NoShow_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Project 2: Investigate a Dataset (No-show appointments!)\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> In this project, we're going to investigate medical appointments dataset collected from Brazil. The dataset focused on answering whether the patients show up for their appointment or not. The dataset has multiple characteristics about the patient such as gender, age, insurance & ...etc. Furthermore, the dataset has 110527 rows in total with 14 columns.\n",
    "\n",
    "> Our analysis will focus on answering two questions: First, the important factors to know whether a patient will show up for their scheduled appointment or not. This will be done by taking No-Show variable as a dependent variable and the rest as independent and test the whole dataset to come up with applicable correlation. Second, we will focus on the significant factors if exist together could prevent patients from showing the same dependent & independents variables as the first part but with significant changes in the dataset. We're going to separate the dataset and dive in the actual no show cases only.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%` not found.\n"
     ]
    }
   ],
   "source": [
    "# Importing the packages to be used in the project.\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## 1. Data Wrangling\n",
    "\n",
    "> In this section of the report, we will prepare the dataset for analysis. This process involves change datatypes when needed, replace or remove null values if exist with values, drop duplicated rows, remove any unnecessary columns and understand the dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load 'oshowappointments-kagglev2-may-2016.csv' dataset \n",
    "db_nsa = pd.read_csv('noshowappointments-kagglev2-may-2016.csv')\n",
    "\n",
    "# Know the size of the dataset\n",
    "db_nsa.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>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>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</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",
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       "      <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>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</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>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</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>2016-04-29T17:29:31Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</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>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.598513e+13</td>\n",
       "      <td>5626772</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T08:36:51Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>76</td>\n",
       "      <td>REPÚBLICA</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>6</th>\n",
       "      <td>7.336882e+14</td>\n",
       "      <td>5630279</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T15:05:12Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>23</td>\n",
       "      <td>GOIABEIRAS</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.449833e+12</td>\n",
       "      <td>5630575</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T15:39:58Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>39</td>\n",
       "      <td>GOIABEIRAS</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.639473e+13</td>\n",
       "      <td>5638447</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T08:02:16Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>21</td>\n",
       "      <td>ANDORINHAS</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>9</th>\n",
       "      <td>7.812456e+13</td>\n",
       "      <td>5629123</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T12:48:25Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>19</td>\n",
       "      <td>CONQUISTA</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "0  2.987250e+13        5642903      F  2016-04-29T18:38:08Z   \n",
       "1  5.589978e+14        5642503      M  2016-04-29T16:08:27Z   \n",
       "2  4.262962e+12        5642549      F  2016-04-29T16:19:04Z   \n",
       "3  8.679512e+11        5642828      F  2016-04-29T17:29:31Z   \n",
       "4  8.841186e+12        5642494      F  2016-04-29T16:07:23Z   \n",
       "5  9.598513e+13        5626772      F  2016-04-27T08:36:51Z   \n",
       "6  7.336882e+14        5630279      F  2016-04-27T15:05:12Z   \n",
       "7  3.449833e+12        5630575      F  2016-04-27T15:39:58Z   \n",
       "8  5.639473e+13        5638447      F  2016-04-29T08:02:16Z   \n",
       "9  7.812456e+13        5629123      F  2016-04-27T12:48:25Z   \n",
       "\n",
       "         AppointmentDay  Age      Neighbourhood  Scholarship  Hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "5  2016-04-29T00:00:00Z   76          REPÚBLICA            0             1   \n",
       "6  2016-04-29T00:00:00Z   23         GOIABEIRAS            0             0   \n",
       "7  2016-04-29T00:00:00Z   39         GOIABEIRAS            0             0   \n",
       "8  2016-04-29T00:00:00Z   21         ANDORINHAS            0             0   \n",
       "9  2016-04-29T00:00:00Z   19          CONQUISTA            0             0   \n",
       "\n",
       "   Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  \n",
       "5         0           0        0             0      No  \n",
       "6         0           0        0             0     Yes  \n",
       "7         0           0        0             0     Yes  \n",
       "8         0           0        0             0      No  \n",
       "9         0           0        0             0      No  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first 10 rows to take a first look on the data\n",
    "db_nsa.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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",
      "PatientId         110527 non-null float64\n",
      "AppointmentID     110527 non-null int64\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null object\n",
      "AppointmentDay    110527 non-null object\n",
      "Age               110527 non-null int64\n",
      "Neighbourhood     110527 non-null object\n",
      "Scholarship       110527 non-null int64\n",
      "Hipertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handcap           110527 non-null int64\n",
      "SMS_received      110527 non-null int64\n",
      "No-show           110527 non-null object\n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check the datatype of the variables & the null values\n",
    "db_nsa.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Age</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>1.105270e+05</td>\n",
       "      <td>110527.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.474963e+14</td>\n",
       "      <td>5.675305e+06</td>\n",
       "      <td>37.088874</td>\n",
       "      <td>0.098266</td>\n",
       "      <td>0.197246</td>\n",
       "      <td>0.071865</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>0.022248</td>\n",
       "      <td>0.321026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.560949e+14</td>\n",
       "      <td>7.129575e+04</td>\n",
       "      <td>23.110205</td>\n",
       "      <td>0.297675</td>\n",
       "      <td>0.397921</td>\n",
       "      <td>0.258265</td>\n",
       "      <td>0.171686</td>\n",
       "      <td>0.161543</td>\n",
       "      <td>0.466873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.921784e+04</td>\n",
       "      <td>5.030230e+06</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.172614e+12</td>\n",
       "      <td>5.640286e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.173184e+13</td>\n",
       "      <td>5.680573e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.439172e+13</td>\n",
       "      <td>5.725524e+06</td>\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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999816e+14</td>\n",
       "      <td>5.790484e+06</td>\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>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          PatientId  AppointmentID            Age    Scholarship  \\\n",
       "count  1.105270e+05   1.105270e+05  110527.000000  110527.000000   \n",
       "mean   1.474963e+14   5.675305e+06      37.088874       0.098266   \n",
       "std    2.560949e+14   7.129575e+04      23.110205       0.297675   \n",
       "min    3.921784e+04   5.030230e+06      -1.000000       0.000000   \n",
       "25%    4.172614e+12   5.640286e+06      18.000000       0.000000   \n",
       "50%    3.173184e+13   5.680573e+06      37.000000       0.000000   \n",
       "75%    9.439172e+13   5.725524e+06      55.000000       0.000000   \n",
       "max    9.999816e+14   5.790484e+06     115.000000       1.000000   \n",
       "\n",
       "        Hipertension       Diabetes     Alcoholism        Handcap  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.197246       0.071865       0.030400       0.022248   \n",
       "std         0.397921       0.258265       0.171686       0.161543   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       4.000000   \n",
       "\n",
       "        SMS_received  \n",
       "count  110527.000000  \n",
       "mean        0.321026  \n",
       "std         0.466873  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore dataset more\n",
    "db_nsa.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         float64\n",
       "AppointmentID       int64\n",
       "Gender             object\n",
       "ScheduledDay       object\n",
       "AppointmentDay     object\n",
       "Age                 int64\n",
       "Neighbourhood      object\n",
       "Scholarship         int64\n",
       "Hipertension        int64\n",
       "Diabetes            int64\n",
       "Alcoholism          int64\n",
       "Handcap             int64\n",
       "SMS_received        int64\n",
       "No-show            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the variables data types separately to apply some changes as needed\n",
    "db_nsa.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
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       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No-show</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
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       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
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       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PatientId  AppointmentID  Gender  ScheduledDay  AppointmentDay  \\\n",
       "No-show                                                                   \n",
       "No           88208          88208   88208         88208           88208   \n",
       "Yes          22319          22319   22319         22319           22319   \n",
       "\n",
       "           Age  Neighbourhood  Scholarship  Hipertension  Diabetes  \\\n",
       "No-show                                                              \n",
       "No       88208          88208        88208         88208     88208   \n",
       "Yes      22319          22319        22319         22319     22319   \n",
       "\n",
       "         Alcoholism  Handcap  SMS_received  \n",
       "No-show                                     \n",
       "No            88208    88208         88208  \n",
       "Yes           22319    22319         22319  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the No-Show variable two cases separtely\n",
    "db = db_nsa.groupby(by='No-show').count()\n",
    "db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JARDIM CAMBURI                 7717\n",
       "MARIA ORTIZ                    5805\n",
       "RESISTÊNCIA                    4431\n",
       "JARDIM DA PENHA                3877\n",
       "ITARARÉ                        3514\n",
       "CENTRO                         3334\n",
       "TABUAZEIRO                     3132\n",
       "SANTA MARTHA                   3131\n",
       "JESUS DE NAZARETH              2853\n",
       "BONFIM                         2773\n",
       "SANTO ANTÔNIO                  2746\n",
       "SANTO ANDRÉ                    2571\n",
       "CARATOÍRA                      2565\n",
       "JABOUR                         2509\n",
       "SÃO PEDRO                      2448\n",
       "ILHA DO PRÍNCIPE               2266\n",
       "NOVA PALESTINA                 2264\n",
       "ANDORINHAS                     2262\n",
       "DA PENHA                       2217\n",
       "ROMÃO                          2215\n",
       "GURIGICA                       2018\n",
       "SÃO JOSÉ                       1977\n",
       "BELA VISTA                     1907\n",
       "MARUÍPE                        1902\n",
       "FORTE SÃO JOÃO                 1889\n",
       "ILHA DE SANTA MARIA            1885\n",
       "SÃO CRISTÓVÃO                  1836\n",
       "REDENÇÃO                       1553\n",
       "SÃO BENEDITO                   1439\n",
       "JOANA D´ARC                    1427\n",
       "                               ... \n",
       "SANTOS REIS                     547\n",
       "ESTRELINHA                      538\n",
       "SANTA CLARA                     506\n",
       "SOLON BORGES                    469\n",
       "PIEDADE                         452\n",
       "SANTA CECÍLIA                   448\n",
       "SANTA LÚCIA                     438\n",
       "SANTA LUÍZA                     428\n",
       "BARRO VERMELHO                  423\n",
       "DO MOSCOSO                      413\n",
       "MÁRIO CYPRESTE                  371\n",
       "BOA VISTA                       312\n",
       "COMDUSA                         310\n",
       "DE LOURDES                      305\n",
       "ARIOVALDO FAVALESSA             282\n",
       "ANTÔNIO HONÓRIO                 271\n",
       "FRADINHOS                       258\n",
       "ENSEADA DO SUÁ                  235\n",
       "SANTA HELENA                    178\n",
       "HORTO                           175\n",
       "UNIVERSITÁRIO                   152\n",
       "SEGURANÇA DO LAR                145\n",
       "NAZARETH                        135\n",
       "MORADA DE CAMBURI                96\n",
       "PONTAL DE CAMBURI                69\n",
       "ILHA DO BOI                      35\n",
       "ILHA DO FRADE                    10\n",
       "AEROPORTO                         8\n",
       "ILHAS OCEÂNICAS DE TRINDADE       2\n",
       "PARQUE INDUSTRIAL                 1\n",
       "Name: Neighbourhood, Length: 81, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore the value count for each Neighbourhood in the dataset\n",
    "db_nsa.Neighbourhood.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the duplicated rows to remove them when exist.\n",
    "sum(db_nsa.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         0\n",
       "AppointmentID     0\n",
       "Gender            0\n",
       "ScheduledDay      0\n",
       "AppointmentDay    0\n",
       "Age               0\n",
       "Neighbourhood     0\n",
       "Scholarship       0\n",
       "Hipertension      0\n",
       "Diabetes          0\n",
       "Alcoholism        0\n",
       "Handcap           0\n",
       "SMS_received      0\n",
       "No-show           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Double check for the null values, if exist remove or replace them.\n",
    "db_nsa.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert PatientID type from float to int.\n",
    "db_nsa.PatientId = db_nsa.PatientId.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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",
      "PatientId         110527 non-null int64\n",
      "AppointmentID     110527 non-null int64\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null object\n",
      "AppointmentDay    110527 non-null object\n",
      "Age               110527 non-null int64\n",
      "Neighbourhood     110527 non-null object\n",
      "Scholarship       110527 non-null int64\n",
      "Hipertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handcap           110527 non-null int64\n",
      "SMS_received      110527 non-null int64\n",
      "No-show           110527 non-null object\n",
      "dtypes: int64(9), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check if the changes reflected or not.\n",
    "db_nsa.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "822145925426128    88\n",
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       "36994987339512     23\n",
       "                   ..\n",
       "98683352133221      1\n",
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       "48689197872217      1\n",
       "9675119787546       1\n",
       "763619586595        1\n",
       "867726299814126     1\n",
       "77425139319169      1\n",
       "39423418767468      1\n",
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       "737858311826761     1\n",
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       "8249496395977       1\n",
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       "31638467315         1\n",
       "89279955685         1\n",
       "5883897911366       1\n",
       "56326578686847      1\n",
       "869587212288428     1\n",
       "735858598529        1\n",
       "2886912523138       1\n",
       "68129842443312      1\n",
       "99264711372         1\n",
       "2212945531847       1\n",
       "57863365759569      1\n",
       "Name: PatientId, Length: 62299, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if the same patient has more than one appointment.\n",
    "db_nsa.PatientId.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the date from time in ScheduledDay variable.\n",
    "\n",
    "split_columns = ['ScheduledDay']\n",
    "\n",
    "# apply split function and the split indicater is 'T'\n",
    "for c in split_columns:\n",
    "    db_nsa[c] = db_nsa[c].apply(lambda x: x.split(\"T\")[0])\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the date from time in AppointmentDay variable.\n",
    "\n",
    "split_columns = ['AppointmentDay']\n",
    "\n",
    "# apply split function and the split indicater is 'T'\n",
    "for c in split_columns:\n",
    "    db_nsa[c] = db_nsa[c].apply(lambda x: x.split(\"T\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PatientId</th>\n",
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       "      <td>2016-04-29</td>\n",
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       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>2016-04-29</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",
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       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PatientId  AppointmentID Gender ScheduledDay AppointmentDay  Age  \\\n",
       "0   29872499824296        5642903      F   2016-04-29     2016-04-29   62   \n",
       "1  558997776694438        5642503      M   2016-04-29     2016-04-29   56   \n",
       "2    4262962299951        5642549      F   2016-04-29     2016-04-29   62   \n",
       "3     867951213174        5642828      F   2016-04-29     2016-04-29    8   \n",
       "4    8841186448183        5642494      F   2016-04-29     2016-04-29   56   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hipertension  Diabetes  Alcoholism  \\\n",
       "0    JARDIM DA PENHA            0             1         0           0   \n",
       "1    JARDIM DA PENHA            0             0         0           0   \n",
       "2      MATA DA PRAIA            0             0         0           0   \n",
       "3  PONTAL DE CAMBURI            0             0         0           0   \n",
       "4    JARDIM DA PENHA            0             1         1           0   \n",
       "\n",
       "   Handcap  SMS_received No-show  \n",
       "0        0             0      No  \n",
       "1        0             0      No  \n",
       "2        0             0      No  \n",
       "3        0             0      No  \n",
       "4        0             0      No  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if the changes reflected or not.\n",
    "db_nsa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the type of ScheduledDay from String to datetime\n",
    "\n",
    "db_nsa['ScheduledDay'] = db_nsa['ScheduledDay'].apply(lambda x: \n",
    "                                    dt.datetime.strptime(x,'%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the type of AppointmentDayfrom String to datetime\n",
    "\n",
    "db_nsa['AppointmentDay'] = db_nsa['AppointmentDay'].apply(lambda x: \n",
    "                                    dt.datetime.strptime(x,'%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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",
      "PatientId         110527 non-null int64\n",
      "AppointmentID     110527 non-null int64\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null datetime64[ns]\n",
      "AppointmentDay    110527 non-null datetime64[ns]\n",
      "Age               110527 non-null int64\n",
      "Neighbourhood     110527 non-null object\n",
      "Scholarship       110527 non-null int64\n",
      "Hipertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handcap           110527 non-null int64\n",
      "SMS_received      110527 non-null int64\n",
      "No-show           110527 non-null object\n",
      "dtypes: datetime64[ns](2), int64(9), object(3)\n",
      "memory usage: 11.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check the changes\n",
    "db_nsa.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the corresponding day of the ScheduledDay date\n",
    "db_nsa['ScheduledDay'] = db_nsa['ScheduledDay'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <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>29872499824296</td>\n",
       "      <td>5642903</td>\n",
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       "      <td>JARDIM DA PENHA</td>\n",
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       "    <tr>\n",
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       "      <td>558997776694438</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-04-29</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>4262962299951</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-04-29</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",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8841186448183</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-04-29</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>95985133231274</td>\n",
       "      <td>5626772</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>76</td>\n",
       "      <td>REPÚBLICA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>733688164476661</td>\n",
       "      <td>5630279</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>23</td>\n",
       "      <td>GOIABEIRAS</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3449833394123</td>\n",
       "      <td>5630575</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>39</td>\n",
       "      <td>GOIABEIRAS</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>56394729949972</td>\n",
       "      <td>5638447</td>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>21</td>\n",
       "      <td>ANDORINHAS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>78124564369297</td>\n",
       "      <td>5629123</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>19</td>\n",
       "      <td>CONQUISTA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PatientId  AppointmentID Gender ScheduledDay AppointmentDay  Age  \\\n",
       "0   29872499824296        5642903      F       Friday     2016-04-29   62   \n",
       "1  558997776694438        5642503      M       Friday     2016-04-29   56   \n",
       "2    4262962299951        5642549      F       Friday     2016-04-29   62   \n",
       "3     867951213174        5642828      F       Friday     2016-04-29    8   \n",
       "4    8841186448183        5642494      F       Friday     2016-04-29   56   \n",
       "5   95985133231274        5626772      F    Wednesday     2016-04-29   76   \n",
       "6  733688164476661        5630279      F    Wednesday     2016-04-29   23   \n",
       "7    3449833394123        5630575      F    Wednesday     2016-04-29   39   \n",
       "8   56394729949972        5638447      F       Friday     2016-04-29   21   \n",
       "9   78124564369297        5629123      F    Wednesday     2016-04-29   19   \n",
       "\n",
       "       Neighbourhood  Scholarship  Hipertension  Diabetes  Alcoholism  \\\n",
       "0    JARDIM DA PENHA            0             1         0           0   \n",
       "1    JARDIM DA PENHA            0             0         0           0   \n",
       "2      MATA DA PRAIA            0             0         0           0   \n",
       "3  PONTAL DE CAMBURI            0             0         0           0   \n",
       "4    JARDIM DA PENHA            0             1         1           0   \n",
       "5          REPÚBLICA            0             1         0           0   \n",
       "6         GOIABEIRAS            0             0         0           0   \n",
       "7         GOIABEIRAS            0             0         0           0   \n",
       "8         ANDORINHAS            0             0         0           0   \n",
       "9          CONQUISTA            0             0         0           0   \n",
       "\n",
       "   Handcap  SMS_received No-show  \n",
       "0        0             0      No  \n",
       "1        0             0      No  \n",
       "2        0             0      No  \n",
       "3        0             0      No  \n",
       "4        0             0      No  \n",
       "5        0             0      No  \n",
       "6        0             0     Yes  \n",
       "7        0             0     Yes  \n",
       "8        0             0      No  \n",
       "9        0             0      No  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check changes\n",
    "db_nsa.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5769215    1\n",
       "5731652    1\n",
       "5707080    1\n",
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       "5758289    1\n",
       "5762391    1\n",
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       "5651786    1\n",
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       "5719362    1\n",
       "5672187    1\n",
       "          ..\n",
       "5744033    1\n",
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       "5739943    1\n",
       "5672324    1\n",
       "5682563    1\n",
       "5680512    1\n",
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       "5496110    1\n",
       "5713200    1\n",
       "5711153    1\n",
       "5717298    1\n",
       "5709110    1\n",
       "5707063    1\n",
       "5729592    1\n",
       "5463358    1\n",
       "5565768    1\n",
       "5776721    1\n",
       "5789023    1\n",
       "5590396    1\n",
       "5606756    1\n",
       "5608807    1\n",
       "5635434    1\n",
       "5621101    1\n",
       "5686470    1\n",
       "5582192    1\n",
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       "5584243    1\n",
       "5598584    1\n",
       "5602682    1\n",
       "5771266    1\n",
       "Name: AppointmentID, Length: 110527, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if the AppointmentID appears more than once.\n",
    "db_nsa.AppointmentID.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop AppointmentID column from the dataset.\n",
    "db_nsa.drop(['AppointmentID'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 13 columns):\n",
      "PatientId         110527 non-null int64\n",
      "Gender            110527 non-null object\n",
      "ScheduledDay      110527 non-null object\n",
      "AppointmentDay    110527 non-null datetime64[ns]\n",
      "Age               110527 non-null int64\n",
      "Neighbourhood     110527 non-null object\n",
      "Scholarship       110527 non-null int64\n",
      "Hipertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handcap           110527 non-null int64\n",
      "SMS_received      110527 non-null int64\n",
      "No-show           110527 non-null object\n",
      "dtypes: datetime64[ns](1), int64(8), object(4)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check changes.\n",
    "db_nsa.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## 2. Exploratory Data Analysis\n",
    "\n",
    "> In this section, we're going to explore the dataset and try to answer our proposed questions. We're going to compute statistics and create visualizations addressing the research questions. At the end of our analysis, we're going to address the findings in the next section. \n",
    "\n",
    "### Q1. What factors are important to know whether a patient will show up for their scheduled appointment or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore all numerical variables with histogram distribution for the whole dataset.\n",
    "db_nsa.hist(figsize=(8, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110477</th>\n",
       "      <td>27984936871939</td>\n",
       "      <td>F</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>67</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110478</th>\n",
       "      <td>5478497292889</td>\n",
       "      <td>F</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>27</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>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110479</th>\n",
       "      <td>4455243979994</td>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>34</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110480</th>\n",
       "      <td>349293493115</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>51</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>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110481</th>\n",
       "      <td>438363374339</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>28</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>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110482</th>\n",
       "      <td>594422525164283</td>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>43</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110483</th>\n",
       "      <td>1642781438442</td>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>60</td>\n",
       "      <td>PRAIA DO CANTO</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>110484</th>\n",
       "      <td>513364984676544</td>\n",
       "      <td>F</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>45</td>\n",
       "      <td>BARRO VERMELHO</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110485</th>\n",
       "      <td>4865334421273</td>\n",
       "      <td>F</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2016-06-06</td>\n",
       "      <td>34</td>\n",
       "      <td>SANTA LUÍZA</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>110486</th>\n",
       "      <td>318415333348194</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>60</td>\n",
       "      <td>MARIA ORTIZ</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>110487</th>\n",
       "      <td>1891645149617</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>20</td>\n",
       "      <td>MARIA ORTIZ</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>110488</th>\n",
       "      <td>793589177751417</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>76</td>\n",
       "      <td>MARIA ORTIZ</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>110489</th>\n",
       "      <td>787436994766271</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>22</td>\n",
       "      <td>MARIA ORTIZ</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>110490</th>\n",
       "      <td>325262361689699</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>51</td>\n",
       "      <td>MARIA ORTIZ</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>110491</th>\n",
       "      <td>756562242135</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>53</td>\n",
       "      <td>MARIA ORTIZ</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>110492</th>\n",
       "      <td>645634214296344</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>33</td>\n",
       "      <td>MARIA ORTIZ</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110493</th>\n",
       "      <td>171213473862</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>21</td>\n",
       "      <td>MARIA ORTIZ</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>110494</th>\n",
       "      <td>289581673196518</td>\n",
       "      <td>F</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>38</td>\n",
       "      <td>MARIA ORTIZ</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>110495</th>\n",
       "      <td>7499488949126</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>40</td>\n",
       "      <td>MARIA ORTIZ</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>110496</th>\n",
       "      <td>85442954737999</td>\n",
       "      <td>F</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>37</td>\n",
       "      <td>MARIA ORTIZ</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110497</th>\n",
       "      <td>793589177751417</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>76</td>\n",
       "      <td>MARIA ORTIZ</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>110498</th>\n",
       "      <td>94336536145654</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>59</td>\n",
       "      <td>MARIA ORTIZ</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>110499</th>\n",
       "      <td>821969177626116</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>66</td>\n",
       "      <td>MARIA ORTIZ</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",
       "    <tr>\n",
       "      <th>110500</th>\n",
       "      <td>443438443334614</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>59</td>\n",
       "      <td>MARIA ORTIZ</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>110501</th>\n",
       "      <td>454425189389</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>44</td>\n",
       "      <td>MARIA ORTIZ</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>110502</th>\n",
       "      <td>731622885364982</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>22</td>\n",
       "      <td>GOIABEIRAS</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>110503</th>\n",
       "      <td>23621816822757</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>64</td>\n",
       "      <td>SOLON BORGES</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>110504</th>\n",
       "      <td>9947982555566</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>4</td>\n",
       "      <td>MARIA ORTIZ</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>110505</th>\n",
       "      <td>56673438855979</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>55</td>\n",
       "      <td>MARIA ORTIZ</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>110506</th>\n",
       "      <td>897388334326</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>5</td>\n",
       "      <td>MARIA ORTIZ</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>110507</th>\n",
       "      <td>476946211846992</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>0</td>\n",
       "      <td>MARIA ORTIZ</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>110508</th>\n",
       "      <td>94336536145654</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>59</td>\n",
       "      <td>MARIA ORTIZ</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>110509</th>\n",
       "      <td>495296829375937</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>33</td>\n",
       "      <td>MARIA ORTIZ</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>110510</th>\n",
       "      <td>23621816822757</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>64</td>\n",
       "      <td>SOLON BORGES</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>110511</th>\n",
       "      <td>823599626588</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>14</td>\n",
       "      <td>MARIA ORTIZ</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>110512</th>\n",
       "      <td>98762456447375</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>41</td>\n",
       "      <td>MARIA ORTIZ</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>110513</th>\n",
       "      <td>86747784995281</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>2</td>\n",
       "      <td>ANTÔNIO HONÓRIO</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>110514</th>\n",
       "      <td>2695685177138</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>58</td>\n",
       "      <td>MARIA ORTIZ</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>110515</th>\n",
       "      <td>645634214296344</td>\n",
       "      <td>M</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>33</td>\n",
       "      <td>MARIA ORTIZ</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110516</th>\n",
       "      <td>69237724436761</td>\n",
       "      <td>F</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2016-06-08</td>\n",
       "      <td>37</td>\n",
       "      <td>MARIA ORTIZ</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110517</th>\n",
       "      <td>5574942418928</td>\n",
       "      <td>F</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>19</td>\n",
       "      <td>MARIA ORTIZ</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>110518</th>\n",
       "      <td>72633149253362</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>50</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110519</th>\n",
       "      <td>65423877893936</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>22</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110520</th>\n",
       "      <td>996997666245785</td>\n",
       "      <td>F</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>42</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110521</th>\n",
       "      <td>36355337746436</td>\n",
       "      <td>F</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>53</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110522</th>\n",
       "      <td>2572134369293</td>\n",
       "      <td>F</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>56</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110523</th>\n",
       "      <td>3596266328735</td>\n",
       "      <td>F</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>51</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110524</th>\n",
       "      <td>15576631729893</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>21</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110525</th>\n",
       "      <td>92134931435557</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>38</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110526</th>\n",
       "      <td>377511518121127</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>54</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              PatientId Gender ScheduledDay AppointmentDay  Age  \\\n",
       "110477   27984936871939      F       Monday     2016-06-06   67   \n",
       "110478    5478497292889      F       Monday     2016-06-06   27   \n",
       "110479    4455243979994      F       Friday     2016-06-06   34   \n",
       "110480     349293493115      F    Wednesday     2016-06-06   51   \n",
       "110481     438363374339      F    Wednesday     2016-06-06   28   \n",
       "110482  594422525164283      F       Friday     2016-06-06   43   \n",
       "110483    1642781438442      F       Friday     2016-06-03   60   \n",
       "110484  513364984676544      F       Friday     2016-06-07   45   \n",
       "110485    4865334421273      F     Thursday     2016-06-06   34   \n",
       "110486  318415333348194      F    Wednesday     2016-06-08   60   \n",
       "110487    1891645149617      M    Wednesday     2016-06-08   20   \n",
       "110488  793589177751417      M    Wednesday     2016-06-01   76   \n",
       "110489  787436994766271      F    Wednesday     2016-06-08   22   \n",
       "110490  325262361689699      M    Wednesday     2016-06-08   51   \n",
       "110491     756562242135      F    Wednesday     2016-06-08   53   \n",
       "110492  645634214296344      M    Wednesday     2016-06-08   33   \n",
       "110493     171213473862      M    Wednesday     2016-06-08   21   \n",
       "110494  289581673196518      F       Monday     2016-06-08   38   \n",
       "110495    7499488949126      M    Wednesday     2016-06-01   40   \n",
       "110496   85442954737999      F       Monday     2016-06-08   37   \n",
       "110497  793589177751417      M    Wednesday     2016-06-01   76   \n",
       "110498   94336536145654      F    Wednesday     2016-06-08   59   \n",
       "110499  821969177626116      F    Wednesday     2016-06-01   66   \n",
       "110500  443438443334614      F    Wednesday     2016-06-08   59   \n",
       "110501     454425189389      M    Wednesday     2016-06-01   44   \n",
       "110502  731622885364982      F    Wednesday     2016-06-08   22   \n",
       "110503   23621816822757      F    Wednesday     2016-06-01   64   \n",
       "110504    9947982555566      F    Wednesday     2016-06-08    4   \n",
       "110505   56673438855979      F    Wednesday     2016-06-01   55   \n",
       "110506     897388334326      M    Wednesday     2016-06-01    5   \n",
       "110507  476946211846992      F    Wednesday     2016-06-08    0   \n",
       "110508   94336536145654      F    Wednesday     2016-06-01   59   \n",
       "110509  495296829375937      M    Wednesday     2016-06-08   33   \n",
       "110510   23621816822757      F    Wednesday     2016-06-01   64   \n",
       "110511     823599626588      F    Wednesday     2016-06-08   14   \n",
       "110512   98762456447375      F    Wednesday     2016-06-08   41   \n",
       "110513   86747784995281      M    Wednesday     2016-06-08    2   \n",
       "110514    2695685177138      F    Wednesday     2016-06-08   58   \n",
       "110515  645634214296344      M       Monday     2016-06-08   33   \n",
       "110516   69237724436761      F      Tuesday     2016-06-08   37   \n",
       "110517    5574942418928      F      Tuesday     2016-06-07   19   \n",
       "110518   72633149253362      F    Wednesday     2016-06-07   50   \n",
       "110519   65423877893936      F    Wednesday     2016-06-07   22   \n",
       "110520  996997666245785      F      Tuesday     2016-06-07   42   \n",
       "110521   36355337746436      F      Tuesday     2016-06-07   53   \n",
       "110522    2572134369293      F      Tuesday     2016-06-07   56   \n",
       "110523    3596266328735      F      Tuesday     2016-06-07   51   \n",
       "110524   15576631729893      F    Wednesday     2016-06-07   21   \n",
       "110525   92134931435557      F    Wednesday     2016-06-07   38   \n",
       "110526  377511518121127      F    Wednesday     2016-06-07   54   \n",
       "\n",
       "          Neighbourhood  Scholarship  Hipertension  Diabetes  Alcoholism  \\\n",
       "110477      RESISTÊNCIA            0             0         1           0   \n",
       "110478      RESISTÊNCIA            0             0         0           0   \n",
       "110479      RESISTÊNCIA            1             0         0           0   \n",
       "110480      RESISTÊNCIA            0             0         0           0   \n",
       "110481      RESISTÊNCIA            0             0         0           0   \n",
       "110482      RESISTÊNCIA            1             0         0           0   \n",
       "110483   PRAIA DO CANTO            0             1         0           0   \n",
       "110484   BARRO VERMELHO            0             0         0           0   \n",
       "110485      SANTA LUÍZA            0             0         0           0   \n",
       "110486      MARIA ORTIZ            0             0         0           0   \n",
       "110487      MARIA ORTIZ            0             0         0           0   \n",
       "110488      MARIA ORTIZ            0             0         0           0   \n",
       "110489      MARIA ORTIZ            0             0         0           0   \n",
       "110490      MARIA ORTIZ            0             0         0           0   \n",
       "110491      MARIA ORTIZ            0             0         0           0   \n",
       "110492      MARIA ORTIZ            0             1         0           0   \n",
       "110493      MARIA ORTIZ            0             0         0           0   \n",
       "110494      MARIA ORTIZ            0             0         0           0   \n",
       "110495      MARIA ORTIZ            0             0         0           0   \n",
       "110496      MARIA ORTIZ            0             1         0           0   \n",
       "110497      MARIA ORTIZ            0             0         0           0   \n",
       "110498      MARIA ORTIZ            0             0         0           0   \n",
       "110499      MARIA ORTIZ            0             1         1           0   \n",
       "110500      MARIA ORTIZ            0             0         0           0   \n",
       "110501      MARIA ORTIZ            0             0         0           0   \n",
       "110502       GOIABEIRAS            0             0         0           0   \n",
       "110503     SOLON BORGES            0             0         0           0   \n",
       "110504      MARIA ORTIZ            0             0         0           0   \n",
       "110505      MARIA ORTIZ            0             0         0           0   \n",
       "110506      MARIA ORTIZ            0             0         0           0   \n",
       "110507      MARIA ORTIZ            0             0         0           0   \n",
       "110508      MARIA ORTIZ            0             0         0           0   \n",
       "110509      MARIA ORTIZ            0             0         0           0   \n",
       "110510     SOLON BORGES            0             0         0           0   \n",
       "110511      MARIA ORTIZ            0             0         0           0   \n",
       "110512      MARIA ORTIZ            0             0         0           0   \n",
       "110513  ANTÔNIO HONÓRIO            0             0         0           0   \n",
       "110514      MARIA ORTIZ            0             0         0           0   \n",
       "110515      MARIA ORTIZ            0             1         0           0   \n",
       "110516      MARIA ORTIZ            0             0         0           0   \n",
       "110517      MARIA ORTIZ            0             0         0           0   \n",
       "110518      MARIA ORTIZ            0             0         0           0   \n",
       "110519      MARIA ORTIZ            0             0         0           0   \n",
       "110520      MARIA ORTIZ            0             0         0           0   \n",
       "110521      MARIA ORTIZ            0             0         0           0   \n",
       "110522      MARIA ORTIZ            0             0         0           0   \n",
       "110523      MARIA ORTIZ            0             0         0           0   \n",
       "110524      MARIA ORTIZ            0             0         0           0   \n",
       "110525      MARIA ORTIZ            0             0         0           0   \n",
       "110526      MARIA ORTIZ            0             0         0           0   \n",
       "\n",
       "        Handcap  SMS_received No-show  \n",
       "110477        0             1      No  \n",
       "110478        0             1      No  \n",
       "110479        0             1     Yes  \n",
       "110480        0             1      No  \n",
       "110481        0             1      No  \n",
       "110482        0             1     Yes  \n",
       "110483        0             0      No  \n",
       "110484        0             0     Yes  \n",
       "110485        0             0      No  \n",
       "110486        0             0      No  \n",
       "110487        0             0      No  \n",
       "110488        0             0      No  \n",
       "110489        0             0      No  \n",
       "110490        0             0      No  \n",
       "110491        0             0      No  \n",
       "110492        0             0     Yes  \n",
       "110493        0             0      No  \n",
       "110494        0             0      No  \n",
       "110495        0             0      No  \n",
       "110496        0             0     Yes  \n",
       "110497        0             0      No  \n",
       "110498        0             0      No  \n",
       "110499        0             0      No  \n",
       "110500        0             0      No  \n",
       "110501        0             0      No  \n",
       "110502        0             0      No  \n",
       "110503        0             0      No  \n",
       "110504        0             0      No  \n",
       "110505        0             0      No  \n",
       "110506        0             0      No  \n",
       "110507        0             0      No  \n",
       "110508        0             0      No  \n",
       "110509        0             0      No  \n",
       "110510        0             0      No  \n",
       "110511        0             0      No  \n",
       "110512        0             0      No  \n",
       "110513        0             0      No  \n",
       "110514        0             0      No  \n",
       "110515        0             0     Yes  \n",
       "110516        0             0     Yes  \n",
       "110517        0             0      No  \n",
       "110518        0             1      No  \n",
       "110519        0             1      No  \n",
       "110520        0             1      No  \n",
       "110521        0             1      No  \n",
       "110522        0             1      No  \n",
       "110523        0             1      No  \n",
       "110524        0             1      No  \n",
       "110525        0             1      No  \n",
       "110526        0             1      No  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the tail 50 rows from the dataset.\n",
    "db_nsa.tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the No-show column with NoShow for easier use because we're going to refer to it a lot. \n",
    "db_nsa.rename(columns={'No-show':'NoShow'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoShow  ScheduledDay\n",
       "No      Friday          15028\n",
       "        Monday          18524\n",
       "        Saturday           23\n",
       "        Thursday        14373\n",
       "        Tuesday         20877\n",
       "        Wednesday       19383\n",
       "Yes     Friday           3887\n",
       "        Monday           4561\n",
       "        Saturday            1\n",
       "        Thursday         3700\n",
       "        Tuesday          5291\n",
       "        Wednesday        4879\n",
       "Name: ScheduledDay, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the Appointment days with noshow variable, to find the relationship between the days and noshow indicator.\n",
    "db_nsa1 = db_nsa.groupby(['NoShow', 'ScheduledDay'])['ScheduledDay'].count()\n",
    "db_nsa1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# source http://benalexkeen.com/bar-charts-in-matplotlib/\n",
    "# visualize Appointment days with noshow variable by a bar chart.\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "N = 6\n",
    "Show_count = (15028, 18524, 23, 14373, 20877, 19383)\n",
    "Noshow_count = (3887, 4561, 1, 3700, 5291, 4879)\n",
    "\n",
    "ind = np.arange(N) \n",
    "width = 0.30      \n",
    "plt.bar(ind, Show_count, width, label='Show Count')\n",
    "plt.bar(ind + width, Noshow_count, width,\n",
    "    label='No Show Count')\n",
    "\n",
    "plt.ylabel('Appointments Count')\n",
    "plt.xlabel('Scheduled Day')\n",
    "plt.title('The relationship between appointment days & No show')\n",
    "\n",
    "plt.xticks(ind + width / 2, ('Friday', 'Monday', 'Saturday', 'Thursday', 'Tuesday', 'Wednesday'))\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize Appointment days with noshow variable by a bar chart.\n",
    "# Old db_nsa1.plot(x='Month', y='number', kind='bar' , title='The relationship between appointment days & No show notification');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ScheduledDay\n",
       "Friday       18915\n",
       "Monday       23085\n",
       "Saturday        24\n",
       "Thursday     18073\n",
       "Tuesday      26168\n",
       "Wednesday    24262\n",
       "Name: NoShow, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total Appointment days with noshow variable to compare it with the above result.\n",
    "db_nsa2 = db_nsa.groupby('ScheduledDay').NoShow.count()\n",
    "db_nsa2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# source http://benalexkeen.com/bar-charts-in-matplotlib/\n",
    "# visualize the total Appointment days with noshow variable by a bar chart.\n",
    "\n",
    "N = 6\n",
    "Total_count = (18915, 23085, 24, 18073, 26168, 24262)\n",
    "\n",
    "ind1 = np.arange(N) \n",
    "width = 0.30      \n",
    "plt.bar(ind1, Total_count, width, label='Total Appointment Count')\n",
    "\n",
    "plt.ylabel('Appointments Count')\n",
    "plt.xlabel('Scheduled Day')\n",
    "plt.title('Total appointments & No show notifications')\n",
    "\n",
    "plt.xticks(ind1 + width / 2, ('Friday', 'Monday', 'Saturday', 'Thursday', 'Tuesday', 'Wednesday'))\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# old Chart visualize the total Appointment days with noshow variable by a bar chart.\n",
    "# db_nsa2.plot(kind='bar', title='Total appointments & No show notifications');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoShow  SMS_received\n",
       "No      0               62510\n",
       "        1               25698\n",
       "Yes     0               12535\n",
       "        1                9784\n",
       "Name: SMS_received, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group SMS_received with noshow variable, to find the relationship between them.\n",
    "db_nsas = db_nsa.groupby(['NoShow', 'SMS_received'])['SMS_received'].count()\n",
    "db_nsas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# source http://benalexkeen.com/bar-charts-in-matplotlib/\n",
    "# visualize the SMS_received with noshow variable by a bar chart.\n",
    "\n",
    "N = 2\n",
    "Show_count = (62510, 25698)\n",
    "Noshow_count = (12535, 9784)\n",
    "\n",
    "ind2 = np.arange(N) \n",
    "width = 0.30      \n",
    "plt.bar(ind2, Show_count, width, label='Show Count')\n",
    "plt.bar(ind2 + width, Noshow_count, width,\n",
    "    label='No Show Count')\n",
    "\n",
    "plt.ylabel('Appointments Count')\n",
    "plt.xlabel('SMS Statues')\n",
    "plt.title('Patients who received SMS vs who did not received them')\n",
    "\n",
    "plt.xticks(ind2 + width / 2, ('Not Recevied SMS', 'Recevied SMS'))\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# old visualize the SMS_received with noshow variable by a bar chart.\n",
    "# db_nsas.plot(kind='bar', title='Patients who received SMS');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoShow  Hipertension\n",
       "No      0               70179\n",
       "        1               18029\n",
       "Yes     0               18547\n",
       "        1                3772\n",
       "Name: Hipertension, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group Hipertension with noshow variable, to find the relationship between them.\n",
    "db_nsah = db_nsa.groupby(['NoShow', 'Hipertension'])['Hipertension'].count()\n",
    "db_nsah"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the age ranges by a box chart.\n",
    "db_nsa['Age'].plot(kind='box', title='Age Ranges visulization by box chart');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoShow  Age \n",
       "No      -1         1\n",
       "         0      2900\n",
       "         1      1858\n",
       "         2      1366\n",
       "         3      1236\n",
       "         4      1017\n",
       "         5      1169\n",
       "         6      1205\n",
       "         7      1126\n",
       "         8      1106\n",
       "         9      1008\n",
       "         10      970\n",
       "         11      948\n",
       "         12      820\n",
       "         13      800\n",
       "         14      802\n",
       "         15      889\n",
       "         16     1049\n",
       "         17     1113\n",
       "         18     1137\n",
       "         19     1151\n",
       "         20     1082\n",
       "         21     1097\n",
       "         22     1025\n",
       "         23     1006\n",
       "         24      921\n",
       "         25      980\n",
       "         26      971\n",
       "         27     1048\n",
       "         28     1116\n",
       "                ... \n",
       "Yes      70       94\n",
       "         71      121\n",
       "         72      101\n",
       "         73       96\n",
       "         74       89\n",
       "         75       81\n",
       "         76       91\n",
       "         77       79\n",
       "         78       89\n",
       "         79       61\n",
       "         80       81\n",
       "         81       63\n",
       "         82       66\n",
       "         83       61\n",
       "         84       35\n",
       "         85       49\n",
       "         86       42\n",
       "         87       27\n",
       "         88       12\n",
       "         89       29\n",
       "         90       23\n",
       "         91       13\n",
       "         92       20\n",
       "         93       10\n",
       "         94        6\n",
       "         95        6\n",
       "         96        1\n",
       "         97        2\n",
       "         98        1\n",
       "         115       3\n",
       "Name: Age, Length: 204, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group age with noshow to read the count & find a relationship.\n",
    "db_nsa3 = db_nsa.groupby(['NoShow', 'Age'])['Age'].count()\n",
    "db_nsa3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Findings\n",
    ">> Based on the above analysis (NoShow as a dependent variable & others as independents) we found the following:\n",
    "1. Ages were from different ranges mostly from 0 to 50. The mean & median equal to 37. Almost elderlies have the most no showing rate comparing with the other age ranges.\n",
    "2. Alcoholism, diabetes and handicap factors have no direct correlation with no showing cases.\n",
    "3. 18029 of patients who have hypertension showed up to their appointments from 21801 in total. This indicates that this factor has a direct effect & rise a concern to patients to show up at their corresponding appointment as scheduled. \n",
    "4. After investigating the relationship between scheduled day and no show variables, we found that Tuesday has the most rate of not showing situations while Saturday & Thursday has the lowest rate.\n",
    "5. There is a positive correlation between SMS messages and showing up to appointments. Based on the provided records, only 9784 of those who received SMS didn’t show to their appointments out of 35482.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2. What are the significant factors if combine will prevent patients from showing?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         22319\n",
       "Gender            22319\n",
       "ScheduledDay      22319\n",
       "AppointmentDay    22319\n",
       "Age               22319\n",
       "Neighbourhood     22319\n",
       "Scholarship       22319\n",
       "Hipertension      22319\n",
       "Diabetes          22319\n",
       "Alcoholism        22319\n",
       "Handcap           22319\n",
       "SMS_received      22319\n",
       "NoShow            22319\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe for no showing cases only.\n",
    "df = db_nsa.loc[db_nsa['NoShow'] == 'Yes']\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 22319 entries, 6 to 110516\n",
      "Data columns (total 13 columns):\n",
      "PatientId         22319 non-null int64\n",
      "Gender            22319 non-null object\n",
      "ScheduledDay      22319 non-null object\n",
      "AppointmentDay    22319 non-null datetime64[ns]\n",
      "Age               22319 non-null int64\n",
      "Neighbourhood     22319 non-null object\n",
      "Scholarship       22319 non-null int64\n",
      "Hipertension      22319 non-null int64\n",
      "Diabetes          22319 non-null int64\n",
      "Alcoholism        22319 non-null int64\n",
      "Handcap           22319 non-null int64\n",
      "SMS_received      22319 non-null int64\n",
      "NoShow            22319 non-null object\n",
      "dtypes: datetime64[ns](1), int64(8), object(4)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# explore the above dataframe.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>Age</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.231900e+04</td>\n",
       "      <td>22319.000000</td>\n",
       "      <td>22319.000000</td>\n",
       "      <td>22319.000000</td>\n",
       "      <td>22319.000000</td>\n",
       "      <td>22319.000000</td>\n",
       "      <td>22319.000000</td>\n",
       "      <td>22319.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.467523e+14</td>\n",
       "      <td>34.317667</td>\n",
       "      <td>0.115507</td>\n",
       "      <td>0.169004</td>\n",
       "      <td>0.064071</td>\n",
       "      <td>0.030333</td>\n",
       "      <td>0.020297</td>\n",
       "      <td>0.438371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.549905e+14</td>\n",
       "      <td>21.965941</td>\n",
       "      <td>0.319640</td>\n",
       "      <td>0.374764</td>\n",
       "      <td>0.244885</td>\n",
       "      <td>0.171505</td>\n",
       "      <td>0.156670</td>\n",
       "      <td>0.496198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.628261e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.176779e+12</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.156794e+13</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.454270e+13</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.996585e+14</td>\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>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId           Age   Scholarship  Hipertension      Diabetes  \\\n",
       "count  2.231900e+04  22319.000000  22319.000000  22319.000000  22319.000000   \n",
       "mean   1.467523e+14     34.317667      0.115507      0.169004      0.064071   \n",
       "std    2.549905e+14     21.965941      0.319640      0.374764      0.244885   \n",
       "min    5.628261e+06      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    4.176779e+12     16.000000      0.000000      0.000000      0.000000   \n",
       "50%    3.156794e+13     33.000000      0.000000      0.000000      0.000000   \n",
       "75%    9.454270e+13     51.000000      0.000000      0.000000      0.000000   \n",
       "max    9.996585e+14    115.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "         Alcoholism       Handcap  SMS_received  \n",
       "count  22319.000000  22319.000000  22319.000000  \n",
       "mean       0.030333      0.020297      0.438371  \n",
       "std        0.171505      0.156670      0.496198  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        0.000000      0.000000      0.000000  \n",
       "50%        0.000000      0.000000      0.000000  \n",
       "75%        0.000000      0.000000      1.000000  \n",
       "max        1.000000      4.000000      1.000000  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore data.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>NoShow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>733688164476661</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>23</td>\n",
       "      <td>GOIABEIRAS</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3449833394123</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>39</td>\n",
       "      <td>GOIABEIRAS</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7542951368435</td>\n",
       "      <td>M</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>29</td>\n",
       "      <td>NOVA PALESTINA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14794966191172</td>\n",
       "      <td>F</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>40</td>\n",
       "      <td>CONQUISTA</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>622257462899397</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>30</td>\n",
       "      <td>NOVA PALESTINA</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12154843752835</td>\n",
       "      <td>F</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>4</td>\n",
       "      <td>CONQUISTA</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>863229818887631</td>\n",
       "      <td>M</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>13</td>\n",
       "      <td>CONQUISTA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>311284853849</td>\n",
       "      <td>M</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>12</td>\n",
       "      <td>NOVA PALESTINA</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>274164858852</td>\n",
       "      <td>F</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>78</td>\n",
       "      <td>SÃO CRISTÓVÃO</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>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>8545415176986</td>\n",
       "      <td>F</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2016-04-29</td>\n",
       "      <td>58</td>\n",
       "      <td>SÃO CRISTÓVÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId Gender ScheduledDay AppointmentDay  Age   Neighbourhood  \\\n",
       "6   733688164476661      F    Wednesday     2016-04-29   23      GOIABEIRAS   \n",
       "7     3449833394123      F    Wednesday     2016-04-29   39      GOIABEIRAS   \n",
       "11    7542951368435      M      Tuesday     2016-04-29   29  NOVA PALESTINA   \n",
       "17   14794966191172      F     Thursday     2016-04-29   40       CONQUISTA   \n",
       "20  622257462899397      F    Wednesday     2016-04-29   30  NOVA PALESTINA   \n",
       "21   12154843752835      F    Wednesday     2016-04-29    4       CONQUISTA   \n",
       "22  863229818887631      M       Monday     2016-04-29   13       CONQUISTA   \n",
       "31     311284853849      M    Wednesday     2016-04-29   12  NOVA PALESTINA   \n",
       "44     274164858852      F     Thursday     2016-04-29   78   SÃO CRISTÓVÃO   \n",
       "48    8545415176986      F      Tuesday     2016-04-29   58   SÃO CRISTÓVÃO   \n",
       "\n",
       "    Scholarship  Hipertension  Diabetes  Alcoholism  Handcap  SMS_received  \\\n",
       "6             0             0         0           0        0             0   \n",
       "7             0             0         0           0        0             0   \n",
       "11            0             0         0           0        0             1   \n",
       "17            1             0         0           0        0             0   \n",
       "20            0             0         0           0        0             0   \n",
       "21            0             0         0           0        0             0   \n",
       "22            0             0         0           0        0             1   \n",
       "31            1             0         0           0        0             0   \n",
       "44            0             1         1           0        0             0   \n",
       "48            0             0         0           0        0             1   \n",
       "\n",
       "   NoShow  \n",
       "6     Yes  \n",
       "7     Yes  \n",
       "11    Yes  \n",
       "17    Yes  \n",
       "20    Yes  \n",
       "21    Yes  \n",
       "22    Yes  \n",
       "31    Yes  \n",
       "44    Yes  \n",
       "48    Yes  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first 10 rows.\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see the distrbutions of the variables.\n",
    "df.hist(figsize=(10, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jOTkZw4YNQ9++fTFnzhzcf//9CAgIgIWFBT777DPY2dlVex6rqny3tGLFCmRnZ2Pt2rU3rfWFF17A0KFDcdddd6G0tBQbNmxQhlAAwGAwYOnSpXjsscfg4+OD5cuX4+TJk3W+e6pUUlKCRx99FA888AAmTpyIM2fOKPNcXFzg7+8PjUaD2NhYjBs3Dj///DNef/11o3X4+PjAwsICW7duxejRo2FlZQUHB4dqfc2ePRuhoaGIiYnB448/jszMTLz66quYMWNGtbO1xvLz88OTTz6JqKgofPjhh/Dx8cGyZcvw66+/1vmcm8qU7UtISEBFRQV69+4NR0dH/Oc//0FxcbES0PVR+TqZPn06Jk+ejOzsbEydOhXjxo0zOhZDQkKQkJCARx55RLkSJjg4GJ988gmeeuqpxm08gBkzZmD+/Pno0qULevfujS1btuD777+/6Vm+uffTxIkT8dprr+HIkSM1XknYJJp0xN9Mhg8fXusHXWVlZeLm5iZz5swREZH8/HwZOXKktGvXTlxcXOSVV16RJ554Qh599FGj5VatWiX33XefWFlZiaOjo/Tu3VuWLl1aaw03XnVTlcFgUD44vXr1qkyePFmcnJykffv2MnbsWHn//fel6lNd0weNNX1YuHbtWvH19ZW2bdtKUFCQfP755wJA9uzZo7TJzs6WsLAw0ev10rZtW+nQoYOMGzdO+aDy9ddfl4CAALG1tRV7e3vp379/jdtwo8zMTAEgOp2u2od0N9YfFRUl/v7+Ym1tLTqdToYOHSq//vqrMr+oqEjCw8PF0dFRXFxcZP78+Q36MLbyua/pr/L5+uCDD8TLy0usra2lX79+kpycLABk27Ztyjrffvtt8fDwEAsLC6XvmvbFxx9/LF26dJE2bdqIh4eHREdHy7Vr15T5N34IKCKyYMEC8fHxuelzeuM2iohcuHBBJk+erOy/+++/X7777jtl/s2Ou/qsPzExUW58mde1fTf66quv5IEHHhBHR0dp166dBAQEyKpVq5T59T2et2zZIj179pS2bduKXq+XZ599Vi5evGi03BdffCEAZNGiRcq0xYsXCwBJTEw0alvTh7F17Zfy8nKZNWuWODs7i62trYwePVoWLlwodnZ2tW6/iOn7yc/Pz+hChZouTBC5fqGJg4ODciVTU9OIqPsXpsrLy9GlSxcMHz5cOSNurdasWYPIyEgUFBTA0dGxpcshUoUJEybg559/NhribW69e/dGnz598P7775tl/a1i6KYhduzYgby8PPTo0QPFxcWIi4tDdnY2IiIiWrq0Bnv33XcxcOBA6HQ6ZGRkYObMmXjyyScZ8kQmysnJwcaNGzFw4EBotVp88803WLNmDT744IMWqScvLw+bNm3Cvn37sG7dOrP1o7qgLy8vxxtvvIFjx46hTZs2uOeee7Bt2zZ069atpUtrsF9++QWxsbEoLCyEt7c3wsPD8dprr7V0WUStllarxZdffolXXnkFpaWl6Ny5M5YtW2aWK13qw83NDU5OTnjvvffg5+dntn7qHLpZunQp9u3bBwcHB2XoIzExEXv37lWuE42KilIu/9q4cSNSU1NhYWGByMjIate1EhFR86rz8soBAwZUu7fGvffei9jYWLz77ru44447sHHjRgDX73myc+dOLFq0CHPmzFE+rSciopZTZ9B37doVdnZ2RtPuu+8+5XrzO++8E4WFhQCuX8/ct29ftGnTBq6urnB3dze6SRERETW/Ro/Rp6amom/fvgCAwsJC+Pv7K/N0Op3yT6AuN36hhuhWodfrkZ+f39JlEFXj4eFRr3aNCvoNGzZAq9Uqt4BtyJWaKSkpSElJAQDExMQ02z3BiRrK0tKSxye1aiYH/fbt27F3717MmzdP+VaZs7MzCgoKlDaFhYW1fvvRYDAY3SOeZ0x0q+IZPd2q6ntGb9K9bjIzM7Fp0ybMnDnT6E55gYGB2LlzJ65du4a8vDzk5ua2yG9JEhHR/9R5eWV8fDwOHjyI4uJiODg4YNSoUdi4cSPKysqUD2n9/f2VXyXasGEDtm3bBgsLC0RERKBHjx71KoRj9HSr4hk93arqe0Z/y9wCgUFPtyoGPd2qzDp0Q3Q7CAsLg5eXl/LbqmFhYS1dEpFJGPRENQgLC0NaWhrCw8ORl5eH8PBwpKWlMeypVVLdvW6ImsKOHTvw1FNPISYmBg4ODoiJiQEAfPrppy1cGVHD8YyeqAYigtmzZxtNmz17doO+K0J0q2DQE9VAo9HgrbfeMpr21ltvNdnvjRI1Jw7dENWgf//+SExMBADExsZi1qxZSExMRHBwcAtXRtRwvLySqBZhYWHYsWMHRAQajQb9+/c322+5EpmC19ETNRFeR0+3Kl5HT0REABj0RESqx6AnIlI5Bj0Rkcox6ImIVI5BT0Skcgx6IiKVY9ATEakcg56ISOUY9EREKsegJyJSOQY9EZHKMeiJiFSOQU9EpHIMeiIilWPQExGpHIOeiEjlGPRERCpX54+DL126FPv27YODgwNiY2MBABcvXkRcXBzOnj0LFxcXvPTSS7Czs4OIYPXq1di/fz+srKwQFRUFX19fs28EERHVrs4z+gEDBiA6OtpoWlJSErp164bFixejW7duSEpKAgDs378fZ86cweLFizF58mSsWrXKPFUTEVG91Rn0Xbt2hZ2dndG0jIwMBAcHAwCCg4ORkZEBANizZw/69+8PjUaDO++8E5cuXcK5c+fMUDYREdVXnUM3Nblw4QKcnJwAAE5OTigqKgIAFBYWQq/XK+2cnZ1RWFiotK0qJSUFKSkpAICYmBij5YhuJZaWljw+qVUzKehrIyLVpmk0mhrbGgwGGAwG5XF+fn5TlkLUZPR6PY9PuiV5eHjUq51JV904ODgoQzLnzp2Dvb09gOtn8FVfEAUFBTWezRMRUfMx6Yw+MDAQaWlpGDFiBNLS0tCrVy9l+rfffot+/frh6NGjsLGxYdDTLcXT07NZ+vnzzz+bpR+i+tBITeMtVcTHx+PgwYMoLi6Gg4MDRo0ahV69eiEuLg75+fnQ6/WYPn26cnllQkICfv75Z7Rt2xZRUVHw8/OrVyE5OTlNskFETa180nBoV37d0mUQVVPfoZs6g765MOjpVsWgp1uVWcfoiYio9WDQExGpHIOeiEjlGPRERCrHoCciUjkGPRGRyjHoiYhUjkFPRKRyDHoiIpVj0BMRqRyDnohI5Rj0REQqx6AnIlI5Bj0Rkcox6ImIVI5BT0Skcgx6IiKVY9ATEakcg56ISOUY9EREKsegJyJSOQY9EZHKMeiJiFSOQU9EpHIMeiIilbNszMKbN29GamoqNBoNvL29ERUVhfPnzyM+Ph4XL15Ep06dMHXqVFhaNqobIiJqBJPP6AsLC5GcnIyYmBjExsaioqICO3fuxKeffophw4Zh8eLFsLW1RWpqalPWS0REDdSooZuKigpcvXoV5eXluHr1KhwdHZGVlYWgoCAAwIABA5CRkdEkhRIRkWlMHlPR6XQIDQ3Fc889h7Zt2+K+++6Dr68vbGxsoNVqlTaFhYU1Lp+SkoKUlBQAQExMDPR6vamlEJnVXwCPT2rVTA76ixcvIiMjA0uWLIGNjQ0WLVqEzMzMei9vMBhgMBiUx/n5+aaWQmR2PD7pVuTh4VGvdiYH/YEDB+Dq6gp7e3sAQJ8+fXD48GFcvnwZ5eXl0Gq1KCwshE6nM7ULIiJqAiaP0ev1ehw9ehRXrlyBiODAgQPw8vJCQEAAdu3aBQDYvn07AgMDm6xYIiJqOJPP6P39/REUFISZM2dCq9WiY8eOMBgM6NmzJ+Lj47F+/Xp06tQJISEhTVkvERE1kEZEpKWLAICcnJyWLoGoRuWThkO78uuWLoOomvqO0fObsUREKsegJyJSOQY9EZHKMeiJiFSOQU9EpHIMeiIilWPQExGpHIOeiEjlGPRERCrHoCciUjkGPRGRyjHoiYhUjkFPRKRyDHoiIpVj0BMRqRyDnohI5Rj0REQqx6AnIlI5Bj0Rkcox6ImIVI5BT0Skcgx6IiKVY9ATEakcg56ISOUY9EREKsegJyJSOcvGLHzp0iUsX74cp06dgkajwXPPPQcPDw/ExcXh7NmzcHFxwUsvvQQ7O7umqpeIiBqoUUG/evVqdO/eHTNmzEBZWRmuXLmCjRs3olu3bhgxYgSSkpKQlJSE8PDwpqqXiIgayOShm8uXL+O3335DSEgIAMDS0hK2trbIyMhAcHAwACA4OBgZGRlNUykREZnE5DP6vLw82NvbY+nSpTh58iR8fX0RERGBCxcuwMnJCQDg5OSEoqKiGpdPSUlBSkoKACAmJgZ6vd7UUojM6i+Axye1aiYHfXl5OU6cOIEJEybA398fq1evRlJSUr2XNxgMMBgMyuP8/HxTSyEyOx6fdCvy8PCoVzuTh26cnZ3h7OwMf39/AEBQUBBOnDgBBwcHnDt3DgBw7tw52Nvbm9oFERE1AZOD3tHREc7OzsjJyQEAHDhwAF5eXggMDERaWhoAIC0tDb169WqaSomIyCSNuupmwoQJWLx4McrKyuDq6oqoqCiICOLi4pCamgq9Xo/p06c3Va1ERGQCjYhISxcBQHlnQHSrKZ80HNqVX7d0GUTVmH2MnoiIWgcGPRGRyjHoiYhUjkFPRKRyDHoiIpVj0BMRqRyDnohI5Rj0REQqx6AnIlI5fjOWWq3yf4QBly+2dBlNw8YO2vfWtnQV1MrU95uxjbrXDVGLunyxWW5NoNfrzX6b4vJJw826frq9ceiGiEjlGPRERCrHoCciUjkGPRGRyjHoiYhUjkFPRKRyDHoiIpVj0BMRqRyDnohI5Rj0REQqx6AnIlI5Bj0Rkcox6ImIVI5BT0Skco2+TXFFRQVmzZoFnU6HWbNmIS8vD/Hx8bh48SI6deqEqVOnwtKSd0MmImopjT6j37p1Kzw9PZXHn376KYYNG4bFixfD1tYWqampje2CiIgaoVFBX1BQgH379mHQoEEAABFBVlYWgoKCAAADBgxARkZG46skIiKTNWpM5eOPP0Z4eDhKSkoAAMXFxbCxsYFWqwUA6HQ6FBYW1rhsSkoKUlJSAAAxMTHQ6/WNKYVuQ38BzXLcWFpamr2f5toWuj2ZHPR79+6Fg4MDfH19kZWV1eDlDQYDDAaD8tjcP9VG6tQcx01z/JQgwNcANZzZfzP28OHD2LNnD/bv34+rV6+ipKQEH3/8MS5fvozy8nJotVoUFhZCp9OZ2gURETUBk4M+LCwMYWFhAICsrCx88803ePHFF7Fo0SLs2rUL/fr1w/bt2xEYGNhkxRIRUcM1+XX048aNw+bNmzF16lRcvHgRISEhTd0FERE1QJNc4B4QEICAgAAAgJubG956662mWC0RETUBfjOWiEjlGPRERCrHoCciUjkGPRGRyjHoiYhUjkFPRKRyGhGRli4CAHJyclq6BGplvvn8fEuX0KRCRzu2dAnUypj9FghELW1oytPQrvza7P00x71uyicNB0abf1vo9sShGyIilWPQExGpHIOeiEjlGPRERCrHoCciUjkGPRGRyjHoiYhUjkFPRKRyDHoiIpVj0BMRqRyDnohI5Rj0REQqx6AnIlI5Bj0Rkcox6ImIVI5BT0Skcgx6IiKVM/kXpvLz87FkyRKcP38eGo0GBoMBQ4cOxcWLFxEXF4ezZ8/CxcUFL730Euzs7JqyZiIiagCTg16r1eKpp56Cr68vSkpKMGvWLNx7773Yvn07unXrhhEjRiApKQlJSUkIDw9vypqJiKgBTB66cXJygq+vLwCgXbt28PT0RGFhITIyMhAcHAwACA4ORkZGRtNUSkREJmmSMfq8vDycOHECnTt3xoULF+Dk5ATg+j+DoqKipuiCiIhMZPLQTaXS0lLExsYiIiICNjY29V4uJSUFKSkpAICYmBjo9frGlkK3mb+AZjluLC0tzd5Pc20L3Z4aFfRlZWWIjY3FQw89hD59+gAAHBwccO7cOTg5OeHcuXOwt7evcVmDwQCDwaA8zs/Pb0wpdJtqjuNGr9c3Sz98DVBDeXh41KudyUM3IoLly5fD09MTjz76qDI9MDAQaWlpAIC0tDT06tXL1C6IiKgJmHxGf/jwYezYsQMdOnTAyy+/DADDATNSAAAEYElEQVQYO3YsRowYgbi4OKSmpkKv12P69OlNViwRETWcyUHfpUsXfPHFFzXOmzdvnskFERFR0+I3Y4mIVK7RV90QtaTyScPN3sdfZu8BgA2/PU7moxERaekiACAnJ6elSyCqUfmk4dCu/LqlyyCqxuxX3RARUevAoCciUjkGPRGRyjHoiYhUjkFPRKRyDHoiIpVj0BMRqRyDnohI5Rj0REQqx6AnIlI5Bj0Rkcox6ImIVI5BT0Skcgx6IiKVY9ATEakcg56ISOUY9EREKsegJyJSOQY9EZHKMeiJiFSOQU9EpHIMeiIilWPQExGpnKW5VpyZmYnVq1ejoqICgwYNwogRI8zVFRER3YRZzugrKiqQkJCA6OhoxMXFIT09HadPnzZHV0REVAezBP2xY8fg7u4ONzc3WFpaom/fvsjIyDBHV0REVAezDN0UFhbC2dlZeezs7IyjR48atUlJSUFKSgoAICYmBnq93hylEBmxsrIybUFPzwY1v3Llimn9EJmBWYJeRKpN02g0Ro8NBgMMBoPyOD8/3xylEBn5888/G7yMXq9v8PHJ45mag4eHR73amWXoxtnZGQUFBcrjgoICODk5maMrIiKqg1mC3s/PD7m5ucjLy0NZWRl27tyJwMBAc3RFRER1MMvQjVarxYQJE7Bw4UJUVFRg4MCB8Pb2NkdXRERUB43UNKDeAnJyclq6BKIamTJGT9QcWnSMnoiIbh0MeiIilWPQExGpHIOeiEjlbpkPY4mIyDx4Rk9Uh1mzZrV0CUSNwqAnIlI5Bj0Rkcox6InqUPXme0StET+MJSJSOZ7RExGpHIOeiEjlGPR029u9ezdGjRpl0o+SELUGDHq67f3444/o0qUL0tPTW7oUIrMwy/3oiVqL0tJSHD58GPPnz8c777yDUaNGoaKiAh999BEOHjwIV1dXiAgGDhyIoKAgHD9+HJ988glKS0thb2+PqKgo/noa3fJ4Rk+3td27d6N79+7w8PCAnZ0djh8/jt27d+Ps2bN499138eyzz+LIkSMAgLKyMnz00UeYMWMG3n77bQwcOBDr1q1r4S0gqhvP6Om2lp6ejmHDhgEA+vbti/T0dJSXlyMoKAgWFhZwdHREQEAAgOs/jnPq1CksWLAAAFBRUcGzeWoVGPR02youLsavv/6KU6dOQaPRoKKiAgDQu3fvWpfx8vLCwoULm6tEoibBoRu6be3atQvBwcFYunQplixZgmXLlsHV1RXt27fHf//7X1RUVOD8+fPIysoCcP1n24qKioyGck6dOtWSm0BULzyjp9tWeno6RowYYTStT58++PPPP6HT6TBjxgzccccd8Pf3h42NDSwtLTFjxgysXr0aly9fRnl5OYYOHcofvqdbHm+BQFSD0tJSWFtbo7i4GNHR0ViwYAEcHR1buiwik/CMnqgGMTExuHTpEsrKyjBy5EiGPLVqPKMnIlI5fhhLRKRyDHoiIpVj0BMRqRyDnohI5Rj0REQq9/82yXhH6OXurwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plot of the ages range.\n",
    "df['Age'].plot(kind='box', title='Age Ranges visulization for no showing only');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Disrbution of gender and age for not showing cases only')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# disrbution using gender and age.\n",
    "df.groupby('Gender').Age.plot(kind='hist', legend = True);\n",
    "plt.xlabel('Age Range')\n",
    "plt.ylabel('Count of no showing')\n",
    "plt.title('Disrbution of gender and age for not showing cases only')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tuesday      5291\n",
       "Wednesday    4879\n",
       "Monday       4561\n",
       "Friday       3887\n",
       "Thursday     3700\n",
       "Saturday        1\n",
       "Name: ScheduledDay, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scheduled days\n",
    "days = df.ScheduledDay.value_counts()\n",
    "days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar chart of the scheuled days & no showing counts. \n",
    "# Source http://benalexkeen.com/bar-charts-in-matplotlib/\n",
    "\n",
    "wday = ['Tuesday', 'Wednesday', 'Monday', 'Friday ', 'Thursday', 'Saturday']\n",
    "NoShow_count = [5291, 4879, 4561, 3887, 3700, 1]\n",
    "\n",
    "x_pos = [i for i, _ in enumerate(wday)]\n",
    "\n",
    "plt.bar(x_pos, NoShow_count, color='green')\n",
    "plt.xlabel(\"Appointment Days\")\n",
    "plt.ylabel(\"No Show Count\")\n",
    "plt.title(\"Scheduled days and No Show counts\")\n",
    "\n",
    "plt.xticks(x_pos, wday)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    14594\n",
       "M     7725\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gender of no showing break down.\n",
    "df.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Findings\n",
    ">> Based on the above analysis (NoShow actual cases as a dependent variable & others as independents) we found the following:\n",
    "1. Females have the most no showing rate comparing with males and based on the histogram distribution females from 24 to 32 years old have the most no showing rate. \n",
    "2. Based on the bar chart of defining the relationship between scheduled days and no showing count situations, it appears that Tuesday & Wednesday has the most no showing rate while Thursday & Friday has the lowest rate.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save changes to new csv file.\n",
    "db_nsa.to_csv('new.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "> In this project, we investigated many aspects of no showing dataset and we successfully identified the most significant factors of no showing situations that directly have a strong relationship with the patients. At the first section, we cleaned the dataset and we applied minor changes to help us with our analysis. While in the second section, we have been trying to answer our research questions. Based on the analysis, we found a direct correlation between SMS factor with no showing element. The patients who received SMS messages prior to their scheduled appointment days showed up to their corresponding appointments. The second factor was associated with the age range based on the provided data from 0 to 28 age range has the most showing range. Moreover, hipertension, gender and the scheduled day have a partial effect on no showing situations.\n",
    "\n",
    "\n",
    "> The dataset has some limitations that should be taken into consideration to have more accurate results: \n",
    "The dataset covered one year only (2016), as a result, no historical data were presented.\n",
    "The dataset of showing and not showing variable was not balanced, and this affects the reliability of the findings as some of them were based on assumptions only without accurate evidence. \n",
    "The dataset datatype most of them were strings which made it more challenging to represent them in graphes, with even limited functions such as count.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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    "name": "ipython",
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