Analysis_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b7ec7713",
   "metadata": {},
   "source": [
    "# <center> Project: Investigating data named (No-show appointments)\n",
    "\n",
    "<center> Practitioner Name: Omar Allam"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9fc2ca5",
   "metadata": {},
   "source": [
    "_________________________________\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\">EDA (Exploratoty Data Analysis)</a></li>\n",
    "<li><a href=\"#Conclusion\">Conclusion</a></li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84339e15",
   "metadata": {},
   "source": [
    "<a id='Intro'></a>\n",
    "## Introduction:\n",
    "_________________________________\n",
    "### 1- Dataset Description:\n",
    "- This dataset collects information from 100k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment. A number of characteristics about the patient are included in each row.\n",
    "- The data has 110527 rows and 14 columns.\n",
    "_________________________________\n",
    "### 2- Columns' description:\n",
    "\n",
    "> `PatientId`: Identification of each patient.\n",
    "\n",
    "> `AppointmentID`: Identification of each appointment.\n",
    "\n",
    "> `Gender`: Gender of the patient. (F : Female , M : Male)\n",
    "\n",
    "> `ScheduledDay`: Day when the patient set up the appointment.\n",
    "\n",
    "> `AppointmentDay`: Day of the appointment at teh hospital.\n",
    "\n",
    "> `Age`: Age of the patient.\n",
    "\n",
    "> `Neighbourhood`: Location of the hospital.\n",
    "\n",
    "> `Scholarship`: If the patient enrolled in Bolsa Família for poor Brazilian or not. (0 : didn't enrol , 1 : enrolled)\n",
    "\n",
    "> `Hipertension`: If the patient suffers Hypertension \"raise in blood pressure\" or not. (0 : doesn't suffer , 1 : suffers) \n",
    "\n",
    "> `Diabetes`: If the patient suffers Diabetes or not. (0 : doesn't suffer , 1 :  suffers)\n",
    "\n",
    "> `Alcoholism`: If the patient is Alcohol addicted or not. (0 : No , 1 : Yes)\n",
    "\n",
    "> `Handcap`: No Info about this column in the documentaion \"the name may be misspilled\"\n",
    "\n",
    "> `SMS_received`: As in the documentation, it is the number of SMS received by the patient from the hospital. (0 : No-SMS , 1 : one-SMS)\n",
    "\n",
    "> `No-show`: If the patient cancel its appointment or not. (Yes : canceled , No : not-canceled)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f6d0d6",
   "metadata": {},
   "source": [
    "<a id='Wrangling'></a>\n",
    "## Data Wrangling:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28402aa8",
   "metadata": {},
   "source": [
    "\n",
    "### 1- Loading Data & Libraries:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f77838c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "438a3375",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.987250e+13</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 16:08:27+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.262962e+12</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:19:04+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.679512e+11</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 17:29:31+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.841186e+12</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:07:23+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender              ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F 2016-04-29 18:38:08+00:00   \n",
       "1  5.589978e+14        5642503      M 2016-04-29 16:08:27+00:00   \n",
       "2  4.262962e+12        5642549      F 2016-04-29 16:19:04+00:00   \n",
       "3  8.679512e+11        5642828      F 2016-04-29 17:29:31+00:00   \n",
       "4  8.841186e+12        5642494      F 2016-04-29 16:07:23+00:00   \n",
       "\n",
       "             AppointmentDay  Age      Neighbourhood  Scholarship  \\\n",
       "0 2016-04-29 00:00:00+00:00   62    JARDIM DA PENHA            0   \n",
       "1 2016-04-29 00:00:00+00:00   56    JARDIM DA PENHA            0   \n",
       "2 2016-04-29 00:00:00+00:00   62      MATA DA PRAIA            0   \n",
       "3 2016-04-29 00:00:00+00:00    8  PONTAL DE CAMBURI            0   \n",
       "4 2016-04-29 00:00:00+00:00   56    JARDIM DA PENHA            0   \n",
       "\n",
       "   Hipertension  Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0             1         0           0        0             0      No  \n",
       "1             0         0           0        0             0      No  \n",
       "2             0         0           0        0             0      No  \n",
       "3             0         0           0        0             0      No  \n",
       "4             1         1           0        0             0      No  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('No_show_data.csv',parse_dates=['ScheduledDay','AppointmentDay']) \n",
    "# I have used parse_dates for these two columns after I invistigated the data.\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab1389f6",
   "metadata": {},
   "source": [
    "_________________________________\n",
    "### 2- InMind Questions:\n",
    "\n",
    "###### General Questions related to the existence of: \n",
    "\n",
    "* missing values?\n",
    "* wrong datatypes for columns?\n",
    "* complete duplicates in the data?\n",
    "* outliers in each column?\n",
    "\n",
    "\n",
    "###### Univariate Questions:\n",
    "\n",
    "* Which `Gender` is more healthier than the other depending on number of enteries?\n",
    "* Which `Age` values are valid or there are values like 1000..,etc?   \n",
    "* Which disease among `Hipertension`, `Diabetes` and `Alcoholism` is most dominant?\n",
    "\n",
    "###### Bivariate Questions:\n",
    "\n",
    "* Is the number of `PatientId` same as `AppointmentID` or may be more than one appointment for the same patient?\n",
    "* Does Receiving `Scholarship` has strong effect on not cancelling the appointment or not?\n",
    "* Does the period between `AppointmentDay` and `ScheduleDay` has effect on cancelling the appointment?\n",
    "_________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "49bea2b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype              \n",
      "---  ------          --------------   -----              \n",
      " 0   PatientId       110527 non-null  float64            \n",
      " 1   AppointmentID   110527 non-null  int64              \n",
      " 2   Gender          110527 non-null  object             \n",
      " 3   ScheduledDay    110527 non-null  datetime64[ns, UTC]\n",
      " 4   AppointmentDay  110527 non-null  datetime64[ns, UTC]\n",
      " 5   Age             110527 non-null  int64              \n",
      " 6   Neighbourhood   110527 non-null  object             \n",
      " 7   Scholarship     110527 non-null  int64              \n",
      " 8   Hipertension    110527 non-null  int64              \n",
      " 9   Diabetes        110527 non-null  int64              \n",
      " 10  Alcoholism      110527 non-null  int64              \n",
      " 11  Handcap         110527 non-null  int64              \n",
      " 12  SMS_received    110527 non-null  int64              \n",
      " 13  No-show         110527 non-null  object             \n",
      "dtypes: datetime64[ns, UTC](2), float64(1), int64(8), object(3)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5f0e8b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89bea8ae",
   "metadata": {},
   "source": [
    "_________________________________\n",
    "##### Above cells' conclusion:\n",
    "\n",
    "* Data has no missing values.\n",
    "* Data has no complete duplicates.\n",
    "* `PatientId` & `AppointmentID` need some modifications.\n",
    "* `Handcap` column isn't understandable from the documentation\n",
    "* We may rename `No-show` as `Canceled` to avoid confusion of the column values.\n",
    "_________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c4754ab",
   "metadata": {},
   "source": [
    "### 3- Cleaning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ec4a19e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108286\n",
       "1      2042\n",
       "2       183\n",
       "3        13\n",
       "4         3\n",
       "Name: Handcap, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# discovering `Handcap` column:\n",
    "\n",
    "df['Handcap'].value_counts() # output values aren't understandable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "afe0cb8b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62299, 110527)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Discovering what is wrong with ID_columns\n",
    "\n",
    "df['PatientId'].nunique() , df['AppointmentID'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ee773b",
   "metadata": {},
   "source": [
    "_________________________________\n",
    "##### From the above, we have noticed that:\n",
    "\n",
    "*It seems some patients have more than one appointment. Therefore:*\n",
    "- We may drop `AppointmentID` for simplicity, because its unique values are as much as the total rows of the DataFrame\n",
    "- We will also drop `HandCap` because its values aren't understandable (in order not to mislead our results)\n",
    "_________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "33973d42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns = ['AppointmentID','Handcap'],axis = 1)\n",
    "df.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0acf40ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping those complete duplicates \"the following will remove the duplicated rows and keep only the `first` row\"\n",
    "\n",
    "df = df.drop_duplicates(ignore_index = True)\n",
    "df.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "587aacc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing `PatientId` enteries with ndarray (from 0 to 62298 \"number of unique values in the column\") \n",
    "\n",
    "rep_array = np.arange(0,df['PatientId'].value_counts().shape[0])\n",
    "df['PatientId'] = df['PatientId'].replace(to_replace = df['PatientId'].unique() , value = rep_array)\n",
    "df['PatientId'] = df['PatientId'].astype('object')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "88340f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming `No-show` column for avoiding confusion of its enteries \"Yes\", \"No\": \n",
    "\n",
    "df.rename(columns = {'No-show':'Canceled'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a7272f",
   "metadata": {},
   "source": [
    "_________________________________\n",
    "* After we have cleaned the wrong datatypes, we are safe to run `.describe()` with continous columns. \n",
    "_________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d5193c72",
   "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>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>SMS_received</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>109909.000000</td>\n",
       "      <td>109909.000000</td>\n",
       "      <td>109909.000000</td>\n",
       "      <td>109909.000000</td>\n",
       "      <td>109909.000000</td>\n",
       "      <td>109909.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.086472</td>\n",
       "      <td>0.098281</td>\n",
       "      <td>0.197236</td>\n",
       "      <td>0.071814</td>\n",
       "      <td>0.030425</td>\n",
       "      <td>0.322831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.121491</td>\n",
       "      <td>0.297696</td>\n",
       "      <td>0.397914</td>\n",
       "      <td>0.258181</td>\n",
       "      <td>0.171755</td>\n",
       "      <td>0.467561</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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Age    Scholarship   Hipertension       Diabetes  \\\n",
       "count  109909.000000  109909.000000  109909.000000  109909.000000   \n",
       "mean       37.086472       0.098281       0.197236       0.071814   \n",
       "std        23.121491       0.297696       0.397914       0.258181   \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       0.000000       0.000000       0.000000   \n",
       "max       115.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "          Alcoholism   SMS_received  \n",
       "count  109909.000000  109909.000000  \n",
       "mean        0.030425       0.322831  \n",
       "std         0.171755       0.467561  \n",
       "min         0.000000       0.000000  \n",
       "25%         0.000000       0.000000  \n",
       "50%         0.000000       0.000000  \n",
       "75%         0.000000       1.000000  \n",
       "max         1.000000       1.000000  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ec0d38",
   "metadata": {},
   "source": [
    "_________________________________\n",
    "##### From the above, we have noticed that:\n",
    "\n",
    "* Columns like `Scholarship`, `Hipertension`, `Diabetes`, `Alcoholism` and `SMS_received` are all categorical columns with values 0,1 not actually continous columns. Therefore, we must change their datatypes.\n",
    "* `Age` column has some invalid negative values.\n",
    "_________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3beb1d4",
   "metadata": {},
   "source": [
    "> ***1. Fixing datatypes***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a1dec51a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col = ['Scholarship', 'Hipertension', 'Diabetes', 'Alcoholism', 'SMS_received']\n",
    "df[cat_col] = df[cat_col].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3100e6",
   "metadata": {},
   "source": [
    "> ***2. Fixing `Age` Column***\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1e8dce17",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### discovering ###\n",
    "\n",
    "\n",
    "outliers_l = 0 # declared to be gloabl variable used inside the following function\n",
    "\n",
    "def plot_age():    \n",
    "    \"\"\" This funcion is to be used in another cell \"\"\"\n",
    "    \n",
    "    # for invalid_ages \"negative & zero\"\n",
    "    invaled_percentage = f\"{(100 * df[df['Age'] <= 0].shape[0] / df.shape[0] ) :0.3f} %\" \n",
    "\n",
    "    #for outliers:\n",
    "    q1,q3 = np.quantile(df['Age'],[0.25, 0.75])\n",
    "    IQR   = q3 - q1\n",
    "    global outliers_l\n",
    "    outliers_l = (q3 + 1.5*IQR) \n",
    "    outliers_percentage= f\"{(100 * df[ df['Age'] > outliers_l ].shape[0] / df.shape[0] ) :0.3f} %\" \n",
    "\n",
    "\n",
    "    # for figuring subplots\n",
    "    \n",
    "    fig,ax = plt.subplots(1,2,figsize=(20,5))\n",
    "    sns.histplot(ax=ax[0],data=df,x='Age',bins= df['Age'].unique().shape[0]-1,color = 'gray') # for histogram\n",
    "    sns.boxplot(ax=ax[1],data=df,x='Age',color = 'skyblue') # for boxplot \n",
    "\n",
    "    # note the above two subplots must be axes-level not figure-level like `displot` or `catplot` to be compatible with subplots\n",
    "    \n",
    "    ax[0].set_ylabel('Number of sample enteries with this age')\n",
    "    ax[0].set_title('Histogram of Age column shows the invaled data')\n",
    "    ax[1].set_title('Boxplot of Age column shows the outliers')\n",
    "    plt.suptitle(f'Age has {invaled_percentage} invaled data & {outliers_percentage} outliers',y=1.1,size =25);\n",
    "\n",
    "plot_age()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0413e9e",
   "metadata": {},
   "source": [
    "__________________________\n",
    "\n",
    "From the above plot, `Age` has **3.213 %** invaled data & **0.004 %** outliers\n",
    "__________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a5be38b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will fill the invaled and outliers ages with either `Males_mean_age or Females_mean_age` depending on the value of \n",
    "# Gender in each entry\n",
    "\n",
    "Condition_1 =  (df['Age'] <= outliers_l) | (df['Age'] > 0)  \n",
    "valid_age_data = df.loc[ Condition_1 ]\n",
    "\n",
    "m_males , m_females = valid_age_data.groupby('Gender').mean()['Age']  # getting the mean_age for males and females\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6f8dadbd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### filling invaled data & outliers ###\n",
    "\n",
    "Condition_M = (df['Gender'] == 'M') & ( (df['Age'] > outliers_l) | (df['Age'] <= 0) )\n",
    "Condition_F = (df['Gender'] == 'F') & ( (df['Age'] > outliers_l) | (df['Age'] <= 0) ) \n",
    "\n",
    "\n",
    "df.loc[ Condition_M , 'Age' ] = m_males\n",
    "df.loc[ Condition_F , 'Age' ] = m_females\n",
    "\n",
    "plot_age()  # function declared in the a previous cell to draw histogram & box_plot for `Age` column\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a629429",
   "metadata": {},
   "source": [
    "_______________________\n",
    "\n",
    "From the above plot, `Age` has **0 %** invaled data & **0 %** outliers\n",
    "__________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44652b1f",
   "metadata": {},
   "source": [
    "_________________________________\n",
    "#### For Cleaning Summary, I did the following:\n",
    "\n",
    "1. dropped `AppointmentID` and `HandCap` Columns.\n",
    "2. replaced `PatientId` values with `0 -> 62298` values\n",
    "3. renamed `No-show` to `Canceled`\n",
    "4. changed the datatypes for `Scholarship`, `Hipertension`, `Diabetes`, `Alcoholism` and `SMS_received`\n",
    "5. replaced wrong values in the `Age` column\n",
    "# <center> _______________________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b591f60",
   "metadata": {},
   "source": [
    "<a id='EDA'></a>\n",
    "## EDA (Exploratory Data Analysis):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec26214",
   "metadata": {},
   "source": [
    "### 1.1- Univariate Analysis:\n",
    "_________________________________\n",
    "* Which `Gender` is more healthier than the other depending on number of enteries?\n",
    "* Which disease among `Hipertension`, `Diabetes` and `Alcoholism` is most dominant?\n",
    "_________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "97ad17cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sY/vuj5xz1ozH5BYt1Pcscx9+24zHXx3ayfkbyO7jnNuHxhq0VulCyjn3B+Bg/MUAZfjTwocAPwPKzeyyFsQfu0K6G/600Cn4Nlc94+L/6R7G39R0Lf4+ZIlYgtczxfAl+NOf4BvG74nYPvx0M/fhm/ZweS2xq+/dzGbGfEWK+TS3Z49Ykr2xBckfwAfB82daME0qbXVfFmlSu08AY4I2Zs8Gbw9updmup/HGoYObGG9Q8JzsX298O7+T8Ntkctzw+NPAsdq/95JV76dJc9rg7E67tfU0/gg0dQoy1bxjyXyz7lm2G76MT57KgW8652Y45xK7dWtODUiLOOcWOedGOee+hK+F+Dx+HykA7mzB/dBOwO9364EznXNvBG094zUn/uZsm/j9OvZ6v13MN9l3oi72IriPWzI9djHfPdXk6THnXA2Nf76+uofLSvc+nEpsne/J9+4gM8vEGaTYdkjVJjOV2Gnbfma2O+1MW+PYnkw27OMiQA4lgIFY+7dUp3daJPgxmBO8/XwTo8ZufPtekmHxCWCy07u7Gp5uM4Pnk5oY5+SWzjRYd7E2YqckGye4YCfpMBrbEJ1hSW6UuwuxA3tTNVixA/vsZDUPQWxJb2jcWpxz9UHN5dfwV4F2Zce+rpsSi//DoGlBMqelKI9Xmqww+Pyx7R6/X8ded031w2tmB+NP/yZOuzHu9SCSO6aJWJuzXXcl1ofz/k2Mc2/wfKSZfW0PlhXbh4eZWSYb1cfW+UlNXBSXdLvTGHM3MtO13ZDgeV4Lp3uaxqTsxuZOFFsfrXRsT2Zj3Ovd2cdFWk27SADNbP/g6rSmxukCnBW8jbbi4p8Mnq9IdqWpmX0RXxsDyXsWiCVzx+H7qYwvA39/vK34RGhEkuHpFrs68RxL0quFmR1D0wfIpvwneL7KzJK11bmQxh+ARA/jf/AH4E+ZpmRmibU5sStum/qnvSl4PjzFj+RVQJP7XEtYEz024P+wxGpLm3s6Khb/QclqGoL9sjnb7Xtm1jNJ+SX4NrUN+KutY6L4m4ODvxdZMjcFz4tpvLqeoFZ7cfD2zCQx96Gxm7ZkYts1WbzN9Wbw3FSi/STwdvD6ATPb3dOMr+LvPADwd/O9kCSVZB/eE//Fb7eB+O2YbFlXJ5swaN84LXj7/yxFjx/BfDpbC3vKSCKWDLXoQrPgCuPfBW/PMLPfNDW+mRWY2Z/Y8c/snh7bk8W1p/u4SKtpFwkg/mrS+ea7nTo//stqZl3N7Az8AST2rz7ZBSC76w58O77OwIuxrnLMd6VzDjAuGO8V59xrSaYvx5/e6IhvU7TAOfdp42znXKy9X1EQf6xdYKY8jv9Bj32+E8D/Uzbf1dozNCYbLfVv/IUuRUCZmR0ZzLuDmV2Cb4OXdN7OuQ9ovKXN7813lTY0Ntx8N2inm9kjNCaaMQvwNWo9gm2UTKy3h8OB22NJkJntZWa/CGJfl2La3THGzB4ys5Fm1j3ucwzBJ7ud8G3PmvtD+Cb+Jr99gnn3D+bX2cyuxHez1Zz4O+G3++HB9B3M7HIabwHygHMulsTErtyO1bicab6rsD7BtH3M7Hb8DaoBbkxSuxr7Ib3RzL4eO81oZsfjt0lTiXKsRvnsoD3t7ngTv90Hmdk+yUYIYj4PfwHA3sBUM/uLmR1lO3bD1cvMzqIx4U2cTy1wbbC804GXzOy42B+OICk52sxGAaluddRizrklwIPB27vN7LJYLbqZHYG/yXmq05MEMVfjvxtvmNlpcdspz8wOM7Mb8ff62+3bGgXr4ejgbYvvNOB8H78PB29vNrMXg2PCp5/N/FXyV+PbDP6KHX8T9/TYnsqe7OMircdlwc0I9/SBrzlzCY/t7NzVWR3w6yTT30SSm3YmjDM6GOemJMOOpfGu8I6duwuaRZLuguKm/2/cuMl6Vvh13PAZTcxnMU3fCHpyE9OmXAf4C1M2xMUQ3xXcfBpv1trim67iTzVtj5v3RvyVZg6f+P65ibjyabyBcvy638CO3Y5NSjLtwwnLXBw8zo0b57aEea+nsculF2ns7ixZbLFphjRnW+AT6dg0sa7T4rvIqwMubeG6/WFC/Bvxia/Dn9q/NlksCfF/My6OjexeV3D1NLb5jJXt1BVcMF0vfOIQG6+Kxq7oltB4c/DFSaY9JC6+WvzVm4uBKS1cb68F8/jWLsbbh8abacdvp3Xs3CXhJyS5yXwwn/9LWK+V+D+F8d17uYRphiQrTxhnRBPrKrEruCoaj5fN6Qruy+x4fK0OYq5hx889uDnfixTLOCkWP0ludN3MbWn4msCquGU3BPtjZUKsU4ABCdPv1rGdpo+ne7KPLybJMX5Xx5fmzIMmfuPixpkcjHPF7mwPPbLr0S5qAJ1zZcAw4Of4H9LYKahu+IPUe/jaos845/6UhuW/g2/I/Xd8d2kd8Afvd4FfAMc5f3uaVCIpXicrS3VfvLRxzkXxV9M9hO9btUPwfBv+ABmrxdm4G/OO4O81OB7fZqcj/gB1E759TXUT09Y7567B/1A8ij94FuL/sX+CP319OY2n/uNdjU8u5wfLHBw8Pr1linPup/gu5WYGcRTgT3H+GH8BQB2t53rgOnxiuTD4HPn4H4qHgM865x5pyQydc7fjb/obqw0swLel+h3+fm5N3e4hZiq+ecITNCYp8/G3qxnhUlyM5Jy7EX8z9mfxiUE3fGI0Ad//cNLT9s7fRPhz+HZ2K/A1MuvwXcp9lsbbbiSbdh6+Ju1FfAK2D36bpmprlcoDwfOFTY3k/JX6p+P/xNyJbzO2CX9rnfrg/UP4/W+oc+6xFPN5CH/8+ge+FrMO3zxhHf5ihp+TuinEbgm22wj8dvwwKK7Cfw+PZRf36XPO/Q9/Md0t+ONrFf7U+2b8PvNb4FDnaxt3V2z9P+iczz5aynm/x9/0+PdBbGvx+2Mtfn3fB3zeOXeSizv7Eky/p8f2ZDHt9j4u0ppsN79XIp8KTrNeAvzeZfZWFSKtLjhFuAyf0AxySXqBkPQKTosuwzdhGOqcWxpySCLtTruoAZTwBO3uYu3okt3sVqRNcf52OX/G18D+ONxoctal+NsUPaDkTyQ9VAMou2RmZ+JPAz6Gv61IbXB135fwF9QMBqY5505oYjYibUawf3+Ib681ODhtJxkQXEjzAf4q8wMTT8uKSOtod13BSVr0xV8h9yugwcw24ts5xfafWMNlkXbBOVdtZlfg2/cNZs/6BpaWGYDvy32ukj+R9FENoOxScCuSb+MvyhiMv21LFf5imwnAP51zG8OKT0RERFpGCaCIiIhIjtFFICIiIiI5RgmgiIiISI5RAigiIiKSY5QAioiIiOQYJYAiIiIiOUYJoIiIiEiOUQIoIiIikmOUAIqIiIjkGCWAIiIiIjlGCaCIiIhIjlECKCIiIpJjlACKiIiI5BglgCIiIiI5RgmgiIiISI5RAigiIiKSY5QAioiIiOQYJYAiIiIiOUYJoIiIiEiOUQIoIiIikmOUAIqIiIjkGCWAIiIiIjlGCaCIiIhIjlECKCIiIpJj2mQCaGZDzMyZWUHYsbQFZjbazG4JOw4RER2/W0bHb0mXjCeAZrbYzGrMrCihPBocFIZkMBaXorzMzG5OUn6mma1qyYHLzK4ws3oz2xr3uGNP4k6XYNsMSTFssplVBfFXmNl/zax/hkNMKVjPU8KOQ6Q90/G7TR+/nZl9JqH8maB8RAZCTMm8hWZWHmYcuSasGsBFwEWxN2Z2BNA5pFiSGQ1camaWUH4p8Jhzrq6F83vLOdct7vGDVoky837gnOsGHAz0BP7e0hmYWX5rByUiGaXjd9v0IXBZ7I2Z9QGOB9aGFlGjU4C9gaFmdkzYweSKsBLAR4jbEYHLgTHxI5jZV81sppltNrOlZnZTqpmZWQ8ze8DMVprZcjO7JZZomNmBZhYxs01BzdX4ZsT3DNAbODluGb2Ar8XiNLOvmFm5mW0Jlvnz5n30HeL+WvDPeaOZTTWzI+OGLTazX5jZbDPbFny+fmb2v2CZrwQxxcb/T/DvdpOZvW5mh6VYZpGZPRcsc72ZvWFmLdoPnHPrgaeAw4N5HmJmLwfzm29m58ctb7SZ3WVmL5jZNuDzZrZvUIO41szWxf+jNrMrzewDM9sQ/JMfHDfMmdnVZrYgGP7v4J/jocDdwAnBP/SNwfhN7kNmdpmZLQli+E2wzk8LhuWZ2fVm9nEw/Akz692S9STSTun4TZs8fj8GXGCNf8IvAp4GauLm3+Rxr6k4g2P9v83s+eAzvm1mBzQztsuBZ4EXgtfxn3n/YFmx9fZvM3s0bvjxwfrfaGazLOTazDbFOZfRB7AYOA2YDxwK5ANLgcGAA4YE440AjsAnqUcCq4GzgmFDgnELgvfPAPcAXfH/It4BvhsMGwvcEMynE3BSM+O8D7g/7v13gWjc+5XAycHrXsBnU8znCmBKkvLPAmuA44J1cHmwbjrGradpQD9gYDDue8BRQEfgNeB3cfO7EugeDPtHQqyjgVuC13/GJ0sdgsfJgDVjfUwGvh28LgqW/0iwzpcC/wcUBJ+rAjgsbtmbgBODbdAVmIWvPewav02As4CPgv2iALgRmBoXgwOew9c+7of/5/qlVOuZpvehYmArcBJQCNwK1AKnBcN/HKz/QcE6vQcYm+nvix56ZNMDHb9j5W3y+A28BHw5KHsHOAFYBowIypo87jUjzvXAsfjj92PAuGbE1gXYDHwFOAf/+1EYN/wt/PG5EH+83gw8GgwbCKwLps0DTg/e9w37u9IWHplfYOMB5MZgZ/4S8HKww3x6AEky3T+AvwevhwTjFgRfsGqgc9y4FwGTgtdjgHuBQS2M8yR84tI5eP8m8JO44Z/gDyp77WI+VwB1wMa4x/HAXcAfEsadD5TGraeL44Y9BdwV9/5a4JkUy+wZrJ8ewfvRNB5Absb/0zqwhetjMrA9iH958OXuC1wAvJEw7j0EB7dg2WPihp2AT9wKkizjf8C34t7nBcscHLx3xP0AAE8A18et550O1E3sQ79lxwNbF/w/4VgC+AFwatzw/vgEcae49dAjVx7o+L2Rtnv8/jZwCT6pHgZ8GAyLTwCbfdxLEWd80v0VYF4zYruE4DcBn1huBL4RDNsvWP9d4sZ/lMYE8JfAIwnzKwMuD/u70hYeYV4F/AjwTfwXbEziQDM7zswmBacJNwFX42ueEg3G/xNaGVQBb8QnIHsHw68DDHjHzOaa2ZXNCc45NwW/U55pZkOBY4DH40Y5B7+DLwlOUZzQxOymOed6xj2mBXH/LBZzEPe+wIC46VbHva5M8r4b+HZ1ZjYqqLbfjD/4QPL19Vd8LdtL5hvdXt/UekjwwyD+gc65i51za4PPcVzC57gY2CduuqVxr/cFlrjk7XAGA/+Mm896/LYbGDfOqrjX2wnWQTK72IcGxMflnNuO/+cYH8vTcbF8ANTjf7BEcp2O323v+A3wX+AL+AT0kSTDUx73mhlns4/PcS4HnnDO1TnnqoMYLw+GDQDWB8fnmPjfk8HAeQnb4SR84iq7ENpl+M65JWa2CP8l/FaSUR4H7sBXV1eZ2T9I/oVYiv8HWZQsqXDOrQKuAjCzk4BXzOx159xHzQhzDL6tyzDgJefcp19g59x0/MGlA/ADfG3Uvs2YZ3zcf3TO/bEF06TyTeBM/D/zxUAPYAP+wLkD59wW4Gf4g9dhwCQzm+6ce3U3l70UiDjnTm9iHJcw/n5mVpBke8XWyWO7EYdLUtbUPrQSv10BMLPOQJ+EWK50zr25G7GItGs6frfN47dzbruZ/Q/4HpCsfV7K456ZXdrcOJvLzAbhE9JjzeycoLgL0Mn8leYrgd5m1iUuCYzfTkvxNYBX7W4MuSzs+wB+C/iCc25bkmHd8Zl/lZkdi/+S7MQ5txLfruFvZrZX0Ij1ADMrBTCz84KdDPzO6vD/aJpjDH5nvwp4OFZoZoVmdrGZ9XDO1eLbJDR3njH3AVcH/5TNzLqabzjdvYXzAb+uqvE1WF2AP6Ua0XzD5QPNzOLibmns8Z4DDjazS82sQ/A4xvyFGcm8g/9Sjwo+cyczOzEYdjfwq+DAFmscfl4z41gNDDKzwriypvahJ4EzzOxzwTS/Z8cD2d3AHy24CMXM+prZmc2MRSQX6PjdNo/fv8afql6cZFhTx71mx9kCl+KvTh4GlASPg/GnpS9yzi0B3gVuCrbbCcAZcdM/ij+OjwxqKDuZ2Yi4fUaaEGoC6Jz72Dn3borB1wA3m9kWfHutJ5qY1WX4BqLl+IPEkzRWAR8DvG1mW4EJwI+cc4uaGd9iYCq+cfKEhMGXAouDqvCr8e0Ymi343Ffh/yVvwFfrX9GSecQZAyzBt80rxzfiTeUg4BX8BRBvAXc65ybv5nJj/0i/CFwIrMCfAvh/+LYcycavx3+BD8S3w1mGb0eIc+7pYNpxwXqdA3y5maG8BswFVplZRVCWch9yzs3FnwYZh09It+AbalcHo/wTv81fCqafhm/wLSLo+E0bPX4751YEp8iTaeq415I4m+ty/GdYFf/AJ6Kx08AX49uOrwNuAcYTHKedc0vxtZK/xp/yXwr8gvArt9oEcy7ZmTOR3GJm3fCNjw9q7g+MiIhklvlbAc1zzv0u7FjaOmXJkrPM7Awz62JmXfG3GXifxobNIiISsqBJ0QFB84Av4Wv8ngk5rHZBfTFKLjsTfyWc4duZXOhUJS4i0mrM7GT8Lb524nzPUruyD/7K4D74JkPfc87NbL0Ic5dOAYuIiIjkGJ0CFhEREckxOgUsItKEoqIiN2TIkLDDEBFpsRkzZlQ45/omG6YEUESkCUOGDOHdd1Pd7UREJHuZ2ZJUw3QKWERERCTHKAEUERERyTFKAEVERERyjBJAERERkRyjBFBEREQkxygBFBEREckxSgBFREREcowSQBEREZEcowRQREREJMeoJxARkSas2VbNv99ZFHYYIpKjvn/s/mmZr2oARURERHKMEkARERGRHKMEUERERCTHKAEUERERyTFKAEVERERyjBJAERERkRyjBFBEREQkxygBlFCZmTOzR+LeF5jZWjN7bhfTjdjVOCIiIpKcEkAJ2zbgcDPrHLw/HVgeYjwiIiLtnhJAyQb/A74avL4IGBsbYGbHmtlUM5sZPA9LnNjMuprZg2Y2PRjvzKD8MDN7x8yiZjbbzA7KyKcRERHJcuoKLotFo1ED9gOGAYOBfsGjCOgdPLoA+fhkPv65BtiU5LEKWAwsAhaVlJSsztgHSm0c8NvglO6RwIPAycGwecApzrk6MzsN+BNwTsL0NwCvOeeuNLOewDtm9gpwNfBP59xjZlaIXy8iIiI5TwlglohGo4OAE4ASfMI3zDl3oJl1SvNyt+MTwrnAjODxXklJyfp0Ljeec262mQ3B1/69kDC4B/BwUHvngA5JZvFF4Otm9vPgfSd84vwWcIOZDQL+65xbkI74RURE2holgCGIRqMFwLH4hO8E59zxZjYwcTwzy0Q4XYDi4HFeXIyLgXeBycDLJSUlH6Y5jgnArcAIoE9c+R+ASc65bwRJ4uQk0xpwjnNufkL5B2b2Nv70cpmZfds591prBy4iItLWKAHMkGg0WgR82Tn3VeBLZtYjNixDiV5LDQke5wJEo9ElwMvB45U01BA+CGxyzr1vZiPiynvQeFHIFSmmLQOuNbNrnXPOzI5yzs00s6HAQufc7cHrIwElgCIikvOUAKZRNBrdF7i4oaHhLDM7xszysjTZa47BwLeDR100Gn0Nf7HG0yUlJZv2dObOuWXAP5MM+gv+FPBPSZ28/QH4BzDb/ApeDHwNuAC4xMxq8W0fb97TOEVERNoDc86FHUO7Eo1GuwLn1NbWfrugoOBEM2vvV1pX46/iHQdMKCkpqQw5HpFWtd+hR7hfPjwh7DBEJEd9/9j9d3taM5vhnBuebJhqAFtJNBo9vq6u7tq8vLxv5OXlde7QIdm1Cu1SR+Cs4LE+Go0+CPy7pKRkcYgxiYiISBOUAO6BaDSaV1dXd3Z9ff0NHTt2LCkoyPnV2Rv4OfDTaDT6HHBHSUnJyyHHJCIiIglyPmPZHdFotGt1dfV3zOxnhYWFA5X47SQP+Drw9Wg0+gH+3n1jS0pK6sMNS0REREA9gbRINBotnDJlyvW1tbXLO3bseFthYeFOt26RnRwKPAKUR6PRS6PRqPY5ERGRkOnHuBmi0WjepEmTvldVVbW8W7duf+7QoUOPXU8lCQ4GxgCzo9Ho2WEHIyIiksuUAO7CpEmTzt6+ffvCXr163dmpU6eisONpBw4DnopGo5FoNHpE2MGIiIjkIjVeS2HChAkHdO/e/ZFevXqdEHYs7dQpwHvRaPTfwG9LSko2hx2QiIhIrlANYIKHHnoob+LEiX/p379/uZK/tCsAfgTMj0ajl4YdjIiISK5QAhjnySef/PzQoUMX7bvvvr/o0KFDYdjx5JB9gDHRaPSlaDQ6IOxgRERE2jslgMBvfvObwmeffXb00KFDX+3Ro8d+YceTw04H3o9Go+eHHYiIiEh7lvMJ4K9+9aviESNGfDR48ODL8/Ly2mxHve1Ib2B8NBp9NBqN6mprERGRNMjZBLC0tDSvtLR0xNSpU39aVVUVdjiys4vxtYFqhykiItLKcjYBBIqB7wJrx4wZ80R1dfW2sAOSnewLTI5Go1eFHYiIiEh7kssJ4ALgI6D7qlWrtr7wwgvPOOfCjkl2VgjcG41G745Gox3CDkZERKQ9yNkEMBKJVAN3AZ2ATpMnT/6ovLz8rZDDktS+C0yKRqP7hB2IiIhIW5ezCSBAJBJZAYwGBgA2evToVzds2LAy3KikCScC06PRaHHYgYiIiLRlOZ0ABt4EpgIDa2pq6h999NEn6+rqasIOSlIaBLwejUaPDTsQERGRtirnE8BIJOKAR4FNQM+PPvpo/eTJk18IOSxpWh/g1Wg0elrYgYiIiLRFOZcAjppZ8blRMyvy48sikchW4E6gJ9Bh4sSJsxYtWjQ7jPik2boBz0ej0XPCDkRERKStyZkE8PPf+nHHP7276hbgDeA3icMjkcjHwBP4U4w8+OCDz2/dunV9ZqOUFioEnohGoxeHHYiIiEhbkhMJ4Lk3/bPkuHP/b35efsEN+M9846iZFackGbUMmAP037x5c81//vOfpxoaGhoyGqy0VB4wOhqNnhl2ICIiIm1Fu04Ai0tH2uX/fOx7R5x+5ps99xk4OG5QPvDoqJkVvePHj0Qi9cADQD3QLRqNrpg+ffqrGQxZdk8Bvvu4U8MOREREpC1otwlgcenITsPPvPj2YSeedkfHLt26JBllX+D+xMJIJLIef3/AvkD+2LFjp65aterjNIcre64j8Ky6jhMREdm1dpkAFpeO7PnZMy4ce2jpl76fl5/f1Gf8xqiZFd9LLIxEIu8D/wMGOed48MEHn66qqlJXcdmvK/BCNBo9IuxAJHuZWb2ZReMeQ8KOSUQk09pdAlhcOrLomLMve+KwL3ztLMvLs2ZMctuomRWHJyn/L7AU6Lt69eptzz///NPqKq5N6AlMiEajRWEHIlmr0jlXEvdYHHZAIiKZ1q4SwOLSkf0/d+FVTx96ysjTzZqT+wG+K7hxo2ZWdI4vjEQiNfhTwYVAp9dff/3j8vLyqa0bsaTJEOAp9R0sIiKSXLtJAItLR+57yuU/mHjg8SNO2o3JDwP+nlgYiURWEtdV3EMPPfTqhg0bVuxZpJIhpwB3hB2EZKXOcad/nw47GBGRZKY8cBsv3XoDl112GZdddhnXXXddq86/XSSAh5/61aFfuOpn/xty1AlH78FsvjtqZkWymwpPxXcXN6i2trZhzJgxT9bW1qqruLbhO9Fo9PthByFZJ/4U8DeSjWBm3zGzd83s3a0bdTtQEcm87RvWsXXdapYvX87y5ctZtWpVq86/zSeAJV8+u/jU7/zixUGHffawVpjdfaNmVuwXXxDXVdwGoNfChQs3TJ48+blWWJZkxj90ZbC0lHPuXufccOfc8G49e+96AhGRVtalVx+69enHwIEDGThwIPvss0+rzt/a8oUNR57+9SNP/e51z/Q74JD9W3G2U4AR1x9VVB9fWFpaOhT4LbAcqP3xj3/8jf333//IVlyupM9CoKSkpGRL2IFI+Mxsq3OuW3PH3+/QI9wvH56QzpBERFL6/rG7n+KY2Qzn3PBkw9psDWBx6cjhx5935SOtnPwBnAT8LrEwEoksBMYTdBV3//33t5uu4qqrq7nkkks4//zzOeecc7jrrrsAuPvuu/niF7/IBRdcwAUXXMAbb7wBQDQa5fzzz+fiiy/mk08+AWDLli1cc801ZOkfiqHAv8MOQkREJFu0yQSwuHTkocWf/+rfhh5zUrpq4G4YNbOiNEn5S8D7QP+tW7fWPPHEE0/W19fXJxmvTSksLOTee+/liSeeYNy4cUydOpXZs2cDcMkllzB+/HjGjx/PySefDMAjjzzCX//6V6699lr+85//AHDvvfdy5ZVX0oKrrzPt0mg0emHYQUj4WlL7JyLSXrW5BLC4dGS/QcVH3XzUV8//XBoXkwc8NmpmRZ/4wriu4uqAbrNmzVrZHrqKMzO6dPGdpdTV1VFXV9dkIldQUEB1dTWVlZUUFBSwdOlS1qxZw/DhSWuZs8ld0Wh0v12PJiIi0r61qQSwuHRklx79Bv7qxIuv/lJ+QUFBmhc3EJ/s7SASiWwA7iboKm7cuHFvrVy58qM0x5J29fX1XHDBBZx66qkcf/zxHHGE70xj3LhxnH/++dx0001s3rwZgCuvvJJbbrmFxx9/nAsvvJA77riDa665Jszwm6snSbapiIhIrmkzCWBx6cj8Dh07fWfEt35yUceu3TJ1CufMUTMrdrqNSNBV3As0dhX3TFVV1dYMxZQW+fn5jB8/nrKyMubMmcNHH33Eeeedx8SJExk3bhxFRUXcdtttAAwbNowxY8Zw3333sWzZMvr27QvAL3/5S2644QbWrVsX5kfZldOi0eglYQchIiISpjaTAAJfP+WKH36/x979987wcm8dNbMiWVvDp4ElQN81a9Zse+6559pFV3Hdu3dn+PDhTJ06lT59+pCfn09eXh5nn302c+bM2WFc5xz3338/3/nOd7jnnnu4+uqr+cpXvsLYsWNDir7ZbotGo7q3h4iI5Kw2kQAWl4485ugzv/nrgYd+5sAQFh/rKq5LfGHQVdw9+K7iOr/xxhsL586d+2YI8e2x9evXs2WLv0NKVVUVb7/9NkOGDGHt2rWfjvPaa69xwAEH7DDdxIkTOfnkk9lrr72oqqoiLy+PvLw8qqqqMhr/bugL/DHsIERERMKS9QlgcenIIQcce/LNxaVf3pNePvbUocA/EguDruIeAvoDNnr06NfWr1+/PMOx7bGKigquuuoqzj//fC655BKOO+44TjnlFP75z39y3nnncf755zN9+nR+/vOffzpNZWUlEydO5LzzzgP81cI///nP+de//vVpWZb7TjQaDXOfEhERCU1W3wi6uHRkz669i/789etGXd6hU+fOYccDnH/9UUX/iS8oLS014NvAccCyoUOH9rrmmmu+26FDh46hRCgtMaWkpOTksIOQ7KYbQYtImHLuRtDFpSMLgO+dcMG3P58lyR/AvaNmVgyOLwi6inucuK7iXnvtNXUV1zacFI1Gvxx2ECIiIpmWtQkgcPKQo44vHTDsiGFhBxKnJzB21MyKHW5BE4lEtgF3AnsBHV544YU5CxcujGY+PNkNt0Sj0ay9e7WIiEg6ZGUCWFw6sk9+h8KLjzn7smPCjiWJE4CbEgsjkcgiYByNXcW9sHXr1qy+H4oA8FngnLCDEBERyaSsSwCLS0cacNFx515R0rl7j2y9VcevRs2s+HyS8leA2cCAbdu21Y4fP75ddBWXA26ORqP5YQchIiKSKVmXAAJHFg0+4NShw08qCTuQJuQBjzbRVVwN0H327Nmr3nnnnVfCCFBa5FDgm2EHISIikilZlQAWl47sAlzxuYu+e3Refn6218gMwN8CZgeRSGQjvqu4InxXcdNWrFixIMOxScv9NOwAREREMiWrEkDgjCO+eNbRPfcZuG/YgTTTGaNmVlybWBiJROYAzwH7Au2iq7gcUBKNRkeEHYSIiEgmZE0CWFw6ckjXXn3OOuK0rye9X00W++uomRWfSVL+LLAY2Hvt2rXbJ0yY8F+XzTddFIAfhx2AiIhIJmRFAhjc8++K4y/49hEFhR2z5Z5/zdUR31Vc1/jCoKu4u4EOQOc333xz0Zw5c9pkV3E55IxoNDo07CBERETSLSsSQOCUXgP2O3zAwYcfGnYgu+kQ4PbEwkgksgp4kMau4iatW7duWaaDk2bLA34YdhAiIiLpFnoCWFw6sitw/vAzLx5qeXmhx7MHrhw1s+KCJOXTgNeBQXV1dQ1jxox5qra2tjrDsUnzXRaNRgvDDkJERCSdsiHhOrlHvwE9+x146JFhB9IK7hk1s2JIfEFcV3HrgN6LFy/eqK7islov4KthByEiIpJOoSaAxaUjOwNfP/rMiw9oA7d9aY4eJO8qbjtwF9AdKHzhhRfmfPzxxzPDCFCa5eKwAxAREUmnsGsAT+zau6jngGGHl4QcR2s6Hrg5sTDoKm4sMBDggQce+N+WLVsqMhybNM/XotFoj7CDEBERSZfQEsDi0pGdgDNLvnzeoLz8goJdTtC2/HLUzIovJCl/BZhF0FXcuHHj1FVcduoInBt2ECIiIukSZg3g0XkFHbrve8TRR4UYQ7rEuoorii+MRCIN+KuCq4Huc+bMWf3222+/HEaAskvqGk5ERNqtUBLA4tKRecAZh5/6tb6FnTp3CyOGDOgPjB41s8LiCxO7ihs/fvzbK1as+DCE+KRpJ0ej0b3CDkJERCQdwqoBHAb0O/C4Ecl60GhPvgr8KLEwEonMBSbS2FXcs5WVlVsyHJs0rQNwathBiIiIpENYCeCXBh3+2W7dehcNDGn5mfT/Rs2sSHaa+1lgIeoqLpt9OewARERE0iHjCWBx6ch9gCMPPuELuZD8ARSSvKu4WuAeoADoPHXq1MWzZ8+eEkaAktLIsAMQERFJhzBqAI8HGooGHzgshGWH5WDgjsTCSCSyGniAoKu4MWPGTF63bt3STAcnKe0XjUaLww5CRESktWU0ASwuHWnASQMOOcI6deveO5PLzgJXjJpZcVGS8neACEFXcQ8//LC6issup4UdgIiISGvLdA3gAKD3AcecMiTDy80Wd4+aWTE0viDoKm4sUAH0XrJkyaZXXnllQijRSTLHhR2AiIhIa8t0AngYwN5Dhx2S4eVmi73wXcV1iC9M7CruxRdfLP/oo4/eCyNA2ckxYQcgIiLS2jKdAJ7Ya8B+dV179emf4eVmk2OBPyQWRiKRxcDjNHYV96K6issKB0aj0V5hByEiItKaMpYAFpeO7APsO+yk0wZlaplZ7LpRMytOT1L+KjATGLB9+/ZYV3F1GY5NdmTA8LCDEBERaU2ZrAE8FKDfgcW5evo3ngFjRs2s6BtfGHQV9xBQRdBV3LRp09RVXPiODTsA8cws38xeCTsOEZG2LpMJ4Oe69Ohdu1dRv8EZXGY22wd4OElXcZuI6yruiSeeeGf58uXzwwhQPlUSdgDiOefqge1m1iPsWERE2rKMJIDFpSO7AYcMO/n0vS0vL6zeR7LRl4GfJBZGIpFyYAJBV3EPPPDAs5WVlZszHJs0OiDsAGQHVcD7ZvaAmd0ee4QdlIhIW5KpZGwYQL8DDtk/Q8trS/48ambFZ5OUTwA+BvqtW7eu8tlnn1VXceFRAphdngd+A7wOzIh7iIhIM2UqATwGqOrWq6hfhpbXlsS6iusWXxjXVVw+0Pmtt95aMmvWrDfCCFDYKxqN7h12EOI55x4GngCmOecejj3CjktEpC0pSPcCgt4/DssrKNjUqXuPvrucIDcdBPwbuDy+MBKJrCktLb0PuBZYPGbMmMmDBg3av6ioaN8wgsxxBwBrwg5CwMzOAG7F/3na38xKgJudc19Px/L27tqR7x+rkxci0r5kogawO9B1nwOL98rLz8/PwPLaqstGzay4OEn5u8AkYGB9fb0LuoqrynBsAgeGHYB86ib8ldkbAZxzUUAZmohIC2QiAdwbaNh7/4N1+nfX7ho1s2KH9mZBV3HjCLqK++STTza9/PLL6iou83T/yuxR55zblFCm9rEiIi2QiQSwH5DXs/8gtaHate4k7yquErgT6AYUlpWVfbBgwQI1es+s3mEHIJ+aY2bfBPLN7CAz+xcwNeygRETakkwkgEOBmu5F/VQD2DzHAH9MLIxEIkuAx4jrKm7z5s1rMxxbLlN3cNnjWny/4tXAWGAz8OMwAxIRaWsykQAeAGzr0rOPEsDm+/momRVfTFI+CXgPGFBZWVk3duxYdRWXOaoBzBLOue3OuRucc8c454YHr9UuVkSkBdKaABaXjswHBnbeq2dtxy5ddef+5ot1FbfDafOgq7jR+Bvh7lVeXr5m6tSpL4UQXy5SAhgyM5toZhNSPcKOT0SkLUl3DWAfIG/AsCN0+5eW64dPApN1FXcnft0WPPnkk9OXLVs2L4wAc4xOAYfvVuBvwCKgErgveGwF5oQYl4hIm5PuBLAfQJ/9hur07+4ZCfwssTASicwDniW4MvWBBx54dvv27eoqLr26hh1ArnPORZxzEeAo59wFzrmJweObwElhxyci0pakOwHsD9C1Z5+eaV5Oe/anUTMrhicpnwh8BPRbv3591TPPPPNUQ0ODboWRPrqHZfboa2ZDY2/MbH9AZxlERFog3QnggUBlQWHHwjQvpz3rgL81TPf4wqCruHvx27DL22+//cmsWbNeDyPAHKEEMHv8BJhsZpPNbDL+4qgfhxqRiEgbk+6u4HoBNfmFhUoA98yB+HZ/l8YXxnUV90N8V3GR559//p0wAmyv6uvr+3bu3Hn6ddddNzbsWMRzzr1oZgcBhwRF85xz1WHGJCLS1qQ7AewM1Bd0KOyY5uXkgktGzax46fqjih5JKJ8BvAqc2tDQULN2rW4N2MoKgc0lJSUbwg5EdnA0MAR/DPuMmeGcGxNuSCIibUe6E8BOQG1+hw6qAWwdd46aWTHt+qOKFsQKIpGIKy0tHQu8jb99jLS+ZWEHII3M7BH8/UWjQH1Q7AAlgCIizZSJGsCq/AKdAm4l3fDtAU+4/qii2lhhJBKpAXQrGMkVw4Fi55wuehIR2U1puwikuHSk4WsA6/NUA9iajgb+HHYQIiGaA+wTdhAiIm1ZOq8Czg/m7/ILOqgNYOv66aiZFV8KOwiRkBQB5WZWpp5ARER2TzpPARcCDQD5BQWqAWxdBjw8ambFkdcfVbQ67GBEMuymsAMQEWnr0pkAflrrl6cEMB32Bh4ZNbPiJ2EH0k7VX39UkdpVZiHnXMTMBgMHOedeMbMu6D6NIiItku4aQJffoTAvLy9fB+f0OB31gZouG1H/v1nJzK4CvgP0xl8NPBC4Gzg1zLhERNqSdLYBLATIy8vTrUmkLaoJOwBJ6fvAicBmAOfcAnyNuIiINFPaE8Da6qr6hob6+l2NLJJlanc9ioSk2jn3aYJuZgX4+wCKiEgzpfMUcEPsRX1tbXVex/wuaVyWSGtTDWD2ipjZr4HOZnY6cA0wMV0L2759O9FoNF2zF5E2oqSkJOwQWlU6awA/7Zuzvra2Ko3LEUkH1QBmr+uBtcD7+LaAzzvnbgg3JBGRtiXdCaAB1NfWqKN2aWu2hx2A7MjMzjSz7zvnGpxz9wGD8b2C/NrMzg05PBGRNiWdCWAVQQJYV1OtGkBpa5aEHYDs5Dog/obPhfiecUYA3wsjIBGRtiqdbQA/rQGsqdq+LY3LEUmHRWEHIDspdM4tjXs/xTm3HlhvZl3DCkpEpC1KWw1geaSsDt+QPr9665Yt6VqOSJooAcw+O9yX0Tn3g7i3fTMci4hIm5bOU8Dgb6ZbWLV1sxJAaWuUAGaft4ObQO/AzL4LvBNCPCIibVY6TwEDrAP6b9+0QQmgtDVKALPPT4BnzOybwHtB2dH4bifPCisoEZG2KBMJ4JBt6yuUAEpbowQwyzjn1gCfM7MvAIcFxc87514LMSwRkTYp3QngWqBw/fLFG9K8HJHWtPb6o4p04VKWChI+JX0iInsg3W0A1wP565Yu2lxbVakfVGkrFocdgIiISDqlOwFcB9QDbF2/dnmalyXSWnT6V0RE2rV0J4DLY8vYuGq5EkBpK5QAiohIu5buBHALsAHotHbxAiWA0lbMDzsAERGRdEprAlgeKXP4H9Puy+a8tyKdyxJpRZPDDkBERCSd0l0DCPAB0Hnr+rWVVdu26GpgyWquoWHx9UcV6RSwiIi0a5lIAJcDDmBLxWqdBpasZnl5r4Qdg4iISLplIgFcARhgG1csVQIo2U73lxMRkXYv7QlgeaSsClgJdFm9cJ4SQMl2SgBFRKTdy0QNIPh2gHstmzNzpWtoaMjQMkVaxDU0zL3+qKLVYcchIiKSbplKABcAHWoqt9VtWbdmaYaWKdIiav8nIiK5IlMJ4KenflfMmz0nQ8sUaSmd/hURkZyQqQRwJf6m0J0+iLxYrtPAkm2cc/VAJOw4REREMiEjCWB5pCz241q0pWL19k2rVyzMxHJFWmDG9UcVbQo7CBERkUzIVA0gwIzY8pbNfe/9DC5XZJfM7L9hxyAiIpIpmUwAPwHWAV0/iLw4r6G+ri6DyxZJyTlXBzwUdhwiIiKZkrEEMOgXeBLQu3LLppoNK5Z+mKllizSlvrbm+euPKloTdhwiIiKZkskaQICZsWV+MvtdXQ0sWaGgsOPtYccgIiKSSZlOAFfhbwnTfd4bLy2or62tzvDyRXZQX1uzBF8zLSIikjMymgDGnQbuVVu1vW7d0oXzMrl8kUSWl3fn9UcVubDjEBERyaRM1wACzAIMsIUzps4KYfkiALiGhtq8/IIHw45DREQk0zKeAJZHyiqAj4EeH775yqKtGypWZDoGEYD6utqJ1x9VVBF2HCIiIpkWRg0g+C63egDMe71sckgxSI7TxR8iIpKrwkoA3wU2AV3KJ72wYJtqASXD6mtrFl1/VJG6fhMRkZwUSgJYHimrAf4L9AWY98bL+iGWzLK8v4cdgoiISFjCqgEEmAZsBrrMfe25D7dtWLcyxFgkh9TVVK/OLyi4J+w4REREwhJaAhjUAj5FrBZwimoBJTNqKrffdP1RRTVhxyEiIhKWMGsAAd4mVgv46sT52zaqFlDSq3r7tiVdevS6N+w4REREwhRqAlgeKasmri3gfLUFlDSrr6m+7vqjihrCjkNERCRMYdcAQlxbwDmvPTd/+8b1q8IOqLkqt2zisV/8H7edfQK3nf05lsyazit3/4U/jzyC2y8cwe0XjmDelJcBWBx9m3+eX8odl5xOxScLP53+wWvOwzl1RJEJVVs3v3/z5w9+Iuw4REREwhZ6AhjUAj4D9MU5yiP/ey3kkJpt4l9/zcGf+wI//e9b/HD8ZPYeejAAJ158NT8cN5kfjpvMISedDsCUR+7i4lsfZOQPbuDtJ0cD8Np9f2PEt36MmYX1EXKGa2hwdTXVV4Ydh4TDzJyZPRL3vsDM1prZc2HGJSISltATwMBUYCvBfQHXLVv8QdgB7UrV1i0sfm8aw8+6BICCDoV07t4j5fh5BQXUVlVRW1VJfkEB65YuYvOalQw9+sRMhZzTtqxb8/Qtpx76bthxSGi2AYebWefg/enA8hDjEREJVUHYAYCvBSwuHfkkcCWw6M3H7n7hKz+9ef+CDoWdwo4tlfXLF9O1Vx+evOlaVn44l4GHfoYzfvFHAN4a/wAzn3uCgcWf4as/vZnOe/VkxJU/4ulbfkaHTp04/w938sLff8fp11wf8qfIDXU11ZVm9t2w45DQ/Q/4KvAkcBEwFjg51IhEJKvdc889rF+/HoCOHTsCsM8++/CXv/wlzLBaRbbUAAJMARYAfTeuXLp1wVuTXgk7oKY01NezYt5sjjv3//jh2EkUdu7C5Idu57jzruAXE6Zz7bhJdC/qx/O3/RaAAcOO4JoxL3LVvc+wftliuvfdB+fg8V9+m/E3fI8t69aE/Inar63r1v7xj6cfpj5/ZRxwoZl1Ao7E34UgKTP7jpm9a2bvbty4MVPxiUiWWb9+PRUVFVRUVLB8+XKWL1/OqlVt5lKFJmVNAlgeKasHRgOdgQ7Tn35kxua1q5aEG1VqPfbuz157D2C/I44G4PBTz2DFvNl077M3efn55OXlcezZl7Js7swdpnPO8doDf+fUq37Gq/f+ldOu/iUlXzmXqWPvC+NjtHvbNlTM79l/0J/DjkPC55ybDQzB1/69sItx73XODXfODe/Zs2cGohORbNS7d2+KioooKipi4MCBDBw4kH322SfssFpFVpwCjimPlC0rLh35LHAWzi15a9x9E0773q+uzi8o6BB2bIm6F/WjZ78BrF38EX2HHMjH77zB3vsPY/PaVezV1+8cc197gX4HHLLDdO9NHMchJ51G5716UltVieXlYXl51FZVhvEx2rWaqsqqlR/OPev+q8/RbV8kZgJwKzAC6BNuKCKS7b773cbWQyUlJeEFkgZZlQAGXgQ+B/Ra/fG89R9OfbXs0FNGfi3soJI545d/ZvwNV1NfW0vvQYM596bbmfCXX7PywzkYRq8B+3LWDbd+On5N5Xbee248V/77PwCcdPHVPPaL/yO/oAMX/ln3Jm5Nzjk+mT39xvuvPmde2LFIVnkQ2OSce9/MRoQci4hIaCwb70FXXDryAOA3wDKg7ozr/nxRrwH7HRxyWNKGrFow98WX/v2nr5RHyrJvB5eMM7OtzrluCWUjgJ8755r8g1lcXOwef/zxNEYnIm1BW6wBNLMZzrnhyYZlTRvAeOWRso+Bp4FBAJHR/5pQW121LdyopK3YUrF6xdzXnj9fyZ/EJCZ/QdnkXSV/IiLtVVYmgIEXgIXA3pvXrNg2u+zpCdlYWynZpba6qmbxzGnnvnz3X7aEHYuIiEi2ytoEsDxSVgvcBxQCHee+9tyHi2a8+WrIYUmW+2T2uzc/dt233go7DhERkWyWtQkgQHmkbCXwCDAQyJvy6F1TVi0onxFyWJKlVn88b/Kbj92lW76IiIjsQlYngIHXgTJgMGCv3D3q+Y0rl30UckySZbauX7t63hsvnV0eKdMtX0RERHYh6xPAoCH/eOBdYFBDfb176d9//M+2jevbx624ZY9Vbtm0dcFbk87+3z9v3hB2LCIiIm1B1ieAAOWRsjrgfuAToH/V1s01r97zl8eqt2/bFHJoErKqbVu2v/v0I9eMv/GaqWHHIiIi0la0iQQQoDxSVgncDmwBijauXLp1yiP/fqyutqY65NAkJDWV26qmjb//94vee+uxsGMRERFpS9pMAghQHinbANwG5APdl38wa+27zzw6vqGhQe2+ckxtVWX1tCceHPXJ7Hf/pnZ/IiIiLdOmEkCA8kjZCuAfQC+g04dvvrqofNLzE8KNSjKprqa69u2nRv998cxpfyyPlNWHHY+IiEhb0+YSQIDySNl84G5gANDhvYnjZn3wetlzrqFBd4pu5+pqa+qm//eROxdOn/K7oG2oiIiItFCbTAAByiNl7+CvDt4XKJj+3zEz3nv+ifEN9XVKCtqp+rra+hnPPn7fgmmTflkeKasJOx4REZG2qs0mgIEX8H0G7wcUzn114vw3H7/34drqqu0hxyWtrKG+rv69ieMfnj/l5Z+VR8p04Y+IiMgeaNMJYHCPwGeAB/Cng7ssmvHmstfuu/WB6m1bdE+4dqKmcnvltCceGv1B5H8/DK4GFxERkT3QphNA8ElgeaQsAvwd6AP0WP3RB+tfvP0PD2zbULEi5PBkD22pWF3x0h233PvR25N/Uh4p2xZ2PCIiIu1Bm08AY8ojZbOAPwGFQNGm1cu3PXfrjaPVbVzbtWL++wuf/9uNf1u/fMlvyyNlW8KOR0REpL1oNwkgQHmk7GPgD8B2oH/1ti21z/3txrGrP54XDTcyaQnX0NAwd9Lz775y16hf1VRuv7U8UrY57JhERETak3aVAAKUR8pW4WsCVwD7NtTVNpT96w/PLnx3yqtON4zOerXVVVVTHr+7bMazj18L/Ee3ehEREWl97S4BBCiPlG0E/gK8D+wP5E159K4pkYdvv79y88a1oQYnKW1dv3bdS3fc8viid9+8pjxSNi24yEdERERaWbtMAOHTvoP/DbwMDAa6fzJr+spn/vize5bNnTnVOafkIous+uiDxc//7Tf/XLd00U/LI2WLw45HRESkPWu3CSBAeaSsFngMuBXoAAyqra5qeO2+W1+eOvbe0VW6VUzoaqurKt97bvybL91xyw3V27aMKo+UbQo7JhERkfbOcqUirLh0ZHfgm8CJwGpgW8eu3Tuccvm1X+x/8GHDw40uN63+eN6CKY/d9e629RV3Am/qlK9ko+LiYvf444+HHYaIhKykpCTsEFrMzGY455LmODmTAAIUl4404LPAt/A1gisAd8jJXzyg5CvnnVnYuUv3UAPMEdXbt21+b+LY6QvemjQFeLA8UvZJ2DGJpKIEUESg/SWABZkOJkxBDdOM4tKRC4FLgeHAynlvvPTx0vdn3HnyZd//Ut/9D/6MmYUbaDvVUF9fvyT69ux3nnp4dvX2rY8Bk3SVr4iISOblVA1gvKA28HPAZYADVgHs95lj+pd8+bxTe+4z8IAw42tv1i1d9NFb4x+YvX7ZomnAmPJI2eqwYxJpDtUAigioBrDdCGoD3ywuHfkhcAVwGLDxk1nTV34ya/qjB5946v5HnPb107r2KhoQaqBt3PbNG9fOfG78ex+/8/p8/AU575ZHynQ/RhERkRDlbA1gvKA28AjgQmAAUAFsxYwjTz/z0INPPLW0S4/e/UINso3ZtGbFog8mvzjvw6mvrQT3HFBWHinbHnZcIi2lGkARgfZXA6gEME5x6cgCfLvAC4Be+KuFKwEO+/xXDx520uknd+vTd1CIIWa1hoaGhoolH82d/dIz81d8MGsbMBMYp9O90pYpARQRUAKYE4pLR3YETgLOAroDa4FtAAefeOr+h54y8qS99h4wVBeLeHW1NdUr5s1+b+bzTyzZtGp5JTANeAlYolu7SFunBFBEQAlgawRTj++iLeYs59ziNC1rMTDcOVexO9MHieBxwDfwNYLrgC0Affbdf69DTh55RP9hhx/ZpUevvVsp5DalevvWjYtnTntv5vP/WV6zfWslvteVyeWRst1a3yLZSAmgiIASwNYIZqtzrluGlrWYPUgAY4pLR3YAjgbOAYqAWnytYC3AoMOO2vug4z9/ZL8DDzmisHPXvfYs6uxWV1NduWHFJx8tmfXOkg8iZetdQ/0GYCIwrTxSti3s+ERamxJAEYH2lwBmxVXAZnY0cBvQDX8BxhXOuZVmNhnfjuxooC/+li2/wl+wMd45d2Mw/TPAvkAn4J/OuXuTLOMS4IdAIfA2cE0w6AF8uz8HPOic+3vitEGXctOKS0dOB4biawVPDJZXuWzuzHXL5s58BbNXDjpuxOD9h594ZNHgA4oLOhR22vO1E76qrZvXV3yycP4ns975+OPpU7a7hvpC4BNgAjA7WD8iIiLSRoSRAHY2s2jwehFwPvAv4Ezn3FozuwD4I3BlME6Nc+4UM/sR8Cw+GVwPfGxmf3fOrQOudM6tN7POwHQzeyooB8DMDsVf2HGic67WzO4ELgbmAgOdc4cH4/VsKvDySFk9sABYUFw68gngEHxbwc8C+Ti3ZcG0SZ8smDZpSYeOnV445JQvHdx/2OEH7rV3/0Gdu/foa22k0aBzzm1dt3bZmoXz53/8zutLVn1U7oB8oAaYDrwBLFD7PhERkbYpjASw0jlXEntjZocDhwMvB/lRPrAybvwJwfP7wFzn3MpguoX4Wr91wA/N7BvBePsCBwXlMafiE8fpwTI6A2vwpy6Hmtm/gOfxFy40S3mkrAaYDcwuLh3ZDX8fwc8DwwBXW1216f2Xn5n//svPfADQuXuPwn2PPGZgv6HDBvXsP2hQtz57D+rQsVOX5i4vnerramsqN29au21Dxdq1ixcs+fDNV1dtXb+2I75WdCMwFf9ZF6vnDhERkbYvG04BGz6xOyHF8OrguSHudex9gZmNAE4DTnDObQ9OGyeeejXgYefcr3ZauNlngJHA9/G1kVcmjrMr5ZGyrfjTym8Xl47sA5QAJwBDgmVb5ZZNVR+++crKD998ZVFsun4HHNJ7wKGfGdRn3/0H9di7/8COXbv3yu9Q2DldFYUN9XV1lVs2rd22Yd3aLWtXr9mw8pM1axZ9uLZiyceVOLcXPjE2YBnwFlAOrFJNn4iISPuSDQngfKCvmZ3gnHvLzDoABzvn5jZz+h7AhiD5OwQ4Psk4rwLPBqeM15hZb/ztXbbhTzE/ZWYfA6P39MOUR8rWBct7Nbh4ZAAwCDg0eOyNr1lj9cfztqz+eN5cfO0aAHkFHfL26rtPl+5F/bp17dWna5e9enbt1L1Ht45du3ft2LVb18LOXbt16NS5q+Xl5bmGhgbnGhpcQ0O9a2hocA319XU1NVV1NVXVtVVVVbXVVVW1Vdurtq5bu2nt4gVr1yz6cKtraOgKdMHXtDpgL4IkHH9698PySNmmPV0PIiIikr1CTwCdczVmdi5wu5n1CGL6Bz4haY4XgavNbDY+mZyWZBnlZnYj8JKZ5eGv3v0+/ibPDwVl4C8waTXBxRFLgsebQY8je+ETwv3xp42H4j+zA2ioq83buHKpbVy5tA7YjD+VXRM8Up1+tWAeBfjELva6I42JXrdgvCXAQnwt3xpgjXroEBERyS26EXTIiktH5gM9ga7Bo1vw3Bvog7//YE98TWcXfDIXf47YgHp8Mrst7rEVf7HMUoJED9im07kiLaPbwIgI6DYw0sqCK4vXseNFK0kFXdUV4hO+hthDSZ2IiIi0hBLANiS4AldX4YpkUJcuXdrkP38Rkabk7XoUEREREWlPlACKiIiI5BglgCIiIiI5RgmgiIiISI5RAigiIiKSY5QAioiIiOQYJYAiIiIiOUYJoIiIiEiOUQIoIiIikmOUAIqIiIjkGHNO3ciKiKRiZluA+WHHEacIqAg7iATZFpPi2bVsi0nx7NruxDTYOdc32QD1BSwi0rT5zrnhYQcRY2bvZlM8kH0xKZ5dy7aYFM+utXZMOgUsIiIikmOUAIqIiIjkGCWAIiJNuzfsABJkWzyQfTEpnl3LtpgUz661aky6CEREREQkx6gGUERERCTHKAEUEUnCzL5kZvPN7CMzuz7seADMbLGZvW9mUTN7N4TlP2hma8xsTlxZbzN72cwWBM+9siCmm8xsebCeomb2lQzGs6+ZTTKzD8xsrpn9KCgPZT01EU8o68jMOpnZO2Y2K4jn90F5aPtREzGFth8Fy883s5lm9lzwvlXXkU4Bi4gkMLN84EPgdGAZMB24yDlXHnJci4HhzrlQ7k9mZqcAW4ExzrnDg7K/AOudc6OCRLmXc+6XIcd0E7DVOXdrpuKIi6c/0N85956ZdQdmAGcBVxDCemoinvMJYR2ZmQFdnXNbzawDMAX4EXA2Ie1HTcT0JULaj4K4fgoMB/Zyzn2ttb9rqgEUEdnZscBHzrmFzrkaYBxwZsgxhc459zqwPqH4TODh4PXD+OQi7JhC45xb6Zx7L3i9BfgAGEhI66mJeELhvK3B2w7BwxHiftRETKExs0HAV4H744pbdR0pARQR2dlAYGnc+2WE+KMZxwEvmdkMM/tO2MEE+jnnVoJPNoC9Q44n5gdmNjs4RZzR09IxZjYEOAp4myxYTwnxQEjrKDi1GQXWAC8750JfPyligvD2o38A1wENcWWtuo6UAIqI7MySlGVDe5kTnXOfBb4MfD84/Sk7uws4ACgBVgJ/y3QAZtYNeAr4sXNuc6aX34x4QltHzrl651wJMAg41swOz9SyU0kRUyjryMy+Bqxxzs1I53KUAIqI7GwZsG/c+0HAipBi+ZRzbkXwvAZ4Gn+qOmyrg3ZmsfZma0KOB+fc6uAHvQG4jwyvp6Ad2VPAY865/wbFoa2nZPGEvY6CGDYCk/Ft7bJiP4qPKcR1dCLw9aDN7zjgC2b2KK28jpQAiojsbDpwkJntb2aFwIXAhDADMrOuQSN+zKwr8EVgTtNTZcQE4PLg9eXAsyHGAnz64xjzDTK4noILCh4APnDO3RY3KJT1lCqesNaRmfU1s57B687AacA8QtyPUsUU1jpyzv3KOTfIOTcEf+x5zTl3Ca28jgr2KEoRkXbIOVdnZj8AyoB84EHn3NyQw+oHPO1/zykAHnfOvZjJAMxsLDACKDKzZcDvgFHAE2b2LeAT4LwsiGmEmZXgT9svBr6bwZBOBC4F3g/alAH8mvDWU6p4LgppHfUHHg6utM8DnnDOPWdmbxHefpQqpkdC3I+SadV9SLeBEREREckxOgUsIiIikmOUAIqIiIjkGCWAIiIiIjlGCaCIiIhIjlECKCIiIpJjlACKiIikkZl9w8ycmR0SdiwiMUoARURE0usiYAr+pr4iWUEJoIiISJoEffCeCHyLIAE0szwzu9PM5prZc2b2gpmdGww72swiZjbDzMoSeqMQaTVKAEVERNLnLOBF59yHwHoz+yxwNjAEOAL4NnACfNpn77+Ac51zRwMPAn8MIWbJAeoKTkREJH0uAv4RvB4XvO8A/Mc51wCsMrNJwfBhwOHAy0GXf/nAyoxGKzlDCaCIiEgamFkf4AvA4Wbm8AmdA55ONQkw1zl3QoZClBymU8AiIiLpcS4wxjk32Dk3xDm3L7AIqADOCdoC9gNGBOPPB/qa2aenhM3ssDACl/ZPCaCIiEh6XMTOtX1PAQOAZcAc4B7gbWCTc64GnzT+PzObBUSBz2UsWskp5pwLOwYREZGcYmbdnHNbg9PE7wAnOudWhR2X5A61ARQREcm858ysJ1AI/EHJn2SaagBFREREcozaAIqIiIjkGCWAIiIiIjlGCaCIiIhIjlECKCIiIpJjlACKiIiI5BglgCIiIiI55v8DaIY1/UQ59PwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting 'Males' vs 'Females'\n",
    "\n",
    "fig, ax = plt.subplots(1,2,figsize=(12,4))\n",
    "\n",
    "data = [ df['Gender'].value_counts()['M'], df['Gender'].value_counts()['F'] ]\n",
    "ax[0].pie(data,labels = ['Males','Females'], explode = [0, 0.1] , autopct='%.0f%%' , shadow = True , colors = ['#CFCFCF','skyblue'])\n",
    "\n",
    "sns.barplot(ax = ax[1], data = df, x = 'Age' , y = 'Gender', palette = [\"skyblue\", \"#CFCFCF\"])\n",
    "\n",
    "ax[0].set_title(\"Males' Vs Femals' Percentage\")\n",
    "ax[1].set_title(\"Males' Vs Femals' Mean_Age\")\n",
    "\n",
    "fig.suptitle(\"Showing details about (Gender) Column\",y = 1.1, size = 24);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef56840d",
   "metadata": {},
   "source": [
    "___________________\n",
    "From the above plots:\n",
    "\n",
    "* `Females` % reserved appointments is > `Males` %.\n",
    "* `Females` who reserved appointments are **40-year** old on average. \n",
    "* `Males` who reserved appointments are **35-year** old on average.\n",
    "___________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "aa1c414b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing each disease percentage for the two Genders\n",
    "\n",
    "M_Al = df[ df['Gender'] == 'M' ]['Alcoholism'].value_counts() [1]\n",
    "M_Di = df[ df['Gender'] == 'M' ]['Diabetes'].value_counts() [1]\n",
    "M_Hi = df[ df['Gender'] == 'M' ]['Hipertension'].value_counts() [1]\n",
    "\n",
    "F_Al = df[ df['Gender'] == 'F' ]['Alcoholism'].value_counts() [1]\n",
    "F_Di = df[ df['Gender'] == 'F' ]['Diabetes'].value_counts() [1]\n",
    "F_Hi = df[ df['Gender'] == 'F' ]['Hipertension'].value_counts() [1]\n",
    "\n",
    "data_M = [ M_Al, M_Di, M_Hi ]\n",
    "data_F = [ F_Al, F_Di, F_Hi ]\n",
    "\n",
    "data_All = np.array(data_M) + np.array(data_F)\n",
    "data_All \n",
    "\n",
    "lab = ['Alcoholism','Diabetes','Hipertension']\n",
    "ex = [0, 0.1,0.1] \n",
    "col =['#ABABAB','#CFCFCF','#9ECDE9'] \n",
    "\n",
    "\n",
    "\n",
    "fig,ax = plt.subplots(1,3,figsize=(16,6))\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax[0],y = lab , x = data_All, palette =col) # 1st plot (all diseases)\n",
    "ax[1].pie(data_M ,labels= lab,explode = ex , autopct='%.0f%%' , shadow = True , colors = col) # 2nd plot (Males)\n",
    "ax[2].pie(data_F ,labels= lab,explode = ex , autopct='%.0f%%' , shadow = True , colors = col) # 3rd plot (Females)\n",
    "\n",
    "ax[0].set_xlabel('No. of people having the disease',size =13)\n",
    "ax[0].set_ylabel('Disease name',size =13)\n",
    "ax[0].set_title('All Sample Diseases',y = -.3,size = 20)\n",
    "ax[1].set_title('Males Diseases',y = -.3,size = 20)\n",
    "ax[2].set_title('Females Diseases',y = -.3,size = 20)\n",
    "\n",
    "fig.tight_layout(pad=4)\n",
    "fig.suptitle('% (Diseases) For Both Genders', y = 1.1,size = 30);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced84b39",
   "metadata": {},
   "source": [
    "___________________\n",
    "From the above plots:\n",
    "\n",
    "* `Hypertension` is by far the most dominant disease for the appointments.\n",
    "* `Females` are less addected to `Alcohol` tha `Males`.\n",
    "___________________"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f67732",
   "metadata": {},
   "source": [
    "### 1.2- Univariate Conclusion:\n",
    ">  #### I have concluded that:\n",
    "\n",
    "* In our sample, `Females` care with their health more than `Males` as they reserved more appointments.\n",
    "* The most dominant disease in our sample is `Hypertension`.\n",
    "* % of `Alcohol` additction is, by far, higher in `Males` than `Females`.   ''' It's a bivariate conclusion '''\n",
    "______________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d6a9616",
   "metadata": {},
   "source": [
    "### 2.1- Bivariate Analysis:\n",
    "_________________________________\n",
    "* Does Receiving `Scholarship` has strong effect on not cancelling the appointment or not?\n",
    "* Does the period between `AppointmentDay` and `ScheduledDay` has effect on cancelling the appointment?\n",
    "* Does `Gender` has effect on Cancelling an appointment?\n",
    "_________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ad94de5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1]\n",
       "Categories (2, int64): [0, 1]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Scholarship'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e7621161",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df, x = 'Canceled',hue='Scholarship', palette =['#ABABAB','#9ECDE9'] )\n",
    "plt.xlabel('Cancelling the appointment or not?',size =13)\n",
    "plt.ylabel('Number of appointmnets',size =13)\n",
    "plt.title('How receiving Scholarship may affect Cancelling appointments',y =1.1, size=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03b9d75",
   "metadata": {},
   "source": [
    "______________________________\n",
    "From the above plot:\n",
    "* Appointments that aren't canceled tend to be for those who received a `Scholarship` more.\n",
    "\n",
    "______________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "93c0be5f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                        109909\n",
       "mean      9 days 16:29:13.462873832\n",
       "std      15 days 05:45:20.798050125\n",
       "min               -7 days +10:10:40\n",
       "25%               -1 days +15:40:32\n",
       "50%                 3 days 10:54:03\n",
       "75%                14 days 06:18:04\n",
       "max               178 days 13:19:01\n",
       "dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['AppointmentDay'] - df['ScheduledDay']).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f91d714",
   "metadata": {},
   "source": [
    "____________________\n",
    "###### The appeared -ve values are misleading. so we will explore those columns in more details: \n",
    "___________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "249de051",
   "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>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-04-29 16:08:27+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-04-29 16:19:04+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-29 17:29:31+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-04-29 16:07:23+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>109904</th>\n",
       "      <td>2016-05-03 09:15:35+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109905</th>\n",
       "      <td>2016-05-03 07:27:33+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109906</th>\n",
       "      <td>2016-04-27 16:03:52+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109907</th>\n",
       "      <td>2016-04-27 15:09:23+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109908</th>\n",
       "      <td>2016-04-27 13:30:56+00:00</td>\n",
       "      <td>2016-06-07 00:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109909 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ScheduledDay            AppointmentDay\n",
       "0      2016-04-29 18:38:08+00:00 2016-04-29 00:00:00+00:00\n",
       "1      2016-04-29 16:08:27+00:00 2016-04-29 00:00:00+00:00\n",
       "2      2016-04-29 16:19:04+00:00 2016-04-29 00:00:00+00:00\n",
       "3      2016-04-29 17:29:31+00:00 2016-04-29 00:00:00+00:00\n",
       "4      2016-04-29 16:07:23+00:00 2016-04-29 00:00:00+00:00\n",
       "...                          ...                       ...\n",
       "109904 2016-05-03 09:15:35+00:00 2016-06-07 00:00:00+00:00\n",
       "109905 2016-05-03 07:27:33+00:00 2016-06-07 00:00:00+00:00\n",
       "109906 2016-04-27 16:03:52+00:00 2016-06-07 00:00:00+00:00\n",
       "109907 2016-04-27 15:09:23+00:00 2016-06-07 00:00:00+00:00\n",
       "109908 2016-04-27 13:30:56+00:00 2016-06-07 00:00:00+00:00\n",
       "\n",
       "[109909 rows x 2 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df [ ['ScheduledDay', 'AppointmentDay'] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "427defa5",
   "metadata": {},
   "source": [
    "_________________________________\n",
    "* we show that the `AppointmentDay` shows only date and not time while `ScheduledDay` show both \n",
    " so if they are the same date, it may appear -ve values thanks to this incompatibility.\n",
    "\n",
    "*Therefore, I will drop **time** and just keep **year**, **month** and **day** of those two columns*\n",
    "_________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1d5bc124",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ScheduledDay'] = pd.to_datetime(df['ScheduledDay'].dt.strftime('%Y-%m-%d') )\n",
    "df['AppointmentDay'] = pd.to_datetime(df['AppointmentDay'].dt.strftime('%Y-%m-%d') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "791bd0e8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>PatientId</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Hipertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>Canceled</th>\n",
       "      <th>ReservationPeriod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26882</th>\n",
       "      <td>3103.0</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-10</td>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>38.0</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54938</th>\n",
       "      <td>24091.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-18</td>\n",
       "      <td>2016-05-17</td>\n",
       "      <td>19.0</td>\n",
       "      <td>SANTO ANTÔNIO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63818</th>\n",
       "      <td>35601.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>22.0</td>\n",
       "      <td>CONSOLAÇÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71077</th>\n",
       "      <td>45038.0</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>2016-05-05</td>\n",
       "      <td>81.0</td>\n",
       "      <td>SANTO ANTÔNIO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-6 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71902</th>\n",
       "      <td>45368.0</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>2016-05-03</td>\n",
       "      <td>7.0</td>\n",
       "      <td>TABUAZEIRO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>-1 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId Gender ScheduledDay AppointmentDay   Age  Neighbourhood  \\\n",
       "26882    3103.0      M   2016-05-10     2016-05-09  38.0    RESISTÊNCIA   \n",
       "54938   24091.0      F   2016-05-18     2016-05-17  19.0  SANTO ANTÔNIO   \n",
       "63818   35601.0      F   2016-05-05     2016-05-04  22.0     CONSOLAÇÃO   \n",
       "71077   45038.0      F   2016-05-11     2016-05-05  81.0  SANTO ANTÔNIO   \n",
       "71902   45368.0      M   2016-05-04     2016-05-03   7.0     TABUAZEIRO   \n",
       "\n",
       "      Scholarship Hipertension Diabetes Alcoholism SMS_received Canceled  \\\n",
       "26882           0            0        0          0            0      Yes   \n",
       "54938           0            0        0          0            0      Yes   \n",
       "63818           0            0        0          0            0      Yes   \n",
       "71077           0            0        0          0            0      Yes   \n",
       "71902           0            0        0          0            0      Yes   \n",
       "\n",
       "      ReservationPeriod  \n",
       "26882           -1 days  \n",
       "54938           -1 days  \n",
       "63818           -1 days  \n",
       "71077           -6 days  \n",
       "71902           -1 days  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ReservationPeriod'] = (df['AppointmentDay'] - df['ScheduledDay']) \n",
    "df[ df['ReservationPeriod'] < pd.Timedelta(0) ]   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e229e0a",
   "metadata": {},
   "source": [
    "_________________________________\n",
    "Even after this cleaning, I have found five enteries with -ve `Reservation_period` value, \n",
    "I will drop them as they are only **5_enteries** out of **109909**\n",
    "________________________________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1d0a8d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# concerting the columns to numeric instead of datetime for further plotting \n",
    "df['ReservationPeriod'] = df['ReservationPeriod'].dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7eafcd88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     87804\n",
       "Yes    22105\n",
       "Name: Canceled, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Canceled'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "496fb78f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ssr/Hbv6xJfO2yDBz8TqwXVMNt8B4yuM4VhFqvCy23KZQ+G5acl86JK/d52LzauDEBsT0B2oSso5FujmdmiQnf7ubSfMlZQuArQu03zvRzeF57XJJezXw0QL9do3zd4v3jbXEe3k9us1N9zriRae89gOpuWN8Xl67AYSkoJoC+/3YTUcgG/u/ZAun56e5dSKv+U6x+QaKHzcvSsyPMRTej38u0c2Bee0uT7T70MW32E1ue63K315p3PnIyFy7Oqbr5iJx1XaukpuuMXXM+zGJ8XyolAxwdKL9GgqcDxKe+8u1zy8l97XY7g3ihegC/R8Q17FK8ra/xqy/edtK0e2Omjt+h27J+pv8bMkzZYPq+NRWNvhMwga4jlCMqpAr4oztRMi4gbjGhWIOEBYyAGY2jFDsZDpwa3776Kj4/UwtsdXlbAvl1T8ws/mEhfgM4Y4VhKqcH8zr5xOERHQ+Nc9CFZKL+4REs5WElQzCHaT6yr2X4zZ3f79QBx6ePcjNixMKdQNMdveHaxlP7rmnQ81spwLtczWwveDub9cWcAGPJoZdaznshjKzfQh3ZgB+6e4b87uJ0z0h/q2tOu8rijTPPUy/k5nVq6YzC89H7GtmdxASMIBn3H1i/N1Sy7VYfEZIrACuc/cPioz/uvj3nLzWuf/j3P3pAv2uAX7f0LgaI5ZzP5BwoPxTLZ3mts9ja1kff+3u1QWa59aFboQrZrlxG2F/CKE2wSX5Pbr7NJq2Bs5x7v6hfaC7VxLuxgPss4XDHkmoMXQ14WpoUu7ZjuPNbMgWDr8ujdnXQk1lAP9091ebOLZl8Xub2jpqoK0Svz+07jQVd88SEo0ehNeRFPMnd19boPl/CRdYISQmSbl5/pi7P1afeOJ2k+vvT+6+oUinDxLuMAwgnLC1lOvdfUF+Q3d/nXCHAD68jZ0Vv5919/8V6HcdIRFNW8bdp+Y3dPeFhLsN8OFpO49wwS1baL8f+98A3Bn/FjtuFRXXiYvj31uT7eK5x/OEu3IX1mNwVxTZj99EuOgDHz62Jf2iSPM/EM4ZO1Kz32/q85HaNNm5Si2ec/fnCjQfSzinh7AOFTofzB1/NjtORrlzn7+7+8pCI3b3lwnJbWdqzvfzbcn6W1/L4ndDztUL6tjQHty94MN0ORberTSjSOvy+P2Su68oMvylFirrODzRfc5owhWwZNKV+z3a3d8xs9nAnmY2yN3nx3iGx24ak5R1jZ98lcC5Xria3FzlFv2AeUWeQ4SwIkEobgOAu681s6cJz5U9bmbXEZKVV919fYFh5I/zi2ZWW3XwffLHmWdcLf3i7uPM7B3Cidj5hKsqwKZakM6Of2/9cN9goUKIrxJuY+8S48k/4e1OmHeLaoulgXLr1AZqkvxCniQUbctfB3OW1JJszk387ke4ilLIM7WsE6+y+Q64RZZrLXYgFF2FcKu+mCcJxZC2MrMd3D23L8jNx9G19Ftbu+aQm6cdgGm1LIvcetmDcDL8oZMuYHyRfpPrQv/E7x0JxbCg9vVwDDUXOBqrWIxQE2f/WrqpTa4ij/s8VPCxibtPN7MXCHfPL6L4CUJjbPG+Nl7Y2y7+bfAFi3p4jFBs61Qz+y9hnzjW3efW3lutaj0ON2hAYX/9WUJ1+XtRU0Q9X20JdcF1y903mNlCQsK8ad0ys47UVGLUkHm+R2I4N5tZoRPonJ7xe1ix+JpBXdtYcj+as3/8rms/kLYt2X/ktsu9zOxDF/ISusXvYset2hxDOMdLViyUdAvhGdCLzew38QJ/IRsId6s+xN2rLVTkcT7FzwneK3ZO4O4rzOxlwvxI9t9U5yO1aYpzlfqYUKihu280s0WEfcBLRfpNvjtvU8UjFt5PmUuUfmm1vw84t+4VW4ea8/j3CKHI+y1mdjjhkYCX4sXmBmlwUtZIW8fvglf6E3JXJLbOa55LqnY3s23i1fpcVjw60c2Fsfld1CRt77j7e1sUdXCLu18EYKEmnJ0JzxhcDFxnZm+5++S8fnIH+s6Eu4h16Zb3//OEhbsv8JP4WW9mLxGucPwreYXdQg1DuTuVfag5Qa9NsasjhU48891GSMYuIJGUEcr89ickrHfn92RmhxJOVPomGq+i5hZwGTXT0YOmTcpy69SieIegmGLrYE7BKzZR8upt0VqfCGWwcwn2BkLRvzcJy/Zuj+9rSmG5FpKcD7Vtv8ka4Lam5gJNfbb9WmuPawa57bOM+m2fUGS+1nIFb0MiQUiuCwMTv2s7Oa9rX9kQ9Vlna1tfCzKzPoQTeihyEYZwYnQo8Fkz+1UtJ0ZbqjH72uQdrFlNFlHk7s+Z2fcJyejH4wczm0O4wHFroTuYdUjuE/tT+zpUlJltHWNI3sVaF4efu2o/kHDhokctg2rourVV4n9D5vl2id8Di3a1uZZ8L9uWbGO56Wip/cCW2pJpyy2vbnz4/KaQLVlWuTunD3jh12XcTSiCvhPwMYonP3WdE+SWQbFzgrqWUaH+m+p8pDZNca7SVONp6HFyG2pqia9vwlRsHWqW41/0PcL6dRQhL/g2sNHMJhJuplxfrIRTvrSqxK/vAXmz7mLSk8uoc8lWrnav3EFtdF77o/OaN5q7V7r7G+7+WcIzBVsDGTPL3+nkrrA/7u5Wn0/eeGYTrqJ9nFA88mXCMjucUNTrbTM7usD4AM6p5zgvKjKZH7qNXkCuCOOO8epATu7K/iPuvjTZQ7xCeichIZtISOB6u3svdx/k4cWWhyR7qUccW2KL1sEmlnx59BB339Pdz3L3233zF+i29HKtS2PmXW39Nue8LiQ3X6fWd/t095lNNO7kel3bdDfX+t+UPk3NCddT9uHqk52aYq07ULx4SWM0al+b0CzroLv/gTDt3yIUrVtAuPN0ETDaQtXnDTkhSF4A3K8Rof2ZkJAtJpzcbuvu3dx9oNe8aDiXLKS9L4bN94Xb1HNZ39zE8TaXlt7/tYTc8rqunstqeEMGbqE699Pj3/OL7HuWUVPKqbZ3yTZ2/jem/1I4HylFye39kHquQ5e3dJDuvszdjyZUOvR7QomkDYSi0z8FpptZvYqetnRSlrtKv30d3eWKSSws0G5M/D7azHaJ3b7hoVwo1CRnyaQt2bypfZtwh2M3Qi1DSbnb9fll6evN3avdfZS7f9PdywlXC84DZhNu894Ri5/kyp4vb+w4GxDbu9QUh7sANu0kc89E3Vagt0MJt5c3Aie7+38L3GVoymcv8uXWwYFW5N0fUW3rYItq6eVaRPIOW23bb7KIU3LeLSjQvrZ+W0Ju+9zRzGq7C9AckvNzu6Jd1d6uVNR2olNIc7zMtTH72nmJ38MbH0ph7j7X3a9y99PdfRChWM4/Y+sK4CsNGNwz1DxzfHptHRYTk8DcHc7/c/ebPO9Z0fgMZVO8QyrfYsKznNCweZ6ML619YVPL7Sdr29ZrfddhCWv0OVAdzqPwYyXFnGlmvYu0q+ucILcMipU2qev4Vaj/Vnc+0sKSxRpLfnt39+fc/fvufgThxsMnCZUBdgNutDre4wotn5TlXuxcHou8fIiZ9SXx7FmBTpJJV37RRTwUUXwb+IiZHUfNjm7MFkddi3gn6Kr49zLb/EV8uYRlsCVentzI8a109zuoOREaxOYra26cZ5lZSyzfXHGlT8WdyqcIzyMsIhRRzJc7oV/oxW/nHlvL+HInIlt61Ta3DnYkvHOrmFwMxcpAt7SWXq75ZlBTocAxtXSXm2+LveZ5MqiZ77XdJcmvoKe55eZpZ7bwxLYR3qXm4eCRtXRXW7vGbguNFitLyVWkcCDQq5ZPruKmM+N+vilt8b42lkjIFQ86pYHj3eJl4O6vu/sXqIn9uAb0O5+a52c+HS9Q1ovVlBMaSM0JbbHKTY6gYSe99eKhcofcMygNmedvECrwgNorXGhNXonfI2vpprZ2pSy3bh8Sn91sarnzoKupfd/Th5DQdKd4ZRkdqXkGbjNxm/lY/Jst1A2wvRV5AXJ8Niq3n0z2X8rnI6kfX+L59Zvxb1rbe+7uZIPmg7uvc/eHqLnw1ZUi61dSS5/c3Ue4pdcV+H6Rbn5IOKmvovBDm7kEbAdqatzJL5qYS9x+Gb+nuvs8ms9fCM9E9SZU557zMDVXYa+uq3YbM0s+CN25tm4JNfnkJIukXR+/dwG+W8f4etRjPHW5h/DsWD/C+zZyRRfvyiuCl5O74zOo0FWDWDvbN2oZX+6A3HdLgnX3SdRs5D8uVJuemZ0IHBz/3pnfPiUtvVw34+5OzfOBXzKzD93NNLPtCO90gQ/Pt1y/R5jZyAL9dqOO6WoGWWpORn9lZrU+o5LcPhsrzs9cLYVfzruYkxvfztTUeFlIo7aFJpI7KZrq7ll3X1XsQ3gYejlh///pJo5ji/e10Y3x+/Nm1pDigHUugzqugEPNvryhRYt/TDjudAPuN7Na76aYWT8zu4+aZ1Jz7z+C8NxyfvcdCe8ObS7/it8nxn1unWIyl1tWF9aVgDflNtuMMvH7Y3mPAQCb1p9L85u3ErcR1u8y4JpCx9scM+vQkIs1ZrY/NTWC3lnbvsdDxXK5/W1td/Z/VOSi54WE91ZCgefkE35SpPl3CNvphkQcpX4+UgrHF6g59znGzGpNzJppe691PphZxzoulBc7Vy+oRZOyeGfk6vj3MjP7eW4jNLO+ZvZLak7MriyUSLn7dGquah5MmMj8BzdHJ9pD8xVdzMW0hJpnJr5hZgNi83WEGgad8GzYODM7IXnCbGY7mNmXzGxC7DbnMDObZGbfMrPdcwvdgsMI74yCMC9eT8TyH8JLhgF+a2bXJq+imllnMzvYzH5HeMB6Sx4cTU77Mmpqz/oB4Xk3KFx0EcLLQlcTrjrck4vNzMrM7ARq3nlRzBvxu7eZ1XbCWpvcBYGPEp4F3CHG0MnMzqNmx/c8H37NQSpaerkW8WvC3Z3+hGeHDkuM/3BChQF9CXfU8l95cR81V4TvM7MzcwcgM9udUHV2U8Xc3cwG1PHpHBOjLxMuKgwFxptZRfKE3swGm9n5ZvYk8Lsmii/n14Qd9iDgiVwyELfxownVBNdWe1NuW9gzuSxaSjxZPC/+rbPqfg8Psj8U/zZpEcZG7msB/kh4rUoX4Gkz+4LFYk5xv7CLmf3UzPJPjnPL4KNmtluR8B40sxvN7BPJk04z629mP6bmznO9qoVPTPNbhItg64E9gYlm9n1LvKIk7lf3M7NfEO7OnpHofxU1dzKuNLOjE8eZvWI85YT9dXO4jXA8MMI+4bu5Y2eMe3g8/uVvd78kvJC2I6Fm4m9b4oKKmfUxs4+b2S0UqU2vxNxNeEbQCMn1JxP7xl0JFzOas0h/s4nFYS+Lf08CnjSzwxPTZ2a2m5l9m7AtndyAweeSq9nuXp/aNXP7qAPj+p1vDeFOxh0WX91h4VU1X6DmfOs/7l6wlkHCBacLzezqxHrcy8x+SE2ydk2BEkKlej6S6vEl4Tpqak68zcyuMLNNj1CYWXczG2lmfyPsF5pabj6cZ4Uv9g0hPDP247iv3VSBooVXHtwe/64Gnq1zbF6/F/NdTpEX1BXodniuWwq/ZLEzYSeU62Yj4SRuY6LZHeS9QC5vGLcmup1QoP2gRHsHzqrPdBYZ10xqefleorttqHkB3R/y2p0XF0gunipC8b51eXH+KNHPyLx262M/VYlmyyn8ssnuhA052f+qAvPZyXuhIjVJ0eUNmEen5A1zah3dfzmv+5WJebcwb3jDC/T/VKL9iriMZpJ48SR1vPiQ8MB9dWI4Swkn57n/kyjwUlbq8fJqankZLvV4eXQtw2325UrdL+k9kpCYJce/is3n44fWydjvjoRnIXPdrksMqxI4dUvnTRy+N+BzWqK/4wjbVq7dhvh/dV4/NzRkXhWI60PTRCjSl9ymVyTGOyexvq0r0G9HYGqi3yXUbAsVie7qXPbU80Whef2ckxj3Xluwr9i3ru1lC+Z1g/e1eevn5EQ3uWPThkSzq/L66Ud4LiTXfmFiGRySN/+T++3lec3upcBLa+s5Tw8nJJTJ4VUSnttK7heqyTu2EopUJbffddTcQasiJH0z4/+L8sZb53KL3RXsP7YbQDhRSca4lHC8yzV7sEB/OxAqikpO89IC87XQS3Fri2dL29W536KW7ZDwTPq8vOWwLPH75ES7Q7ZkPSkSb9F9Qn2mO9HNzbGbm4u0/y6bb0eVhO0yuZydAi/vLTK8rnF5O/DHevZTRnhGyYE/J5pfFJvNJLyoOHdesCQvvonAVgWGe3lsP4Zw4S6371icN81PkvcC9sQwtvR8ZGSumwLtNk1XLfNkeGIcw/PaNeXxpT7rUNFtiLCfeDpvXVke51NyvlU19fpLzcvdPa4Pc+IwnyswDz0u88V5y6+SvBd/F/u0+LMp7r7e3c8mvEDvvzH4XvH7v4Qa6T7thYu+5TyT+P2hWhU9lLfP3RLObSzNysMVoVyxiq9ZoniXu/+bUF3mFYQiU6sIdxTWETb0vxHKDCevCL5EKLp0LaHWxUWEYie5fn4P7O6FXza5xt3PJTy/cxvhCmkHwntbFhDm2feAnb2e1XTW4b9s/gBqsWqxc/FdR7hqNoYwLzoSqov9K6EYzetFew4qCLWGvUWownRY/PStb8Du/mfCVeDbgfcICc9a4EVC5S0HeePeI9TkUliuhWIYSziB+BMwJY7f4u8/UmSdjP2+SyhuciXhGTUjrM8Z4DAP5a9bnLs/Sdg+f0C4cr+csC5VE/Yj/yIkjF9vhnFnCOvhvYRtqAvhxOFqQq16ueK+ywr0u4Fwl+WfhINED2q2hZ753TeD3JXqKe7+Rq1d1hhFzTQ1tIKQOm3hvjbX77uEef5Vwr5pKWE+zie8XPQnhP1Osp+lhGdN7iLsw/pQswxyz2J9nXA1/DFC8mSEokxzCXcOz/RQ62pt79yqbZrHEbbJc4F/E56pXkc4ri4hrNO/Imybmx1bPbx09SDCXYRFhO15Zfx/mLsXK/HQJNx9EeHE8nxqjiM9CPP+ZcId9x8W6G8GYbv5DOFO0rzYX2fCvuUBwt3YQ5sz/qbi4aW2+xAehZhJzb7xHkJtxMn3Sy5r4fAazUPto7sRtp9JhGnrS9g+XyKczxxGuGhQH2dSc7yv8y59jGEjNUUHz7cCRfzd/RrCC6wfJ+z/qwmJyU+BQ919cR3j+D7hYtU4wra0nrDf+SbwcQ939Av1V3LnIyVwfEnGsoiw3/4k4XzhPcKxshthv/tf4P9ohoqa3P12wsWp5wh3U7clzINc5SvvE84P/kxYXvMI82cD4fzhGsJFywz1YDHTExGREmNmvyKclI5299oqWBGRNspCpWVPEK6496rjorU0gJldBNwEzPIGVskf+78c+BnhhfAjmzI2aX/Sek+ZiIjUIj4n8/n49/E0YxGRdJiZUfPc0dNKyETaLiVlIiIpMbNvmNllZrZT7gFhM+tiocatZwmVnyykpmi0iLQxZnaUmV1lZuUWaqLNVYJxAKEirWMIj2L8Ps04RaR5day7ExERaSY7Ep43+A2w0cyWE16tkds3Lwc+VdezDCLSqvUh7Ae+CWBmSwnPy+SeS3Tg0vhMr4i0UUrKRETScwuhpq6PAYOBrQgPeM8gVIpxdXNV2iIiJeNFQkUyxxAu1OSq+H+XUK3/39y92EuLRaSNUEUfIiIiIiIiKdIzZSIiIiIiIilSUiYiIiIiIpIiJWUiIiIiIiIpUlImIiIiIiKSIiVlIiIiIiIiKVJSJiIiIiIikiIlZSIiIiIiIilSUiYiIiIiIpIiJWUiIiIiIiIpUlImIiIiIiKSIiVlIiIiIiIiKVJSJiIiIiIikiIlZSIiIiIiIilSUiYiIiIiIpIiJWUiIiIiIiIpUlImIiIiIiKSIiVlIiIiIiIiKVJSJiIiIiIikiIlZSIiIiIiIilSUiYiIiIiIpIiJWUiIiIiIiIpUlImIiIiIiKSIiVlIiIiIiIiKVJSJiIiIiIikiIlZSIiIiIiIilSUiYiIiIiIpIiJWUiIiIiIiIpUlImIiIiIiKSIiVlIiIiIiIiKVJSJiIiIiIikiIlZSIiIiIiIilSUiYiIiIiIpIiJWUiIiIiIiIpUlImIiIiIiKSIiVlIiIiIiIiKVJSJiIiIiIikiIlZSIiIiIiIilSUiYiIiIiIpIiJWUiIiIiIiIpUlImIiIiIiKSIiVlIiIiIiIiKeqYdgClaMCAAT58+PC0wxARkWbw8ssvL3L3gWnHISIikqOkrIDhw4eTzWbTDkNERJqBmc1KOwYREZEkFV8UERERERFJkZIyERERERGRFCkpExERERERSZGeKaunqqoq5syZw7p169IOpV66du3KkCFD6NSpU9qhiIiIiIhILZSU1dOcOXPo1asXw4cPx8zSDqdW7s7ixYuZM2cOO+ywQ9rhiIiIiIhILVR8sZ7WrVvHVlttVfIJGYCZsdVWW7Wau3oiIiIiIu2ZkrIGaA0JWU5rilVEREREpD1TUtZI8+fP59Of/jQ77rgjBxxwAIceeigPPPBAo4c7ZswYTj755CaIUERERERESpmeKWsEd+e0007jwgsv5I477gBg1qxZPPTQQy0ey4YNG+jYUYtT0pXJZNIOoVWpqKhIOwQREREpAbpT1gijR4+mc+fOfPnLX97UbNiwYXz9619n48aNfPe73+XAAw9kn3324R//+AcQ7oCNHDmSiooKdtttN8477zzcHYDHH3+c3XbbjSOOOIL7779/0zBXr17NZz/7WQ488ED2228//vOf/wBw8803c9ZZZ3HKKadw/PHHt+CUi4iIiIhIU9GtlUaYPHky+++/f8F2//rXv+jTpw8vvfQSlZWVHH744ZsSp1dffZXJkyez3XbbcfjhhzNu3DjKy8v5whe+wOjRo9lpp504++yzNw3rV7/6FUcffTQ33ngjy5Yt46CDDuLYY48F4IUXXmDSpEn079+/+SdYRERERESanJKyJvS1r32N5557js6dOzNs2DAmTZq0qTjX8uXLmT59Op07d+aggw5iyJAhAIwYMYKZM2fSs2dPdthhB3beeWcAzj//fK6//noAnnjiCR566CH++Mc/AqEmyNmzZwNw3HHHKSETEREREWnFlJQ1wp577sl999236f8111zDokWLKC8vZ+jQofz1r3/lhBNO2KyfMWPG0KVLl03/y8rK2LBhA1C8xkR357777mPXXXfdrPn48ePp0aNHU02OiIiIiIikQM+UNcLRRx/NunXruPbaazc1W7NmDQAnnHAC1157LVVVVQC89dZbrF69uuiwdtttN2bMmME777wDwJ133rmp3QknnMBf//rXTc+evfrqq00+LSIiIiIikg7dKWsEM+PBBx/kW9/6Fr///e8ZOHAgPXr04He/+x1nnXUWM2fOZP/998fdGThwIA8++GDRYXXt2pXrr7+ek046iQEDBnDEEUfwxhtvAPCTn/yESy65hH322Qd3Z/jw4TzyyCMtNJUiIiLS2mUymd5Af6ALUBY/ABuBDcB6YFlFRcWyVAIUaecsd/dFapSXl3s2m92s2ZQpU9h9991TimjLtMaYpXVTlfgNoyrx02FmL7t7edpxiDSVTCYzBNgtfnYFtgUGAFslvjvVc3BVwKL4WRg/84HpwBRgSkVFxdymjF9EdKdMREREpFXIZDIdgf2BjwL7Abu5+65m1rMJR9OJkNRtW0scy4GphCTtdWAc8HJFRcWGJoxDpF1RUiYiIiJSgjKZTA/gUOAId/8ocIiZdU92U6ySsGbWBzg4fnJWZzKZF4Bn42d8RUXFujSCE2mNlJSJiIiIlIhMJrMD8El3PxX4qJl1hNSSr4boARwbPwCVmUzmOeA/wIMVFRXvpRaZSCugpExEREQkRZlMZifgU9XV1ed06NBhb2gVSVhdugDHxM9fMpnMy8A9wN0VFRWzUo1MpAQpKRMRERFpYZlMpjvw6Y0bN36trKxsBECHDm36TUUHxM/vYjHHfwJ3VlRUrE03LJHSoKRMREREpIVkMpndqqqqvtGhQ4cLysrKepaVldXdU9tzaPz8IZPJ3AxcW1FR8Xa6IYmkS0lZK1JWVsbee++96f+DDz7I8OHD0wtIRERE6pTJZMrc/fQNGzZ8q1OnTod16lTf2unbvP7At4FvZTKZJ4C/A49UVFRUpxuWSMtTUraFmvp9TPV5X1G3bt2YOHFik45XREREmkcmk+lQWVn5mQ4dOvy8U6dOQ5WMFWXACfEzLZPJXA7co+RM2pM2VXjZzG40swVm9kaiWX8ze9LMpsfvfmnGKCIiIm1bJpPpcOutt15YWVk5s0uXLjd16tRpaNoxtSK7AncCr2UymTMzmUyrr/FEpD7aVFIG3Ax8PK/ZZcDT7r4z8HT83yqtXbuWESNGMGLECE4//fS0wxEREZGETCZjt91223mVlZXvdu/e/eYuXbpsn3ZMrdheQAZ4JZPJnJJ2MCLNrU0VX3T3Z81seF7jTwIj4+9bgDHA91suqqaj4osiIiKl6Z///OfBXbp0ualbt267px1LGzMCeCiTyYwDvlpRUTEp5XhEmkWbSsqKGOTu8wDcfZ6ZbZ12QCIiItI2XHvttX07dOjwz379+p3RoUMHFbVrPocDL2cymb8AP6uoqFiVdkAiTak9JGX1YmZfBL4IMHSoin6LSPO7K6v3p9bXOeXD0g5B5EOuueaab/Tt2/dXXbp06Zl2LO1ER0JtjZ/KZDLfrKiouD/tgESaSkknZWZ2FLDR3Z9txGDmm9m28S7ZtsCCQh25+/XA9QDl5eXeiPGJiIhIG3bVVVft3bt377sGDRq0R9qxtFNDgPsymcyjwNcqKip0hUtavZJKysxsLPBDdx9nZt8nXA3ZYGbXuPuvt3CwDwEXAr+N3/9piljrU4V9U1u1SnfqRURE0lJeXt7h3HPP/eXgwYO/27FjR9Vvn76TgI9mMpn/q6iouC3tYEQao9RqX9wLeDH+/gKhgo5DgC/Xp2czuxN4AdjVzOaY2ecIydhxZjYdOC7+FxEREam37373u8M/97nPvTps2LAfKiErKb2BWzOZzJ2ZTKZP2sGIbKmSulNGSBLdzD4CmLtPAajvu8Xc/dwirY5povhERESkHSkvL7cTTzzxM/vss8/funXrpmfHStc5wEGZTObsioqKbNrBiDRUqd0pew74G/BH4AGAmKAtSjMoERERaX/Ky8u7nHXWWXfstddeNykhaxV2BMZlMplvph2ISEOVWlJ2EbAMmAT8LDbbDbg6pXhERESkHTr11FOHXXDBBRM+8pGPnKOq7luVzsBVmUzmpkwmo2Km0mqUWlKGu//Q3X/m7qvj/0fd/aqUwxIREZF24vzzzz/8pJNOenHw4MH7pB2LbLGLgFGZTKZej8CIpK3UkrL3zOw/ZnammXVOOxgRERFpP8rLy+1LX/rShccdd9zjW2211TZpxyONdhTwQiaT2THtQETqUmpJ2TDgaeAy4AMzu97Mjkg5ppJhZlxwwQWb/m/YsIGBAwdy8sknpxiViIhI61deXt7xox/96BVHHnnk9T169NDzY23HrsD4TCZzWNqBiNSmpGpfdPeFwF+Av5jZrsAFwG1m5sDtwL/cvSReEHhXtmnDOKd8WJ3d9OjRgzfeeIO1a9fSrVs3nnzySQYPHtykcYiIiLQ35eXl3UeOHPn3gw466DN6fqxNGgCMzmQy51ZUVDyQdjAihZTanbKkbeKnN/AOMBh41cwuSzWqlH3iE5/g0UcfBeDOO+/k3HOLvQVARERE6lJeXt7/iCOOuE0JWZvXBbg7k8mcnnYgIoWUVFJmZnua2W/MbDZwLTAd2Mfdj3P3zwH7Az9MNciUnXPOOdx1112sW7eOSZMmcfDBB6cdkoiISKtUXl7e//DDD7/5sMMOO10JWbvQiZCYnZZ2ICL5SiopA54FegEV7r6Hu//O3d/PtXT3mcBVKcVWEvbZZx9mzpzJnXfeyYknnph2OCIiIk3KzNzM/pT4f6mZXd7U4ykvL9/qiCOOuOXwww8/WQlZu9IJuEeJmZSaUkvKtnX3/3P3CcU6cPeftmRApejUU0/l0ksvVdFFERFpiyqBM8xsQHONoLy8fMARRxxx82GHHXaSErJ2KZeYfTLtQERySiopc/f1ZjbIzE4xs4vN7LO5T9qxlZLPfvaz/PSnP2XvvfdOOxQREZGmtgG4HvhWfgszG2ZmT5vZpPg9tKEDLy8vH1BeXv4PJWTtXifg3kwmc3zagYhAiSVlZnYaoVKPXwD/AL4evy+opbd2Z8iQIXzzm99MOwwREZHmcg1wnpn1yWv+N+BWd98H+DehxuZ6Ky8v77/TTjtdOXLkyFOVkAk1idmeaQciUlJV4gNXABe7+71mttTd9zOzi4GS21jqU4V9U1u1atWHmo0cOZKRI0e2eCwiIiLNxd1XmNmtwDeAtYlWhwJnxN+3Ab+v7zDLy8u7DRo06Mcnn3zymR07diy18x9JT2/g0Uwmc3BFRcX8tIOR9quk7pQBQ9393rxmtwCfSSMYERERSc1VwOeAHrV04/UZUHl5eVnPnj2/esYZZ1zYtWvX7k0RnLQpw4D/ZDKZrmkHIu1XqSVlC8xsUPw908wOBT4ClKUYk4iIiLQwd18C3ENIzHKeB86Jv88DnqtrOOXl5VZWVnb2mWeeeUmfPn36N32k0kYcDNySyWRUrFVSUWpJ2Q3AEfH3n4FngNeAv6cWkYiIiKTlT0CyFsZvABeb2STC8+b1ecD6qJNPPvnH22677ZDmCFDalE8R6jUQaXElVaba3X+X+H2rmY0Berj7lPSiquHumLWOCyju9SrRISIiUlLcvWfi93yge+L/TODo+g6rvLx87/333/8nu+++++5NGqS0ZT/KZDLPV1RU/DftQKR9KbU7ZZtx99mlkpB17dqVxYsXt4pkx91ZvHgxXbuqaLSIiLRP5eXlgwYNGvSDo4466tC0Y5FWxYBbM5nM4LQDkfYl9TtlZvYe9XhQ190b/C6SpjRkyBDmzJnDwoUL0wyj3rp27cqQISqpISIi7U95eXnnsrKyr5522mlHd+rUqUva8UirMwD4dyaTObqioqI67WCkfUg9KQPOT/w+ELiQ8N6RWYTacP4PuDWFuDbTqVMndthhh7TDEBERkbqd+fGPf/y0fv36Daq7U5GCjgS+B/w27UCkfUi9+KK7j819gIuAj7v7De7+hLvfAJwEXJxqkCIiItIqlJeX773rrrteuNdee+2ddizS6v0ik8nsn3YQ0j6knpTl2Q7If0PyKkDlekVERKRW5eXlfTt16vSV448//iBrLTVzSSnrBNyWyWQ6pR2ItH2llpQ9BDxkZseZ2e5mdjzwQGwuIiIiUlB5ebkBFx5//PEH9ejRo1/a8UibsQfwnbSDkLav1JKyLwMvANcBrwDXAuNjcxEREZFiRmy77bZH7rnnniPSDkTanJ9kMplhaQchbVspVPSxibuvAy6LHxEREZE6lZeXdwcuOvHEE/fv0KFDWdrxSJvTnVAJ3SfTDkTarlK7UyYiIiLSUKcecsgh+wwcOFB3M6S5nJrJZE5NOwhpu5SUiYiISKtVXl4+vHv37qcceuihB6Ydi7R5f8lkMt3TDkLaJiVlIiIi0iqVl5eXARcec8wxu3Tp0qVH2vFImzcMuDTtIKRtUlImIiIirdWhvXv33n3XXXfdJ+1ApN34ViaT6Zt2ENL2lFRFHwCxGvwRQM9kc3f/aSOH+y3g84ADrwMXx4pFREREpJUpLy/vApx17LHH7tixY8fOaccj7UZfQhX5P0k5DmljSupOmZn9DbgdOADYPvEZ0sjhDga+AZS7+15AGXBO46IVERGRFB3Wr1+/QR/5yEf2SzsQaXe+mclk+qcdhLQtpXan7FxghLu/1wzD7gh0M7MqQtWmc5thHCIiItLMysvLuwFnHnvssTuXlZWV2rmMtH29gO8CP0g7EGk7SupOGbAYWNbUA3X394E/ArOBecByd3+iqccjIiIiLeKIAQMGDNxhhx32TTsQabf+L5PJDEw7CGk7Si0p+xPwbzM71Mx2TH4aM1Az60d44d8OwHZADzM7P6+bL5pZ1syyCxcubMzoREREpJmUl5f3AM4YOXLkDnpRtKSoJ/CttIOQtqPUbvlfG79PzmvuhOfAttSxwAx3XwhgZvcDhxGeXwsjcL8euB6gvLzcGzEuERERaT4f69KlS/dhw4bt3dIjfuSRR3j66acxM4YOHcpXv/pVKisr+fOf/8zChQsZOHAg3/72t+nZsydTp07lhhtuoFOnTnzzm99k2223ZfXq1fz5z3/mRz/6EWbW0uFL0/t8JpO5vKKiYn3agUjrV1J3yty9Q5FPY6+EzQYOMbPuFvaCxwBTGh+xiIiItJTy8vJOwImHH3741p06derakuNevHgxjz32GL/97W+58sorqa6uZty4cTz44IPsvffe/PWvf2XvvffmwQcfBODhhx/m0ksv5dxzz+WJJ8ITE5lMhtNPP10JWdsxEDgr7SCkbSippKy5uPt4IAO8QqgOvwPxrpiIiIi0GnsBPXffffdUalysrq5m/fr1bNy4kcrKSvr3789LL73EyJEjARg5ciQTJkwAoKysjPXr17N+/XrKysr44IMPWLJkCXvuuWcaoUvz+WraAUjbkHrxRTN73N0/Hn//j1BU8UPc/WONGY+7/wz4WWOGISIiIukoLy834BO77757z169eg1q6fFvtdVWnHLKKXzlK1+hc+fO7Lvvvuy7774sX76cfv36AdCvXz9WrFgBwOmnn84//vEPOnfuzNe//nVuvfVWzjlHb+Npgw7LZDL7VlRUvJZ2INK6pZ6UAbcmfv8ztShERESklA0Bdj7wwAN3S2Pkq1at4qWXXuKaa66hR48eXHnllTz77LNFu99hhx349a9/DcCbb75Jv379cHeuvPJKOnbsyGc+8xn69u3bQtFLM/sq8KW0g5DWLfXii+5+R+L3LcU+acYoIiIiqftY7969O2yzzTa7pzHy119/na233po+ffrQsWNHDj74YKZNm0afPn1YunQpAEuXLqV3796b9efu3HfffVRUVHDvvfdy9tln89GPfpTHHnssjcmQ5nFeJpPpXXdnIsWlnpSJiIiI1CZWgz/yoIMOGphWNfgDBgxg+vTpVFZW4u68/vrrDBkyhPLycsaMGQPAmDFjOPDAAzfrb8yYMey///707NmTyspKzIwOHTqwfr0q7GtDegCnpx2EtG6lUHxRREREpDb7AB133HHHPdIKYOedd+aQQw7he9/7HmVlZQwfPpxjjz2WdevWceWVVzJ69GgGDBjAt7/97U39VFZWMnbsWH784x8DcMopp/DHP/6Rjh07cskll6Q0JdJMPgWoZJdsMXPXK7nylZeXezabTTsMkVYnk8mkHUKrsmH4gXV3JACcUz6syYZlZi+7e3mTDVCaXXl5+WVbbbXVRz7/+c9/zlSfvJSmKmCbioqKJWkHIq2Tii+KiIhIySovL+8H7LL//vtvp4RMSlgn4NS0g5DWK/Xii2b2i/p05+4/be5YREREpOTsBTBs2LBUal0UaYBPAjenHYS0TqVwp2z7xGdn4DLgGGAn4Oj4f+fUohMREZE0Hd6zZ8/K/v37D087EJE6HJ/JZLqlHYS0TqnfKXP3i3O/zewu4Fx3vy/R7AzgrDRiExERkfSUl5f3BnbZe++9e3To0KEULiSL1KY74YbCo2kHIq1Pqe3gPgE8mNfsP8CJLR+KiIiIpGxXgO23377panoRaV4fSzsAaZ1KLSl7G/haXrOvAu+kEIuIiIika2+gcuDAgcPTDkSknpSUyRYptaTs88C3zWyOmY03s/eB78TmIiIi0k6Ul5cbsE+vXr3W9ezZc+u04xGppwMymUz3tIOQ1if1Z8qS3P1VM9sZOATYDpgHvODuVelGJiIiIi1sK6D3Hnvs0VVV4Usr0gk4GHgm7UCkdSm1O2UADhg1sent1iIiIu3PcIDtt99+eLphiDSYijBKg5XUnTIz2w14GOgKzCFUk7/OzE5x9ympBiciIiItaQ+gauDAgUPTDkSkgT6adgDS+pTanbK/A9cDQ939UHcfAlwXm4uIiEg7kHuezMyW9ezZc1Da8Yg00IFpByCtT6klZSOAK909WWTxqthcRERE2oc+QP/Bgwd3LSsrK6lSPSL10DuTyWyXdhDSupRaUjYXODKv2UdjcxEREWkfBgK+3XbbDUw7EJEttHvaAUjrUmpXn34IPGRmjwCzgGHAScD5qUYlIiIiLWkgYAMHDlRSJq3VbsDTaQchrUdJ3Slz94eA/YE3gF7x+wB3/0+qgYmIiEhLGgpU9evXT0mZtFa6UyYNUmp3ynD3t4Ar0o5DREREUrMDsKZXr14D0g5EZAspKZMGST0pM7Pr3f2L8fdtFHkvmbt/pkUDExERkRYXa17cHljao0cPJWXSWu2WdgDSupRC8cUZid9vA+8U+YiIiEjb1xPo2q1bNzp27Ngl7WBEttB2mUwm9Zsf0nqkvrK4+28AzKwMeA+4w93XpRuViIiIpGQroLpv377d0w5EpJEGAB+kHYS0DqVwpwwAd99IeEeZEjIREZH2qwdgffv27ZF2ICKNpOK3Um8lk5RFD5vZKWkHISIiIqnpAdCzZ08lZdLaKSmTeku9+GKerkDGzF4gFGXcVOmHKvoQERFpF7oDHXr06KHii9LaKSmTeiu1pOyN+BEREZH2qT+woXv37rpTJq2dkjKpt5JKytz952nHICIiIqnqB1R17dq1W9qBiDSSkjKpt1J7pgwzO87M/mVmD8f/5WZ2dNpxiYiISIvoB6zv2LFjSV04FtkCKoIr9VZSSZmZfR24FpgOfCw2Xgtc0QTD7mtmGTObamZTzOzQxg5TREREmlxPYIOZldQ5isgW0IUFqbdS2+FdAhzr7r8FqmOzqcCuTTDsq4HH3X03YF9gShMMU0RERJpWR6C6Q4cOZWkHItJIWoel3kotg+9FqHURampe7ASsb8xAzaw34c7bRQDuvr6xwxQREZFmUUai9mVpPq+99tqoqqqqqrTjaGvWr1/fd+edd3584MCBo9OORVqPUkvKngUuA36VaPYN4JlGDndHYCFwk5ntC7wMfNPdV+c6MLMvAl8EGDp0aCNHJyIiIluojFBaRolZM1u8eDETJkyYnXYcbdCyZ5999u5sNvt22oFI61FqSdnXCS+Q/gLQy8ymASuAxr5QuiOwP/B1dx9vZlcTkr+f5Dpw9+uB6wHKy8t1IBAREUmHAbi7jsXNbK+99uo7YcKEV9OOow2qAj5IOwhpXUoqKXP3eWZ2IHAgMIxQlHGCu1fX3med5gBz3H18/J8hJGUiIiJSWqoBq66ubuyxX+qw9dZb73bZZZfdUlFRoSKMIikrtYo+IMTUKf4uI14xawx3/wB4z8xyFYYcA7zZ2OGKiIhIk9sIUFlZuTbtQNqBPoBeOyRSAkrqTpmZ7QM8CHQB3geGAOvM7HR3f62Rg/868G8z6wy8C1zcyOGJiIhI09sAdFq3bp2SspZxOjAqv+FNryw4Gejf8uG0G3Mu3n9rVQQim5RUUgbcCFwDXOnubmYGfCs2P6AxA3b3iUB5oyMUERGR5rQC6L1mzZo1aQfSTnwyk8l8taKiohpgxJEndAM6fu33/zqjY6fOuoDdfB4BlJTJJqVWfHEX4Krcw73x+2pg51SjEhERkZayEui0Zs0a3SlrGdsAhwKMOPKETsCvgaufydwyONWo2r6NaQcgpaXUkrLHgFPzmp0CPJpCLCIiItLylgEdV61apTtlLed0gIljR1UR5v+yt159cfyGqvWVqUbVtm1IOwApLaWWlJUBd5nZ82Z2t5k9D9wNlJnZrblPyjGKiIhI81kGdFq5cqXulLWc0xO/nwP6bKhaX71o7uy30gqoHdD6LZsptaTsDcJt81GE2hFHxf+TgXcSHxEREWmbVgEsXbp0ddqBtCM7ZjKZfeLvycTzwxlvvjY1vZDavEVpByClpaQq+nD3n6cdg4iIiKRqLeBr166tqqysXNmlS5deaQfUTpwOTALmx0+P18c9Pf3g40/b0KGsrKTOF9uIhWkHIKWl1O6UiYiISPu2EnCAVatW6W5CyzkDYOLYUU4owth/7eqVVUvmv/9uumG1WVq3ZTNKykRERKSULCaen6xcuXJxyrG0J/tkMpkd4+9JgAHMmvb6lPRCatN0p0w2o6RMRERESskyQnXhHZYtW6a7CS0rV+HHHGA50PX1caOneXW1pxhTW6V1WzajpExERERKRjabrQbmAd0XLVqkE9eWlasavxoYBwxYvnjB2mWL5s9KN6w2Seu2bKbkkjIzO97Mvmdmv0h+0o5LREREWsxsoNvcuXNVfLFlHZrJZAbF368RizDOeXuKijA2vTlpByClpaSSMjP7G3A7cACwfeIzJM24REREpEXNArrOnTt32YYNG/QC45bTATgt/p4BrAM6v/7CM1PdVYKxCb1/8f5br0w7CCktJZWUAecCB7j72e5+ceLz2bQDExERkRazIPdj2bJluqPQsnJFGDcALwJbLZwzc8XKZYvnphtWm6I7j/IhpZaULSY84CsiIiLt1/zcjwULFsxOM5B26OhMJtMn/n6F+E7bue9MUyLRdDQv5UNKLSn7E/BvMzvUzHZMftIOTERERFrMAkLRuU6zZs1SJRMtqxNwUvw9nVATZscpLz03Nb2Q2hwlZfIhpZaUXQucTKjx5+3EZ3qaQYmIiEjLiTUwTgH6TJs27f3q6uqNacfUzuSKMK4n3C3rP/utNxatWblcNQY2DSVl8iEllZS5e4cin7K0YxMREZEW9TrQfd26dRtWrFgxL+1g2plPZDKZrvH3eKArwLyZb+tuWdPQfJQPKamkLMfMhsYijNunHYuIiIikYhbgAIsWLdJzZS2rB3B8/D0NqAY6THvlBd3haSR3n3vx/lt/kHYcUnpKKikzs23NbCyhyOL9wDtm9qyZbZdyaCIiItKy3ickA2UzZsx4N+1g2qFcEcY1wGSg3/SJ4+dWrl29It2wWjcz+1/aMUhpKqmkjPBM2WtAP3ffFugHvApcl2pUIiIi0qKy2WwV4SJt70mTJs3U+8pa3CmZTCb3+MjzQE+AD2a9q6J3jfNs2gFIaSq1pOwI4Dvuvhogfn8POCzVqERERCQNWaBXVVXVxoULF76TdjDtzFbAx+LvqYSipPb2pKyKMDaOkjIpqNSSsqXAHnnNdkXvLhMREWmP3gQMYMaMGbpD0/LOAJg4dtRy4B2gz5sTxs6qWl+5Nt2wWid3X0IoCiryIaWWlP0eeMrMfmtmXzGz3wJPxuYiIiLSvnwALAa6v/zyy9M2bty4Ie2A2pnTMpmMxd//A3pXb9zoC+bMnJZmUK2Vmf3v4v239rTjkNJUUkmZu98AnA0MAE6J3+e6+/WpBiYiIiItLpvNOvAc0H/16tXrFy1apCKMLWsIUB5/byq2OGPyqyrCuGXGph2AlK6SSsoA3H20u3/e3U+M36PTjklERERSM5F4vvL222+/kW4o7VKuFsaFhBoxe73+/Oh3N26oWp9uWK3SI2kHIKWrY9oBmNmP3P1X8fcvinXn7j9tuahERESkRMwmPFvebcKECVMPOuigtZ06deqWckztyenAD+Pv54BPrV+3duWieXPeHrT9Dvn1AEgR1Rs3vvm5A7ednnYcUrpK4U7ZkMTv7Wv5iIiISDuTzWarCTXWDaisrNwwa9asiSmH1N7slslkdou/3yBWvDJr6qRmr3hlyQfv8/svns6PzjyCH5/1MZ68IzzNsmr5Uv741bO47LRD+ONXz2L1imUATJ84gZ+ePZJfXHAC89+bAcCalcv509fOxj3dR7k6lJXdnWoAUvJST8rc/SuJ3xcX+6QZo4iIiKTqBUIyYBMmTMimfYLdDp0Rv+cCS4Duk5576q3q6o0bm3OkHco6cva3fs6v7nuOH938GKPvvYn3353GYzf/ld0P/Ci/ffBFdj/wozx2818BGHX7tXztDzdy5td+yDP33gzAQzdcycmf/SZmVsuYWkQm7QCktKWelCWZ2ZIizRe0dCwiIiJSGrLZ7HxC9fhbzZ49e8mSJUveTTumdib3XFlNxSsrllUuXTBvRnOOtO/AQQzbfR8AuvXoybY77MyyBR/w6tjHOfzkswE4/OSzeWXMfwEo69iR9ZVrWb9uDWUdO7LgvZksWziPXQ9I93W31Rs3vnXx/lu/mWoQUvJKKikDOuU3MLNOQFmBbkVERKT9eALoATB58uRsyrG0N+WZTCb3KMlrxPPH996a3GLvjls0dzazp77Bjnvtz4rFC+k7cBAQEreVSxYBcNLF3+SWKy7lyTuu55izP8f9f/81p3/lspYKsSjr0OGetGOQ0lcSSZmZ/c/MngW6mtmzyQ8wDXi+CcZRZmavmplqvhEREWl93gRWAN3Gjx8/rbKycmXaAbUzp8XvWcAqoMvrz4+e6i1QlnTdmtVc893Pce6lv6Rbz15Fuxu66178+Jb/8r3rH2Dh+7PoO3Ab3J1rL/sC1//4qyxfnE7BKzPT82RSp5JIyoB/AjcCG4B/JT7/BL5CTVnmxvgmiXdsiIiISOuRzWY3EO6WDdy4cWP19OnTx6cdUzuTK8JYTbhYvtWS+XNXr1iycE5zjnRDVRXXfPezHPKJMzng6JMA6L3VQJYtnA/AsoXz6dV/wGb9uDuP/PPPnPL5b/PQ9X/ktC99j0NPrOCpu/7ZnKEWi3/CxftvrVc5SJ1KIilz91vc/WZgv/g797nV3Ue5e1Vjhm9mQ4CTCEmeiIiItE4vxO8OTz/99IT169evSTWa9uVjmUxmq/h7IvHRkjlvT2m2C97uzk2//Bbb7rAzJ5z/5U3N9/vYCYx7JNx8GvfI3ex35Mc362/cw3ezzxHH0qN3XyrXrcU6dMCsA+vXrW2uUIsqKyu7qsVHKq1SSSRlOe4+1cwGmdkpZnaxmX0292nkoK8CvgdUNz5KERERSUM2m10CjAO2Wbt2bdW0adMa/XiD1FsZcGr8/Q5QBXSa/OLYZkvKpk+cwAuP3svUl57jZ+cezc/OPZpJzz3FiRd9nTfHj+Wy0w7hzfFjOfGir2/qp3LtGp5/5G6OOitU3H3C+V/mmu9+lvv+9iuOqriouUItaOOGDUusQ4f7WnSk0mql/vLoJDM7DbgdmA7sCUwG9iLU9HPjFg7zZGCBu79sZiNr6e6LwBcBhg4duiWjEhERkeb3KHAE0GH06NETdt1118M6d+7cPe2g2onTgZsmjh1VNeLIEyYAB82bOf2DVcuXzu/Zp9+gph7ZLvsdzI0vzy/Y7rvXFc51unTrzveufyAxjEP45T1jmzq0enGvvuHi/bden8rIpdUpqTtlwBXAxe6+H7A6fn8ReLkRwzwcONXMZgJ3AUeb2e35Hbn79e5e7u7lAwcObMToREREpLlks9kPgP8R75ZNnTp1XNoxtSPHZTKZnvF3FugMMPfdt/TMfh53r+7YqfM1acchrUepJWVD3f3evGa3AJ/Z0gG6+w/cfYi7DwfOAUa7+/mNiFFERETS9RjhNTplzzzzzEvr169fnXZA7URXIPcA11vARqBs6svjWqxq/NZiw/rKURfvv/V7acchrUepJWULzCx3+3ummR0KfAS9p0xERESixN2yQWvXrq2aPHnys2nH1I6cATBx7Kh1hHeW9Z8x+dX5a1evXJpuWKWlU5euP0s7BmldSi0pu4FQThzgz8AzhA3+700xcHcf4+4nN8WwREREJFWPEp6NL3vyySdfWrly5QdpB9ROnJTJZDrH3y8C3QA+mPm2ijBGa1evGnvx/lu/lHYc0rqUVFLm7r9z9/vi71uBXYAD3P0n6UYmIiIipSSbzc4HRgPbVVdX+5gxYx5tgfcYC/QGjo6/p8Vvmz5xgoowEqrxL+vY8dtpxyGtT0klZWZ2SaL4Iu4+29115UVEREQK+Q+wDug+efLkOe+9996raQfUTuReJL0SmAr0m/LyuPfWr1u7Kt2w0rd29cqnvnTo0FfSjkNan5JKyoCRwAwzeyq+p6x32gGJiIhIacpmsysJr9IZBPDf//73qaqqqpZ/Q3D788lMJpM7h3wO6Ik789+bMa22nto6d/dOnbtcknYc0jqVVFLm7qcB2xGqrr8AmGdm95nZGakGJiIiIqVqPKEmwK2XLl265rXXXns67YDagUHAYfF3rtiivfP6y+26dNPaVSse++Ih209OOw5pnUoqKQNw92Xu/k93PxrYHegF5FeTLyIiIkI2m60GbgO6A2VPP/30K0uXLp2dcljtQa4I4xJgFtB78otjZmyoWr8u3bDSsXHjhqou3bp/Pe04pPUquaQMwMyOMLNrgJeAwYCqFRUREZGCstnsbOBxYLC7+8MPP/zAhg0bKtOOq407PfH7OaDPhqr11Qvfnz09rYDStGLxwms+f9DgGWnHIa1XSSVlZvYHM5tFKB++CjjB3fd09ytSDk1ERERK28PAUqDP3Llzl2Wz2f+mHVAbt0Mmk9k3/t5UZG/mlIntrgjj2lUr51dXV38v7TikdSuppAzoCZzv7sPd/fvuPjHtgERERKT0ZbPZ1cA/gH5AxzFjxrw2d+7cN1IOq63L3S2bHz89Xx83+u2NGzdsSDGmFrd88fwvX3riflVpxyGtW8kkZWZWBhwPTEg7FhEREWl9stnsW4Q7ZkMA7rvvvofXrFmzNN2o2rQzACaOHeXAOKDf2tUrq5Z88P476YbVcpYu/ODpH5x+2INpxyGtX8kkZe6+EdgIdE07FhEREWm1HgZmAFuvXr16/ahRo+6trq7emHZQbdTemUzmI/H3JMAAZk97o10UYaxaX7mucu2az6Qdh7QNJZOURVcB95jZkWb2ETPbMfdJOzAREREpfdlsdj1wHdAR6D5t2rR5L7300iMph9WW5YowzgFWAN0mjXv6rerq6uoUY2oRi+a995MfnHbI3LTjkLah1JKyvwHHAc8A04G346dd1uQjIiIiDZfNZucDNwDbAGXPPPPMxOnTp49LOay2Klc1fjWhFsb+K5YsXLt80fxZ6YbVvBbNfW/8D08/7I9pxyFtR0klZe7eocinLO3YREREpFXJEooyDgXsvvvue2r+/PlT6+hHGu7QTCazTfw9ESgDeG/6m212Xq9dvXLlzCkTK9KOQ9qWkkrKcsxsezM7JO04REREpHXKZrMO3E9IzoYA3HXXXfevWLFiXqqBtT0GnBZ/zwDWAp3feOGZKe6eWlDNpXrjxuoZb0784jXf/dycYt1Y8JyZfSLR7FNm9njLRCmtUUklZWY21MzGAVOBp2KzCjP7Z7qRiYiISGuTzWY3Av8kPO80aO3atVWZTObOysrKlSmH1tbkijBuBF4EBix8f9bKlUsXv59uWE1v1tRJt//hS2feVVs3HrLRLwNXmllXM+sB/Ar4WkvEKK1TSSVlhPeLPAr0AnLve3iS8JyZiIiISINks9m1wF8I5xV9FyxYsPLhhx/+d1VV1dqUQ2tLjspkMn3j71cIlazw/rvT2lQRxoXvz5r60D+v/EJ9unX3NwjFZ78P/Ay4HfiRmb1kZq+a2ScBzGxPM5tgZhPNbJKZ7dxsEyAlrdSSsoOA37p7NeAA7r4c6JNqVCIiItJqZbPZxcCfgZ5A97fffnv+ww8/fKsSsybTCTgp/p5OSIA7TpnwvzZTNf6KJQsXvzLmv5+YOHbU+gb09nPg08AnCK98Gu3uBwJHAX+Id9C+DFzt7iOAcsJdXWmHSi0pmw/slGxgZnsAs9MJR0RERNqCbDY7g3DHbCDQ/a233vrgkUceua2qqmpdyqG1FbkijOsJd8u2em/65MVrVi5flG5Yjbdu9ao145948Ny7/vTTmQ3pz91XA3cDtxFKfV1mZhOBMYQkbSjwAvBDM/s+MMzddaGgnSq1pOyPwCNmdjHQ0czOJazMv0s3LBEREWntstnsJEJitjXQbdq0afMeffRRJWZN4+OZTKZr/D0B6AIwd8b0Vn23bEPV+g0vjnrgsjt+/6Mnt3AQ1fFjwJnuPiJ+hrr7FHe/AziVUEHKKDM7uolCl1ampJIyd78R+B5wFvAecCHwE3f/d6qBiYiISJuQzWZfJSRmg4BuU6dOnfvYY4/dvmHDhsqUQ2vtegAnxN/TCI+hdJj2ygutNimrrq72l0c/9rfX/vfENU0wuFHA183MAMxsv/i9I/Cuu/8FeAjYpwnGJa1QSSVlAO7+oLuf6O57uvvH3f3BtGMSERGRtiObzb4C/I3wculuU6ZMef/hhx++pbKyclXKobV2uSKMa4A3gP5vvzZh3ro1q5enG9aWmfzi2AdffPy+78cXYzfWLwnP3k0yszfif4CzgTdiscbdgFubYFzSCpVUUmZm55rZ7vH3LmY21sxGm9luaccmIiIibUc2m82SSMymTZs2784777xh1apVC1IOrTU7JZPJdIy/nyfcPWP+7HdaXS2MU19+/tnR9974mQZW7PEh7n65u//R3de6+5fcfW9338vdT47tfxNvRIyINyOWNM0USGtTUkkZcAWQWxn/BLwEPAv8PbWIREREpE3KZrMvAVcDA4A+H3zwwYqbb775xkWLFr2bcmitVX/gY/H3VEIRRnt7UrZVJWWTXxzzwqjbrz1r4thRunMqLabUkrKB7j7fzLoCRwA/An4BjEg1KhEREWmTYlHGXxOKlg1ctWpV5U033fTv2bNnv5JyaK1VrgjjcuBtoM+bE56dVVVZuSbdsOrn9edHj3/q7n+dO3HsKN0xlRZVaknZQjPbifA+h5fcvZJQZailG5aIiIi0Vdls9h3CMz4rgcEbN26svuOOOx5+4403nnJ3Tzm81ua0TCaTO297DuhdvXGjL5gzY1qaQdXHpHFPvzD63pvOnjh21Ky0Y5H2p9SSsl8CLwP/Av4Qmx0DvJZaRCIiItLmZbPZ+cCvCHd3hgH2yCOPjHviiSduW79+/ep0o2tVhgAHxt+bal58d/KrJV2E8bXnnnr+mczN5yghk7SUVFLm7jcD2wJD3D33PojxwDmpBSUiIiLtQjabXQn8GRgH7AB0efXVV2fcdttt1y1ZsmRmqsG1LrkijAuB94Ferz8/+p2NG6oaVWlGc6jeuHHj+FEPjB5z3y3nTBw7anba8Uj7VVJJWdQNONPMvhf/d4wfERERkWaVzWbXE0rs3Eh4l1m/hQsXrrrhhhtunTx58ujq6uqmqB69rTs98ft/QL+qynUbF82b83ZaARWyvnLduifuuP6hFx+//8KJY0e9l3Y80r6VVFJmZkcSXjh4HvCT2Hhn4NrUghIREZF2JZvNejabHUOobKwK2N7defjhh//3yCOP3LhmzRpVW167XTOZzO7x9+Rcw1lTJpXMi6RXLV+6/MF//P62aa88/7WJY0fNSTsekZJKyoCrgLPd/ePAhthsPHBQYwZqZtub2TNmNsXMJpvZNxsZp4iIiLRx2Wx2JvAz4EVCccZub7755vv/+Mc/rp02bdr/qqurN6YaYGk7I37PJbzuqPukcU9Nr67emPo8W/j+7Hn3XP2LP8+bMf3SiWNHzUs7HhEovaRsuLs/HX/najtaT+OLL24AvuPuuwOHAF8zsz0aOUwRERFp47LZ7Grgn8A1QF9gu8rKyo0PPPDA6Hvvvfe6JUuWqGKIwnLPlTmhFsatVq9YVrl0/rwZaQb19qSXpt5z9c9/snLpot9MHDtqRZqxiCSVWlL2ppmdkNfsWOD1xgzU3ee5+yvx90pCbUCDGzNMERERaR9iccbxwA8JNUIPB3rNmDFj0fXXX3/z+PHj/1NVVbU21SBLzwGZTGZo/P0a8fVGs9+anEoRxvWV69aOvvem0Y/e9Jdvb6haf+PEsaNKrtIRad9KLSn7DvBvM7sF6GZm/wBuBr7bVCMws+HAfoRikSIiIiL1ks1mFwN/B/5ESDKGAh2feeaZif/617/+OnPmzKwqAtnMafF7FrAK6PL6809Pa+l3vy2aO3vOnX/6yX2vPz/6uxPHjvpvvHsnUlJKqlZDd3/RzPYlVPRxI/AecJC7N8kDmGbWE7gPuMTdV+S1+yLwRYChQ4cW6HvLZDKZJhtWW7dh+IF1dySbnFM+LO0QRETanWw268Ck8vLyHwEnAicDa5ctW7bgrrvuenTw4MHjjjrqqJGDBw/ex8ys1oG1facDf5k4dlT1iCNPeB44eumCeXOXL17wXt8Bg5ruZKuI6urq6knPPfXq2AdvfxD36yeOHbWguccpsqVK7U4Z7v6+u//e3b/m7r8F+pvZvY0drpl1IiRk/3b3+wuM93p3L3f38oEDBzZ2dCIiItKGZbPZtdls9j7gp8BsQkUg/d9///1lt99++4N33XXXNfPmzZvcwjeFSs1HM5nMgPj7VeLNgDlvT2n2Ioyrli1Z/NANf3ps7AO3XYH775SQSakriaTMzLqb2S/N7GEzu9LMepvZjmb2APA80KgNKV6p+hcwxd2vbIqYRURERLLZ7HvA7+JnMSE56ztr1qzFt9xyS+a+++677oMPPpjS0kX2SkQZcGr8/S6h8rZOk18cO7W5RrhxQ9X6ic+OGn/zFd+5fdbUSd+ZOHbUgxPHjqpqrvGJNJVSKb54DeE5r1HAJ4C9gd2AW4AvuPuiRg7/cOAC4HUzmxib/dDdH2vkcEVERKSdi0Ua3ywvL/8lsBfwKUJlIEvefvvt+W+//fY92267bZ9DDz20fPjw4ft37ty5e5rxtrDTgRsnjh1VNeLIEyYAB30w6+0PVi1b8kHPvv23acoRzZ0x/a2n7rph0tIF854G/j1x7KiVTTl8keZUKknZCcAId19gZn8lFAM40t3/1xQDd/fniLX+iIiIiDSHbDZbTXjebDIwgpCcDQPWzps3b+H999//dOfOncccdthhe+2+++4H9enTZ7s0420hx2UymZ4VFRWrgCxwBMDcGW9N3WW/Q5okKVuzcvni/z10Z3ZqdtzrhAv6k1WZh7Q2pZKU9XT3BQDuPsfMVjVVQiYiIiLSkrLZ7Ebg5fLy8lcJJX+OIyRp1evXr58/ZsyY18aMGfPa7rvvvt0+++wzYvDgwXt07ty5R5oxN6MuhFJQ9wJvARuBsqnZcVN22e+QkY0ZcOXaNSsnjx876YXHMm9tqFp/P/DkxLGjKhsdsUgKSiUp62hmR5G4m5X/391HpxGYiIiIyJaId87eJBRt3Jpwl+g4oCuwfMqUKfOmTJkyt0OHDv8dMWLEDrvtttue22yzza5tMEE7Hrh34thR60YcecJrwO4z3py4YO2qlUu69ezVv6EDW79u7ao3J/zv5ecfvee9qvWVLwN3Thw7an6TRy3SgkolKVtAqAI/Z3Hefwd2bNGIRERERJpINptdANxfXl7+GLAvcCywE+DV1dXLXnnllXdfeeWVd83skb333nv7XXbZZbett956eK9evbZpbVXruzurVq2aP2vWrA/69u172ZAhQ0YlWr8IHAAwb9bbU3fcc7/D6jvc9evWrpry0nPZcY/cPadqfeUs4B7gdRVVlLagJJIydx+edgwiIiIizS2bza4DxgPjy8vLBxAStI8Rnj1zd185adKkOZMmTZoN0LNnzy577LHH0KFDhw4fOHDgsF69em3boUOHkqg9O2fjxo0bVqxYMXfhwoWz58yZM3vKlCmLVq5c2YNQdHFurAglZyrhYnuH6RPHT6lPUrZ6+dIF014d/8b4x++bu75y3XuEZGzSxLGj9KJuaTNKIikTERERaW+y2ewi4Gng6Zig7Q4cBuxMeITDVq1atXLChAnvTJgwYTpA9+7dO++yyy7bbb311gP79+8/sHfv3gN79uw5sKWKPFZWVq5avXr1opUrVy5etmzZovfee+/9adOmLa2qqupFOK80QtX3Y4DXgRnJ/ieOHbVqxJEnTAWGTH35+TlHnXnhqs5du/XMH0/1xo0b57/37uSJzz4x7a1XX1wDzCU8lzZp4thRG5t5MkVanJIyERERkZTFBO1/wP/Ky8u7ANsT3nm2HyFJ6wB0WLNmzbqJEycuItRUvelOUd++fbsNHTp06379+vXp0aNHj27duvXo2rVrj65du/bo3Llzjy5duvQoKyvrbGYdzKzMzDoQ7sw5wMaNG9dXVVWtqaqqWrt+/fq169evX1tZWblm3bp1a5cvX75s/vz5i+bMmbN49erVBnQDehISsA7ABuBZYFqMa2He3bF8zwGfx33J/Pfenbr9znuW51qsXbViyTtvvPLqhFEPvr9y2WInJHUPA68pGZO2TEmZiIiISAnJZrOVwNvx82R5eXknQpI2lPD+s+HAYEJCBGDLli2rWrZs2SpgKeFOVRWhpsOGMKAT4fywE6H4YVdC8lcGbAssB+YQalJ8B5idzWYb+j6wKXFc9s6kl6cMHDxsp7kzpr/55vhnZ77zejYX+xhCkjpHz4xJe6CkTERERKSEZbPZKuDd+AGgvLy8A9AfGBg/Q4C+QL/43Z+QWFUTnuGqjcWPA6viZzGwCJgFLIy/F8dn4hpl4thRS0ccecIMoN9r456a99pzT95HSPrmAo8Br04cO2pNY8cj0pooKRMRERFpZWJ1+4viZ0p++/Ly8txdrx6Eu10dCIlP7u6ax89GYA2wFqiso9hhUxoHfA73+YRE7BVglu6KSXulpExERESkjYnJ1fr4KUVjCc+gzVUtiiJKykRERESkhU0cO2oD4dk0EaHmFraIiIiIiIikQEmZiIiIiIhIipSUiYiIiIiIpEhJmYiIiIiISIqUlImIiIiIiKRISZmIiIiIiEiKlJSJiIiIiIikSEmZiIiIiIhIipSUiYiIiIiIpEhJmYiIiIiISIqUlImIiIiIiKRISZmIiIiIiEiKlJSJiIiIiIikSEmZiIiIiIhIipSUiYiIiIiIpEhJmYiIiIiISIqUlImIiIiIiKRISZmIiIiIiEiKlJSJiIiIiIikqN0kZWb2cTObZmZvm9llaccjIiIiIiIC7SQpM7My4BrgE8AewLlmtke6UYmIiIiIiLSTpAw4CHjb3d919/XAXcAnU45JRERERESEjmkH0EIGA+8l/s8BDk52YGZfBL4Y/64ys2ktFJvUGAAsSjuI1uLctAOQpqB1vp6aeH0f1rSDExERaZz2kpRZgWa+2R/364HrWyYcKcTMsu5ennYcIi1F67yIiIhA+ym+OAfYPvF/CDA3pVhEREREREQ2aS9J2UvAzma2g5l1Bs4BHko5JhERERERkfZRfNHdN5jZ/wGjgDLgRnefnHJY8mEqPirtjdZ5ERERwdy97q5ERERERESkWbSX4osiIiIiIiIlSUmZiIiIiIhIipSUSSrMzM3sT4n/l5rZ5SmGJNLkLHjOzD6RaPYpM3s8zbhERESktCgpk7RUAmeY2YC0AxFpLh4e2v0ycKWZdTWzHsCvgK+lG5mIiIiUEiVlkpYNhJrnvpXfwsyGmdnTZjYpfg9t+fBEmoa7vwE8DHwf+BlwO/AjM3vJzF41s08CmNmeZjbBzCbGdX/nFMMWERGRFqTaFyUVZrYK2A6YBOwLfAHo6e6Xm9nDQMbdbzGzzwKnuvtp6UUr0jjxDtkrwHrgEWCyu99uZn2BCcB+wG+BF9393/F9imXuvjatmEVERKTlKCmTVJjZKnfvaWa/AKqAtdQkZYuAbd29ysw6AfPcXcUcpVWL6/oq4FNAV8LdYoD+wAmExOxHwK3A/e4+PY04RUREpOW1i5dHS0m7inAH4aZautGVA2kLquPHgDPdfVpe+ylmNh44CRhlZp9399EtHaSIiIi0PD1TJqly9yXAPcDnEo2fB86Jv88DnmvpuESa0Sjg62ZmAGa2X/zeEXjX3f8CPATsk16IIiIi0pKUlEkp+BOQLJ74DeBiM5sEXAB8M5WoRJrHL4FOwCQzeyP+BzgbeMPMJgK7EYoxioiISDugZ8pERERERERSpDtlIiIiIiIiKVJSJiIiIiIikiIlZSIiIiIiIilSUiYiIiIiIpIiJWUiIiIiIiIpUlIm0oqY2TZmdpeZvWNmb5rZY2a2SwuMd1UDu7/czC5trnhERERE2hIlZSKtRHzZ8APAGHf/iLvvAfwQGJRuZCIiIiLSGErKRFqPo4Aqd78u18DdJwKvmtnTZvaKmb1uZp8EMLPhZjbFzG4ws8lm9oSZdYvtdjKzp8zstdjfR2Lz75rZS2Y2ycx+XiiIYt2Y2Y/MbJqZPQXs2nyzQURERKRtUVIm0nrsBbxcoPk64HR335+QuP0p3lUD2Bm4xt33BJYBZ8bm/47N9wUOA+aZ2fGx+4OAEcABZvax5IiKdWNmBwDnAPsBZwAHNsUEi4iIiLQHHdMOQEQazYBfxwSqGhhMTZHGGfFuGoSEbriZ9QIGu/sDAO6+DjYlXMcDr8buexISsGcT4yrWTS/gAXdfE4f1UBNPo4iIiEibpaRMpPWYDFQUaH4eMBA4wN2rzGwm0DW2q0x0txHoRkjiCjHgN+7+j1piKNiNmV0CeF0TICIiIiIfpuKLIq3HaKCLmX0h18DMDgSGAQtiQnZU/F+Uu68A5pjZaXEYXcysOzAK+KyZ9YzNB5vZ1nm9F+vmWeB0M+sW78Sd0gTTKyIiItIu6E6ZSCvh7m5mpwNXmdllhGfJZgKXA38xsywwEZhaj8FdAPzDzH4BVAFnufsTZrY78EJ8JG0VcD6wIBFDwW7c/RUzuzuOfxbwv0ZPsIiIiEg7Ye4qcSQiIiIiIpIWFV8UERERERFJkZIyERERERGRFCkpExERERERSZGSMhERERERkRQpKRMREREREUmRkjIREREREZEUKSkTERERERFJkZIyERERERGRFCkpExERERERSZGSMhERERERkRQpKRMREREREUmRkjIREREREZEUKSkTERERERFJkZIyERERERGRFCkpExERERERSZGSMhERERERkRQpKRMREREREUmRkjIREREREZEUKSkTERERERFJkZIyERERERGRFCkpK8LM3Mx2ir+vM7OfpB1Ta2Fmq8xsx7TjSGrMMjSzMWb2+S3s9ytmNj/Ok622ZBgtrZHTu8X9Su3MbKaZHdvS/TYnM5tsZiPTjkOalpldYWaLzOyDtGOpr7a4fbUFyXOxluy3OZXiOZKUhlaflMWd4XozG5DXfGLcIIc3dhzu/mV3/2Vjh5PPzEaaWXXcQFea2TQzu7ipx9OcCp2Eu3tPd3+3iYa9Ls6fRWZ2v5ltuyXDaq5lWBsz6wRcCRwf58niAt10NrPLzWy6ma2O6/ONTbHelhIzOzdOm+U172hmC8zs5AYOz83sdTPrkGh2hZnd3ETx9jazq8xsdlz/3o7/B9Tdd+sU10M3s4Naapzuvqe7j6lPt2me+JrZRWb2XBrjbi5xfV5qZi+Y2eBE8/PM7OpGDHd74DvAHu6+TZFu2sX2ZWY/MLNnCzQfEM9b9mrAsIbH7fPRvOa3m9nlTRAuZratmf3LzObFc5KpZvZzM+vRFMMvRWZ2s5ltMLPtWmqcDTlHSjOxjMeE29MYd3vV6pOyaAZwbu6Pme0NdEsvnAaZ6+49gd7At4AbzGzXlhq5mXVsqXFtof+L82cXoC/w54YOwMzKmjqoehoEdAUm19JNBjgV+DTQB9gXeBk4ptmja1kPEJbfkXnNPw448PgWDHM74JzGhfVhZtYZeBrYkxBfb+AwYDHQYglLS4rJ8gXAEuDClMORZhYT7wOAbYDngB/E5n2AS4GfNmLww4DF7r6gyLjb0/Z1G3CYme2Q1/wc4HV3f2MLhnmImR3e+NA2Z2b9gRcI506Hunsv4DjCfvsjTT2+UhCTzTOB5cB5KYcjAu7eqj/ATODHwEuJZn8EfkQ42Rsem3WJzWcD84HrgG6Jfr4LzAPmAp+N/e4U290MXBF/9wMeARYCS+PvIYnhjAF+CYwDVgJPAAOKxD4SmJPXbAFwVvzdAbgMeIdwwLoH6B/bdQVuj82XAS8Bg2K7PsC/4vS8D1wBlMV2F8XY/kw4AftN7H+vRAwDgbXA1rVNL/ArYCOwDlgF/C02T867PsCtsf9ZcVl1SMTyXFwuSwnJ9Sfy5uXnE/+/BrwRf+8GPBmnYRrwqUR3NwPXAo8Bq4Fjk8swdvMF4O3Y/0PAdol2xwFTCTvqvwFjk3HkLa8uwFWE9WZu/N2FkESujvNiFTC6QL/Hxvm8fS3r98XAFMK69C7wpfz1h3BVekFc3hcn2ncD/hTn+/I4r7vFdocAz8dl/xowspb5/tkYw1JgFDBsC+fV9cCNec3uAa6MvwcQ1q9lcbn8L7euFBiWA98HpgMdY7MrgJsT3ZxKSIiXxWnaPW+/cSkwKcZ+N9A1tvs8YR/Rs5blktsuVwJvAqcn2l1E7et1f+CmuL4sBR5MtDsZmBhjfh7YJy/mY+vaN8T2F8TlvpiwL9zUb5Hp+RhhXTw/9tM5b3rGAX+N82oqcEze+vIbYEJs/5+8WOpaDrlpujxOx61xvk4GymO724DqGOMq4HvA8LgeXAy8F+fll4ED43JdRtwn1XNd9tj/9Nj+GsCA3Qn7uI1x3Mti9yfGZb+SsJ+9tNj8LbUPcDbwm/j748Bj8fffgE/Xo/+C+3Vq9mnVcV7dXKDfdrV9Ec4BfprXbALwjfh7J8J+czmwCLi7yHBy6/v3gWcSzW8HLk/8r+3YVnAdj+2uAF6nyD43dnM1YVtbQbh4+NFEu8spsv3G9tsD98d1ZjGJbZO6t8vc+cQWn8cVmZ7PxOn5JvHcIm96MoRjw0rgFWDfvPXlB4T1cylhnevagOWQPL+8Bng0jmc88JHY7tnY7WrC9nQ2Ncf971Fz3D+NsD96K47vh4lx1XYeOTwO/8I4TxcBP0rsF9YDVXHcryW2v3djrDOA89Len7WlT+oBNHoC4s6QcGK+O1AWN7JhbJ6UXRU3jP5AL+BhNj8ozQf2AnoAdxTYaHJJ2VaEKyvd43DuZfOd/pi48u9COCkeA/y2SOwjiUlZ3HBOJRzM9ovNLgFeBIYQdkb/AO6M7b4Up6F7nOYDgN6x3YOx2x6ExGoC8WQ+blAbgK8DHWOMNwK/SsT1NeDxBkzv5/OmKznvbiWcpPUi7ADeAj6XiKWKsPMqA75C2Jla/rAJJ+yjCSdnPeIyvjhOw/6EncmeieW1HDg8zteuecvw6Nj9/nG+/hV4NjGeFUAF0Ilw93JD/jQmpvUXcRltTUhmnwd+GdsNj/OiY5F+fwuMrWP9PolwldIId5nWAPsn1p8NMYZOhJ3yGqBfbH9NnIeD4/w9LE7vYMLO+cQ4f46L/wcWmO+nEQ4su8d5/WPg+S2cV4fH7nOJYR/CCdyI+P83hINsp/j5aG5dKDAsB3YmnBjkYt2UlFGTFB8Xh/W9OB2dE/uNCYS7bf0JJwRfju3uAm6pY7mcFfvtQDhQrga2red6/SjhQN8vxnZkbL4/4SB7cOzvwhhnl+S+rh77hj0IB9GPxXZXxuVSW1L2L8LBulNcF85ItLso9v+t2P5swvaVO7CPISQluf3nfcDtDVgOyaRsHWG9LIvrw4v5+/rE/+FxPbiOsI0fH/t/kLA9Do7zMzd/T6PIupxYpx4h3BkYSjh5/HhiHjyXN8/mEU9K47LcvymPbc35icvqOcL+/w/xUw48Wc/+a9uvjyTvYmNev+1q+yLcgZme+L8r4WQ3t7+9k5DY5Y5VRxQZTm5970nY3nKxbkrKqOXYVo91/EXg53Usl/MJ5wQdCRcDP6DmYtblFNl+4//XCBeDeySnk/ptl7nziavYwvO4ItPzNPB7QqmWDSS24Tg9VdQc3y4lJCGdEuvLG4Rksz/hwlWd5xgFpulmQiJ1UJz+fwN3Feo2sX1tINzN7kTYDhbG6e1FuAO9DtixHuvy8Dj8Gwj7gn2BSuKFszgPbk+MuwfhGL5r/L8t8bxLnybaN6cdQKMnoCYp+3HcCXyccAelY1zZhhNOaFcTrz7E/g4FZsTfN5JInAgnEgWTsgLjHwEsTfwfA/w48f+rxASnQL8jCUnYsrghbAQuSbSfwuZXpLcl7CQ6Eq4CbXalL3YzKA4refXoXOKVNcIBbXZeP8cC7yb+jwM+04DpLZiUEXbElYRnC3LtvgSMScTydqJd99jvNolhr4nz533Czmog4SD9v7xx/gP4WWJ53ZrXftMyJJyA/j7Rrmecr8MJV86SJ4JGuCpVLNF4Bzgx8f8EYGb8PZzak7IbSOx867m+Pwh8M7H+rE0On3DScQjhAL+WxJW9RDffB27LazYKuDB/mQL/JZ5sxf8d4jIZ1tB5FbuZTrwSTziYvJZo9wvCiV7Rg2iBdexEwhW+LmyelP0EuCcv7veJdwQJ+43zE+1/D1wXfz9JkQsptcQzEfhkXes1YRuuJibOecO4lpjQJ5pNo+akciY1J2K17Rt+yuYH9R6Ek8BiJ43dCQfa0xLb0n8S7S8icdIbm00ALkisL8n95x5xfGX1XA7JpOypvOGsTfzf1G3e9jU40WwxcHbi/33EfSq1rMuJdeqIRPt7gMsS8yA/KZtN2J/1bsi6UiofQpL9GiGBGUDY7+8OfINwhf7fQN8C/dW1Xx9J7UlZe92+Dov/f8Xm29ethFIEQ4pNf9763pFwXpFLeJJJWdFjWz3W8enEC1MNWC5LiccYatl+CedbCylwLKR+2+VONPI8rsB4h8Z1ZUT8Pwq4OtH+cjY/vnVg8wsxM5Pzi3AseqcByyF5fvnPvOFMTfzfbBqoOe7nSj/1it0cnOjmZWr257Wty7l1KlnaawJwTmIe5CdlywgX6jedY+rTdJ+28kwZhDsonybssG/NazeQsGN82cyWmdkywjMsA2P77Qh3XnJmFRuJmXU3s3+Y2SwzW0E4ePXNe24pWePUGsIGWcxcd+9LKFf/F8IVlpxhwAOJmKcQErdBcXpHAXeZ2Vwz+32sWGIY4erJvER//yBcOc5JTiuEO1DdzOxgMxtGSLweaMD0FjMA6Mzm83MW4Qp2zqZ55e5r4s/k/PqGu/d198Hufp67L4zTeHBu+uI0nkc4KBebxqTtkjG5+yrCydxg8tYFD3uieg8r/q7vA8OLCTvIoszsE2b2opktidN5ImG+bhqGu29I/M+tbwMIVyPfKTDYYcBZefPviCKxDAOuTnS3hHBw3JJ5BWHb/Ez8fQFwS6LdHwhXTJ8ws3fN7LI6hoW7P0Y4Of5iXqv8ZVwdYyu47rH5dlqf5fIZC5UJ5ebLXmy+XIqt19sDS9x9aYHBDgO+k7dctqfw+lTbviF/uayO01TM6YQrr4/F//8GPmFmAxPdvB+Xb07+ep6//+xEmB/1WQ5J+cukaz2ee52f+L22wP/ccq1tXS42/tr23WcStsdZZjbWzA6tI86S4u5/dvd93f1s4oUuwonnFwnPtE4hFHvKV5/9em3a1fYV47sX+Ex8dvM8Nt/vfY+wHk6wUBvpZ4sNK+EGYJCZnZLXvLZjW05j9nvfMbMpZrY8zpc+FFkubL79bg/MyjtW5dRnu4QmPI+LLgCmuPvE+P/fwKfjeVROcjlXEy461rbfy7Wrz3JIash+B8Jxf2P8vTZ+17bfK7YuN2j8cV0/m1AEdp6ZPWpmu9URqzRAm0nK3H0W4dbyiYRyy0mLCCvpnvEEv6+79/FQgQSEqx/bJ7ofWsuovkMofnCwu/cmFGGAsBNpTPyVhDsYe5vZabHxe4Sy8n0Tn67u/r67V7n7z919D0KxtJOpKR9dSXiOLddPb3ffMzm6vHFXE66YnUtIbB9x95X1nN7NhpVnEeGKzLBEs6GEK+WN8R6h2F9yvvR0968kuqktrrnJmOLDvlvFuDZbF+JBdPv8ARQbFmH65tZzOp4CDjKzIYVamlkXwpX+PxKeF+xLOHGuz7q2iFCEodAD2u8R7pQl518Pd/9tkW6/lNdtN3d/nobPKwhJ2THxBPYQQpELANx9pbt/x913BE4Bvm1m9anw5MeE4j/dE83yl3Eutvqse08BJxSrcSxeuLgB+D9gq7hc3qB+y+U9oL+Z9S3S7ld587q7u99ZpNuC+wY+vFy6E9bvYi4kHIRnW6jC/F5CUnVuopvBcR7m5K/n+fvPKsI62JjlkK+2bbo+aluXGzxud3/J3T9JuOD1IGEf2uqY2SDCna5fEJKfSe5eRXhOeZ8CvTR2v97eti8ISdinCMV4exGKEALg7h+4+xfcfTvCcvi71VHbXlw+Pyc8v56cL7Ud2+ryFHC6JWq0TTKzjxLOUT5FuBPZl1CMub7LZWiRCyz13S6b8jwOwvnSjmb2QdzvXUlIMD+R6Ca5nDsQigDWtt/LtWvMcmhqta3LdSm03xvl7scREviphG1VmkibScqizwFHx2x+k5h03AD82cy2BjCzwWZ2QuzkHuAiM9sj7mB/Vss4ehF2DMss1FZUW7cN4u7rCRUz5Gq+ug74VTxIYWYDzeyT8fdRZrZ3vGO1gnCQ3Oju8wgPFv/JQrXDHczsI2Z2ZB2jv4NwBeQ8EifK1D2984GC79uIV3LuidPQK07HtwnFLRrjEWAXM7vAzDrFz4Fmtns9+78DuNjMRsTE59fAeHefSXgeYU8zOyMeQL7B5nfg8t0J/DgumwGEZVev6XP3pwhFeR4wswMsVA/fy8y+HK+WdiYUy1sIbDCzTxCem6nPsKsJxTmuNLPtzKzMzA6N03s7cIqZnRCbd7XweoZCyeF1wA/MbE8ItbOZ2VmxXUPnVe7iyXOE+faku2+6QmdmJ5vZTvHEfQXhat7GwkPabJhjCA+oX5hofA9wkpkdE698fodwsaI+J+C3EQ5k95nZbnEb2srMfmhmJxKKcDhhuWDhNRb1qto6bp//JZx49Yvrbu5Cxw3Aly3csTYz62FmJ5lZrwKDKrpvIDycfrKZHWGhprtfUGRfb6Eq9GMIF3VGxM++wO/YfH5uDXwjxnsWoZjbY4n25yf2n78AMontf0uXQ76i+5p6qm1drs+4h8T5mXuVxXlm1ieeIOfW19boSkLR7zWEC5sHmllPQjGpD1Xb3QT79XazfSX8j1Ds63pC0cf1uRZmdlZi37s0Tnt91qXbCMeHjyea1XZsq8uVhBI7tySme7CZXWlm+xDOBTYQiyGa2U9j9/UxgZA0/TbO965WU4NkvbbLpjyPs3BR8COE57hGxM9ehPmX3O8dkDi+XULYd72YaP81Mxti4dzoh4SiwNC45ZCvKfZ7xdbl+ox7uMVE3cwGmdmpFpLMSsKzla11v1eS2lRS5u7vuHu2SOvvE4pGvWihGN5ThDtAuPt/CQ+Qjo7djK5lNFcRHohcRNg4t6Qq79rcSLiidAqhpqOHCMW5VsbxHRy724ZwcFhBuB09lpqD4mcIJ/S5WoEy1FEswd3HE8prb0c4qOVcRe3TezVQYeF9N38pMOivx+G+SzgZvyNO4xaLd/GOJ1QrPJdw6/13hANUffp/mvCsy32EA8VH4rBw90WEh8x/SyhusDPhWYtirgCyhNreXifU0HRFAyangnByezfhquMbhAfun4rT+Q3CwWYp4S7mQw0Y9qUxppcIRUJ+R6hZ6z3gk4SDyELCCdJ3KbA/cPcHYn93xe3mDeKVxC2YVzm3EK4i5hcz3pmwXa4iVM38d6/nO6wId8v6J+KeRngo/a+EdfcU4JTkyVAx8a71sYSrgE8StrEJhKuo4939TcLFkxcIB629qd9051xAuIgylfAM4CVxvFnCc3Z/IyzvtwnFsQspum9w98mEynruIKzfSwnFborFMtHdn4hX7D+IifJfgH2s5j1K4wnLZxHhmZgK3/y9e7cRno34gFBs9hsxli1eDgX8hnABZJmZXdrQnmtbl+thNKE2uQ/MbFFsdgEwMw7ry4TpbFXM7CjCc2MPALj7BMLFlveAowjbdiFbvF9vZ9tXbpqdsL8rtN87EBhvZqviOL/p7jPqmsiYHP+Mzfd7RY9t9RjeEkKpm6oYz0pCRRjLCfNqFOHc4C1C0bx11F1cPRnrKYRnw2YT5tfZsV1DtsumOo+7kPBc3+t5+72rCQl3bp7+J8a5lLBenREvwuTcQbgI/m78XBFj2eLlUMDlhER5mZl9agv6r+08si73xu/FZvYK4RzhO4TzriWEyse+ugUxSRG52opEREQ+xMwuIlTeckSR9mMID4P/syXjEhFpLhZeyL2Tuxe82GJmMwn7xadaMi5p29rUnTIREREREZHWRkmZiIiIiIhIilR8UUREREREJEW6UyYiIiIiIpIiJWUiIiIiIiIpUlImIiIiIiKSIiVlIiIiIiIiKVJSJiIiIiIikiIlZSIiIiIiIin6f2GW9ifjQ9GvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize = (12,5))\n",
    "\n",
    "sns.barplot(ax= ax[0], data =df, x = 'Canceled', y= 'ReservationPeriod',estimator = np.median, palette = ['#ABABAB','#9ECDE9'], ci = False, hue ='Gender')\n",
    "ax[1].pie(df['Canceled'].value_counts().values , labels = ['No', 'Yes'], autopct='%.0f%%',explode = [0.1,0],shadow =True,colors = ['#ABABAB','#9ECDE9'])\n",
    "\n",
    "\n",
    "ax[1].set_title('% of Canceled Vs NonCanceled Appointments', y = -.3)\n",
    "ax[0].set_title('Median ReservationPeriod of Canceled Vs NonCanceled Appointments', y = -.3)\n",
    "ax[0].set_ylabel('Reservation period in days', size = 12)\n",
    "\n",
    "fig.tight_layout(pad=3)\n",
    "fig.suptitle('How ReservationPeriod Length Affects Cancelling The Appointments', y = 1.1,size = 25,horizontalalignment = 'center');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f92a30f",
   "metadata": {},
   "source": [
    "_________________________________\n",
    "*It appears from the above plots that:* \n",
    "\n",
    "* `20 %` of our sample appointments are cancelled.\n",
    "* Longer`ReservationPeriod` contributes to those cancelled appointments.\n",
    "* `Gender` has no notable effect on cancelling appointments.\n",
    "________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6178c73",
   "metadata": {},
   "source": [
    "### 2.2- Bivariate Conclusion:\n",
    "\n",
    "_________________________________\n",
    "> ### I have concluded that:\n",
    "\n",
    "* Receiving `Scholarship` has strong effect on not cancelling the appointment.\n",
    "* Longer`ReservationPeriod` __ period between `AppointmentDay` and `ScheduledDay` __ contributes to those cancelled appointments.\n",
    "* `Gender` has no notable effect on cancelling appointments.\n",
    "\n",
    "_________________________________\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9045f22",
   "metadata": {},
   "source": [
    "<a id='Conclusion'></a>\n",
    "## Conclusion:\n",
    "_______________________\n",
    "> ## Reached Results:\n",
    "\n",
    "I went through a 110527-row and 14-column dataset about medical appointments in Brazil in order to discover which features affects the meeting being Canceled or not.\n",
    "\n",
    "#### First, the data has to be cleaned and I:\n",
    "\n",
    "1. dropped `AppointmentID` and `HandCap` Columns.\n",
    "2. replaced `PatientId` values with `0 -> 62298` values.\n",
    "3. renamed `No-show` to `Canceled` for avoiding confusion.\n",
    "4. changed the datatypes for `Scholarship`, `Hipertension`, `Diabetes`, `Alcoholism` and `SMS_received`.\n",
    "5. replaced wrong values in the `Age` column.\n",
    "\n",
    "\n",
    "#### Then, I came with some insights from the analysis that:\n",
    "\n",
    "1. In our sample, `Females` care with their health more than `Males` as they reserved more appointments.\n",
    "2. The most dominant disease in our sample is `Diabetes`.\n",
    "3. % of `Alcohol` additction is, by far, higher in `Males` than `Females`.  \n",
    "4. Receiving `Scholarship` has strong effect on not cancelling the appointment.\n",
    "5. Longer`ReservationPeriod` __ period between `AppointmentDay` and `ScheduledDay` __ contributes to those cancelled appointments.\n",
    "6. `Gender` has no notable effect on cancelling appointments.\n",
    "\n",
    "#### After that, I concluded the main objective that:\n",
    "\n",
    "* Receiving financial `Scholarship` & geting shorter `ReservationPeriod` for the appointment are the most two features that may tend to lower the number of Canceled appointments \n",
    "\n",
    "\n",
    "> ## Limitations:\n",
    "\n",
    "#### There are two limitations in this data:\n",
    "\n",
    "1. `Handcap` has no documentation in the kaggle description & its name has no translation so it may be important but we cannot understand it.\n",
    "2. Discription of data on kaggle shows that `SMS_received` is a field indicates number of message received but in this data it is only binary field with either **0** or **1** which may reflect misleading understand.\n"
   ]
  }
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