investigate-Doctor_No_Show-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Investigate Doctor_No_Show Dataset\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "This dataset collects information from 100k medical appointments in Brazil and it's focused on:\n",
    "1. the question of whether or not patients show up for their appointment\n",
    "2. what factors are effecting their decision.\n",
    "\n",
    "##### *dataset's columns:*\n",
    "* Be careful about the encoding of the 'Attendance' column: it says ‘No’ if the patient showed up to their appointment, and ‘Yes’ if they did not show up.\n",
    "* 'SMS_Received' indecates whether or not the patient received a reminder message.\n",
    "* ‘Subsidy’ indicates whether or not the patient is enrolled in Brasilian welfare program Bolsa Família.\n",
    "* ‘Scheduled_Day’ tells us on what day the patient set up their appointment.\n",
    "* 'Appointment_Day' tells us on what date was the appointment.\n",
    "* 'Hipertension', 'Diabetes', 'Alcoholism', 'Handicap' columns: 1 if the pateint Infected with any of these chronic illnesses and 0 if not.\n",
    "\n",
    "##### *You can find the original datast [here](https://www.kaggle.com/joniarroba/noshowappointments)*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing important packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>SMS_Received</th>\n",
       "      <th>Subsidy</th>\n",
       "      <th>Scheduled_Day</th>\n",
       "      <th>Appointment_Day</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40101</th>\n",
       "      <td>No</td>\n",
       "      <td>F</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-05-04T12:52:34Z</td>\n",
       "      <td>2016-05-06T00:00:00Z</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>44617</th>\n",
       "      <td>No</td>\n",
       "      <td>F</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-05-05T14:21:10Z</td>\n",
       "      <td>2016-05-19T00:00:00Z</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10197</th>\n",
       "      <td>No</td>\n",
       "      <td>M</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-15T08:05:22Z</td>\n",
       "      <td>2016-05-04T00:00:00Z</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103516</th>\n",
       "      <td>No</td>\n",
       "      <td>F</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-03T09:31:17Z</td>\n",
       "      <td>2016-06-08T00:00:00Z</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36972</th>\n",
       "      <td>No</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-05-03T14:15:31Z</td>\n",
       "      <td>2016-05-12T00:00:00Z</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Attendance Gender  Age  SMS_Received  Subsidy         Scheduled_Day  \\\n",
       "40101          No      F   51             0        0  2016-05-04T12:52:34Z   \n",
       "44617          No      F   14             0        0  2016-05-05T14:21:10Z   \n",
       "10197          No      M   77             0        0  2016-04-15T08:05:22Z   \n",
       "103516         No      F   29             1        0  2016-06-03T09:31:17Z   \n",
       "36972          No      F   20             1        0  2016-05-03T14:15:31Z   \n",
       "\n",
       "             Appointment_Day  Hipertension  Diabetes  Alcoholism  Handicap  \n",
       "40101   2016-05-06T00:00:00Z             0         0           0         0  \n",
       "44617   2016-05-19T00:00:00Z             0         0           0         0  \n",
       "10197   2016-05-04T00:00:00Z             1         1           0         0  \n",
       "103516  2016-06-08T00:00:00Z             0         0           0         0  \n",
       "36972   2016-05-12T00:00:00Z             0         0           0         0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the dataset and investigating it  \n",
    "dataframe = pd.read_csv('KaggleV2-May-2016.csv') \n",
    "dataframe.sample(5)\n",
    "\n",
    "# PS: I changed some column names in the excel sheet + dropped the columns i don't need for analysis just to make it cleaner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#my own shape function\n",
    "def myshape (df):\n",
    "    #first counting rows:\n",
    "    rows = 0\n",
    "    for row in df.iterrows():\n",
    "        rows += 1\n",
    "    #now columns:\n",
    "    columns = 0\n",
    "    for column in df.columns:\n",
    "        columns += 1\n",
    "    return (rows, columns)\n",
    "\n",
    "myshape(dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#testing\n",
    "dataframe.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count   Dtype \n",
      "---  ------           --------------   ----- \n",
      " 0   Attendance       110527 non-null  object\n",
      " 1   Gender           110527 non-null  object\n",
      " 2   Age              110527 non-null  int64 \n",
      " 3   SMS_Received     110527 non-null  int64 \n",
      " 4   Subsidy          110527 non-null  int64 \n",
      " 5   Scheduled_Day    110527 non-null  object\n",
      " 6   Appointment_Day  110527 non-null  object\n",
      " 7   Hipertension     110527 non-null  int64 \n",
      " 8   Diabetes         110527 non-null  int64 \n",
      " 9   Alcoholism       110527 non-null  int64 \n",
      " 10  Handicap         110527 non-null  int64 \n",
      "dtypes: int64(7), object(4)\n",
      "memory usage: 9.3+ MB\n"
     ]
    }
   ],
   "source": [
    "dataframe.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>SMS_Received</th>\n",
       "      <th>Subsidy</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.321026</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.110205</td>\n",
       "      <td>0.466873</td>\n",
       "      <td>0.297675</td>\n",
       "      <td>0.397921</td>\n",
       "      <td>0.258265</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>115.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Age   SMS_Received        Subsidy   Hipertension  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean       37.088874       0.321026       0.098266       0.197246   \n",
       "std        23.110205       0.466873       0.297675       0.397921   \n",
       "min        -1.000000       0.000000       0.000000       0.000000   \n",
       "25%        18.000000       0.000000       0.000000       0.000000   \n",
       "50%        37.000000       0.000000       0.000000       0.000000   \n",
       "75%        55.000000       1.000000       0.000000       0.000000   \n",
       "max       115.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "            Diabetes     Alcoholism       Handicap  \n",
       "count  110527.000000  110527.000000  110527.000000  \n",
       "mean        0.071865       0.030400       0.022248  \n",
       "std         0.258265       0.171686       0.161543  \n",
       "min         0.000000       0.000000       0.000000  \n",
       "25%         0.000000       0.000000       0.000000  \n",
       "50%         0.000000       0.000000       0.000000  \n",
       "75%         0.000000       0.000000       0.000000  \n",
       "max         1.000000       1.000000       4.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "722"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(dataframe.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Quality Issues:*\n",
    "\n",
    "* erroneous data type of (Gender, Scheduled_Day, Appointment_Day, Subsidy, Hipertension, Diabetes, Alcoholism, Handicap, SMS_Received, Attendance) columns.\n",
    "* inaccurate data. (Age < 0), (Handicap Values > 1), (some appointment dates are after the schudled dates)\n",
    "* inconsistent dates in Appointment_Day column and Scheduled_Day column.\n",
    "* duplicated data.\n",
    "\n",
    "### *Tidness Issues:*\n",
    "\n",
    "-  1 variable (Diseases) is divided into 4 columns (Hipertension, Diabetes, Alcoholism, Handicap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's take a copy first\n",
    "df = dataframe.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110327, 11)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping the inaccurate data\n",
    "df.drop(df.query('Handicap > 1').index, inplace = True)\n",
    "df.drop(df.query('Age < 0').index, inplace = True)\n",
    "\n",
    "# testing:\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attendance                   bool\n",
       "Gender                   category\n",
       "Age                         int64\n",
       "SMS_Received                 bool\n",
       "Subsidy                      bool\n",
       "Scheduled_Day      datetime64[ns]\n",
       "Appointment_Day    datetime64[ns]\n",
       "Hipertension                int64\n",
       "Diabetes                    int64\n",
       "Alcoholism                  int64\n",
       "Handicap                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correctio the data types\n",
    "df.Scheduled_Day = pd.to_datetime(df.Scheduled_Day).dt.floor('d').astype('datetime64[ns]') #notice this may lead to more duplicated data.\n",
    "df.Appointment_Day = pd.to_datetime(df.Appointment_Day).astype('datetime64[ns]')\n",
    "\n",
    "df.Gender = df.Gender.astype('category')\n",
    "\n",
    "# make the values in attendance column more logical\n",
    "df.Attendance.replace({'Yes': 0, 'No': 1}, inplace = True)\n",
    "df[['Subsidy', 'SMS_Received', 'Attendance']] = df[['Subsidy', 'SMS_Received', 'Attendance']].astype('bool')\n",
    "\n",
    "# testing:\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>Chronic_Illnesses</th>\n",
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      "text/plain": [
       "        Hipertension  Diabetes  Alcoholism  Handicap Chronic_Illnesses\n",
       "10040              0         0           0         0              Zero\n",
       "53310              1         0           0         0               One\n",
       "104354             1         0           0         1               Two\n",
       "66301              1         0           0         0               One\n",
       "85635              0         0           0         0              Zero"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the divided data\n",
    "df['Chronic_Illnesses'] = df.Hipertension + df.Diabetes + df.Alcoholism + df.Handicap\n",
    "df.Chronic_Illnesses.replace({0 : 'Zero', 1 : \"One\", 2 : \"Two\", 3 : \"Three\", 4 : \"Four\"}, inplace = True)\n",
    "df.Chronic_Illnesses = df.Chronic_Illnesses.astype(\"category\")\n",
    "\n",
    "# testing\n",
    "df[['Hipertension', 'Diabetes', 'Alcoholism', 'Handicap','Chronic_Illnesses']].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attendance                     bool\n",
       "Gender                     category\n",
       "Age                           int64\n",
       "SMS_Received                   bool\n",
       "Subsidy                        bool\n",
       "Scheduled_Day        datetime64[ns]\n",
       "Appointment_Day      datetime64[ns]\n",
       "Hipertension                   bool\n",
       "Diabetes                       bool\n",
       "Alcoholism                     bool\n",
       "Handicap                       bool\n",
       "Chronic_Illnesses          category\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correct the erroneous datatypes\n",
    "df[['Hipertension', 'Diabetes', 'Alcoholism', 'Handicap']] = df[['Hipertension', 'Diabetes', 'Alcoholism', 'Handicap']].astype('bool')\n",
    "\n",
    "# testing \n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scheduled_Day</th>\n",
       "      <th>Appointment_Day</th>\n",
       "      <th>Waiting_Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27474</th>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52008</th>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67538</th>\n",
       "      <td>2016-05-16</td>\n",
       "      <td>2016-05-16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21283</th>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-05-19</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9728</th>\n",
       "      <td>2016-04-14</td>\n",
       "      <td>2016-05-02</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Scheduled_Day Appointment_Day  Waiting_Days\n",
       "27474    2016-04-29      2016-05-03             4\n",
       "52008    2016-05-09      2016-05-12             3\n",
       "67538    2016-05-16      2016-05-16             0\n",
       "21283    2016-04-27      2016-05-19            22\n",
       "9728     2016-04-14      2016-05-02            18"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating the waiting time in days\n",
    "df['Waiting_Days'] = (df.Appointment_Day - df.Scheduled_Day).dt.days\n",
    "\n",
    "#testing\n",
    "df[['Scheduled_Day', 'Appointment_Day', 'Waiting_Days']].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Attendance</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>SMS_Received</th>\n",
       "      <th>Subsidy</th>\n",
       "      <th>Scheduled_Day</th>\n",
       "      <th>Appointment_Day</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>Chronic_Illnesses</th>\n",
       "      <th>Waiting_Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Attendance, Gender, Age, SMS_Received, Subsidy, Scheduled_Day, Appointment_Day, Hipertension, Diabetes, Alcoholism, Handicap, Chronic_Illnesses, Waiting_Days]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping the erroneous data:\n",
    "df.drop(df.query('Waiting_Days < 0').index, inplace = True)\n",
    "\n",
    "# testing \n",
    "df.query('Waiting_Days < 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop duplicates\n",
    "df.drop_duplicates(inplace = True)\n",
    "\n",
    "#testing\n",
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>Chronic_Illnesses</th>\n",
       "      <th>Subsidy</th>\n",
       "      <th>SMS_Received</th>\n",
       "      <th>Waiting_Days</th>\n",
       "      <th>Scheduled_Day</th>\n",
       "      <th>Appointment_Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72568</th>\n",
       "      <td>True</td>\n",
       "      <td>M</td>\n",
       "      <td>71</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Two</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>2016-06-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72569</th>\n",
       "      <td>True</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zero</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>2016-06-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Attendance Gender  Age  Hipertension  Diabetes  Alcoholism  Handicap  \\\n",
       "72568        True      M   71          True      True       False     False   \n",
       "72569        True      M   20         False     False       False     False   \n",
       "\n",
       "      Chronic_Illnesses  Subsidy  SMS_Received  Waiting_Days Scheduled_Day  \\\n",
       "72568               Two    False         False             0    2016-06-08   \n",
       "72569              Zero     True         False             0    2016-06-08   \n",
       "\n",
       "      Appointment_Day  \n",
       "72568      2016-06-08  \n",
       "72569      2016-06-08  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# final touch \n",
    "df = df[['Attendance', 'Gender', 'Age', 'Hipertension', 'Diabetes', 'Alcoholism', 'Handicap', 'Chronic_Illnesses', 'Subsidy', 'SMS_Received','Waiting_Days', 'Scheduled_Day', 'Appointment_Day']]\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Storing the final dataset\n",
    "df.to_csv('Final_Dataset.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "\n",
    "### What factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>Chronic_Illnesses</th>\n",
       "      <th>Subsidy</th>\n",
       "      <th>SMS_Received</th>\n",
       "      <th>Waiting_Days</th>\n",
       "      <th>Scheduled_Day</th>\n",
       "      <th>Appointment_Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25487</th>\n",
       "      <td>False</td>\n",
       "      <td>F</td>\n",
       "      <td>58</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>Two</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>11</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-05-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66627</th>\n",
       "      <td>False</td>\n",
       "      <td>M</td>\n",
       "      <td>47</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zero</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>2016-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13853</th>\n",
       "      <td>True</td>\n",
       "      <td>M</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zero</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>16</td>\n",
       "      <td>2016-04-20</td>\n",
       "      <td>2016-05-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19076</th>\n",
       "      <td>True</td>\n",
       "      <td>F</td>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zero</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-04-27</td>\n",
       "      <td>2016-05-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31965</th>\n",
       "      <td>True</td>\n",
       "      <td>F</td>\n",
       "      <td>57</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Zero</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>2016-05-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Attendance Gender  Age  Hipertension  Diabetes  Alcoholism  Handicap  \\\n",
       "25487       False      F   58          True      True       False     False   \n",
       "66627       False      M   47         False     False       False     False   \n",
       "13853        True      M   30         False     False       False     False   \n",
       "19076        True      F   11         False     False       False     False   \n",
       "31965        True      F   57         False     False       False     False   \n",
       "\n",
       "      Chronic_Illnesses  Subsidy  SMS_Received  Waiting_Days Scheduled_Day  \\\n",
       "25487               Two    False          True            11    2016-04-29   \n",
       "66627              Zero    False         False             0    2016-06-01   \n",
       "13853              Zero    False          True            16    2016-04-20   \n",
       "19076              Zero    False          True             7    2016-04-27   \n",
       "31965              Zero    False         False            12    2016-05-04   \n",
       "\n",
       "      Appointment_Day  \n",
       "25487      2016-05-10  \n",
       "66627      2016-06-01  \n",
       "13853      2016-05-06  \n",
       "19076      2016-05-04  \n",
       "31965      2016-05-16  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Waiting_Days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>72570.000000</td>\n",
       "      <td>72570.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.089321</td>\n",
       "      <td>14.019416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.141220</td>\n",
       "      <td>16.784029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>115.000000</td>\n",
       "      <td>179.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age  Waiting_Days\n",
       "count  72570.000000  72570.000000\n",
       "mean      40.089321     14.019416\n",
       "std       23.141220     16.784029\n",
       "min        0.000000      0.000000\n",
       "25%       21.000000      2.000000\n",
       "50%       41.000000      7.000000\n",
       "75%       58.000000     21.000000\n",
       "max      115.000000    179.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's check the statistical model of our data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now take a look of that spread\n",
    "plt.style.use(\"ggplot\")\n",
    "\n",
    "df.hist(figsize = (15,10))\n",
    "\n",
    "plt.tight_layout;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we can see that the majorty of patients are in their 50s, with waiting time almost 2 weeks.. lots of schedules were made May and June 2016 for appointments in May-August  of the same year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reassign the index to time to make a time series analysis.\n",
    "Scheduled_df = df.copy()\n",
    "Scheduled_df.set_index('Scheduled_Day', inplace = True)\n",
    "monthly_scheduled = Scheduled_df.resample('m').agg({'Attendance': 'sum'})\n",
    "\n",
    "#plotting scheduled days over time\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "plt.plot_date(x = monthly_scheduled.index, y = monthly_scheduled['Attendance'], linestyle = '--')\n",
    "\n",
    "plt.title(\"Scheduled Appointments Over Months\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Average number of Scheduled Appointments\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### there was a raising with peak on June 2016 but suddenly went down again. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reassign the index to time to make a time series analysis.\n",
    "Appointment_df = df.copy()\n",
    "Appointment_df.set_index('Appointment_Day', inplace = True)\n",
    "monthly_appointments = Appointment_df.resample('m').agg({'Attendance': 'sum'})\n",
    "\n",
    "#plotting account activity over time\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "plt.plot_date(x = monthly_appointments.index, y = monthly_appointments['Attendance'], linestyle = '--')\n",
    "\n",
    "plt.title(\"Appointments Per Month Over Years\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Appointments\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### constant raising till the peak on -again- june 2016 then dropping again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#how many of them actually miss their appointment?\n",
    "plt.figure(figsize=(15,15))\n",
    "\n",
    "x = df.Attendance.value_counts()\n",
    "labels = 'Absentees', 'Attendees'\n",
    "sizes = [x[0], x[1]]\n",
    "\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%')\n",
    "\n",
    "plt.title(\"Percentage Of Attendees\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Well, the majority seems to be committed 😊"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#do the gender has an effect o the attendance?\n",
    "plt.figure(figsize=(15,15))\n",
    "\n",
    "x1 = df.groupby(\"Gender\").Attendance.sum()\n",
    "labels = 'Females', 'Males'\n",
    "sizes = [x1[0], x1[1]]\n",
    "\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%')\n",
    "plt.title(\"Which gender is more committed?\")\n",
    "\n",
    "\n",
    "#and is it really because females are more comitted or are they just  majorty in the dataset?\n",
    "plt.figure(figsize=(15,15))\n",
    "\n",
    "x2 = df.Gender.value_counts()\n",
    "labels = 'Females', 'Males'\n",
    "sizes = [x2[0], x2[1]]\n",
    "\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%')\n",
    "plt.title(\"Percentage Of each gander in the dataset\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### most of the attendees are females? well, the majorty of sample are females!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gorping age for analysis seek\n",
    "df['Age_Groups'] = pd.cut(df.Age, [0, 14, 24, 64, 120], labels = ['Children', 'Youth', 'Adults', 'Seniors'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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R4gAAAAAAvtGtsDhnzhx9/vnnslgsuvnmmyVJJpNJDz/8sEeLAwAAAAD4RrfCoiTdcsstqq2tVXl5ueLi4jR48GBP1gUAAAAA8KFuhcXa2lqtW7dOhw4dkiS9/fbbKikpUVlZmRYsWODJ+gAAAAAAPtCtr8544403lJCQoM2bN8vP70y+HD58uL744guPFgcAAAAA8I1uhcWKigrNmDFDZvP/Xz0oKMj93YsAAAAAgN6lW2ExLCxMx44dO6etqqpKdrvdI0UBAAAAAHyrW9cs3nXXXXr55Zc1Y8YMdXR0aOfOncrNzdWMGTM8XB4AAAAAwBe6FRanTJmi4OBgffTRR7LZbCosLNR9992n0aNHe7o+AAAAAIAPdPurM0aPHk04BAAAAICrRLfCosvl0kcffaTi4mLV19fr1Vdf1VdffaUTJ04oKSnJ0zUCAAAAALysWze42bJliz7++GPddtttqq2tlSTZbDa9//77Hi0OAAAAAOAb3QqLBQUFWr58ucaPHy+TySRJioyMVE1NjUeLAwAAAAD4RrfCYkdHh/r27XtOW3Nzs6ENAAAAANA7dCssJiQk6K233lJra6ukM9cwbtmyRSNHjvRocQAAAAAA3+hWWJw9e7YcDofmzJmjxsZGzZ49W8ePH9cDDzzg6foAAAAAAD7QrbuhBgUF6emnn9aJEydUW1sru92u/v37e7g0AAAAAICvdBoWOzo6DG2hoaEKDQ09Z7nZ3K3JSQAAAADAFaTTsHj//fd36wW2bNly2YoBAAAAAPQMnYbFzMxM9+M9e/aopKRE99xzj+x2u2pra/X+++9rzJgxXikSAAAAAOBdnYbFiIgI9+MPP/xQ6enp6tevnyQpKipKMTEx+ud//mfdfvvtnq8SAAAAAOBV3brgsLGxUT/88MM5bS0tLWpsbPRIUQAAAAAA3+rW3VCTk5O1cuVKTZs2TTabTXV1dfrf//1fJScne7o+AAAAAIAPdCssPvjggxowYICKi4vldDrVv39/3XHHHUpJSenWTrKzs7Vnzx6FhYVpzZo1kqStW7fqo48+ct9d9f7779eIESMkSbm5ucrPz5fZbFZqaqri4+MlSZWVlcrKylJLS4sSEhKUmpoqk8mk1tZWZWZmqrKyUiEhIUpLS1NkZOTF/iwAAAAAAP9Pt8Ki2WzW7bff/pOvT5w8ebLuvPNOZWVlndM+bdo03X333ee0VVVVqbi4WGvXrpXT6dTKlSu1bt06mc1m5eTkaP78+RoyZIheeukllZWVKSEhQfn5+erXr582bNigoqIivfPOO1q8ePFPqhUAAAAA0M2wKEmff/65Dh06pObm5nPa77vvvgtue9NNN6mmpqZb+yktLVVSUpL8/f0VGRmpAQMGqKKiQhEREWpqalJcXJwkadKkSSotLVVCQoJ2796te++9V5I0duxYbdq0SS6XSyaTqbvdAwAAAAD8SLfC4saNG/XJJ59o6NCh6tOnz2Xb+fbt21VYWKiYmBjNnj1bwcHBcjgcGjJkiHud8PBwORwOWSwW2Ww2d7vNZpPD4ZAkORwO9zKLxaKgoCCdOnXKfYrrj+Xl5SkvL0+SlJ6eLrvdftn6g57Bz8+P3yvQCcZH7/e9rwvAVY33l96NY8jVp1thsaioSK+88spl/c9x++23a+bMmZKkLVu26K233tLjjz8ul8t13vU7a+9sWWeziikpKedca1lbW3sxZeMKcPa7QAEYMT4AeBLvL70bx5DeKSoqqtNl3frqjJCQEPd3LF4u/fv3l9lsltls1m233aYDBw5Ikvtuq2c5HA6Fh4cb2uvq6hQeHm7Ypr29XY2NjQoODr6s9QIAAADA1aRbYXH69Olav369ysvL9f3335/z76dyOp3ux7t27VJ0dLQkKTExUcXFxWptbVVNTY2qq6sVGxsrq9WqwMBAlZeXy+VyqbCwUImJiZKkkSNHaseOHZKkkpISDR06lOsVAQAAAOASdOs01N/85jeSpD179hiWbdmy5YLbv/baa/rqq6906tQpLViwQLNmzdK+fft06NAhmUwmRURE6LHHHpMkRUdHa9y4cVqyZInMZrPmzp0rs/lMpp03b56ys7PV0tKi+Ph4JSQkSJKmTJmizMxMLVq0SMHBwUpLS+tW5wEAAAAA52dydXUx4FXg6NGjvi4Blxnn0wOdY3z0fu2P3n3hlQAPseR84OsS4EEcQ3qnS75m8aza2lqVl5dfckEAAAAAgJ6tW6eh1tbWat26dTp06JAk6e2331ZJSYnKysq0YMECT9YHAAAAAPCBbs0svvHGG0pISNDmzZvl53cmXw4fPlxffPGFR4sDAAAAAPhGt8JiRUWFZsyY4b7RjCQFBQWpsbHRY4UBAAAAAHynW2ExLCxMx44dO6etqqpKdrvdI0UBAAAAAHyrW9cs3nXXXXr55Zc1Y8YMdXR0aOfOncrNzdWMGTM8XB4AAAAAwBe6FRanTJmi4OBgffTRR7LZbCosLNR9992n0aNHe7o+AAAAAIAPdCssfvvttxo9erQhHFZUVCg2NtYjhQEAAAAAfKdb1yy++OKL521ftWrVZS0GAAAAANAzdDmz2NHRIUlyuVzuf2d9//33slgsnq0OAAAAAOATXYbF+++/3/34l7/85TnLzGaz7rnnHs9UBQAAAADwqS7DYmZmplwul1asWKFf/epX7naTyaTQ0FAFBAR4vEAAAAAAgPd1GRYjIiIkSXfeeaf78Y99+OGHmj59umcqAwAAAAD4TLducPPf//3fF9UOAAAAALiydTmzuHfvXklSe3u7+/FZx44dU2BgoOcqAwAAAAD4TJdh8de//rUkqbW11f1YOnPNYv/+/fXII494tjoAAAAAgE90GRazsrIknbnRzRNPPOFuP3z4sAoKCpSTk6PExETPVggAAAAA8Louw+JZTzzxhOrr67Vz504VFBTo0KFDuvHGGzVnzhwPlwcAAAAA8IUuw2JbW5t2796tgoIClZWVacCAARo/frxqamq0ePFihYWFeatOAAAAAIAXdRkWH330UZnNZiUnJ+vee+9VTEyMJOlPf/qTV4oDAAAAAPhGl1+dce211+r06dOqqKjQgQMH1NDQ4K26AAAAAAA+1OXM4ooVK3T8+HEVFBToD3/4g958800NHz5cP/zwg9rb271VIwAAAADAyy54g5uIiAjNnDlTM2fO1P79+1VQUCCTyaRly5bp1ltv1YMPPuiNOgEAAAAAXtStu6GedcMNN+iGG25Qamqqdu3apcLCQk/VBQAAAADwoYsKi2cFBARowoQJmjBhwuWuBwAAAADQA3R5gxsAAAAAwNWJsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADwiIAAAAAwICwCAAAAAAw8PPGTrKzs7Vnzx6FhYVpzZo1kqSGhgZlZGTo+PHjioiI0OLFixUcHCxJys3NVX5+vsxms1JTUxUfHy9JqqysVFZWllpaWpSQkKDU1FSZTCa1trYqMzNTlZWVCgkJUVpamiIjI73RNQAAAADolbwyszh58mQ9++yz57Rt27ZNw4YN0/r16zVs2DBt27ZNklRVVaXi4mKtXbtWzz33nDZu3KiOjg5JUk5OjubPn6/169fr2LFjKisrkyTl5+erX79+2rBhg6ZNm6Z33nnHG90CAAAAgF7LK2Hxpptucs8anlVaWqrk5GRJUnJyskpLS93tSUlJ8vf3V2RkpAYMGKCKigo5nU41NTUpLi5OJpNJkyZNcm+ze/duTZ48WZI0duxY7d27Vy6XyxtdAwAAAIBeyWfXLJ48eVJWq1WSZLVaVV9fL0lyOByy2Wzu9cLDw+VwOAztNptNDofDsI3FYlFQUJBOnTrlra4AAAAAQK/jlWsWL0ZnM4JdzRSeb5nJZDrvunl5ecrLy5Mkpaeny263/4Qq0ZP5+fnxewU6wfjo/b73dQG4qvH+0rtxDLn6+CwshoWFyel0ymq1yul0KjQ0VNKZGcO6ujr3eg6HQ+Hh4Yb2uro6hYeHn7ONzWZTe3u7GhsbDae9npWSkqKUlBT389raWk90Dz5kt9v5vQKdYHwA8CTeX3o3jiG9U1RUVKfLfHYaamJiogoKCiRJBQUFGjVqlLu9uLhYra2tqqmpUXV1tWJjY2W1WhUYGKjy8nK5XC4VFhYqMTFRkjRy5Ejt2LFDklRSUqKhQ4d2OrMIAAAAALgwk8sLd4J57bXX9NVXX+nUqVMKCwvTrFmzNGrUKGVkZKi2tlZ2u11Llixxzwa+9957+vjjj2U2mzVnzhwlJCRIkg4cOKDs7Gy1tLQoPj5ejzzyiEwmk1paWpSZmamDBw8qODhYaWlpuuaaa7pV29GjRz3Wb/gGf/UCOsf46P3aH73b1yXgKmbJ+cDXJXSJ8QFf6qnjo6uZRa+ExZ6MsNj78GEY6Bzjo/fjwzB8qad+GD6L8QFf6qnjo0eehgoAAAAA6LkIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADwiIAAAAAwICwCAAAAAAwICwCAAAAAAwIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMPDzdQEwan/0bl+XcEX73tcFXOEsOR/4ugQAAAD0AMwsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADwiIAAAAAwICwCAAAAAAwICwCAAAAAAwIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADwiIAAAAAwMDP1wUsXLhQffv2ldlslsViUXp6uhoaGpSRkaHjx48rIiJCixcvVnBwsCQpNzdX+fn5MpvNSk1NVXx8vCSpsrJSWVlZamlpUUJCglJTU2UymXzYMwCe0P7o3b4u4Yr2va8L6AUsOR/4ugQAALzC52FRkl544QWFhoa6n2/btk3Dhg3TjBkztG3bNm3btk0PPvigqqqqVFxcrLVr18rpdGrlypVat26dzGazcnJyNH/+fA0ZMkQvvfSSysrKlJCQ4MNeAQAAAMCVq0eehlpaWqrk5GRJUnJyskpLS93tSUlJ8vf3V2RkpAYMGKCKigo5nU41NTUpLi5OJpNJkyZNcm8DAAAAALh4PWJmcdWqVZKkv//7v1dKSopOnjwpq9UqSbJaraqvr5ckORwODRkyxL1deHi4HA6HLBaLbDabu91ms8nhcJx3X3l5ecrLy5Mkpaeny263e6RPl4LTxOBLPXFM/BjjA77GGAE6x/gAOtfTx8f5+Dwsrly5UuHh4Tp58qRefPFFRUVFdbquy+W6qPbzSUlJUUpKivt5bW1t94sFrgKMCaBrjBGgc4wPoHM9dXx0lb98fhpqeHi4JCksLEyjRo1SRUWFwsLC5HQ6JUlOp9N9PaPNZlNdXZ17W4fDofDwcEN7XV2d+3UBAAAAABfPp2GxublZTU1N7sdffPGFBg0apMTERBUUFEiSCgoKNGrUKElSYmKiiouL1draqpqaGlVXVys2NlZWq1WBgYEqLy+Xy+VSYWGhEhMTfdYvAAAAALjS+fQ01JMnT+rVV1+VJLW3t2vChAmKj4/X4MGDlZGRofz8fNntdi1ZskSSFB0drXHjxmnJkiUym82aO3euzOYzeXfevHnKzs5WS0uL4uPjuRMqAAAAAFwCk+tiLvjrhY4ePerrEgz4Hjn4Uk//DjnGB3yNMQJ0jvEBdK6njo8efc0iAAAAAKDnISwCAAAAAAwIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADwiIAAAAAwICwCAAAAAAwICwCAAAAAAwIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADwiIAAAAAwICwCAAAAAAwICwCAAAAAAwIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADP18XcDmVlZXpzTffVEdHh2677TbNmDHD1yUBAAAAwBWp18wsdnR0aOPGjXr22WeVkZGhoqIiVVVV+bosAAAAALgi9ZqwWFFRoQEDBuiaa66Rn5+fkpKSVFpa6uuyAAAAAOCK1GvCosPhkM1mcz+32WxyOBw+rAgAAAAArly95ppFl8tlaDOZTIa2vLw85eXlSZLS09MVFRXl8dou2v/Z7esKgJ6L8QF0jTECdI7xAVyUXjOzaLPZVFdX535eV1cnq9VqWC8lJUXp6elKT0/3ZnnwomeeecbXJQA9FuMD6BpjBOgc4+Pq02vC4uDBg1VdXa2amhq1tbWpuLhYiYmJvi4LAAAAAK5IveY0VIvFokceeUSrVq1SR0eHbr31VkVHR/u6LAAAAAC4IvWasChJI0aM0IgRI3xdBnwsJSXF1yUAPRbjA+gaYwToHOPj6mNyne/OMAAAAACAq1qvuWYRAAAAAHD5EBbRI5w4cUKvvfaaFi1apMWLF+ull15SXl5ep3etff3111VVVSVJWrhwoerr6w3rbN26VR988IFH6wZ8yeVy6fnnn9df/vIXd1txcbFWrVrV7dc4ffq0tm/f7n6+b98+7haNXmHXrl2aNWuWvvvuu/MuX7FihQ4cONDla/z4OLJjxw6+vxlXtPfee09LlizRU089pWXLlunbb7+96Nc4cOCANm3a5IHq0FP1qmsWcWVyuVxavXq1kpOTlZaWJkk6dOiQdu/u/LuQFixY8JP3197eLovF8pO3B3oKk8mkRx99VBkZGRo6dKg6Ojr0X//1X3r22We7/RqnT5/Wn/70J91xxx0erBTwvp07d+qGG25QUVGRZs2adcmvt2PHDkVHRys8PPwyVAd4V3l5uT777DO9/PLL8vf3V319vdra2i76dQYPHqzBgwd3e30+c135CIvwuX379snPz0+33367u+26667T6dOntXfvXq1Zs0ZHjhxRTEyMFi1aJJPJpBUrVuihhx4yvGG99957KigokN1uV0hIiGJiYiSd+QtyXFycvvnmGyUmJmro0KHavHmzmpubFRoaqscff1xWq1UrVqxQbGys9u3bp8bGRi1YsEA33nijV38ewMUYNGiQRo4cqffff18//PCDJk6cqLfeeks1NTXq06ePHnvsMV177bXaunWr+vbtq7vvvluStHTpUi1fvly//e1vdezYMS1btkzDhw/XiBEj1NzcfN5xB1wpmpub9c033+iFF17QK6+8olmzZqmlpUXZ2dmqqqrSwIED1dLS4l7/oYce0ttvvy1JKikp0WeffaaFCxe6l5eUlOjAgQNav369AgICtGrVKv3+97/X7t27ZbFYNHz4cM2ePdvr/QS6y+l0KiQkRP7+/pKk0NBQSVJlZeVFfR7at2+f/vCHP+iZZ55RQ0ODsrOzz3u8cTqdOn78uEJCQvSP//iPys7OVltbm1wul5YuXaq/+7u/8+WPAxeBsAif++tf/6rrr7/+vMsOHjyotWvXymq16vnnn9c333yjG2644bzrVlZWqqioSK+88ora29u1fPlyd1iUpMbGRv3qV79SW1ubVqxYoaefflqhoaEqLi7W7373Oz3++OOSpI6ODr300kvas2eP3n33XT3//POXv9PAZTRz5kwtX75cfn5+uuGGG3T99dfr6aef1t69e5WZmanVq1d3uu0//dM/6ciRI+519u3bd1HjDuiJdu3apfj4eEVFRSk4OFiVlZX66quvFBAQoFdffVWHDx/W8uXLu/16Y8eO1R//+Ef3HykbGhq0a9cuvfbaazKZTDp9+rQHewNcultuuUXvvvuunnzySQ0bNkxJSUmKi4vTpk2bfvLnoa1bt3Z6vKmsrNTKlSsVEBCgTZs2aerUqZo4caLa2trU0dHh9f7jpyMsokeLjY2VzWaTdGa2saamptMPrV9//bVGjx6tPn36SJISExPPWZ6UlCRJOnr0qI4cOaKVK1dKOvNmaLVa3euNHj1akhQTE6OamprL2yHAA/r27aukpCT17dtXRUVFWrp0qSTp5ptvVkNDgxobGy/q9S5m3AE9UVFRkaZNmybpzHt/UVGRqqurNXXqVEnStddeq2uvvfYnv35gYKACAgL0+uuva8SIERo5cuRlqRvwlL59++rll1/W119/rX379ikjI0O/+MUvLunz0P79+zs93iQmJiogIECSFBcXp/fee091dXUaM2YMs4pXGMIifC46OlqffvrpeZedPV1Cksxm8wX/GtXVqXJnQ6Qk/exnP+v0JiBn99md/QE9hclkkslkUmffhmSxWM5Z9uNT8P7WxY47oCc5deqU9u7dqyNHjshkMrn//3Z2Bot07rGjq7FxlsVi0b/927/pyy+/VHFxsf74xz/qhRdeuPTiAQ8ym80aOnSohg4dqkGDBmn79u2X9Hmoq2/f+/FnrgkTJig2NlZ79uzRqlWrtGDBAt18882X2Bt4C3dDhc/dfPPNam1tVV5enrutoqJCX3311UW9zo033qhdu3appaVFTU1N+uyzz867XlRUlOrr61VeXi5Jamtr05EjR356B4Ae5MYbb9Sf//xnSWdOKQ0JCVFQUJAiIiJ08OBBSWdODzr7V+LAwEA1NTX5rF7gcispKVFycrKys7OVlZWlX//614qMjNT111+vnTt3Sjpz+cPhw4fd24SFhamqqkodHR3atWvXeV+3b9++7rHS3NysxsZGjRgxQnPmzNGhQ4c83i/gUhw9elTV1dXu54cOHdLAgQMv6fNQZ8ebv/X999/rmmuu0dSpU5WYmHjO2EPPx8wifM5kMumpp57Sf/zHf+j999+Xv7+/IiIiNGrUqIt6nZiYGCUlJWnZsmWKiIjo9LQ5Pz8/LV26VG+++aYaGxvV3t6uqVOnKjo6+nJ0B/CpWbNmKTs7W0899ZT69OnjvknH2LFjVVhYqGXLlmnw4MGKioqSJIWEhOjnP/+5li5dqvj4eI0YMcKX5QOXrKioSDNmzDinbcyYMTp06JBaWlr01FNP6brrrlNsbKx7+QMPPKCXX35ZNptN0dHRam5uNrzu5MmTlZOTo4CAAD377LN65ZVX1NraKpfLpYcfftjT3QIuSXNzszZt2qTTp0/LYrFowIABeuyxx5SSkvKTPw91drz5W8XFxfrzn/8si8Wi/v37a+bMmZeza/Awk6urOWQAAAAAwFWJ01ABAAAAAAaERQAAAACAAWERAAAAAGBAWAQAAAAAGBAWAQAAAAAGhEUAAAAAgAHfswgAwP+zYsUKHT58WG+88Yb8/f09tp8vvvhCubm5OnDggPz8/GSz2TR+/HhNnTpVAQEBHtsvAAAXg5lFAAAk1dTU6Ouvv5Yk7d6922P7+eSTT7RmzRqNHz9e2dnZ2rRpkxYvXiyHw6G6urrzbtPe3u6xegAA6IzJ5XK5fF0EAAC+9u677+rzzz9XbGysqqur9cwzz7iXnTp1SllZWfr6668VFRWlW265Rfv27dPKlSslSd999502bdqkyspKhYaG6r777lNSUpJhHy6XS48//rimTp2qu+66q9Natm7dqiNHjsjf31+fffaZZs+erYSEBOXk5Gj//v0KDg7WP/zDPyglJUWSlJWVJZvNpl/+8peSpH379mnDhg16/fXXJUkLFy5USkqKCgsLdeLECY0aNUrz5s1jFhMA0CVmFgEAkFRQUKAJEyZo4sSJ+vzzz3XixAn3so0bN6pv37564403tHDhQhUUFLiXNTc368UXX9SECRP0m9/8Rk8++aQ2btyoI0eOGPZx9OhR1dXVacyYMResZ/fu3Ro7dqzefPNNTZw4UevWrZPNZtO///u/a+nSpfrd736nL7/8stv927lzp5577jlt2LBB1dXVeu+997q9LQDg6kRYBABc9fbv36/a2lqNGzdOMTExuuaaa7Rz505JUkdHhz799FPNmjVLffr00c9+9jMlJye7t92zZ48iIiJ06623ymKxKCYmRmPGjFFJSYlhP6dOnZIk9e/f39322muvac6cOXrwwQdVWFjobo+Li9Po0aNlNptVX1+v/fv364EHHlBAQICuu+463XbbbeesfyF33HGH7Ha7goODdc8996ioqOhif0wAgKsMN7gBAFz1duzYoeHDhys0NFSSNGHCBBUUFGj69Omqr69Xe3u7bDabe/0fPz5+/Li+/fZbzZkzx93W3t6uSZMmGfYTHBwsSTpx4oQiIyMlSWlpaZKk559/Xh0dHefdh9PpVHBwsAIDA91tdrtdBw4c6HYf7Xa7+3FERIQcDke3twUAXJ0IiwCAq1pLS4s++eQTdXR06NFHH5UktbW16fTp0zp06JAGDRoki8Wiuro6RUVFSdI5N6Kx2Wy66aab9Pzzz19wXwMHDlR4eLg+/fTTLq9Z/FtWq1UNDQ1qampyB8ba2lqFh4dLkvr06aMffvjBvf6PT6E9q7a29pzHZ7cFAKAznIYKALiq7dq1S2azWRkZGVq9erVWr16tjIwM3XjjjSosLJTZbNbo0aP1+9//Xj/88IO+++67c65ZHDlypKqrq1VYWKi2tja1tbWpoqJCVVVVhn2ZTCbNnj1b7777rvLy8tTQ0CCXy6Xq6mqdPHmy0xrtdrt+/vOf67e//a1aWlp0+PBhffzxx5o4caIk6brrrtNf/vIXNTQ06MSJE/qf//kfw2ts375ddXV1amhoUG5ursaNG3cZfnoAgN6MmUUAwFWtoKBAt9566zmnaUpnrvF788039cADD2ju3LnKysrSY489pqioKI0fP16VlZWSpMDAQP3Lv/yLNm/erM2bN8vlcunaa6/Vww8/fN79JSUlKSgoSLm5udq8ebP8/f1lt9uVkpKisWPHdlrnk08+qZycHM2fP1/BwcG69957NXz4cEnSpEmT9OWXX2rhwoWKiIjQ5MmT9eGHH56z/YQJE/Tiiy/K6XQqMTFRv/jFLy7lxwYAuArw1RkAAFyk//zP/9SJEyf0xBNP+LqUblm4cKHmz5/vDpcAAHQHp6ECAHAB3333nQ4fPiyXy6WKigp9/PHHGj16tK/LAgDAozgNFQCAC2hqatK6devkdDoVFham6dOna9SoUb4uCwAAj+I0VAAAAACAAaehAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAACD/wv1QIEmdFzyzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#what about their ages?\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "x = df.groupby(\"Age_Groups\").Attendance.sum()\n",
    "labels = 'Children', 'Youth', 'Adults', 'Seniors'\n",
    "sizes = [x[0], x[1], x[2], x[3]]\n",
    "plt.bar(labels, sizes)\n",
    "\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Attendees');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Well, teens are careless anyways. 🤷‍♀️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's be more specific \n",
    "plt.figure(figsize=(15,5))\n",
    "x = df.groupby([\"Age_Groups\", 'Gender']).Attendance.sum()\n",
    "x_labels = 'Children', 'Youth', 'Adults', 'Seniors'\n",
    "x_loc = np.arange(len(x_labels))\n",
    "w = 0.25\n",
    "\n",
    "females = [x[0], x[2], x[4], x[6]] \n",
    "plt.bar(x_loc, females, width = w, label = 'Females')\n",
    "\n",
    "males = [x[1], x[3], x[5], x[7]] \n",
    "plt.bar(x_loc + w, males, width = w, label = 'Males')\n",
    "\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Attendees')\n",
    "plt.title(\"Gender-Age\")\n",
    "plt.xticks(ticks = x_loc+w/2, labels = x_labels)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### woha! G-R-E-A-T job women, adulting really means commeting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nk08++aAlLjk5+ZC3u/zyyzVu3Dg9+eSTeu+995SamqpTTjml5j4l6cMPP1Tbtm0PuG29evWO6tidg/nhhx90wQUX6I477tCjjz6q1NRUff/997r88suPOJzgYK/f0Tx3p9NZq8j89nru/+/1t8sO97wOdT/7fw/+/u/q95/7fD516NBBH3/88QH3HxcXV+vzQ/3iZcOGDbr44osPmROA/VB6ANSSnZ2t3NxcrVy5smZVo6qqSgsWLNB1111Xc71hw4bp/fffV1RUVM3AgmHDhunjjz/WokWL9Mgjj9R5toULF6qiokJPPfVUzW90f38Q9NFq3bq1hg0bppdeekkzZszQFVdccdhzihzt63I0xo8frxEjRuiJJ56odfm3336rcePG6ZFHHlFsbKyioqIOmE722xv5/S/PyMhQkyZNtGbNmlqrR8cqOTlZWVlZmjt3rk477bSay+fOnVvrej179tSKFSsOWaATEhKOOk+nTp3UqVMn3XLLLbr22ms1YcKEw5ae3+vZs6cKCwtVWVl5yBWyHj166NVXX9W2bdsOutrTs2dPvf7668rKyjpgpeBw/vCHP+iWW27RF198obfeeksXX3xxzfdQdna2YmJitHHjxlqv5f6ys7Ml7ZviN2LECEn7VqgWLlyoDh06HPJx58yZo7S0ND3wwAM1l02aNOmoc+/veJ+7v2RnZ+utt96qtVo0Z86cWtfp2bOn3nzzTSUlJSk9Pf24Hmfy5MmqV6/eCecFEDqY3gaglmHDhql37966+OKLNXfuXC1fvlyXXXaZKisr9Ze//KXW9ZYtW6bFixfX/DZ/2LBheueddxQZGal+/frVebY2bdrI4XDo8ccf16ZNm/TJJ5/oX//613Hf3zXXXKMJEyZo5cqVuuqqqw573aN9XY4kPz9fkyZN0qWXXlrzhv+3/6688kpVVVXpww8/lLRvQtvcuXO1detW5ebmyufzqVmzZnI6nfryyy+1Z8+emoldDz74oJ555hk98MADWr58udasWaNPPvnkmAqEJN166616+umn9dZbb2ndunV6/PHHa03lk6R//etf+vTTT3XzzTdr8eLF2rBhg77++mtdeeWVqqioOKo869ev12233aY5c+Zoy5Ytmj9/vmbPnl1r++DRGDZsmIYPH65zzz1XH3/8sTZu3KhFixbp2Wef1UsvvSRpXzlp1qyZzjzzTH3zzTfatGmTpk+frvfff1+SdMMNN8jr9erss8/W7NmztXnzZs2ZM0d33XWX5s2bd8jHrlevnk4//XT961//0sKFC3XZZZfVfC0hIUF33nmn7rzzTj333HNas2aNVqxYoYkTJ+q2226TtK94n3nmmbr++us1Y8aMmu/DkpKSwz7ndu3aae/evXrllVe0ceNGvfnmm/rvf/97TK/bb473ufvLX/7yF+3du1dXX321Vq1apenTp+uuu+6qdZ1LLrlELVq00Omnn66pU6dq8+bN+uGHH/TQQw/V2oZ5ODfddNMBE/4A2BulB0AtDodDn3zyidq3b6/TTz9dvXr10q5duzRt2jSlpaXVXK9Xr15KTExUx44day4fMmSILMvSgAEDFB0dXefZunTpomeffVbjx49Xx44d9dhjj+mpp5467vs7++yzlZycrBEjRqhFixaHve7Rvi5H8sYbb8iyLJ111lkHfC0+Pl6nn356zTl77rvvPhUVFaldu3Zq0KCBtm7dqoyMDD300EN6+OGH1bBhw5r7ufTSS/XBBx/oiy++UO/evdWrVy/de++9BxzrciQ33XSTbrzxRt18883KycnR/Pnzdc8999S6zkknnaRvv/1Wy5Yt06BBg9SlSxfdfPPNSkxMrNnudKQ88fHxWrduncaMGaO2bdvqvPPOU//+/Q+6zepwHA6HPvvsM5177rm65ZZbav5+vvjiC7Vq1UrSvi1P3333nTp16qQxY8aoQ4cOuv7662sKWkZGhubPn6+0tDSde+65ateunS655BJt2bJFDRs2POzjX3755Vq8eLE6deqkbt261fra3XffrSeffFIvv/yyunbtqoEDB+rJJ59U8+bNa67z6quvKicnR6NHj9aQIUOUlZWlc84557CPOXr0aN11112688471blzZ02cOFGPPvroMb1uvzmR5+4PWVlZ+vzzz7VgwQLl5OTopptuOmBFNCYmRt9995169uypK664Qm3bttW5556rBQsWqFmzZkf1OBs2bFBubq4/ngKAIOWwjndTMQCEuPz8fGVlZentt9/WeeedZzoOAADwE47pARB23G63du/erfvvv1+NGjXS2WefbToSAADwI0oPgLAzd+5cnXTSSWrRooXefPPNQ47XBQAA9sD2NgAAAAC2xiADAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeAAAAALZG6QEAAABga5QeIIy4XC7l5OQoOztbXbt21RNPPCGfzydJ+vHHH3XjjTce9HbNmzdXbm5uzZ8lafPmzXr33XcPev3NmzcrNjZWOTk56tixoy677DK53e46fS79+/ev0/sDAAD2RekBwkhsbKwWL16sFStWaNq0afryyy913333SZJ69uypZ5555qjv63ClR5JatWqlxYsXa9myZdq2bZs++OCDE86/v3nz5tXp/QEAAPui9ABhKj09XRMmTNBzzz0ny7I0c+ZMjR49WpKUl5enkSNHqlu3brrmmmtkWVbN7Ro0aCBJuv322zV79mzl5OToySefPOTjuFwu9e7dW9u3b5ckLVq0SEOGDFGPHj00atQo7dy5U5K0fv16DR8+XF27dlX37t21YcMGSdKjjz6qXr16qUuXLho3blzN/SYkJEiSLrroIn355Zc1l48dO1YfffSRvF6v/v73v9fcdvz48XXxsgEAgBBE6QHCWMuWLeXz+bRnz55al993330aOHCgfv75Z5155pnaunVrzdcWLlwoSXr44Yc1aNAgLV68WDfffPMhH6OyslI//PCDTjnlFLndbv31r3/VpEmTtGjRIv3pT3/SXXfdJUm65JJLdP3112vJkiWaN2+eGjZsqKlTp2rdunVasGCBFi9erEWLFmnWrFm17n/MmDF6//33JUnV1dWaPn26TjvtNL3yyitKTk7WwoULtXDhQr300kvatGlTnbxuAAAgtESYDgDArP1XcX4za9YsTZ48WZJ0+umnKzU19Zjvd8OGDcrJydG6det0/vnnq0uXLlq+fLmWL1+uESNGSJK8Xq8aNmyokpISbd++Xeecc44kKSYmRpI0depUTZ06Vd26dZMklZaWat26dRo8eHDN45x66qm68cYbVVVVpa+//lqDBw9WbGyspk6dqqVLl2rSpEmSpKKiIq1bt04tWrQ45ucCAABCG6UHCGMbN26Uy+VSenq6Vq1aVetrDofjhO77t2N6du7cqaFDh+qzzz5TixYtlJ2drfnz59e6bnFx8UHvw7Is3XHHHbrmmmsO+TgxMTEaOnSopkyZovfff19/+MMfam777LPPatSoUSf0PAAAQOhjexsQpvbu3atrr71WN9xwwwEFZ/DgwXrnnXckSV999ZUKCgoOuH1iYqJKSkqO+DgNGzbUww8/rIceekjt2rXT3r17a0qP2+3WihUrlJSUpMaNG+uTTz6RJFVVVam8vFyjRo3Sq6++qtLSUknS9u3bD9iKJ+3b4vbaa69p9uzZNSVn1KhReuGFF2qmxq1du1ZlZWVH+eoAAAA7ofQAYaSioqJmZPXw4cM1cuTIWsMBfjNu3DjNmjVL3bt319SpU9W0adMDrtOlSxdFRESoa9euhx1kIElnn322ysvL9cMPP2jSpEm67bbb1LVrV+Xk5NRMYXvrrbf0zDPPqEuXLurfv7927dqlkSNH6uKLL1a/fv3UuXNnnX/++QctWiNHjtSsWbM0fPhwRUVFSZKuuuoqdezYUd27d1enTp10zTXXyOPxHM/LBgAAQpzDOtiGfgAAAACwCVZ6AAAAANgapQcAAACArVF6AAAAANgapQcAAACArVF6AAAAANgapQcAAACArVF6AAAAANgapQcAAACArVF6AAAAANgapQcAAACArVF6AAAAANgapQcAAACArVF6AAAAANhahOkAAAB7stzVsjweyemUw+mSnA7J6ZLD4TAdDQAQZig9AABJkuXzyVeYL29RgayKMvkqKmRVlsuqqJCvokzWr5/7KitkVZTL9+vXrMry2tetLJdVWSF5vQd/IIdDcjolh1MOl1NyuiSH49di9Ovnzv0+dzjlcLnkiI2VMyFZzoREOROT5Ez87c+/fkz43edx8YF9AQEAQcthWZZlOgQAwL98lRXy5u2RN3fvvo95e+TN27vfx73yFuQeuqiEoogIOeN/LUgJ+/2XnCJXWoYiMhrV/OdMTDKdFgDgR5QeAAhxvopyebZvlXfvLnlqFZlfP+bvlVVWajpmUHPEJygivZEiMhrK9VsZSm8oV2bWvlLEqhEAhDRKDwCECF9ZqdxbN+7775dN8mzdJPfWjfLm7pb4Ue5XzsRkudIbKiKzkSLSG8n168eIRo0V0bCJHC6X6YgAgMOg9ABAkPEWF8q9daM8WzfKvXXTvv9+2Shffq7paDgIR1S0Ipq0UGTz1ops3lpRv3501UszHQ0A8CtKDwAY4i0pknvj2l9XbzbJ88u+j76iAtPRUAecScmKbNZakS3aKKplO0W2bq/Ipi3kcDFDCAACjdIDAAFg+Xxyb92o6lVLVbVqqapXL5Vn+1bTsRBgjqjofStCrdsrqlV7RbXuoMhmreSIjDQdDQBsjdIDAH7gKy1R1eplql69TFWrlqh67QpZ5WWmYyEYRUQqslkrRXfooujO3RXduYdcyammUwGArVB6AOAEWZYlzy+bfl3BWaaq1cvk+WUTwwVw3CKatFB05x6K6dxd0Z26c3wQAJwgSg8AHCNfeZmq1yxT1ap9KznVa5bLV1psOhZsLCKrqaI79VB0lx6K7tRdEWnppiMBQEih9ADAEVher6rXrVTlT/NV+dP3ql6zQvLZ6CSeCDkRDRsrulP3X7fD9VREeqbpSAAQ1Cg9AHAQnj27akpO1ZKFrOQgqLkyGtWUoJic3opoQAkCgP1RegBAkuWuVtWyn1Tx41xVLponz7YtpiMBxy2yVTvF9h2i2D5DFNWqnek4AGAcpQdA2PLm56pi4RxVLpyjysULZFWUm44E1DlXesN9BajvEEV36sZ5ggCEJUoPgLBhWZbc61epYsFsVSyYI/eG1UxYQ1hxJiQppucAxfYdopge/eSMizcdCQACgtIDwPaqVi1V+XdTVD53unz5uabjAMEhMkoxXXvWbINjLDYAO6P0ALCl6s3r9xWd76bKu3u76ThAcHM4FNWmY802uMhmrUwnAoA6RekBYBue3Tv2FZ2ZX8u9ZYPpOEDIimjURLEDTlb8yaMV2aS56TgAcMIoPQBCmrcwX+Wzpqr8uymqXr3MdBzAdqLaZit++BmKGzJKzoRE03EA4LhQegCEHF9Zqcrnfavy76aoasmPnCgUCITIKMX2Gaz44aMV072fHC6X6UQAcNQoPQBCglVdpYoFs1U+c4oqfpwruatNRwLClrNemuKHnqr44aM5/gdASKD0AAhqVauXqfSryaqY962s8jLTcQD8TmTrDoofPlpxQ0bJlZRiOg4AHBSlB0DQ8VVVqnzm1yr9YtK+c+kACH4RkYrtPVDxw89QTM/+nAQVQFCh9AAIGu7tW1X65SSVf/M/+UqLTccBcJycKfUUN/QUxQ8/Q1Et2piOAwCUHgBmWV6vKhfMVumXk1T58w8SP5IAW4nKzlHiWRcrtt9QOZxO03EAhClKDwAjvIX5KpvyiUq/mizv3l2m4wDwM1dGlhLPvEjxI8+SMy7edBwAYYbSAyCgqlYuVun/PlT53G8lj9t0HAAB5oiLV/yIM5V45hhFZGaZjgMgTFB6APidr7JC5TO+VOkXH8m9aa3pOACCgdOl2L6DlXj2JYrOzjGdBoDNUXoA+I1n13aVfPqeyqb/T1ZZqek4AIJUZJuOSjz7YsUNHC5HBFPfANQ9Sg+AOufetlnFH7ym8plfS16v6TgAQoSrfroSRl+o+FPPkSsx2XQcADZC6QFQZ6o3r1fxxFdUMXe65POZjgMgRDmiYxR38ulKPOsPimzc3HQcADZA6QFwwqrXrdxXdn6YxchpAHXH4VBMj/5KuugKRXfMMZ0GQAij9AA4blUrF6v4vVdU+dN801EA2FxM935KuvRaRbfNNh0FQAii9AA4ZpWLF6h44iuqWrbIdBQAYSam9yAl//FaRbVqZzoKgBBC6QFw1CoWzFHx+6+qevVS01EAhDOHQ7H9hirpkmsU1by16TQAQgClB8BhWZalinnfqvj9V+XesMZ0HAD4/5xOxQ0aoaRLrlZkVjPTaQAEMUoPgIOyfD6Vz5qq4g9elWfLRtNxAODQnC7FnXSKkv/wZ0U0bGw6DYAgROkBcIDKxQtU+MpTcm9cazoKABw9l0vxw89Q0pirFJGeaToNgCBC6QFQw711kwpffVqVC+eYjgIAxy8iUgmjzlbSRX+Sq34D02kABAFKDwB5iwpU9PZ4lU35WPJ6TccBgDrhiIpW/KnnKumCsXKl1jcdB4BBlB4gjFnVVSr55D0Vf/iarPIy03EAwC8cMbFKumCsEs+7VI7IKNNxABhA6QHCkGVZKv9uioreeF7ePTtNxwGAgHBlZin1qpsV22+o6SgAAozSA4SZqhWLVfjyE6peu9J0FAAwIqZ7P6Vcc6siGzc3HQVAgFB6gDDh3vGLil59RhXzZ5iOAgDmRUQo8YwxSrr4KjnjEkynAeBnlB7A5rwlRSp+72WVfvGh5PGYjgMAQcWZUl/JY69X/PAz5HA4TMcB4CeUHsCmLLdbpf97X8UTX5WvtNh0HAAIalHtOinl2r8rum226SgA/IDSA9hQ1fKflf/sg/Js22w6CgCEDodD8cNHK3nsX+VKqWc6DYA6ROkBbMRXVqrCV59W2ZRPJP5pA8BxccQnKPkPVynhzDFyuCJMxwFQByg9gE2Uz52ughcflS8/13QUALCFiCYtlHrNrYrp1td0FAAniNIDhDhP7h4VvvCIKr7/znQUALCl2L5DlHLt3xXRINN0FADHidIDhCjLslT6xSQVvfGcrPIy03EAwNYccfFKuepmJYw623QUAMeB0gOEIPfWjcp/5gFVr1pqOgoAhJWY7v2UeuNdrPoAIYbSA4QQy+1W8fuvqvjD1yWP23QcAAhLrPoAoYfSA4SIqhWLlf/MA4yhBoAgwaoPEDooPUCQ85WVqvC1Z1T29ceMoQaAIMOqDxAaKD1AECuf+60Kxz8qb95e01EAAIcR06P/vlWftAzTUQAcBKUHCEK+slIVPP+Qyr+bYjoKAOAoOeITlHLl31j1AYIQpQcIMlUrlyjvsbvl3b3DdBQAwHFg1QcIPpQeIEhYXu++yWzvvSz5vKbjAABOgCM+Yd+xPiPPMh0FgCg9QFDw7N2l/EfvVtWKn01HAQDUoZie/ZX6V1Z9ANMoPYBh5XOnq+CZB+UrLTYdBQDgB474RNW76Z+KG3Cy6ShA2KL0AIb4KitVOOExlU35xHQUAEAAxJ92vlL/fLMcUdGmowBhh9IDGFC9YbXy/nOXPNu2mI4CAAigyBZtVf/2fyuycXPTUYCwQukBAsiyLJV+8o4KX39e8rhNxwEAGOCIiVXqdbcp/uTRpqMAYYPSAwSItyBP+U/cq8qf5puOAgAIAnEnn67U626XMybWdBTA9ig9QABU/DhX+U/eJ19hvukoAIAgEtG4uerf/pCiWrQxHQWwNUoP4EeWu1qFrz2r0s8mSvxTAwAchCM6WqnX3aH44Wx3A/yF0gP4iSd3t3If+Lvc61aajgIACAHxo85W6rV/Z7ob4AeUHsAPqlYuVu6Dt8lXmGc6CgAghES2aq+0Ox9RRGaW6SiArVB6gDpWOuUTFfz3EaazAQCOizMhSfVuvU+xvQeZjgLYBqUHqCOW16PC8Y+r9IsPTUcBAIQ6h0OJF4xV8h+vlcPlMp0GCHmUHqAOeIsKlffQP1S17CfTUQAANhLdtZfq3/6QXEkppqMAIY3SA5yg6o1rlXv/rfLu2Wk6CgDAhiIaNVHavU8pMquZ6ShAyKL0ACegfPY3yn/yXllVlaajAABszJmQpPp3/kcxXXuajgKEJEoPcBwsn09Fb72gkg9eMx0FABAuIiKUev0dShh5lukkQMih9ADHyFdeqrxH71blgtmmowAAwlDi+ZcreewNcjgcpqMAIYPSAxwD9/atyv3XLfJs22w6CgAgjMX2P0n1br1fzpgY01GAkEDpAY5SxY/zlPefu2SVlZiOAgCAItt0VIN7npCrXprpKEDQo/QAR6F40hsqeuN5yeczHQUAgBquBhlKu+dJRbVsazoKENQoPcBhWF6vCp5/SGVTPjEdBQCAg3LExqn+Px5UbO9BpqMAQYvSAxyCr6pSeQ/fwcACAEDwczqVcuXflHj2xaaTAEGJ0gMchLe4ULn33aLq1UtNRwEA4KjFn3a+Uq/9PzlcEaajAEGF0gP8jmfPTu29+wZ5tm0xHQUAgGMW072f6t/xkJxxCaajAEGD0gPsp3rjWu0dd6N8+bmmowAAcNwimrVUg3ufVkR6Q9NRgKBA6QF+VbnkR+U+cKus8jLTUQAAOGGuBhlq8OALisxqajoKYBylB5BUPucb5T16t+Rxm44CAECdcabWV4MHnldU89amowBGUXoQ9kqnfKKC5/7NOXgAALbkTEpWg389p6g2HUxHAYyh9CCsFX/0popefcZ0DAAA/MoRF68G9z2t6I45pqMARlB6ELYKX39OJR++bjoGAAAB4YiOUdrdjyumWx/TUYCAo/Qg7Fg+nwpeeERlX35kOgoAAIEVGaW0Ox9RbO9BppMAAUXpQVixPB7lP36PymdNNR0FAAAzIiJU///uV9ygEaaTAAFD6UHYsKqrlPvgP1T541zTUQAAMMvpVOqN/1TCiDNNJwECgtKDsGC5q5X7r1tV+dN801EAAAgODodSrv27EkdfaDoJ4HeUHtie5fEo98G/q3LBbNNRAAAIOslX/FVJ519uOgbgV5Qe2Jrl9SjvoTtUMX+G6SgAAAStpDFXKfnSa03HAPyG0gPbsrxe5T32T1XMmmY6CgAAQS/hnEuUetXNpmMAfkHpgS1ZPp/ynxin8hlfmY4CAEDIiD/1XKVef4ccDofpKECdcpoOANQ1y7JU8MwDFB4AAI5R2VeTVfjSE6ZjAHWO0gPbKfjvwyqb9pnpGAAAhKTST99T0bsTTMcA6hSlB7ZSMP4xlX35kekYAACEtOJ3Jqjks4mmYwB1htID2yh85WmV8gMaAIA6UTjhcZVN/5/pGECdoPTAFgrfeF4lk98yHQMAAPuwLOU/fb8q5s80nQQ4YZQehLyid19SyQevmY4BAID9eL3KfeROVS5ZaDoJcEIoPQhpxR+8ruJ3xpuOAQCAfbmrlXv/rapas9x0EuC4UXoQsko+fkdFbzxnOgYAALZnVZQrd9xNcm/ZYDoKcFwoPQhJpVM+UeHLT5qOAQBA2PCVFGnv3TfIs2u76SjAMaP0IORU/DhPBc8/ZDoGAABhx5u3V3vuuk7e/FzTUYBjQulBSKneuFZ5D98heb2mowAAEJa8u7Zr7z+vl7ekyHQU4KhRehAyPLl7lHvv32RVlJmOAgBAWHNv2aDccTfJV1FuOgpwVCg9CAm+8jLl3vs3efP2mI4CAAAkVa9Zrtz7/0+Wu9p0FOCIKD0IepbXo7yH75B701rTUQAAwH6qlixQ3n/ukmVZpqMAh0XpQdAreP4RVS6aZzoGAAA4iIp5M1T05n9NxwAOi9KDoFb8wesqm/Kx6RgAAOAwSj54TWUzvjIdAzgkSg+CVvmsqSp683nTMQAAwFEoeOYBVa1ebjoGcFAOi02YCEJVKxdrz53XSRwcCQBAyHCm1lfGk28ookGm6ShALaz0IOi4t29R7v23UngAAAgxvoI85d5/q3yVlaajALVQehBUvEWF++b+F3PCMwAAQpF7wxrlP3EPE90QVCg9CBpWdZVy/3WLPDu3mY4CAABOQMXcb1X89njTMYAalB4EBcuylPfYPapevdR0FAAAUAeKJ76s8u+mmI4BSKL0IEgUvfG8KuZONx0DAADUofyn/qWqtStMxwAoPTCvfN4MlXz4uukYAACgjlnVVcq9/1Z5cveYjoIwR+mBUe7tW5X/5L2mYwAAAD/x5efum+hWxUQ3mEPpgTG+qkrlPXSbrPIy01EAAIAfudevUv6T9zHRDcZQemBMwfMPyb1pnekYAAAgACpmT1Pxey+ZjoEwRemBEaVfTVb59C9MxwAAAAFU/O5LKp/9jekYCEOUHgRc9bpVKhj/mOkYAAAg0CxL+U/dJ/e2zaaTIMxQehBQ3pIi5T50m+SuNh0FAAAYYFVWKO/hO2VVV5mOgjBC6UHAWJal/MfukXf3DtNRAACAQe5Na1X48lOmYyCMUHoQMMXvv6LKH+eajgEAAIJA6Rcfqnzut6ZjIExQehAQlT//oOJ3JpiOAQAAgkj+0/fLs2en6RgIA5Qe+J0nd7fyHv2n5POZjgIAAIKIVVaivEfukuX1mI4Cm6P0wK8sj0d5D90uX1GB6SgAACAIVa9eqqI3XzAdAzZH6YFfFb7ylKpXLzMdAwAABLGSj95UxaL5pmPAxig98JvyWVNV+tlE0zEAAECwsyzlPzFO3vxc00lgUw7LsizTIWA/7h2/aPeNl8iqKDcdBQgK/WcsU7zLKZfDIZfDoS8GdtCK4nLduXyrqrw+uRwOPdipqXJS4g+47Subduu9X3JlSfpDkzRd1SJDkvTv1ds0c2+xOibF6qmuLSRJH23PU2G1R1f+eh0ACCXRXXupwQPPy+Hk9/KoW3xHoc5ZXq/yHx9H4QF+5/2+7fT1oI76YmAHSftKy99aN9TXgzrq1raN9O/V2w64zZqSCr33S64+H9BBUwZ21PQ9RdpUVqlit1eLCso0dVBH+SxpdXGFKr0+fbgtT5c1Sw/0UwOAOlG1ZKGKP3jVdAzYEKUHda7ko7dUvXqp6RhA0HPIoRKPV5JU4vEqIzrygOusK61U95R4xbqcinA61Ldeor7eVSinQ3L7fLIsS5VenyKcDr24cZf+1DxdkU5HoJ8KANSZ4ndeUtXyn03HgM1QelCnqjetU9E7403HAIKOQ9IfF6zVaXNW6Z2teyVJ4zo21r9Xb1Ofb5fqgVXbdFv7rANu1y4xRj/kl6qg2qMKr08z9hZpZ2W1EiJcOjUzVafOWaUmcVFKjHBpSVG5RmakBPaJAUBd83mV9+g/5S0pMp0ENsIxPagzlsej3X+7TO5Na01HAYLOrspqZcZEKbfKrUsWrNO/spvoi50F6lsvUac1TNXnO/P17tZcvden7QG3nfhLrt7cskdxLpfaJMQoxuXUuI5Nal3nH0s367Jm6VpWXK5Ze4vVISlWN7ZuGKinBwB1Lqb3IDUY96TpGLAJVnpQZ4rfnUDhAQ4hMyZKkpQWHalRGSlaXFimj7bn6dTMFEnS6MxULSkqO+htxzRJ05cDO2pSv3ZKiYpQi/joWl9fXrTv+LmW8dH6aFueXujeUmtKKrSprNJ/TwgA/KxywWyVfPqe6RiwCUoP6kTV6uUq/vAN0zGAoFTu8ar012N3yj1ezc4tVrvEWGVER+n7/FJJ0ty8EjWPizno7XOr3JKk7RXV+npXgc5sVK/W1x9bu0O3tG0kt2XJq32L905JFV6fn54RAARG4avPyL11o+kYsIEI0wEQ+nxVlcp/cpzk85qOAgSlvdUeXb1ogyTJY1k6u1E9DW2QrLjOLt278hd5LUvRToce7txU0r6tcLct26I3erWRJF3z00YVuD2KdDh0f3ZTpUT+/x/dU3YVqmtKXM1KUo+UBI2YtUIdkuLUMSkuwM8UAOqYx638p+9X+qOvMMYaJ4RjenDCCsY/xklIAQCA36RcdbMSz7nEdAyEMCozTkjlkoUq/fx90zEAAICNFb31gjw7DzyXGXC0KD04br7yUuU/9S+JxUIAAOBHVlWl8p95QGxQwvGi9OC4FU54Qt49O03HAAAAYaBq6Y8q+/pj0zEQoig9OC4VC2arbNpnpmMAAIAwUvjq0/Lk7jYdAyGI0oNj5i0uVP4zD5iOAQAAwoxVXqaC5x4yHQMhiNKDY1bw/MPyFeSZjgEAAMJQ5cI5KpvxlekYCDGUHhyTsplfq2LON6ZjAACAMFY44TF5C/NNx0AIofTgqPlKilU4/jHTMQAAQJjzFRep4MVHTcdACKH04KgVvvm8fMWFpmMAAACoYvY0lc+faToGQgSlB0elet0qxkQCAICgUvDfh+UrLTEdAyGA0oMjsixLBf99WPL5TEcBAACo4cvPVeHLT5iOgRBA6cERlU35RNVrV5iOAQAAcICyaZ+r8qfvTcdAkKP04LC8JUUqeuM50zEAAAAOKf/ZB+WrKDcdA0GM0oPDKnr9OfmKi0zHAAAAOCTvnp0q/uA10zEQxCg9OKSqtStUNvVT0zEAAACOqPSTd+XZvcN0DAQpSg8OyvL5VPDfRxheAAAAQoJVXaXCV542HQNBitKDgyqb8rHc61aajgEAAHDUKuZOV+Xyn0zHQBCi9OAA3uJCFb3xX9MxAAAAjlnhhMdlsVMFv0PpwQGKXn9OvhKGFwAAgNDj3rBGZdM+Mx0DQYbSg1qqVi9neAEAAAhpRW++IF95mekYCCKUHtSwfD4VvPCIZFmmowAAABw3X2Geit9/1XQMBBFKD2qUfTVZ7vWrTMcAAAA4YSWfvifPzm2mYyBIUHogSfIWFaroTYYXAAAAm3BXq/BVRlhjH0oPJEnF746Xr7TYdAwAAIA6UzFvhiqX/mg6BoIApQfy7Nqu0q8/Nh0DAACgzhVOeIIR1qD0QCp6Z7zk8ZiOAQAAUOfcm9YymRaUnnDn3rJB5TO/Nh0DAADAb4reekG+8lLTMWAQpSfMFb31gsSSLwAAsDFfYb6K33vFdAwYROkJY1Vrlqti/kzTMQAAAPyu5LOJcu/4xXQMGELpCWNFbzCiGgAAhAmPW0WvP2c6BQyh9ISpyiULVbVkgekYAAAAAVMx71tVb1xrOgYMoPSEqaI3njcdAQAAILAsS0Vvv2g6BQyg9IShivkzVb1muekYAAAAAVf5wyxVr1tpOgYCjNITZiyfT4VvcSwPAAAIX6z2hB9KT5gpn/mVPFs2mo4BAABgTOWP81S1aqnpGAggSk8YsTweFb0zwXQMAAAA41jtCS+UnjBS+vXH8u7abjoGAACAcVWLF6hy+U+mYyBAKD1hwldZqeL3ORMxAADAb4rfYrUnXFB6wkTp5xPly881HQMAACBoVC3/idWeMEHpCQO+8lKVfPSW6RgAAABBp/i9l01HQABQesJA6ZcfyVdSZDoGAABA0KlavEBVqzl/od1RemzO8nhU+tn7pmMAAAAEreKJrPbYHaXH5sq/+1revD2mYwAAAAStyoVzVL1htekY8CNKj82VTH7bdAQAAICgVzyRKbd2RumxsYpF8+XevN50DAAAgKBXMX+m3Fs2mI4BP6H02FjJZCa2AQAAHBXLUvH7r5pOAT+h9NhU9YY1qlq8wHQMAACAkFE++xt5du8wHQN+QOmxKY7lAQAAOEY+r0q/+NB0CvgBpceGPLm7VT57qukYAAAAIadsyqfyVVaajoE6RumxodJP3pO8XtMxAAAAQo6vtFjlM78yHQN1jNJjM77yUpVO+dh0DAAAgJBV+jkndrcbSo/NlH41WVZ5mekYAAAAIcu9eb0ql/5oOgbqEKXHRiyPR6Wf8ZsJAACAE8V7Knuh9NhI+awp8ubuNh0DAAAg5FX8MEuePbtMx0AdofTYCGOqAQAA6ojPq9L/fWA6BeoIpccmKn/6Xu5N60zHAAAAsI2yqZ/KV8X4ajug9NhEyces8gAAANQlX0mRymd+bToG6gClxwbc27eo8qfvTccAAACwHcZX2wOlxwbKvv7EdAQAAABbcm9ap8pli0zHwAmi9IQ4y+1W2fT/mY4BAABgW6z2hD5KT4ir+H6mfEUFpmMAAADYVsX87+TZy/jqUEbpCXGlX31sOgIAAIC9+bwq/WKS6RQ4AZSeEObZuU1VSxeajgEAAGB7ZVM+llVdZToGjhOlJ4SVTvlEsizTMQAAAGzPV1yk8u+mmo6B40TpCVGW16Oybz43HQMAACBsMDwqdFF6QlTFD7PlK8gzHQMAACBsVC3/iYEGIYrSE6LKpn1mOgIAAEB4sSyVz/jadAocB0pPCPIW5Kly0TzTMQAAAMJO2cwvTUfAcaD0hKCyGV9JXq/pGAAAAGHHs2WjqjesNh0Dx4jSE4LKOYgOAADAmLIZX5mOgGNE6Qkx1etWyb15vekYAAAAYav8uymy2HUTUig9IYYx1QAAAGb58nNVtYQTxIcSSk8Isdxulc+aYjoGAABA2Cv79gvTEXAMKD0hpGLBLPmKi0zHAAAACHsV82fKV1lhOgaOEqUnhJRN5zcKAAAAwcCqrFDFvBmmY+AoUXpChK+8TJU/fW86BgAAAH5VzhS3kEHpCRGVC+dI7mrTMQAAAPCrysUL5M3PNR0DR4HSEyLKWT4FAAAILj6vyr9jyFQooPSEAKu6SpWL5pmOAQAAgN8pm/Gl6Qg4CpSeEFD50/eyKspNxwAAAMDvuDeskXvrRtMxcASUnhDA1jYAAIDgVcZAg6BH6QlyltejygWzTccAAADAIZR/N9V0BBwBpSfIVS39Ub4STkgKAAAQrLy7t8u9ZYPpGDgMSk+QY2sbAABA8KtgZ05Qo/QEMcvnU8X8maZjAAAA4AgqFs4xHQGHQekJYtWrl8pXkGc6BgAAAI6getUyeTkkIWhReoIYW9sAAABChM+ryh85r2KwovQEsYp5M01HAAAAwFFi4m7wovQEqeoNq+Xdvd10DAAAABylyp++l+X1mI6Bg6D0BKkKtrYBAACEFF9psapWLjEdAwdB6QlSHM8DAAAQeioXMMUtGFF6gpB722Z5tm40HQMAAADHiPP1BCdKTxDi3DwAAAChybNtszw7t5mOgd+h9AShyp8XmI4AAACA48RqT/Ch9AQZy12t6tUcAAcAABCqKD3Bh9ITZKpWLZVVVWU6BgAAAI5T1fKf5SsvMx0D+6H0BJmqJT+ajgAAAIAT4XGr8ufvTafAfig9QaZy6ULTEQAAAHCCKhhdHVQoPUHEV1mh6rUrTMcAAADACar8ca4syzIdA7+i9ASRquU/Sx6P6RgAAAA4Qb7CfLk3rDEdA7+i9ASRKra2AQAA2EbVip9NR8CvKD1BpJIhBgAAALZRtWKx6Qj4FaUnSPhKS+TeyBIoAACAXVStXGw6An5F6QkSlcsWST6f6RgAAACoI76CPLl3/GI6BkTpCRpVSzieBwAAwG6q2eIWFCg9QYLz8wAAANgPwwyCA6UnCHgL8+XZstF0DAAAANQxjusJDpSeIMDWNgAAAHvybN8qb1GB6Rhhj9ITBBhVDQAAYF+MrjaP0hMEOCkpAACAfXFcj3mUHsM8e3fJs3Ob6RgAAADwk2qO6zGO0mNY9erlpiMAAADAj6o3rJGvstJ0jLBG6TGsesNq0xEAAADgT16vqtcsM50irFF6DKtev8p0BAAAAPgZx/WYRekxzM1KDwAAgO0xwc0sSo9Bnj075SsuMh0DAAAAfla9Zrksr9d0jLBF6TGoej2rPAAAAOHAqiiXe+Na0zHCFqXHIDfH8wAAAISNqlVLTEcIW5Qeg5jcBgAAED5Y6TGH0mMQ29sAAADCh3vLetMRwhalxxBP7h75CvNNxwAAAECAuLdslOXzmY4Rlig9hnA8DwAAQHixqirl2bXddIywROkxhON5AAAAwo97M1vcTKD0GMLxPAAAAOHHvWWD6QhhidJjiJuVHgAAgLDDSo8ZlB4DvPm58ubtNR0DAAAAAUbpMYPSYwDH8wAAAIQnz45fZFVXmY4Rdig9BnA8DwAAQJjyeeX+ZZPpFGGH0mMAx/MAAACEL7a4BR6lx4DqTetMRwAAAIAh7s1McAs0Sk+AWR6PvHt2mY4BAAAAQxhbHXiUngDz7Nkp+bymYwAAAMAQtrcFHqUnwDw7t5mOAAAAAIO8eXvkKyk2HSOsUHoCzLPzF9MRAAAAYFj1FlZ7AonSE2Cs9AAAAIAtboFF6QkwSg8AAAAoPYFF6Qkwzy5KDwAAQLjzbNtiOkJYofQEkGVZ8u7abjoGAAAADPPm7jYdIaxQegLIm7dXVlWV6RgAAAAwzJO7R5ZlmY4RNig9AcTWNgAAAEiS3NXyFeSZThE2KD0BxBADAAAA/Mazd5fpCGGD0hNAnh2cowcAAAD7cFxP4FB6AojtbQAAAPiNdw8rPYFC6Qkgz04mtwEAAGAftrcFDqUngNjeBgAAgN94KT0BQ+kJEG9JkayyEtMxAAAAECQ8ezmmJ1AoPQHC5DYAAADsz0vpCRhKT4B4KT0AAADYj68oX5a72nSMsEDpCRBv/l7TEQAAABBMLIstbgFC6QkQb1Gh6QgAAAAIMgwzCAxKT4D4igtNRwAAAECQ4biewKD0BAilBwAAAL/HuXoCg9ITIN6iAtMRAAAAEGTY3hYYlJ4AYaUHAAAAv0fpCQxKT4BQegAAAPB7TG8LDEpPAFher3ylJaZjAAAAIMj4SopMRwgLlJ4A8JUWSz6f6RgAAAAIMr6yUtMRwgKlJwB8DDEAAADAwbirZbmrTaewPUpPAHg5ngcAAACHwGqP/1F6AsBXVGg6AgAAAIIUpcf/KD0B4CtmexsAAAAOziqn9PgbpScAWOkBAADAobDS43+UngDgmB4AAAAciq+8zHQE26P0BAArPQAAADgUi5Uev6P0BICPlR4AAAAcgo9jevyO0hMAXs7TAwAAgEPgmB7/o/QEgK+02HQEAAAABClWevyP0hMAVlWV6QgAAAAIUhzT43+UnkDwuE0nAAAAQJBie5v/UXoCwPJ6TEcAAABAkGJ7m/9RegLAcrPSAwAAgIOzOE+P31F6AoGVHgAAABwC29v8j9LjZ5bXI1mW6RgAAAAIUmxv8z9Kj59ZblZ5AAAAcGhsb/M/So+/sbUNAAAAh2F5eL/ob5QeP7MYVw0AAIDD8flMJ7A9So+/0dwBAABwOBalx98oPX7GSg8AAAAOy7JkMfjKryg9fkbpAQAAwBGxxc2vKD3+xvY2AAAAHAmlx68oPX7GSg8AAACOiON6/IrS42eMIAQAAMCRWD6O6fEnSo+/UXoAAABwJD6v6QS2FmE6gN2x0gMA/19Rg6Z6e9BftKQi2nQUAAgqk1xRijcdwsYoPX7GMT0AIFkOh2YOv1avO1qrJN8riV8IAcD+HA6H6Qi2RunxN5YqAYS5bW37aHz2RVpR4JHEz0QAOBgnpcevKD1+5ohiCweA8FQVl6SPR9ykj0qS5C1gZQcAYA6lx88oPQDC0ZK+52l8/YHaVeSWxEQiADgSVnr8i9LjZ5QeAOGkMKOlXh9wrWblSyrjmEYAOFpOOo9fUXr8zBFN6QFgf15nhL4deZ3e8DZVeT4n2AOAY8UgA/+i9PiZIzrGdAQA8KstHQbqxXbnaU2hWxKFBwCOlcvpkIulHr+i9PiZI4rSA8CeKuNTNWnETfq0KE7eQrayAcDxiolwmo5ge5QeP2N7GwA7WjTwD5qQ1Et7C5nKBgAnKibSZTqC7VF6/IxBBgDsJC+rrV7t82fNz7ekcgoPANSFWEqP31F6/MzhdEoRkZKHrR8AQpfXFampo27QW9VZqmRQAQDUKba3+R+lJwAc0TGyKD0AQtSGzsM0vuUZWl/EoAIA8AdWevyP0hMAjuhoWWUlpmMAwDEpT0rT+yffpP8VRMsq4hc3AOAv0ZQev6P0BADH9QAINT8MuUwvxeUov4DjdgDA31jp8T9KTwAwwQ1AqNjbNFsv97xCC/N9UgWFBwACISaSY3r8jdITAE5OUAogyLkjY/TVqL/qvYoMVTGoAAACKiaClR5/o/QEACcoBRDM1uWM0n+bnqItxQwqAAAT2N7mf5SeAOCYHgDBqDQ1U++e9FdNyY+UVcygAgAwhe1t/kfpCQCO6QEQbOYMu1KvRmWrMJ/jdgDAtBhWevyO0hMArPQACBa7WuRoQs6lWlzglSopPAAQDBKjeUvub7zCAeCIjTMdAUCYc0fH67NRN+qDsnpyF3hNxwEA7Cc1LtJ0BNuj9ASAKznVdAQAYWxlz9F6seHJ2lbslmSZjgMA+J2U2CjTEWyP0hMATkoPAAOK05ro7cHX6Zt8l1TCoAIACFas9PgfpScAKD0AAslyOPTd8Gv0mqONSvLZygYAwY6VHv+j9ASAK6We6QgAwsT2Nr00odMftKzAI4nCAwChgJUe/6P0BICT0gPAz6pjE/XxyJs0qSRZ3gKmsgFAqIiPcinSxXl6/I3SEwCupBTTEQDY2NK+52p82iDtLGJQAQCEmtQ4trYFAqUnAJzJqZLDIVm8GQFQdwozmuuNAX/Rd/kOqZRBBQAQilJj2doWCJSeAHC4XHImJslXXGQ6CgAb8Dld+nbEX/SG1UJlDCoAgJCWwkpPQFB6AsSZUp/SA+CEbWk/QOPbn6fVhQwqAAA7YIhBYFB6AsSVWl+erRtNxwAQoirjUzVpxI36tChe3kIGFQCAXaSwvS0gKD0B4qrfwHQEACHqpwFjNCG5t/ZQdgDAdjhHT2BQegLEVY/SA+DY5Ddqo1f7Xq15+ZZUTuEBADtKS6D0BAKlJ0BY6QFwtLyuSE0beYPe8mSpIt9nOg4AwI8aJsWYjhAWKD0BQukBcDQ2dRqqF1qdpfVFbkkUHgCwu4bJlJ5AoPQEiKt+uukIAIJYeVJ9fXDyTfpfYYx8RZxzBwDCQYTToQYJ0aZjhAVKT4Cw0gPgUBYMvlQvxXdTXgHH7QBAOElPjJbT4TAdIyxQegLEVS9NcjgkyzIdBUCQ2Nukg17udaUW5vukCgoPAISbRmxtCxhKT4A4XBFyJteTrzDPdBQAhnkio/TVyBv1blWmqhhUAABhK5MhBgFD6QmgiPRMVVN6gLC2LmekXmx6qjYVM6gAAMIdk9sCh9ITQBGNm6l67QrTMQAYUJaSoXeH/VVf50fJKmZQAQCAlZ5AovQEUGRWM9MRABgw76Q/6ZXoTirI57gdAMD/1yg51nSEsEHpCaCIJs1NRwAQQLtadNXLOZfppwKvVEnhAQDUxkpP4FB6AiiClR4gLLijYvW/UTdpYnl9uQu8puMAAIKQ0yFlJnKOnkCh9ARQZKMmktMp+Th4GbCrVT1G68VGJ+uXErckRtQDAA4uLSFaES6n6Rhhg9ITQI6oaLkaNJR393bTUQDUseK0xnpn8PWalu+SShhUAAA4vCyO5wkoSk+ARTZpRukBbMRyODTr5Kv1mqutivPZygYAODot68ebjhBWKD0BFpHVTPpxnukYAOrAjtY9Nb7zxVpW4JE8FB4AwNFrXj/OdISwQukJsMjGDDMAQl11bLw+HfE3fViaIk8BU9kAAMeuBSs9AUXpCbCIrOamIwA4Acv6nK3xDYZoRzGDCgAAx69lGqUnkCg9ARbJuXqAkFSY3kxvDrxOM/MdUimDCgAAxy8xOkINEhhXHUiUngBz1UuTIzZeVkWZ6SgAjoLP6dKM4dfqdbVUGYMKAAB1gON5Ao/SY0BE42Zyr1tpOgaAI9javr/Gtz9fqwo9kig8AIC6wfE8gUfpMSAyi9IDBLPK+BRNHn6jPi5OkLeQQQUAgLpF6Qk8So8BEU2Y4AYEq5/7X6gJqX21u4iyAwDwD0pP4FF6DIhkghsQdPIbttbr/a7WnHxJZRQeAID/tOSYnoCj9BgQwbl6gKDhdUZo2qjr9banicrzfabjAABsLjbSpcykGNMxwg6lx4CIRk0kp1Py8QYLMGlT9hC92PpsrStyS+LfIwDA/5rXi5PD4TAdI+xQegxwRscoonFzebZuNB0FCEsVCan6YMTf9HlhrHxFnHMHABA4rRpwPI8JlB5DottlU3oAAxYO/qNeSuiu3AKO2wEABF7HzCTTEcISpceQqLbZKpv2uekYQNjIbdxBr/S+Uj/k+6RyCg8AwAxKjxmUHkOi2nUyHQEIC57IKH098ka9U5WpKgYVAAAMinQ51DY9wXSMsETpMSSyeWs5oqNlVVWZjgLY1vquw/VCs9O1qZhBBQAA81qnJSjS5TQdIyxRegxxuCIU2aq9qlcuMR0FsJ2ylAxNHPZXfZkfJauYQQUAgODQITPRdISwRekxKKptNqUHqGPzTrpCr0R3VkE+x+0AAIILx/OYQ+kxKLpdJ5WaDgHYxO7mXfRy98u1KN8rVVJ4AADBh5Uecyg9BkW1zTYdAQh57qhYfTHqRk0sT1N1vtd0HAAADio6wqmWaZyjxxRKj0ERmVlyJqfKV1RgOgoQklZ3P1UvZo3U1hK3JMt0HAAADqldeqIinAwxMIXSY1hU22xVLpxjOgYQUkrqN9I7Q27Q1PwIqYRBBQCA4MfWNrMoPYZFtetE6QGOkuVwaM6wq/RKRHsVs5UNABBCGGJgFqXHMI7rAY7OjlY9NKHLJVpa4JG8FB4AQGjpyEqPUZQew6LbZksOh2RxPAJwMNWx8fp0xN/0YWmKPAVMZQMAhJ7kmAg1qxdnOkZYo/QY5kxMUkTDJvLs2Go6ChB0VvQ6Sy9mDNX2YgYVAABCV7fGKXI4HKZjhDVKTxCIapdN6QH2U9Sgqd4adJ2+zXdKpQwqAACEtm5NUk1HCHuUniAQ1TZb5TO+Mh0DMM7ndGnm8Gv0ulqplEEFAACb6NEkxXSEsEfpCQJR7TqZjgAYt61tX73Y8UKtLPRIovAAAOwhMTpCbdITTMcIe5SeIBDVsp0cMbGyKitMRwECriouSZNH/E2TSxLlLWRQAQDAXnIap8jJ8TzGUXqCgCMyUtGduqnyx3mmowABtbjfBZpQr792FTGoAABgT2xtCw6UniAR070fpQdhoyCzlV7rf43m5EsqY1ABAMC+ulN6ggKlJ0jEdOtrOgLgd15nhKaPvE5vepuqPN9nOg4AAH6VEB2hdhmclDQYUHqCRGTTFnI1yJB3727TUQC/2Jw9SC+2Pldri9ySKDwAAPvLyUrmeJ4gQekJIjE5fVQ27TPTMYA6VZGQqg+H/02fFcXKV8RWNgBA+GBrW/Cg9ASRmO59KT2wlR8HXqyXknpqL1PZAABhqDsnJQ0alJ4gEp3TW3I6JR9bfxDachu316u9r9T3+ZZUTuEBAISf+CiX2nM8T9Cg9AQRV1KKolq3V/XalaajAMfFExGpKSNv1DvVDVXJoAIAQBjr1TRVLifH8wQLSk+QienWl9KDkLShyzC90PwMbSxmUAEAAP1b1jcdAftxmg6A2qK7M7oaoaU8OV2vnHO//lHvlF8LDwAAGNAyzXQE7IeVniAT3b6LHLHxsirKTEcBjuj7oWP1UmwXFRRw3A4AAL9p0yBB6YnRpmNgP5SeIOOIiFB05+6qXDDbdBTgkPY066yXe1yuH/N9UgWFBwCA/Q1sxda2YEPpCUIx3ftSehCU3JEx+nLUjZpYka4qBhUAAHBQA1pQeoINpScIxXTvZzoCcIA13U7Ri01GaQuDCgAAOKTkmAh1apRsOgZ+h9IThCKzmsqVkSXv7u2mowAqTW2od076q6bkR0gMKgAA4LD6tqjPqOogROkJUjHdeqvs649Nx0AYsxwOzR12lV6JbK+ifK/pOAAAhIT+bG0LSpSeIBXTvS+lB8bsbNlNE7r+UUsKvJKXwgMAwNFwOqT+LeqZjoGDoPQEqZicPlJEpORhOxECxx0dr89G3aQPylLlLqDsAABwLLIzk5QSF2U6Bg6C0hOknPEJiunWR5UL55iOgjCxsteZeiHjJG0vdkuyTMcBACDkDGBUddCi9ASxuEHDKT3wu+K0Jnpz8HX6Nt8llbKyCADA8RrYMs10BBwCpSeIxfYdKkVGSe5q01FgQ5bDoZnDr9XrjtYqYVABAAAnpElKrNplJJqOgUOg9AQxZ3zCvhOV/jDLdBTYzLa2fTQ++yKtKPBIovAAAHCiTm6XbjoCDoPSE+TiBo2g9KDOVMUl6eMRN+mjkiR5Czym4wAAYBvDKT1BjdIT5GL7DJYjKlpWdZXpKAhxS/qep/H1B2pXEYMKAACoS01T2doW7Cg9Qc4ZF6+YHv1VMX+G6SgIUYUZLfX6gGs1K19SGYMKAACoa2xtC36UnhAQN2g4pQfHzOuM0Lcjr9Mb3qYqz/eZjgMAgG2xtS34UXpCQEyfwXJER8uqYosbjs6WDgP1YrvztKbQLYnCAwCAvzRNjVPbdLa2BTtKTwhwxsQqpudAVcydbjoKglxlfKomjbhJnxbFyVvIVjYAAPxteLsGpiPgKFB6QkTcoBGUHhzWooF/0ISkXtpbyFQ2AAACZXi7DNMRcBQoPSEiptdAOWJiZVVWmI6CIJOX1Vav9vmz5udbUjmFBwCAQGlWL05t0hNMx8BRoPSECGdMjGJ6D1TFrGmmoyBIeF2RmjrqBr1VnaVKBhUAABBwTG0LHZSeEBI3cASlB5KkDZ2HaXzLM7S+iEEFAACYMoLSEzIoPSEkttcAOWLjZFWUm44CQ8qT0vT+yTfpfwXRsooYVAAAgCltGiSodQO2toUKSk8IcURFK7bPYJXP/Np0FBjww5DL9FJcjvILOG4HAADTzujc0HQEHANKT4iJGzic0hNm9jbN1ss9r9DCfJ9UQeEBAMC0SJdDp3bMNB0Dx4DSE2Jieg6QMylZvuIi01HgZ+7IGH016q96ryJDVQwqAAAgaAxqlaaU2EjTMXAMKD0hxhEZqfiTz1DJx2+bjgI/WpczSi80PUWbixlUAABAsDmTrW0hx2k6AI5d/KnnmI4APylNzdSEcx/U7Skn/1p4AABAMGmQEKW+zeubjoFjxEpPCIrMaqboLj1VtfRH01FQh+YMu1KvRmWrMJ/jdgAACFandsyUy+kwHQPHiNITohJOPZfSYxO7WuRoQs6lWlzglSopPAAABLMzOzcyHQHHgdITomL7nSRnSj35CvNNR8FxckfH67ORN+qD8npyF3hNxwEAAEfQpVGymtWLMx0Dx4HSE6IckZGKH36GSia9YToKjsPKnqP1YsOTta3ELckyHQcAABwFBhiELgYZhLCEU86RHOwpDSXFaU3033Mf0j8TBv9aeAAAQCiIjXRpePt00zFwnFjpCWERDRsrOqe3qn7+wXQUHIHlcOi7k6/Wa862KslnKxsAAKFmWNsGio/irXOo4m8uxCWcei6lJ8htb9NLEzr9QcsKPJIoPAAAhKJzujDAIJRRekJcbN8hcqbWl68gz3QU/E51bKI+HnmTJpUky1vAVDYAAEJV+4xEdW2cYjoGTgClJ8Q5XBGKH3GmSj54zXQU7Gdp33M1Pm2QdhYxqAAAgFB3YffGpiPgBDHIwAYSTjlHcvJXGQwKM5rr6XMf0b0xfbWzlEEFAACEutS4SI1qn2E6Bk4QKz02EJHRSDHd+qpy0TzTUcKWz+nStyP+ojesFipjUAEAALZxdpdGiorgl8uhjtJjE/GnnkvpMWRL+wEa3/48rS5kUAEAAHbicjp0Xk6W6RioA5Qem4jtPVCu+uny5u0xHSVsVManaNKIm/RpUby8hQwqAADAboa2TlNGYozpGKgDrNXZhMMVofiRZ5mOETZ+GjBGfxtyjyYXxsvLnAIAAGzpoh5NTEdAHaH02Ej8qLMkl8t0DFvLb9RGj537qB6I7K495azuAABgV20aJKgbY6ptg+1tNhLRIFNxg0aqfOZXpqPYjtcVqWkjb9BbnixV5PtMxwEAAH52EWOqbYXSYzOJF1yu8u++liz2XNWVTZ2G6oVWZ2l9kVsShQcAALtLjo3UKR0ZU20nlB6biWreWjG9BqpywWzTUUJeeVJ9fXDyTfpfYYx8RZxzBwCAcHFW54aKjuCQATuh9NhQ0gVjKT0naMHgS/VSfDflFXDcDgAA4STC6dAF3djaZjeUHhuK7thV0Z26q2r5T6ajhJy9TTrolV5XakG+T6qg8AAAEG5O7ZipzCTGVNsNpcemEi+4nNJzDDyRUfpq5I16typTVQwqAAAgLDkd0uV9mpmOAT+g9NhUbM8BimzZVu6Na01HCXrrckbqxaanalMxgwoAAAhnw9qmq1m9ONMx4Aecp8fGki4YazpCUCtLydBL5z6g21OG/1p4AABAOBvLKo9tsdJjY7EDTlZEoyby7PjFdJSgM++kP+mV6E4qyOe4HQAAIPVrUU/tMhJNx4CfsNJjYw6XS4nnXmo6RlDZ1aKrHjjncT1mtVdBJYUHAADsc0Xf5qYjwI8oPTYXP3y0nPXSTMcwzh0Vq4/PuF1/a/lH/VTgNR0HAAAEkZysZHVrnGI6BvyI0mNzjsgoJZ59sekYRq3qMVr/N+rfequknqq9luk4AAAgyIzty7E8duewLIt3gTbnKy/TjrGjZZWVmI4SUMVpjfXO4Os1LZ8zKgMAgINrm56gdy7vbToG/IyVnjDgjItX4hkXmo4RMJbDoe+GX6Mbe9xM4QEAAIfFxLbwwEpPmPAWFWrnFaNlVVWajuJXO1r31PjOF2tZAUMKAADA4TVNjdWHV/aV0+EwHQV+xsjqMOFKTlH8yLNU+vn7pqP4RXVsvD4d8Td9WJoiD4UHAAAchT/1bU7hCROUnjCSeO6lKv1qsuSx14k4l/U5W+MbDNGOYrckFi4BAMCRtUyL16nZmaZjIEAoPWEkIj1TCaedp9LPJpqOUicK05vpzYHXaWa+Qyq1V5EDAAD+9ZeBLVnlCSMc0xNmvEWF2nnVWbLKy0xHOW4+p0szhl+r19VSZdWccwcAAByb7IZJev2PPU3HQAAxvS3MuJJTlHTeZaZjHLet7fvr7jMf1fPVzSg8AADguFw3qKXpCAgwVnrCkK+yUruuPkfevL2moxy1yvgUTR5+oz4uThDnFwUAAMerV9NU/feibqZjIMBY6QlDzpgYJV1yjekYR+3n/hfq5qH3aFIRhQcAAJyY6we3Mh0BBjDIIEzFDz9DJZ+8K8/WjaajHFJ+w9Z6vd/VmpMvqYwx1AAA4MQMbZ2m7IZJpmPAAFZ6wpTD5VLK2BtMxzgorzNCX596k27sdO2+wgMAAHCCnA7pWo7lCVus9ISx2D6DFZ3dTVUrfjYdpcam7CF6sfXZWlfkluQzHQcAANjEKR0y1SotwXQMGMIggzBXtXq59tw61nQMVSSk6oMRf9PnhbHy8R0JAADqUITToUlX9lVWSqzpKDCElZ4wF92+k2IHnKyKudONZVg4+I96KaG7cgs4bgcAANS9c7o2ovCEOUoPlHz59ar44TvJE9jSkdu4g17pfaV+yPdJ5RQeAABQ9xKjI3R1/xamY8AwSg8UmdVUCaeco9L/fRiQx/NERunrkTfqnapMVeVz3A4AAPCfq/q3UEpclOkYMIxjeiBJ8hbma+dV58iqKPPr46zvOlwvNDtdm4rdfn0cAACAFvXj9O7Y3opwMrA43PEdAEmSK6WeEs/9o9/uvzw5Xa+c+4BuSx1J4QEAAAFx80ltKDyQxEoP9uOrrNDOq86WryCvTu933klX6JXoziqo5LgdAAAQGANb1teT53U1HQNBguqLGs6YWCVfcnWd3d/u5l304LmP6zGrA4UHAAAETITToZuHtTEdA0GEQQaoJX7kWSr9/AO5t2w47vtwR8Xqi1E3amJ5mqrzvXWYDgAA4Mgu6t5YTVPjTMdAEGF7Gw5QtWKx9tz2Z+k4vjVWdz9VL2aN1NYSjtsBAACBVy8uSh9d1VcJ0fxuH/8f3w04QHR2juKGna7y6f876tuU1G+kd4bcoKn5ERKFBwAAGHLdoJYUHhyAlR4clLcwX7uuOV++0uLDXs9yODRn2FV6JaK9iqvYygYAAMzpkJGo1y/tKafDYToKggyDDHBQrpR6Sr7susNeZ0erHrrv7Mf1pLcNhQcAABh367A2FB4cFGt/OKT4U89V6bTP5F63stbl1bHx+nTE3/RhaYo8BUxlAwAA5p3WMVNdG6eYjoEgxfY2HFb1upXafctYyeeTJK3odZZezBiq7aUctwMAAIJDSmykPvxTH6XERZmOgiDFSg8OK6pNR8Wfcq52LFygtwZdp2/znRKFBwAABJGbT2pN4cFhsdKDI/KWleiid5dpS2Gl6SgAAAC19GmWqucu7GY6BoIcgwxwRK74RF09sJXpGAAAALVERzh1+8j2pmMgBFB6cFRGdsjQgJb1TccAAACo8ef+LdQ4JdZ0DIQASg+O2m3D2yo20mU6BgAAgNqmJ+iSXk1Mx0CIoPTgqDVMjtXVA1qYjgEAAMKc0yHdNaq9Ipy8lcXR4TsFx+QPPZqofUai6RgAACCMXdi9sTpmJpmOgRBC6cExcTkduueUDop0cbZjAAAQeJlJ0frLwJamYyDEUHpwzNqkJ+jP/dnmBgAAAu/2Ee0UF8WpJnFsKD04Lpf1bqZODVlWBgAAgTOqQ4YGtEwzHQMhiNKD4+JyOjTutA6KjuBbCAAA+F96YrT+Mbyt6RgIUbxjxXFrXi9e1w3ipKUAAMC/HJLuOaWDkmIiTUdBiKL04IT8oUdjdW+SYjoGAACwsQu7N1af5vVMx0AIc1iWZZkOgdC2vbBCF7++QOVur+koAFAnLJ9XK5+5TlFJ9dXmT/9W/tLvtGPaG6rcs1Udbnhe8U3aHfR2mz54VEWrvldEQoo63fpKzeXbvpygotULFNeotVqMuV2SlLdomjwVxcoYeF5AnhMQqprXi9Nbl/VSDCdIxwlgpQcnLCslVjcNbW06BgDUmd1zJis2vWnN57EZzdX60vuU0KLLYW+X1nOU2lz5UK3LPBWlKt28Qtm3vCzL51P5zo3yuauUu2iKGvQ7yy/5AbtwOR267/SOFB6cMEoP6sS5OVnqy7IzABuoLtyrotU/KK33aTWXxWY0U0x6kyPeNrFlF0XE1Z5s6XA4ZXk9sixLPk+VHK4I7Zr5vtIHnCOni7G7wOFc2a85JyFFnaD0oM7cfUoHJUbzP3AAoe2Xz59X49Oulhx1cxJmV0ycUjoP0sqnrlF0aqZcMfEq27ZGqdkD6uT+Abvq1DBJV/RtZjoGbILSgzqTnhit/zuZUZIAQlfhyvmKSEhVfOO6/VnWcOgYZd88QU3O+It2THlNWSPHau8PX2jD2//Sjulv1+ljAXYQE+nUfad3VISTt6qoG3wnoU6dlp2pUR0yTMcAgONSumWFClfO09KHLtbGdx5QyYbF2vjev+vs/su3r5MkRTdorLyfpqnVH+9Rxa5Nqty7rc4eA7CDG4e0VtPUONMxYCPsRUKdu2NkO63aVaytBRWmowDAMWl86lVqfOpVkqTiDYu1+7sP1PIPd9bZ/W+f8pqanXeLLK9Xls8nad8xPz53VZ09BhDq+javpwu6NTYdAzbDSg/qXHxUhP59RidFufj2AmAPBcvnaMmDF6lsy0qte+1OrX35NklSdVGu1r5yR831Nr7zgFY//1dV7f1FSx68SHsXfFnrPuKbtFdUcpoiYhOU0KyjVjxxlSSH4hpxomdAklLjIjXu1A6mY8CGOE8P/OaDn7bp0elrTccAAAAhwCHp6fO7ql+L+qajwIb4VTz85sLujXVSmwamYwAAgBBwWZ9mFB74DaUHfnX3Ke3VKDnGdAwAABDEumYl69qBLUzHgI1ReuBXiTGRenB0tiKcdXO+CwAAYC/JsZF68IxsxlPDr/jugt91apSs6wa1NB0DAAAEGYekcad2UEYiu0LgX5QeBMQfezVVf/bpAgCA/Vzau6kGtUozHQNhgNKDgHA4HLrvtA5KT4g2HQUAAASB7k1SdN0gxrUjMCg9CJiUuCjdP7qjXBzfAwBAWKsfH6UHR2fzngABQ+lBQHVvkqqbhrY2HQMAABjicjj04BnZSmP3BwKI0oOA+0OPJhrdKdN0DAAAYMC1g1qoR5NU0zEQZig9MOKOEe2V3TDJdAwAABBAw9ula2yf5qZjIAxRemBEVIRT/zmrs+rFRZmOAgAAAqB9RqLGndrBdAyEKUoPjElPjNZ/zurEiUsBALC5enFRevTszoqJdJmOgjBF6YFRXRun6P9Obms6BgAA8JNIl0P/ObuzMpM4ASnMofTAuPNysnROl0amYwAAAD+4fUQ7dc1KNh0DYY7Sg6Dw9+Ft1aURPxABALCTMT0a68zO/GIT5lF6EBQiXU49clYnpTOzHwAAW+jTLFV/G9rGdAxAEqUHQSQtIVr/ObuzoiP4tgQAIJQ1TY3Vv8/sJBfDihAkeHeJoJLdMEnjTu0gfkQCABCa4qNcevycLkqKiTQdBahB6UHQGdE+Q38d0sp0DAAAcIycDunBM7LVvH686ShALZQeBKVLezfTBd2yTMcAAADH4OaT2mhAyzTTMYADUHoQtG4d1laDW/ODEwCAUHBpr6Ya06OJ6RjAQVF6ELRcToceHJ2tjpmJpqMAAIDDOKUDW9MR3Cg9CGoxkS49eW5XNUrmLM4AAASj3s1S9w0hcjCGCMGL0oOgVy8+Sk+f11XJMRGmowAAgP20aZCgR87qrAgXbykR3PgORUhoXj9ej57TRVH8UAUAICg0TIrRM+d3VUI0v5RE8OMdJEJGt8YpGnca5/ABAMC05JgIPXN+V6UlRJuOAhwVSg9CykjO4QMAgFHREU49fk4XzsWDkELpQci5tHczXd6nmekYAACEHadDuv/0bHVtnGI6CnBMKD0ISTcMbqULuzU2HQMAgLBy67C2OqltA9MxgGNG6UHI+r+T2+j07EzTMQAACAt/GdhSF3bnF44ITZQehCyHw6G7T+mgk/mNEwAAfvWnvs30p37NTccAjhulByHN5XTogdHZ6t+ivukoAADY0sU9m+gvgxgihNBG6UHIi3A59chZndS9SYrpKAAA2Mr5OVm6+aQ2pmMAJ4zSA1uIiXTpiXO7KLthkukoAADYwlmdG+ofw9uajgHUCUoPbCM+KkJPn9dVrdM4bwAAACfilA4ZunNUezkcnBIc9kDpga0kx0bquQu7qWlqnOkoAACEpGFtG+je0zrKSeGBjVB6YDv146P034ty1CQl1nQUAABCysBW9fXg6Gy5nBQe2AulB7aUkRij8X/orub1WPEBAOBo9G1eT4+c2VkRLt4ewn74roZtNUiI1otjuqslx/gAAHBYvZqm6tGzOysqgreGsCeHZVmW6RCAPxWWV+v6Dxdr7Z5S01EAAAg6A1vV18NndlJ0hMt0FMBvKD0IC8WVbt3w4WKt2lViOgoAAEFjeLt03X96R7a0wfYoPQgbpVUe3ThpsZbtKDYdBQAA407PztTdp3RgaAHCAqUHYaW82qO/fbRUP28rNB0FAABjzsvJ0m3D23IeHoQNSg/CTqXbq1smL9XCrQWmowAAEHB/7NVUNw1tbToGEFCUHoSlKo9Xf/9kmeZvyjcdBQCAgLm6fwv9eUAL0zGAgKP0IGxVe3y6638rNHPdXtNRAADwuxuHtNalvZuajgEYQelBWPNZlv7zzVp9tHi76SgAAPiFQ9I/hrfV+d0am44CGEPpASS9Mn+TXpyzyXQMAADqVITTobtPaa/TshuajgIYRekBfvXp0h16aOoaefknAQCwgfgolx45q7P6NK9nOgpgHKUH2M/sDbm68/PlqnT7TEcBAOC4NUiI0lPndVXb9ETTUYCgQOkBfmfZjiLdPHmpiircpqMAAHDMWqbF6+nzuiozKcZ0FCBoUHqAg9icX6YbP1yincWVpqMAAHDUejRJ0aNnd1ZiTKTpKEBQofQAh5BbWqUbJy3Rur2lpqMAAHBEozpkaNypHRTpcpqOAgQdSg9wGKVVHv39k6X6cWuh6SgAABzSZb2b6obBreRwOExHAYISpQc4ArfXpwenrNYXK3aZjgIAQC0uh0O3ntxGF3AOHuCwKD3AUXprwVY9N2u9fPyLAQAEgZhIpx4Yna0hrRuYjgIEPUoPcAzmbMjVP/+3QmXVXtNRAABhLD0hWv85u7OyGyaZjgKEBEoPcIw25pbp1o+XalthhekoAIAw1KVRsh45q5PSEqJNRwFCBqUHOA6FFW7d8dkyBhwAAALqrM4NdduIdkxoA44RpQc4Th6fT499s04fLdluOgoAwOZcToduOamNLuzOwALgeFB6gBP0wU/b9MSMdfIy4QAA4AcpsZF6+MxO6tE01XQUIGRReoA6sGBLvu74bLmKKz2mowAAbKRNgwQ9dk5nNUqONR0FCGmUHqCO/FJQrv/7ZJk25paZjgIAsIHh7dJ1zykdFBvlMh0FCHmUHqAOVbq9enjaGk5kCgA4bg5J1w5sqT/1a246CmAblB7ADz5ZukOPTV+rKo/PdBQAQAhJionQvad11KBWaaajALZC6QH8ZM3uEt3x2XL9wvl8AABHoXPDJD14RrYacvwOUOcoPYAflVZ5dN9XqzRz3V7TUQAAQcoh6ZJeTXX9oJaK4Pw7gF9QeoAAePfHrXr2uw3yMNYaALCf5JgIjWM7G+B3lB4gQJZsL9Kdny/XnpIq01EAAEGgS6NkPXhGtjKTYkxHAWyP0gMEUGF5te7+YqW+35xvOgoAwBCHpEt7N9VfBrVUhJPtbEAgUHqAAPNZll7/fosmzNskL9vdACCspMRG6t7TOmpAy/qmowBhhdIDGLJiZ7Hu+WKlthaUm44CAAiAnKxkPXhGJ6UnRpuOAoQdSg9gUKXbq6dmrNdHS7abjgIA8BOX06Er+jTTVf1byOV0mI4DhCVKDxAEZm/I1QNfr1Z+ebXpKACAOtS8XpzuPa2jshsmmY4ChDVKDxAkCsqr9cCU1Zq1Ptd0FADACXJIGtOjia4f3FLRES7TcYCwR+kBgswnS3foiW/XqcLtNR0FAHAcGibF6J5TO6hn01TTUQD8itIDBKFfCso17ouVWraz2HQUAMAxGN0pU7cOa6uE6AjTUQDsh9IDBCmvz9Kr32/WK/M3M9oaAIJcvbgo3TmqnYa0bmA6CoCDoPQAQW7N7hLd//UqrdlTajoKAOAgTmrTQHeObKeUuCjTUQAcAqUHCAEen0/vLvxFE+ZtUpXHZzoOAEBSUkyEbh3WRqdlNzQdBcARUHqAEPJLQbn+PXW1ftxaaDoKAIS1UR0ydPNJbVQ/ntUdIBRQeoAQ9OnSHXp65nqVVHlMRwGAsNIoOUa3j2infi3qm44C4BhQeoAQlVtapcemr9X0tXtNRwEA23M5HbqkZxP9uX8LxURy3h0g1FB6gBA3c91e/eebNdpbWm06CgDYUnbDJN05sp3apieajgLgOFF6ABsorfLo2e/W6+MlO8Q/aACoG/FRLl03qJXO75Ylp8NhOg6AE0DpAWxkyfYiPTZ9rVbvLjEdBQBC2tA2DfT3k9sqPTHadBQAdYDSA9iMz7L0ydIdemH2RhVWuE3HAYCQkpkUrf8b1lZD2nCSUcBOKD2ATZVUujV+7iZNWrxdXh//zAHgcGIjXbqsd1P9sVdTBhUANkTpAWxu/d5SPf7tWs7tAwAH4ZB0SscM3TC4NVvZABuj9ABhYvqaPXp65nrtLK40HQUAgkLnhkm6ZVgbdWqUbDoKAD+j9ABhpNLt1ZsLtujNBVtV5fGZjgMARqQnRuuGwa10SocMOZjKBoQFSg8QhnYWVejpmes5sSmAsBId4dSlvZrq8j7NOG4HCDOUHiCMLd9RpOdmbdCiXwpNRwEAvxrVIUM3DG6lzKQY01EAGEDpAaB5m/L0/KwNWrun1HQUAKhTPZqk6LpBrdQli+N2gHBG6QEgSbIsS1NX79aLczZpW2GF6TgAcEI6ZibqukGt1Kd5PdNRAAQBSg+AWjxenz5eukOvzN+svLJq03EA4Ji0SovXtQNbaignFwWwH0oPgIOqqPbqvUW/6M0FW1RW7TUdBwAOq0lKrK4e0EIjO2TIyUQ2AL9D6QFwWIUVbr3+/WZNWrydMdcAgk56YrSu7NdcZ3ZuqAin03QcAEGK0gPgqOSWVumthVv18ZIdqnCz8gPArNS4SI3t00zn5WQpOoLx0wAOj9ID4JgUllfrvUW/6IOft6u0ymM6DoAwUy8uSn/o2VgXdmusuKgI03EAhAhKD4DjUlrl0Qc/bdN7i35RYYXbdBwANtcoOUaX9mqqMzo3ZGUHwDGj9AA4IRXVXk1esl1vL9yqXKa9AahjrdPidXmfZhrRPkMuJwMKABwfSg+AOlHt8emzZTv05oKt2llcaToOgBDXNStZY/s008BWaaajALABSg+AOuXx+fTVyt16a8EWbcorNx0HQIgZ0LK+xvZpppzGKaajALARSg8Av7AsSz9sztfEn7Zp3sY88YMGwKG4HA4Nb5+uy3s3U5v0BNNxANgQpQeA323JL9cHP23T/5bvVDnjrgH8KjUuUmd1bqTzcrKUmRRjOg4AG6P0AAiY0iqPPlu2Qx/8tE3bizjuBwhXHTITdVG3xhrRPkNREZxQFID/UXoABJzPsjR7Q64mLtqmH7cWmI4DIAAiXQ4Nb5euC7s1VqdGyabjAAgzlB4ARq3fW6qJP/2ir1fuVpXHZzoOgDqWnhit87pm6ewujVQvPsp0HABhitIDICgUV7r11cpd+mTJDq3PLTMdB8AJ6t4kRRd2a6whbdIU4WQLGwCzKD0Ags7yHUX6ZNkOTVu1h8EHQAhJi4/SadmZGt2poVrUjzcdBwBqUHoABK2yao+mrtqtz5fv1LIdxabjADiIKJdTg1unaXSnhurbvJ5cTofpSABwAEoPgJCwOb9M/1u+S1+u2Km9pdWm4wBhr2NmokZ3aqhRHTKUFBNpOg4AHBalB0BI8fosfb85X/9bvlOzN+Qy/AAIoLT4KJ3acd/2tZZpbF8DEDooPQBCVnm1R7PW52ramj2avylPbi8/zoC6FhPp1MCWaTo9O1P9WtRn+xqAkETpAWALpVUezVy3V9NW79GCLfny+PjRBhyv2EiXBrSsr+Ht0jWgZX3FRLpMRwKAE0LpAWA7RRVuzVi3V9NW79airYXy8mMOOKK4SJcGtqqvk9ulq38Lig4Ae6H0ALC1/LJqfbt2j6at2aPF2wrFAhDw/8VHuTSoVZpObpeufi3qKTqCogPAnig9AMJGflm15m7K09wNufp+c77KqjkHEMJPYnSEBrVK07B2DdSveX1FRXDiUAD2R+kBEJY8Xp9+3laoORvyNGdjnrYWlJuOBPhNy7R4DWxZXwNa1leXrGRFOCk6AMILpQcAJG0tKN9XgDbk6udthQxCQEiLiXSqR5NUDWhZXwNb1lfD5FjTkQDAKEoPAPxOWbVHP2zK15yNefphc772lFaZjgQcUeu0ePVtUV/9WtRTTlYK29YAYD+UHgA4gl8KyrXol0L9uLVAP/1SoL2l1aYjAcpMila3xinq2TRV/VrUV4OEaNORACBoUXoA4BhtyS/Xol8KtGhrgRb9Uqi8MkoQ/K9papy6NUlW98Yp6tY4hS1rAHAMKD0AcII255XttxJUqPxyShBOjNMhtUpLULfGKerWZF/JqR8fZToWAIQsSg8A1LHthRVauatYK3YWa+WuEq3eXaIKN+OxcWjxUS61TU9Up4ZJymmcopzGyUqKiTQdCwBsg9IDAH7m9VnalFemlbuKtXJniVbsKtb6vaVMiAtT8VEutctIVPuMRHXISFT7zEQ1S42Tw+EwHQ0AbIvSAwAGVHm8WruntKYIbcgt1eb8clV5fKajoQ7tX3A6ZiaqfUaSmqbGUnAAIMAoPQAQJHyWpe2FFdqYW6aNeWU1H7dQhoJefJRLzerFqXm9eDWvv+9jy7R4Cg4ABAlKDwAEOa/P0vaifWVoU16ZNvz6cXthhcqqOVYokBokRNUqNr99TE9kXDQABDNKDwCEsKIKt3YWV2pHUYV2FlVqR1GldhTv+/POokqVM0DhmKTERiojMVqZSTHKTIpRRmKMMpOi1Sg5Vs3rxyk+KsJ0RADAcaD0AICNFZZXa0fxvgK0s7hSeWXVKiivVn55tQrK3covr1ZhuVvVXntvn4twOpQcG6mU2EjVi4v6tdDsV26SopWZGKOYSJfpqAAAP6D0AABUWuX5tQy59ytF+4pRWbVHFdVeVbh9qnB7VOH2qbzaW/PnCrdXXj9PootyORUdsd9/kS5FRzgVHxWhlNhIJf/6X0pspJJjIpUS9+vHXy9PiGaFBgDCGaUHAHDCqj0+lbu9qnR7VV69rwRZsmRZkiXJsqxfP/7uz9r3B5/2rcZERzgV82uhiY5wKSbCqagIp5wMAwAAnABKDwAAAABbc5oOAAAAAAD+ROkBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBAAAAYGuUHgAAAAC2RukBACCMuVwu5eTkqFOnTjrjjDNUWFhYp/d/2mmn1cl93nvvvXrsscdOPBCAsETpAQAgjMXGxmrx4sVavny56tWrp+eff75O7//LL79USkpKnd4nABwrSg8AAJAk9evXT9u3b5ckbdiwQaeccop69OihQYMGafXq1ZKk3bt365xzzlHXrl3VtWtXzZs3T5L09ttvq3fv3srJydE111wjr9crSWrevLlyc3N122236b///W/NY9177716/PHHJUmPPvqoevXqpS5dumjcuHE113nwwQfVrl07DR8+XGvWrAnIawDAnig9AABAXq9X06dP15lnnilJuvrqq/Xss89q0aJFeuyxx3TddddJkm688UYNGTJES5Ys0U8//aTs7GytWrVK77//vubOnavFixfL5XLpnXfeqXX/Y8aM0fvvv1/z+QcffKALLrhAU6dO1bp167RgwQItXrxYixYt0qxZs7Ro0SJNnDhRP//8syZPnqyFCxcG7sUAYDsRpgMAAABzKioqlJOTo82bN6tHjx4aMWKESktLNW/ePF1wwQU116uqqpIkffvtt3rzzTcl7TseKDk5WW+99ZYWLVqkXr161dxnenp6rcfp1q2b9uzZox07dmjv3r1KTU1V06ZN9cwzz2jq1Knq1q2bJKm0tFTr1q1TSUmJzjnnHMXFxUlSTRkDgONB6QEAIIz9dkxPUVGRRo8ereeff15jx45VSkqKFi9efFT3YVmWLr/8cj300EOHvd7555+vSZMmadeuXRozZkzNbe+44w5dc801ta771FNPyeFwHNdzAoDfY3sbAABQcnKynnnmGT322GOKjY1VixYt9OGHH0raV0yWLFkiSTr55JP1wgsvSNq3Ja64uFgnn3yyJk2apD179kiS8vPztWXLlgMeY8yYMZo4caImTZqk888/X5I0atQovfrqqyotLZUkbd++XXv27NHgwYP18ccfq6KiQiUlJfr888/9/hoAsC9KDwAAkLRvC1rXrl01ceJEvfPOO3rllVfUtWtXZWdn69NPP5UkPf3005oxY4Y6d+6sHj16aMWKFerYsaMeeOABjRw5Ul26dNGIESO0c+fOA+4/OztbJSUlysrKUsOGDSVJI0eO1MUXX6x+/fqpc+fOOv/881VSUqLu3bvroosuUk5Ojs477zwNGjQooK8FAHtxWJZlmQ4BAAAAAP7CSg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW6P0AAAAALA1Sg8AAAAAW/t/rysmQnuv/bIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#maybe they forgot of the appointment and just need a little reminder?\n",
    "x = df.groupby(\"SMS_Received\").Attendance.sum()\n",
    "plt.figure(figsize=(15,15))\n",
    "\n",
    "labels = \"Did't Receive\", 'Received'\n",
    "sizes = [x[0], x[1]]\n",
    "\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%')\n",
    "\n",
    "plt.title(\"How many of Attendees received a reminder?\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### if you were really sick you won't need that msg much to rush to the hospital right 🤷‍♀️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gorping waiting time for easier analysis\n",
    "df['Waiting_Duration'] = pd.cut(df.Waiting_Days ,[0, 1, 7, 15, 30, 90, 180], labels = ['Same_Day', 'Up_To_Week', 'Almost_2_Weeks', 'Up_To_Month', 'Up_To_3_Months', 'Up_to_6_Months'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#How much did they wait\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "x = df.groupby(\"Waiting_Duration\").Attendance.sum()\n",
    "labels = 'Same_Day', 'Up_To_Week', 'Almost_2_Weeks', 'Up_To_Month', 'Up_To_3_Months', 'Up_to_6_Months'\n",
    "sizes = [x[0], x[1], x[2], x[3], x[4], x[5]]\n",
    "plt.bar(labels, sizes)\n",
    "\n",
    "plt.xlabel(\"Waiting_Duration\")\n",
    "plt.ylabel('Attendees');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### waiting 6 months for a doctor's appointment? it's either the pain already gone or the patient  is already gone! 😁"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Waiting_Duration  SMS_Received\n",
       "Same_Day          False           2897\n",
       "                  True               0\n",
       "Up_To_Week        False           8039\n",
       "                  True            6423\n",
       "Almost_2_Weeks    False           3182\n",
       "                  True            4913\n",
       "Up_To_Month       False           3486\n",
       "                  True            6148\n",
       "Up_To_3_Months    False           2246\n",
       "                  True            4081\n",
       "Up_to_6_Months    False             56\n",
       "                  True             101\n",
       "Name: Attendance, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"Waiting_Duration\", 'SMS_Received']).Attendance.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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po9GoiRMnKiIiQpL066+/asmSJSovL1d4eLgmTZrk0GMn9u/fX6/b15TxzRscdb5vM322uIawceOYAkfQT+Ao+krDKykpUcuWLRu6jFq5u7ufl8dONBenes9ON0LotEDY0AiEp8eBFo4iEMIRHFPgCPoJHEVfaXgEwqanLoHQaY+dAAAAAAA0LgRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAjVpwcLCGDh2qq6++WnfccYeOHj16Xpd/2223nZdlLliwQMuWLTsPFTmPe0MXAAAAAKDpGPX29vO6vA9vDT3jNJ6enlq7dq0k6YEHHtDKlSv1wAMPnLca/vnPf563ZTU1jBACAAAAaDJ69eqlgwcPSpL27NmjW2+9VUOHDtUNN9ygXbt2SZIOHz6syZMnKzo6WtHR0crKypIkvf/++xoxYoSGDh2qRx55RFVVVZKkK6+8UhaLRc8995xWrlxpX9efR/yWLl2q4cOHKzo6Wi+88IJ9mkWLFulvf/ubxo4dq19//dUZu+C8IhACAAAAaBKqqqq0fv16DRs2TJL0yCOPaPbs2Vq7dq1mzpypxx57TJI0c+ZM9e3bV6mpqfr888/VpUsX7dy5U2vWrFFKSorWrl0rNzc3ffDBBzWWP2rUKH300Uf21x999JFGjhyp9PR07d69W5988om++OILbdmyRRs3btSWLVu0Zs0affHFF3rttdf0448/Om9nnCecMgoAAACgUSsrK9PQoUOVm5ursLAwDRo0SMeOHdOmTZs0depUGQwG2Ww2lZeXS5I2bNigRYsWSZLc3Nzk4+Oj999/Xz/99JOGDx9uX6bZbK6xnh49eig/P18HDx5UQUGBfH191a5dOy1fvlzp6en2IFpSUqLdu3eruLhY1157rby8vCRJQ4cOddYuOW8IhAAAAAAatRPXEBYVFemOO+7QypUrNWbMGPn4+Gjt2rVyd3dXZWVlrcuw2Wy6+eab7aOIpzNixAh98sknysvL06hRo+zzTps2TbfddluNaZOTk2UwGM5t4xoYp4wCAAAAaBJ8fHw0e/ZsLVu2TJ6engoODraf4mmz2bR161ZJ0sCBA/Xmm29Kqj7N9I8//tDAgQP18ccfKz8/X5JUWFio3Nzck9YxatQoffjhh/rkk080YsQISdJVV12lVatW6dixY5KkAwcOKD8/X3379tVnn32m0tJSFRcX229805QwQggAAACgyejRo4e6deumDz/8UC+//LIee+wxJSUlqaKiQqNGjVL37t317LPP6pFHHtE777wjo9Go559/XpGRkXrkkUc0fvx42Ww2ubu767nnnlP79u1rLL9Lly46duyYAgMD1bZtW0lSVFSUdu7cqeuvv16S1LJlS7300ksKCwvTyJEjNWzYMLVv315XXnml0/fHuTLYbDZbQxfhDPv372/oEhots9ls/6YEqE3V369v6BIkSW7Jaxq6BNSCYwocQT+Bo+grDa+kpEQtW7Zs6DJq5cgpo67kVO9ZUFDQKafllFEAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAp+UitxxpVurynhEIAQAAAJyW0Wjkhi1NSGVlpYxGx2Mej50AAAAAcFqenp4qKyvT8ePHG+1D2C+44AIdP368octocDabTUajUZ6eng7PQyAEAAAAcFoGg0FeXl4NXUateDzJ2eOUUQAAAABwUQRCAAAAAHBRBEIAAAAAcFFOuYZw//79SkxMtL/Oy8vTmDFjFBUVpcTERB0+fFht2rRRXFycvL29JUmrV69WWlqajEajYmNjFR4eLknKycnR4sWLVV5eroiICMXGxjbai1sBAAAAoDFzyghhUFCQ5s+fr/nz52vu3Llq0aKF+vTpo5SUFIWFhSkpKUlhYWFKSUmRJOXm5iozM1MLFy7UE088oeXLl8tqtUqSkpOTNXXqVCUlJengwYPavHmzMzYBAAAAAJodp58y+tNPPykwMFBt2rRRVlaWoqKiJElRUVHKysqSJGVlZal///7y8PBQQECAAgMDtWvXLhUWFqq0tFQhISEyGAwaNGiQfR4AAAAAQN04/bETGzZs0IABAyRJR48elclkkiSZTCYVFRVJkiwWizp37myfx8/PTxaLRW5ubvL397e3+/v7y2KxnHI9qampSk1NlSQlJCTIbDbXy/Y0B+7u7uwfOORQQxfw/9BfGzeOKXAE/QSOoq/AEfSTs+fUQFhZWalNmzbplltuqXU6m81Wp/ZTiY6OVnR0tP01zyU5PZ7bgqaG/tq4cUyBI+gncBR9BY6gn5xZUFDQKdudesroDz/8oEsvvVStW7eWJPn6+qqwsFCSVFhYKB8fH0nVI38FBQX2+SwWi/z8/E5qLygokJ+fn/M2AAAAAACaEacGwj+fLipJkZGRSk9PlySlp6erd+/e9vbMzExVVFQoLy9PBw4cUKdOnWQymeTl5aXs7GzZbDZlZGQoMjLSmZsAAAAAAM2G004ZPX78uLZs2aI777zT3hYTE6PExESlpaXJbDYrPj5ekhQcHKx+/fopPj5eRqNRkydPltFYnV2nTJmiJUuWqLy8XOHh4YqIiHDWJgAAAABAs2Kw1eXCvCZs//79DV1Co8U513BU1d+vb+gSJEluyWsaugTUgmMKHEE/gaPoK3AE/eTMGsU1hAAAAACAxoNACAAAAAAuikAIAAAAAC6KQAgAAAAALopACAAAAAAuikAIAAAAAC6KQAgAAAAALopACAAAAAAuikAIAAAAAC6KQAgAAAAALopACAAAAAAuikAIAAAAAC6KQAgAAAAALsq9oQsAULtRb29v6BLsPmjoAgAAAHBeMUIIAAAAAC6KQAgAAAAALopACAAAAAAuikAIAAAAAC6KQAgAAAAALopACAAAAAAuikAIAAAAAC6KQAgAAAAALooH0wMA4EJGvb29oUuQJG14YGBDlwAAkBMD4bFjx7Rs2TLt27dPBoNBd999t4KCgpSYmKjDhw+rTZs2iouLk7e3tyRp9erVSktLk9FoVGxsrMLDwyVJOTk5Wrx4scrLyxUREaHY2FgZDAZnbQYAAAAANBtOO2V0xYoVCg8P14svvqj58+erXbt2SklJUVhYmJKSkhQWFqaUlBRJUm5urjIzM7Vw4UI98cQTWr58uaxWqyQpOTlZU6dOVVJSkg4ePKjNmzc7axMAAAAAoFlxSiAsKSnRtm3bdPXVV0uS3N3ddeGFFyorK0tRUVGSpKioKGVlZUmSsrKy1L9/f3l4eCggIECBgYHatWuXCgsLVVpaqpCQEBkMBg0aNMg+DwAAAACgbpxyymheXp58fHy0ZMkS/fbbb7rssss0ceJEHT16VCaTSZJkMplUVFQkSbJYLOrcubN9fj8/P1ksFrm5ucnf39/e7u/vL4vF4oxNAAAAAIBmxymBsKqqSrt379akSZPUuXNnrVixwn566KnYbLY6tZ9KamqqUlNTJUkJCQkym811qtmVuLu7s3/QpNBfGzeOKXAE/QSOoq/AEfSTs+eUQOjv7y9/f3/7qF/fvn2VkpIiX19fFRYWymQyqbCwUD4+PvbpCwoK7PNbLBb5+fmd1F5QUCA/P79TrjM6OlrR0dH21/n5+fWxac2C2Wxm/6BJob82bhxT4IjKykr6CRzCMQWOoJ+cWVBQ0CnbnXINYevWreXv76/9+/dLkn766Se1b99ekZGRSk9PlySlp6erd+/ekqTIyEhlZmaqoqJCeXl5OnDggDp16iSTySQvLy9lZ2fLZrMpIyNDkZGRztgEAAAAAGh2nPbYiUmTJikpKUmVlZUKCAjQPffcI5vNpsTERKWlpclsNis+Pl6SFBwcrH79+ik+Pl5Go1GTJ0+W0VidXadMmaIlS5aovLxc4eHhioiIcNYmAAAAAECz4rRAeMkllyghIeGk9qeeeuqU048ePVqjR48+qb1jx45asGDBea8PAAAAAFyN055DCAAAAABoXAiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCiCIQAAAAA4KIIhAAAAADgogiEAAAAAOCi3J21onvvvVeenp4yGo1yc3NTQkKCiouLlZiYqMOHD6tNmzaKi4uTt7e3JGn16tVKS0uT0WhUbGyswsPDJUk5OTlavHixysvLFRERodjYWBkMBmdtBgAAAAA0Gw4FwtzcXHl7e6t169YqKyvTmjVrZDQaNXLkSF1wwQUOr+zpp5+Wj4+P/XVKSorCwsIUExOjlJQUpaSkaMKECcrNzVVmZqYWLlyowsJCzZ49W4sWLZLRaFRycrKmTp2qzp076/nnn9fmzZsVERFR9y0HAAAAABfn0CmjixYtUklJiSTpzTff1LZt25Sdna1XX331nFaelZWlqKgoSVJUVJSysrLs7f3795eHh4cCAgIUGBioXbt2qbCwUKWlpQoJCZHBYNCgQYPs8wAAAAAA6sahEcLDhw8rKChINptNWVlZWrBggVq0aKFp06bVaWXPPfecJGno0KGKjo7W0aNHZTKZJEkmk0lFRUWSJIvFos6dO9vn8/Pzk8VikZubm/z9/e3t/v7+slgsdaoBAAAAAFDNoUDo4eGh0tJS5ebmyt/fXz4+PqqqqlJFRYXDK5o9e7b8/Px09OhRzZkzR0FBQaed1maz1an9VFJTU5WamipJSkhIkNlsdnheV+Pu7s7+QZNCf23cOKbAEfQTOIq+AkfQT86eQ4FwwIABevbZZ1VaWqprr71WkrR7924FBAQ4vCI/Pz9Jkq+vr3r37q1du3bJ19dXhYWFMplMKiwstF9f6O/vr4KCAvu8FotFfn5+J7UXFBTYl/tX0dHRio6Otr/Oz893uFZXYzab2T9oUuivjRvHFDiisrKSfgKHcEyBI+gnZ3a6ATmHriGcOHGixo0bpylTptgDocFg0B133OHQysvKylRaWmr/95YtW9ShQwdFRkYqPT1dkpSenq7evXtLkiIjI5WZmamKigrl5eXpwIED6tSpk0wmk7y8vJSdnS2bzaaMjAxFRkY6VAMAAAAAoCaHHztx+eWXKz8/X9nZ2QoJCVHHjh0dXsnRo0f1wgsvSJKqqqo0cOBAhYeHq2PHjkpMTFRaWprMZrPi4+MlScHBwerXr5/i4+NlNBo1efJkGY3V2XXKlClasmSJysvLFR4ezh1GAUDSqLe3N3QJdhseGNjQJQAAAAc5FAjz8/O1aNEi7dmzR5L0z3/+Uxs3btTmzZt11113nXH+tm3bav78+Se1t2rVSk899dQp5xk9erRGjx59UnvHjh21YMECR8oGAAAAANTCoVNGX331VUVEROiNN96Qu3t1huzZs6e2bNlSr8UBAAAAAOqPQ4Fw165diomJsZ+2KUktW7a0P5sQAAAAAND0OBQIfX19dfDgwRptubm53NoVAAAAAJowh64hHDlypObOnauYmBhZrVatX79eq1evVkxMTD2XBwAAAACoLw4Fwquvvlre3t5at26d/P39lZGRobFjx6pPnz71XR8AAAAAoJ44/NiJPn36EAABAAAAoBlxKBDabDatW7dOmZmZKioq0gsvvKBffvlFR44cUf/+/eu7RgAAAABAPXDopjKrVq3Sl19+qSFDhig/P1+S5O/vrw8//LBeiwMAAAAA1B+HAmF6eroeffRRDRgwQAaDQZIUEBCgvLy8ei0OAAAAAFB/HAqEVqtVnp6eNdrKyspOagMAAAAANB0OBcKIiAi9+eabqqiokFR9TeGqVavUq1evei0OAAAAAFB/HAqEt99+uywWiyZOnKiSkhLdfvvtOnz4sG699db6rg8AAAAAUE8custoy5Yt9cgjj+jIkSPKz8+X2WxW69at67k0AAAAAEB9Om0gtFqtJ7X5+PjIx8enxs+NRocGGQEAAAAAjcxpA+H48eMdWsCqVavOWzEAAAAAAOc5bSB8+eWX7f/+/vvvtXHjRt1www0ym83Kz8/Xhx9+qCuvvNIpRQIAAAAAzr/TBsI2bdrY//3xxx8rISFBF154oSQpKChIl112mR577DENGzas/qsEAAAAAJx3Dl0AWFJSouPHj9doKy8vV0lJSb0UBQAAAACofw7dZTQqKkqzZ8/WiBEj5O/vr4KCAv33v/9VVFRUfdcHAAAAAKgnDgXCCRMmKDAwUJmZmSosLFTr1q11zTXXKDo6ur7rAwAAAADUE4cCodFo1LBhw7heEAAAAACaEYcCoST9+OOP2rNnj8rKymq0jx079rwXBQAAAACofw4FwuXLl+vrr79W9+7ddcEFF9R3TQAAAAAAJ3AoEG7YsEHz5s2T2Ww+p5VZrVbNmDFDfn5+mjFjhoqLi5WYmKjDhw+rTZs2iouLk7e3tyRp9erVSktLk9FoVGxsrMLDwyVJOTk5Wrx4scrLyxUREaHY2FgZDIZzqgsAAAAAXJFDgbBVq1b2ZxCei08//VTt2rVTaWmpJCklJUVhYWGKiYlRSkqKUlJSNGHCBOXm5iozM1MLFy5UYWGhZs+erUWLFsloNCo5OVlTp05V586d9fzzz2vz5s2KiIg459oAAABQbdTb2xu6BLsNDwxs6BKAZs2h5xBed911SkpKUnZ2tg4dOlTjP0cVFBTo+++/15AhQ+xtWVlZ9kdXREVFKSsry97ev39/eXh4KCAgQIGBgdq1a5cKCwtVWlqqkJAQGQwGDRo0yD4PAAAAAKBuHBohfO211yRJ33///Uk/W7VqlUMrWrlypSZMmGAfHZSko0ePymQySZJMJpOKiookSRaLRZ07d7ZP5+fnJ4vFIjc3N/n7+9vb/f39ZbFYHFo/AAAAAKAmhwKho6HvdDZt2iRfX19ddtll2rp16xmnt9lsdWo/ldTUVKWmpkqSEhISzvn6x+bM3d2d/YMmhf7auHFMgSPoJ3AUfQWOoJ+cPYcfOyFJ+fn5slgsCgkJqdNKduzYoe+++04//PCDysvLVVpaqqSkJPn6+qqwsFAmk0mFhYXy8fGRVD3yV1BQYJ/fYrHIz8/vpPaCggL5+fmdcp3R0dGKjo6uUTtOzWw2s3/QpNBfG7fKykreI5wR/QSOoq/AEfw9e2ZBQUGnbHfoGsL8/HzNnDlTcXFxmj17tiRp48aNWrZsmUMrv+WWW7Rs2TItXrxYDz74oHr06KH7779fkZGRSk9PlySlp6erd+/ekqTIyEhlZmaqoqJCeXl5OnDggDp16iSTySQvLy9lZ2fLZrMpIyNDkZGRDtUAAAAAAKjJoUD46quvKiIiQm+88Ybc3asHFXv27KktW7ac08pjYmK0ZcsW3X///dqyZYtiYmIkScHBwerXr5/i4+P13HPPafLkyTIaq0udMmWKXnnlFd1///1q27YtdxgFAAAAgLPk0Cmju3bt0owZM+yhTJJatmypkpKSOq+we/fu6t69u6Tqx1k89dRTp5xu9OjRGj169EntHTt21IIFC+q8XgAAAABATQ6NEPr6+urgwYM12nJzc7lwEwAAAACaMIdGCEeOHKm5c+cqJiZGVqtV69ev1+rVq+2neAIAAAAAmh6HAuHVV18tb29vrVu3Tv7+/srIyNDYsWPVp0+f+q4PAAAAAFBPHAqEO3fuVJ8+fU4KgLt27VKnTp3qpTAAAAAAQP1y6BrCOXPmnLL9ueeeO6/FAAAAAACcp9YRQqvVKkmy2Wz2/044dOiQ3Nzc6rc6AAAAAEC9qTUQjh8/3v7vcePG1fiZ0WjUDTfcUD9VAQAAAADqXa2B8OWXX5bNZtOsWbP0zDPP2NsNBoN8fHzUokWLei8QAAAAAFA/ag2Ebdq0kSRde+219n//2ccff6zrrruufioDAAAAANQrh24q8/7779epHQAAAADQ+NU6Qvjzzz9Lkqqqquz/PuHgwYPy8vKqv8oAAAAAAPWq1kC4dOlSSVJFRYX931L1NYStW7fWpEmT6rc6AAAAAEC9qTUQLl68WFL1zWWmTZtmb//tt9+Unp6u5ORkRUZG1m+FAACg2Tl0Q/+GLsHOLXlNQ5cAAA2m1kB4wrRp01RUVKT169crPT1de/bsUdeuXTVx4sR6Lg8AAAAAUF9qDYSVlZX67rvvlJ6ers2bNyswMFADBgxQXl6e4uLi5Ovr66w6AQAAAADnWa2B8O9//7uMRqOioqJ0880367LLLpMkffHFF04pDgAAAABQf2p97MTFF1+sY8eOadeuXfr1119VXFzsrLoAAAAAAPWs1hHCWbNm6fDhw0pPT9dHH32kFStWqGfPnjp+/LiqqqqcVSMAAAAAoB6c8aYybdq00U033aSbbrpJ27dvV3p6ugwGg6ZPn67BgwdrwoQJzqgTAAAAAHCeOXSX0RNCQ0MVGhqq2NhYffvtt8rIyKivugAAAAAA9axOgfCEFi1aaODAgRo4cOD5rgcAAAAA4CRnFQgBADgdHjgOAEDTUetdRgEAAAAAzZdTRgjLy8v19NNPq7KyUlVVVerbt6/GjBmj4uJiJSYm6vDhw2rTpo3i4uLk7e0tSVq9erXS0tJkNBoVGxur8PBwSVJOTo4WL16s8vJyRUREKDY2VgaDwRmbAQAAAADNilNGCD08PPT0009r/vz5mjdvnjZv3qzs7GylpKQoLCxMSUlJCgsLU0pKiiQpNzdXmZmZWrhwoZ544gktX75cVqtVkpScnKypU6cqKSlJBw8e1ObNm52xCQAAAADQ7DglEBoMBnl6ekqSqqqqVFVVJYPBoKysLEVFRUmSoqKilJWVJUnKyspS//795eHhoYCAAAUGBmrXrl0qLCxUaWmpQkJCZDAYNGjQIPs8AAAAAIC6cdpNZaxWqx599FEdPHhQ11xzjTp37qyjR4/KZDJJkkwmk4qKiiRJFotFnTt3ts/r5+cni8UiNzc3+fv729v9/f1lsVictQkAAAAA0Kw4LRAajUbNnz9fx44d0wsvvKC9e/eedlqbzVan9lNJTU1VamqqJCkhIUFms7luBbsQd3d39g+aFPorHEVfgSPoJ40bf6fAEfSTs+f0x05ceOGF6tatmzZv3ixfX18VFhbKZDKpsLBQPj4+kqpH/goKCuzzWCwW+fn5ndReUFAgPz+/U64nOjpa0dHR9tf5+fn1tEVNn9lsZv+gSaG/wlH0FTiCftK4VVZW8h7hjPh79syCgoJO2e6UawiLiop07NgxSdV3HP3pp5/Url07RUZGKj09XZKUnp6u3r17S5IiIyOVmZmpiooK5eXl6cCBA+rUqZNMJpO8vLyUnZ0tm82mjIwMRUZGOmMTAAAAAKDZccoIYWFhoRYvXiyr1SqbzaZ+/fqpV69eCgkJUWJiotLS0mQ2mxUfHy9JCg4OVr9+/RQfHy+j0ajJkyfLaKzOrlOmTNGSJUtUXl6u8PBwRUREOGMTAAAAAKDZcUogvPjiizVv3ryT2lu1aqWnnnrqlPOMHj1ao0ePPqm9Y8eOWrBgwXmv0ZUduqF/Q5dg55a8pqFLAAAAAFyGU04ZBQAAAAA0PgRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBRBEIAAAAAcFEEQgAAAABwUQRCAAAAAHBR7g1dAAAAAHA6h27o39AlSJLcktc0dAlAvWCEEAAAAABcFIEQAAAAAFwUgRAAAAAAXJRTriHMz8/X4sWLdeTIERkMBkVHR2v48OEqLi5WYmKiDh8+rDZt2iguLk7e3t6SpNWrVystLU1Go1GxsbEKDw+XJOXk5Gjx4sUqLy9XRESEYmNjZTAYnLEZAAAAANCsOGWE0M3NTbfddpsSExP13HPP6fPPP1dubq5SUlIUFhampKQkhYWFKSUlRZKUm5urzMxMLVy4UE888YSWL18uq9UqSUpOTtbUqVOVlJSkgwcPavPmzc7YBAAAAABodpwSCE0mky677DJJkpeXl9q1ayeLxaKsrCxFRUVJkqKiopSVlSVJysrKUv/+/eXh4aGAgAAFBgZq165dKiwsVGlpqUJCQmQwGDRo0CD7PAAAAACAunH6Yyfy8vK0e/duderUSUePHpXJZJJUHRqLiookSRaLRZ07d7bP4+fnJ4vFIjc3N/n7+9vb/f39ZbFYTrme1NRUpaamSpISEhJkNpvra5OavEMNXcCf8D7BEfQTOIq+AkfQT+AI+knj5u7uznt0lpwaCMvKyrRgwQJNnDhRLVu2PO10NputTu2nEh0drejoaPvr/Px8xwtFg+F9giPoJ3AUfQWOoJ/AEfSTxs1sNvMenUFQUNAp2512l9HKykotWLBAf/vb33TllVdKknx9fVVYWChJKiwslI+Pj6Tqkb+CggL7vBaLRX5+fie1FxQUyM/Pz1mbAAAAAADNilMCoc1m07Jly9SuXTtdd9119vbIyEilp6dLktLT09W7d297e2ZmpioqKpSXl6cDBw6oU6dOMplM8vLyUnZ2tmw2mzIyMhQZGemMTQAAAACAZscpp4zu2LFDGRkZ6tChg6ZPny5JGj9+vGJiYpSYmKi0tDSZzWbFx8dLkoKDg9WvXz/Fx8fLaDRq8uTJMhqrs+uUKVO0ZMkSlZeXKzw8XBEREc7YBAAAAABodpwSCENDQ/Xuu++e8mdPPfXUKdtHjx6t0aNHn9TesWNHLViw4LzWBwAAAACuyGnXEAIAAAAAGhcCIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgCIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgCIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgCIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgCIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgCIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgCIQAAAAC4KAIhAAAAALgoAiEAAAAAuCgCIQAAAAC4KHdnrGTJkiX6/vvv5evrqwULFkiSiouLlZiYqMOHD6tNmzaKi4uTt7e3JGn16tVKS0uT0WhUbGyswsPDJUk5OTlavHixysvLFRERodjYWBkMBmdsAgAAAAA0O04ZIbzqqqv0+OOP12hLSUlRWFiYkpKSFBYWppSUFElSbm6uMjMztXDhQj3xxBNavny5rFarJCk5OVlTp05VUlKSDh48qM2bNzujfAAAAABolpwSCLt162Yf/TshKytLUVFRkqSoqChlZWXZ2/v37y8PDw8FBAQoMDBQu3btUmFhoUpLSxUSEiKDwaBBgwbZ5wEAAAAA1F2DXUN49OhRmUwmSZLJZFJRUZEkyWKxyN/f3z6dn5+fLBbLSe3+/v6yWCzOLRoAAAAAmhGnXENYFzabrU7tp5OamqrU1FRJUkJCgsxm8znX1lwdaugC/oT3CY6gn8BR9BU4gn4CR9BPGjd3d3feo7PUYIHQ19dXhYWFMplMKiwslI+Pj6Tqkb+CggL7dBaLRX5+fie1FxQUyM/P77TLj46OVnR0tP11fn5+PWwFzjfeJziCfgJH0VfgCPoJHEE/adzMZjPv0RkEBQWdsr3BThmNjIxUenq6JCk9PV29e/e2t2dmZqqiokJ5eXk6cOCAOnXqJJPJJC8vL2VnZ8tmsykjI0ORkZENVT4AAAAANHlOGSF88cUX9csvv+iPP/7QXXfdpTFjxigmJkaJiYlKS0uT2WxWfHy8JCk4OFj9+vVTfHy8jEajJk+eLKOxOrdOmTJFS5YsUXl5ucLDwxUREeGM8gEAAACgWXJKIHzwwQdP2f7UU0+dsn306NEaPXr0Se0dO3a0P8cQAAAAAHBuGuyUUQAAAABAwyIQAgAAAICLIhACAAAAgIsiEAIAAACAiyIQAgAAAICLIhACAAAAgIsiEAIAAACAiyIQAgAAAICLIhACAAAAgIsiEAIAAACAiyIQAgAAAICLIhACAAAAgIsiEAIAAACAiyIQAgAAAICLIhACAAAAgIsiEAIAAACAiyIQAgAAAICLIhACAAAAgIsiEAIAAACAiyIQAgAAAICLIhACAAAAgIsiEAIAAACAiyIQAgAAAICLcm/oAs7G5s2btWLFClmtVg0ZMkQxMTENXRIAAAAANDlNLhBarVYtX75cTz75pPz9/fXYY48pMjJS7du3b+jSAAAAAJcx6u3tDV2C3YYHBjZ0CU1WkwuEu3btUmBgoNq2bStJ6t+/v7KysgiEAAAAgIs6dEP/hi7Bzi15TUOXUCdN7hpCi8Uif39/+2t/f39ZLJYGrAgAAAAAmiaDzWazNXQRdfH111/rxx9/1F133SVJysjI0K5duzRp0qQa06Wmpio1NVWSlJCQ4PQ6AQAAAKCxa3IjhP7+/iooKLC/LigokMlkOmm66OhoJSQkEAYdMGPGjIYuAU0EfQWOoJ/AEfQTOIq+AkfQT85ekwuEHTt21IEDB5SXl6fKykplZmYqMjKyocsCAAAAgCanyd1Uxs3NTZMmTdJzzz0nq9WqwYMHKzg4uKHLAgAAAIAmp8kFQkm64oordMUVVzR0Gc1GdHR0Q5eAJoK+AkfQT+AI+gkcRV+BI+gnZ6/J3VQGAAAAAHB+NLlrCAEAAAAA5weBEAAAAABcFIGwCfjggw8UHx+vhx9+WNOnT9fOnTuduv57771XDz30kB566CHFxcXpnXfeUUVFhVNrQE15eXl66KGHarS9++67WrNmjcPL+PLLLzV9+nRNnz5d48eP10MPPaTp06fr7bffdmj++fPn69tvv7W/fuCBB/T+++/bX7/wwgv65ptvHK7nhFmzZunXX3+t83yu5Ntvv9WYMWP0+++/Szp1fziftm7dqh07dtQ6zccff6y4uDg9/PDDevbZZ3X48OHTTkvfaTwaw7EkLy9PY8aM0TvvvGNvKyoq0vjx47V8+XKH6/jrMtevX29//dVXX531slxBY+gHhw8f1qOPPqrp06crPj5eX3zxRa3Tz5o1S3fffbf+fOXTvHnzdNtttzlc81998sknOn78uP31uSyrOTof/URy7DOlNpmZmYqLi1N8fLwWLVp02uk4tjiuSd5UxpVkZ2dr06ZNmjt3rjw8PFRUVKTKykqn1/H000/Lx8dHZWVleuWVV/TKK69o2rRpTq8D58/gwYM1ePBgSdWh/8R77KguXbooOztbffr00R9//CFPT09lZ2fbf75z505NmTLlvNcNaf369QoNDdWGDRs0ZsyYel/f1q1b5enpqS5dupx2mksuuUQJCQm64IIL9MUXX+itt95SXFzcKael7zQv53oskaS2bdvq+++/17hx4yRJGzduVPv27c+6psOHD2v9+vUaOHDgWS8DdXOu/cBkMmnOnDny8PBQWVmZHnroIUVGRsrPz++081x44YXasWOHQkNDdezYMR05cuSctuHTTz/V3/72N11wwQXntBzUzpHPlNM5cOCAUlJSNHv2bHl7e+vo0aO1Ts+xxTEEwkausLBQrVq1koeHhyTZD67vvfeeNm3apPLycoWEhOjOO++UwWDQrFmzdMkll2j37t0qKirSvffeq5SUFO3du1f9+/e3/0JkZGTov//9ryorK9W5c2dNmTJFRuOZB4w9PT3197//XXfffbeKi4vl7u6uefPm6dixY6qsrNS4cePUu3dvvfPOO/Lx8dHw4cMlSf/+97/l6+trf436c6IP7Nq1S6Wlpbr77rvVqVMnh+a12Wx66623tHnzZknSjTfeqP79+59y2i5duuitt96SJO3YsUO9evXSDz/8IJvNpsOHD6tFixZq3bq1fvzxR7377ruqrKxU27Ztdc8998jT01M5OTl64403VFZWJh8fH91zzz0ymUz25VutVi1ZskRms9nebyGVlZVpx44devrppzVv3ryTAuFXX32lb7/9VlarVfv27dPIkSNVWVmpjIwMeXh46LHHHpO3t7f27Nmj5ORkHT9+XG3bttXdd98tb29vffrpp1q7dq3c3NzUvn173XLLLVq7dq2MRqP+97//adKkSeratetJdfXo0cP+786dO+t///vfabfBmX1nzJgxWrp0qXJyciRV/9F63XXXndN74CqcdSyRpBYtWqhdu3b69ddf1bFjR2VmZqpfv34qLCyUVP1H2NKlS1VUVGR/z81msxYvXiwvLy/l5OToyJEjmjBhgvr27at//etfys3N1fTp0xUVFSVvb28VFhbqueee06FDh9SnTx9NmDDhnPeRK3BWP3B3//9/klZUVMhqtZ5x+f3799eGDRsUGhqqb775Rn369NG+fftqXffWrVv1n//8R61atdK+fft02WWX6b777tN///tfWSwWPfPMM/Lx8dHTTz8tqfrvl++//14tWrTQ9OnT1bp1a3399dd67733ZDQa1bJlSz3zzDMO7Y/mzNF+kpeXd9JnitlsPuXv96msW7dO11xzjby9vSVJvr6+tdbVUMcWq9XapD57CISN3OWXX6733ntPDzzwgMLCwtS/f39169ZN1157rW666SZJ0ksvvaRNmzYpMjJSUvVB9ZlnntGnn36q+fPnKyEhQd7e3rrvvvs0YsQIHT16VJmZmZo9e7bc3d312muv6X//+5+ioqIcqqlly5YKCAjQgQMHdNlll+nhhx9Wy5YtVVRUpCeeeEKRkZG6+uqrtWDBAg0fPlxWq1WZmZn6xz/+UW/7CTWVlZVpzpw5+uWXX7R06VItWLDAofm++eYb7dmzR/Pnz1dRUZEee+wxde3atcYf2ydcdtll2rdvnyorK5Wdna1u3brp0KFD+v3337V792516dJFRUVF+uCDDzRz5kx5enoqJSVFH3/8sWJiYvT666/rkUcekY+PjzIzM/Xvf/9b99xzjySpqqpKSUlJ6tChg0aPHn1e901T9+233yo8PFxBQUHy9vZWTk6O/YPxhH379mnevHmqqKjQfffdp1tvvVXz5s3TypUrlZ6erhEjRujll1/WpEmT1K1bN61atUrvvfeeJk6cqA8//FAvv/yyPDw8dOzYMV144YUaOnSoPD09df311ztUY1pamsLDw0/7c2f2nZycHFksFvvvwLFjx85ux7soZxxLThgwYIA2bNig1q1by2g0ys/Pz/5H2/LlyzVo0CBdddVVSktLs/cBSTpy5IieffZZ7d+/X3PnzlXfvn11yy236KOPPtKMGTMkVX9RsmfPHs2bN0/u7u568MEHde211572j07U5Kx+kJ+fr4SEBB08eFATJkyodXRQksLCwvTKK6/Y/86488477aefn27dkrR7924tXLhQJpNJM2fO1I4dOzR8+HB98sknNUY2jx8/rs6dO2v8+PF66623tG7dOt14441677339MQTT8jPz49jyp840k8CAgJO+kxJSEg47e/3X+3fv1+SNHPmTFmtVt188821ft5IDXNsKSoqalKfPQTCRs7T01Nz587Vtm3btHXrViUmJurWW2+Vp6en1qxZo+PHj6u4uFjBwcH2QHji/x06dFD79u3tB962bduqoKBA27dv1+7du/XYY49JksrLy+t8es8JNptN//73v7Vt2zYZDAZZLBYdPXpUAQEB8vb21u7du3X06FFdcsklatWq1XnYI5Akg8FQa/uJUxm6deumkpIS+x/2Z7J9+3YNGDBARqNRrVu3Vrdu3fTrr7/a+9SfeXh4KDg4WDk5Odq5c6euv/56HTp0SDt27NDu3bsVEhKinTt3Kjc3VzNnzpQkVVZWKiQkRPv379e+ffs0e/ZsSdUjOn/+AyE5OVn9+vUjDJ7Chg0bNGLECEn//9vxa665psY03bt3l5eXl7y8vNSyZcsax4S9e/fa+0S3bt0kSVFRUUpMTLRPk5SUpN69e6tPnz51ri8jI0M5OTmaNWvWaadxZt8JCAhQXl6eXn/9dV1xxRXq2bNnnbepOWsMx5ITwsPDtWrVKrVu3fqkUaSdO3fq4YcfliQNGjSoxnVpvXv3ltFoVPv27Ws9faxHjx5q2bKlJKl9+/bKz88nEP4/jaUfmM1mvfDCC7JYLJo/f7769u2r1q1bn3b5RqNRoaGhyszMVHl5uQICAs64bi8vL3Xq1En+/v6Sqk93z8vLU2ho6EnLd3d3V69evSRVf5G1ZcsWSdVnOSxevFj9+vXTlVdeecb90FzUVz+p7ff7r6xWqw4cOKCnn35aFotFTz31lBYsWFDrehri2NK+ffsm9dlDIGwCjEajunfvru7du6tDhw5au3at9u7dq+eff15ms1nvvvuuysvL7dOfOL3UYDDY/33idVVVlWw2m6KionTLLbecVT2lpaXKy8vTRRddpPXr16uoqEgJCQlyd3fXvffea69lyJAh+uqrr3TkyBH7dQU4P1q1aqXi4uIabcXFxTU+DP/sdAfxcxUSEqJt27aptLRU3t7e6ty5sz777DPt2bNHw4YNU15ensLCwvTggw/WmG/v3r1q3769nnvuudMud+vWrbruuuvUokWLeqm9Kfrjjz/0888/a9++fTIYDPZTqv4aCP/8e280Gu2nYhmNRlVVVdW6jscee0y//PKLvvvuO73//vtauHChw/Vt2bJFq1ev1qxZs2rUcCrO6jve3t6aP3++Nm/erM8++0yZmZn20UQ0nmOJVP3H96WXXqqPPvpICxcu1KZNmxya7899rbZHK//19+JMvwuupDH1A0ny8/NTcHCwtm/frr59+9Y6bf/+/fXCCy/o5ptvdnj5f+0Lpzs91c3Nzb6tf+4zd955p3bu3Knvv/9ejzzyiObNm+cSX3o3hn7i5+enkJAQubu7KyAgQEFBQTpw4ECtpzE3xLGlqX32cJfRRm7//v06cOCA/fWePXsUFBQkSfabvNT1bnxhYWHauHGj/duO4uLiWu8I+GdlZWV67bXX1Lt3b3l7e6ukpES+vr5yd3fXzz//XGM5ffr00ebNm/Xrr7+ecTgfdePp6SmTyaSffvpJUvV7+OOPP9q/4czMzJRU/Q1py5Yt7d9cnUnXrl319ddfy2q1qqioSNu2bav1INulSxelpqbq4osvliRdfPHF2rlzp/3bsZCQEO3YsUMHDx6UVH36zf79+xUUFKSioiL7jUQqKyvt131I0tVXX62IiAgtXLiQP9r+ZOPGjYqKitKSJUu0ePFiLV26VAEBASooKKjTclq2bClvb29t27ZNUvWoXteuXWW1WpWfn68ePXpowoQJKikpUVlZmby8vFRWVlbrMnfv3q3k5GQ98sgjZ7ymQ3Je3ykqKpLValXfvn01btw47d69u077qrlrLMeSE0aOHKlbb731pD+uQ0JC7LWcuKlSbby8vFRaWupQrWgc/aCgoMD+hXJxcbF27Nhh/3vnTOuIiYnRgAEDznrdJ3h6ep7xWCdJBw8eVOfOnTV27Fi1atWqzsfgpup89ZO/fqbU5fe7T58++vnnnyVV3zH0wIEDatu27Rlrd/axpal99jBC2MiVlZXp9ddf17Fjx+Tm5qbAwEDdeeeduvDCC/XQQw8pICBAHTt2rNMy27dvr3HjxmnOnDmy2Wxyc3PT5MmT1aZNm9POc+KCaavVqj59+ujGG2+UVH16wNy5czVjxgxdcsklateunX0ed3d3de/eXRdeeKFDN6xB3UybNk3Lly/Xm2++KUm66aabFBgYKEny9vbWk08+ab+w21F9+vRRdna2pk+fLkmaMGFCrafrdOnSRYcOHVJMTIyk6m9TfX19ZTabZTQa5ePjo3vvvVeLFi2yP6pk3LhxCgoK0kMPPaQVK1aopKREVVVVGj58uIKDg+3Lvu6661RSUqKXXnpJ999/P31I1aeLntjXJ1x55ZVavXp1nZd177332m8qExAQoHvuuUdWq1UvvfSSSkpKJEkjRozQhRdeqF69emnhwoXKyso67U1l3nrrLZWVldlHFM1msx599NHTrt9ZfScmJkZLly61jwCc7ZkRzVljOJacEBwcXOO9PCE2NlZLly7VmjVr7Dd+qE2HDh3k5uZW48YPqF1D94Pff/9db775pgwGg2w2m0aOHKkOHTqccR0Gg+GU1zefbt0nHtdzKtHR0frHP/4hk8lkv6nMqbz11lv2L+t79Ohh/2LLFZyPfvLXz5S6/H5ffvnl+vHHHxUXFyej0agJEyY4NDrr7GOLxWJpUp89BlttY6DAObBarXr00UcVHx+viy66qKHLcRmzZs3SbbfdVucvCgDgzziWQKIfwDH0k6aNr9xRL3Jzc3X//fcrLCyMMAgAAAA0UowQwu7xxx+3n5p1wn333efQKRto3L788kt9+umnNdq6dOni0MO/9+7dq5deeqlGm4eHB48RcVEffPCBvv766xptp7sjLH2n+eFYAsm5/WD+/PnKy8ur0Xbrrbdyb4Im4Fz6CZ81zkUgBAAAAAAXxSmjAAAAAOCiCIQAAAAA4KIIhACAZm3btm164IEHTvvz/Px83Xbbbad9OHVz8r///U9z5sxp6DIAAI0I1xACABql1atXa/v27Xrsscfsbffff78uuuiik9rGjh170oOpT+fee+/V1KlT1bNnz/Ne8+m8++67Wr16tTw8PCRJJpNJPXv21OjRo2UymeplnXl5eZo2bZr+/e9/y83NrV7WAQBo+hghBAA0Sl27dtX27dvtI3dHjhxRVVWVcnJyarQdPHhQXbt2bchSHdKvXz+9+eabev311/Xwww/ryJEjmjFjhgoLC89qea4wogkAqH/uDV0AAACn0qlTJ1VVVWnPnj267LLL9Msvv6h79+46dOhQjba2bdvqxx9/1Jo1a1RQUCAfHx+NGjVKQ4cOlSRt3bpVL730kpYtW6aXXnpJ+fn5mjt3roxGo2666Sb169evxkjarFmzFBoaqq1bt+q3335TSEiI7r//fvn4+EiS0tPTtWrVKpWVlWn48OH68ssv6zTi6O7uruDgYMXFxenRRx/VRx99pNtvv11fffWV1q1bp9mzZ9unHTNmjJKSkhQYGKjFixerRYsWys/P1y+//KLp06ersrJS77zzjg4dOqSWLVtq8ODBGjNmjCTp6aefliRNnDhRkjRz5kzt37+/xjp27NihlStXav/+/QoKCtLEiRPVpUsXSTrjfgAANA+MEAIAGiV3d3d17txZv/zyi6TqawFDQ0MVGhpao61r167y9fXVo48+qjfeeEP33HOP3njjDeXk5Jy0zPvuu09ms1mPPvqo/vnPf2rUqFGnXPeGDRt0991367XXXlNlZaU++ugjSVJubq5ee+013X///Xr11VdVUlIii8VyVttnNBoVGRmp7du3OzzP+vXrdcMNN+iNN95QaGioLrjgAk2bNk0rVqzQjBkztHbtWn377beSpGeeeUaStHLlSv3zn/9USEhIjWUVFxcrISFB/+f//B+9/vrrGjFihBISEvTHH3+ccT8AAJoPAiEAoNHq2rWrtm3bJknavn27unbtelJbt27ddMUVVygwMFAGg0HdunVTz5496xS0/uqqq65SUFCQWrRooX79+mnPnj2SpI0bN6pXr14KDQ2Vu7u7xo4de07bZzKZVFxc7PD0vXv3VmhoqIxGo1q0aKHu3burQ4cOMhqNuvjiizVgwAB7WD6T77//XoGBgRo0aJDc3Nw0cOBABQUFadOmTfZpTrcfAADNB6eMAgAarW7duunzzz9XcXGxioqKdNFFF8nX11eLFy9WcXGx9u7dq27duumHH37Qe++9p/3798tms+n48ePq0KHDWa+3devW9n9fcMEFKisrkyRZLBaZzeYaP2vVqtVZr8discjb29vh6f39/Wu83rlzp/71r39p7969qqysVGVlpfr27evwutu0aVOjrU2bNjVGPE+3HwAAzQcjhACARiskJEQlJSVKTU21X9vWsmVLmUwmpaamys/PTyaTSQsWLNDIkSOVnJyslStXKiIiol7qMZlMKigosL8uLy+vcYplXVitVm3atEmhoaGSqgNXeXm5/edHjhw5aR6DwVDjdVJSknr16qWlS5fqjTfe0NChQ3Xi5uF/nfav/Pz8dPjw4Rpt+fn58vPzO5vNAQA0UQRCAECj1aJFC3Xs2FGffPKJPThJUmhoqD755BN17dpVlZWVqqiokI+Pj9zc3PTDDz9oy5Ytp11m69atlZeXd1b19O3bV5s2bdKOHTtUWVmpd999t87LqKysVG5urhYtWqQjR47ouuuukyRdfPHF2rdvn/bs2aPy8nKHll1aWipvb2+1aNFCu3bt0vr16+0/8/HxkcFg0KFDh045b0REhA4cOKD169erqqpKmZmZys3N1RVXXFHnbQIANF2cMgoAaNS6deum7OzskwLhZ599pq5du8rLy0uxsbFKTExURUWFevXqpcjIyNMuLyYmRq+//rreeustjR492uFTLCUpODhYkyZN0osvvqjjx49r+PDh8vHxsT9fsDZff/21srKyJP3/5xDOnTvXPiIXFBSkm266SbNnz1aLFi00fvx4paam1rrMKVOm2B9l0a1bN/Xr10/Hjh2TVD3iOHr0aM2cOVNVVVV6/PHHa8zbqlUrzZgxQytWrFBycrICAwM1Y8YM7iIKAC6GB9MDAHCWysrKNHHiRCUlJSkgIKChywEAoM44ZRQAgDr47rvvdPz4cZWVlenNN99Uhw4dTro5CwAATQWnjAIAUAffffedXn75ZdlsNnXs2FEPPvigDAaD/vGPf9gfh/FnN9xwg0aPHt0AlQIAcGacMgoAAAAALopTRgEAAADARREIAQAAAMBFEQgBAAAAwEURCAEAAADARREIAQAAAMBFEQgBAAAAwEX9X4AsALfe9t29AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#that needs a deeper look..\n",
    "plt.figure(figsize=(15,5))\n",
    "x = df.groupby([\"Waiting_Duration\", 'SMS_Received']).Attendance.sum()\n",
    "x_labels = 'Same_Day', 'Up_To_Week', 'Almost_2_Weeks', 'Up_To_Month', 'Up_To_3_Months', 'Up_to_6_Months'\n",
    "x_loc = np.arange(len(x_labels))\n",
    "w = 0.25\n",
    "\n",
    "Didnt_Receive  = [x[0], x[2], x[4], x[6], x[8], x[10]] \n",
    "plt.bar(x_loc + w, Didnt_Receive, width = w, label = \"Didn't Receive\")\n",
    "\n",
    "Received = [x[1], x[3], x[5], x[7], x[9], x[11]] \n",
    "plt.bar(x_loc, Received, width = w, label = 'Received')\n",
    "\n",
    "plt.xlabel(\"Waiting_Duration\")\n",
    "plt.ylabel('Attendees')\n",
    "plt.title(\"Waiting...\")\n",
    "plt.xticks(ticks = x_loc+w/2, labels = x_labels)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a lil reminder really helps when the waiting time is too long.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Maybe they can't afford the visit?\n",
    "plt.figure(figsize=(15,15))\n",
    "x = df.groupby(\"Subsidy\").Attendance.sum()\n",
    "labels = 'Not Registered', 'Registered'\n",
    "sizes = [x[0], x[1]]\n",
    "plt.pie(sizes, labels=labels, autopct='%1.1f%%')\n",
    "plt.title(\"How many of them is registered in the subsidy?\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chronic_Illnesses\n",
       "Four         8\n",
       "One      11705\n",
       "Three      471\n",
       "Two       5194\n",
       "Zero     35278\n",
       "Name: Attendance, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#testing\n",
    "df.groupby(\"Chronic_Illnesses\").Attendance.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wrg8AAAAAYIJmhcVOnTqptrZWZWVlcrlcioiIkN/vV319fUvXBwAAAAAwQbPC4ogRI/Tkk0+qtrZWN998syTpiy++UFRUVIsWBwAAAAAwR7PC4vTp0/XRRx/JZrPpmmuukSRZLBbdc889LVocAAAAAMAczQqLknTttdeqsrJSRUVF6tu3r/r06dOSdQEAAAAATNSssFhZWanVq1ertLRUkvTKK69oz549Kiws1P3339+S9QEAAAAATNCsr8544YUXlJSUpJdfflkhIWfy5cCBA/Xxxx+3aHEAAAAAAHM0KywWFxdr0qRJslr//+7h4eHB714EAAAAALQvzQqL3bt319GjR5uMlZWVye12t0hRAAAAAABzNeuexVtuuUXLli3TpEmT1NjYqPfff1/btm3TpEmTWrg8AAAAAIAZmhUWx44dK7vdrh07dsjlcikvL0933HGHhgwZ0tL1AQAAAABM0OyvzhgyZMi3DoeVlZXKzMzU119/LYvFotTUVP3kJz/RyZMntWrVKlVUVCgyMlJz586V3W6XJG3btk05OTmyWq1KS0tTYmKiJKmkpESZmZny+XxKSkpSWlqaLBaL6uvrlZGRoZKSEnXr1k3p6emKior6VvUCAAAAQEfXrHsWA4GAsrOz9eSTT+qXv/ylJOnAgQPKz89v1klsNpumTZumVatWafHixXrnnXdUVlam7du3a8CAAVqzZo0GDBig7du3SzpzP2R+fr5Wrlypxx57TBs2bFBjY6MkKSsrS7NmzdKaNWt09OhRFRYWSpJycnLUtWtXrV27VuPHj9fmzZsv8aMAAAAAAJzVrLC4ZcsWvfvuuxo3bpwqKyslSS6XS2+88UazTuJwOBQXFydJCgsLU69eveTxeFRQUKCUlBRJUkpKigoKCiRJBQUFGj58uDp16qSoqChFR0eruLhYXq9XtbW16tu3rywWi0aNGhU8Zu/evRo9erQkaejQodq/f78CgUDzPwkAAAAAQFCzwmJubq4WLFigESNGyGKxSJKioqJUXl5+yScsLy/XF198ofj4eB0/flwOh0PSmUBZXV0tSfJ4PHK5XMFjnE6nPB6PYdzlcsnj8RiOsdlsCg8P14kTJy65PgAAAABAM+9ZbGxsVJcuXZqM1dXVGcYupq6uTitWrND06dMVHh5+3v3OtyJ4oZXCc207G2y/KTs7W9nZ2ZKkpUuXtsmv/zhmdgEwTVvsx44iJCSEzx+moPdgBvoOZqH3Li/NCotJSUnatGmT7rnnHklngtmWLVs0ePDgZp+ooaFBK1as0MiRI3X99ddLOvP9jV6vVw6HQ16vVxEREZLOrBhWVVUFj/V4PHI6nYbxqqoqOZ3OJse4XC75/X7V1NQEH5bzTampqUpNTQ3+fPayWqAtoB/N43a7+fxhCnoPZqDvYBZ6r+2JiYk577ZmXYZ69913y+PxaPr06aqpqdHdd9+tiooK3XXXXc0qIBAIaP369erVq5cmTJgQHE9OTlZubq6kM5e6XnfddcHx/Px81dfXq7y8XEeOHFF8fLwcDofCwsJUVFSkQCCgvLw8JScnS5IGDx6snTt3SpL27Nmj/v37n3NlEQAAAABwcZbAJTwF5uuvv1ZlZaXcbrd69OjR7JMcPHhQv/71r9W7d+9ggJsyZYoSEhK0atWq4HvOmzcvuBr4+uuv691335XVatX06dOVlJQkSTp06JDWrVsnn8+nxMRE3XvvvbJYLPL5fMrIyNAXX3whu92u9PR09ezZ86K1HT58uNnzaC3++yaaXQJMYst60+wSOiz+0gmz0HswA30Hs9B7bc+FVhbPGxbPflXFxVitzVqcbLMIi2hLCIvm4T9eMAu9BzPQdzALvdf2XCgsnveexSlTpjTrzbds2XLpFQEAAAAA2rTzhsWMjIzg63379mnPnj269dZbg38NeOONN4IPqgEAAAAAtC/nDYuRkZHB12+//baWLl2qrl27SjqzVBkXF6dHHnlEN954Y8tXCQAAAABoVc264bCmpkanT59uMubz+VRTU9MiRQEAAAAAzNWs71lMSUnRokWLNH78+OD3Gf75z39WSkpKS9cHAAAAADBBs8Li1KlTFR0drfz8fHm9XvXo0UM33XRTky+3BwAAAAC0H80Ki1arVTfeeCP3JwIAAABAB9GssChJH330kUpLS1VXV9dk/I477vjeiwIAAAAAmKtZYXHDhg3avXu3+vfvr86dO7d0TQAAAAAAkzUrLO7atUvPPPOM3G53S9cDAAAAAGgDmvXVGd26dQt+xyIAAAAAoP1r1srihAkTtGbNGt16663q3r17k209e/ZskcIAAAAAAOZpVlh88cUXJUn79u0zbNuyZcv3WxEAAAAAwHTNCosEQgAAAADoWJp1z+JZlZWVKioqaqlaAAAAAABtRLNWFisrK7V69WqVlpZKkl555RXt2bNHhYWFuv/++1uyPgAAAACACZq1svjCCy8oKSlJL7/8skJCzuTLgQMH6uOPP27R4gAAAAAA5mhWWCwuLtakSZNktf7/3cPDw1VTU9NihQEAAAAAzNOssNi9e3cdPXq0yVhZWZncbneLFAUAAAAAMFez7lm85ZZbtGzZMk2aNEmNjY16//33tW3bNk2aNKmFywMAAAAAmKFZYXHs2LGy2+3asWOHXC6X8vLydMcdd2jIkCEtXR8AAAAAwATNCouff/65hgwZYgiHxcXFio+Pb5HCAAAAAADmadY9i0899dQ5xxcvXvy9FgMAAAAAaBsuuLLY2NgoSQoEAsF/zjp27JhsNlvLVgcAAAAAMMUFw+KUKVOCr++8884m26xWq2699daWqQoAAAAAYKoLhsWMjAwFAgE98cQT+s1vfhMct1gsioiIUGhoaIsXCAAAAABofRcMi5GRkZKkm2++Ofj6m95++21NmDChZSoDAAAAAJimWQ+4+dOf/nRJ4wAAAACAy9sFVxb3798vSfL7/cHXZx09elRhYWHNOsm6deu0b98+de/eXStWrJAkbd26VTt27FBERISkM/dHDho0SJK0bds25eTkyGq1Ki0tTYmJiZKkkpISZWZmyufzKSkpSWlpabJYLKqvr1dGRoZKSkrUrVs3paenKyoqqvmfAgAAAACgiQuGxeeff16SVF9fH3wtnblnsUePHrr33nubdZLRo0fr5ptvVmZmZpPx8ePHa+LEiU3GysrKlJ+fr5UrV8rr9WrRokVavXq1rFarsrKyNGvWLCUkJGjJkiUqLCxUUlKScnJy1LVrV61du1a7du3S5s2bNXfu3GbVBgAAAAAwumBYPBvuMjIyNHv27OD4l19+qdzcXGVlZSk5OfmiJ+nXr5/Ky8ubVVBBQYGGDx+uTp06KSoqStHR0SouLlZkZKRqa2vVt29fSdKoUaNUUFCgpKQk7d27V7fffrskaejQodq4caMCgYAsFkuzzgkAAAAAaOqCYfGs2bNnq7q6Wu+//75yc3NVWlqqq6++WtOnT/9OJ3/nnXeUl5enuLg43X333bLb7fJ4PEpISAju43Q65fF4ZLPZ5HK5guMul0sej0eS5PF4gttsNpvCw8N14sSJ4CWuAAAAAIBLc8Gw2NDQoL179yo3N1eFhYWKjo7WiBEjVF5errlz56p79+7f+sQ33nijbrvtNknSli1btGnTJj3wwAMKBALn3P984+fbdr5VxezsbGVnZ0uSli5dKrfbfamlt7hjZhcA07TFfuwoQkJC+PxhCnoPZqDvYBZ67/JywbB43333yWq1KiUlRbfffrvi4uIkSf/zP//znU/co0eP4Otx48Zp2bJlks6sGFZVVQW3eTweOZ1Ow3hVVZWcTmeTY1wul/x+v2pqamS328953tTUVKWmpgZ/rqys/M5zAb4v9KN53G43nz9MQe/BDPQdzELvtT0xMTHn3XbBr874l3/5F506dUrFxcU6dOiQTp48+b0V5fV6g68/+OADxcbGSpKSk5OVn5+v+vp6lZeX68iRI4qPj5fD4VBYWJiKiooUCASUl5cXvF9y8ODB2rlzpyRpz5496t+/P/crAgAAAMB3cMGVxSeeeEIVFRXKzc3VW2+9pZdeekkDBw7U6dOn5ff7m32S5557TgcOHNCJEyd0//33a/Lkyfr0009VWloqi8WiyMhI/exnP5MkxcbGatiwYZo3b56sVqtmzJghq/VMpp05c6bWrVsnn8+nxMREJSUlSZLGjh2rjIwMzZkzR3a7Xenp6d/y4wAAAAAASJIlcKGbAf/JwYMHlZubq927d8tms2nMmDGaOnVqS9bX4g4fPmx2CQb++yZefCe0S7asN80uocPishiYhd6DGeg7mIXea3sudBlqs56GetZVV12lq666Smlpafrggw+Ul5f3nYsDAAAAALQ9lxQWzwoNDdUNN9ygG2644fuuBwAAAADQBlzwATcAAAAAgI6JsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADwiIAAAAAwICwCAAAAAAwICwCAAAAAAwIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAIMTsAgC0Hf77JppdgmmOmV2AiWxZb5pdAgAAaINYWQQAAAAAGBAWAQAAAAAGhEUAAAAAgAFhEQAAAABgQFgEAAAAABgQFgEAAAAABoRFAAAAAIABYREAAAAAYBDSGidZt26d9u3bp+7du2vFihWSpJMnT2rVqlWqqKhQZGSk5s6dK7vdLknatm2bcnJyZLValZaWpsTERElSSUmJMjMz5fP5lJSUpLS0NFksFtXX1ysjI0MlJSXq1q2b0tPTFRUV1RpTAwAAAIB2qVVWFkePHq1HH320ydj27ds1YMAArVmzRgMGDND27dslSWVlZcrPz9fKlSv12GOPacOGDWpsbJQkZWVladasWVqzZo2OHj2qwsJCSVJOTo66du2qtWvXavz48dq8eXNrTAsAAAAA2q1WCYv9+vULrhqeVVBQoJSUFElSSkqKCgoKguPDhw9Xp06dFBUVpejoaBUXF8vr9aq2tlZ9+/aVxWLRqFGjgsfs3btXo0ePliQNHTpU+/fvVyAQaI2pAQAAAEC71CqXoZ7L8ePH5XA4JEkOh0PV1dWSJI/Ho4SEhOB+TqdTHo9HNptNLpcrOO5yueTxeILHnN1ms9kUHh6uEydOKCIiwnDe7OxsZWdnS5KWLl0qt9vdMhP8Do6ZXQBMY3Y/0nsdk9l919GFhITwO0Cro+9gFnrv8mJaWDyf860IXmil8FzbLBbLOfdNTU1Vampq8OfKyspLrBBoOfQjzEDfmcvtdvM7QKuj72AWeq/tiYmJOe82056G2r17d3m9XkmS1+sNrgK6XC5VVVUF9/N4PHI6nYbxqqoqOZ1OwzF+v181NTWGy14BAAAAAM1nWlhMTk5Wbm6uJCk3N1fXXXddcDw/P1/19fUqLy/XkSNHFB8fL4fDobCwMBUVFSkQCCgvL0/JycmSpMGDB2vnzp2SpD179qh///7nXVkEAAAAAFxcq1yG+txzz+nAgQM6ceKE7r//fk2ePFmTJk3SqlWrlJOTI7fbrXnz5kmSYmNjNWzYMM2bN09Wq1UzZsyQ1Xom086cOVPr1q2Tz+dTYmKikpKSJEljx45VRkaG5syZI7vdrvT09NaYFgAAAAC0W5ZAB39s6OHDh80uwcB/30SzS4BJbFlvmnp+eq9jMrvvOjru34EZ6DuYhd5re9rkPYsAAAAAgLaLsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADwiIAAAAAwICwCAAAAAAwICwCAAAAAAwIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADwiIAAAAAwICwCAAAAAAwICwCAAAAAAwIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMCAsAgAAAAAMQswu4MEHH1SXLl1ktVpls9m0dOlSnTx5UqtWrVJFRYUiIyM1d+5c2e12SdK2bduUk5Mjq9WqtLQ0JSYmSpJKSkqUmZkpn8+npKQkpaWlyWKxmDgzAAAAALh8mR4WJWnhwoWKiIgI/rx9+3YNGDBAkyZN0vbt27V9+3ZNnTpVZWVlys/P18qVK+X1erVo0SKtXr1aVqtVWVlZmjVrlhISErRkyRIVFhYqKSnJxFkBAAAAwOWrTV6GWlBQoJSUFElSSkqKCgoKguPDhw9Xp06dFBUVpejoaBUXF8vr9aq2tlZ9+/aVxWLRqFGjgscAAAAAAC5dm1hZXLx4sSTpRz/6kVJTU3X8+HE5HA5JksPhUHV1tSTJ4/EoISEheJzT6ZTH45HNZpPL5QqOu1wueTyeVpwBAODb8t830ewSTHXM7AJMZMt60+wSAAAXYHpYXLRokZxOp44fP66nnnpKMTEx5903EAhc0vi5ZGdnKzs7W5K0dOlSud3uSyu4FXTk/3Ho6MzuR3qvY6LvYBaze68jCwkJ4fOHKei9y4vpYdHpdEqSunfvruuuu07FxcXq3r27vF6vHA6HvF5v8H5Gl8ulqqqq4LEej0dOp9MwXlVVFXzff5aamqrU1NTgz5WVlS0xLeBboR9hBvoOZqH3zON2u/n8YQp6r+250GKdqfcs1tXVqba2Nvj6448/Vu/evZWcnKzc3FxJUm5urq677jpJUnJysvLz81VfX6/y8nIdOXJE8fHxcjgcCgsLU1FRkQKBgPLy8pScnGzavAAAAADgcmfqyuLx48e1fPlySZLf79cNN9ygxMRE9enTR6tWrVJOTo7cbrfmzZsnSYqNjdWwYcM0b948Wa1WzZgxQ1brmbw7c+ZMrVu3Tj6fT4mJiTwJFQAAAAC+A0vgUm74a4cOHz5sdgkGHf1hDx2Z2Q97oPc6JvoOZjG79zoyLgWEWei9tqfNXoYKAAAAAGibCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADwiIAAAAAwICwCAAAAAAwICwCAAAAAAwIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADAiLAAAAAAADwiIAAAAAwICwCAAAAAAwICwCAAAAAAwIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAIMbuA71NhYaFeeuklNTY2aty4cZo0aZLZJQEAgDbKf99Es0swzTGzCzCZLetNs0sALgvtZmWxsbFRGzZs0KOPPqpVq1Zp165dKisrM7ssAAAAALgstZuwWFxcrOjoaPXs2VMhISEaPny4CgoKzC4LAAAAAC5L7SYsejweuVyu4M8ul0sej8fEigAAAADg8tVu7lkMBAKGMYvFYhjLzs5Wdna2JGnp0qWKiYlp8dou2X/tNbsCdFT0HsxA38Es9B5gijb5/984p3azsuhyuVRVVRX8uaqqSg6Hw7Bfamqqli5dqqVLl7ZmebgEDz/8sNkloAOi72AWeg9moO9gFnrv8tJuwmKfPn105MgRlZeXq6GhQfn5+UpOTja7LAAAAAC4LLWby1BtNpvuvfdeLV68WI2NjRozZoxiY2PNLgsAAAAALkvtJixK0qBBgzRo0CCzy8B3lJqaanYJ6IDoO5iF3oMZ6DuYhd67vFgC53oyDAAAAACgQ2s39ywCAAAAAL4/7eoyVLRdH3zwgf7whz80Gfvqq6/08MMPKykpyaSq0FFUVVVpw4YNKisrUyAQ0KBBgzRt2jSFhPCvQLScEydO6Mknn5Qkff3117JarYqIiJAkLVmyhP7D9+J8fVZRUSGHw6FVq1aZXCE6ijvuuEO9e/cO/jx//nxFRUWZWBG+D1yGClNkZ2frvffe08KFC2W1XniBOxAIKBAIXHQ/4FwCgYAeffRR3XjjjRozZowaGxv129/+Vna7XdOmTTO7PHQQW7duVZcuXTRx4kSzS0E79s0+Ky8v17Jly7RixYoLHuP3+2Wz2VqpQrRn06ZN0yuvvPK9vR+92TbwZ020usOHD+uPf/yjnnrqKVmtVr355pvavXu36uvrNWTIEE2ePFnl5eVasmSJ+vfvr6KiIs2fP19/+ctfVFhYKEn613/9Vw0fPtzcieCysH//foWGhmrMmDGSJKvVqnvuuUezZ89WVFSUPvnkE50+fVrHjh3TkCFDNHXqVEnSRx99pK1bt6qhoUE9e/bUAw88oC5dupg5FVzmAoGAFixYoGXLlqm0tFQPPfSQ1q1bJ7fbrTlz5mj58uWqrq7W888/r+rqakVEROiBBx6Q2+02u3RcphobG7V+/XoVFRXJ6XTqoYceUmhoqJ544gn17dtXf//735WcnKz+/fvr5ZdfVl1dXbDvHA6Hjh49qg0bNqi6ulqdO3fWrFmz1KtXL7OnhctIaWmpsrKydPr0afXs2VP//u//LrvdrieeeELTpk1Tnz59VF1drUceeUSZmZnauXOn9u3bJ5/Pp9OnT2vhwoVmT6HDY6kGraqhoUFr1qzRtGnT5Ha79dFHH+nIkSN6+umn9cwzz6ikpEQHDhyQdCZUjho1Ss8884wOHTqk0tJSPfvss3r88cf1yiuvyOv1mjwbXA7+8Y9/6Ic//GGTsfDwcLndbvn9fpWWlmru3Llavny58vPzVVlZqerqar3++ut6/PHHtWzZMsXFxentt982aQZoLywWi+rr61VTU6ODBw+qT58++uyzz1RRUaGIiAh17txZGzZs0KhRo7R8+XLdcMMN2rhxo9ll4zJ25MgR3XzzzVq5cqXCw8O1Z8+e4Laamhr95je/0Y9//GNt3LhRv/jFL7Rs2TKNGTNGr776qiTphRde0L333qtly5Zp2rRpevHFF82aCi4DPp9P8+fP1/z58/Xss89KkjIyMnTXXXdp+fLl6t27t/74xz9e9H2Kioo0e/ZsgmIbwcoiWtWWLVt0xRVXaMSIEZLOrN58/PHHeuihhyRJdXV1Onr0qNxut9xut/r27StJOnjwoEaMGCGr1aoePXqoX79+OnTokJKTk02bCy4fFovFMBYIBGSxWHTNNdcoPDxcknTFFVeosrJSp06dUllZmR5//HFJZ/7IcbYXge/i7GrOgQMHdOutt6qwsFCBQEBXX321JOnzzz/XL3/5S0nSqFGjtHnzZjPLxWUuKipKP/jBDyRJcXFxqqioCG47e3XO4cOH9Y9//EOLFi2SdGY10uFwqK6uTn//+9+1cuXK4DENDQ2tVzwuO6GhocGQKJ35g8SpU6fUr18/SVJKSkqz7qEdOHCg7HZ7i9WJS0NYRKv59NNP9de//lXLli1rMj5p0iT96Ec/ajJWXl7OJX/4XlxxxRX661//2mSspqZGVVVVslqt6tSpU3DcarXK7/crEAhowIABSk9Pb+Vq0d5dffXV+uyzz1RZWank5GS98cYbkqTBgwebXBnao3/+95vP5wv+3Llz5+DrK664QosXL25ybE1Njbp27drkf/6B74vNZtPZx6bU19c32fbN3oT5uAwVreLkyZNat26dZs+erbCwsOD4tddeq3fffVd1dXWSJI/Ho+PHjxuOv/rqq7V79241Njaqurpan332meLj41utfly+BgwYoNOnTys3N1fSmb+ab9q0SaNHjz7vf5DOrv4cPXpUknT69GkdPny41WpG+3X11VfrvffeU3R0tKxWq+x2uz788ENdeeWVks70Xn5+viTp/fff11VXXWVmuegAYmJiVF1draKiIklnVg//8Y9/KDw8XFFRUdq9e7ekM1djlJaWmlgpLjfh4eGy2+367LPPJEl5eXnBqygiIyNVUlIiSU0uj0bbw8oiWsX//u//qrq6WllZWU3Gb731Vt1www167LHHJEldunTRnDlzDE8+HTJkSPBBN5I0depU9ejRo1Vqx+XNYrHol7/8pV588UX96U9/UiAQUFJSkqZMmaJdu3ad85iIiAg9+OCDWr16dfAvnnfeeadiYmJas3S0Q2cfI3/2sqwrr7xSVVVVwUuu0tLS9Pzzz+vNN98MPmgEaEkhISH6xS9+oZdeekk1NTXy+/36yU9+otjYWP385z9XVlaWXn/9dTU0NGjEiBHBy1qB5njwwQeDD7iJiooK/jvtlltu0apVq5SXl6drrrnG5CpxIXx1BgAAAADAgMtQAQAAAAAGhEUAAAAAgAFhEQAAAABgQFgEAAAAABgQFgEAAAAABoRFAECblpmZqddee82UcwcCAa1bt05paWl65JFHvtV7TJ48OfidnS3t6aef1s6dO7/Te2zdulVr1qyRJJWXl2vy5Mny+/3fQ3UAgMsN37MIALgkDz74oHw+n9auXasuXbpIknbs2KH33ntPTzzxhLnFfc8OHjyojz/+WM8//3xwrv/M6/Xqtdde04cffqi6ujo5nU4NHz5cEydOPO8xLeXRRx/9VsdNnjxZa9asUXR09PdcEQDgcsbKIgDgkvn9fv33f/+32WVcssbGxkvav6KiQpGRkecNfSdPntSvfvUr+Xw+PfXUU9q0aZN+9atf6dSpUzp27Ngl18cKHgCgLWFlEQBwySZOnKg33nhDN910k7p27dpkW3l5uWbPnq1XX31VNptNkvTEE09o5MiRGjdunHbu3KkdO3aoT58+2rlzp+x2u+bMmaMjR45oy5Ytqq+v19SpUzV69Ojge1ZXV2vRokX6/PPP9cMf/lCzZ89WZGSkJOn//u//tHHjRpWUlCgiIkJ33HGHhg8fLunMJayhoaGqrKzUgQMHNH/+fA0cOLBJvR6PR1lZWTp48KDsdrt++tOfKjU1VTk5OdqwYYMaGho0bdo03XLLLZo8eXKTY99++2116dJFc+bMkdV65u+vbrdbaWlpTfb7+OOP9fTTT+vEiRMaMWKEZsyYIYvF0uSzyM3N1U033aSJEydq48aN+vDDD9W5c2eNGzdOt956q6xWa3D/hIQEvfvuuwoPD9fMmTOVlJRk+JwlKTs7W//1X/+lqqoquVwuzZkzR3Fxcd/69/7EE0/oqquu0qeffqovv/xSffv21c9//nNFRERIkoqKirRp0yaVlZUpMjJS06dPV//+/SVJO3fu1B//+EdVV1erW7duuvPOOzVy5EgdPXpUzz//vEpLSxUSEqJrrrlGc+fOvejvdt++fXrllVdUVVWlsLAwjR8/XhMnTvzWcwMAGBEWAQCXLC4uTv3799dbb72lO++885KP//zzzzV27Fht3LhRW7du1XPPPafBgwdrzZo1OnDggFasWKGhQ4cGV/Tef/99Pfzww0pISNDvfvc7rVmzRosWLVJdXZ2eeuopTZ48WY8++qi+/PJLLV68WLGxsYqNjQ0e+8gjj2jBggVqaGgw1LJ69WrFxsbqt7/9rQ4fPqxFixapZ8+eGjt2rKxWq3bs2KFFixadcx6ffPKJrr/++mBQPJ99+/ZpyZIlqq2t1YIFC5ScnKzExMTgZzF8+HC9+OKL8vv9euGFF1RTU6OMjAydOHFCixcvlsPh0NixYyVJxcXFSklJ0YYNG5Sdna3169dr/fr1slgsTc65e/du/eEPf9D8+fPVp08fHTt2LBjev4tdu3bpkUcekdvt1tNPP6233npLd911lzwej5YuXarZs2crMTFR+/fv14oVK/Tcc88pNDRUL730kpYsWaKYmBh5vV6dPHlSkvTaa6/p2muv1cKFC9XQ0KCSkhJJuujvdv369Zo7d66uvvpqnTx5UuXl5d95bgCAprgMFQDwrUyePFl//vOfVV1dfcnHRkVFacyYMbJarRo+fLiqqqp02223qVOnTrr22msVEhLS5KEwgwYNUr9+/dSpUydNmTJFRUVFqqys1L59+xQZGakxY8bIZrMpLi5O119/vfbs2RM89rrrrtNVV10lq9Wq0NDQJnVUVlbq4MGDuuuuuxQaGqof/OAHGjdunPLy8po1jxMnTqhHjx4X3W/SpEnq2rWr3G63+vfvr9LS0uA2h8OhH//4x7LZbAoJCVF+fr7+7d/+TWFhYYqKitKECROa1ON2u5Wamiqr1aqUlBR5vV4dP37ccM6cnBz99Kc/VXx8vCwWi6Kjo4Orsd/F6NGjFRMTo9DQUA0bNiw4l7y8PCUlJWnQoEGyWq0aOHCg+vTpo3379kmSLBaLvvrqK/l8PjkcjmCYDwkJUUVFhbxer0JDQ3XVVVdJ0kV/tzabTWVlZaqpqZHdbv9OK6YAgHNjZREA8K307t1bgwcP1vbt29WrV69LOrZ79+7B12cD3DdDV2hoqOrq6oI/u1yu4OsuXbrIbrfL6/WqoqJCn3/+uaZPnx7c7vf7NWrUqHMe+8+8Xq/sdrvCwsKCY263W4cOHWrWPLp166avv/76ovt9c26dO3duMje32x18XV1drYaGhiZjkZGR8ng8530vSU3e76zKykr17NmzOdO4JOebS2Vlpfbs2aO//e1vwe1+v1/9+/dXly5dlJ6errfeekvr16/XlVdeqbvvvlu9evXS1KlT9dprr+nRRx9V165dNWHCBI0dO/aiv9tf/OIXev311/X73/9evXv31l133aW+fft+7/MFgI6MsAgA+NYmT56sBQsWaMKECcGxs5eOnj59WuHh4ZLUrEB1IVVVVcHXdXV1OnnypBwOh1wul/r166fHH3/8vMf+8+WZ3+RwOHTy5EnV1tYGA2NlZaWcTmez6howYIA++OAD3XbbbRe9FLU5IiIiZLPZVFlZqSuuuOKS6/kmt9v9rR6y8225XC6NHDlS999//zm3JyYmKjExUT6fT6+99pp++9vf6sknn1SPHj2Cxxw8eFCLFi1Sv379Lvq7jY+P10MPPaSGhgb95S9/0apVq/T888+32PwAoCPiMlQAwLcWHR2tYcOG6c9//nNwLCIiQk6nU++9954aGxuVk5PznUPLhx9+qIMHD6qhoUGvvfaaEhIS5Ha7NXjwYB05ckR5eXlqaGhQQ0ODiouLVVZW1qz3dbvduvLKK/X73/9ePp9PX375pd59912NHDmyWcdPmDBBtbW1yszMVEVFhaQzD8x5+eWX9eWXX17yPK1Wq4YNG6ZXX31VtbW1qqio0Ntvv93ser5p7Nixeuutt1RSUqJAIKCjR48Ga2wJI0eO1N/+9jcVFhaqsbFRPp9Pn376qaqqqvT1119r7969qqurU0hIiLp06RIM17t37w7+MeDsw5KsVusFf7cNDQ167733VFNTo5CQEIWHh38vYR0A0BQriwCA7+S2227Te++912Rs1qxZevHFF/Xqq69q7Nix3/nywBEjRugPf/iDioqKFBcXp5///OeSpLCwMP3qV7/Syy+/rJdfflmBQED/8i//onvuuafZ7/0f//EfysrK0qxZs2S323X77bcbnph6Pna7XYsWLQpeRnn69Gk5nU6NGDHiW39n4b333quNGzdq9uzZCg0N1bhx4zRmzJhLfp9hw4bpxIkTWr16tTwej6Kiopo8Rfb75na79dBDD+l3v/udVq9eLavVqvj4eN13330KBAJ66623tHbtWlksFv3gBz/QzJkzJUmHDh3Sf/7nf6qmpkY9evRQWlqaoqKiJOmCv9u8vDxt3LhRjY2NiomJ0Zw5c1pkXgDQkVkCgUDA7CIAAAAAAG0L12wAAAAAAAwIiwAAAAAAA8IiAAAAAMCAsAgAAAAAMCAsAgAAAAAMCIsAAAAAAAPCIgAAAADAgLAIAAAAADAgLAIAAAAADP4ficQl8ir7JLMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#maybe their history with chronic diseases has an effect\n",
    "plt.figure(figsize=(15,5))\n",
    "x = df.groupby(\"Chronic_Illnesses\").Attendance.sum()\n",
    "labels = 'Zero', 'One', 'Two', 'Three', 'Four'\n",
    "sizes = [x[4], x[1], x[3], x[2], x[0]]\n",
    "plt.bar(labels, sizes)\n",
    "plt.xlabel(\"Number of Chronic Illnesses\")\n",
    "plt.ylabel(\"Attendees\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### it's sad to see that people are not learning from their pain.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chronic_Illnesses  Subsidy\n",
       "Four               False          8\n",
       "                   True           0\n",
       "One                False      10464\n",
       "                   True        1241\n",
       "Three              False        445\n",
       "                   True          26\n",
       "Two                False       4743\n",
       "                   True         451\n",
       "Zero               False      30700\n",
       "                   True        4578\n",
       "Name: Attendance, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#testing\n",
    "df.groupby([\"Chronic_Illnesses\", 'Subsidy']).Attendance.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's be more specific \n",
    "plt.figure(figsize=(15,5))\n",
    "x = df.groupby([\"Chronic_Illnesses\", 'Subsidy']).Attendance.sum()\n",
    "x_labels = 'Zero', 'One', 'Two', 'Three', 'Four'\n",
    "x_loc = np.arange(len(x_labels))\n",
    "w = 0.25\n",
    "\n",
    "No = [x[8], x[2], x[6], x[4], x[0]]\n",
    "plt.bar(x_loc + w, No, width = w, label = 'Not Registered')\n",
    "\n",
    "Yes = [x[9], x[3], x[7], x[5], x[1]]\n",
    "plt.bar(x_loc, Yes, width = w, label = 'Registered')\n",
    "\n",
    "\n",
    "plt.xlabel(\"Number of Chronic Illnesses\")\n",
    "plt.ylabel(\"Attendees\")\n",
    "plt.title(\"Illness\")\n",
    "plt.xticks(ticks = x_loc+w/2, labels = x_labels)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I don't think the subsidy itself affects much"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "# Conclusions:\n",
    "### Although most of them made it to their appointment, there might be several effects on the reason why people miss their doctor's appointment.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Timing Effect:\n",
    "#### Hence, I noticed from the graphs the increase in scheduled appointments in June 2016 so, I searched the internet on what was going on then in Brazil and I found out it was the outbreak of the Zika virus. maybe not all the recorded were for the infected... but surely the epidemic made them more aware of the importance of their general health.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gender and Age:\n",
    "\n",
    "#### Not surprising that the graphs show what looks like women are more committed but do not jump to conclusions, that does not mean men are less caring. It is only because women are much of the population.\n",
    "\n",
    "#### So, let us consider the ages then; teens are the least committed, maybe it is just the nature of this age stage ... carless life :)\n",
    "#### Adults, on the other hand, are the most committed because they are grown enough to know the importance of their health and punctuality. meanwhile, we cannot blame children as they do not have control over stuff like this it is the parent's fault."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do you remember me?\n",
    "\n",
    "#### Another important factor is the waiting time, we can see that many of them are committed when the waiting time is up to a week even if they did not receive a reminder SMS. And the least committed are those with waiting time up to 6 months!\n",
    "#### like who would remember an appointment 6 months later! maybe they don't even need that appointment anymore. however, receiving an SMS for waiting times more than 10 days may help."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## They know the pain...\n",
    "\n",
    "#### It is sad to see that people with a history of chronic diseases are less committed to comparing to those who don’t... maybe they had enough injections and are just tired of lots of medications? we need to look after them more closely, especially those who are not registered in the Bolsa Família subsidy as they might not be able to afford the treatment expenses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limitations:\n",
    "\n",
    "#### 1. The most important thing is it was not clear if this appointment was a dentist, a cardiologist, or other!\n",
    "#### 2. Unbalanced data where women are already a majority in the ample so we cant decide if committing to the appointment is related to gender or not.\n",
    "#### 3. Most of the collected data are nominal so we couldn't make many inferential tests and we can't also show a strong correlation between them.\n",
    "#### 4. We don't have many details on some factors; for example, some poor are registered in Bolsa Família but that doesn't state that others are rich enough to afford the fee."
   ]
  }
 ],
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