{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Lung Disease Recovery Predictor**\n",
    "\n",
    "In this notebook, we aim to achieve the following:\n",
    "\n",
    "- Clean the data, if necessary\n",
    "- Train 3 binary classification models (Naive Bayes, SVMs, & Random Forests)\n",
    "- Evaluate the models using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "We are going to explore the data if there is any inconsistencies, missing values, or imbalanced classes, and deal with them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Smoking Status</th>\n",
       "      <th>Lung Capacity</th>\n",
       "      <th>Disease Type</th>\n",
       "      <th>Treatment Type</th>\n",
       "      <th>Hospital Visits</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>4.49</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Therapy</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bronchitis</td>\n",
       "      <td>Surgery</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.95</td>\n",
       "      <td>COPD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bronchitis</td>\n",
       "      <td>Medication</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.60</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Surgery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Gender Smoking Status  Lung Capacity Disease Type Treatment Type  \\\n",
       "0  71.0  Female             No           4.49         COPD        Therapy   \n",
       "1  34.0  Female            Yes            NaN   Bronchitis        Surgery   \n",
       "2  80.0    Male            Yes           1.95         COPD            NaN   \n",
       "3  40.0  Female            Yes            NaN   Bronchitis     Medication   \n",
       "4  43.0    Male            Yes           4.60         COPD        Surgery   \n",
       "\n",
       "   Hospital Visits Recovered  \n",
       "0             14.0       Yes  \n",
       "1              7.0        No  \n",
       "2              4.0       Yes  \n",
       "3              1.0        No  \n",
       "4              NaN       Yes  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./lung_disease_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5200 entries, 0 to 5199\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Age              4900 non-null   float64\n",
      " 1   Gender           4900 non-null   object \n",
      " 2   Smoking Status   4900 non-null   object \n",
      " 3   Lung Capacity    4900 non-null   float64\n",
      " 4   Disease Type     4900 non-null   object \n",
      " 5   Treatment Type   4900 non-null   object \n",
      " 6   Hospital Visits  4900 non-null   float64\n",
      " 7   Recovered        4900 non-null   object \n",
      "dtypes: float64(3), object(5)\n",
      "memory usage: 325.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>Lung Capacity</th>\n",
       "      <th>Hospital Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4900.000000</td>\n",
       "      <td>4900.000000</td>\n",
       "      <td>4900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.449796</td>\n",
       "      <td>3.501865</td>\n",
       "      <td>7.528571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20.126882</td>\n",
       "      <td>1.461179</td>\n",
       "      <td>3.996401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>2.220000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age  Lung Capacity  Hospital Visits\n",
       "count  4900.000000    4900.000000      4900.000000\n",
       "mean     54.449796       3.501865         7.528571\n",
       "std      20.126882       1.461179         3.996401\n",
       "min      20.000000       1.000000         1.000000\n",
       "25%      37.000000       2.220000         4.000000\n",
       "50%      54.000000       3.480000         8.000000\n",
       "75%      72.000000       4.800000        11.000000\n",
       "max      89.000000       6.000000        14.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Smoking Status', 'Lung Capacity', 'Disease Type',\n",
       "       'Treatment Type', 'Hospital Visits', 'Recovered'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "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": [
    "(df.isna().sum() / len(df) * 100).plot(kind=\"bar\") # check NaN data by percent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the features have a consistent 300 missing values. We will impute them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Smoking Status</th>\n",
       "      <th>Lung Capacity</th>\n",
       "      <th>Disease Type</th>\n",
       "      <th>Treatment Type</th>\n",
       "      <th>Hospital Visits</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bronchitis</td>\n",
       "      <td>Surgery</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.95</td>\n",
       "      <td>COPD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bronchitis</td>\n",
       "      <td>Medication</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.60</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Surgery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.61</td>\n",
       "      <td>Lung Cancer</td>\n",
       "      <td>Surgery</td>\n",
       "      <td>11.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5181</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5.12</td>\n",
       "      <td>Lung Cancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5182</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>4.11</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Therapy</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5188</th>\n",
       "      <td>80.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>2.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Medication</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>29.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>3.36</td>\n",
       "      <td>Asthma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5196</th>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.50</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Medication</td>\n",
       "      <td>4.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1964 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  Gender Smoking Status  Lung Capacity Disease Type Treatment Type  \\\n",
       "1     34.0  Female            Yes            NaN   Bronchitis        Surgery   \n",
       "2     80.0    Male            Yes           1.95         COPD            NaN   \n",
       "3     40.0  Female            Yes            NaN   Bronchitis     Medication   \n",
       "4     43.0    Male            Yes           4.60         COPD        Surgery   \n",
       "7     72.0    Male            NaN           2.61  Lung Cancer        Surgery   \n",
       "...    ...     ...            ...            ...          ...            ...   \n",
       "5181   NaN    Male            Yes           5.12  Lung Cancer            NaN   \n",
       "5182   NaN  Female             No           4.11         COPD        Therapy   \n",
       "5188  80.0    Male             No           2.49          NaN     Medication   \n",
       "5191  29.0  Female             No           3.36       Asthma            NaN   \n",
       "5196  21.0     NaN            Yes           1.50         COPD     Medication   \n",
       "\n",
       "      Hospital Visits Recovered  \n",
       "1                 7.0        No  \n",
       "2                 4.0       Yes  \n",
       "3                 1.0        No  \n",
       "4                 NaN       Yes  \n",
       "7                11.0       Yes  \n",
       "...               ...       ...  \n",
       "5181              1.0       Yes  \n",
       "5182              3.0       Yes  \n",
       "5188             13.0       Yes  \n",
       "5191              6.0       Yes  \n",
       "5196              4.0        No  \n",
       "\n",
       "[1964 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isna().any(axis=1)] # Show rows with NaN values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confirms that we have alot of `NaN` data. Rows may have missing data but its columns has data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                0\n",
       "Gender             0\n",
       "Smoking Status     0\n",
       "Lung Capacity      0\n",
       "Disease Type       0\n",
       "Treatment Type     0\n",
       "Hospital Visits    0\n",
       "Recovered          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute numerical features with their mean\n",
    "numerical_columns = ['Age', 'Lung Capacity', 'Hospital Visits']\n",
    "df[numerical_columns] = df[numerical_columns].fillna(df[numerical_columns].mean())\n",
    "\n",
    "# Impute categorical features with their mode (most frequent value)\n",
    "categorical_columns = ['Gender', 'Smoking Status', 'Disease Type', 'Treatment Type', 'Recovered']\n",
    "df[categorical_columns] = df[categorical_columns].fillna(df[categorical_columns].mode().iloc[0])\n",
    "\n",
    "# Check if all NaNs are handled\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5200 entries, 0 to 5199\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Age              5200 non-null   float64\n",
      " 1   Gender           5200 non-null   object \n",
      " 2   Smoking Status   5200 non-null   object \n",
      " 3   Lung Capacity    5200 non-null   float64\n",
      " 4   Disease Type     5200 non-null   object \n",
      " 5   Treatment Type   5200 non-null   object \n",
      " 6   Hospital Visits  5200 non-null   float64\n",
      " 7   Recovered        5200 non-null   object \n",
      "dtypes: float64(3), object(5)\n",
      "memory usage: 325.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Smoking Status</th>\n",
       "      <th>Lung Capacity</th>\n",
       "      <th>Disease Type</th>\n",
       "      <th>Treatment Type</th>\n",
       "      <th>Hospital Visits</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Therapy</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.501865</td>\n",
       "      <td>Bronchitis</td>\n",
       "      <td>Surgery</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Medication</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.501865</td>\n",
       "      <td>Bronchitis</td>\n",
       "      <td>Medication</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Surgery</td>\n",
       "      <td>7.528571</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Gender Smoking Status  Lung Capacity Disease Type Treatment Type  \\\n",
       "0  71.0  Female             No       4.490000         COPD        Therapy   \n",
       "1  34.0  Female            Yes       3.501865   Bronchitis        Surgery   \n",
       "2  80.0    Male            Yes       1.950000         COPD     Medication   \n",
       "3  40.0  Female            Yes       3.501865   Bronchitis     Medication   \n",
       "4  43.0    Male            Yes       4.600000         COPD        Surgery   \n",
       "\n",
       "   Hospital Visits Recovered  \n",
       "0        14.000000       Yes  \n",
       "1         7.000000        No  \n",
       "2         4.000000       Yes  \n",
       "3         1.000000        No  \n",
       "4         7.528571       Yes  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see the distribution of the features\n",
    "fig, axes = plt.subplots(nrows=4, ncols=2, figsize=(10,15))\n",
    "\n",
    "# Age\n",
    "axes[0,0].hist(df['Age'])\n",
    "axes[0,0].set_xlabel('Distribution')\n",
    "axes[0,0].set_ylabel('Frequency')\n",
    "axes[0,0].set_title('Age')\n",
    "\n",
    "# Lung Capacity\n",
    "axes[0,1].hist(df['Lung Capacity'])\n",
    "axes[0,1].set_xlabel('Distribution')\n",
    "axes[0,1].set_ylabel('Frequency')\n",
    "axes[0,1].set_title('Lung Capacity')\n",
    "\n",
    "# Lung Capacity\n",
    "axes[1,0].hist(df['Hospital Visits'])\n",
    "axes[1,0].set_xlabel('Distribution')\n",
    "axes[1,0].set_ylabel('Frequency')\n",
    "axes[1,0].set_title('Hospital Visits')\n",
    "\n",
    "# Gender vs rECOVERED\n",
    "count_data = df.groupby(['Gender', 'Recovered']).size().unstack(fill_value=0)\n",
    "count_data.plot(kind='bar', stacked=False, ax=axes[1, 1])\n",
    "axes[1, 1].set_xlabel('Gender')\n",
    "axes[1, 1].set_ylabel('Count')\n",
    "axes[1, 1].set_title('Age Count by Recovery')\n",
    "axes[1, 1].legend(title='Recovered')\n",
    "\n",
    "# Smoking vs Recovered\n",
    "count_data = df.groupby(['Smoking Status', 'Recovered']).size().unstack(fill_value=0)\n",
    "count_data.plot(kind='bar', stacked=False, ax=axes[2, 0])\n",
    "axes[2, 0].set_xlabel('Smoking Status')\n",
    "axes[2, 0].set_ylabel('Count')\n",
    "axes[2, 0].set_title('Smoker Count by Recovery')\n",
    "axes[2, 0].legend(title='Recovered')\n",
    "\n",
    "\n",
    "# Disease Type vs Recovered\n",
    "count_data = df.groupby(['Disease Type', 'Recovered']).size().unstack(fill_value=0)\n",
    "count_data.plot(kind='bar', stacked=False, ax=axes[2, 1])\n",
    "axes[2, 1].set_xlabel('Disease Type')\n",
    "axes[2, 1].set_ylabel('Count')\n",
    "axes[2, 1].set_title('Disease Type by Recovery')\n",
    "axes[2, 1].legend(title='Recovered')\n",
    "\n",
    "# Treatment type vs Recovered\n",
    "count_data = df.groupby(['Treatment Type', 'Recovered']).size().unstack(fill_value=0)\n",
    "count_data.plot(kind='bar', stacked=False, ax=axes[3, 0])\n",
    "axes[3, 0].set_xlabel('Treatment Type')\n",
    "axes[3, 0].set_ylabel('Count')\n",
    "axes[3, 0].set_title('Treatment Type by Recovery')\n",
    "axes[3, 0].legend(title='Recovered')\n",
    "\n",
    "# Disease type vs treatment type\n",
    "count_data = df.groupby(['Disease Type', 'Treatment Type']).size().unstack(fill_value=0)\n",
    "count_data.plot(kind='bar', stacked=False, ax=axes[3, 1])\n",
    "axes[3, 1].set_xlabel('Disease Type')\n",
    "axes[3, 1].set_ylabel('Count')\n",
    "axes[3, 1].set_title('Disease Type by Treatment Type')\n",
    "axes[3, 1].legend(title='Treatment')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no alarming inconsistencies or outliers.\n",
    "\n",
    "Next we will convert the categorical variables to numerical for our model training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Smoking Status</th>\n",
       "      <th>Lung Capacity</th>\n",
       "      <th>Disease Type</th>\n",
       "      <th>Treatment Type</th>\n",
       "      <th>Hospital Visits</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Therapy</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.501865</td>\n",
       "      <td>Bronchitis</td>\n",
       "      <td>Surgery</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Medication</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.501865</td>\n",
       "      <td>Bronchitis</td>\n",
       "      <td>Medication</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Surgery</td>\n",
       "      <td>7.528571</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Gender Smoking Status  Lung Capacity Disease Type Treatment Type  \\\n",
       "0  71.0  Female             No       4.490000         COPD        Therapy   \n",
       "1  34.0  Female            Yes       3.501865   Bronchitis        Surgery   \n",
       "2  80.0    Male            Yes       1.950000         COPD     Medication   \n",
       "3  40.0  Female            Yes       3.501865   Bronchitis     Medication   \n",
       "4  43.0    Male            Yes       4.600000         COPD        Surgery   \n",
       "\n",
       "   Hospital Visits Recovered  \n",
       "0        14.000000       Yes  \n",
       "1         7.000000        No  \n",
       "2         4.000000       Yes  \n",
       "3         1.000000        No  \n",
       "4         7.528571       Yes  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" \\n# target encoding due to many classes\\nencoder = TargetEncoder(cols=['Disease Type'], smoothing=0.3)\\ndf['DiseaseTypeEncoded'] = encoder.fit_transform(df['Disease Type'], df['Recovered'])\\n\\nencoder = TargetEncoder(cols=['Treatment Type'], smoothing=0.3)\\ndf['TreatmentTypeEncoded'] = encoder.fit_transform(df['Treatment Type'], df['Recovered']) \""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from category_encoders import TargetEncoder\n",
    "\n",
    "# label encoding for binary\n",
    "df['Gender'] = df['Gender'].map({'Male': 1, 'Female': 0})\n",
    "df['Recovered'] = df['Recovered'].map({'Yes': 1, 'No': 0})\n",
    "df['Smoking Status'] = df['Smoking Status'].map({'Yes': 1, 'No': 0})\n",
    "df['Age'] = df['Age'].astype(int)\n",
    "df['Hospital Visits'] = df['Hospital Visits'].astype(int)\n",
    "\n",
    "disease_type = pd.get_dummies(df['Disease Type'], prefix='Disease Type', dtype=int)\n",
    "treatment_type = pd.get_dummies(df['Treatment Type'], prefix='Treatment Type', dtype=int)\n",
    "\n",
    "\n",
    "\"\"\" \n",
    "# target encoding due to many classes\n",
    "encoder = TargetEncoder(cols=['Disease Type'], smoothing=0.3)\n",
    "df['DiseaseTypeEncoded'] = encoder.fit_transform(df['Disease Type'], df['Recovered'])\n",
    "\n",
    "encoder = TargetEncoder(cols=['Treatment Type'], smoothing=0.3)\n",
    "df['TreatmentTypeEncoded'] = encoder.fit_transform(df['Treatment Type'], df['Recovered']) \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, disease_type], axis=1)\n",
    "df = pd.concat([df, treatment_type], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Smoking Status</th>\n",
       "      <th>Lung Capacity</th>\n",
       "      <th>Disease Type</th>\n",
       "      <th>Treatment Type</th>\n",
       "      <th>Hospital Visits</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Disease Type_Asthma</th>\n",
       "      <th>Disease Type_Bronchitis</th>\n",
       "      <th>Disease Type_COPD</th>\n",
       "      <th>Disease Type_Lung Cancer</th>\n",
       "      <th>Disease Type_Pneumonia</th>\n",
       "      <th>Treatment Type_Medication</th>\n",
       "      <th>Treatment Type_Surgery</th>\n",
       "      <th>Treatment Type_Therapy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Therapy</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.501865</td>\n",
       "      <td>Bronchitis</td>\n",
       "      <td>Surgery</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Medication</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.501865</td>\n",
       "      <td>Bronchitis</td>\n",
       "      <td>Medication</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>COPD</td>\n",
       "      <td>Surgery</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Smoking Status  Lung Capacity Disease Type Treatment Type  \\\n",
       "0   71       0               0       4.490000         COPD        Therapy   \n",
       "1   34       0               1       3.501865   Bronchitis        Surgery   \n",
       "2   80       1               1       1.950000         COPD     Medication   \n",
       "3   40       0               1       3.501865   Bronchitis     Medication   \n",
       "4   43       1               1       4.600000         COPD        Surgery   \n",
       "\n",
       "   Hospital Visits  Recovered  Disease Type_Asthma  Disease Type_Bronchitis  \\\n",
       "0               14          1                    0                        0   \n",
       "1                7          0                    0                        1   \n",
       "2                4          1                    0                        0   \n",
       "3                1          0                    0                        1   \n",
       "4                7          1                    0                        0   \n",
       "\n",
       "   Disease Type_COPD  Disease Type_Lung Cancer  Disease Type_Pneumonia  \\\n",
       "0                  1                         0                       0   \n",
       "1                  0                         0                       0   \n",
       "2                  1                         0                       0   \n",
       "3                  0                         0                       0   \n",
       "4                  1                         0                       0   \n",
       "\n",
       "   Treatment Type_Medication  Treatment Type_Surgery  Treatment Type_Therapy  \n",
       "0                          0                       0                       1  \n",
       "1                          0                       1                       0  \n",
       "2                          1                       0                       0  \n",
       "3                          1                       0                       0  \n",
       "4                          0                       1                       0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_oh = df.copy() # create copy for backup purposes\n",
    "df_oh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the categorical variables\n",
    "df_oh = df_oh.drop(['Disease Type', 'Treatment Type'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reordering columns for ease of normalization\n",
    "\n",
    "df_oh = df_oh[['Age', 'Hospital Visits', 'Lung Capacity', 'Gender', 'Smoking Status',\n",
    "            'Recovered', 'Disease Type_Asthma', 'Disease Type_Bronchitis',\n",
    "            'Disease Type_COPD', 'Disease Type_Lung Cancer',\n",
    "            'Disease Type_Pneumonia', 'Treatment Type_Medication',\n",
    "            'Treatment Type_Surgery', 'Treatment Type_Therapy']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Hospital Visits</th>\n",
       "      <th>Lung Capacity</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Smoking Status</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Disease Type_Asthma</th>\n",
       "      <th>Disease Type_Bronchitis</th>\n",
       "      <th>Disease Type_COPD</th>\n",
       "      <th>Disease Type_Lung Cancer</th>\n",
       "      <th>Disease Type_Pneumonia</th>\n",
       "      <th>Treatment Type_Medication</th>\n",
       "      <th>Treatment Type_Surgery</th>\n",
       "      <th>Treatment Type_Therapy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71</td>\n",
       "      <td>14</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>7</td>\n",
       "      <td>3.501865</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>3.501865</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Hospital Visits  Lung Capacity  Gender  Smoking Status  Recovered  \\\n",
       "0   71               14       4.490000       0               0          1   \n",
       "1   34                7       3.501865       0               1          0   \n",
       "2   80                4       1.950000       1               1          1   \n",
       "3   40                1       3.501865       0               1          0   \n",
       "4   43                7       4.600000       1               1          1   \n",
       "\n",
       "   Disease Type_Asthma  Disease Type_Bronchitis  Disease Type_COPD  \\\n",
       "0                    0                        0                  1   \n",
       "1                    0                        1                  0   \n",
       "2                    0                        0                  1   \n",
       "3                    0                        1                  0   \n",
       "4                    0                        0                  1   \n",
       "\n",
       "   Disease Type_Lung Cancer  Disease Type_Pneumonia  \\\n",
       "0                         0                       0   \n",
       "1                         0                       0   \n",
       "2                         0                       0   \n",
       "3                         0                       0   \n",
       "4                         0                       0   \n",
       "\n",
       "   Treatment Type_Medication  Treatment Type_Surgery  Treatment Type_Therapy  \n",
       "0                          0                       0                       1  \n",
       "1                          0                       1                       0  \n",
       "2                          1                       0                       0  \n",
       "3                          1                       0                       0  \n",
       "4                          0                       1                       0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_oh.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5200, 13), (5200,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split\n",
    "X = df_oh.drop('Recovered', axis=1).values\n",
    "y = df_oh['Recovered'].values\n",
    "\n",
    "import numpy as np\n",
    "X = np.array(X)\n",
    "y = np.array(y)\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;saga&#x27;)</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 fitted 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 fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(solver=&#x27;saga&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(solver='saga')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    " \n",
    "scaler = MinMaxScaler()\n",
    "X_train[:, :3] = scaler.fit_transform(X_train[:, :3]) # scaling only the continous variables\n",
    "X_test[:, :3] = scaler.transform(X_test[:, :3])\n",
    "\n",
    "nb = GaussianNB()\n",
    "rf = RandomForestClassifier(n_estimators=200, class_weight='balanced')\n",
    "svm = SVC(kernel='rbf')\n",
    "xgb = XGBClassifier(n_estimators=300, learning_rate=0.05, max_depth=8)\n",
    "lg = LogisticRegression(penalty='l2', solver='saga')\n",
    "\n",
    "nb.fit(X_train, y_train)\n",
    "rf.fit(X_train, y_train)\n",
    "svm.fit(X_train, y_train)\n",
    "xgb.fit(X_train, y_train)\n",
    "lg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "def evaluate_model(model, model_name: str, X_test: np.array, y_test: np.array) -> None:\n",
    "    \"\"\" \n",
    "    Evaluates given model in its accuracy, precision, recall, F1 score, and plots a confusion matrix.\n",
    "    \"\"\"\n",
    "    \n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    \n",
    "    print(f'{model_name} | Acc: {acc:.2f}% | Precision: {precision:.2f}% | F1 Score: {f1:.2f}% | Recall: {recall:.2f}%')\n",
    "    \n",
    "    results = dict({\n",
    "        'Accuracy': acc,\n",
    "        'Precision': precision,\n",
    "        'F1 Score': f1,\n",
    "        'Recall': recall\n",
    "    })\n",
    "    \n",
    "    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15,5))\n",
    "    \n",
    "    cm = confusion_matrix(y_test, y_pred, labels=model.classes_)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                                display_labels=model.classes_)\n",
    "    disp.plot(ax=axes[0])\n",
    "    axes[0].set_title(f'Confusion Matrix of {model_name}')\n",
    "    \n",
    "    axes[1].bar(results.keys(), results.values())\n",
    "    axes[1].set_ylim(0,1) # 0 to 1 limit\n",
    "    axes[1].set_title(f'{model_name} Evaluation Metrics')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes | Acc: 0.53% | Precision: 0.57% | F1 Score: 0.62% | Recall: 0.67%\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABUkAAAHqCAYAAAA5289qAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAcLJJREFUeJzt3Ql4FFXWxvGTBJIQdkF2ZBEFkU1BERcERfATHXEbdVAQEccFF1BEFEFAxRVxHBQXEDcUdcQRdVDABRUcBMQdFAFBdkR2IZDU97zXqbY7dEJCupMK/f/5lEl3V1ff6q7Qt06dc2+S53meAQAAAAAAAECCSi7uBgAAAAAAAABAcSJICgAAAAAAACChESQFAAAAAAAAkNAIkgIAAAAAAABIaARJAQAAAAAAACQ0gqQAAAAAAAAAEhpBUgAAAAAAAAAJjSApAAAAAAAAgIRGkBQAAAAAAABAQiNIirj48ccfrXPnzlaxYkVLSkqyN954I6bbX7ZsmdvuhAkTYrrdkqxDhw5uKWp79uyxW265xerWrWvJycnWrVu3Im8DxwMAADgQFVf/Dgfm56n+8p133llsrx80ei/0ngCAjyDpAeynn36yv//979awYUNLT0+3ChUq2AknnGCPPPKI/f7773F97Z49e9rXX39td999tz3//PPWpk0bO1Bcdtll7stU72e091EBYj2u5cEHHyzw9letWuW+sBcsWGAlwfjx4+2BBx6w888/35599lnr169fruuqU6j35ayzzso10Lk/71lx8dscvui4aNWqlf3zn/+0rKys4m4iAACIM12kVR9A/e2VK1dG7f80a9bMShK/z+Yvqamp1qBBA7vyyittxYoVVpJF67+FL/fee6+VZO+8807gAqF+MFIJFdGOny1btliZMmXcOn379t2v17jnnntinpgDIPGUKu4GID7efvttu+CCCywtLc169OjhOmaZmZn2ySef2IABA+zbb7+1J598Mi6vrcDh7Nmz7fbbb9/vL7l9qVevnnud0qVLW3EoVaqU7dixw6ZMmWJ//etfIx578cUXXSd5586d+7VtBUmHDRtm9evXd8G2/HrvvfesOLz//vtWu3Zte/jhh/P9nLfeesvmzZtnrVu3PiCOh4svvtjOOOMM9/vmzZtd5/S6666zn3/+2QWQAQDAgW/Xrl0uwPboo4/GbJvF1b+TOnXq2MiRI93vOo/47rvvbOzYsfbuu+/a999/bxkZGVaShfffwh111FFWkqkfOmbMmKiBUvWXdR5TXHRu+tJLL7kqtHCvv/56obetIKmSNgpS1TZ48GC79dZbC/3aAA4cBEkPQEuXLrWLLrrIBY4UwKpZs2bosWuvvdYWL17sgqjxsn79evezUqVKcXsN/2p9cX7BKytXX/I5g6QTJ060rl272r/+9a8iaYuCteqk6gp/cVi3bl2BPutDDjnEtm7d6gLBb7755gFxPBx99NF2ySWXhG5fc8011rZtW3csECQFACAx6OL2U089ZYMGDbJatWrFZJvF1b8TDZsV3r8RZZMqCeLTTz+10047zUqynP23RFCc/WVRUDpakLSoz5+2b99uZcuWdQHj4gwaAwgeyu0PQPfff79t27bNxo0bFxEg9TVq1MhuuOGGiDElR4wYYYceeqgL/imD8bbbbnNXw8Pp/jPPPNNlox577LHuS1al/M8991xoHV2xVHBWlLGq4JWe55ep+7/vayyYadOm2YknnuiCb+XKlbPGjRu7Nu1rDEoFhU866ST3pafnnn322e5Kd7TXU7BYbdJ66gT26tXLBRzz629/+5v95z//sU2bNoXu+/zzz125vR7LaePGjXbzzTdb8+bN3T6pLPv//u//7Msvvwyt8+GHH9oxxxzjfld7/LIffz/9ci1lYbZv394FR/33JecYRxryQJ9Rzv3v0qWLVa5c2WWs7qvzcNNNN7mxRnVc6DNQKbzneRGfwQcffOAyk/22ah/yUr58eVeSryzc+fPn57luft6z8Lb475PaqdvK5MxJJy464fjtt99C9/33v/+1008/3R0Hek9PPvlk1/nfX3rt6tWr79Xp+ve//+06gDpx0nuqvzn97YWX5Q8dOtRlxPoXG8KpxE3Ha3iWso5B/5jXe6vt6/MIt2bNGnc8KSNEr6t/F/S3ofcNAADEhvpk+k7PT7n2M888Y6eccopVq1bNfTc3bdrUHn/88b3WC+/frV271vUtdKE5p0WLFrn+h4b78amPeuONN4b6cjoHuO+++yw7O3u/97FGjRruZ3gfR/0tXSBWX1El01WqVHEVbeH9jCVLlrj2Ras8mjVrlntMwTOfhi24/PLLXX9KbT/yyCPdEE85KWtXj6n/pv6thvhSwC0WdN6jc51o2rVrFzGcWH4/z9yGasjZJ1N/Ome/+uOPP3bvqxIO9Br6XNWnDh/+S+c2yiKV8CEE8hqT9IsvvnD9a/Wz1d8+9dRT7bPPPovaTvWP+/fvbwcffLDre55zzjlR+6y50TmShhRbuHBhRD9V53DRzp9E56TqH+v49fdbQdbwc1W1TecuGvrL32e9F+HnfsqE1mvoONF5ZvhjOb3wwgvufNc/rnTeFZ7VPXfuXHdOVbVqVXfM6+KBjlcAJR9B0gOQgk/6Qj/++OPztf4VV1xhQ4YMcVdT1XFRgEilNcpGzUmBRZUx6MrxQw895L409AXkB2XOPffcUOdHJSwaj3T06NEFar+2pU6JvviGDx/uXucvf/nLPoNW06dPd19WymzUF56+wNXpUsZntGCQMkCV0ah91e/68o/W6cyN9lVfquHlIeqUNWnSxL2XOalzqHFytG+jRo1yQWSN26r32w9YHnHEEW6f/YCY3j8t+mL2/frrr64jo2wFvbcdO3aM2j6NPasOjIKlfhDuiSeecF/w6lDmleGgQKjec32WCh6qver4qs16X0XbVtu0vwq++W3VPuyLgvQ6dvY1XlJ+3rNo9Hnqs3nllVf2ekz3aVIxvb6oU6b3V2MhqQOmUh2dVKijO2fOHMsPBdc3bNjgFrVZndOpU6e69z6cjjF1PvUe6vPRcAP62wsv87n00kvdhYtJkyZFPFdlbq+99pqdd955oSwAvd8KimqbOum54447XAdQHb/wY17PmTx5sguUPvbYY3b99de7Y3/58uX52j8AALBvCpRomCtlk+7rYrQCaEosUGBVfV0FfhRo9ANc0ShgqD5QtP6N+g0pKSkuiOb3TbSugj1q0z/+8Q/XJ9bFYr8vty/qP/r9m9WrV7s+kx+s0rbCkwTU59a5g17nqquushkzZrjgrp+AoHMTPUfDUuWk+3ShVxdw/WDwcccd5/r2ylpVn0mv2bt374jzCr3P6tMoIKn71Y9X/1gXvwvafwtf1A+TCy+80FXoaf/CKSisIGL4udL+fJ4F9eqrr7o2X3311a4vr/Me/dTn69N8FH6Gr98315LXeZcutisBQYFH9SW1z/rsor2PGk5K6+o4UDt03lmQ4dXU59Z5Q3ggW8eu+rLq0+akgL7OSZQAoTkNtL8qp9c5ij4fn/ZRAVTti7/Pei/C6W9D75/6+n369Mm1jTqO1B9X0oLOy3Rbn6eOf9G5ps4l1NdWH15t6t69+16BZQAllIcDyubNm5Xm55199tn5Wn/BggVu/SuuuCLi/ptvvtnd//7774fuq1evnrtv5syZofvWrVvnpaWleTfddFPovqVLl7r1HnjggYht9uzZ020jp6FDh7r1fQ8//LC7vX79+lzb7b/GM888E7qvVatWXrVq1bxff/01dN+XX37pJScnez169Njr9S6//PKIbZ5zzjlelSpVcn3N8P0oW7as+/3888/3Tj31VPd7VlaWV6NGDW/YsGFR34OdO3e6dXLuh96/4cOHh+77/PPP99o338knn+weGzt2bNTHtIR799133fp33XWXt2TJEq9cuXJet27d9rmPb7zxRuh54bS/SUlJ3uLFiyNe98gjj9znNnOuq/dJrzFv3rzQe7G/71m046Fdu3Ze69atI547Z84ct95zzz3nbmdnZ3uHHXaY16VLF/e7b8eOHV6DBg280047Lc/98V832nL11VdHbNPfbk5///vfvYyMDLev4W1v27ZtxHqvv/662+4HH3zgbm/dutWrVKmS16dPn4j11qxZ41WsWDF0/2+//Rb17xEAAMSG+h/6rlUf7qeffvJKlSrlXX/99Xn2laL1CdQfadiwYZ79uyeeeMK91tdffx2xXtOmTb1TTjkldHvEiBGuv/rDDz9ErHfrrbd6KSkp3vLly/PcJ7/PmXM54ogjXJ9yX/sye/bsiD5XeNu///770H2ZmZle1apVXf/a17t3b69mzZrehg0bIrZ50UUXuT6O/3o638lvHzS//Tctart/XpXzPEfuv/9+1x/++eefC/15+seO2hRO/b3wfl9urzFy5Mi92nLttddGnFuF0/06F/LpvCA1NdUdt75Vq1Z55cuX99q3b79XOzt16hTRv+3Xr587njZt2uTlxT//0vmdzjMbNWoUeuyYY47xevXqFWqf2u97/vnn3bncxx9/HLE9nQtp3U8//TR0n4738OMo52tffPHFuT7m+/HHH93r6bww5zmIv9+TJ08O/b0DOPCQSXqAUTac6Gpsfgf2lpxXlFVmLTnHLtWVWl2h8ymbUBmGyp6LFX98S5Um57ccSFe3VbqhrNaDDjoodH+LFi3c1VR/P8PpKnc47ZeyNP33MD9UsqEyGL9MRD9zKxXR1U3N6Ohfmddr+UMJ7KvsPOd2lBGYH7rKqauougqqzFdlICqbdF/0fikbQVfncx4X6r+oxLuw/GzSvLJ3C/Oe6eqyhiX46aefIq5Ua5t+poKOGX94BG3bzyBQuY5KjWbOnJmvY1BZvxoiQovGUtLYv3qfc/5dqRzHp0xOvZaOO13VDi87UkaArt6Ht11ZFrqKrawQ0Wsp41UZ2+HZD/rcNB6qhkHwX1PDC+g4DR9iAAAAxJ4yJpWFpglS1T/NTXifQJM+6jtc3/HqU+t2btSfU6l7eMXJN9984ypJwjPrlHWoPob6WuH9hE6dOrk+lfo4+6Jhsvz+jfp+ytZU21TRFF5iHb4vu3fvdn0qZX6qTx/eX1Olj/qi4dmkmgRK7fLHBlU/U30pZQ3q9/C2K3NSr+9vU9v/5Zdf9sr0zK/w/lv4ovMd8Yd5UuauP9yU6L1XpqvK3gv7eRZE+Guor6rXUOWg2qaS+YLScaAKM2Vmhg8roGGZ1DfWEGs5z4v0noWXp+sY03aiDXGVG21b1Yn63PyfuZ0/6ThWlZoq18KPBVV8id/fzY+c537RqIJNfX9VevnnID5/v/1zVU1Eq+MdwIGFIOkBRl/mfgAmP/SFpi8AdWRyjjekL4CcX3jhnQGfOl+xDL6og6dyHA0DoLIilbKoc5JXsMpvp4JnOemL1Q985bUvfvl1QfZFg48rIK3Okjp8Gk8053vpU/tVGnLYYYe5QJ3GsFGQ+auvvipQ50kzyRdkEH+VpyhwrICgSqA0VtK+6P1UOX7OYLtfSl+QjlBuNP6nxsnS5E25dewK856ppEbHtn8SoQ6kOlr+mEuiAKmoLF7bDV+efvppN+RDfj4btU8nHVp08qLxwFRipZMJDQ8QXtKksZu072qDXsc/KQh/Hf0NaH/9kwg9po6YSnn8DprfdnUSc7ZdHV6VAom2o1J8ndzo70llThq3WAF9AAAQe5oxWyXbeY1NqmGk1G/wx9HX97c/znxefQ/1hXQhN7zkXn0dBU7VB/Gpn6Chf3L2EfSa4vcT8qK2+f0bDb+kC9zqt2n80/B905iYCir5Y5/6/TVdzA3fF+2ngp/hpdbq66hv6we9FHzV8xRkztl2P0nAb/vAgQPdxXONHam+mC5SF2RM+fD+W/ji9xP9PtmKFSts9uzZ7rYuYOsifHhAujCfZ0FomCQ/IUT7rdfwL57vz2vovdaF+tzOn9QP177H+vzpqKOOckFPHQf6/HXe6X/+Oek4Vv8557Fw+OGH5/s4Dh8OY1/0+er8wQ+UR6P3XENZKdFDx7qSLzQmbc75PACUTEzldoDRl7qCW7qiXBDRBqyORllq0YRfXS3oa4RPWuNfJdXVbV0ZVCarOnjq/OnLU8Gf3NpQUIXZF586guqQapBwXSnOa4xNjX+jcX40qLcm61EHR1/CChQWZAD98KvI+aEApN+BUMBOmYdBoc62gqDqZEQbu7Yw75n+DnR1WycR6qRqnCB1LhUw9Pnb0Az0GsMqGnVC94dOYBQs1bGsiafU4VenSn+jyuzVpE3KplA2hDr54fujDqfGYVXHUScdGotUHa/wGVj99TXmkj+JQrjwCRX0fumkRFfHlbGh91Rj8Sr7WR1VAAAQO8rK03e2An3h446HB2LUT1CgSGOuK7ioC+Cq5FG/aF99HCUQKGCoC+Dqv6ivo+0pYOPTNlRNlXMWcZ8fZCoojaeui73hmagap1JBIvU3NKGRHle/X+3MuS+qltFFa41hqv6Rgq66sOxn7fnr6/3LObZ7eKWYH8hTwFYXknW+oAxUjb2uvlNB5hnIi/pPmrxH77GyNvVTbfXHfi3s55nf8yPd1uepSU3Vb9RrKSCrCa4UOC3MZFxFff4kyhzVOK5KyFDAOWfWpk/7peNE72s0eq/jdQ6V12emvrnOLTQmq/rWOlfRWLS6b3/PHQAEA0HSA5CCK+qU6YqnOip50QDj+vLRVbrwCXc0YLqCOv5M9bGgwE/4TPC+aFmJ+qJUZ0OLvhQVLLv99ttd4NS/Ap5zP0QdpZxUxqxOozoS8aAvec22qTZHm+zKpy9TTbI0bty4iPv1noR3avMbsM4PZc+qE62roerYKYNQmYzKeM2L3k8Nlq+M5PBsUr8kPFbHhZ9NquBytI5wft+z3KjTpY63jgsF2tXJVWfXp0ClKHAZ7bgqDH/Q/23btrmfKndX+Zkm+gqfiEuD40ejkwhdmVYJkoKlCmZq9tacbVdmcH7arvU1XIIW/b3rpEqdOU3oAAAAYp9Nqu/Y8IuzPgVWdPFTAcLwzLz8lg6rPFrDKfnVMj/88IObkCnn9776ILHu3/gBO79/4/fX1I9Tv8K3c+fOqP1+ZaQqE1B9Gw0PpExGDU/g02Pqe+o18tN29e/V39OiSS6VvHD33Xe798Of6LIwtH2dWymwq3MSvee6CB8+AWphPk8/EzPne5Xz/EiJDvqclZgRPlGThgfIKb/nEnqv1TfO7fxJ5zYFCUIW9PxJwWwNSZHXxFI6jjVRlM4J97VfsTiH0uvp3FjDV+SWQOHTkAtadLwpK1YVXy+//LKrhgRQclFufwDSFWN9oesfaAU7c9LVTs0S6ZeLS84sPv9qXbRZBgvzpaNSEJVK+/TFqFm3w+kKaU7+l1RuZQwaO0frqOMQ3slQRq2yT/39jAcF8ZTlqKzBaBl94Vdec15lVYdLV4DD+cHcaB3LgtKVZmVP6n3RZ6qxpdSJ3Vc5iN4vdU61T+F0NVwdEJWsx4qCpCpLUnbl/r5nuVEpjLbx0ksvueepkxseLFc2hI5LDUkQ3tn3hY+3VVDqMEvLli1D+yLh+6POvDIeotF7rECwTq4++uijiCxS0bhcCu7qAkK08ZD8tuvkQycq4bTPOgGhLAgAgPjQd62+uzVGec4hbqL1CdRHVjZmfqjfpH6AshoVlFHWogKn4TT+pxImlOWWk/qY/sXcglLgT30mv3+TW39NM37nzIb0K11U1aS2T5gwwWUJ+pmh/rbUf1NWaLTKuPC+mS4+h9P7oMQAtSWWY0UqALtq1So3FJMCdjlL7QvzefoXvcMzc/W+KeFlX6+h3/1zuv05l9A2NX+B5oHQTO0+nT8q6HfiiSdGDD0QS9pvnX+qsknDJeRGx7H6/U899dRej2mYh/Dh1LTfhT1/0t+RgsM6L8mZneu/9xpaIOfxvq9zVQAlB5mkByB96eiLTV/gyg7V1cZmzZq5gIxKWxQsUlmGqIOjoJm+iP1y4Dlz5rigmr4kFACMFWVZKminTEZNCKTgjcosVO4TPqi7vpTUUVCAVhmLKhVXIKlOnTruyzo3KplWYEnZs71793ZfnOqgKVsxrzL4wtIXqbIF9kUBOu2bMjuV1akrwrqKHj5Quv/5qfM7duxYF8jSF76utOdnHJ1wKqXW+zZ06FA7+uij3X3qrHXo0MGVWyurNDfKttRnr+xddZp0nCjYrE6Ugpp+hy4W9Pmo7D5aWVR+37PcKMtS+6EAsbJic3Zq9dmpw6vjRlmaeh2Ni6XOmE4C1DH0g5150fHrZ2TqdWbMmOE692qzOp+i35UtoL83Hf8KNuvKeW7lSaVLl3Z/MwpUqxObc5gEtU1/P8q+0OerdZURoKC4hqnQuL56rrIOdPVdnUydOOjkRBcm1AHOK/MZAAAUjvpR+q5Xpl54NYj6Bgroqb+ljFAFHRUEUr8lr8mewqlPoyCs+noKmPqTyfgGDBjgMhvVl1K/XxeGFVBSX0qZn+rf7asqR4E+v3+joKr2Q30PlS2HDyOg19B+qk+nvoaCs6pIqlKlStTt6txE4+SrrxUt01bjneox9X/79OnjtqkkCvW3tF0/oULvoxIU1OfRuOvff/+96/voHCI/k9iG99/CqZ8bXo3nz0Fw8803h4K44Qrzeeq4UDaiMl+1XxpaSoHvnEFslderXWqD+qnqB6qvGW0sUH3Wov6mjg21Obc+31133eWyUXWOpeor9RMV2FewL69zhVhQ/39f1M9VQF2TLumY0GetILIyXXW/LgK0adMmtN86PtTvV6avzp10DBWE5pbQ360SYJQxrMxkDa+myi5tU0FdnSfr707ntPpM1PfX563PJJ6JOQCKSO4T36Ok++GHH7w+ffp49evX91JTU73y5ct7J5xwgvfoo496O3fuDK23e/dub9iwYV6DBg280qVLe3Xr1vUGDRoUsY7Uq1fP69q1616vc/LJJ7vFt3TpUkV9vAceeGCvdd977z2vWbNmrj2NGzf2XnjhBW/o0KFufd+MGTO8s88+26tVq5ZbTz8vvvhitz85X+OZZ56J2P706dPdPpYpU8arUKGCd9ZZZ3nfffddxDr+661fvz7ifm1L92vbeenZs6dXtmzZPNeJ9h7o/bzpppu8mjVruvapnbNnz97r/ZN///vfXtOmTb1SpUpF7KfWO/LII6O+Zvh2tmzZ4j6vo48+2n2+4fr16+clJye7187L1q1b3bp6/3VcHHbYYW5/srOz93rd3NoUrY3R1v3tt9+8ihUr7vd7ltvxIE899ZR7TMf/77//HrVdX3zxhXfuued6VapU8dLS0tx799e//tUdi3nxXzd80WfWsGFDb8CAAe49DPfpp596xx13nNsXva+33HKL9+6777rnffDBB3ttf86cOe6xzp0759oGPa9Lly7u/UtPT/cOPfRQ77LLLvPmzp3rHt+wYYN37bXXek2aNHHHrdZr27at98orr+S5bwAAIH/8PuTnn38etd+ox3L2f958802vRYsW7rtbffX77rvPGz9+/F590Wj9RL+vp/6E1ld/Ohr1Q9Snb9SoketTV61a1Tv++OO9Bx980MvMzMxzn/Sa4f2bpKQk76CDDvL+8pe/ePPmzdurH9erVy+3/XLlyrl+ycKFC11/Svsfjd4P9Ud/+eWXqI+vXbvW9V90XqJ+aI0aNbxTTz3Ve/LJJ0PrPPHEE1779u1D/Tf1gdT/2rx5c4H7b+FLtDZ3797dPdapU6eo2yzM5/nTTz+57Wofqlev7t12223etGnT9uof6pxG6+k91nut87wvv/xyrz7wnj17vOuuu847+OCD3ecWfp6l33UuFG7+/PnuM9N2MzIyvI4dO3qzZs3K1zGu9uXWj83P+VdOWkefezgdq3o/dczoPapcubLXunVrd/4a/lnrmNPx4P9d+J9jXq+d8zzUp8/uqKOOCr2ePjN9Jv77pXPTQw45xD1erVo178wzzwz1vQGUbEn6X1EFZAEA+aeSLpXvPPfccxHjdQEAAJRkGmtdWZOqvgEAICgYkxQAAkqlO5ohU6U+AAAAB4K5c+faggULIiYgAgAgCBiTFAACRuOgalZNjRXct2/fiMmmAAAASiJNxDRv3jx76KGH3KSrOceKBwCguBEkBYCAue6669zEShr8PdqEVgAAACWNJozShJyNGze2l156ydLT04u7SQAARKDcHgACRjPO/v777/bGG2/ka3ZWAEVn5syZbgZjzXKblJTk/k735cMPP7Sjjz7azZCrmXMnTJhQJG0FgCC58847LTs7281Cf/LJJxd3cwAA2AtBUgAAgHzavn27tWzZ0saMGZOv9ZcuXWpdu3a1jh07ujH4brzxRrviiivs3XffjXtbAQAAAOQfs9sDAADsB2WSTp482bp165brOgMHDrS3337bjcXnu+iii2zTpk02derUImopAAAAgAN6TFKVa6xatcqVo+pEBQCA/aVrhlu3bnVl1MnJwSi02Llzp2VmZsZse6mpqYwBV8Rmz55tnTp1irivS5cuLqM0N7t27XJLeH9n48aNVqVKFfo7AAAAQJzO80p0kFQB0rp16xZ3MwAAB5AVK1ZYnTp1AhEgbVCvnK1ZlxWzbdaoUcOVfxMoLTpr1qyx6tWrR9yn21u2bHFjD5cpU2av54wcOZJJ2wAAAIAiPs8r0UFSf0KTE+0MK2Wli7s5QGAtfrxVcTcBCLzs33fZqv73BmayLGWQKkD687z6VqF84TNbt2zNtnqtl7ntEiQNtkGDBln//v1Dtzdv3myHHHKI69hVqFChWNsGAAAAlCRKTlCCZX7O80p0kNQvOVOAtFQSQVIgN8llCIgA+RW0cuZy5ZPcUljZFqz9ShTK3l27dm3EfbqtYGe0LFJJS0tzS056DkFSAAAAID7neSU6SAoAwIEuy8u2LC8220HRa9eunb3zzjsR902bNs3dDwAAACA4gjEzBQAAQAmwbds2W7BggVtEY7zq9+XLl4dK5Xv06BFa/6qrrrIlS5bYLbfcYgsXLrTHHnvMXnnlFevXr1+x7QMAAACAvZFJCgBAgGWb55ZYbAeFN3fuXOvYsWPotj92aM+ePW3ChAm2evXqUMBUGjRoYG+//bYLij7yyCNusPinn37azXAPAAAAIDgIkgIAAORThw4dzPNyDzgrUBrtOV988UWcWwYAAACgMAiSAgAQYNnuv9hsBwAAAAAQHUFSAAACLMvz3BKL7QAAAAAAomPiJgAAAAAAAAAJjUxSAAACjImbAAAAACD+CJICABBgCm5mESQFAAAAgLii3B4AAAAAAABAQiOTFACAAKPcHgAAAADij0xSAAAAAAAAAAmNTFIAAAIsy/PcEovtAAAAAACiI0gKAECAZf9vicV2AAAAAADRUW4PAAAAAAAAIKGRSQoAQIBlmeeWWGwHAAAAABAdQVIAAAIsy/tjicV2AAAAAADRUW4PAAAAAAAAIKGRSQoAQIAxcRMAAAAAxB9BUgAAAizbkizLkmKyHQAAAABAdJTbAwAAAAAAAEhoZJICABBg2d4fSyy2AwAAAACIjkxSAAAAAAAAAAmNTFIAAAIsK0ZjksZiGwAAAABwoCJICgBAgBEkBQAAAID4o9weAAAAAAAAQEIjkxQAgADL9pLcEovtAAAAAACiI0gKAECAUW4PAAAAAPFHuT0AAAAAAACAhEYmKQAAAZZlyW4p/HYAAAAAALkhkxQAAAAAAABAQiOTFACAAPNiNHGTtgMAAAAAiI4gKQAAAcbETQAAAAAQf5TbAwAAAAAAAEhoZJICABBgWV6yWwq/nZg0BwAAAAAOSARJAQAIsGxLsuwYFH5kG1FSAAAAAMgN5fYAAAAAAAAAEhqZpAAABBgTNwEAAABA/BEkBQAgIcYkpdweAAAAAHJDuT0AAAAAAACAhEYmKQAAgZ+4qfCl8rHYBgAAAAAcqMgkBQAAAAAAAJDQyCQFACDAsi3ZsmJwTTPbGJMUAAAAAHJDkBQAgABj4iYAAAAAiD/K7QEAAAAAAAAkNDJJAQAIeLm9lsJvh0xSAAAAAMgNQVIAAAIsy0tySyy2AwAAAACIjnJ7AAAAAAAAAAmNTFIAAAIsK0az22dRbg8AAAAAuSJICgBAgGV7yW4p/HYIkgIAAABAbii3BwAAAAAAAJDQyCQFACDAKLcHAAAAgPgjkxQAAAAAAABAQiOTFACAAMtWFqiXFJPtAAAAAACiI0gKAECAZVuyW2KxHQAAAABAdJwxAQAAAAAAAEhoZJICABBgWV6yW2KxHQAAAABAdARJAQAIsGxLcksstgMAAAAAiI60EgAAAAAAAAAJjUxSAAACjHJ7AAAAAIg/zpgAAAAAAAAAJDQySQEACLAsS3ZLLLYDAAAAAIiOICkAAAGW7SW5JRbbAQAAAABER1oJAAAAAAAAgIRGJikAAAGWHaNye20HAAAAABAdQVIAAAIs20t2Syy2AwAAAACIjjMmAACwl8cff9xatGhhFSpUcEu7du3sP//5T+jxDh06WFJSUsRy1VVXRWxj+fLl1rVrV8vIyLBq1arZgAEDbM+ePcWwNwAAAACQNzJJAQAIsCxLcksstlMQderUsXvvvdcOO+ww8zzPnn32WTv77LPtiy++sCOPPNKt06dPHxs+fHjoOQqGhl4vK8sFSGvUqGGzZs2y1atXW48ePax06dJ2zz33FHp/AAAAACCWCJICABBgxVVuf9ZZZ0Xcvvvuu1126WeffRYKkiooqiBoNO+995599913Nn36dKtevbq1atXKRowYYQMHDrQ777zTUlNTC7E3AAAAABBblNsDAIA8KSv05Zdftu3bt7uye9+LL75oVatWtWbNmtmgQYNsx44docdmz55tzZs3dwFSX5cuXWzLli327bffFvk+AAAAAEBeyCQFACDAsvajVD637YiClOHS0tLcEs3XX3/tgqI7d+60cuXK2eTJk61p06busb/97W9Wr149q1Wrln311VcuQ3TRokX2+uuvu8fXrFkTESAV/7YeAwAAAIAgIUgKAEACqVu3bsTtoUOHuvL3aBo3bmwLFiywzZs322uvvWY9e/a0jz76yAVKr7zyytB6yhitWbOmnXrqqfbTTz/ZoYceGvf9AAAAAIBYIkgKAEACjUm6YsUKN1u9L7csUtG4oY0aNXK/t27d2j7//HN75JFH7Iknnthr3bZt27qfixcvdkFSjVU6Z86ciHXWrl3rfuY2jikAAAAAFBfGJAUAIMCyvOSYLaIAafiSV5A0p+zsbNu1a1fUx5RxKsooFZXpq1x/3bp1oXWmTZvmXtMv2QcAAACAoCBICgAA9qKJmGbOnGnLli1zwU7d/vDDD6179+6upF4z1c+bN889/uabb1qPHj2sffv21qJFC/f8zp07u2DopZdeal9++aW9++67NnjwYLv22msLFJgNojFjxlj9+vUtPT3dZdDmzJjNafTo0W7ogjJlyrjhDvr16+fGeQUAAAAQHJTbAwAQYJ4lWXYMJm7SdgpCGaAKfK5evdoqVqzogp8KdJ522mmuZH/69Oku+KcZ7xX4O++881wQ1JeSkmJvvfWWXX311S6rtGzZsm5M0+HDh1tJNmnSJOvfv7+NHTvWBUj1HnTp0sVNWlWtWrW91p84caLdeuutNn78eDv++OPthx9+sMsuu8ySkpJs1KhRxbIPAAAAAPZGkBQAgAALL5Uv7HYKYty4cbk+pqCoJnDal3r16tk777xjBxIFNvv06WO9evVytxUsffvtt10QVMHQnGbNmmUnnHCC/e1vf3O3lYF68cUX23//+98ibzsAAACA3FFuDwAAkA+ZmZluiIFOnTqF7ktOTna3Z8+eHfU5yh7Vc/yS/CVLlrjA8RlnnJHr62jc1y1btkQsAAAAAOKLTFIAAAIs20tySyy2g8LZsGGDZWVlWfXq1SPu1+2FCxdGfY4ySPW8E0880TzPsz179thVV11lt912W66vM3LkSBs2bFjM2w8AAAAgd2SSAgAQYFmWHLMFRU+TXd1zzz322GOP2fz58+3111935fma+Co3miRr8+bNoUVjwAIAAACILzJJAQAA8qFq1apuQqq1a9dG3K/bNWrUiPqcO+64wy699FK74oor3O3mzZu7ya6uvPJKu/322125fk5paWluAQAAAFB0SCsBAKAElNvHYkHhpKamWuvWrW3GjBmh+7Kzs93tdu3aRX3Ojh079gqEKtAqKr8HAAAAEAxkkgIAAORT//79rWfPntamTRs79thjbfTo0S4z1J/tvkePHla7dm03rqicddZZNmrUKDvqqKOsbdu2tnjxYpddqvv9YCkAAACA4keQFACAAMu2ZLfEYjsovAsvvNDWr19vQ4YMsTVr1lirVq1s6tSpocmcli9fHpE5OnjwYEtKSnI/V65caQcffLALkN59993FuBcAAAAAciJICgBAgGV5SW6JxXYQG3379nVLbhM1hStVqpQNHTrULQAAAACCi7QSAAAAAAAAAAmNTFIAAAIsVpMuMXETAAAAAOSOICkAAAHmecmW7SXHZDsAAAAAgOg4YwIAAAAAAACQ0MgkBQAgwLIsyS2x2A4AAAAAIDoySQEAAAAAAAAkNDJJAQAIsGwvNpMuaTsAAAAAgOgIkiJfmrXdZhdcs94Oa77DqtTYY3deXt9mT60YsU7dRjut9+DV1uK4bZZSyuznH9JsRJ/6tn5lqnv8/7r/ah3P+c0aNf/dypbPtnObNLPtW1KKaY+A2Kr81morP2+Tpa7Zadmlk21no7K2/oI6trtmemid0ut22cGTfrH0H7ZZ0p5s29G8oq3rXteyKpZ2j5fasMuqvLnaMr7faimbd9ueSqVta7sq9utZNcxKkfifqLJjNHFTLLYBAAAAAAcqzpiQL+kZ2bbk23T75211oj5es94uG/XGYluxOM0GnH+oXXXq4TZxdHXL3Pln9lN6mWyb+2F5e/nRakXYcqBoZCzaZptOPdiWD25iv9x8mFmWZ3Ue+tGSdmW5x/Wz9oM/mJL5frnlcFtxWxMXKK39yOJQil/q6p2mFdb2rGc/33Wkrb+4rlX8cL1VfW1VMe8dAAAAAAAHtkBkko4ZM8YeeOABW7NmjbVs2dIeffRRO/bYY4u7WQgz94MKbsnNZbeusTnvV7Bxd9UK3bf657SIdSY/fbD72aLdtji2FCgeK286LOL22t717dAbvrL0ZTvs98blrcyP2630hkxbPqypZZf5I4N6zRUN7NC+C1zm6I4jK7jMUi2+3dXS7Lc1O63iB+ttw0XRL1DgwJdtSW6JxXYAAAAAAAHNJJ00aZL179/fhg4davPnz3dB0i5duti6deuKu2nIp6Qkz449dYutXJJmd0/8ySZ99a098taP1u70zcXdNKDYJP/+RwZpVtk/rkUpa1QxKq/Un4Eqr3SSu6/Mj7lfOEjekWXZ/9sGElOWlxSzBQAAAAAQ0CDpqFGjrE+fPtarVy9r2rSpjR071jIyMmz8+PHF3TTkU6WqeyyjXLZd2HedyzYddHFD+3RqBRvy9DJrfhxZo0hA2Z4d/NIv9vthZS2zThl3186GZS07LdmqvrrSknZlu/L7qpN+saRss5RNu6NupvTanVZpxjrb3KFqEe8AAAAAAACJpVjTkzIzM23evHk2aNCg0H3JycnWqVMnmz179l7r79q1yy2+LVu2FFlbkbuk/4XaZ79bwSY/9UdJ/ZJvy1jTNjusa49f7evPyhVvA4EiVu2F5Zb2y++24rbGofuyKpS21dccatWe+9kqTV/nMki3tj3IdtbLiHq5qtRvmVZ71GLb1qaybT75j78rJCYmbgIAAACAAzxIumHDBsvKyrLq1atH3K/bCxcu3Gv9kSNH2rBhw4qwhciPLRtTbM9uzWb/5yzesuLHNDvy2O3F1i6gOFR7frmVXbDZVgxqbHsOSo14bEezCrbs/uaWvHWPWYpZdkYpa3jDl7b74MoR66X8lml17vvBdjYqa2svq1fEe4BAjkkag1J5xiQFAAAAgNyVqLQSZZxu3rw5tKxYsaK4mwQz27M72X74MsPqHPpnlq/UbrjL1v0SGSQCDlie5wKk5eZvcrPX7zk4cuKycNnlS7kAaZnvtljK1j22rVWliAzSugqQ1s+wNb3rmyUT2AIAAAAA4IDOJK1ataqlpKTY2rVrI+7X7Ro1auy1flpamltQ9NIzsqxWg8zQ7Rp1M63hkb/b1k0ptn5lqr36WDW7bezP9s1nZe3LWeWsTcetdtxpW2zA+YeGnlP54N1Wudoeq9Xgj2Bqgya/247ten5p27qJiWlQslV7foWV/2yjrbr+UDd7fcrmP8YZ1e9e6h/Xoyp8vMEya6VbVvnSlr54m1WbuMJ+61zNdtdMDwVI69z7g+2ummobLqzjAqi+rIqli2nPUNy8GM1ur+0AAAAAAKIr1shUamqqtW7d2mbMmGHdunVz92VnZ7vbffv2Lc6mIYfDW/5uD/zrp9Dtq4atcj/fm1TZHup3iM2aWtH+cWttu6jvOrt6xEr7ZUmajehT376d8+d4pBqf9NKb/gyIP/TGH9t78Ma6Nu2Vg4p0f4BYq/TBevdTWaDh1vSuZ1tO/GPipdQ1O63qaystZXuWC4T+elZN29S5WmjdjG+3WOq6XW5p2P/riO388EzrItkPAAAAAAASUbGn7/Xv39969uxpbdq0sWOPPdZGjx5t27dvd7PdIzi+ml3OutRqmec6771cxS25eeGhGm4BDkT5CWJuuKCOW3KjYKofUAV8Go80JmOSxmAbAAAAAHCgKvYg6YUXXmjr16+3IUOG2Jo1a6xVq1Y2derUvSZzAgAgETG7PQAAAAAkQJBUVFpPeT0AAAAAAACAhA2SAgCA6Ci3BwAAAID4I0gKAECAZcdodvtYbAMAAAAADlQMUAYAAAAAAAAgoZFJCgBAgFFuDwAAAADxR5AUAIAAI0gKAAAAAPFHuT0AAAAAAACAhEYmKQAAAUYmKQAAAADEH5mkAAAAAAAAABIamaQAAAQYmaQAAAAAEH8ESQEACDBPAU5Lisl2AAAAAADRUW4PAAAAAAAAIKGRSQoAQIBRbg8AAAAA8UeQFACAACNICgAAAADxR7k9AAAAAAAAgIRGJikAAAFGJikAAAAAxB+ZpAAAAAAAAAASGpmkAAAEGJmkAAAAABB/BEkBAAgwz0tySyy2AwAAAACIjnJ7AAAAAAAAAAmNTFIAAAIs25LcEovtAAAAAACiI0gKAECAMSYpAAAAAMQf5fYAAAAAAAAAEhqZpAAABBgTNwEAAABA/BEkBQAgwCi3BwAAAID4o9weAAAAAAAAQEIjkxQAgACj3B4AAAAA4o9MUgAAAAAAAAAJjUxSAAACTBmgsRhPlExSAAAAxFL9W98u7iagBFh2b1crKQiSAgAQYJ4LcMZmOwAAAACA6Ci3BwAAAAAAAJDQyCQFACDAsi3J/ReL7QAAAAAAoiNICgBAgDG7PQAAAADEH+X2AAAAAAAAABIamaQAAASYZrZPikEWqLYDAAAAAIiOTFIAAAAAAAAACY1MUgAAAszz/lhisR0AAAAAQHQESQEACDAmbgIAAACA+KPcHgAAAAAAAEBCI5MUAIAAI5MUAAAAAOKPICkAAAHG7PYAAAAAEH+U2wMAAAAAAABIaGSSAgAQYMxuDwAAAADxR5AUAIDAB0ljMSZpTJoDAAAAAAckyu0BAAAAAAAAJDQySQEACDBmtwcAAACA+COTFAAAAAAAAEBCI5MUAIAA01CisRhOlCFJAQAAACB3BEkBAAgwyu0BAEgM9W99u7ibgBJg2b1di7sJwAGLcnsAAAAAAAAACY0gKQAAJaHePhZLATz++OPWokULq1ChglvatWtn//nPf0KP79y506699lqrUqWKlStXzs477zxbu3ZtxDaWL19uXbt2tYyMDKtWrZoNGDDA9uzZE6t3BgAAAABihiApAABB9r9y+8Iu2k5B1KlTx+69916bN2+ezZ0710455RQ7++yz7dtvv3WP9+vXz6ZMmWKvvvqqffTRR7Zq1So799xzQ8/PyspyAdLMzEybNWuWPfvsszZhwgQbMmSIlXRjxoyx+vXrW3p6urVt29bmzJmT5/qbNm1yAeWaNWtaWlqaHX744fbOO+8UWXsBAAAA7BtjkgIAgL2cddZZEbfvvvtul1362WefuQDquHHjbOLEiS54Ks8884wdccQR7vHjjjvO3nvvPfvuu+9s+vTpVr16dWvVqpWNGDHCBg4caHfeeaelpqZaSTRp0iTr37+/jR071gVIR48ebV26dLFFixa5bNmcFCQ+7bTT3GOvvfaa1a5d237++WerVKlSsbQfAAAAQHRkkgIAEGCeF7tFtmzZErHs2rVrn21QVujLL79s27dvd2X3yi7dvXu3derUKbROkyZN7JBDDrHZs2e72/rZvHlzFyD1KZio1/SzUUuiUaNGWZ8+faxXr17WtGlTFyzVcALjx4+Pur7u37hxo73xxht2wgknuAzUk08+2Vq2bFnkbQcAAACQO4KkAAAEWCxK7UMl92ZWt25dq1ixYmgZOXJkrq/99ddfu/FGVSJ+1VVX2eTJk11gcM2aNS4TNGc2pAKiekz0MzxA6j/uP1YSKStUAeLw4HBycrK77QeHc3rzzTddYFnl9tr/Zs2a2T333OMCz7lR4DpnMBsAAABAfFFuDwBAAlmxYoWbiMmnAGhuGjdubAsWLLDNmze7UvGePXu68UcT1YYNG1xwM1rwd+HChVGfs2TJEnv//fete/fubhzSxYsX2zXXXOMycYcOHRr1OQpcDxs2LC77AAAAACA6gqQAAATZfky6lOt2zEKz1eeHskUbNWrkfm/durV9/vnn9sgjj9iFF17osio1IVF4Nqlmt69Ro4b7XT9zTmikx/3HEkV2drYbj/TJJ5+0lJQU9z6uXLnSHnjggVyDpIMGDXLjnvqUSaoMYAAAAADxQ7k9AADId8BPpeAK9JUuXdpmzJgRekwTFy1fvtyVlot+qlx/3bp1oXWmTZvmArQq2S+Jqlat6gKdfrA3WnA4J81or9ns9TyfJrjSkAMKNEej7F4/mF2QoDYAAACA/UeQFACABJq4Kb+UzThz5kxbtmyZC3bq9ocffujKxjWWae/evV224wcffODG6dRERgqMamZ76dy5swuGXnrppfbll1/au+++a4MHD3Zjc+ZV4h9kyqxVgDg8OKzAsW77weGcNFmTSuy1nu+HH35wwVNtDwAAAEAwECQFACDIvBguBaAM0B49erhxSU899VRXaq9A52mnneYef/jhh+3MM8+08847z9q3b+8yKV9//fXQ85U5+dZbb7mfCiBecsklbnvDhw+3kkyB4aeeesqeffZZ+/777+3qq6+27du3uyCxaB8VUPbpcc1uf8MNN7jg6Ntvv+0mblKwGAAAAEBwMCYpAADYy7hx4/J8PD093caMGeOW3NSrV89NVnQg0Xis69evtyFDhriS+VatWtnUqVNDkzlpyAHNeO/TWKIKLvfr189atGhhtWvXdgHTgQMHFuNeAAAAAMiJICkAAAHmeUluicV2EBt9+/Z1SzQakiAnZdJ+9tlnRdAyAAAAAPuLICkAAEFXwFJ5AAAAAEAcgqRvvvlmvjf4l7/8pYBNAAAAAAAAAICAB0m7deuWr40lJSVZVlZWYdsEAAD+h3J7AAAAAAhIkDQ7Ozv+LQEAAAAAAACAkjYm6c6dO93stgAAII7jkcZiTFLGNQWQYOrf+nZxNwElwLJ7uxZ3EwAAAZFc0CeonH7EiBFWu3ZtK1eunC1ZssTdf8cdd9i4cePi0UYAABJYUgwXAAAAAEBMgqR33323TZgwwe6//35LTU0N3d+sWTN7+umnC7o5AAAAAAAAAChZQdLnnnvOnnzySevevbulpKSE7m/ZsqUtXLgw1u0DACCxeTFcAAAAAACxGZN05cqV1qhRo6iTO+3evbugmwMAAHlhTFIAAAAACF4madOmTe3jjz/e6/7XXnvNjjrqqFi1CwAAAAAAAACCmUk6ZMgQ69mzp8soVfbo66+/bosWLXJl+G+99VZ8WgkAQKLykv5YYrEdAAAAAEBsMknPPvtsmzJlik2fPt3Kli3rgqbff/+9u++0004r6OYAAEAePC92CwAAAAAgRpmkctJJJ9m0adP256kAAAAAAAAAUPKDpDJ37lyXQeqPU9q6detYtgsAAAgTNwEAAABA8IKkv/zyi1188cX26aefWqVKldx9mzZtsuOPP95efvllq1OnTjzaCQAAAAAAAADBGJP0iiuusN27d7ss0o0bN7pFv2sSJz0GAADiMHFTLBYAAAAAQGwyST/66CObNWuWNW7cOHSffn/00UfdWKUAACB2krw/llhsBwAAAAAQo0zSunXrukzSnLKysqxWrVoF3RwAAAAAAAAAlKwg6QMPPGDXXXedm7jJp99vuOEGe/DBB2PdPgAAEpsXwwUAAAAAsP/l9pUrV7akpD/HMtu+fbu1bdvWSpX64+l79uxxv19++eXWrVu3/GwSAADkR6zGE2VMUgAAAAAoXJB09OjR+VkNAAAAAAAAAA7MIGnPnj3j3xIAALC3WJXKU26PQqp/69vF3QSUAMvu7VrcTQAAACia2e3D7dy50zIzMyPuq1ChQmE2CQAAwhEkBQAAAIDgTdyk8Uj79u1r1apVs7Jly7rxSsMXAAAAAAAAADigg6S33HKLvf/++/b4449bWlqaPf300zZs2DCrVauWPffcc/FpJQAAiYrZ7QEAAAAgeOX2U6ZMccHQDh06WK9eveykk06yRo0aWb169ezFF1+07t27x6elAAAAAAAAABCETNKNGzdaw4YNQ+OP6raceOKJNnPmzNi3EACAROYlxW4BAAAAAMQmSKoA6dKlS93vTZo0sVdeeSWUYVqpUqWCbg4AAOQhyYvdAgAAAACIUZBUJfZffvml+/3WW2+1MWPGWHp6uvXr188GDBhQ0M0BAAAAAAAAQLEq8JikCob6OnXqZAsXLrR58+a5cUlbtGgR6/YBAJDYYjXpEpmkAAAAABC7IGlOmrBJCwAAAAAAAAAcsEHSf/zjH/ne4PXXX1+Y9gAAAAAAAABA8IKkDz/8cL42lpSURJAUAIAY0pz0sZh0ibntAQAAAKCQQVJ/NvugSmp5hCWlpBV3M4DAWtplXHE3AQi8LVuzrXJxNwIAAAAAUDLHJAUAAHHkJf2xxGI7AAAAAICoCJICABBkzG4PAAAAAHGXHP+XAAAAAAAAAIDgIpMUAIAgI5MUAAAAAOKOICkAAAGmme1jMrs9QVIAAAAAiG25/ccff2yXXHKJtWvXzlauXOnue/755+2TTz7Zn80BAAAAAAAAQMkJkv7rX/+yLl26WJkyZeyLL76wXbt2ufs3b95s99xzTzzaCABA4vJiuAAAAAAAYhMkveuuu2zs2LH21FNPWenSpUP3n3DCCTZ//vyCbg4AAOSFICkAAAAABC9IumjRImvfvv1e91esWNE2bdoUq3YBAAAAAAAAQDCDpDVq1LDFixfvdb/GI23YsGGs2gUAAMImborFAgAAAACIUZC0T58+dsMNN9h///tfS0pKslWrVtmLL75oN998s1199dUF3RwAAAAAAAAAFKtSBX3CrbfeatnZ2Xbqqafajh07XOl9WlqaC5Jed9118WklAACJykv6Y4nFdgAAAAAAsQmSKnv09ttvtwEDBriy+23btlnTpk2tXLlyBd0UAADYl1hNukS5PQAAAADELkjqS01NdcFRAAAAAAAAAEioIGnHjh1dNmlu3n///cK2CQAA/E+sJl1i4iYAAAAAiGGQtFWrVhG3d+/ebQsWLLBvvvnGevbsWdDNAQCAvFBuDwAAAADBC5I+/PDDUe+/88473fikAAAAAAAAAFCSJMdqQ5dccomNHz8+VpsDAADyv3L7wi5kkgIAAABAHCZuymn27NmWnp4eq80BAACh3B4AAAAAghckPffccyNue55nq1evtrlz59odd9wRy7YBAAAAAAAAQPCCpBUrVoy4nZycbI0bN7bhw4db586dY9k2AABAJikAAAAABCtImpWVZb169bLmzZtb5cqV49cqAAAAAAAAAAjixE0pKSkuW3TTpk3xaxEAAAiJxaRNocmbAAAAAACxmd2+WbNmtmTJkoI+DQAAAAAAAAAOjCDpXXfdZTfffLO99dZbbsKmLVu2RCwAAAAAAAAAcECOSaqJmW666SY744wz3O2//OUvlpSUFDHLvW5r3FIAABAjTNwEAAAAAMEJkg4bNsyuuuoq++CDD+LbIgAAEBKr8UQZkxQAAAAAYhAkVaaonHzyyfl9CgAAAAAAAAAcOEFSCS+vBwAARYQsUAAAAAAITpD08MMP32egdOPGjYVtEwAAAAAAAAAEM0iqcUkrVqwYv9YAAIBITNwEAAAAAMEKkl500UVWrVq1+LUGAABEYOImAAAAAIi/5PyuyHikAAAAAAAAAA5EBZ7dHgAAFCHK7QEAAAAgOEHS7Ozs+LYEAADshXJ7AAAAAAhQuT0AAADMxowZY/Xr17f09HRr27atzZkzJ1/Pe/nll93wRd26dYt7GwEAAAAUDEFSAABKQrl9LBYU2qRJk6x///42dOhQmz9/vrVs2dK6dOli69aty/N5y5Yts5tvvtlOOumkImsrAAAAgPwjSAoAQJARJA2UUaNGWZ8+faxXr17WtGlTGzt2rGVkZNj48eNzfU5WVpZ1797dhg0bZg0bNizS9gIAAADIH4KkAAAA+ZCZmWnz5s2zTp06he5LTk52t2fPnp3r84YPH27VqlWz3r17F1FLAQAAAMRt4iYAAFD0mLgpODZs2OCyQqtXrx5xv24vXLgw6nM++eQTGzdunC1YsCDfr7Nr1y63+LZs2VKIVgMAAADIDzJJAQAA4mDr1q126aWX2lNPPWVVq1bN9/NGjhxpFStWDC1169aNazsBAAAAECQFACDYimlMUgXqjjnmGCtfvrwrFdeM7IsWLYpYp0OHDm629vDlqquuilhn+fLl1rVrVzdup7YzYMAA27Nnj5VECnSmpKTY2rVrI+7X7Ro1auy1/k8//eQmbDrrrLOsVKlSbnnuuefszTffdL/r8WgGDRpkmzdvDi0rVqyI2z4BAAAA+APl9gAABFmsZqYv4DY++ugju/baa12gVEHN2267zTp37mzfffedlS1bNrSeJjHSmJs+BUN9Kk1XgFQBxFmzZtnq1autR48eVrp0abvnnnuspElNTbXWrVvbjBkzXNBYsrOz3e2+ffvutX6TJk3s66+/jrhv8ODBLsP0kUceyTVDNC0tzS0AAAAAig5BUgAAsJepU6dG3J4wYYLLBNXERe3bt48IikbLopT33nvPBVWnT5/uxu1s1aqVjRgxwgYOHGh33nmnCzqWNP3797eePXtamzZt7Nhjj7XRo0fb9u3b3Wz3oiBw7dq1XSZuenq6NWvWLOL5lSpVcj9z3g8AAACgeFFuDwBACZi4KRZLYajsWw466KCI+1988UVXhq6gn8rEd+zYEXpMM743b948YqKjLl26uImIvv32WyuJLrzwQnvwwQdtyJAhLuirCZkUUPb3UcMLKGMWAAAAQMlCJikAAAlUbp9zpvT8lHarpPzGG2+0E044ISID8m9/+5vVq1fPatWqZV999ZXLENW4pa+//rp7fM2aNVFngvcfK6lUWh+tvF4+/PDDPJ+rjFwAAAAAwUOQFACABJJzHMyhQ4e60ve8aGzSb775xj755JOI+6+88srQ78oYrVmzpp166qluQqJDDz00xi0HAAAAgPghSAoAQIDFolTe345opvQKFSqE7t9XFqkyJt966y2bOXOm1alTJ89127Zt634uXrzYBUk1VumcOXMi1vFnhs9tHFMAAAAAKA6MSQoAQEkot4/FYuYCpOFLbkFSz/NcgHTy5Mn2/vvvW4MGDfbZVI3PKcoolXbt2rnZ3detWxdaZ9q0ae51mzZtGpv3BwAAAABigExSAAAQtcR+4sSJ9u9//9vKly8fGkO0YsWKVqZMGVdSr8fPOOMMq1KlihuTtF+/fm7m+xYtWrh1O3fu7IKhl156qd1///1uG4MHD3bb3lcGKwAAAAAUJTJJAQBIoEzS/Hr88cfdjPYdOnRwmaH+MmnSJPd4amqqTZ8+3QVCmzRpYjfddJOdd955NmXKlNA2UlJSXKm+fiqr9JJLLrEePXrY8OHDY/0uAQAAAEChkEkKAACiltvvawKojz76aJ/bqVevnr3zzjsxbBkAAAAAxB5BUgAAAizpf0sstgMAAAAAiI4gKQAAQbYfpfK5bgcAAAAAEBVjkgIAAAAAAABIaGSSAgAQYEneH0sstgMAAAAAiI4gKQAAQUa5PQAAAADEHeX2AAAAAAAAABIamaQAAAQdWaAAAAAAEFdkkgIAAAAAAABIaGSSAgAQYEzcBAAAAADxR5AUAIAgY+ImAAAAAIg7yu0BAAAAAAAAJDQySQEACDDK7QEAAAAg/giSAgAQZJTbAwAAAEDcUW4PAAAAAAAAIKGRSQoAQIBRbg8AAAAA8UeQFACAIKPcHgAAAADijnJ7AAAAAAAAAAmNTFIAAIKMTFIAAAAAiDsySQEAAAAAAAAkNDJJAQAIMCZuAgAAAID4I0gKAECQUW4PAAAAAHFHuT0AAAAAAACAhEYmKQAAAZbkeW6JxXYAAAAAANERJAUAIMgotwcAAACAuKPcHgAAAAAAAEBCI5MUAIAAY3Z7AAAAAIg/MkkBAAAAAAAAJDQySQEACDLGJAUAAACAuCNICgBAgFFuDwAAAADxR7k9AAAAAAAAgIRGJikAAEFGuT0AAAAAxB1BUgAAAoxyewAAAACIP8rtAQAAAAAAACQ0MkkBAAgyyu0BAAAAIO4IkgIAEHCUygMAAABAfFFuDwAAAAAAACChkUkKAECQed4fSyy2AwAAAACIikxSAAAAAAAAAAmNTFIAAAI+HmksxiRlXFMAAAAAyB1BUgAAgozZ7QEAAAAg7ii3BwAAAAAAAJDQyCQFACDAkrL/WGKxHQAAAABAdARJAQAIMsrtAQAAACDuCJIiX5oduc7OP+87O6zRb1alyu82bMRJNvuzuqHHK1X63Xr3WmBHH7XGypbNtG++rWaPjW1tq1ZVCK1z/8jp1qLFuojtvv1OI3t0zLFFui9APEx5toq9/VxVW7si1d2u13inde+3xo45Zau7vWpZqj01vJZ9O6ec7c5MstYdt9i1d620ygfvcY+vWZFqEx+ubgs+LWe/rS9tVarvtlPO/c0uvmGtlU4lugUAAAAAwAEbJJ05c6Y98MADNm/ePFu9erVNnjzZunXrVpxNQi7S0/fY0qWV7b1ph9qQwR/neNSzoYNn2p6sZBs2or3t2FHazj1noY28+3278qozbdeuPw+zd6Yeas+/0CJ0e9dO4vQ4MBxcc7ddftsqq91gl3lekk17tbLd2auBjXnvB6tRN9Nuu/hQa9j0d7vv1cVu/Wfvr2lDejawR9760ZKTzVYsTrPsbLMb7vvFajXYZcsWptvoAXVt545ku3LoquLePRQjZrcHAAAAgPgr1gjV9u3brWXLlnb55ZfbueeeW5xNwT7MnVfLLdHUrrXVjjjiV/v71WfYz8srufseHXOMvfTC69bx5GU29b1GEUHR334rU2TtBorKcZ23RNzudesae+u5qrZwXob9urq0yzAd894iK1v+j4EhBzzys513RHNb8Ek5O7r9Njum41a3+GrWy7RfflrntkGQNMF53h9LLLYDAAAAAAhekPT//u//3IKSrXTpP4I+mZkpofuUSbd7d4odeeT6iCBpx47L7JSOy+y339Ltv3Nq28SXm0VkmgIHgqwss4+nVLJdO5LtiDbbbfWyNLMkiyibL53mWVKyufJ7BUmj2b41xcpXyirClgMAAAAAkJiITqHQVvxSwdauy7Bel31p//jnsbZzZ4qd022RHXzwDjuo8u+h9T74qL6tW5dhv/6aYQ0a/GaX91pgdepssRF3ty/W9gOxsvT7dLvxrMMsc1eylSmbbUPGLbV6h++yilX2WHpGto27u5b1ulVZoUk27u6alp2VZBvXRf9neOXSVPv3+IOtz5CVRb4fCBbK7QEAAAAg/kpUkHTXrl1u8W3ZElneiuKRlZXsAp39bvjMXpv0mmVlJdkXC2rYnM9rWlLSn+v9Z+qfGaXLfq5kGzeWsftGvm81a2y11WvKF0/jgRiqc+gue2zaItuxNcU+fquSPXhDPXvg9R9doHTwE8vs0UF17N/jqroM0o7dfrNGzXe433PasLq03d79UGt/5iY7o/vG4tgVAAAAAAASSokKko4cOdKGDRtW3M1AFIsXH2TXXneGZWRkWulS2bZ5S7qNHvWu/fjjQbk+Z+Giqu5nrVoESXFgUDl97QaZ7vfDWvxuixZk2BtPH2w33P+Lte6w1SbM/t42/5piKaXMylXMsotaHmk1D/nzwo/8uqaU3XLBoda0zXa74YEVxbQnCBRlgMYiC5RMUgAAAADIVZQcpuAaNGiQbd68ObSsWEEAIWh27Eh1AdJatbbYYY022uzP6uS67qENf3M/lVEKHIg0T87uzMh/ZitWyXIBUk3YtGlDqYgJn5RBOuD8RnZY89/tpoeXu1nvAb/cPhYLAAAAAOAAyCRNS0tzC4peevpuq1Xrz8llatTYbg0b/mZbt6ba+vVl7aQTl9vmzWm2bn1Zq19/k1195TwXIJ3/RU23vkrqO3ZYZnPm1ratW1KtQYNNdmWf+fbV19Vs6bLKxbhnQGyMv6emHXPKFju49m77fVuyfTC5sn01q5zdPfEn9/i7Lx9khxy2041P+v28svb4kNp2zpXrrW6jXREB0mq1M63PkFW2+dc//3k+qNqeYtsvAAAAAAASQbEGSbdt22aLFy8O3V66dKktWLDADjroIDvkkEOKs2nI4fDDNtr9984I3f57n/nu57TpDeyhh9u5CZquvGK+Vaq00zb+lm4zZjRwM9f7du9Jtlat1lq3sxdZevoeW78+wz79tK69FLYOUJIpK/SB6+u5iZgyymdZgyN2ugBp65P/uLjwy09p9szImrZ1U4pVr5tpF1+/1s69cn3o+fNnlrdVS9Pc0r31kRHbfnfVgiLfHwQsJVlLLLYDAAAAAAhekHTu3LnWsWPH0O3+/fu7nz179rQJEyYUY8uQ01dfV7fTu/4t18f/PaWxW3KzYUNZu+XWTnFqHVD8+o/Ke/iP3revdktuOl+40S1ATsxuDwAAAAAHeJC0Q4cO5pHZAgAAAAAAAKAYlagxSQEASDjMbg8AAAAAccfcyQAAAAAAAAASGpmkAAAEGGOSAgAAAED8ESQFACDIsr0/llhsBwAAAAAQFeX2AAAAAAAAABIamaQAAAQZEzcBAAAAQNwRJAUAIMCSYjSeqLYDAAAAAIiOcnsAAAAAAAAACY1MUgAAgszz/lhisR0AAAAAQFRkkgIAEGAqtY/VgtgYM2aM1a9f39LT061t27Y2Z86cXNd96qmn7KSTTrLKlSu7pVOnTnmuDwAAAKB4ECQFAADIp0mTJln//v1t6NChNn/+fGvZsqV16dLF1q1bF3X9Dz/80C6++GL74IMPbPbs2Va3bl3r3LmzrVy5ssjbDgAAACB3BEkBACgJs9vHYkGhjRo1yvr06WO9evWypk2b2tixYy0jI8PGjx8fdf0XX3zRrrnmGmvVqpU1adLEnn76acvOzrYZM2YUedsBAAAA5I4gKQAAQD5kZmbavHnzXMm8Lzk52d1Wlmh+7Nixw3bv3m0HHXRQHFsKAAAAoKCYuAkAgABL8jy3xGI7KJwNGzZYVlaWVa9ePeJ+3V64cGG+tjFw4ECrVatWRKA1p127drnFt2XLlkK0GgAAAEB+kEkKAECQZcdwQbG699577eWXX7bJkye7SZ9yM3LkSKtYsWJo0TimAAAAAOKLICkAAEA+VK1a1VJSUmzt2rUR9+t2jRo18nzugw8+6IKk7733nrVo0SLPdQcNGmSbN28OLStWrIhJ+wEAAADkjiApAAAloNw+FgsKJzU11Vq3bh0x6ZI/CVO7du1yfd79999vI0aMsKlTp1qbNm32+TppaWlWoUKFiAUAAABAfDEmKQAAQRarmemJkcZE//79rWfPni7Yeeyxx9ro0aNt+/btbrZ76dGjh9WuXduVzMt9991nQ4YMsYkTJ1r9+vVtzZo17v5y5cq5BQAAAEAwECQFAADIpwsvvNDWr1/vAp8KeLZq1cpliPqTOS1fvtzNeO97/PHHLTMz084///yI7QwdOtTuvPPOIm8/AAAAgOgIkgIAEGQqk49FqTzl9jHTt29ft0Tz4YcfRtxetmxZEbUKAAAAQGEQJAUAIMCSvD+WWGwHAAAAABAdEzcBAAAAAAAASGgESQEAKAnl9rFYCkATDx1zzDFWvnx5q1atmnXr1s0WLVoUsc7OnTvt2muvtSpVqrhJiM477zxbu3ZtxDoao7Nr166WkZHhtjNgwADbs2dPTN4aAAAAAIgVgqQAAGAvH330kQuAfvbZZzZt2jTbvXu3de7c2c3k7uvXr59NmTLFXn31Vbf+qlWr7Nxzzw09npWV5QKkmrho1qxZ9uyzz9qECRPcpEcAAAAAECSMSQoAQIAlZf+xxGI7BaEZ28MpuKlM0Hnz5ln79u1t8+bNNm7cOJs4caKdcsopbp1nnnnGjjjiCBdYPe644+y9996z7777zqZPn+5mf9dM8CNGjLCBAwe6md1TU1MLv2MAAAAAEANkkgIAkEDl9lu2bIlYdu3ala9mKCgqBx10kPupYKmySzt16hRap0mTJnbIIYfY7Nmz3W39bN68uQuQ+rp06eJe99tvv43p2wQAAAAAhUGQFACABFK3bl2rWLFiaNHYo/uSnZ1tN954o51wwgnWrFkzd9+aNWtcJmilSpUi1lVAVI/564QHSP3H/ccAAAAAICgotwcAIMiUAOrFaDtmtmLFCqtQoULo7rS0tH0+VWOTfvPNN/bJJ5/EoCEAAAAAEDwESQEACLAkz3NLLLYjCpCGB0n3pW/fvvbWW2/ZzJkzrU6dOqH7a9So4SZk2rRpU0Q2qWa312P+OnPmzInYnh73HwMAAACAoKDcHgAA7MXzPBcgnTx5sr3//vvWoEGDiMdbt25tpUuXthkzZoTuW7RokS1fvtzatWvnbuvn119/bevWrQutM23aNBekbdq0aRHuDQAAAADkjUxSAACCLGzSpUJvpwBUYq+Z6//9739b+fLlQ2OIahzTMmXKuJ+9e/e2/v37u8mcFPi87rrrXGBUM9tL586dXTD00ksvtfvvv99tY/DgwW7b+SnzBwAAAICiQpAUAADs5fHHH3c/O3ToEHH/M888Y5dddpn7/eGHH7bk5GQ777zzbNeuXW7m+sceeyy0bkpKiivVv/rqq13wtGzZstazZ08bPnx4Ee8NAAAAAOSNICkAAEGmBNDsGG2nIKvnI/M0PT3dxowZ45bc1KtXz955552CvTgAAAAAFDGCpAAAJNDETQAAAACAvTFxEwAAAAAAAICERiYpAABBpgTQmEzcFIvGAAAAAMCBiSApAABBVkyz2wMAAABAIqHcHgAAAAAAAEBCI5MUAIAg08z2STHaDgAAAAAgKoKkAAAEGLPbAwAAAED8UW4PAAAAAAAAIKGRSQoAQJAxcRMAAAAAxB2ZpAAAAAAAAAASGpmkAAAEGZmkAAAAABB3BEkBAAgygqQAAAAAEHeU2wMAAAAAAABIaGSSAgAQZNlmlhSj7QAAAAAAoiJICgBAgCV5nltisR0AAAAAQHSU2wMAAAAAAABIaGSSAgAQZEzcBAAAAABxR5AUAIAgy/ZUKx+b7QAAAAAAoqLcHgAAAAAAAEBCI5MUAIAgo9weAAAAAOKOTFIAAAAAAAAACY1MUgAAAi1GmaTaDgAAAAAgKoKkAAAEGeX2AAAAABB3lNsDAAAAAAAASGhkkgIAEGTZygD1YrQdAAAAAEA0BEkBAAgyL/uPJRbbAQAAAABERbk9AAAAAAAAgIRGJikAAEHGxE0AAAAAEHdkkgIAAAAAAABIaGSSAgAQZEzcBAAAAABxR5AUAIAgo9weAAAAAOKOcnsAAAAAAAAACY1MUgAAgsxV28cikzQWjQEAAACAAxNBUgAAgoxyewAAAACIO8rtAQAAAAAAACQ0MkkBAAiy7Gz9L0bbAQAAAABEQ5AUAIAgo9weAAAAAOKOcnsAAAAAAAAACY1MUgAAgoxMUgAAAACIOzJJAQAAAAAAACQ0MkkBAAiybGWAejHaDgAAAAAgGoKkAAAEmOdluyUW2wEAAAAAREe5PQAAAAAAAICERiYpAABBpgmXYlEqz8RNAAAAAJArgqQAAASZC24SJAUAAACAeKLcHgAAAAAAAEBCI5MUAIAgy842S4rBpEtM3AQAAAAAuSKTFAAAAAAAAEBCI5MUAIAgY0xSAAAAAIg7gqQAAASYl51tXgzK7T3K7QEAAAAgV5TbAwAAAAAAAEhoZJICABBklNsDAAAAQNwRJAUAIMiyPbMkgqQAAAAAEE+U2wMAAAAAAABIaGSSAgAQZC4DNAaTLpFJCgAAAAC5IkgKAECAedmeeTEot/cIkgIAAABArii3BwAAAAAAAJDQCJICABBkXnbsFsTEmDFjrH79+paenm5t27a1OXPm5Ln+q6++ak2aNHHrN2/e3N55550iaysAAACA/CFICgAAkE+TJk2y/v3729ChQ23+/PnWsmVL69Kli61bty7q+rNmzbKLL77YevfubV988YV169bNLd98802Rtx0AAABA7giSAgAQ9DFJY7Sg8EaNGmV9+vSxXr16WdOmTW3s2LGWkZFh48ePj7r+I488YqeffroNGDDAjjjiCBsxYoQdffTR9s9//rPI2w4AAAAgdwRJAQAIMsrtAyMzM9PmzZtnnTp1Ct2XnJzsbs+ePTvqc3R/+PqizNPc1gcAAABQPEr07Pb+TL17snYVd1OAQNuyleAIsC9btmUHchb4PbbbzIvRdlAoGzZssKysLKtevXrE/bq9cOHCqM9Zs2ZN1PV1f2527drlFt/mzZvdzy1btlhxyt61o1hfHyVDcR+n4ThmkR8csyhpOGZR0mwp5mPWf/38nOeV6CDp1q1b3c+Pv3m4uJsCBFrlw4u7BUDJ+m6pWLFicTfDUlNTrUaNGvbJmthN8qPtabsItpEjR9qwYcP2ur9u3brF0h6gICqOLu4WAAXDMYuShmMWJU3F0SXnPK9EB0lr1aplK1assPLly1tSUlJxNwf/i9DrJE6fS4UKFYq7OUAg8XcSTLqyqC9OfbcEgWZCX7p0qSvxjhUFSLVd7J+qVataSkqKrV27NuJ+3VYAOhrdX5D1ZdCgQW5yKF92drZt3LjRqlSpQn8nQPi3HCUNxyxKGo5ZlDQcsyX/PK9EB0k1DlidOnWKuxmIQv8g8I8CkDf+ToInCBmk4RTQJKgZHAoyt27d2mbMmOFmqPcDmLrdt2/fqM9p166de/zGG28M3Tdt2jR3f27S0tLcEq5SpUox2w/EFv+Wo6ThmEVJwzGLkoZjtuSe55XoICkAAEBRUoZnz549rU2bNnbsscfa6NGjbfv27W62e+nRo4fVrl3blczLDTfcYCeffLI99NBD1rVrV3v55Zdt7ty59uSTTxbzngAAAAAIR5AUAAAgny688EJbv369DRkyxE2+1KpVK5s6dWpocqbly5e7Shff8ccfbxMnTrTBgwfbbbfdZocddpi98cYb1qxZs2LcCwAAAAA5ESRFTKk8cOjQoXuVCQL4E38nQMmm0vrcyus//PDDve674IIL3IIDC/+Wo6ThmEVJwzGLkoZjtuRL8jSCKQAAAAAAAAAkqD/rwQAAAAAAAAAgAREkBQAAAAAAAJDQCJICAAAAQAJISkpyk8fFel0AQOGE/5u7bNkyd3vBggXF3ayEQ5AUMTVmzBirX7++paenW9u2bW3OnDnF3SQgUGbOnGlnnXWW1apVi5MPAIih2bNnW0pKinXt2rW4mwLky2WXXeb6AlpSU1OtUaNGNnz4cNuzZ0/cXnP16tX2f//3fzFfF4l3zIYvixcv3u9+blZWlt17773WpEkTK1OmjB100EHuPPLpp58ugj0C9j62S5cubQ0aNLBbbrnFdu7cWdxNQxEjSIqYmTRpkvXv39/N5jZ//nxr2bKldenSxdatW1fcTQMCY/v27e5vQxcUAACxM27cOLvuuuvcSfqqVauKrR2ZmZnF9tooeU4//XQXjPzxxx/tpptusjvvvNMeeOCBuB1XNWrUyPesywVZF4l3zIYvCijtbz932LBh9vDDD9uIESPsu+++sw8++MCuvPJK27RpU9z2gX+nkdexvWTJEndMPvHEEy62gcRCkBQxM2rUKOvTp4/16tXLmjZtamPHjrWMjAwbP358cTcNCAxlZNx11112zjnnFHdTAOCAsW3bNnex9uqrr3aZpBMmTIh4fMqUKXbMMce4SpeqVatG/Bu8a9cuGzhwoNWtW9cFhJTNp4CraDuVKlWK2JYyo5Rp4lNQq1WrVi7rSYECvYZMnTrVTjzxRPf8KlWq2Jlnnmk//fRTxLZ++eUXu/jii13mVNmyZa1Nmzb23//+15XZJScn29y5cyPWHz16tNWrV8+ys7Nj+O6hOOmYUzBSn6uO306dOtmbb77pspq6detmd999t8vKa9y4sVt/xYoV9te//tUdVzpuzj77bHe8hFPf+8gjj3TbrlmzpvXt2zf0WHh2nwJFekzr6LhVG0aOHBl1Xfn666/tlFNOcdl+OqYVyNLfns9v84MPPui2qXWuvfZa2717d1zfQxTPMRu+KIt/f/u5Ot6vueYau+CCC9y/oQqy9u7d226++ebQOvo37/7773f/Puv1DznkEPe3UdBjc3/+npB4x7b6Azpe9O/xtGnTQseg/n3UMarjTMfpa6+9FvH8b7/91n3XV6hQwcqXL28nnXRS6Hv/888/t9NOO831QSpWrGgnn3yySyxD8BAkRUyokzVv3jz3D4lPnXvdVvkbAABAvLzyyiuuVFMnvpdccokLEnme5x57++233Qn7GWecYV988YXNmDHDjj322NBze/ToYS+99JL94x//sO+//95ljpQrV65Ar69S03/961/2+uuvh8YPU0aVKmwU6NRrql+kdvgBTp3A6yRp5cqVLkjw5ZdfutI+Pa6hi9SHeuaZZyJeR7d1sq9t4cCkk28/y03HzaJFi9xJ+ltvveWCjarS0sn3xx9/bJ9++qk7VpX95D/n8ccfd4FJBYkUONKxpcBSNDrm9bj+fvQ6L774ojv2otHxrNeuXLmyO9l/9dVXbfr06REBWFEWoIIC+vnss8+6Cw05L1oA4RSUev/99239+vW5rjNo0CBXkn/HHXe4bNOJEyda9erVC3Rs7s/fExLXN998Y7NmzXJDoYgCpM8995xLBFMwtF+/fq6/8dFHH7nH9V3evn17F2jV8azYyOWXXx4aPmXr1q3Ws2dP++STT+yzzz6zww47zPVLdD8CxgNiYOXKlToT8WbNmhVx/4ABA7xjjz222NoFBJn+ZiZPnlzczQCAEu/444/3Ro8e7X7fvXu3V7VqVe+DDz5wt9u1a+d179496vMWLVrk/i2eNm1a1MefeeYZr2LFihH36d/t8C700KFDvdKlS3vr1q3Ls43r1693z/v666/d7SeeeMIrX7689+uvv0Zdf9KkSV7lypW9nTt3utvz5s3zkpKSvKVLl+b5Oig5evbs6Z199tnu9+zsbHccpqWleTfffLN7rHr16t6uXbtC6z///PNe48aN3bo+PV6mTBnv3Xffdbdr1arl3X777fnqe1x33XXeKaecErG93NZ98skn3fG4bdu20ONvv/22l5yc7K1Zsya0P/Xq1fP27NkTWueCCy7wLrzwwv1+jxAs+oxTUlK8smXLhpbzzz+/UP3cb7/91jviiCPcsdS8eXPv73//u/fOO++EHt+yZYv7u3jqqaeiPj+/x+b+/D0hMY9tHW86fnUMvfbaa+57OCMjY69YR+/evb2LL77Y/T5o0CCvQYMGXmZmZr5eLysry/UBpkyZEvVvRt/1uv3FF1/EdD+xb1yGBgAAQImlzCBNFKmydSlVqpRdeOGFoZJ5ZXaeeuqpUZ+rx1QmqozOwlCZ8sEHHxxxn8aYVJsaNmzoSu/8DL3ly5eHXvuoo45yJZ7RqNRPbZs8ebK7rWy8jh075prph5JJGW3KXlO5u0qVdexqCAdp3rx5KItJlG2srGVlvuk5WnT8aGIRZW9qHgCNx5vb8Z6TspJ1HCoD+/rrr7f33nsv13WVZa3yUg0L4TvhhBNc5rP+Bn0q8/dLr0Vl98xPcGDRv0M6bvxFGcmFoWHalLWn7Dpl3ul40eRPV1xxRejY07AouR3X+T02C/r3hMQ9tjXsjbI+NYzgeeed546THTt2uHJ5/1jRosxS/1jR81Rer0mfolm7dq0bmlAZpCq3V79AFSV+nwDBUaq4G4ADg8bWUIdIf/zhdFslFAAAAPGgYKjK2TTOnE8JGSp5++c//+nKl3OT12Oisna/bN8XbXzF8JNzn07yFTx96qmnXNt0wt6sWbNQGee+Xlsn8xoKQCX25557risvfeSRR/J8DkrmSblK5PV56zhRkD+340on1K1bt3Zl8TkpSF/QYRiOPvpoW7p0qf3nP/9x5ckam1HDPOQcZ68gcgYINK4pY+geWHRc5jaEw/7Ssatxo7XceOON9sILL9ill15qt99++z7/rcyvgv49IbGPbQ3bo+C7+hj67vaH76ldu3bEc/zJ7fZ1nCro+uuvv7rvcfUN9Lx27doxtEMAkUmKmFDHTl8yGuvFpw6RbuuPHwAAINYUHFUmx0MPPRSR2aQMIQWcNNZoixYtIvon4ZRZpP6KP6ZYtBNljRemMe98/pijedGJkDKYBg8e7LKfjjjiCPvtt98i1lG7tK2NGzfmuh1lUil49dhjj7l9VbAUB+ZJuSaiCQ+Q5hbUVIZytWrV3HPCF2UmKSNOmca5He/RKJtJ2asK5mvyM42tG+2Y1DGsv6vwvwWN4ajglj8JDhAryi4VHW/KvFMAKrfjen+PzX39PSGx6fi57bbb3Pe4jkcFNZX1mfNY0SRP/ne6xrbNbaI6HZPK2Nc4pP7Eehs2bCjivUJ+ECRFzGhyAnWwNEi7yh40Q6e+rJSmDuDPq9b+Sbwog0O/U2oBAPtXqqzgo2ZCVqZH+KISOWWADB061AVL9VP9E01mc99997nnK6Ck7A6VeGoWb/2b/OGHH7qJbKRt27aWkZHhTpRUUqdszvxMQqMJRDTD8pNPPunK9DSJg/pJ4VSKr2obldXr5GnJkiUuQBU+4aVO/o877jgbOHCgWz9WGVUombp37+6qtzQDt07G/eNVJ96//PKLW0el+rpooBJoBYA0e/Kjjz4adXujRo1yfxsLFy60H374wU14o2NSM31He20NCaC/F5VGa2Km6667zmX7+RPoAPvTzz3//PPt4YcfdiXOP//8szumNfnY4Ycf7ibk03GnfwM1sZ1f3qzSfH9Ilf09NvPz94TEdsEFF7hqWU3oePPNN7vJmhTr0DHo/9uq26KJwrZs2WIXXXSRm7BR//4+//zzoSEfFOzXbfVDdKzr+OM7PZgIkiJmdBX6wQcftCFDhlirVq3cF+LUqVPpOAFh9KWpMei0iE6a9bv+bgAABaOTZJUHR8v6UZBU/+ZqjDkFfzSLt/onp5xyihvD1KdSZ52kX3PNNe6EXGOG+RlJeq7KPt955x2XdaqAkj9e5L4yUF5++WU3u60CtjqxeuCBB/aqwtEYkMpiUmaJtq/Zm8PHcxQFgFWOp0AuEpsC9jNnznRZp8oqVhBdx4fGUFRGqChQNHr0aJd9rGylM888052sR6PM0/vvv9/atGnjypyXLVvmjvVoZft67XfffddlmWpd/c0oS1pDWgCF6edqhvkpU6a4IUoUGNUxrH+L9e+jn12tWe1vuukmtx0d9zrv9Me63d9jMz9/T0hsOv4U/NS/k4MGDXLHoWa517Fy+umnu/L7Bg0auHV1YVQXRHWhQOOcq8pWCWT+ECTqr+iirjKYFcBXMF7f/wieJM3eVNyNAAAAALC3ESNGuCDvV199VdxNAQAAOKCRSQoAAAAEjLJRVDqqbCiVjgIAACC+CJICAAAAAaMSP5XrdejQgVJ7AACAIkC5PQAAAAAAAICERiYpAAAAAAAAgIRGkBQAAAAAAABAQiNICgAAAAAAACChESQFAAAAAAAAkNAIkgIAAAAAAABIaARJgYC47LLLrFu3bqHbHTp0sBtvvLHI2/Hhhx9aUlKSbdq0Kdd19Pgbb7yR723eeeed1qpVq0K1a9myZe51FyxYUKjtAAAAAAAA5ESQFNhH4FKBOS2pqanWqFEjGz58uO3Zsyfur/3666/biBEjYhbYBAAAAAAAQHSlcrkfwP+cfvrp9swzz9iuXbvsnXfesWuvvdZKly5tgwYN2mvdzMxMF0yNhYMOOigm2wEAAAAAAEDeyCQF9iEtLc1q1Khh9erVs6uvvto6depkb775ZkSJ/N133221atWyxo0bu/tXrFhhf/3rX61SpUou2Hn22We7cnFfVlaW9e/f3z1epUoVu+WWW8zzvIjXzVluryDtwIEDrW7duq5NymodN26c227Hjh3dOpUrV3YZpWqXZGdn28iRI61BgwZWpkwZa9mypb322msRr6PA7+GHH+4e13bC25lfape2kZGRYQ0bNrQ77rjDdu/evdd6TzzxhGu/1tP7s3nz5ojHn376aTviiCMsPT3dmjRpYo899liB2wIAAAAAAFBQBEmBAlIwURmjvhkzZtiiRYts2rRp9tZbb7ngYJcuXax8+fL28ccf26effmrlypVzGan+8x566CGbMGGCjR8/3j755BPbuHGjTZ48Oc/X7dGjh7300kv2j3/8w77//nsXcNR2FXT817/+5dZRO1avXm2PPPKIu60A6XPPPWdjx461b7/91vr162eXXHKJffTRR6Fg7rnnnmtnnXWWG+vziiuusFtvvbXA74n2Vfvz3Xffudd+6qmn7OGHH45YZ/HixfbKK6/YlClTbOrUqfbFF1/YNddcE3r8xRdftCFDhriAs/bvnnvuccHWZ599tsDtAQAAAAAAKAjK7YF8UqanAqLvvvuuXXfddaH7y5Yt6zIg/TL7F154wWVw6j5ldYrK9ZU1qrFDO3fubKNHj3bl+gpQioKY2m5ufvjhBxdgVCBWmayijM2cpfnVqlVzr+NnnirQOH36dGvXrl3oOQrKKsB68skn2+OPP26HHnqoC9qKMmG//vpru++++wr03gwePDj0e/369e3mm2+2l19+2WXI+nbu3OkCtrVr13a3H330Uevatat7bWXqDh061P3uvyfKflXQVW3t2bNngdoDAAAAAABQEARJgX1QdqgyNpUhquDn3/72Nzdbu6958+YR45B++eWXLmtS2ZXhFCT86aefXIm5sj3btm0beqxUqVLWpk2bvUrufcryTElJcYHN/FIbduzYYaeddlrE/cpmPeqoo9zvytgMb4f4AdWCmDRpkstw1f5t27bNTWxVoUKFiHUOOeSQUIDUfx29n8p+1Xul5/bu3dv69OkTWkfbqVixYoHbAwAAAAAAUBAESYF90DidyrhUIFTjjiqgGU6ZpOEUJGzdurUrH8/p4IMP3u8S/4JSO+Ttt9+OCE6KxjSNldmzZ1v37t1t2LBhbpgBBTWVRepnpxakrSrTzxm0VXAYAAAAAAAgngiSAvugIKgmScqvo48+2mVWqvQ9Zzalr2bNmvbf//7X2rdvH8qYnDdvnntuNMpWVdalxhL1y+3D+ZmsmhDK17RpUxcMXb58ea4ZqJokyZ+EyvfZZ59ZQcyaNctNanX77beH7vv555/3Wk/tWLVqlQs0+6+TnJzsSvyrV6/u7l+yZIkLuAIAAAAAABQlJm4CYkxBvqpVq7oZ7TVx09KlS91YpNdff7398ssvbp0bbrjB7r33XnvjjTds4cKFbgKjTZs25bpNjfOpcTkvv/xy9xx/mxqnVBSk1PinGhpg/fr1LjNTJewaG1STNWnyI5Wzz58/340F6k+GdNVVV9mPP/5oAwYMcGXvEydOdBMwFcRhhx3mAqDKHtVrqOw+2iRUmrFe+6DhCPS+6P3QDPcaj1SUiaqJpvR8jcGqsVE1luuoUaMK1B4AAAAAAICCIkgKxFhGRobNnDnTjcGpSYiUramxNjUmqZ9ZetNNN9mll17qgoYam1MBzXPOOSfP7ark//zzz3cB1SZNmrixO7dv3+4eUzm9goyamV5ZmX379nX3jxgxws0Qr+Cj2nH66ae78ntNiiRq47/+9S8XeG3ZsqWbQEqTPRXEX/7yFxeI1Wu2atXKZZbqNXNSNq7ejzPOOMNNXtWiRQt77LHHQo9fccUVbrIrBUaVOavsVwVs/bYCAAAAAADES5KX20wxAAAAAAAAAJAAyCQFAAAAAAAAkNAIkgIAAAAAAABIaARJAQAAAAAAACQ0gqQAAAAAAAAAEhpBUgAAAAAAAAAJjSApAAAAAAAAgIRGkBQAAAAAAABAQiNICgAAAAAAACChESQFAAAAAAAAkNAIkgIAAAAAAABIaARJAQAAAAAAACQ0gqQAAAAAAAAALJH9P2HevlZkEQLVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(nb, 'Naive Bayes', X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM | Acc: 0.52% | Precision: 0.56% | F1 Score: 0.63% | Recall: 0.72%\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(svm, 'SVM', X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost | Acc: 0.53% | Precision: 0.58% | F1 Score: 0.59% | Recall: 0.60%\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABUkAAAHqCAYAAAA5289qAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAeOxJREFUeJzt3Qd4FFXXwPGTBBIIhNCkV0Up0hQUAUVQBAQVFLsCIqAioIAiokhV8UWlqAgWmq8i2FAp0gULINL86CqgIB0RQk1Cdr7nXN5Zd5NNI7PJhP3/fMZkZ2ZnZ2Yn7J2z59wbZlmWJQAAAAAAAAAQosJzegcAAAAAAAAAICcRJAUAAAAAAAAQ0giSAgAAAAAAAAhpBEkBAAAAAAAAhDSCpAAAAAAAAABCGkFSAAAAAAAAACGNICkAAAAAAACAkEaQFAAAAAAAAEBII0gKAAAAAAAAIKQRJIXr/Pbbb9KiRQuJjY2VsLAw+fLLLx3d/h9//GG2O2XKFEe3m5s1bdrUTNnt7Nmz8swzz0j58uUlPDxc2rVrl+37AAAAAGTEQw89JJUqVcqx19fX1n3AOXo/p/d1en8HAE4gSIqAtm/fLo8++qhcfPHFki9fPilUqJA0btxYxo4dK6dPnw7qa3fq1Ek2bNggL730kvz3v/+V+vXry4VCGzX6Qa7nM9B51ACxLtfptddey/T29+7dK0OGDJH169dLbjBp0iR59dVX5c4775SpU6dKnz59Aq538OBBKVq0qNxwww0pliUmJkqtWrVMo/HkyZN+y3bu3Ck9e/aUyy67TKKjo81Uo0YN6dGjh/zf//2f37p63uxzr5MGbUuXLi233HKLrFy5UnJabntvAQBAxjz44IOmvf3rr7+mWPbKK6+Ydsns2bP95sfHx8ubb74p1157rRQpUkQiIyOlTJkyctttt8nHH38sSUlJKRIEfCdti9atW1feeustv3Vzyttvv52pBIbkx+M7PfbYY5KbLV++3LT5jh49Km4LRur0ww8/pFhuWZZJetDl2nbOjmsAAIIhT1C2ilxtzpw5ctddd0lUVJR07NhRatasKQkJCeYDsV+/frJp0yZ59913g/LaGjhcsWKFPP/88ya4FQwVK1Y0r5M3b17JCXny5JFTp07JrFmz5O677/Zb9tFHH5lG8pkzZ847kDZ06FATMNSGb0YtWLBAcsKSJUukbNmyMnr06DTXK1GihPznP/+RRx55xARTNZBue/3112Xjxo3mfBYoUMA7X28m7rnnHnO+H3jgAalTp44JfG7dulW++OILGT9+vAmi6vXgS+cXLFhQPB6P7N69W9577z1p0qSJrFq1KlPn1Gnn+94CAAB3GzVqlMydO9cE97RtZNN2yrBhw6R9+/Z+gadDhw7JzTffLGvWrJGWLVvKwIEDzZfJ+/fvl0WLFsn9998vv//+u7zwwgt+r3PfffdJ69atze/Hjh0zr9mrVy/5888/zZfWOUkDZMWLF89UluRNN91k7lWS0y/Hc3uQVNt8ei4KFy7st2zbtm2mPZtT9D5l2rRpJjjva9myZfLXX3+Z+8fsvAY6dOgg9957b5ZeFwB8ESSFH22M6QeNBo60kaaZdDbNvtMGlwZRg0UbfSp5g8BJ+g2nfsDnFP0Q16xc/ZY/eZBUGx1t2rSRzz//PFv2RYO1ml2p2Qc5QTNEM/ped+3aVT744AN5+umnzY1CsWLFvDcPd9xxh9/Ng2ZC29fx4sWL/a5jpQFXbYgFamRqVqs20GzaBYB+UfDpp58SnAQAAI5L7cvgxx9/3Hypr5VcyQND69atM+1FbQP5GjBggKxevdoE05K78sorTdaqTbffoEED0/7M6SDp+dBgqO/xhIKcDgZqkF3bxG+88YZJRLDpNVSvXj05fPhwtuyHVo9pckRERISZAMAplNvDz8iRI+XEiRMyceLEFIElVaVKFXnyySf9+pQcPny4XHLJJeZDW7PcnnvuOVMC5EvnaxBLs1GvvvpqE6TUUn4Netm0rMTO6tOMVQ1m2n3+pNb/j10i7WvhwoXm200NvmlGYNWqVc0+pdcnqQaFr7vuOvOBq89t27atbNmyJeDrabDY/nZX+07t3LmzCThmlH7D/8033/iV0fz888+m3F6XJXfkyBETHNSycj0mLZHSDIJffvnFu87SpUvlqquuMr/r/tglMfZxap+jGuzTrAPNjNTgqH1ekvdJqo1zfY+SH79mK2hJl2Y1ptdweeqpp0zZjV4X+h5o9wFaiuP7Hnz77bcmM9neVz2G1OjyCRMmmMwHPRd2414baNpQS34d6z5Mnjw54HWsz3niiSfM/qWnVKlS3uckD/B26dJFSpYsac6VZqrqjU1mz0VGrtv03lsAAJC76ZfB+iW6tnH+/vtvmT59usybN09efPFFU3Vj04qr+fPnm4Bq8gCpTbuq0iqa9GhbQtsxyds4Sr9Mvvzyy03bRcv4NVkiUPm3Bsw0OJY/f37zJbMGLffs2eO3jma4avulXLlyZnvaNtN2tt2PpLbxtT2o2Yh2G8eJvvK1Kk3bVIHa6JpVq208u6uBr776yiQq6LHqPuq9jd7jpNcVgbbRArVhA91vaFdPev9gd2emr//www+b99v3XkPvg1TlypW958P3XCXPtNyxY4epAtRsYm3fX3PNNSmSWuz9/OSTT0yXZvpe6D7ceOON5r4mo/S86f5qu9WmFYefffZZwHsYpdVZY8aMMdeTvqZec9qt2z///ONdJ61rwC7112Xa9tcvFXT/0+qTVO+zrr/+eomJiTH3TdqO1kCuTe+5NENb3wPdJ92eJljofQaA0EYmKfxoybJ+cDdq1CjDDToNDGn2nQaCfvrpJxkxYoQJrs2cOdNvXf0A1vU0sKRBOO2PUj/ktWGlH5ra0NMAkfZLaZcDacMmM/TDVYOxtWvXNhmG2sjR1/3xxx/TfJ6WJmnQUY9dGydajq/9PGljde3atSkCtJoBqg0XPVZd/v7773uzADJCj1VLqrTsWxtHSj+4q1WrZr7lT04bPzqAlTaA9HUPHDgg77zzjvnw37x5s2nQVa9e3RzzoEGDTMNZA77K973URo0epzYCtBGrjZRANGNBg8b6PmljXL+h1dfTsnztJ1ZfLzUa/NP+sDQAqu+1Zl9qY14bfNpo1tL6iy66yGxHG2kalNfzqPQY0qLXid486Pra6NGbB91X35sHu9ReA/qaHZFZGpC2G3S6v9pA1saTb9avXh/acNNrSxvg+p7oTYJez3oDYX+RkJFzkZHrNiPvLQAAyL000KNtrSuuuEK6d+8u33//vQl2anAyeVtdnU8GpQYL7Uy/uLg4E0jStpRmn/rStrCWezdv3tzsi2alandE+oW+tk3sLqs0QKXBTw1AadtM26faLtN1NNPVrhbSYJS2dbS0X9vU+kWzBtl27dplHmsATZdpu1+73FKptVF9afdUgTIXNSimVVLa7dK4ceO8XYn5ngc9j9pus7MQ9Vj09fv27Wt+ajtY2116npzKstVj1ja9njMNztldmOlP7f9erwG9R9C+abXiTNuJdnWTtp0D0XOu7UE9Jk0A0EorvTfT9qcGLm+//fYUfdxqJZW2pzUgqIkFGlDXe7iM0PerYcOGZv/0nkLpdaTb0vuL5IkLSgOi9rWi+6iVYNoXrl4j9vWUkWtAA6R6HvR9ST4OgS99Lb2/0vsGvbb1OtTX0mtdA7ka1NXED03q0dfU90Lb5Xr/oO14TYABEMIs4H+OHTumqW1W27ZtM7T++vXrzfpdu3b1m//000+b+UuWLPHOq1ixopn33XffeecdPHjQioqKsp566invvJ07d5r1Xn31Vb9tdurUyWwjucGDB5v1baNHjzaPDx06lOp+268xefJk77y6detaJUqUsP7++2/vvF9++cUKDw+3OnbsmOL1Hn74Yb9t3n777VaxYsVSfU3f4yhQoID5/c4777RuvPFG83tSUpJVqlQpa+jQoQHPwZkzZ8w6yY9Dz9+wYcO8837++ecUx2a7/vrrzbIJEyYEXKaTr/nz55v1X3zxRWvHjh1WwYIFrXbt2qV7jF9++aX3eb70eMPCwqzff//d73Uvv/xyKzNOnTplXXzxxeY16tWrZ509ezbgdRxoX//55x9zbdiTbiv5e5t8Kly4sDVv3jy/7YwZM8Ys+/DDD73zEhISrIYNG5rzFBcXl6lzkZHrNq33FgAAXBgGDBhgPu8jIiKsNWvWpFiubU5dfvToUb/5p0+f9mvjaJvHZrctA03du3e3PB6PX/s8MjLSatGihV/b86233jLrT5o0ydvu0bZzzZo1zWvbZs+ebdYbNGiQeaz7Eahtn5y2B5O3RdOS2vHo9PHHH5t19LjKli1rtW/f3u+5n3zySYr7Et82oe3RRx+1oqOjTTs8tXuSb7/91mxLf6Z3vxHoNXRfk++Lniudp9tITl9b98HWu3dvs+7333/vnXf8+HGrcuXKVqVKlbzvob2f1atXt+Lj473rjh071szfsGGDlRY9Dl1P26N6LcTExHiP56677rKaNWvm3b82bdp4n6f7pc/76KOP/Lanbevk81O7BuzXvvbaa1O0++1l9rnSvwvdtwYNGvhdl8q+ztetW2ee8+mnn6Z5zABCE+X28NJvSpVm6GWEdvau9BtXX5pRqpKXeeio4nYGnNJvArWkWL9RdYr9jbWWzGgmYEbs27fPjBiu3yZrmYpNs/q0Q3j7OH0lHzVTj0uzNO1zmBH6TaaWvmgJkn5brT9TK1PRzEK7/0wt+9HXskuyNZM1o3Q7+i1uRrRo0cJ882v3+anZlJrhkB49X/qtvH5TnPy60DatftucFZoZYH/DqyVCyfshst+DQFnImv2p1509aXZBctq/l37Tr1mzWq6v/V1pBoR2ou97jPqts2Y82/RbcD1mzYzVcqDMnIvzuW4BAMCFx84c1Kod7SYpudTaOdolkW8bJ/nAOkqrUbSNo5O2dzRLVdt2vm15ra7STLvevXv79d3erVs3k6Fpt++131PNCNXsPt++/rVkXSuj7PW0DF/bbtrm9S2vdoKW7NvH4zs1a9bMLNfMTM0g1faYts9sM2bMMFVIvudI99N2/Phxk6Gq7XvN0NRBP53g+xp2FqyWxqvMtOd96bFpV2a+x6LXhr7XWoKuFWe+9D7AdywC+94sM/djWl2lVVWaeannSn+mdg+jlVbabtd7Kj1ee9JKQt1PrbbKKL0G0+t/VN9/3adnn302xRgUdhdt9n2EVndlprs0AKGBICm8tOGj9IMlI3QkTG08aVmzLw0eadBHl/uqUKFCim1o/5ZONpi0rEZL5LUbAC3R0LIP7XsnrcCTvZ8acExOy5z1gzx5SUfyY9HjUJk5Fu1OQAPS2lDTUe21VCn5ubTp/mvJzaWXXmoCndqA1gaw9m2Umb5ztEGYmUGatO9MDRxrEFnLZ7RLgfTo+dSGffJgu11Kn/y6yCwt49KSGb1x0H1K3o+S/bq+jWGb3gho4+nDDz9MdfvaX6uWl2ljTgPnOvCTblPLcXyPUd+L5AM/JT/GjJ6L87luAQDAhWX37t0yePBg08bR37UUOrnU2jn6ha4dJNQv+gPRtou2cXTSL8C15FmDnFrqvGHDhjTbxdp+1G6pfNs4gdZTGiS1l2u7Vbuj0i+GtY2j7Sw9Lk0OyCrtR9I+Ht/Jt0xb21ga0Pv666+9500Dixo89R3XQEvetTRdA2h6T6TtbLtLA6f6qdQunbRLJt0/DZjqa2iXTVl5DT3Pqd3D2MudvofR/dbzrF2FaddhmsChXaoFon1/6rHpPYRvEF8nfS800J5R9rlKiw7eqgJ9weC7Hf1iQLtL03sqLb3XxAn6IwWgCJLCSxsEGtDZuHFjpp6XfOCk1KT2zV/yAWwy8xrJO1PXBsd3331nvgXXkT81iKiNIw14pdfxemZk5Vhs2mjUBqr2G6T9t6b2Dax6+eWXzYe5Niw1wKfffGojWPvayUwgzfcb7IzQYKTdeLEbz264edAR5zXTUxvsyfvq0satDggQ6DrWPkq1UacByYzSb7n1efoNf1r9H2VFdl23AADAvbSfc6UBRQ3iad/tyTP8NACpkrdzdIBIO0hoB74yQqtylLZDgkWzUrWfTe23VLP7XnjhBRPE03ZmsGmmpvajqV8+K+2LVIOm2s6yaT+U2s+/DoiqFVS6jraz7bEG0mprZ/Qexc7AfO+997zjEmhbVvvJTO81nOTEPYzvILSawax9k9pVUcnpcWmANFDGr056voN1H5OW119/3bS3dZBUvR606kvvq/766y/HXgNA7kSQFH508Bj9Bk4H60mPjkSvH3z6DWHyDsS1sWGPVO8EbewFGlEzUFaiZvdpg2/UqFGmxEQbmFrOnlo5h72f2il9clpeo98w6oj3waANDG0gavauZg+mRjte19KhiRMnmvW0FF4bwcnPSUYD1hmhAUEtydFuErRkR7/11w7706Pnc+/evSkyku1SpaxcF/bNg2aQaiBU31ttYOoIsL601EszTFetWiVOOHv2rF/Whh6DXvfJG7TJjzEz5yK969bJ9xYAALiLfmGu2Y46YKRmSGp2Z6Avg7WtrrQKKVhtnEDtYi3B1wF3fNs4gdaz5yVv7+lo8drdkLbbNMCr29NAlS2Y7RwNTmowUrsq0AouDZraZe5KuwLQrqx0wB/N9NRznNFgs71O8jZ58nsUzdTU6iQtA9dBsTRrVb8M1+zc5DJzLvQ8p3YPYy8PBt1/bbvqgFNpJXro+67nVhMUAmX91qlTx9FrQF9PZSTpp1atWjJw4EDzBYEOlKaDN2nQF0BoI0gKP88884wJCGrZrwY7k9MAqpY72+XiShtxvjTIYweqnKIfeFoCod/4+fYlqg3KQCOT+9IRxZWOYBiIBtt0Hc3o9G3g6IerNuTs4wwGDXxqY1jLnbSbgrS+9U3+Da/28aMf5r7sYG6ggHJm9e/f34w6qudF31NtUOpo96mdR5ueL/32XI/Jl3YXoI0feyTM87150G+cNVtCaYmY9mmkWba+/cHqdRwdHW1Gtgx0HWfm23K9prQ/Un1/7O4G9Bi1TEwb2r43GW+++abJPNVshMyci4xct06+twAAwD30y1TNZNOR7e3ufbS6S9uIGtzTNp9Ng00aXNNR0bUv80Ay087RrEllB6s0cKXBWf1C2nc7+kW9tsXt9n39+vVNu0iDSr5tQ80u3LJli3c97fNR+99M3q7XbgN8n6ftnGC1cTRrVF9L27R6PjVoGii70vd4NYj79ttvp7ttDULq85Nn4iZ/bqDXCHQfldk2n7Y1NSnAN8FFEx30+tC2uyY7BIO2d8ePHy9DhgyRW2+9NdX19FxrW1iv5eS07ex7jE5cA5pIoteWZi0nv+7sc6/3DPaXA74BUw36pnefA+DClyendwDuoo0W7V9GGxNaBtOxY0fTp4s2FDRQpI007afRbkxp0Ew/hO0yFf2Q1gaIlkPbnaY7QbMnNWin31pqI1IbXPrBrIPq+HZ0rgE0baRow0wbLVoqro0U/UY+UAf2tldffdUErBo2bChdunQxZRca8NLSbf3wDxb9MNZvMNOj32jrsWlmZ6NGjUzpu2YQJP/2Wd8/LXfRBqs2ELSxoaXiGenDx5dmMOp509L2K6+80szTQYx04CMtkQrUR5ZNG0r63j///POmw3i9TjTYrA15Lbeyv+E935sH30GQ9Pzpseox6uvpe2b3uaXXsQ6spP00PfDAA2Y/tHGkWRC6TJ+r10WgrF1t+Om6mgWqNwX67b++jv0Nt2bWav+m+rewZs0a0wjV5/3444+msWv3F5bRc5GR69ap9xYAALiLtgW1zaEl2L7l0JpFqu1qbTO0atXK277Qrpf0sba3tf1qZz3qF7jadY+2KQJ9Ka1tZrtfdm1baWajDuCkbUsNLintK3LAgAEm21Ff47bbbjOZitou0f7z7X46dcBKLUfXtqneA2ibS7+Y1mQKbRf16dPHrKdl9lopo8EyDdjlyZPHfPGt6/pWUemX3tq2f/HFF00f/RqAveGGG9I8b7rtQP3Ma5+fGki2aVtWt6ntMQ2C+ZbaKz1+PX96X6PtTG3v/fe//81QsFnvFbRrBG2D6vO0vaYDGSXva1O7NbP7Y01MTDTjBGibUNulyem5ULq/eo70XGubMlBlm2amfvzxx+b91n3XsQT0mtHt6nubvP98J+n5So9eGzoQrAYtdYwDvc70eLQiS+8r9Xqx+zM9n2sgOT3PmoygCT96vWqWq7632pWC3j/qudH7HK1O0/dN7yU1YKrvt/7tad++AEJc6gPfI5T9+uuvVrdu3axKlSpZkZGRVkxMjNW4cWPrzTfftM6cOeNdLzEx0Ro6dKhVuXJlK2/evFb58uWtAQMG+K2jKlasaLVp0ybF61x//fVmsu3cuVNbI9arr76aYt0FCxZYNWvWNPtTtWpV68MPP7QGDx5s1rctXrzYatu2rVWmTBmznv687777zPEkf43Jkyf7bX/RokXmGPPnz28VKlTIuvXWW63Nmzf7rWO/3qFDh/zm67Z0vm47LZ06dbIKFCiQ5jqBzoGez6eeesoqXbq02T/dzxUrVqQ4f+qrr76yatSoYeXJk8fvOHW9yy+/POBr+m4nLi7OvF9XXnmleX999enTxwoPDzevnZbjx4+bdfX863Vx6aWXmuPxeDwpXje1ffL15JNPmtddtWpVwOU9e/Y0y1evXu03//fff7e6d+9uValSxcqXL585d9WqVbMee+wxa/369QHfW99J36uGDRtan3zySYrXPHDggNW5c2erePHi5lqrVatWimsqo+ciI9dtWu8tAADInbTtEhERYdoygWjbR9s4TzzxhN/806dPW2PGjDHtFG23atugVKlS1i233GJ99NFH1tmzZ1O0LX0nXf/iiy+2+vXrZ9oqyb311lumzaRtl5IlS5r21D///JNivRkzZlhXXHGFFRUVZRUtWtR64IEHrL/++su7/PDhw1aPHj3MtrRdFRsbazVo0CBF22r//v3mXkHvOXT/krdvk0t+PL5ToOc+//zzZpm2CQP58ccfrWuuuca0FbUd9swzz1jz5883z/n222/92vLaTval9wXt27e3oqOjrSJFiliPPvqotXHjxhRtNT0vt99+u1W4cGFzHu666y5r7969Zj1th/oaPny4VbZsWfPe+95j6GvrPvjavn27deedd5rtanv36quvtmbPnu23jh6DbufTTz/1m5/afVFy9r3Ozz//nOZ6qd3zvfvuu1a9evXM+dX3WNvNeo71+NO7BtJ67dTuwb7++murUaNG3vs6PScff/yxWbZjxw7r4Ycfti655BJzvvS6bdasmbkXBIAw/V9OB2oBAAAAAAAAIKfQJykAAAAAAACAkEaQFAAAAAAAAEBII0gKAAAAAAAAIKQRJAUAAMggHTVaRxkuU6aMGcn4yy+/TPc5S5cuNaMrR0VFmRF7p0yZki37CgAAACDjCJICAABk0MmTJ6VOnToybty4DK2/c+dOadOmjTRr1kzWr18vvXv3lq5du8r8+fODvq8AAAAAMo7R7QEAAM6DZpLOnDlT2rVrl+o6/fv3lzlz5sjGjRu98+699145evSozJs3L5v2FAAAAEB68kgu5vF4ZO/evRITE2NuVAAAOF/6neHx48dNGXV4uDsKLc6cOSMJCQmObS8yMlLy5cvn2PaQvhUrVkjz5s395rVs2dJklKYmPj7eTL7tnSNHjkixYsVo7wAAAABBus/L1UFSDZCWL18+p3cDAHAB2b17t5QrV84VAdLKFQvK/oNJjm2zVKlSpvybQGn22b9/v5QsWdJvnj6Oi4uT06dPS/78+VM8Z8SIETJ06NBs3EsAAADgwpaR+7xcHSTVDFJ1rbSWPJI3p3cHcK0DjzfI6V0AXC8p4Yz8+v4w72dLTtMMUg2Q/rmmkhSKyXpma9xxj1Ss94fZLkFSdxswYID07dvX+/jYsWNSoUIF07ArVKhQju4bAAAAkJtocoImWGbkPi9XB0ntkjMNkOYJI0gKpCYiioAIkFFuK2cuGBNmpqzyiLuOK1Ro9u6BAwf85uljDXYGyiJVUVFRZkpOn0OQFAAAAAjOfV6uDpICAHChS7I8kmQ5sx1kv4YNG8rcuXP95i1cuNDMBwAAAOAe7hiZAgAAIBc4ceKErF+/3kxK+3jV33ft2uUtle/YsaN3/ccee0x27NghzzzzjGzdulXefvtt+eSTT6RPnz45dgwAAAAAUiKTFAAAF/OIZSYntoOsW716tTRr1sz72O47tFOnTjJlyhTZt2+fN2CqKleuLHPmzDFB0bFjx5rO4t9//30zwj0AAAAA9yBICgAAkEFNmzYVy0o94KyB0kDPWbduXZD3DAAAAEBWECQFAMDFPOY/Z7YDAAAAAAiMICkAAC6WZFlmcmI7AAAAAIDAGLgJAAAAAAAAQEgjkxQAABdj4CYAAAAACD6CpAAAuJgGN5MIkgIAAABAUFFuDwAAAAAAACCkkUkKAICLUW4PAAAAAMFHJikAAAAAAACAkEYmKQAALpZkWWZyYjsAAAAAgMAIkgIA4GKe/01ObAcAAAAAEBjl9gAAAAAAAABCGpmkAAC4WJJYZnJiOwAAAACAwAiSAgDgYknWucmJ7QAAAAAAAqPcHgAAAAAAAEBII5MUAAAXY+AmAAAAAAg+gqQAALiYR8IkScIc2Q4AAAAAIDDK7QEAAAAAAACENDJJAQBwMY91bnJiOwAAAACAwMgkBQAAAAAAABDSyCQFAMDFkhzqk9SJbQAAAADAhYogKQAALkaQFAAAAACCj3J7AAAAAAAAACGNTFIAAFzMY4WZyYntAAAAAAACI0gKAICLUW4PAAAAAMFHuT0AAAAAAACAkEYmKQAALpYk4WbK+nYAAAAAAKkhkxQAAAAAAABASCNICgCAi1n/G7gpq5NuJzPGjx8vtWvXlkKFCpmpYcOG8s033wTYP0tuvvlmCQsLky+//NJv2a5du6RNmzYSHR0tJUqUkH79+snZs2ezfE4AAAAAwGmU2wMA4GI5NXBTuXLl5JVXXpFLL73UBEKnTp0qbdu2lXXr1snll1/uXW/MmDEmQJri9ZKSTIC0VKlSsnz5ctm3b5907NhR8ubNKy+//HKWjwcAAAAAnEQmKQAASOHWW2+V1q1bmyDpZZddJi+99JIULFhQVq5c6V1n/fr18vrrr8ukSZNSPH/BggWyefNm+fDDD6Vu3bom23T48OEybtw4SUhIyOajAQAAAIC0ESQFAMDFkqxwx6bz3oekJJk+fbqcPHnSlN2rU6dOyf3332+CnpotmtyKFSukVq1aUrJkSe+8li1bSlxcnGzatOm89wUAAAAAgoFyewAAXMwjYeJx4DtNj1jmpwYpfUVFRZkpkA0bNpig6JkzZ0wW6cyZM6VGjRpmWZ8+faRRo0amBD+Q/fv3+wVIlf1YlwEAAACAmxAkBQAghJQvX97v8eDBg2XIkCEB161ataopqT927Jh89tln0qlTJ1m2bJn8/vvvsmTJEtM/KQAAAABcCAiSAgAQQgM37d6924xWb0sti1RFRkZKlSpVzO/16tWTn3/+WcaOHSv58+eX7du3S+HChf3Wb9++vVx33XWydOlSU4K/atUqv+UHDhwwPwOV5wMAAABATiJICgCAi2W1P9F/t3Ou3F4DpL5B0szweDwSHx8vQ4cOla5du/ot0/5HR48ebQZ8Ulqmr4M9HTx4UEqUKGHmLVy40Ly2XbIPAAAAAG5BkBQAAKQwYMAAMyJ9hQoV5Pjx4zJt2jSTITp//nyTCRooG1TXrVy5svm9RYsWJhjaoUMHGTlypOmHdODAgdKjR480s1cBAAAAICcQJAUAwPUDN2W93D6z29AM0I4dO8q+ffskNjZWateubQKkN910U4aeHxERIbNnz5bu3bubrNICBQqYPk2HDRt2nkcAAAAAAMFDkBQAAKQwceLETK1v/a+c31fFihVl7ty5Du4VAAAAAAQHQVIAAFzMI+GSJFnvk9QjKYOYAAAAAIBzCJICABBCAzcBAAAAAFLK+l0XAAAAAAAAAORiZJICAODycnudsr4dMkkBAAAAIDUESQEAcLEkK8xMTmwHAAAAABAY5fYAAAAAAAAAQhqZpAAAuFiSQ6PbJ1FuDwAAAACpIkgKAICLeaxwM2V9OwRJAQAAACA1lNsDAAAAAAAACGlkkgIA4GKU2wMAAABA8JFJCgAAAAAAACCkkUkKAICLeTQL1ApzZDsAAAAAgMAIkgIA4GIeCTeTE9sBAAAAAATGHRMAAAAAAACAkEYmKQAALpZkhZvJie0AAAAAAAIjSAoAgIt5JMxMTmwHAAAAABAYaSUAAAAAAAAAQhqZpAAAuBjl9gAAAAAQfNwxAQAAAAAAAAhpZJICAOBiSRJuJie2AwAAAAAIjCApAAAu5rHCzOTEdgAAAAAAgZFWAgAAAAAAACCkkUkKAICLeRwqt9ftAAAAAAACI0gKAICLeaxwMzmxHQAAAABAYNwxAQAAAAAAAAhpZJICAOBiSRJmJie2AwAAAAAIjCApAAAuRrk9AAAAAAQfd0wAAAAAAAAAQhqZpAAAuFiSQ6Xyuh0AAAAAQGBkkgIAAAAAAAAIaWSSAgDgYvRJCgAAAADBR5AUAAAXS7LCzeTEdgAAAAAAgXHHBAAAkAnjxo2TSpUqSb58+aRBgwayatWqNNcfM2aMVK1aVfLnzy/ly5eXPn36yJkzZ7JtfwEAAACkj0xSAABczJIw8TgwcJNuB1k3Y8YM6du3r0yYMMEESDUA2rJlS9m2bZuUKFEixfrTpk2TZ599ViZNmiSNGjWSX3/9VR566CEJCwuTUaNG5cgxAAAAAEiJTFIAAHJBub0TE7JOA5vdunWTzp07S40aNUywNDo62gRBA1m+fLk0btxY7r//fpN92qJFC7nvvvvSzT4FAAAAkL24YwIAAMiAhIQEWbNmjTRv3tw7Lzw83DxesWJFwOdo9qg+xw6K7tixQ+bOnSutW7dO9XXi4+MlLi7ObwIAAAAQXJTbAwDgYh4rzExObAdZc/jwYUlKSpKSJUv6zdfHW7duDfgczSDV51177bViWZacPXtWHnvsMXnuuedSfZ0RI0bI0KFDHd9/AAAAAKkjkxQAABdLknDHJmS/pUuXyssvvyxvv/22rF27Vr744guZM2eODB8+PNXnDBgwQI4dO+addu/ena37DAAAAIQiMkkBAAAyoHjx4hIRESEHDhzwm6+PS5UqFfA5L7zwgnTo0EG6du1qHteqVUtOnjwpjzzyiDz//POmXD+5qKgoMwEAAADIPqSVAACQC8rtnZiQNZGRkVKvXj1ZvHixd57H4zGPGzZsGPA5p06dShEI1UCr0vJ7AAAAAO5AkBQAAKQwfvx4qV27thQqVMhMGgT85ptvzLIjR45Ir169pGrVqpI/f36pUKGCPPHEE6Y03NeuXbukTZs2ZvT3EiVKSL9+/UyfnLlZ37595b333pOpU6fKli1bpHv37iYzVEe7Vx07djTl8rZbb73VnMvp06fLzp07ZeHChSa7VOfbwVIAAAAAOY9yewAAXMwj4WZyYjuZUa5cOXnllVfk0ksvNRmPGhRs27atrFu3zjzeu3evvPbaa1KjRg35888/zWBEOu+zzz4zz9cBjjRAqmXoy5cvl3379pkAYt68eU0fnbnVPffcI4cOHZJBgwbJ/v37pW7dujJv3jzvYE4aGPbNHB04cKCEhYWZn3v27JGLLrrIBEhfeumlHDwKAAAAAMmFWbm41isuLk5iY2OlqbSVPGF5c3p3ANfa37tRTu8C4HpJ8Wdky9vPmWxIzZx0y2dc9+/vkKiCWf+Miz+RKOOv+yJLx1e0aFF59dVXpUuXLimWffrpp/Lggw+arMo8efKYrNNbbrnFBE7tAOKECROkf//+JsiopevI3LXglmsTAAAAuBDb0pTbAwCANGlWqJaLawA0tb437UaHBkjVihUrzCBFdoBUtWzZ0jRSNm3alG37DgAAAAAZQbk9AAAu5tSgS/Y2NEiZ0ZHUN2zYYIKiZ86ckYIFC8rMmTNNeX1yhw8fluHDh5sR221aiu4bIFX2Y10GAAAAAG5CJikAAC5mWeHicWDS7ajy5cubchN7GjFiRKqvrQMzrV+/Xn766SczQFGnTp1k8+bNfuto0FX7HtXg6ZAhQ4J+PgAAAAAgGMgkBQAghOzevduvL57UskiV9htapUoV83u9evXk559/lrFjx8o777xj5h0/flxatWolMTExJstUB2Wy6YBNq1at8tvegQMHvMsAAAAAwE3IJAUAwMWSJMyxSWmA1HdKK0ianMfjkfj4eG8GaYsWLUwg9euvv5Z8+fL5ratl+lquf/DgQe+8hQsXmtcMVLIPAAAAADmJTFIAAJDCgAED5Oabb5YKFSqYjNFp06bJ0qVLZf78+d4A6alTp+TDDz80j+2+Ti+66CKJiIgwyzUY2qFDBxk5cqTph3TgwIHSo0ePTAVmAQAAACA7ECQFAMDFPNa/gy5ldTuZoRmgHTt2lH379pm+S2vXrm0CpDfddJMJlmo/pcoux7ft3LlTKlWqZAKls2fPNn2ZalZpgQIFTJ+mw4YNy/KxAAAAAIDTCJIiQ2o2OCF3PX5ILq11SoqVOitDHq4kK+bFepcXLp4oXZ7fJ/WuPy4FYpNk48qCMm5gWdm781y2UMlyCfLBqi0Bt/3iIxXl+9mFs+1YgGB4uMFaufGyHVK52FGJT4yQ9XtLyZhl18ifR4p413n/3q/kqgp7/Z736foa8uKC6/3m3VZzq3So/4tULHpMTsbnlQXbLpERi5pk27HAXeyBl5zYTmZMnDgx1WVNmzYVy0o/6lqxYkWZO3dupl4XAAAAAHICQVJkSL5oj+zYlE/mf1xUBk/6I9lSy8xLOhsmQzpXllMnwuWORw7JKzO2S7frq0r86Qg5tDev3FvHvw+61g/+LXd2PyQ/L4nJ1mMBgqF++b0yY11N2bSvhESEe6RXk59kwl2z5Y5J98rpxH8Hs/nsl+ry9g9Xex+fSfT/Z1iDox2v+kVGLb1GNuwrKfnznpUysefKmAEAAAAAwAU8cNO4ceNMaZ4O+tCgQYMUo+Ei563+tpBMHVlalvtkj9rKXpwgNeqfkjefLSe//hItf23PZ36PymdJs9uPmnU8njD551Bev6nRzcfku1mF5cypiBw4IsBZj392i3y9sZps/7uo/HqouAyae4OUiT0h1Use8ltPg6J/n4z2TicTIr3LYqLipcd1q+T5OTfIN1suk7+Oxspvh4rJst8r58ARwS08EubYBAAAAABwaZB0xowZ0rdvXxk8eLCsXbtW6tSpIy1btvQbDRfuljfSY34mxP97A25ZYZKYECaXX3Uy4HOq1DolVWqeMZmpwIWoYFSC+Rl3xn+AmtY1fpOlPSfL552nyxNNVkq+PIneZQ0r7ZbwMEtKxJyUmV0+lgXdP5CRty2QkjEnsn3/4R5JVphjEwAAAADApUHSUaNGSbdu3aRz585mFNwJEyZIdHS0TJo0Kad3DRm0+/d8cuCvvPLwgH1SMPas5Mnrkbt7HJSLyiRK0ZL/BoB8tbrviPz5a5RsXl0g2/cXCLYwseSZG3+UdX+Vkt8PF/PO/2bLpfL87Bul6/TbZOJPV8otl/8qL9+y2Lu8XOE4EyTtes1aeXVxY3nqyxYSm++MvHP3LMkTnpRDRwMAAAAAwIUvR/skTUhIkDVr1siAAQO888LDw6V58+ayYsWKFOvHx8ebyRYXRz99bqB9kQ7rUkn6jtotn2/ZJElnRdZ9HyOrFsdIWIDEpch8Hml2+z8ybUzJnNhdIOieu+k7uaT4EXnoo3Z+8z//5d9+eTV4evhEtLx37ywpV/iYKa0PC7Mkb4RH/rP4WlnxR3mz3rOzbpLFPabK1RX2yPI/KmT7sSB0B24CAAAAgFCSo0HSw4cPS1JSkpQs6R8s08dbt25Nsf6IESNk6NCh2biHyKjfN0TL4zdVleiYJMmb15JjR/LI2Nm/ya//lz/Fute1OSpR+S1Z9Cml9rjwDGj+vTS55E95+ON2cvBEwTTX1YGZVIX/BUkPnziXWb39cBHvOv+czi9HT+eTUoUouQ9Vpj9RB0rl6ZMUAAAAAFKXq9JKNOP02LFj3mn37t05vUtI5tTxCBMgLVM5Xi6tc0pWzE850FPL+47IygWFzHrAhcMyAdIbLt0p3WbcJnuOFUr3GVVLHDY/D508Fxxdv6eU+Vmp6LkBz1ShfGekcP4zsi8uJmh7DgAAAABAqMvRKFXx4sUlIiJCDhw44DdfH5cqdS5Y4CsqKspMyH75opOkTOVzA9GoUuUT5OLLT8vxoxFyaE+kXHfLUTn2dx45uCevVK5+Rh4btkdWzIuVtcv8AztlKsVLrWtOygsPMlo3LizP3fS93Fz9N+k982YzYn2xAqfM/BPxkRJ/No8pqW9d/Tf5fkdFOXY6Si4t8bf0a7ZcVu8ubUawV3/+U1iW/FZJ+t/4gwyb31ROJuSVJ5r8JH8cKSw/7yqTw0eInGI5NDK9bgcAAAAA4MIgaWRkpNSrV08WL14s7dqd67vP4/GYxz179szJXUMyl9U5La9+vt37+LGhe83PBTOKyOt9KpgBmh4dslcKFz8rRw7mkUWfFgnY52jLe4/I4X15ZU2y4CmQ291zxSbzc9J9X/nNf2FuM/l6YzVJTIqQBpX+kgfq/5/kz3tW9h8vKIt+vVjeW1HPb/2Bc26Ufjf8KG/dOceUWK/ZXUa6f3qLnPVEZOvxAAAAAAAQSsIsy7JycgdmzJghnTp1knfeeUeuvvpqGTNmjHzyySemT9LkfZUmpwM3xcbGSlNpK3nC8mbbPgO5zf7ejXJ6FwDXS4o/I1vefs5051KoUPrdJQSb/RnXflEnyVsgMsvbSzyZIJ83n+qa40PmrwXeOwAAACB4bekc7xTynnvukUOHDsmgQYNk//79UrduXZk3b166AVIAAEIBo9sDAAAAQPDleJBUaWk95fUAAAAAAAAAQjZICgAAAtO+aXVyYjsAAAAAgMAIkgIA4GIeh0a3d2IbAAAAAHChooMyAAAAAAAAACGNTFIAAFyMcnsAAAAACD6CpAAAuBhBUgAAAAAIPsrtAQAAAAAAAIQ0MkkBAHAxMkkBAAAAIPjIJAUAAAAAAAAQ0sgkBQDAxcgkBQAAAIDgI0gKAICLWRrglDBHtgMAAAAACIxyewAAAAAAAAAhjUxSAABcjHJ7AAAAAAg+gqQAALgYQVIAAAAACD7K7QEAAAAAAACENDJJAQBwMTJJAQAAACD4yCQFAAAAAAAAENLIJAUAwMXIJAUAAACA4CNICgCAi1lWmJmc2A4AAAAAIDDK7QEAAAAAAACENDJJAQBwMY+EmcmJ7QAAAAAAAiNICgCAi9EnKQAAAAAEH+X2AAAAAAAAAEIamaQAALgYAzcBAAAAQPARJAUAwMUotwcAAACA4KPcHgAAAAAAAEBII5MUAAAXo9weAAAAAIKPTFIAAAAAAAAAIY1MUgAAXEwzQJ3oT5RMUgAAAABIHUFSAABczDIBTme2AwAAAAAIjHJ7AACQwvjx46V27dpSqFAhMzVs2FC++eYb7/IzZ85Ijx49pFixYlKwYEFp3769HDhwwG8bu3btkjZt2kh0dLSUKFFC+vXrJ2fPns2BowEAAACAtBEkBQDAxTwS5tiUGeXKlZNXXnlF1qxZI6tXr5YbbrhB2rZtK5s2bTLL+/TpI7NmzZJPP/1Uli1bJnv37pU77rjD+/ykpCQTIE1ISJDly5fL1KlTZcqUKTJo0CDHzxEAAAAAZBXl9gAAuFhOjW5/6623+j1+6aWXTHbpypUrTQB14sSJMm3aNBM8VZMnT5bq1aub5ddcc40sWLBANm/eLIsWLZKSJUtK3bp1Zfjw4dK/f38ZMmSIREZGZvmYAAAAAMApZJICABBC4uLi/Kb4+Ph0n6NZodOnT5eTJ0+asnvNLk1MTJTmzZt716lWrZpUqFBBVqxYYR7rz1q1apkAqa1ly5bmNe1sVAAAAABwC4KkAAC4mI5s79SkypcvL7Gxsd5pxIgRqb72hg0bTH+jUVFR8thjj8nMmTOlRo0asn//fpMJWrhwYb/1NSCqy5T+9A2Q2svtZQAAAADgJpTbAwAQQnbv3m0GYrJpADQ1VatWlfXr18uxY8fks88+k06dOpn+RwEAAADgQkOQFAAAF7Osc5MT21H2aPUZodmiVapUMb/Xq1dPfv75Zxk7dqzcc889ZkCmo0eP+mWT6uj2pUqVMr/rz1WrVvltT5fbywAAAADATSi3BwAgFwzc5MSUVR6Px/RhqgHTvHnzyuLFi73Ltm3bJrt27TJ9lir9qeX6Bw8e9K6zcOFCE6DVkn0AAAAAcBMySQEAQAoDBgyQm2++2QzGdPz4cTOS/dKlS2X+/PmmL9MuXbpI3759pWjRoibw2atXLxMY1ZHtVYsWLUwwtEOHDjJy5EjTD+nAgQOlR48eaZb4AwAAAEBOIEgKAICLOZUFmtltaAZox44dZd++fSYoWrt2bRMgvemmm8zy0aNHS3h4uLRv395kl+rI9W+//bb3+RERETJ79mzp3r27CZ4WKFDA9Gk6bNiwLB8LAAAAADiNICkAAC6mo9KHOVEqn8ltTJw4Mc3l+fLlk3HjxpkpNRUrVpS5c+dm6nUBAAAAICfQJykAAAAAAACAkEYmKQAAITS6PQAAAAAgJYKkAAC4PkjqRJ+kjuwOAAAAAFyQKLcHAAAAAAAAENLIJAUAwMVyanR7AAAAAAglZJICAAAAAAAACGlkkgIA4GLalagT3YnSJSkAAAAApI4gKQAALka5PQAAAAAEH+X2AAAAAAAAAEIamaQAALgZ9fYAAAAAEHRkkgIA4Gb/K7fP6qTbgTPGjRsnlSpVknz58kmDBg1k1apVaa5/9OhR6dGjh5QuXVqioqLksssuk7lz52bb/gIAAABIH5mkAAAAGTRjxgzp27evTJgwwQRIx4wZIy1btpRt27ZJiRIlUqyfkJAgN910k1n22WefSdmyZeXPP/+UwoUL58j+AwAAAAiMICkAAC5mWecmJ7aDrBs1apR069ZNOnfubB5rsHTOnDkyadIkefbZZ1Osr/OPHDkiy5cvl7x585p5moUKAACQ21V6dk5O7wJyiT9eaSO5AeX2AAC4mBOl9t6Se2SJZoWuWbNGmjdv7p0XHh5uHq9YsSLgc77++mtp2LChKbcvWbKk1KxZU15++WVJSkpK9XXi4+MlLi7ObwIAAAAQXARJAQAAMuDw4cMmuKnBTl/6eP/+/QGfs2PHDlNmr8/TfkhfeOEFef311+XFF19M9XVGjBghsbGx3ql8+fKOHwsAAAAAfwRJAQBwM3vQJScmZDuPx2P6I3333XelXr16cs8998jzzz9vyvRTM2DAADl27Jh32r17d7buMwAAABCK6JMUAAAgA4oXLy4RERFy4MABv/n6uFSpUgGfoyPaa1+k+jxb9erVTeaplu9HRkameE5UVJSZAAAAAGQfgqQAALgYAze5hwY0NRt08eLF0q5dO2+mqD7u2bNnwOc0btxYpk2bZtbT/kvVr7/+aoKngQKkAIDQxSA4uJAGwAFyI8rtAQBwM8vBCVnWt29fee+992Tq1KmyZcsW6d69u5w8edI72n3Hjh1NubxNl+vo9k8++aQJjs6ZM8cM3KQDOQEAAABwDzJJAQAAMkj7FD106JAMGjTIlMzXrVtX5s2b5x3MadeuXd6MUaWDLs2fP1/69OkjtWvXlrJly5qAaf/+/XPwKAAAAAAkR5AUAAAXs6wwMzmxHThDS+tTK69funRpinkNGzaUlStXZsOeAQAAADhfBEkBAHA7SuUBAAAAIOeDpF9//XWGN3jbbbdlZX8AAAAAIMsYBAcZwSA4AIBMBUntEVzTExYWJklJSRlaFwAApI9yewAAAABwSZDU4/EEf08AAAAAAAAAIAf8O/zqeThz5oxzewIAAAL3R+rUBAAAAABwJkiq5fTDhw+XsmXLSsGCBWXHjh1m/gsvvCATJ07M7OYAAECawhycAAAAAACOBElfeuklmTJliowcOVIiIyO982vWrCnvv/9+ZjcHAAAAAAAAALkrSPrBBx/Iu+++Kw888IBERER459epU0e2bt3q9P4BABDaKLcHAAAAAHcM3ORrz549UqVKlYCDOyUmJjq1XwAAQBwMcBIkBQAAAADnMklr1Kgh33//fYr5n332mVxxxRWZ3RwAAAAAAAAA5K5M0kGDBkmnTp1MRqlmj37xxReybds2U4Y/e/bs4OwlAAChygo7NzmxHQAAAACAM5mkbdu2lVmzZsmiRYukQIECJmi6ZcsWM++mm27K7OYAAEAaLMu5CQAAAADgUCapuu6662ThwoXn81QAAAAAAAAAyP1BUrV69WqTQWr3U1qvXj0n9wsAACgGbgIAAAAA9wVJ//rrL7nvvvvkxx9/lMKFC5t5R48elUaNGsn06dOlXLlywdhPAAAAAAAAAHBHn6Rdu3aVxMREk0V65MgRM+nvOoiTLgMAAEEYuMmJCQAAAADgTCbpsmXLZPny5VK1alXvPP39zTffNH2VAgAA54RZ5yYntgMAAAAAcCiTtHz58iaTNLmkpCQpU6ZMZjcHAAAAAAAAALkrSPrqq69Kr169zMBNNv39ySeflNdee83p/QMAILRZDk4AAAAAgPMPkhYpUkSKFi1qps6dO8v69eulQYMGEhUVZSb9fe3atfLwww9nZHMAAMDlfZKOGDFCrrrqKomJiZESJUpIu3btZNu2bX7r7N+/Xzp06CClSpWSAgUKyJVXXimff/653zrad/kDDzwghQoVMgM+dunSRU6cOOHIqQEAAACAbO2TdMyYMY69IAAAcD/tg7xHjx4mUHr27Fl57rnnpEWLFrJ582YTEFUdO3aUo0ePytdffy3FixeXadOmyd13320qTK644gqzjgZI9+3bJwsXLjTd9eiXrY888ohZFwAAAAByVZC0U6dOwd8TAACQklOl8pncxrx58/weT5kyxWSUrlmzRpo0aWLm6UCO48ePl6uvvto8HjhwoIwePdqso0HSLVu2mO38/PPPUr9+fbOODvTYunVr00UPfZnnLpWenZPTu4Bc4I9X2uT0LgAAAGRPn6S+zpw5I3FxcX4TAABwb5+kyT+34+PjM7Qbx44dMz+16x1bo0aNZMaMGaak3uPxyPTp003boGnTpmb5ihUrTIm9HSBVzZs3l/DwcPnpp5+cPU8AAAAAkJ1B0pMnT0rPnj1NNomW22l/pb4TAABwr/Lly0tsbKx30r5H06MB0N69e0vjxo2lZs2a3vmffPKJKaEvVqyY6aP80UcflZkzZ0qVKlW8fZZqe8FXnjx5TKBVlwEAAABAriq39/XMM8/It99+a8rrdLCGcePGyZ49e+Sdd96RV155JTh7CQBAqHK43H737t1mECWbBjfTo32Tbty4UX744Qe/+S+88ILpk3TRokWmT9Ivv/zS9En6/fffS61atRzYaQAAAABwaZB01qxZ8sEHH5hSOh184brrrjMZIxUrVpSPPvrIDNAAAADcSQOkvkHS9Gj1yOzZs+W7776TcuXKeedv375d3nrrLRM8vfzyy828OnXqmACpfoE6YcIEM+r9wYMH/bang0Bpeb4uAwAAAIBcW26vNzYXX3yx+V1vsvSxuvbaa80NFAAAcJAV5tyUmZe1LBMg1fL5JUuWSOXKlf2Wnzp1yvzU/kV9RUREmPJ81bBhQ5NpqgM52XRburxBgwZZOCkAAAAAkMNBUg2Q7ty50/xerVo10x+ZnWGqgzMAAADnhFnOTZmhJfYffvihTJs2TWJiYkwfojqdPn3a2wbQShLth3TVqlUms/T111+XhQsXSrt27cw61atXl1atWkm3bt3MOj/++KMJvN57772MbA8AAAAgdwdJtcT+l19+Mb8/++yzpqQuX7580qdPH+nXr18w9hEAAGQz7XtcR7TX7nVKly7tnXQ0e5U3b16ZO3euXHTRRXLrrbdK7dq1TXc8U6dOldatW3u3o13xaED1xhtvNPO18uTdd9/NwSMDAAAAAAf6JNVgqK158+aydetWU0an2SR6gwQAANw7cFOGV7fSf8Kll14qn3/+eZrr6Ej2mo0KAAAAABdUkDQ5HbBJJwAAAAAAAAC4YIOkb7zxRoY3+MQTT2RlfwAAAAAAAADAfUHS0aNHZ2hjYWFhBEkBAHCQjkmf2UGXUtsOAAAAACALQVJ7NHu3Smh+pXjy5svp3QBc65dn3s7pXQBcL+64R4rwpwIAAAAAISnLfZICAIAgssLOTU5sBwAAAAAQEEFSAADcLIdGtwcAAACAUBKe0zsAAAAAAAAAADmJTFIAANyMTFIAAAAACDqCpAAAuJiObO/I6PYESQEAAADA2XL777//Xh588EFp2LCh7Nmzx8z773//Kz/88MP5bA4AAAAAAAAAck+Q9PPPP5eWLVtK/vz5Zd26dRIfH2/mHzt2TF5++eVg7CMAAKHLcnACAAAAADgTJH3xxRdlwoQJ8t5770nevHm98xs3bixr167N7OYAAEBaCJICAAAAgPuCpNu2bZMmTZqkmB8bGytHjx51ar8AAAAAAAAAwJ1B0lKlSsnvv/+eYr72R3rxxRc7tV8AAMBn4CYnJgAAAACAQ0HSbt26yZNPPik//fSThIWFyd69e+Wjjz6Sp59+Wrp3757ZzQEAAAAAAABAjsqT2Sc8++yz4vF45MYbb5RTp06Z0vuoqCgTJO3Vq1dw9hIAgFBlhZ2bnNgOAAAAAMCZIKlmjz7//PPSr18/U3Z/4sQJqVGjhhQsWDCzmwIAAOlxatAlyu0BAAAAwLkgqS0yMtIERwEAAAAAAAAgpIKkzZo1M9mkqVmyZElW9wkAAPyPU4MuMXATAAAAADgYJK1bt67f48TERFm/fr1s3LhROnXqlNnNAQCAtFBuDwAAAADuC5KOHj064PwhQ4aY/kkBAAAAAAAAIDcJd2pDDz74oEyaNMmpzQEAAPW/cvusTmSSAgAAAEAQBm5KbsWKFZIvXz6nNgcAABTl9gAAAADgviDpHXfc4ffYsizZt2+frF69Wl544QUn9w0AAAAAAAAA3BckjY2N9XscHh4uVatWlWHDhkmLFi2c3DcAAEAmKQAAAAC4K0ialJQknTt3llq1akmRIkWCt1cAAAAAAAAA4MaBmyIiIky26NGjR4O3RwAAwMuJQZu8gzcBAAAAAJwZ3b5mzZqyY8eOzD4NAAAAAAAAAC6MIOmLL74oTz/9tMyePdsM2BQXF+c3AQAAAAAAAMAF2SepDsz01FNPSevWrc3j2267TcLCwvxGudfH2m8pAABwCAM3AQAAAIB7gqRDhw6Vxx57TL799tvg7hEAAPByqj9R+iQFAAAAAAeCpJopqq6//vqMPgUAAAAAAAAALpwgqfItrwcAANmELFAAAAAAcE+Q9LLLLks3UHrkyJGs7hMAAAAAAAAAuDNIqv2SxsbGBm9vAACAPwZuAgAAAAB3BUnvvfdeKVGiRPD2BgAA+GHgJgAAAAAIvvCMrkh/pAAAAAAAAAAuRJke3R4AAGQjyu0BAAAAwD1BUo/HE9w9AQAAKVBuDwAAAAAuKrcHAACAyLhx46RSpUqSL18+adCggaxatSpDz5s+fbrpvqhdu3ZB30cAAAAAmUOQFACA3FBu78SUCSNGjJCrrrpKYmJizKCNGtjbtm1bivVWrFghN9xwgxQoUEAKFSokTZo0kdOnT3uXHzlyRB544AGzrHDhwtKlSxc5ceKE5FYzZsyQvn37yuDBg2Xt2rVSp04dadmypRw8eDDN5/3xxx/y9NNPy3XXXZdt+woAAAAg4wiSAgDgZjkUJF22bJn06NFDVq5cKQsXLpTExERp0aKFnDx50i9A2qpVKzNfsyl//vln6dmzp4SH/9u80ADppk2bzDZmz54t3333nTzyyCOSW40aNUq6desmnTt3lho1asiECRMkOjpaJk2alOpzkpKSzHkYOnSoXHzxxdm6vwAAAAAc7pMUAACEjnnz5vk9njJliskoXbNmjckWVX369JEnnnhCnn32We96VatW9f6+ZcsWsx0NntavX9/Me/PNN6V169by2muvSZkyZSQ3SUhIMMc/YMAA7zwNCDdv3twEjFMzbNgwc+40i/b777/Ppr0FAAAAkBlkkgIAkAsGbnJiUnFxcX5TfHx8hvbj2LFj5mfRokXNTy0v/+mnn0zwr1GjRlKyZEm5/vrr5YcffvA+RwOHWmJvB0iVBhQ1sKjPzW0OHz5sskL1WH3p4/379wd8jp6PiRMnynvvvZfh19H3JPn7BAAAACC4CJICABBCypcvL7Gxsd5J+x5Nj8fjkd69e0vjxo2lZs2aZt6OHTvMzyFDhpjyc80YvfLKK+XGG2+U3377zSzTwKEGUX3lyZPHBFpTCypeSI4fPy4dOnQwAdLixYtn+Hn6nvi+R/qeAQAAAAguyu0BAHCz8+hPNNXtiMju3bvNIEq2qKiodJ+qfZNu3LjRL0tUA6fq0UcfNf1zqiuuuEIWL15s+ufMSPA1t9FAZ0REhBw4cMBvvj4uVapUivW3b99uBmy69dZbU5w3DRbrQFiXXHJJiudpOb8ODmXTTFICpQAAAEBwESQFACCEgqQaIPUNkqZHB2KyB1wqV66cd37p0qXNTx28yFf16tVl165d5ncNHCYf9f3s2bNmxPtAQUW3i4yMlHr16plAcLt27bxBT32s5ym5atWqyYYNG/zmDRw40GSYjh07NtXApwauMxK8BgAAAOAcgqQAACAFy7KkV69eMnPmTFm6dKlUrlzZb3mlSpXMwEuaDenr119/lZtvvtn83rBhQzl69KgZ7EiDi2rJkiUmsNigQYNsPBrnaIZnp06dTD+rV199tYwZM0ZOnjzpzabt2LGjlC1b1mTS5suXz9s9gU37aFXJ5wMAAADIWQRJAQBwMd9Bl7K6nczQEvtp06bJV199JTExMd4+RLWPzPz580tYWJj069dPBg8eLHXq1JG6devK1KlTZevWrfLZZ595s0pbtWpl+iydMGGCJCYmmozLe++9N9eNbG+755575NChQzJo0CBzTvS4tT9WezAnzaLVgakAAAAA5C4ESQEACKFy+4waP368+dm0aVO/+ZMnT5aHHnrI/K6DOZ05c0b69OljSug1WLpw4UK/fjY/+ugjExjVAZ00eNi+fXt54403JDfT4wlUXq806zYtU6ZMCdJeAQAAAMgKgqQAACBguX1GPPvss2ZKjY5krxmpAAAAAOBmBEkBAHCxnCq3BwAAAIBQQpAUAAA3y6FyewAAAAAIJYwsAAAAAAAAACCkkUkKAICbkUkKAAAAAEFHJikAAAAAAACAkEYmKQAALhb2v8mJ7QAAAAAAAiNICgCAm1FuDwAAAABBR7k9AAAAAAAAgJBGJikAAC4WZp2bnNgOAAAAACAwgqQAALgZ5fYAAAAAEHSU2wMAAAAAAAAIaWSSAgDgdmSBAgAAAEBQkUkKAAAAAAAAIKSRSQoAgIsxcBMAAAAABB9BUgAA3IyBmwAAAAAg6Ci3BwAAAAAAABDSyCQFAMDFKLcHAAAAgOAjSAoAgJtRbg8AAAAAQUe5PQAAAAAAAICQRiYpAAAuRrk9AAAAAAQfQVIAANyMcnsAAAAACDrK7QEAAAAAAACENDJJAQBwMzJJAQAAACDoyCQFAAAAAAAAENLIJAUAwMUYuAkAAAAAgo8gKQAAbka5PQAAAAAEHeX2AAAAAAAAAEIamaQAALhYmGWZyYntAAAAAAACI0gKAICbUW4PAAAAAEFHuT0AAAAAAACAkEYmKQAALsbo9gAAAAAQfGSSAgAAAAAAAAhpZJICAOBm9EkKAAAAAEFHkBQAABej3B4AAAAAgo9yewAAAAAAAAAhjUxSAADcjHJ7AAAAAAg6gqQAALgY5fYAAAAAEHyU2wMAAAAAAAAIaWSSAgDgZpTbAwAAAEDQESQFAMDlKJUHAAAAgOCi3B4AAKQwYsQIueqqqyQmJkZKlCgh7dq1k23btgVc17IsufnmmyUsLEy+/PJLv2W7du2SNm3aSHR0tNlOv3795OzZs9l0FAAAAACQMQRJAQBwM8tybsqEZcuWSY8ePWTlypWycOFCSUxMlBYtWsjJkydTrDtmzBgTIE0uKSnJBEgTEhJk+fLlMnXqVJkyZYoMGjQoS6cEAAAAAJxGuT0AAEhh3rx5fo81uKmZoGvWrJEmTZp4569fv15ef/11Wb16tZQuXdrvOQsWLJDNmzfLokWLpGTJklK3bl0ZPny49O/fX4YMGSKRkZHZdjwAAAAAkBYySQEAcHl/pE5NWXHs2DHzs2jRot55p06dkvvvv1/GjRsnpUqVSvGcFStWSK1atUyA1NayZUuJi4uTTZs2ZW2HAAAAAMBBZJICABBCo9trgNJXVFSUmdLi8Xikd+/e0rhxY6lZs6Z3fp8+faRRo0bStm3bgM/bv3+/X4BU2Y91GQAAAAC4BUFSAABCSPny5f0eDx482JS+p0X7Jt24caP88MMP3nlff/21LFmyRNatWxe0fQUAAACA7EKQFAAAFwvznJuc2I7avXu3FCpUyDs/vSzSnj17yuzZs+W7776TcuXKeedrgHT79u1SuHBhv/Xbt28v1113nSxdutSU4K9atcpv+YEDB8zPQOX5AAAAAJBTCJICABBC5fYaIPUNkqa6umVJr169ZObMmSbgWblyZb/lzz77rHTt2tVvnvY/Onr0aLn11lvN44YNG8pLL70kBw8eNIM+qYULF5rXr1GjhgMHBQAAAADOIEiKdN3fer1cV+8PqVD6mMQnRMim30vKu59dJbv3/5s91LfjD3JljT1SvPApOR2fVzb9XkLe+fRq7zqXlP9b7mv9i9S69IDEFjwj+w8XlFlLq8vni/7t2w7IzWZNLSZzPiguB3afG627YtUz8kCf/XLVDcfN437tq8j/rSjo95zWHQ7Lk//5y/u4ZZm6KbY74O0/pGm7o0HffyBQif20adPkq6++kpiYGG8forGxsZI/f36TCRooG7RChQregGqLFi1MMLRDhw4ycuRIs42BAweabaeXwQoAAAAAIRMk1dK9V199VdasWSP79u0z2Srt2rXLyV1CAHWq7pcvl9SQbTsvkogIj3S9Y7WM7DtPOg9sL2cS8pp1fv2zuCxaeYkc+LugFCoQL53arpVXn/pG7n/mHvFY4XJZxcNyNC6/vPxuUzn4TwG5/JID8lSnHyTJEyZfLrk8pw8RyLKLSifKw8/tlbKV48WywmThp0VkSOfKMm7Br1Kp6hmzzs0PHJaO/f4drCYqf8oa6qdG75L6zf4dWKdgoaRsOgK4lRMj09vbyYzx48ebn02bNvWbP3nyZHnooYcytI2IiAhTqt+9e3eTVVqgQAHp1KmTDBs2LHM7AwAAAAAXcpD05MmTUqdOHXn44YfljjvuyMldQRr6j27l9/iVSU3ky7EfyWWVDsv//VrazJu9rJp3+YG/Y2TSzHoycdhMKVX8hOw9VEi++aGq3zb2HSokl1c5aDJUCZLiQnBNC/8Rwzs/u19mf1Bctq6J9gZJo/JbUrTE2TS3o0HR9NZBiLGsc5MT28nU6pl/zUDPqVixosydOzfT2wIAAACAkAmS3nzzzWZC7lIgf4L5GXcycKlkvshEaXXtb7L3UIwcPFIgze0cT2UbQG6WlCTy/azCEn8qXKrXP+md/+0XRWTJ50WkSIlEueamOLm/937JF+0fVHrr+bIy+unyUqpivNzS4W9pce8RCQvLgYMAAAAAACCE0CcpMiUszJKe962UDb+VlD/2FPVb1rbZZnn0rlWSP99Z2bUvVvq9drOcTYoIuB0tt2921Q4ZMLZlNu05EHw7t+ST3rdeKgnx4ZK/gEcGTdwpFS+LN8ua3f6PlCiXIMVKJsrOLfll4kul5a/tUTJo4h/e53fst0/qNj5hyvDXLIuRN58rJ6dPhku7rodz8KgQquX2AAAAABBKclWQND4+3ky2uDj/8lYE35MP/iiVy/4jvUacG7nY16KVVWT1prJSrPApubvlBhncfbH0fPlWSTzrf5lVKntEXnxioUz9+kpZvalcNu49EFzlLomXtxduk1PHI+T72YXltScryqtf/GYCpa0f/Nu7XuXqZ6RoiUTpf3cV2ftHpJSpdC47+4E+B7zrVKl1Ws6cCpdPx5cgSAoAAAAAQJCFSy4yYsQIM6quPZUvXz6ndymkPPHAcmlYZ7f0GdlGDv+Tsoz+5OlI2XMw1vRTOuTtG6V86WNyXb0//dapWOYfef3puTJ7WVX5cPYV2bj3QPDljbSkbOUEubT2aXn4uX1SucZp+fL9iwKuW+3KU+bn3j9S73JC1zm8L1IS4qm3D2mWgxMAAAAAIPcHSQcMGCDHjh3zTrt3787pXQoRlgmQXnvlH9J3ZGvZfzgm3WdoH4phYknePP+OzF2pzD8yqt8cWbD8Upn4xVVB3mcg5+kYNokJgf+Z3b4xv/mpGaWp2b4pvxQsfFYio4huhTK73N6JCQAAAABwAZTbR0VFmQnZq/eDy+XGa7bLwDduklNn8kqRQqe8maMJiXmk9EVxpn9RLZ0/ejyfXFTkpNzX+heJT8wjP/1feW+J/ah+c+XnjeXkk/m1vNvwWGFy7Pi5YBGQm016ubRcdUOcXFQ2UU6fCJdvZxaR/1teUF6att2U1Ovjq2+Mk5giSbJzcz55Z0hZqXXNCbm4xrmR71cuKCT/HMoj1eudkrxRHln7XYxMf6OE3PnYoZw+NAAAAAAALng5GiQ9ceKE/P77797HO3fulPXr10vRokWlQoUKOblr8NH2hi3m55hn5/jNf2ViE5n/42WSkBghtS7bL+1v2igxBRLkn7j88n/bSkmvl2+Vo/8LgF5ff6cUKXRGWjT63Uy2/YcLyn3P3JvNRwQ47+jhPPLqExXlyME8Eh2TZPod1QBpvetPyME9eWXd9zEy8/2LTD+jF5VJlGtbH5X7ev/bB2lEXktmTSku7wyJMhmo2k/po0P2ys0P/NuXKUKUXhA6ObEdAAAAAID7gqSrV6+WZs2aeR/37dvX/OzUqZNMmTIlB/cMvpo93DXN5X8fLSADxrRKc52pX9UzE3Ch6jsq9e4/SpRNlNe++PfLgUCuanbcTEByjG4PAAAAABd4kLRp06ZikdkCAAAAAAAAIAflqj5JAQAIOU6NTM93kgAAAABwYYxuDwAAAAAAAABOI5MUAAAXo09SAAAAAAg+gqQAALiZxzo3ObEdAAAAAEBAlNsDAAAAAAAACGlkkgIA4GYM3AQAAAAAQUeQFAAAFwtzqD9R3Q4AAAAAIDDK7QEAAAAAAACENDJJAQBwM8s6NzmxHQAAAABAQGSSAgDgYlpq79QEZ4wbN04qVaok+fLlkwYNGsiqVatSXfe9996T6667TooUKWKm5s2bp7k+AAAAgJxBkBQAACCDZsyYIX379pXBgwfL2rVrpU6dOtKyZUs5ePBgwPWXLl0q9913n3z77beyYsUKKV++vLRo0UL27NmT7fsOAAAAIHUESQEAyA2j2zsxIctGjRol3bp1k86dO0uNGjVkwoQJEh0dLZMmTQq4/kcffSSPP/641K1bV6pVqybvv/++eDweWbx4cbbvOwAAAIDUESQFAADIgISEBFmzZo0pmbeFh4ebx5olmhGnTp2SxMREKVq0aBD3FAAAAEBmMXATAAAuFmZZZnJiO8iaw4cPS1JSkpQsWdJvvj7eunVrhrbRv39/KVOmjF+gNbn4+Hgz2eLi4rKw1wAAAAAygkxSAADczOPghBz1yiuvyPTp02XmzJlm0KfUjBgxQmJjY72T9mMKAAAAILgIkgIAAGRA8eLFJSIiQg4cOOA3Xx+XKlUqzee+9tprJki6YMECqV27dprrDhgwQI4dO+addu/e7cj+AwAAAEgdQVIAAHJBub0TE7ImMjJS6tWr5zfokj0IU8OGDVN93siRI2X48OEyb948qV+/frqvExUVJYUKFfKbAAAAAAQXfZICAOBmTo1MT4zUEX379pVOnTqZYOfVV18tY8aMkZMnT5rR7lXHjh2lbNmypmRe/ec//5FBgwbJtGnTpFKlSrJ//34zv2DBgmYCAAAA4A4ESQEAADLonnvukUOHDpnApwY869atazJE7cGcdu3aZUa8t40fP14SEhLkzjvv9NvO4MGDZciQIdm+/wAAAAACI0gKAICbaZm8E6XylNs7pmfPnmYKZOnSpX6P//jjj2zaKwAAAABZQZAUAAAXC7POTU5sBwAAAAAQGAM3AQAAAAAAAAhpZJICAOBmlNsDAAAAQNCRSQoAAAAAAAAgpJFJCgCAi4V5zk1ObAcAAAAAEBhBUgAA3IxyewAAAAAIOsrtAQAAAAAAAIQ0MkkBAHAzTQB1IgmURFIAAAAASBVBUgAAXCzMsszkxHYAAAAAAIFRbg8AAAAAAAAgpJFJCgCAmzFwEwAAAAAEHZmkAAAghREjRshVV10lMTExUqJECWnXrp1s27bNu/zIkSPSq1cvqVq1quTPn18qVKggTzzxhBw7dsxvO7t27ZI2bdpIdHS02U6/fv3k7NmzOXBEAAAAAJA6gqQAALiZJoB6HJgymUi6bNky6dGjh6xcuVIWLlwoiYmJ0qJFCzl58qRZvnfvXjO99tprsnHjRpkyZYrMmzdPunTp4t1GUlKSCZAmJCTI8uXLZerUqWa9QYMGOX2WAAAAACBLKLcHAMDFcmrgJg14+tLgpmaCrlmzRpo0aSI1a9aUzz//3Lv8kksukZdeekkefPBBkymaJ08eWbBggWzevFkWLVokJUuWlLp168rw4cOlf//+MmTIEImMjMzycQEAAACAE8gkBQAA6bLL6IsWLZrmOoUKFTIBUrVixQqpVauWCZDaWrZsKXFxcbJp06Zs2GsAAAAAyBgySQEAcDNNAHVk4KZzPzRA6SsqKspMafF4PNK7d29p3LixySAN5PDhwyZL9JFHHvHO279/v1+AVNmPdRkAAAAAuAWZpAAA5IbR7Z2YRKR8+fISGxvrnXSApvRo36Ta7+j06dMDLtfAq/Y9WqNGDVNGDwAAAAC5DZmkAACEkN27d5uSeFt6WaQ9e/aU2bNny3fffSflypVLsfz48ePSqlUriYmJkZkzZ0revHm9y0qVKiWrVq3yW//AgQPeZQAAAADgFmSSAgDgZh4HJxETIPWdUguSWpZlAqQa+FyyZIlUrlw5YAapjnivAzB9/fXXki9fPr/lDRs2lA0bNsjBgwe98xYuXGheV7NOAQAAAMAtyCQFAMDFcmp0ey2xnzZtmnz11VcmS9TuQ1RL9PPnz+8NkJ46dUo+/PBD89ju7/Siiy6SiIgIs1yDoR06dJCRI0eabQwcONBsO70MVgAAAADITgRJAQBACuPHjzc/mzZt6jd/8uTJ8tBDD8natWvlp59+MvOqVKnit87OnTulUqVKJlCqpfrdu3c3WaUFChSQTp06ybBhw7LxSAAAAAAgfQRJAQBwM59Bl7K8nUytnvb6GjxNbx1VsWJFmTt3bqZeGwAAAACyG32SAgAAAAAAAAhpZJICAOBmOZRJCgAAAAChhCApAABuRpAUAAAAAIKOcnsAAAAAAAAAIY1MUgAA3MwjImEObQcAAAAAEBBBUgAAXCzMsszkxHYAAAAAAIFRbg8AAAAAAAAgpJFJCgCAmzFwEwAAAAAEHUFSAADczGNprbwz2wEAAAAABES5PQAAAAAAAICQRiYpAABuRrk9AAAAAAQdmaQAAAAAAAAAQhqZpAAAuJpDmaS6HQAAAABAQARJAQBwM8rtAQAAACDoKLcHAAAAAAAAENLIJAUAwM08mgFqObQdAAAAAEAgBEkBAHAzy3NucmI7AAAAAICAKLcHAAAAAAAAENLIJAUAwM0YuAkAAAAAgo5MUgAAAAAAAAAhjUxSAADcjIGbAAAAACDoCJICAOBmlNsDAAAAQNBRbg8AAAAAAAAgpJFJCgCAm5lqeycySZ3YGQAAAAC4MBEkBQDAzSi3BwAAAICgo9weAAAAAAAAQEgjkxQAADfzePR/Dm0HAAAAABAIQVIAANyMcnsAAAAACDrK7QEAAAAAAACENDJJAQBwMzJJAQAAACDoyCQFAAAAAAAAENLIJAUAwM08mgFqObQdAAAAAEAgBEkBAHAxy/KYyYntAAAAAAACo9weAAAAAAAAQEgjkxQAADfTAZecKJVn4CYAAAAASBVBUgAA3MwENwmSAgAAAEAwUW4PAAAAAAAAIKSRSQoAgJt5PCJhDgy6xMBNAAAAAJAqMkkBAAAAAAAAhDQySQEAcDP6JAUAAACAoCNICgCAi1kej1gOlNtblNsDAAAAQKootwcAACmMGDFCrrrqKomJiZESJUpIu3btZNu2bX7rnDlzRnr06CHFihWTggULSvv27eXAgQN+6+zatUvatGkj0dHRZjv9+vWTs2fPZvPRAAAAAEDaCJICAOBmWibv1JQJy5YtMwHQlStXysKFCyUxMVFatGghJ0+e9K7Tp08fmTVrlnz66adm/b1798odd9zhXZ6UlGQCpAkJCbJ8+XKZOnWqTJkyRQYNGuToKQIAAACArKLcHgAAN/NYImHZ3yfpvHnz/B5rcFMzQdesWSNNmjSRY8eOycSJE2XatGlyww03mHUmT54s1atXN4HVa665RhYsWCCbN2+WRYsWScmSJaVu3boyfPhw6d+/vwwZMkQiIyOzflwAAAAA4AAySQEACCFxcXF+U3x8fIaep0FRVbRoUfNTg6WaXdq8eXPvOtWqVZMKFSrIihUrzGP9WatWLRMgtbVs2dK87qZNmxw+MgAAAAA4fwRJAQBwM1Mq73FgOpdJWr58eYmNjfVO2vdoejwej/Tu3VsaN24sNWvWNPP2799vMkELFy7st64GRHWZvY5vgNRebi8DAAAAALeg3B4AABezPJZYDpTbW/8Lku7evVsKFSrknR8VFZXuc7Vv0o0bN8oPP/yQ5f0AAAAAADcikxQAgBCiAVLfKb0gac+ePWX27Nny7bffSrly5bzzS5UqZQZkOnr0qN/6Orq9LrPXST7avf3YXgcAAAAA3IAgKQAAbuZIqf3/psy8rGWZAOnMmTNlyZIlUrlyZb/l9erVk7x588rixYu987Zt2ya7du2Shg0bmsf6c8OGDXLw4EHvOgsXLjTB2Ro1akhuNW7cOKlUqZLky5dPGjRoIKtWrUpz/U8//dT016rrax+tc+fOzbZ9BQAAAJAxBEkBAEDAEvsPP/zQjF4fExNj+hDV6fTp02a59mfapUsX6du3r8ky1YGcOnfubAKjOrK9atGihQmGdujQQX755ReZP3++DBw40Gw7I2X+bjRjxgxzzIMHD5a1a9dKnTp1zGBUvoFgX8uXL5f77rvPnKt169ZJu3btzKTdFwAAAABwD4KkAAC4vU9Sh6bMGD9+vBnRvmnTplK6dGnvpEFC2+jRo+WWW26R9u3bS5MmTUwJ/RdffOFdHhERYUr19acGTx988EHp2LGjDBs2THKrUaNGSbdu3UxAWAPAEyZMkOjoaJk0aVLA9ceOHSutWrWSfv36SfXq1WX48OFy5ZVXyltvvZXt+w4AAAAgdQzcBACAm5kyeY9D28n8QE9p0fJxLT3XKTUVK1a8YMrLtQ9WzZgdMGCAd154eLg0b95cVqxYEfA5Ol8zT31p5umXX34Z9P0FAAAAECJBUvsG7uzZMzm9K4CrxR13IMACXODiTngyHBzMTmclUcRyaDvIksOHD0tSUpKULFnSb74+3rp1a8DnaBcFgdbX+amJj483k00zelVcXJzkJE/8qRx9feQOOX2d+uKaRUZwzSK34ZpFbhSXg9et/doZuc/L1UHS48ePm5+rlo7I6V0BXK3IZTm9B0Du+mzR/jZzWmRkpClf/2G/c1mYuj3dLtxtxIgRMnTo0BTzy5cvnyP7A2RG7Jic3gMgc7hmkdtwzSI3ih2TO+7zcnWQtEyZMrJ7924zoERYWFhO7w7+F6HXmzh9X3T0YgAp8XfiTvrNon5w6meLG2gp+86dO02Jt1M0QKrbxfkpXry46V/1wIEDfvP1sQagA9H5mVlfaTm/b4m+x+ORI0eOSLFixWjvuAj/liO34ZpFbsM1i9yGazb33+fl6iCp9gNWrly5nN4NBKD/IPCPApA2/k7cxw0ZpL40oElQ0z00yFyvXj1ZvHixGaHeDmDq4549ewZ8jg5Ypct79+7tnbdw4UIzPzVRUVFm8lW4cGHHjgPO4t9y5DZcs8htuGaR23DN5t77vFwdJAUAAMhOmuHZqVMnqV+/vlx99dUyZswYOXnypBntXnXs2FHKli1rSubVk08+Kddff728/vrr0qZNG5k+fbqsXr1a3n333Rw+EgAAAAC+CJICAABk0D333COHDh2SQYMGmcGX6tatK/PmzfMOzrRr1y5T6WJr1KiRTJs2TQYOHCjPPfecXHrppWZk+5o1a+bgUQAAAABIjiApHKXlgYMHD05RJgjgX/ydALmbltanVl6/dOnSFPPuuusuM+HCwr/lyG24ZpHbcM0it+Gazf3CLO3BFAAAAAAAAABC1L/1YAAAAAAAAAAQggiSAgAAAAAAAAhpBEkBAAAAIASEhYWZweOcXhcAkDW+/+b+8ccf5vH69etzerdCDkFSOGrcuHFSqVIlyZcvnzRo0EBWrVqV07sEuMp3330nt956q5QpU4abDwBw0IoVKyQiIkLatGmT07sCZMhDDz1k2gI6RUZGSpUqVWTYsGFy9uzZoL3mvn375Oabb3Z8XYTeNes7/f777+fdzk1KSpJXXnlFqlWrJvnz55eiRYua+8j3338/G44ISHlt582bVypXrizPPPOMnDlzJqd3DdmMICkcM2PGDOnbt68ZzW3t2rVSp04dadmypRw8eDCndw1wjZMnT5q/Df1CAQDgnIkTJ0qvXr3MTfrevXtzbD8SEhJy7LWR+7Rq1coEI3/77Td56qmnZMiQIfLqq68G7boqVapUhkddzsy6CL1r1nfSgNL5tnOHDh0qo0ePluHDh8vmzZvl22+/lUceeUSOHj0atGPg32mkdW3v2LHDXJPvvPOOiW0gtBAkhWNGjRol3bp1k86dO0uNGjVkwoQJEh0dLZMmTcrpXQNcQzMyXnzxRbn99ttzelcA4IJx4sQJ82Vt9+7dTSbplClT/JbPmjVLrrrqKlPpUrx4cb9/g+Pj46V///5Svnx5ExDSbD4NuCrdTuHChf22pZlRmmli06BW3bp1TdaTBgr0NdS8efPk2muvNc8vVqyY3HLLLbJ9+3a/bf31119y3333mcypAgUKSP369eWnn34yZXbh4eGyevVqv/XHjBkjFStWFI/H4+DZQ07Sa06Dkfq+6vXbvHlz+frrr01WU7t27eSll14yWXlVq1Y16+/evVvuvvtuc13pddO2bVtzvfjStvfll19utl26dGnp2bOnd5lvdp8GinSZrqPXre7DiBEjAq6rNmzYIDfccIPJ9tNrWgNZ+rdns/f5tddeM9vUdXr06CGJiYlBPYfImWvWd9Is/vNt5+r1/vjjj8tdd91l/g3VIGuXLl3k6aef9q6j/+aNHDnS/Pusr1+hQgXzt5HZa/N8/p4Qete2tgf0etF/jxcuXOi9BvXfR71G9TrT6/Szzz7ze/6mTZvMZ32hQoUkJiZGrrvuOu/n/s8//yw33XSTaYPExsbK9ddfbxLL4D4ESeEIbWStWbPG/ENi08a9PtbyNwAAgGD55JNPTKmm3vg++OCDJkhkWZZZNmfOHHPD3rp1a1m3bp0sXrxYrr76au9zO3bsKB9//LG88cYbsmXLFpM5UrBgwUy9vpaafv755/LFF194+w/TjCqtsNFAp76mtot0P+wAp97A603Snj17TJDgl19+MaV9uly7LtI21OTJk/1eRx/rzb5uCxcmvfm2s9z0utm2bZu5SZ89e7YJNmqVlt58f//99/Ljjz+aa1Wzn+znjB8/3gQmNUikgSO9tjSwFIhe87pc/370dT766CNz7QWi17O+dpEiRczN/qeffiqLFi3yC8AqzQLUoID+nDp1qvmiIfmXFoAvDUotWbJEDh06lOo6AwYMMCX5L7zwgsk2nTZtmpQsWTJT1+b5/D0hdG3cuFGWL19uukJRGiD94IMPTCKYBkP79Olj2hvLli0zy/WzvEmTJibQqtezxkYefvhhb/cpx48fl06dOskPP/wgK1eulEsvvdS0S3Q+XMYCHLBnzx69E7GWL1/uN79fv37W1VdfnWP7BbiZ/s3MnDkzp3cDAHK9Ro0aWWPGjDG/JyYmWsWLF7e+/fZb87hhw4bWAw88EPB527ZtM/8WL1y4MODyyZMnW7GxsX7z9N9t3yb04MGDrbx581oHDx5Mcx8PHTpknrdhwwbz+J133rFiYmKsv//+O+D6M2bMsIoUKWKdOXPGPF6zZo0VFhZm7dy5M83XQe7RqVMnq23btuZ3j8djrsOoqCjr6aefNstKlixpxcfHe9f/73//a1WtWtWsa9Pl+fPnt+bPn28elylTxnr++ecz1Pbo1auXdcMNN/htL7V13333XXM9njhxwrt8zpw5Vnh4uLV//37v8VSsWNE6e/asd5277rrLuueee877HMFd9D2OiIiwChQo4J3uvPPOLLVzN23aZFWvXt1cS7Vq1bIeffRRa+7cud7lcXFx5u/ivffeC/j8jF6b5/P3hNC8tvV60+tXr6HPPvvMfA5HR0eniHV06dLFuu+++8zvAwYMsCpXrmwlJCRk6PWSkpJMG2DWrFkB/2b0s14fr1u3ztHjRPr4GhoAAAC5lmYG6UCRWrau8uTJI/fcc4+3ZF4zO2+88caAz9VlWiaqGZ1ZoWXKF110kd887WNS9+niiy82pXd2ht6uXbu8r33FFVeYEs9AtNRP923mzJnmsWbjNWvWLNVMP+ROmtGm2Wta7q6lynrtahcOqlatWt4sJqXZxpq1rJlv+hyd9PrRgUU0e1PHAdD+eFO73pPTrGS9DjUD+4knnpAFCxakuq5mWWt5qXYLYWvcuLHJfNa/QZuW+dul10rL7hmf4MKi/w7pdWNPmpGcFdpNm2btaXadZt7p9aKDP3Xt2tV77Wm3KKld1xm9NjP794TQvba12xvN+tRuBNu3b2+uk1OnTplyefta0UkzS+1rRZ+n5fU66FMgBw4cMF0Tagaplttru0ArSuw2AdwjT07vAC4M2reGNoj0j9+XPtYSCgAAgGDQYKiWs2k/czZNyNCSt7feesuUL6cmrWVKy9rtsn1boP4VfW/ObXqTr8HT9957z+yb3rDXrFnTW8aZ3mvrzbx2BaAl9nfccYcpLx07dmyaz0HuvCnXEnl9v/U60SB/ateV3lDXq1fPlMUnp0H6zHbDcOWVV8rOnTvlm2++MeXJ2jejdvOQvJ+9zEgeINB+TelD98Ki12VqXTicL712td9onXr37i0ffvihdOjQQZ5//vl0/63MqMz+PSG0r23ttkeD79rG0M9uu/uesmXL+j3HHtwuvetUg65///23+RzXtoE+r2HDhnTt4EJkksIR2rDTDxnt68WmDSJ9rH/8AAAATtPgqGZyvP76636ZTZohpAEn7Wu0du3afu0TX5pZpO0Vu0+xQDfK2l+Y9nlns/scTYveCGkG08CBA032U/Xq1eWff/7xW0f3S7d15MiRVLejmVQavHr77bfNsWqwFBfmTbkOROMbIE0tqKkZyiVKlDDP8Z00M0kz4jTTOLXrPRDNZtLsVQ3m6+Bn2rduoGtSr2H9u/L9W9A+HDW4ZQ+CAzhFs0uVXm+aeacBqNSu6/O9NtP7e0Jo0+vnueeeM5/jej1qUFOzPpNfKzrIk/2Zrn3bpjZQnV6TmrGv/ZDaA+sdPnw4m48KGUGQFI7RwQm0gaWdtGvZg47QqR9WmqYO4N9vre2beKUZHPo7pRYAcH6lyhp81JGQNdPDd9ISOc0AGTx4sAmW6k9tn+hgNv/5z3/M8zWgpNkdWuKpo3jrv8lLly41A9moBg0aSHR0tLlR0pI6zebMyCA0OoCIjrD87rvvmjI9HcRB20m+tBRfq220rF5vnnbs2GECVL4DXurN/zXXXCP9+/c36zuVUYXc6YEHHjDVWzoCt96M29er3nj/9ddfZh0t1dcvDbQEWgNAOnrym2++GXB7o0aNMn8bW7dulV9//dUMeKPXpI70Hei1tUsA/XvR0mgdmKlXr14m288eQAc4n3bunXfeKaNHjzYlzn/++ae5pnXwscsuu8wMyKfXnf4bqAPb2eXNWppvd6lyvtdmRv6eENruuusuUy2rAzo+/fTTZrAmjXXoNWj/26qPlQ4UFhcXJ/fee68ZsFH//f3vf//r7fJBg/36WNsheq3r9cdnujsRJIVj9Fvo1157TQYNGiR169Y1H4jz5s2j4QT40A9N7YNOJ6U3zfq7/t0AADJHb5K1PDhQ1o8GSfXfXO1jToM/Ooq3tk9uuOEG04epTUud9Sb98ccfNzfk2meYnZGkz9Wyz7lz55qsUw0o2f1FppeBMn36dDO6rQZs9cbq1VdfTVGFo31AahaTZpbo9nX0Zt/+HJUGgLUcTwO5CG0asP/uu+9M1qlmFWsQXa8P7UNRM0KVBorGjBljso81W+mWW24xN+uBaObpyJEjpX79+qbM+Y8//jDXeqCyfX3t+fPnmyxTXVf/ZjRLWru0ALLSztUR5mfNmmW6KNHAqF7D+m+x/vtoZ1frqPZPPfWU2Y5e93rfafd1e77XZkb+nhDa9PrT4Kf+OzlgwABzHeoo93qttGrVypTfV65c2ayrX4zqF6L6RYH2c65VtppAZndBou0V/VJXM5g1gK/BeP38h/uE6ehNOb0TAAAAAFIaPny4CfL+3//9X07vCgAAwAWNTFIAAADAZTQbRUtHNRtKS0cBAAAQXARJAQAAAJfREj8t12vatCml9gAAANmAcnsAAAAAAAAAIY1MUgAAAAAAAAAhjSApAAAAAAAAgJBGkBQAAAAAAABASCNICgAAAAAAACCkESQFAAAAAAAAENIIkgIu8dBDD0m7du28j5s2bSq9e/fO9v1YunSphIWFydGjR1NdR5d/+eWXGd7mkCFDpG7dulnarz/++MO87vr167O0HQAAAAAAgOQIkgLpBC41MKdTZGSkVKlSRYYNGyZnz54N+mt/8cUXMnz4cMcCmwAAAAAAAAgsTyrzAfxPq1atZPLkyRIfHy9z586VHj16SN68eWXAgAEp1k1ISDDBVCcULVrUke0AAAAAAAAgbWSSAumIioqSUqVKScWKFaV79+7SvHlz+frrr/1K5F966SUpU6aMVK1a1czfvXu33H333VK4cGET7Gzbtq0pF7clJSVJ3759zfJixYrJM888I5Zl+b1u8nJ7DdL2799fypcvb/ZJs1onTpxottusWTOzTpEiRUxGqe6X8ng8MmLECKlcubLkz59f6tSpI5999pnf62jg97LLLjPLdTu++5lRul+6jejoaLn44ovlhRdekMTExBTrvfPOO2b/dT09P8eOHfNb/v7770v16tUlX758Uq1aNXn77bczvS8AAAAAAACZRZAUyCQNJmrGqG3x4sWybds2WbhwocyePdsEB1u2bCkxMTHy/fffy48//igFCxY0Gan2815//XWZMmWKTJo0SX744Qc5cuSIzJw5M83X7dixo3z88cfyxhtvyJYtW0zAUberQcfPP//crKP7sW/fPhk7dqx5rAHSDz74QCZMmCCbNm2SPn36yIMPPijLli3zBnPvuOMOufXWW01fn127dpVnn3020+dEj1WPZ/Pmzea133vvPRk9erTfOr///rt88sknMmvWLJk3b56sW7dOHn/8ce/yjz76SAYNGmQCznp8L7/8sgm2Tp06NdP7AwAAAAAAkBmU2wMZpJmeGhCdP3++9OrVyzu/QIECJgPSLrP/8MMPTQanztOsTqXl+po1qn2HtmjRQsaMGWPK9TVAqTSIqdtNza+//moCjBqI1UxWpRmbyUvzS5QoYV7HzjzVQOOiRYukYcOG3udoUFYDrNdff72MHz9eLrnkEhO0VZoJu2HDBvnPf/6TqXMzcOBA7++VKlWSp59+WqZPn24yZG1nzpwxAduyZcuax2+++aa0adPGvLZm6g4ePNj8bp8TzX7VoKvua6dOnTK1PwAAAAAAAJlBkBRIh2aHasamZohq8PP+++83o7XbatWq5dcP6S+//GKyJjW70pcGCbdv325KzDXbs0GDBt5lefLkkfr166coubdplmdERIQJbGaU7sOpU6fkpptu8puv2axXXHGF+V0zNn33Q9kB1cyYMWOGyXDV4ztx4oQZ2KpQoUJ+61SoUMEbILVfR8+nZr/qudLndunSRbp16+ZdR7cTGxub6f0BAAAAAADIDIKkQDq0n07NuNRAqPY7qgFNX5pJ6kuDhPXq1TPl48lddNFF513in1m6H2rOnDl+wUmlfZo6ZcWKFfLAAw/I0KFDTTcDGtTULFI7OzUz+6pl+smDthocBgAAAAAACCaCpEA6NAiqgyRl1JVXXmkyK7X0PXk2pa106dLy008/SZMmTbwZk2vWrDHPDUSzVTXrUvsStcvtfdmZrDoglK1GjRomGLpr165UM1B1kCR7ECrbypUrJTOWL19uBrV6/vnnvfP+/PPPFOvpfuzdu9cEmu3XCQ8PNyX+JUuWNPN37NhhAq4AAAAAAADZiYGbAIdpkK948eJmRHsduGnnzp2mL9InnnhC/vrrL7POk08+Ka+88op8+eWXsnXrVjOA0dGjR1Pdpvbzqf1yPvzww+Y59ja1n1KlQUrt/1S7Bjh06JDJzNQSdu0bVAdr0sGPtJx97dq1pi9QezCkxx57TH777Tfp16+fKXufNm2aGYApMy699FITANXsUX0NLbsPNAiVjlivx6DdEeh50fOhI9xrf6RKM1F1oCl9vvbBqn2jal+uo0aNytT+AAAAAAAAZBZBUsBh0dHR8t1335k+OHUQIs3W1L42tU9SO7P0qaeekg4dOpigofbNqQHN22+/Pc3tasn/nXfeaQKq1apVM313njx50izTcnoNMurI9JqV2bNnTzN/+PDhZoR4DT7qfrRq1cqU3+ugSEr38fPPPzeB1zp16pgBpHSwp8y47bbbTCBWX7Nu3boms1RfMznNxtXz0bp1azN4Ve3ateXtt9/2Lu/atasZ7EoDo5o5q9mvGrC19xUAAAAAACBYwqzURooBAAAAAAAAgBBAJikAAAAAAACAkEaQFAAAAAAAAEBII0gKAAAAAAAAIKQRJAUAAAAAAAAQ0giSAgAAAAAAAAhpBEkBAAAAAAAAhDSCpAAAAAAAAABCGkFSAAAAAAAAACGNICkAAAAAAACAkEaQFAAAAAAAAEBII0gKAAAAAAAAIKQRJAUAAAAAAAAgoez/AclNVuE3v5J4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(xgb, 'XGBoost', X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forests | Acc: 0.54% | Precision: 0.59% | F1 Score: 0.59% | Recall: 0.60%\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(rf, 'Random Forests', X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression | Acc: 0.56% | Precision: 0.58% | F1 Score: 0.69% | Recall: 0.86%\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(lg, 'LogisticRegression', X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Hospital Visits</th>\n",
       "      <th>Lung Capacity</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Smoking Status</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Disease Type_Asthma</th>\n",
       "      <th>Disease Type_Bronchitis</th>\n",
       "      <th>Disease Type_COPD</th>\n",
       "      <th>Disease Type_Lung Cancer</th>\n",
       "      <th>Disease Type_Pneumonia</th>\n",
       "      <th>Treatment Type_Medication</th>\n",
       "      <th>Treatment Type_Surgery</th>\n",
       "      <th>Treatment Type_Therapy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71</td>\n",
       "      <td>14</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>7</td>\n",
       "      <td>3.501865</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>3.501865</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Hospital Visits  Lung Capacity  Gender  Smoking Status  Recovered  \\\n",
       "0   71               14       4.490000       0               0          1   \n",
       "1   34                7       3.501865       0               1          0   \n",
       "2   80                4       1.950000       1               1          1   \n",
       "3   40                1       3.501865       0               1          0   \n",
       "4   43                7       4.600000       1               1          1   \n",
       "\n",
       "   Disease Type_Asthma  Disease Type_Bronchitis  Disease Type_COPD  \\\n",
       "0                    0                        0                  1   \n",
       "1                    0                        1                  0   \n",
       "2                    0                        0                  1   \n",
       "3                    0                        1                  0   \n",
       "4                    0                        0                  1   \n",
       "\n",
       "   Disease Type_Lung Cancer  Disease Type_Pneumonia  \\\n",
       "0                         0                       0   \n",
       "1                         0                       0   \n",
       "2                         0                       0   \n",
       "3                         0                       0   \n",
       "4                         0                       0   \n",
       "\n",
       "   Treatment Type_Medication  Treatment Type_Surgery  Treatment Type_Therapy  \n",
       "0                          0                       0                       1  \n",
       "1                          0                       1                       0  \n",
       "2                          1                       0                       0  \n",
       "3                          1                       0                       0  \n",
       "4                          0                       1                       0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_oh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def prediction(model, age: int, gender: str,\n",
    "               smoke_status: str, lung_capacity: float,\n",
    "               disease_type: str, treatment_type: str,\n",
    "               hospital_visits: int\n",
    "               ) -> int:\n",
    "    \n",
    "    df_input = pd.DataFrame(\n",
    "        {'Age': [age],\n",
    "         'Hospital Visits': [hospital_visits],\n",
    "         'Lung Capacity': [lung_capacity],\n",
    "         'Gender': [1 if gender == \"Male\" else 0],\n",
    "         'Smoking Status': [1 if smoke_status == \"Yes\" else 0],\n",
    "         'Disease Type_Asthma': [1 if disease_type in 'Disease Type_Asthma' else 0],\n",
    "         'Disease Type_Bronchitis': [1 if disease_type in 'Disease Type_Bronchitis' else 0],\n",
    "        'Disease Type_COPD': [1 if disease_type in 'Disease Type_COPD' else 0],\n",
    "        'Disease Type_Lung Cancer': [1 if disease_type in 'Disease Type_Lung Cancer' else 0],\n",
    "        'Disease Type_Pneumonia': [1 if disease_type in 'Disease Type_Pneumonia' else 0],\n",
    "        \n",
    "        'Treatment Type_Medication': [1 if treatment_type in 'Treatment Type_Medication' else 0],\n",
    "        'Treatment Type_Surgery': [1 if treatment_type in 'Treatment Type_Surgery' else 0],\n",
    "        'Treatment Type_Therapy': [1 if treatment_type in 'Treatment Type_Therapy' else 0]\n",
    "         }\n",
    "    )\n",
    "    \n",
    "    input_arr = np.array(df_input)\n",
    "    \n",
    "    prediction = model.predict(input_arr)[0]\n",
    "    \n",
    "    return prediction.item()\n",
    "    \n",
    "    \n",
    "prediction(lg, 20, 'Male', 'Yes', 7.14, 'COPD', 'Surgery', 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the models\n",
    "from joblib import dump\n",
    "from pathlib import Path\n",
    "import os\n",
    "\n",
    "\n",
    "model_dir = Path('./models/')\n",
    "\n",
    "if not os.path.exists(model_dir):\n",
    "    os.makedirs(model_dir, exist_ok=True)\n",
    "\n",
    "dump(nb, model_dir/'GaussianNB.pkl')\n",
    "dump(rf, model_dir/'RandomForests.pkl')\n",
    "dump(xgb, model_dir/'XGBoost.pkl')\n",
    "dump(svm, model_dir/'SVM.pkl')\n",
    "dump(lg, model_dir/'LogisticRegression.pkl')\n",
    "df_oh.to_csv('preprocessed_data.csv', index=False)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}