5QhgAAFSRtra2PPbYY7n22mszbFj/b+eHDRuWRYsWZdOmTWlra6vQhAAwdAlhAABQRTZv3pwkmTRp0n63771/734AwD5CGAAAVJFx48YlSdavX7/f7Xvv37sfALCPEAYAAFVk2rRpOeWUU7J48eJ0d3dn1apVWb16dVatWpXu7u7ccMMNGT9+fKZNm1bpUQFgyKmr9AAAAMCBq62tzdKlSzNv3rwcddRR6ezsTJIsW7YsjY2Nee6557J8+fLU1tZWeFIAGHqcEQYAAFWot7f3effV1NTs934A4DeEMAAAqCK7d+/OggULcskll+Tpp5/OkiVLcvHFF2fJkiV56qmncskll2ThwoXZvXt3pUcFgCFHCAMAgCrS1taWxx57LFOnTs0ZZ5yRhQsX5u67787ChQtzxhln5JxzzsmmTZvS1tZW6VEBYMgRwgAAoIps3rw5SbJo0aI0Nzenra0tt99+e9ra2tLc3Jxrr722334AwD5CGAAAVJHRo0cnSc4777ysXLkyU6ZMSWNjY6ZMmZKVK1fm3HPP7bcfALCPEAYAAH9EampqKj0CAAxZQhgAAFSRjo6OJMkDDzyQOXPmZO3atens7MzatWszZ86cPPDAA/32AwD2EcIAAKCKjBs3LkmyePHirFu3LtOnT8+VV16Z6dOnZ/369fmHf/iHfvsBAPsIYQAAUEWmTZuWU045JWvWrMlPfvKTtLa2Zv78+Wltbc3DDz+cBx98MOPHj8+0adMqPSoADDlCGAAAVJHa2tosXbo0d911Vy677LI0NDTkrLPOSkNDQy677LLcddddWbJkSWprays9KgAMOXWVHgAAADg4c+fOzfLly7NgwYJMnz697/7x48dn+fLlmTt3bgWnA4ChSwgDAIAqNHfu3Fx66aW59957841vfCMXXXRRzj//fGeCAcCL8NZIAAAAAIoghAEAQBVasWJFJkyYkJaWlixbtiwtLS2ZMGFCVqxYUenRAGDIEsIAAKDKrFixIvPmzUtzc3Pa2tpy++23p62tLc3NzZk3b54YBgAvQAgDAIAqsnv37ixYsCCzZ8/OnXfemeeeey4PPfRQnnvuudx5552ZPXt2Fi5cmN27d1d6VAAYcoQwAACoIm1tbXnssccyderUvPzlL+/31siXv/zlOeecc7Jp06a0tbVVelQAGHKEMAAAqCKbN29OkixatGi/b4289tpr++0HAOwjhAEAQBUZPXp0kuS8887LypUrM2XKlDQ2NmbKlClZuXJlzj333H77AQD7CGEAAFCldu/enVWrVmX16tVZtWpVdu/enZqamkqPBQBDVl2lBwAAAA5cR0dHkuSBBx7IUUcdlc7OziTJsmXL0tjYmOeee67ffgDAPs4IAwCAKjJu3LgkSW9v7/O21dTU9N2/dz8AYB8hDAAAqsjUqVNTV1eXMWPGZOvWrWltbc38+fPT2tqap59+OmPGjEldXV2mTp1a6VEBYMg5qBB2ww035KyzzsrIkSMzevTozJkzJw8//HC/fd75znempqam36+zzz673z5dXV256qqrcvzxx2fEiBF505velJ///OeDfzUAAPBHbs2aNenp6UlHR0fmzZuXH/3oR9m1a1d+9KMfZd68eeno6EhPT0/WrFlT6VEBYMg5qGuErVq1Ku973/ty1llnpaenJx/+8Icza9as/OhHP8qIESP69nvDG96Qz3/+8323hw8f3u95rr766vzrv/5rvvzlL+e4447LggULMnv27LS3t6e2tnaQLwkAAP54bd68OUnygQ98IP/0T/+Uu+66q29bXV1dPvCBD+RTn/pU334AwD4HFcK++c1v9rv9+c9/PqNHj057e3umT5/ed39DQ0PGjh273+d49tln87nPfS633nprXv/61ydJvvSlL+Wkk07Kt7/97Vx44YUH+xoAAKAYe6/99elPfzpvfOMb09LSkkceeSSnnXZaWltb8+lPf7rffgDAPoO6Rtizzz6bJDn22GP73X/fffdl9OjRefnLX573vOc9/X5iTXt7e7q7uzNr1qy++0444YRMmjTJ6dsAAPB77L1G2OjRo7N8+fK84hWvyPDhw/OKV7wiy5cvz+jRo10jDABewEGdEfbbent7M3/+/Jx33nmZNGlS3/0XXXRR3vzmN+fkk0/Opk2b8nd/93d53etel/b29jQ0NGTLli0ZPnx4jjnmmH7PN2bMmGzZsmW/x+rq6kpXV1ff7W3btiVJuru7093dPdCXUNX2vu5SXz/WANYA1gC/YR2UZ/Xq1enp6ckvf/nLHHPMMens7EySLFu2LI2NjX23V69enRkzZlRyVA4TXwewBrAGDvy1DziEvf/9789//ud/5v777+93/1ve8pa+30+aNCmvec1rcvLJJ+frX/965s6d+4LP19vbm5qamv1uu+GGG3L99dc/7/577rknTU1NA3wFfxxaW1srPQIVZg1gDWANkFgHJVm9enXf73fv3t1v22/f/sY3vpEdO3YctrmoPF8HsAYoeQ3s3LnzgPYbUAi76qqr8i//8i9ZvXp1TjzxxBfdd9y4cTn55JPzyCOPJEnGjh2bXbt2ZevWrf3OCuvo6HjB07cXLVqU+fPn993etm1bTjrppMyaNSujRo0ayEuoet3d3WltbU1LS0vq6+srPQ4VYA1gDWANkFgHJWpsbMyyZcsyderUtLa2ZvXq1X1rYPr06WlpacmaNWty4YUX5vzzz6/0uBwGvg5gDWAN7Hv34O9zUCGst7c3V111Vb761a/mvvvuy/jx43/vY5566qn87Gc/67tY5+TJk1NfX5/W1tZcfvnlSX7zk2/Wr1+fG2+8cb/P0dDQkIaGhufdX19fX+wf8F4+B1gDWANYAyTWQUn2/pT1YcOG5YgjjsgFF1yQrq6uXHDBBamtrc2wYcP69rMmyuLrANYAJa+BA33dB3Wx/Pe973350pe+lNtuuy0jR47Mli1bsmXLlr7rEGzfvj0LFy7Mgw8+mMceeyz33XdfLrnkkhx//PH5sz/7syTJUUcdlXe9611ZsGBB/u3f/i3/8R//kbe97W1pbm7u+ymSAADA/u39QVQPPPBA5syZk7Vr16azszNr167NnDlz8sADD/TbDwDY56DOCPvsZz+bJJk5c2a/+z//+c/nne98Z2pra7Nu3bp88YtfzDPPPJNx48bl/PPPzx133JGRI0f27f+JT3widXV1ufzyy9PZ2ZkLLrggX/jCF/r+7xYAALB/e99psXjx4vyv//W/Mn369L5t48ePzz/8wz/k2muv7dsPANjnoN8a+WIaGxvzrW996/c+zxFHHJGbbropN91008EcHgAAijdt2rSccsopWbNmTX784x/nn/7pn/Kd73wnr3vd6/K+970vl19+ecaPH59p06ZVelQAGHIG/FMjAQCAw6+2tjZLly7NZZddlmOPPbbvMiV33313/u7v/i6dnZ258847vdsCAPbjoK4RBgAADB3PPffci94GAPpzRhgAAFSR3bt356//+q+TJBdffHFmzZqVRx55JKeddlruueeefP3rX89f//Vf59JLL3VWGAD8DmeEAQBAFbnvvvvS0dGR8847L8uXL093d3d++tOfpru7O8uXL8+5556bjo6O3HfffZUeFQCGHCEMAACqyN7AdcIJJ2TkyJFZuHBh7r777ixcuDAjR47MS1/60n77AQD7eGskAABUoa985SsZM2ZMrr/++jQ0NKSrqysf+chH8pWvfKXSowHAkOWMMAAAqCJTp05NktTX12fTpk059dRTs27dupx66qnZtGlT6uvr++0HAOzjjDAAAKgiP/rRj5Ik3d3dOe6449LZ2ZkkWbZsWRobG9Pd3d2330UXXVSxOQFgKHJGGAAAVJHHHnus7/ddXV39tu3atWu/+wEAvyGEAQBAFTnllFP6Pp544on9tp144ok5+eST++0HAOzjrZEAAFBFmpubkyTbtm3Lz372s3z2s5/Nd77znbzuda/LX//1X/fFsb37AQD7CGEAAFBFnnrqqSTJ008/nZEjR2bPnj1JkrvvvjvXXHNN3+29+wEA+3hrJAAAVJFx48b1/b63t7fftt++/dv7AQC/IYQBAEAVmTp1aurq6jJmzJhs3749S5YsycUXX5wlS5Zk+/btGTNmTOrq6jJ16tRKjwoAQ44QBgAAVWTNmjXp6elJR0dHrrjiirz2ta/N29/+9rz2ta/NFVdckY6OjvT09GTNmjWVHhUAhhwhDAAAqsjmzZuTJLfeemvWrVuX6dOn58orr8z06dOzfv363Hrrrf32AwD2EcIAAKCK7L3216mnnppHH300ra2tmT9/flpbW/PII4/kT/7kT/rtBwDsI4QBAEAVmTZtWk455ZQsXrw43d3d+cEPfpANGzbkBz/4Qbq7u3PDDTdk/PjxmTZtWqVHBYAhp67SAwAAAAeutrY2S5cuzWWXXZampqa+nxR5991354Mf/GB6e3tz5513pra2tsKTAsDQ44wwAACoMmvXrk2S1NTU9Lt/2LBh/bYDAP0JYQAAUEV27dqVT3ziExkzZkx27tzZ7xphO3bsyJgxY/KJT3wiu3btqvSoADDkCGEAAFBFbr755vT09ORjH/tY6ur6X+mkrq4uH/3oR9PT05Obb765QhMCwNAlhAEAQBXZuHFjkt+8LXLChAlpaWnJsmXL0tLSkgkTJvS9PXLvfgDAPkIYAABUkVNPPTVJ8u53vzvNzc1pa2vL7bffnra2tjQ3N+c973lPv/0AgH2EMAAAqCLvfe97kyTDhw/PHXfckeeeey4PPfRQnnvuudxxxx0ZPnx4v/0AgH3qfv8uAADAUPHd7343yW8umn/kkUdmz549SZJly5Zl2LBhfbe/+93vZubMmZUaEwCGJGeEAQBAFdm8eXPf73t7e/tt++3bv70fAPAbQhgAAFSR0aNHJ0nOO++87Ny5M0uWLMnFF1+cJUuWZOfOnTn33HP77QcA7OOtkQAAUKWGDx+eD3zgA5kwYUIuvvji1NbWpqamptJjAcCQ5YwwAACoIh0dHUmSBx54IHPmzMnatWvT2dmZtWvXZs6cOXnggQf67QcA7COEAQBAFRk3blySZPHixVm3bl2mT5+eK6+8MtOnT8/69evzD//wD/32AwD2EcIAAKCKTJs2LaecckrWrFmTH/zgB7nkkkty8skn55JLLsn3v//9PPjggxk/fnymTZtW6VEBYMhxjTAAAKgitbW1Wbp0aS677LIcddRRffc//vjjfbfvvPPO1NbWVmpEABiynBEGAABV5otf/OKgtgNAqYQwAACoIp2dnfna176W4cOHZ+fOnWltbc38+fPT2tqanTt3Zvjw4fna176Wzs7OSo8KAEOOEAYAAFXkgx/8YJJk/vz5aWxszIwZMzJ9+vTMmDEjjY2Nufrqq/vtBwDsI4QBAEAVeeSRR5Ik7373u/e7/V3vele//QCAfYQwAACoIqeddlqS5P/8n/+z3+2f+9zn+u0HAOwjhAEAQBX5+Mc/niRZtmxZdu3a1W/brl278slPfrLffgDAPkIYAABUkcbGxlx66aXZtWtXRo4cmWuvvTa/+MUvcu2112bkyJHZtWtXLr300jQ2NlZ6VAAYcuoqPQAAAHBwVq5cmTlz5uRrX/talixZ0m/bpZdempUrV1ZmMAAY4pwRBgAAVWjlypXZuXNn/uqv/iqvfvWr81d/9VfZuXOnCAYAL8IZYQAAUKUaGxvz6U9/OnfffXcuvvji1NfXV3okABjSnBEGAAAAQBGEMAAAAACKIIQBAAAAUATXCAMAgArauXNnNmzYMODHb+/sypp1G3PM8d/LkY0NA36eiRMnpqmpacCPB4BqIIQBAEAFbdiwIZMnTx7089w4yMe3t7fnzDPPHPQcADCUCWEAAFBBEydOTHt7+4Af//DmZzL//67Lsjc35/RxRw9qDgD4YyeEAQBABTU1NQ3qTKxhjz+VhrbOnDHpVXn1yccdwskA4I+Pi+UDAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEU4qBB2ww035KyzzsrIkSMzevTozJkzJw8//HC/fXp7e3PdddflhBNOSGNjY2bOnJkf/vCH/fbp6urKVVddleOPPz4jRozIm970pvz85z8f/KsBAAAAgBdwUCFs1apVed/73pe1a9emtbU1PT09mTVrVnbs2NG3z4033phly5blM5/5TB566KGMHTs2LS0t+fWvf923z9VXX52vfvWr+fKXv5z7778/27dvz+zZs7N79+5D98oAAAAA4LfUHczO3/zmN/vd/vznP5/Ro0envb0906dPT29vbz75yU/mwx/+cObOnZskueWWWzJmzJjcdtttee9735tnn302n/vc53Lrrbfm9a9/fZLkS1/6Uk466aR8+9vfzoUXXniIXhoAAAAA7HNQIex3Pfvss0mSY489NkmyadOmbNmyJbNmzerbp6GhITNmzMiaNWvy3ve+N+3t7enu7u63zwknnJBJkyZlzZo1+w1hXV1d6erq6ru9bdu2JEl3d3e6u7sH8xKq1t7XXerrxxrAGsAa4DesA3p6evo+Wgdl8nUAawBr4MBf+4BDWG9vb+bPn5/zzjsvkyZNSpJs2bIlSTJmzJh++44ZMyaPP/543z7Dhw/PMccc87x99j7+d91www25/vrrn3f/Pffck6ampoG+hD8Kra2tlR6BCrMGsAawBkisg5L9bHuS1GXt2rX5xfpKT0Ml+TqANUDJa2Dnzp0HtN+AQ9j73//+/Od//mfuv//+522rqanpd7u3t/d59/2uF9tn0aJFmT9/ft/tbdu25aSTTsqsWbMyatSoAUxf/bq7u9Pa2pqWlpbU19dXehwqwBrAGsAaILEOSH7wX08n676Xs88+O6962bGVHocK8HUAawBrYN+7B3+fAYWwq666Kv/yL/+S1atX58QTT+y7f+zYsUl+c9bXuHHj+u7v6OjoO0ts7Nix2bVrV7Zu3drvrLCOjo5MnTp1v8draGhIQ0PD8+6vr68v9g94L58DrAGsAawBEuugZHV1dX0frYGy+TqANUDJa+BAX/dB/dTI3t7evP/978+KFSvyne98J+PHj++3ffz48Rk7dmy/U/F27dqVVatW9UWuyZMnp76+vt8+mzdvzvr1618whAEAAADAYB3UGWHve9/7ctttt+VrX/taRo4c2XdNr6OOOiqNjY2pqanJ1VdfncWLF+e0007LaaedlsWLF6epqSlvfetb+/Z917velQULFuS4447Lsccem4ULF6a5ubnvp0gCAAAAwKF2UCHss5/9bJJk5syZ/e7//Oc/n3e+851JkmuuuSadnZ35m7/5m2zdujVTpkzJPffck5EjR/bt/4lPfCJ1dXW5/PLL09nZmQsuuCBf+MIXUltbO7hXAwAAAAAv4KBCWG9v7+/dp6amJtddd12uu+66F9zniCOOyE033ZSbbrrpYA4PAAAAAAN2UNcIAwAAAIBqJYQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCHWVHgAASrVz585s2LBhwI/f3tmVNes25pjjv5cjGxsGNcvEiRPT1NQ0qOcAAIChTggDgArZsGFDJk+ePOjnufEQzNLe3p4zzzzzEDwTAAAMXUIYAFTIxIkT097ePuDHP7z5mcz/v+uy7M3NOX3c0YOeBQAA/tgJYQBQIU1NTYM6C2vY40+loa0zZ0x6VV598nGHcDIAAPjj5GL5AAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAh1lR4AAACq2aYnd2RHV0/Fjr/xVzv6PtbVVebb+xENdRl//IiKHBsADoYQBgAAA7TpyR05f8l9lR4jSbJg+bqKHv/ehTPFMACGPCEMAAAGaO+ZYJ98y6szYfSRlZmhsyt33fdgZs88JyMaGw778R/t2J6r7/h+Rc+KA4ADddAhbPXq1fn4xz+e9vb2bN68OV/96lczZ86cvu3vfOc7c8stt/R7zJQpU7J27dq+211dXVm4cGFuv/32dHZ25oILLsjNN9+cE088ceCvBAAAKmTC6CMz6aVHVeTY3d3d2fKS5MyTj0l9fX1FZgCAanHQF8vfsWNHXvWqV+Uzn/nMC+7zhje8IZs3b+77dffdd/fbfvXVV+erX/1qvvzlL+f+++/P9u3bM3v27OzevfvgXwEAAAAAHICDPiPsoosuykUXXfSi+zQ0NGTs2LH73fbss8/mc5/7XG699da8/vWvT5J86UtfykknnZRvf/vbufDCCw92JAAAAAD4vQ76jLADcd9992X06NF5+ctfnve85z3p6Ojo29be3p7u7u7MmjWr774TTjghkyZNypo1a/4Q4wAAAADAob9Y/kUXXZQ3v/nNOfnkk7Np06b83d/9XV73utelvb09DQ0N2bJlS4YPH55jjjmm3+PGjBmTLVu27Pc5u7q60tXV1Xd727ZtSX5zPYTu7u5D/RKqwt7XXerrxxrAGiDp6enp+2gdlMvXgsoaCn8PK70GhsLnoHSVXgNUnjWANXDgr/2Qh7C3vOUtfb+fNGlSXvOa1+Tkk0/O17/+9cydO/cFH9fb25uampr9brvhhhty/fXXP+/+e+65J01NTYMfuoq1trZWegQqzBrAGijXz7YnSV3Wrl2bX6yv9DRUmq8FlbH37+H999+fxyvzQyP7VGoNDKXPQel8HcAaoOQ1sHPnzgPa75CHsN81bty4nHzyyXnkkUeSJGPHjs2uXbuydevWfmeFdXR0ZOrUqft9jkWLFmX+/Pl9t7dt25aTTjops2bNyqhRo/6wL2CI6u7uTmtra1paWvx0oEJZA1gD/OC/nk7WfS9nn312XvWyYys9DhXia0Fl/fCJbVmybm3OO++8vPKEynxfWuk1MBQ+B6Wr9Bqg8qwBrIF97x78ff7gIeypp57Kz372s4wbNy5JMnny5NTX16e1tTWXX355kmTz5s1Zv359brzxxv0+R0NDQxoaGp53f319fbF/wHv5HGANYA2Uq66uru+jNYCvBZUxlP4eVmoNDKXPQel8HcAaoOQ1cKCv+6BD2Pbt2/Poo4/23d60aVO+//3v59hjj82xxx6b6667LpdddlnGjRuXxx57LNdee22OP/74/Nmf/VmS5Kijjsq73vWuLFiwIMcdd1yOPfbYLFy4MM3NzX0/RRIAAAAADrWDDmHf+973cv755/fd3vuWxXe84x357Gc/m3Xr1uWLX/xinnnmmYwbNy7nn39+7rjjjowcObLvMZ/4xCdSV1eXyy+/PJ2dnbngggvyhS98IbW1tYfgJQEAAADA8x10CJs5c2Z6e3tfcPu3vvWt3/scRxxxRG666abcdNNNB3t4AAAAABiQYZUeAAAAAAAOByEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAi1FV6AACoZpue3JEdXT0VOfbGX+3o+1hXV7l/0kc01GX88SMqdnwAADhQQhgADNCmJ3fk/CX3VXqMLFi+rtIj5N6FM8UwAACGPCEMAAZo75lgn3zLqzNh9JGH//idXbnrvgcze+Y5GdHYcNiPnySPdmzP1Xd8v2JnxQEAwMEQwgBgkCaMPjKTXnrUYT9ud3d3trwkOfPkY1JfX3/Yjw8AANXGxfIBAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIdZUeAACqWU3dtmza9nCGHXHkYT92T09Pnuh5Ij9++sepq6vMP+mbtm1PTd22ihwbAAAOlhAGAINQf/R3c+2/L67oDDd/8+aKHr/+6AuSXFzRGQAA4EAIYQAwCN3PTMnSN741p46uzBlhD9z/QM4979yKnRG2sWN7PvDPGytybAAAOFhCGAAMQm/PqIwfdXpecdxRh/3Y3d3d2VS3KWcce0bq6+sP+/GTZM9zz6a351cVOTYAABwsF8sHAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwgCq0e/furFq1KqtXr86qVauye/fuSo8EAAAw5AlhAFVmxYoVmTBhQlpaWrJs2bK0tLRkwoQJWbFiRaVHAwAAGNKEMIAqsmLFisybNy/Nzc1pa2vL7bffnra2tjQ3N2fevHliGAAAwIsQwgCqxO7du7NgwYLMnj07K1euzJQpU9LY2JgpU6Zk5cqVmT17dhYuXOhtkgAAAC9ACAOoEm1tbXnsscdy7bXXZtiw/l++hw0blkWLFmXTpk1pa2ur0IQAAABDmxAGUCU2b96cJJk0adJ+t++9f+9+AAAA9CeEAVSJcePGJUnWr1+/3+1779+7HwAAAP0JYQBVYtq0aTnllFOyePHi7Nmzp9+2PXv25IYbbsj48eMzbdq0Ck0IAAAwtAlhAFWitrY2S5cuzV133ZU5c+Zk7dq16ezszNq1azNnzpzcddddWbJkSWprays9KgAAwJBUV+kBADhwc+fOzfLly7NgwYJMnz697/7x48dn+fLlmTt3bgWnAwAAGNqEMIAqM3fu3Fx66aW59957841vfCMXXXRRzj//fGeCAQAA/B5CGECF7Ny5Mxs2bBjw44c3jkjdMS/N8MYR+cEPfjDg55k4cWKampoG/HgAAIBqIYQBVMiGDRsyefLkQT/PjYN8fHt7e84888xBzwEAADDUCWEAFTJx4sS0t7cP+PEPb34m8//vuix7c3NOH3f0oOYAAAAogRAGUCFNTU2DOhNr2ONPpaGtM2dMelVeffJxh3AyAACAP07DKj0AAAAAABwOQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCLUVXoAAACoZjV127Jp28MZdsSRFTl+T09Pnuh5Ij9++sepqzv8395v2rY9NXXbDvtxAWAghDAAABiE+qO/m2v/fXGlx8jN37y5YseuP/qCJBdX7PgAcKCEMAAAGITuZ6Zk6RvfmlNHV+6MsAfufyDnnnduRc4I29ixPR/4542H/bgAMBBCGAAADEJvz6iMH3V6XnHcURU5fnd3dzbVbcoZx56R+vr6w378Pc89m96eXx324wLAQAhhADBAnd27kyTrf/FsRY6/o7Mr3/tVMvbxrRnR2FCRGR7t2F6R4wIAwEAIYQAwQBv/OwL9jxXrKjhFXW599KEKHv83RjT4lgIAgKHPd60AMECzXjk2SXLq6CPTWF972I//8OZns2D5uiyd15zTx1XmLVnJbyLY+ONHVOz4AABwoIQwABigY0cMzxWvfVnFjt/T05MkOfUlIzLppZULYQAAUC2GVXoAAAAAADgchDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFKGu0gMAAJRq586d2bBhw6CeY3tnV9as25hjjv9ejmxsGPDzTJw4MU1NTYOaBQBgqBPCAAAqZMOGDZk8efIhea4bB/n49vb2nHnmmYdkFgCAoUoIAwCokIkTJ6a9vX1Qz/Hw5mcy//+uy7I3N+f0cUcPahYAgD92QhgAQIU0NTUN+iysYY8/lYa2zpwx6VV59cnHHaLJAAD+OLlYPgAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBEOOoStXr06l1xySU444YTU1NRk5cqV/bb39vbmuuuuywknnJDGxsbMnDkzP/zhD/vt09XVlauuuirHH398RowYkTe96U35+c9/PqgXAgAAAAAv5qBD2I4dO/KqV70qn/nMZ/a7/cYbb8yyZcvymc98Jg899FDGjh2blpaW/PrXv+7b5+qrr85Xv/rVfPnLX87999+f7du3Z/bs2dm9e/fAXwkAAAAAvIi6g33ARRddlIsuumi/23p7e/PJT34yH/7whzN37twkyS233JIxY8bktttuy3vf+948++yz+dznPpdbb701r3/965MkX/rSl3LSSSfl29/+di688MJBvBwAAAAA2L+DDmEvZtOmTdmyZUtmzZrVd19DQ0NmzJiRNWvW5L3vfW/a29vT3d3db58TTjghkyZNypo1a/Ybwrq6utLV1dV3e9u2bUmS7u7udHd3H8qXUDX2vu5SXz/WAElPT0/fR+ugTNYAiXVQaUPh81/p7wmGwuegdJVeA1SeNYA1cOCv/ZCGsC1btiRJxowZ0+/+MWPG5PHHH+/bZ/jw4TnmmGOet8/ex/+uG264Iddff/3z7r/nnnvS1NR0KEavWq2trZUegQqzBsr1s+1JUpe1a9fmF+srPQ2VYA2QWAeVtvfzf//99+fxIys7S6W+JxhKn4PS+b4Qa4CS18DOnTsPaL9DGsL2qqmp6Xe7t7f3eff9rhfbZ9GiRZk/f37f7W3btuWkk07KrFmzMmrUqMEPXIW6u7vT2tqalpaW1NfXV3ocKsAa4Af/9XSy7ns5++yz86qXHVvpcagAa4DEOqi0Hz6xLUvWrc15552XV55Qme9LK/09wVD4HJSu0muAyrMGsAb2vXvw9zmkIWzs2LFJfnPW17hx4/ru7+jo6DtLbOzYsdm1a1e2bt3a76ywjo6OTJ06db/P29DQkIaGhufdX19fX+wf8F4+B1gD5aqrq+v7aA2UyRogsQ4qbSh9/iv1PcFQ+hyUzveFWAOUvAYO9HUf9E+NfDHjx4/P2LFj+52Kt2vXrqxataovck2ePDn19fX99tm8eXPWr1//giEMAAAAAAbroM8I2759ex599NG+25s2bcr3v//9HHvssXnZy16Wq6++OosXL85pp52W0047LYsXL05TU1Pe+ta3JkmOOuqovOtd78qCBQty3HHH5dhjj83ChQvT3Nzc91MkAQAAAOBQO+gQ9r3vfS/nn39+3+291+56xzvekS984Qu55ppr0tnZmb/5m7/J1q1bM2XKlNxzzz0ZOXJk32M+8YlPpK6uLpdffnk6OztzwQUX5Atf+EJqa2sPwUsCAAAAgOc76BA2c+bM9Pb2vuD2mpqaXHfddbnuuutecJ8jjjgiN910U2666aaDPTwAAAAADMghvUYYAAAAAAxVQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQhLpKDwAAANWqs3t3kmT9L56t2Aw7OrvyvV8lYx/fmhGNDYf9+I92bD/sxwSAgRLCAABggDb+dwT6HyvWVXiSutz66EMVnWBEg/+0AGDo868VAAAM0KxXjk2SnDr6yDTW11Zkhoc3P5sFy9dl6bzmnD7uqIrMMKKhLuOPH1GRYwPAwRDCAABggI4dMTxXvPZlFZ2hp6cnSXLqS0Zk0ksrE8IAoFq4WD4AAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAItRVeoBS7dy5Mxs2bBjw47d3dmXNuo055vjv5cjGhgE/z8SJE9PU1DTgxwMAAABUCyGsQjZs2JDJkycP+nluHOTj29vbc+aZZw56DgAAAIChTgirkIkTJ6a9vX3Aj3948zOZ/3/XZdmbm3P6uKMHNQcAAABACYSwCmlqahrUmVjDHn8qDW2dOWPSq/Lqk487hJMBAAAA/HFysXwAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAh1lR4AoJptenJHdnT1VOTYG3+1o+9jXV3lvpyPaKjL+ONHVOz4AAAAB0oIAxigTU/uyPlL7qv0GFmwfF2lR8i9C2eKYQAAwJAnhAEM0N4zwT75lldnwugjD//xO7ty130PZvbMczKiseGwHz9JHu3Ynqvv+H7FzooDAAA4GEIYwCBNGH1kJr30qMN+3O7u7mx5SXLmycekvr7+sB8fAACg2rhYPgAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABShrtIDAABUs01P7siOrp6KHX/jr3b0fayrq8y3diMa6jL++BEVOTYAwMEQwgAABmjTkzty/pL7Kj1GkmTB8nUVPf69C2eKYQDAkCeEAQAM0N4zwT75lldnwugjKzNDZ1fuuu/BzJ55TkY0Nhz24z/asT1X3/H9ip4VBwBwoIQwAIBBmjD6yEx66VEVOXZ3d3e2vCQ58+RjUl9fX5EZAACqhYvlAwAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFqKv0AADVrKZuWzZtezjDjjjysB+7p6cnT/Q8kR8//ePU1VXmy/mmbdtTU7etIscGAAA4WEIYwCDUH/3dXPvviys6w83fvLmix68/+oIkF1d0BgAAgAMhhAEMQvczU7L0jW/NqaMrc0bYA/c/kHPPO7diZ4Rt7NieD/zzxoocGwAA4GAJYQCD0NszKuNHnZ5XHHfUYT92d3d3NtVtyhnHnpH6+vrDfvwk2fPcs+nt+VVFjg0AAHCwXCwfAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEAR6io9AABANaup25ZN2x7OsCOOrMjxe3p68kTPE/nx0z9OXd3h/9Zu07btqanbdtiPCwAwEEIYAMAg1B/93Vz774srPUZu/ubNFTt2/dEXJLm4YscHADhQQhgAwCB0PzMlS9/41pw6unJnhD1w/wM597xzK3JG2MaO7fnAP2887McFABgIIQwAYBB6e0Zl/KjT84rjjqrI8bu7u7OpblPOOPaM1NfXH/bj73nu2fT2/OqwHxcAYCBcLB8AAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEeoqPUA12/Tkjuzo6qnIsTf+akffx7q6yvwxjmioy/jjR1Tk2AAAAAAHSwgboE1P7sj5S+6r9BhZsHxdRY9/78KZYhgAAABQFYSwAdp7Jtgn3/LqTBh95OE/fmdX7rrvwcyeeU5GNDYc9uM/2rE9V9/x/YqdEQcAAABwsISwQZow+shMeulRh/243d3d2fKS5MyTj0l9ff1hPz4Ag7dz585s2LBhwI9/ePMz6dryaH68vjF7njp6ULNMnDgxTU1Ng3oOAAAY6oQwAKiQDRs2ZPLkyYN+nrfeMvhZ2tvbc+aZZw7+iQAAYAgTwgCgQiZOnJj29vYBP357Z1e+fu+DeeP55+TIQb5NfuLEiYN6PAAAVAMhDAAqpKmpaVBnYXV3d2frkx0557Wv8TZ5AAA4AMMqPQAAAAAAHA5CGAAAAABFEMIAAAAAKIIQBgAAAEARXCwfYIA6u3cnSdb/4tmKHH9HZ1e+96tk7ONbM2KQPzFwoB7t2F6R4wIAAAyEEAYwQBv/OwL9jxXrKjhFXW599KEKHv83RjT45wQAABj6/JcLwADNeuXYJMmpo49MY33tYT/+w5ufzYLl67J0XnNOH3fUYT/+XiMa6jL++BEVOz4AAMCBEsIABujYEcNzxWtfVrHj9/T0JElOfcmITHpp5UIYAABAtXCxfAAAAACKIIQBAAAAUAQhDAAAAIAiuEYYAMAAdXbvTpKs/8WzFZthR2dXvverZOzjWzOiseGwH//R//4JugAA1UAIAwAYoI3/HYH+x4p1FZ6kLrc++lBFJxjR4NtKAGDo8x0LAMAAzXrl2CTJqaOPTGN9bUVmeHjzs1mwfF2WzmvO6eMq8xNkRzTUZfzxIypybACAgyGEAQAM0LEjhueK176sojP09PQkSU59yYhMemllQhgAQLVwsXwAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIdZUeoJrV1G3Lpm0PZ9gRRx72Y/f09OSJnify46d/nLq6w//HuGnb9tTUbTvsxwUAAAAYKCFsEOqP/m6u/ffFFZ3h5m/eXLFj1x99QZKLK3Z8AAAAgIMhhA1C9zNTsvSNb82poytzRtgD9z+Qc887tyJnhG3s2J4P/PPGw35cAAAAgIESwgaht2dUxo86Pa847qjDfuzu7u5sqtuUM449I/X19Yf9+Hueeza9Pb867McFAAAAGCgXywcAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQhEMewq677rrU1NT0+zV27Ni+7b29vbnuuutywgknpLGxMTNnzswPf/jDQz0GAAAAAPTzBzkj7JWvfGU2b97c92vdunV922688cYsW7Ysn/nMZ/LQQw9l7NixaWlpya9//es/xCgAAAAAkOQPFMLq6uoyduzYvl8veclLkvzmbLBPfvKT+fCHP5y5c+dm0qRJueWWW7Jz587cdtttf4hRAAAAACDJHyiEPfLIIznhhBMyfvz4XHHFFfnpT3+aJNm0aVO2bNmSWbNm9e3b0NCQGTNmZM2aNX+IUQAAAAAgSVJ3qJ9wypQp+eIXv5iXv/zl+eUvf5mPfexjmTp1an74wx9my5YtSZIxY8b0e8yYMWPy+OOPv+BzdnV1paurq+/2tm3bkiTd3d3p7u4+1C/hgPT09PR9rMQMe49Z6uun8muAyvP3EF8HSHwtwBrAvwdYA1gDyYG/9kMewi666KK+3zc3N+ecc87JqaeemltuuSVnn312kqSmpqbfY3p7e59332+74YYbcv311z/v/nvuuSdNTU2HaPKD87PtSVKX+++/P48fWZERkiStra0VOe5Qef1Ubg1QeXv/Hq5duza/WF/paagkXwfK5msB1gB7+fcAa4CS18DOnTsPaL9DHsJ+14gRI9Lc3JxHHnkkc+bMSZJs2bIl48aN69uno6PjeWeJ/bZFixZl/vz5fbe3bduWk046KbNmzcqoUaP+YLO/mB8+sS1L1q3Neeedl1eecPhn6O7uTmtra1paWlJfX3/Yj1/p10/l1wCV94P/ejpZ972cffbZedXLjq30OFSArwMkvhZgDeDfA6wBrIFk37sHf58/eAjr6urKj3/840ybNi3jx4/P2LFj09ramj/90z9NkuzatSurVq3KP/7jP77gczQ0NKShoeF599fX11fsD7iurq7vYyUXWaU+B0Pl9VPZvwdUlr+H7OXrQNl8LcAaYC//HmANUPIaONDXfchD2MKFC3PJJZfkZS97WTo6OvKxj30s27Ztyzve8Y7U1NTk6quvzuLFi3PaaafltNNOy+LFi9PU1JS3vvWth3oUAAAAAOhzyEPYz3/+81x55ZV58skn85KXvCRnn3121q5dm5NPPjlJcs0116SzszN/8zd/k61bt2bKlCm55557MnLkyEM9CgAAAAD0OeQh7Mtf/vKLbq+pqcl1112X66677lAfGgAAAABe0LBKDwAAAAAAh4MQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBHqKj1Aters3p0kWf+LZyty/B2dXfner5Kxj2/NiMaGw378Rzu2H/ZjAgAAAAyGEDZAG/87BP2PFesqOEVdbn30oQoePxnRYAkBAAAA1UHFGKBZrxybJDl19JFprK897Md/ePOzWbB8XZbOa87p44467MdPfhPBxh8/oiLHBgAAADhYQtgAHTtieK547csqdvyenp4kyakvGZFJL61MCAMAAACoJi6WDwAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUoa7SAwAAlGrnzp3ZsGHDoJ7j4c3PpGvLo/nx+sbseeroAT/PxIkT09TUNKhZAACGOiEMAKBCNmzYkMmTJx+S53rrLYN7fHt7e84888xDMgsAwFAlhAEAVMjEiRPT3t4+qOfY3tmVr9/7YN54/jk5srFhULMAAPyxE8IAACqkqalp0GdhdXd3Z+uTHTnnta9JfX39IZoMAOCPk4vlAwAAAFAEIQwAAACAIghhAAAAABTBNcIAKmTnzp3ZsGHDgB//8OZn0rXl0fx4fWP2PHX0gJ9n4sSJaWpqGvDjAQAAqoUQBlAhGzZsyOTJkwf9PG+9ZXCPb29vH/TFugEAAKqBEAZQIRMnTkx7e/uAH7+9sytfv/fBvPH8c3JkY8Og5gAAACiBEAZQIU1NTYM6E6u7uztbn+zIOa99Terr6w/hZAAAAH+chDAAAKgg14wEgMNHCAMAgApyzUgAOHyEMAAAqCDXjASAw0cIAwCACnLNSAA4fIZVegAAAAAAOByEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCHWVHqBUO3fuzIYNGwb8+Ic3P5OuLY/mx+sbs+epowf8PBMnTkxTU9OAHw8AAABQLYSwCtmwYUMmT5486Od56y2De3x7e3vOPPPMQc8BAAAAMNQJYRUyceLEtLe3D/jx2zu78vV7H8wbzz8nRzY2DGoOAAAAgBIIYRXS1NQ0qDOxuru7s/XJjpzz2tekvr7+EE4GAAAA8MfJxfIBAAAAKIIQBgAAAEARhDAAAAAAiiCEAQAAAFAEIQwAAACAIghhAAAAABRBCAMAAACgCEIYAAAAAEUQwgAAAAAoghAGAAAAQBGEMAAAAACKIIQBAAAAUAQhDAAAAIAiCGEAAAAAFEEIAwAAAKAIQhgAAAAARRDCAAAAACiCEAYAAABAEYQwAAAAAIoghAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFqKv0AAAAAAzMrl27ctNNN+U73/lOHn300Vx11VUZPnx4pccCGLKcEQYAAFCFrrnmmowYMSILFy7M3XffnYULF2bEiBG55pprKj0awJDljDAAAIAqc8011+TjH/94ampq+t2/e/fufPzjH0+S3HjjjZUYDWBIc0YYAABAFdm1a1eWLl2aJOnt7e23be/tpUuXZteuXYd9NoChzhlhAAAAVeSmm27Knj17kiSjR4/On//5n2fHjh0ZMWJE/vmf/zkdHR3Zs2dPbrrppixYsKDC0wIMLUIYAABAFVm9enWSZMSIERk+fHg+8YlP9G078cQT09TUlJ07d2b16tVCGMDvEMIAAACqyM9//vMkyY4dO7Jjx479bvvd3wPwG64RBgAAUEVOOOGEQ7ofQEmEMAAAgCoybty4Q7ofQEmEMAAAgCqydevWQ7ofQEmEMAAAgCry0EMPHdL9AEoihAEAAFQRZ4QBDJwQBgAAUEV6e3sP6X4AJRHCAAAAqsgxxxxzSPcDKIkQBgAAUEWOPvroQ7ofQEmEMAAAgCry85///JDuB1ASIQwAAKCKuFg+wMAJYQAAAFXkty+CX1NT02/bb992sXyA5xPCAAAAqtTvxi7xC+DF1VV6AAAAgJLt3LkzGzZsOOD9hw0blj179hzQfv/v//2/A37eiRMnpqmp6YD3B6hGQhgAAEAFbdiwIZMnTz7kz7tnz56Det729vaceeaZh3wOgKFECAMAAKigiRMnpr29/YD37+zszHnnnfd797v//vvT2Nh4UHMA/LETwgAAACqoqanpoM/EuvTSS/O1r33tRbefe+65gx0N4I+Oi+UDAABUmZUrV+bSSy/d77ZLL700K1euPLwDAVQJIQwAAKAKrVy5Mjt37szlb///csQpf5rL3/7/ZefOnSIYwIvw1kgAAIAq1djYmEX/vxvz3c+uzaK/PvugrgkGUCJnhAEAAABQBCEMAAAAgCIIYQAAAAAUQQgDAAAAoAhCGAAAAABFEMIAAAAAKEJdpQcAAACodpue3JEdXT0VOfbGX+3o+1hXV5n/xBvRUJfxx4+oyLEBDoYQBgAAMAibntyR85fcV+kxsmD5uooe/96FM8UwYMgTwgAAAP7/7d15XFTl/gfwz7AjMCC4AaK4geCGhCgugWvW1V/WrbxSKmlZmaK5ZpqZ5pa5VVrpqxRvhZqaedMUN4yrJiCSC7viUmLuDCr7fH9/cDk5ggIDMsB83q+Xr5ec7XnmnO855znfec4zlVDcE2zFUB+0bmRb/eVn5+LnyKMYFBQAG2vLai8/7eodTNwUb7AecUREFcFEGBERERERURVo3cgW7V3tq73c/Px8XGkI+DavD3Nz82ovn4ioNuFg+UREREREREREZBTYI4yIiIiIiKiSVGYapGuSYWJV/a9GFhQU4HLBZSTeTDTIYPnpmjtQmWmqvVwiIn0wEUZERERERFRJ5g7H8F70AoPWYfXu1QYr29yhL4BnDFY+EVF5MRFGRERERERUSfm3u2LpP4LRygCD5RcUFODwfw+jR88eBukRdvbqHYR+d7bayyUi0gcTYURERERERJUkBWq0UHvC28kwg+Wnm6XDy9HLIIPla3MyIQXXqr1cIiJ9MBFGRERERERUCdn5hQCA039mGqT8u9m5iL0GNLlwCzbWltVeftrVO9VeJhGRvpgIIyIiIiIiqoSz/0sEvbvtlAFrYYZ/p8UYsHzAxpKPl4bg7++PmJi/j32XLl0QHR1twBoR1WwGvVKtXr0aS5YsQUZGBtq1a4cVK1agV69ehqwSUYUc/+MCLmf9pde6ubk5+PPSRb3L1moLkZyUjHO4DRMTU7234+rWDJaWVnqt62LXGE80ba532URERER1wYB2TQAArRrZwtpc/3aZvpIzMjF5yyksfaEDPJ2r/9VMoCgJ1qKBjUHKNmYqlarEtJiYGKhUKoiIAWpk3NKv38Xd3AK91s3Ovof0tBS9yy4sLET8qbO4ZxYFU1P9r0MtWnvA2rqe3uvXhmuBwRJhmzZtwsSJE7F69Wr06NEDX331FZ5++mkkJCSgWbNmhqoWUbmlX7+LYRuXw7LhfsNVoglw+PZ/KreNm/qvmnutL/aEzK/xFzoiIiKix8nRxgL/8jfcM0xBQdGDd6uGNmjvaphEGFW/0pJgD85nMqz6pF+/i96fROq9fu6VNFwJm1hl9dFXk5ErYNmkdaW2cXBKUI1+RjRYImzZsmUYPXo0XnvtNQDAihUrsGfPHnzxxRdYuHChoapFVG53cwuQf7srJgYMgZtjxTPmVdUjzLOtp0F6hF26eQ9LUjP0/saDiIiIiIrcu3cPSUlJeq+fnHEbuVfSkHjaGtobDnpvp23btqhXT/+eIFR9/P39lf8PGzYMYWFh2LVrF5555hmMHDkS4eHhynJ8TbJ6FD8XrRjqg9Z6/HpsdrYv0v/lo3f5hYWFiD8RD5/OPgbrEZZ29Q4mboqv8c+IBkmE5eXl4fjx43j33Xd1pg8YMABHjhwpsXxubi5yc3OVvzUaDYCiX0fJz89/vJWtoYo/t7F+/pogKzsXUqCGvUlzuNVTV3j9bNU95Jvof4Eo1BbiSv4NNIQLTCuTCLNqodeFLvfuXUjBXRQUFDAODYTXAWIMEMA4IMZAXXD69Gl07dq10tsJDqvc+seOHUPnzp0rXQ9jdPNuHrafSsKdglt6rX83KxNpp0+Ue/lTV0/BqnnRl9lm7Vvi1cUf4Oq1a/jh1DGYtW+pzDt19RRGLJhV7u22bt8ZNnb69Sps4+SMp7089Fq3LsjKzoXKTIOztxMB84r3hsrJzUFGrv4dJQoLtMg0v44/cy7A1MxE7+1oM7WwytFv6JxLd7KhMtMY7BmxvGWqxAB9JS9fvgxXV1ccPnwY3bt3V6YvWLAAYWFhSE5O1ll+zpw5+PDDD0ts5/vvv+c3FmQwR/9SYeM5/RNQdaXr60yfAjSyrsIKERERERmZ3Nxc/PHHH3qvn68FbuYAjlaAuf7Pv2jatCksLav/VyfrgqN/qbBNc8Cww6YYWO61vpjStDcaG+mzAWOgSO61vpjatLdBnhHv3buH4OBgZGZmQq1+eGcVgw6W/+A7zSJS6nvOM2bMwKRJk5S/NRoN3NzcMGDAgEd+uLosPz8fe/fuRf/+/WFubm7o6hilbnfz0CHxKlo2tNFrUNTs7I44/2IHvcsvLCjEqVOn0KFDB5ia6Z+Qc2/VRu+urzaWpnB3qrnvftd1vA4QY4AAxgExBogxUBN0u5uHFqdccadgkF7rV7RH2A9rlir/f3HMZGi1Wly9dg2NGjaEiYlJifnlVakeYZ2Nu0dYcQzY270AKz2ez3Jyc5DxR+V6hKWlpaJ16zaV6hHm3LQZrPT8MTUAcLZrCF8Xw/ygWvHbg2UxSCKsQYMGMDU1xZUrV3SmX716FY0bNy6xvKWlZanfTJibmxv9hZ77wHAaO5jj5YAWldiCEwLauum9dn5+PuxwD888E8QYMHK8DhBjgADGATEGiDFgSI0dzPFGrycqt5H/+2e5F03aHoGYmBgAQMHpcyXGCMu5kAMA6NKlCza891Hl6kXlUiUxUInV8/PzlRgw1utAeT93JTrO6s/CwgJPPPEE9u7dqzN97969Oq9KEhEREREREZGu+wfADw8Ph4WFBYYMGQILCwtloPwHlyOiIgZ7NXLSpEkYPnw4/Pz8EBAQgDVr1uDixYt48803DVUlIiIiIiIiolrhYUML3T+fiEoyWCJs6NChuHHjBubOnYuMjAy0b98eu3btQvPmhnmXlIiIiIiIiKg2ERH4+/srr0kCRa9DsicY0cMZdLD8sWPHYuzYsYasAhEREREREVGtFR0dzfGhiCrAIGOEERERERERERERVTcmwoiIiIiIiIiIyCgwEUZEREREREREREaBiTAiIiIiIiIiIjIKTIQREREREREREZFRYCKMiIiIiIiIiIiMAhNhRERERERERERkFJgIIyIiIiIiIiIio8BEGBERERERERERGQUmwoiIiIiIiIiIyCgwEUZEREREREREREaBiTAiIiIiIiIiIjIKTIQREREREREREZFRYCKMiIiIiIiIiIiMAhNhRERERERERERkFJgIIyIiIiIiIiIio8BEGBERERERERERGQUmwoiIiIiIiIiIyCgwEUZEREREREREREaBiTAiIiIiIiIiIjIKTIQREREREREREZFRYCKMiIiIiIiIiIiMAhNhRERERERERERkFJgIIyIiIiIiIiIio8BEGBERERERERERGQUmwoiIiIiIiIiIyCgwEUZEREREREREREaBiTAiIiIiIiIiIjIKTIQREREREREREZFRYCKMiIiIiIiIiIiMAhNhRERERERERERkFJgIIyIiIiIiIiIio8BEGBERERERERERGQUmwoiIiIiIiIiIyCgwEUZEREREREREREaBiTAiIiIiIiIiIjIKTIQREREREREREZFRYCKMiIiIiIiIiIiMAhNhRERERERERERkFJgIIyIiIiIiIiIio8BEGBERERERERERGQUzQ1dAHyICANBoNAauieHk5+fj3r170Gg0MDc3N3R1yAAYA8QYIMYAAYwDYgwQY4AYA8QYAP7OERXnjB6mVibCsrKyAABubm4GrgkREREREREREdUUWVlZsLe3f+h8lZSVKquBtFotLl++DDs7O6hUKkNXxyA0Gg3c3Nxw6dIlqNVqQ1eHDIAxQIwBYgwQwDggxgAxBogxQIwBoKgnWFZWFlxcXGBi8vCRwGpljzATExM0bdrU0NWoEdRqtdEGORVhDBBjgBgDBDAOiDFAjAFiDBBj4FE9wYpxsHwiIiIiIiIiIjIKTIQREREREREREZFRYCKslrK0tMQHH3wAS0tLQ1eFDIQxQIwBYgwQwDggxgAxBogxQIyBiqiVg+UTERERERERERFVFHuEERERERERERGRUWAijIiIiIiIiIiIjAITYUREREREREREZBSYCCOqo0JCQjBkyBBDV8NoBQUFYeLEiY+1jDlz5sDHx+exlkF1k7u7O1asWPHQ+efPn4dKpUJ8fHy5t7l+/Xo4ODhUum5kWGXFBj0e3O/0oOpoRxA9SJ/7P1UfHp+qY2boChAR1UXbtm2Dubm5oatBpBc3NzdkZGSgQYMGVbrdkJAQ3L59G9u3b6/S7RIREdVEQUFB8PHxqTWJ7sd1/yeqadgjjIhKJSIoKCgwdDVqLUdHR9jZ2Rm6GvQY5eXlGboKJVRVnUxNTdGkSROYmfH7Mn3U5dggIqKqU9Pa27z/k7FgIqyG2b17N3r27AkHBwc4OTlh0KBBOHv2rDL/yJEj8PHxgZWVFfz8/LB9+/YS3SMTEhLwzDPPwNbWFo0bN8bw4cNx/fp1A3waAoCsrCy8/PLLsLGxgbOzM5YvX67T3T0vLw/Tpk2Dq6srbGxs0LVrV0RGRirrF79utGfPHnh5ecHW1hYDBw5ERkaGskxhYSEmTZqkxM20adMgIjr1EBF8/PHHaNmyJaytrdGpUyds2bJFmR8ZGQmVSoU9e/bAz88PlpaWiIqKeqz7pi67/xi7u7vjo48+wogRI2Bra4vmzZvjp59+wrVr1/Dss8/C1tYWHTp0QGxsrLJ+8XHfvn07PDw8YGVlhf79++PSpUvlKrPYkCFDEBISovy9evVqtGnTBlZWVmjcuDFeeOEFZV5ZMVLXBQUFYdy4cRg3bpxyLs2aNUs5l4qPY0hICOzt7fH6668DKLouP/nkk7C2toabmxtCQ0Nx9+5dZbuP2udbtmxBhw4dYG1tDScnJ/Tr109ZtzzHU986AcC9e/cwatQo2NnZoVmzZlizZo0yr7Su9zt27ECbNm1gbW2N3r17IywsDCqVCrdv39bZ7sOuVXPmzEFYWBh++uknqFQqqFQqnWtdTWZssXG/ixcvKtcptVqNl156CX/99RcAIDMzE6ampjh+/DiAomuIo6MjunTpoqwfHh4OZ2fniu7yOqestsD9Sjv/bt++XeKcOXPmDP7xj39ArVbDzs4OvXr1UtqMWq0Wc+fORdOmTWFpaQkfHx/s3r1bWTcvLw/jxo2Ds7MzrKys4O7ujoULFyrzMzMzMWbMGDRq1AhqtRp9+vTB77//XuX7hcpv9+7dsLe3x4YNGwAA33zzDdq1awdLS0s4Oztj3LhxyrIqlQpfffUVBg0ahHr16sHLywtHjx5FWloagoKCYGNjg4CAAJ1nDKp6ISEhOHToEFauXKnc99avX19qezs3NxehoaFo1KgRrKys0LNnT8TExCjbKm6n79y5E506dYKVlRW6du2KU6dO6ZS5detWJS7c3d2xdOlSnfnu7u5YsGBBue//xeXu378ffn5+qFevHrp3747k5OTHt+MIWq0WixcvRuvWrWFpaYlmzZph/vz5JZYrLCzE6NGj0aJFC1hbW8PT0xMrV67UWSYyMhL+/v6wsbGBg4MDevTogQsXLgAAfv/9d/Tu3Rt2dnZQq9V44okndJ5H6jShGmXLli2ydetWSUlJkRMnTsjgwYOlQ4cOUlhYKBqNRhwdHeWVV16RM2fOyK5du8TDw0MAyIkTJ0RE5PLly9KgQQOZMWOGJCYmSlxcnPTv31969+5t2A9mxF577TVp3ry57Nu3T06dOiXPPfec2NnZyYQJE0REJDg4WLp37y6//vqrpKWlyZIlS8TS0lJSUlJERGTdunVibm4u/fr1k5iYGDl+/Lh4eXlJcHCwUsbixYvF3t5etmzZIgkJCTJ69Gixs7OTZ599Vlnmvffek7Zt28ru3bvl7Nmzsm7dOrG0tJTIyEgRETl48KAAkI4dO0pERISkpaXJ9evXq20/1TWBgYHKMW7evLk4OjrKl19+KSkpKfLWW2+JnZ2dDBw4UDZv3izJyckyZMgQ8fLyEq1WKyJ/H3c/Pz85cuSIxMbGir+/v3Tv3l0p44MPPpBOnTqVWmaxZ599VkaOHCkiIjExMWJqairff/+9nD9/XuLi4mTlypXKsmXFSF0XGBgotra2MmHCBElKSpJvv/1W6tWrJ2vWrBGRouOoVqtlyZIlkpqaKqmpqXLy5EmxtbWV5cuXS0pKihw+fFg6d+4sISEhIvLofX758mUxMzOTZcuWSXp6upw8eVJWrVolWVlZSn0edTz1rVPxeo6OjrJq1SpJTU2VhQsXiomJiSQmJoqISHp6us69JT09XczNzWXKlCmSlJQk4eHh4urqKgDk1q1bIlL2tSorK0teeuklGThwoGRkZEhGRobk5uZW6TF8XIwtNpYvXy4iIlqtVjp37iw9e/aU2NhY+e2338TX11cCAwOV5X19feWTTz4REZH4+HipX7++WFhYSGZmpoiIjBkzRoYOHVplx6K2KqstcP9+f/D8ExG5deuWAJCDBw+KiMgff/whjo6O8vzzz0tMTIwkJyfLN998I0lJSSIismzZMlGr1RIeHi5JSUkybdo0MTc3V9oWS5YsETc3N/n111/l/PnzEhUVJd9//72IFB33Hj16yODBgyUmJkZSUlJk8uTJ4uTkJDdu3KiW/UW653l4eLjY2dnJ9u3bRURk9erVYmVlJStWrJDk5GSJjo5W4kdEBIC4urrKpk2blDaGu7u79OnTR3bv3i0JCQnSrVs3GThwoAE+mfG4ffu2BAQEyOuvv67c9/bt21dqezs0NFRcXFxk165dcubMGRk5cqTUr19fOeeK2+leXl4SEREhJ0+elEGDBom7u7vk5eWJiEhsbKyYmJjI3LlzJTk5WdatWyfW1taybt06pU4Vvf8Xl9u1a1eJjIyUM2fOSK9evXTao1T1pk2bJvXr15f169dLWlqaREVFydq1a0scn7y8PJk9e7ZER0fLuXPnlPbJpk2bREQkPz9f7O3tZcqUKZKWliYJCQmyfv16uXDhgoiItGvXTl555RVJTEyUlJQU2bx5s8THxxvqY1crJsJquKtXrwoAOXXqlHzxxRfi5OQk2dnZyvy1a9fqnAzvv/++DBgwQGcbly5dEgCSnJxcnVUnEdFoNGJubi4//PCDMu327dtSr149mTBhgqSlpYlKpZI///xTZ72+ffvKjBkzRKTo4RKApKWlKfNXrVoljRs3Vv52dnaWRYsWKX/n5+dL06ZNlUTYnTt3xMrKSo4cOaJTzujRo2XYsGEi8veNrriRRZXzYCLslVdeUeZlZGQIAHn//feVaUePHhUAkpGRISJ/H/fffvtNWSYxMVEAyLFjx0Sk4omwrVu3ilqtFo1GU6K+5YmRui4wMFAnGSkiMn36dPHy8hKRouM4ZMgQnXWGDx8uY8aM0ZkWFRUlJiYmkp2d/ch9fvz4cQEg58+ff2h9ypPsqGidite7Pya1Wq00atRIvvjiCxEp2RCePn26tG/fXmebM2fOLJEIK+taNXLkSJ0EfW1hbLFR/EAdEREhpqamcvHiRWX5M2fOCACJjo4WEZFJkybJoEGDRERkxYoV8sILL4ivr6/s3LlTREQ8PDyUuDJWZbUFRCqeCJsxY4a0aNFCeQB+kIuLi8yfP19nWpcuXWTs2LEiIjJ+/Hjp06ePTkwX279/v6jVasnJydGZ3qpVK/nqq68q8tGpEorP81WrVom9vb0cOHBAmefi4iIzZ8586LoAZNasWcrfxW2Mr7/+WpkWHh4uVlZWj6fypHjwel1ae/vOnTtibm4u3333nTItLy9PXFxc5OOPP9ZZb+PGjcoyN27cEGtrayXpERwcLP3799cpf+rUqeLt7a38XdH7f3G5+/btU9bZuXOnANB5JqWqo9FoxNLSUtauXVtiXmn3hweNHTtW/vnPf4pIUYwAeOiX2nZ2drJ+/foqqXdtw1cja5izZ88iODgYLVu2hFqtRosWLQAUvZqQnJyMjh07wsrKSlne399fZ/3jx4/j4MGDsLW1Vf61bdtW2TZVr3PnziE/P1/nONnb28PT0xMAEBcXBxGBh4eHzjE7dOiQzvGqV68eWrVqpfzt7OyMq1evAih6fSEjIwMBAQHKfDMzM/j5+Sl/JyQkICcnB/3799cpZ8OGDSXi4v71qOp07NhR+X/jxo0BAB06dCgxrfi4AiWPY9u2beHg4IDExES96tC/f380b94cLVu2xPDhw/Hdd9/h3r17ACoWI3VZt27doFKplL8DAgKQmpqKwsJCACXPj+PHj2P9+vU6++ypp56CVqtFenr6I/d5p06d0LdvX3To0AEvvvgi1q5di1u3blW4zhWtU7H7Y1KlUqFJkyY68Xe/5ORkndfdgJL3H+DR16razphio1hiYiLc3Nzg5uamTPP29ta5DgUFBSEqKgparRaHDh1CUFAQgoKCcOjQIVy5cgUpKSkIDAyscN3rkrLaAvqIj49Hr169Sv1RFo1Gg8uXL6NHjx4603v06KEct5CQEMTHx8PT0xOhoaGIiIhQljt+/Dju3LkDJycnnVhJT083qvtBTbB161ZMnDgRERER6N27N4CidsLly5fRt2/fR65bnnZHTk4ONBrNY6g5leX+6/PZs2eRn5+vc86am5vD39+/RJvv/va+o6MjPD09lWUSExNLPe/vv1cBFbv/l7ZO8evudeX+XtMkJiYiNze3zHO82Jdffgk/Pz80bNgQtra2WLt2LS5evAigKEZCQkLw1FNPYfDgwVi5cqXO8DqTJk3Ca6+9hn79+mHRokVGdY3nKHg1zODBg+Hm5oa1a9fCxcUFWq0W7du3R15eHkREpxEOoMQ4UFqtFoMHD8bixYtLbJtjdFS/4uPzsOOm1WqV8VVMTU11lrG1tVX+/2BDV6VSlTj2j6LVagEAO3fuhKurq848S0tLnb9tbGzKvV0qv/uPYXE8lDat+Fg9OL2saQBgYmJSIi7y8/OV/9vZ2SEuLg6RkZGIiIjA7NmzMWfOHMTExFQoRozZg+eHVqvFG2+8gdDQ0BLLNmvWDBYWFg/d5w4ODti7dy+OHDmCiIgIfPbZZ5g5cyaOHTuGFi1alHk89a1TsdKuKw/GX7Hy3H8ets2KXKtqs7oUG8VKO+4PTn/yySeRlZWFuLg4REVFYd68eXBzc8OCBQvg4+ODRo0awcvLq5Q9ZjzKags8yMTEpMT8B4+vtbV1meWWVl7xNF9fX6Snp+OXX37Bvn378NJLL6Ffv37YsmULtFotnJ2dSx3Dz8HBocxyqer4+PggLi4O69atQ5cuXaBSqcp17AH92x1UPe6/Pj/qGvGwNt/9ipepzL26rDhg7FSf8p7jALB582a88847WLp0KQICAmBnZ4clS5bg2LFjyjLr1q1DaGgodu/ejU2bNmHWrFnYu3cvunXrhjlz5iA4OBg7d+7EL7/8gg8++AAbN27Ec8899zg+Wo3CHmE1yI0bN5CYmIhZs2ahb9++8PLy0vkGuG3btjh58iRyc3OVaQ8OZufr64szZ87A3d0drVu31vnHBEf1a9WqFczNzREdHa1M02g0SE1NBQB07twZhYWFuHr1aonj1aRJk3KVYW9vD2dnZ/z222/KtIKCAmXwYqDoG3xLS0tcvHixRDn3f9NPNUtBQYHOOZ6cnIzbt28rvTwf1LBhwxI/onD69GmdZczMzNCvXz98/PHHOHnyJM6fP48DBw4wRv7n/vOo+O82bdqUSFQXK77mPrjPWrduDQsLCwAP3+dAUWOyR48e+PDDD3HixAlYWFjgxx9/BFC+46lvnSqqbdu2OoP2AiXvP+VhYWGh8610bWKMseHt7Y2LFy/q/EhHQkICMjMzleSWvb09fHx88Pnnn0OlUsHb2xu9evXCiRMn8PPPPxt9bzCg7LbAgxo2bAgAOsf4/oHzgaLeGVFRUaUmQNVqNVxcXPDf//5XZ/qRI0d0kpJqtRpDhw7F2rVrsWnTJmzduhU3b96Er68vrly5AjMzsxJx0qBBgwp/ftJfq1atcPDgQfz0008YP348gKIvtdzd3bF//34D147Kozz3veJr8P3nbH5+PmJjY0t8kXD/vejWrVtISUlR2oXe3t6lnvceHh4PvVdRzVP8w0TlOcejoqLQvXt3jB07Fp07d0br1q1L7dXVuXNnzJgxA0eOHEH79u3x/fffK/M8PDzwzjvvICIiAs8//zzWrVtXpZ+npmIirAapX78+nJycsGbNGqSlpeHAgQOYNGmSMj84OBharRZjxoxBYmIi9uzZg08++QTA35n5t99+Gzdv3sSwYcMQHR2Nc+fOISIiAqNGjaq1Dx+1mZ2dHUaOHImpU6fi4MGDOHPmDEaNGgUTExOoVCp4eHjg5ZdfxogRI7Bt2zakp6cjJiYGixcvxq5du8pdzoQJE7Bo0SL8+OOPSEpKwtixY3V+yc3Ozg5TpkzBO++8g7CwMJw9exYnTpzAqlWrEBYW9hg+OVUFc3NzjB8/HseOHUNcXBxeffVVdOvWrdRX0gCgT58+2LlzJ3bu3FlqHPz888/49NNPER8fjwsXLmDDhg3QarXw9PRkjPzPpUuXMGnSJCQnJyM8PByfffYZJkyY8NDlp0+fjqNHj+Ltt99GfHw8UlNTsWPHDuWB5VH7/NixY1iwYAFiY2Nx8eJFbNu2DdeuXVMavWUdT33rpI833ngDSUlJmD59OlJSUrB582asX78ewMN7KJbG3d0dJ0+eRHJyMq5fv17qQ3xNZYyx0a9fP3Ts2BEvv/wy4uLiEB0djREjRiAwMFDntZ6goCB8++23CAwMhEqlQv369eHt7Y1NmzYhKCio/Du5jiqrLfAga2trdOvWDYsWLUJCQgJ+/fVXzJo1S2eZcePGQaPR4F//+hdiY2ORmpqKf//738ovuU2dOhWLFy/Gpk2bkJycjHfffRfx8fFKzC5fvhwbN25EUlISUlJS8MMPP6BJkyZwcHBAv379EBAQgCFDhmDPnj04f/48jhw5glmzZhnPr4nVIB4eHjh48KDymiRQ9Cu8S5cuxaefforU1FTExcXhs88+M2xFqVTu7u44duwYzp8/j+vXr5fai8rGxgZvvfUWpk6dit27dyMhIQGvv/467t27h9GjR+ssO3fuXOzfvx+nT59GSEgIGjRogCFDhgAAJk+ejP3792PevHlISUlBWFgYPv/8c0yZMqU6PipVESsrK0yfPh3Tpk1Thij57bff8PXXX5dYtnXr1oiNjcWePXuQkpKC999/X+eLy/T0dMyYMQNHjx7FhQsXEBERgZSUFHh5eSE7Oxvjxo1DZGQkLly4gMOHDyMmJsZoenHz1cgaxMTEBBs3bkRoaCjat28PT09PfPrpp0ojUq1W4z//+Q/eeust+Pj4oEOHDpg9ezaCg4OVccNcXFxw+PBhTJ8+HU899RRyc3PRvHlzDBw4UOlqT9Vr2bJlePPNNzFo0CCo1WpMmzYNly5dUo7ZunXr8NFHH2Hy5Mn4888/4eTkhICAADzzzDPlLmPy5MnIyMhASEgITExMMGrUKDz33HPIzMxUlpk3bx4aNWqEhQsX4ty5c3BwcICvry/ee++9Kv/MVDXq1auH6dOnIzg4GH/88Qd69uyJb7755qHLjxo1Cr///jtGjBgBMzMzvPPOO8qYIkDRKy3btm3DnDlzkJOTgzZt2iA8PBzt2rUDwBgBgBEjRiA7Oxv+/v4wNTXF+PHjMWbMmIcu37FjRxw6dAgzZ85Er169ICJo1aoVhg4dCuDR+zwxMRG//vorVqxYAY1Gg+bNm2Pp0qV4+umnAZR9PPWtkz5atGiBLVu2YPLkyVi5ciUCAgIwc+ZMvPXWWxV6dfb1119HZGQk/Pz8cOfOHRw8eLDWJEqMMTZUKhW2b9+O8ePH48knn4SJiQkGDhxY4oG7d+/eWLZsmc6xDAwMRHx8PHuE/U9ZbYEHffPNNxg1ahT8/Pzg6emJjz/+GAMGDFDmOzk54cCBA5g6dSoCAwNhamoKHx8fZXyg0NBQaDQaTJ48GVevXoW3tzd27NiBNm3aACgafmHx4sVITU2FqakpunTpgl27diltxV27dmHmzJkYNWoUrl27hiZNmuDJJ59Uxpqi6uXp6YkDBw4gKCgIpqamWLp0KXJycrB8+XJMmTIFDRo0wAsvvGDoalIppkyZgpEjR8Lb2xvZ2dkP7W2zaNEiaLVaDB8+HFlZWfDz88OePXtQv379EstNmDABqamp6NSpE3bs2KH06PX19cXmzZsxe/ZszJs3D87Ozpg7dy5CQkIe98ekKvb+++/DzMwMs2fPxuXLl+Hs7Iw333yzxHJvvvkm4uPjMXToUKhUKgwbNgxjx47FL7/8AqDoWSIpKQlhYWG4ceMGnJ2dMW7cOLzxxhsoKCjAjRs3MGLECPz1119o0KABnn/+eXz44YfV/XENQiXGMnhHHfXdd9/h1VdfRWZmZoXeJybDuXv3LlxdXbF06dIS3/IQFVu/fj0mTpxYrl4eVDWCgoLg4+ODFStWGLoqtcL8+fPx5Zdf6rw2V1cxNqiqsS1ARBURGRmJ3r1749atWxyrj6gKsEdYLbNhwwa0bNkSrq6u+P333zF9+nS89NJLTILVYCdOnEBSUhL8/f2RmZmJuXPnAgCeffZZA9eMiKj8Vq9ejS5dusDJyQmHDx/GkiVLMG7cOENXi6hWYFuAiIio5mAirJa5cuUKZs+ejStXrsDZ2Rkvvvgi5s+fb+hqURk++eQTJCcnw8LCAk888QSioqI44CwR1Sqpqan46KOPcPPmTTRr1gyTJ0/GjBkzDF0tolqDbQEiIqKaga9GEhERERERERGRUeDo6UREREREREREZBSYCCMiIiIiIiIiIqPARBgRERERERERERkFJsKIiIiIiIiIiMgoMBFGRERERERERERGgYkwIiIiIiIiIiIyCkyEERERERERERGRUWAijIiIiIiIiIiIjAITYUREREREREREZBT+H++hF447Axn3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,14))\n",
    "plt.title(\"No outlier\")\n",
    "df_no_outlier.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "34c6846f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age              21.000\n",
      "gender            1.000\n",
      "impluse          21.000\n",
      "pressurehight    33.000\n",
      "pressurelow      20.000\n",
      "glucose          56.000\n",
      "kcm               2.500\n",
      "troponin          0.018\n",
      "class             1.000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "q1 = df_no_outlier.quantile(0.25)\n",
    "q3 = df_no_outlier.quantile(0.75)\n",
    "iqr = q3-q1\n",
    "print(iqr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6cfba544",
   "metadata": {},
   "outputs": [],
   "source": [
    "lowerbound = q1 - iqr *1.5\n",
    "upperbound = q3+iqr *1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "739a1b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_outlier2 = df_no_outlier[~((df_no_outlier>upperbound)|(df_no_outlier<lowerbound)).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cc0e422b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(623, 9)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_outlier2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f16781a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'No outlier 2'}>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,14))\n",
    "plt.title(\"No outlier 2\")\n",
    "df_no_outlier2.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6c074b8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age              20.500\n",
      "gender            1.000\n",
      "impluse          20.000\n",
      "pressurehight    34.000\n",
      "pressurelow      20.000\n",
      "glucose          51.000\n",
      "kcm               2.145\n",
      "troponin          0.012\n",
      "class             1.000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "q1 = df_no_outlier2.quantile(0.25)\n",
    "q3 = df_no_outlier2.quantile(0.75)\n",
    "iqr = q3-q1\n",
    "print(iqr)\n",
    "\n",
    "lowerbound = q1 - iqr *1.5\n",
    "upperbound = q3+iqr *1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bd89fd99",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_outlier3 = df_no_outlier2[~((df_no_outlier>upperbound)|(df_no_outlier<lowerbound)).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6a6656d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(567, 9)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_outlier3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cd3a1e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'No outlier 3'}>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,14))\n",
    "plt.title(\"No outlier 3\")\n",
    "df_no_outlier3.boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "190168db",
   "metadata": {},
   "source": [
    "## Persentase outlier yang dihilangkan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4418732a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_penuh = df.shape[0]\n",
    "outlier1 = df.shape[0]-df_no_outlier.shape[0]\n",
    "outlier2 = df.shape[0]-df_no_outlier2.shape[0]\n",
    "outlier3 = df.shape[0]-df_no_outlier3.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cde525e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1319\n",
      "530\n",
      "696\n",
      "752\n"
     ]
    }
   ],
   "source": [
    "print(data_penuh)\n",
    "print(outlier1)\n",
    "print(outlier2)\n",
    "print(outlier3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "68394081",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = round((outlier1/data_penuh)*100)\n",
    "b = round((outlier2/data_penuh)*100)\n",
    "c = round((outlier3/data_penuh)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dd51943d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40 %\n",
      "53 %\n",
      "57 %\n"
     ]
    }
   ],
   "source": [
    "print(a,\"%\")\n",
    "print(b,\"%\")\n",
    "print(c,\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf8ec26",
   "metadata": {},
   "source": [
    "Karena banyak abget data ilang, kita pakai yang 40% saja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f1b4394a",
   "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>gender</th>\n",
       "      <th>impluse</th>\n",
       "      <th>pressurehight</th>\n",
       "      <th>pressurelow</th>\n",
       "      <th>glucose</th>\n",
       "      <th>kcm</th>\n",
       "      <th>troponin</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>118</td>\n",
       "      <td>66</td>\n",
       "      <td>134.0</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.017</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>109</td>\n",
       "      <td>65</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>121</td>\n",
       "      <td>49</td>\n",
       "      <td>98.0</td>\n",
       "      <td>6.48</td>\n",
       "      <td>0.021</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>156</td>\n",
       "      <td>76</td>\n",
       "      <td>82.0</td>\n",
       "      <td>6.78</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>105</td>\n",
       "      <td>80</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.97</td>\n",
       "      <td>0.146</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.030</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>93</td>\n",
       "      <td>78</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.068</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>117</td>\n",
       "      <td>83</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.052</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1311</th>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "      <td>115</td>\n",
       "      <td>69</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.062</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>125</td>\n",
       "      <td>55</td>\n",
       "      <td>149.0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.172</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>345 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender  impluse  pressurehight  pressurelow  glucose   kcm  \\\n",
       "15     61       1       81            118           66    134.0  1.49   \n",
       "19     45       1       60            109           65     89.0  1.60   \n",
       "32     68       1       61            121           49     98.0  6.48   \n",
       "35     64       1       58            156           76     82.0  6.78   \n",
       "43     55       1       97            105           80    100.0  2.97   \n",
       "...   ...     ...      ...            ...          ...      ...   ...   \n",
       "1293   53       1       55            120           79    114.0  2.93   \n",
       "1301   47       0       58             93           78    170.0  1.19   \n",
       "1303   55       0       80            117           83    200.0  0.78   \n",
       "1311   85       1      112            115           69    114.0  2.19   \n",
       "1315   66       1       84            125           55    149.0  1.33   \n",
       "\n",
       "      troponin  class  \n",
       "15       0.017      1  \n",
       "19       0.020      1  \n",
       "32       0.021      1  \n",
       "35       0.012      1  \n",
       "43       0.146      1  \n",
       "...        ...    ...  \n",
       "1293     0.030      1  \n",
       "1301     0.068      1  \n",
       "1303     0.052      1  \n",
       "1311     0.062      1  \n",
       "1315     0.172      1  \n",
       "\n",
       "[345 rows x 9 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_outlier[df_no_outlier[\"class\"]==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c8234b14",
   "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>gender</th>\n",
       "      <th>impluse</th>\n",
       "      <th>pressurehight</th>\n",
       "      <th>pressurelow</th>\n",
       "      <th>glucose</th>\n",
       "      <th>kcm</th>\n",
       "      <th>troponin</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>160</td>\n",
       "      <td>83</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>160</td>\n",
       "      <td>77</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>112</td>\n",
       "      <td>58</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>179</td>\n",
       "      <td>68</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>154</td>\n",
       "      <td>81</td>\n",
       "      <td>135.0</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>150</td>\n",
       "      <td>75</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>136</td>\n",
       "      <td>68</td>\n",
       "      <td>141.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>118</td>\n",
       "      <td>68</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>179</td>\n",
       "      <td>68</td>\n",
       "      <td>147.0</td>\n",
       "      <td>5.22</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>122</td>\n",
       "      <td>67</td>\n",
       "      <td>204.0</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>444 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender  impluse  pressurehight  pressurelow  glucose   kcm  \\\n",
       "0      64       1       66            160           83    160.0  1.80   \n",
       "2      55       1       64            160           77    270.0  1.99   \n",
       "5      58       0       61            112           58     87.0  1.83   \n",
       "6      32       0       40            179           68    102.0  0.71   \n",
       "8      44       0       60            154           81    135.0  2.35   \n",
       "...   ...     ...      ...            ...          ...      ...   ...   \n",
       "1298   67       0       81            150           75     80.0  4.80   \n",
       "1299   62       1       90            136           68    141.0  1.83   \n",
       "1312   48       1       84            118           68     96.0  5.33   \n",
       "1313   86       0       40            179           68    147.0  5.22   \n",
       "1314   44       1       94            122           67    204.0  1.63   \n",
       "\n",
       "      troponin  class  \n",
       "0        0.012      0  \n",
       "2        0.003      0  \n",
       "5        0.004      0  \n",
       "6        0.003      0  \n",
       "8        0.004      0  \n",
       "...        ...    ...  \n",
       "1298     0.009      0  \n",
       "1299     0.014      0  \n",
       "1312     0.006      0  \n",
       "1313     0.011      0  \n",
       "1314     0.006      0  \n",
       "\n",
       "[444 rows x 9 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_outlier[df_no_outlier[\"class\"]==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2885c2b7",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "74a1d94f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import minmax_scale, scale, normalize\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, accuracy_score,confusion_matrix,roc_auc_score,roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a14dd7c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d7e84795",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df_no_outlier.drop([\"class\"],axis=1)\n",
    "y = df_no_outlier[\"class\"]\n",
    "\n",
    "modelgbr = GradientBoostingClassifier()\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)\n",
    "modelgbr.fit(X_train, y_train)\n",
    "predictiongbr = modelgbr.predict(X_test)\n",
    "\n",
    "# Hitung matriks konfusi\n",
    "confusion = confusion_matrix(y_test, predictiongbr)\n",
    "\n",
    "# Ambil nilai TP, FP, FN, TN dari matriks konfusi\n",
    "TP = confusion[1, 1]\n",
    "FP = confusion[0, 1]\n",
    "FN = confusion[1, 0]\n",
    "TN = confusion[0, 0]\n",
    "\n",
    "# Visualisasikan matriks konfusi dengan heatmap (diputar)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion.T, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=['Actual Negative', 'Actual Positive'],\n",
    "            yticklabels=['Predicted Negative', 'Predicted Positive'])\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.title('Confusion Matrix\\nTP = {}\\nFP = {}\\nFN = {}\\nTN = {}'.format(TP, FP, FN, TN))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a7e0e48e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98        87\n",
      "           1       1.00      0.94      0.97        71\n",
      "\n",
      "    accuracy                           0.97       158\n",
      "   macro avg       0.98      0.97      0.97       158\n",
      "weighted avg       0.98      0.97      0.97       158\n",
      "\n",
      "AUC: 0.97\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelgbr = GradientBoostingClassifier()\n",
    "modelgbr.fit(X_train, y_train)\n",
    "predictiongbr = modelgbr.predict(X_test)\n",
    "print(classification_report(y_test, predictiongbr))\n",
    "\n",
    "# Menggunakan predict_proba pada objek modelgbr\n",
    "probas = modelgbr.predict_proba(X_test)[:, 1]  # Ambil probabilitas kelas positif\n",
    "\n",
    "fpr, tpr, _ =   roc_curve(y_test, probas)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Cetak AUC\n",
    "print(\"AUC: {:.2f}\".format(roc_auc))\n",
    "\n",
    "# Gambarkan kurva ROC\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = {:.2f})'.format(roc_auc))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cee7861e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70f42bc2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
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}