{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Investigate Medical Appointment Dataset**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A person takes a doctor's appointment, receives all the instructions, and no-show. Who's to blame?\n",
    "\n",
    "In this project, I will try to analyse why would some patient not show up for their medical appointment or whether there are reasons for that using the data we have.\n",
    "I will try to find some correlation between the different attributes and whether the patient shows up or not. The dataset I'm going to use contains 110.527 medical appointments and its 14 associated variables ( PatientId, AppointmentID, Gender, ScheduledDay, AppointmentDay, Age, Neighbourhood, Scholarship, Hypertension, Diabetes, Alcoholism, Handcap', SMS_received, No-show )\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions to answer\n",
    "\n",
    "*   What is the percentage of no-show?\n",
    "*   What factors are important for to know in order to predict if a patient will show up for their scheduled appointment?\n",
    "    *   Is the time gender related to whether a patient will show or not?\n",
    "    *   Are patients with scholarship more likely to miss their appointment?\n",
    "    *   Are patients who don't recieve sms more likely to miss their appointment?\n",
    "    *   Is the time difference between the scheduling and appointment related to whether a patient will show?\n",
    "    *   Does age affect whether a patient will show up or not?\n",
    "    *   What is the percentage of patients missing their appointments for every neighbourhood\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing needed modules\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "#choose plots style\n",
    "sns.set_style('darkgrid')\n",
    "#make sure plots are inline with the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling\n",
    "\n",
    "### Loading the dataset and checking the columns we have\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.589978e+14</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Loading the dataset and checking the columns we have\n",
    "\n",
    "### Load data and print out a few lines. Perform operations to inspect data\n",
    "### Types and look for instances of missing or possibly errant data.\n",
    "df = pd.read_csv('data/no-showappointments.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(110527, 14)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PatientId</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AppointmentID</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScheduledDay</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AppointmentDay</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighbourhood</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scholarship</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hipertension</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diabetes</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcoholism</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Handcap</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMS_received</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No-show</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0\n",
       "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"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Get the shape and types of our data\n",
    "print(df.shape)\n",
    "pd.DataFrame(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>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",
       "</table>\n",
       "</div>"
      ],
      "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Get some statistics about our data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   PatientId       110527 non-null  float64\n",
      " 1   AppointmentID   110527 non-null  int64  \n",
      " 2   Gender          110527 non-null  object \n",
      " 3   ScheduledDay    110527 non-null  object \n",
      " 4   AppointmentDay  110527 non-null  object \n",
      " 5   Age             110527 non-null  int64  \n",
      " 6   Neighbourhood   110527 non-null  object \n",
      " 7   Scholarship     110527 non-null  int64  \n",
      " 8   Hipertension    110527 non-null  int64  \n",
      " 9   Diabetes        110527 non-null  int64  \n",
      " 10  Alcoholism      110527 non-null  int64  \n",
      " 11  Handcap         110527 non-null  int64  \n",
      " 12  SMS_received    110527 non-null  int64  \n",
      " 13  No-show         110527 non-null  object \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 11.8+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PatientId         False\n",
       "AppointmentID     False\n",
       "Gender            False\n",
       "ScheduledDay      False\n",
       "AppointmentDay    False\n",
       "Age               False\n",
       "Neighbourhood     False\n",
       "Scholarship       False\n",
       "Hipertension      False\n",
       "Diabetes          False\n",
       "Alcoholism        False\n",
       "Handcap           False\n",
       "SMS_received      False\n",
       "No-show           False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Check if there is any missing values in our data\n",
    "df.info()\n",
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Check if there is any duplicated rows in our data\n",
    "df.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:blue'>Notes on data exploration</h3>\n",
    "\n",
    " It's clear that some columns need to have their type corrected like dates. Another great finding is that the data has no duplicated or missing values. Also, the column no-show can be a bit confusing and we can invert the values to make it more intuitive (show instead of no-show) and we can also turn it to integer instead of yes or no.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "\n",
    "*   Drop irrelevant columns\n",
    "*   Modify column names\n",
    "*   Correct data types\n",
    "*   Invert no-show column in to show with integer values\n",
    "*   Create a new column for days difference between scheduling an appointment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>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",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>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>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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender          ScheduledDay        AppointmentDay  Age      Neighbourhood  \\\n",
       "0      F  2016-04-29T18:38:08Z  2016-04-29T00:00:00Z   62    JARDIM DA PENHA   \n",
       "1      M  2016-04-29T16:08:27Z  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "2      F  2016-04-29T16:19:04Z  2016-04-29T00:00:00Z   62      MATA DA PRAIA   \n",
       "3      F  2016-04-29T17:29:31Z  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI   \n",
       "4      F  2016-04-29T16:07:23Z  2016-04-29T00:00:00Z   56    JARDIM DA PENHA   \n",
       "\n",
       "   Scholarship  Hipertension  Diabetes  Alcoholism  Handcap  SMS_received  \\\n",
       "0            0             1         0           0        0             0   \n",
       "1            0             0         0           0        0             0   \n",
       "2            0             0         0           0        0             0   \n",
       "3            0             0         0           0        0             0   \n",
       "4            0             1         1           0        0             0   \n",
       "\n",
       "  No-show  \n",
       "0      No  \n",
       "1      No  \n",
       "2      No  \n",
       "3      No  \n",
       "4      No  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Drop irrelevant columns\n",
    "df.drop(['PatientId','AppointmentID'],axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <td>appointmentday</td>\n",
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       "      <td>age</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neighbourhood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>scholarship</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>hipertension</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>alcoholism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>handcap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sms_received</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>no_show</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "                 0\n",
       "0           gender\n",
       "1     scheduledday\n",
       "2   appointmentday\n",
       "3              age\n",
       "4    neighbourhood\n",
       "5      scholarship\n",
       "6     hipertension\n",
       "7         diabetes\n",
       "8       alcoholism\n",
       "9          handcap\n",
       "10    sms_received\n",
       "11         no_show"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Change all cloumns name to lower case and replace all - with _\n",
    "df.columns=df.columns.str.lower().str.replace('-','_')\n",
    "pd.DataFrame(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Change data columns to data type\n",
    "df['scheduledday']=pd.to_datetime(df['scheduledday'])\n",
    "df['appointmentday']=pd.to_datetime(df['appointmentday'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['No' 'Yes']\n",
      "[1 0]\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduledday</th>\n",
       "      <th>appointmentday</th>\n",
       "      <th>age</th>\n",
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       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
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       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:19:04+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 17:29:31+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:07:23+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender              scheduledday            appointmentday  age  \\\n",
       "0      F 2016-04-29 18:38:08+00:00 2016-04-29 00:00:00+00:00   62   \n",
       "1      M 2016-04-29 16:08:27+00:00 2016-04-29 00:00:00+00:00   56   \n",
       "2      F 2016-04-29 16:19:04+00:00 2016-04-29 00:00:00+00:00   62   \n",
       "3      F 2016-04-29 17:29:31+00:00 2016-04-29 00:00:00+00:00    8   \n",
       "4      F 2016-04-29 16:07:23+00:00 2016-04-29 00:00:00+00:00   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  show  \n",
       "0        0             0     1  \n",
       "1        0             0     1  \n",
       "2        0             0     1  \n",
       "3        0             0     1  \n",
       "4        0             0     1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Turn no_show column to show\n",
    "print(df.no_show.unique())\n",
    "df.no_show=df.no_show.map({'No':1,'Yes':0})\n",
    "df.rename(columns={'no_show':'show'},inplace=True)\n",
    "print(df.show.unique())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Create a new column for days difference between scheduling and appointment\n",
    "day_diff=(df.appointmentday.dt.date-df.scheduledday.dt.date).dt.days \n",
    "df.insert(3,'day_diff',day_diff)\n",
    "df.day_diff.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                         object\n",
       "scheduledday      datetime64[ns, UTC]\n",
       "appointmentday    datetime64[ns, UTC]\n",
       "day_diff                        int64\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",
       "show                            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Check data one last time\n",
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our data cleaned and with the proper type for every column and also created a new Time difference column we can start analyzing our data and try to find the correlation between different variables and the show column.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define function to get the ratio of show in different categories\n",
    "def plot_rat(x):\n",
    "    df.groupby(x).show.mean().plot(kind='bar',\n",
    "                                    edgecolor='black',\n",
    "                                    figsize=(14,8)).set_ylabel('Ratio of show');\n",
    "    display(df.groupby(x)[['show']].mean())\n",
    "#     plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the percentage of no-show?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>110527.000000</td>\n",
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       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "      <td>110527.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.183702</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",
       "      <td>0.798067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.254996</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",
       "      <td>0.401444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-6.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.000000</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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>179.000000</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",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            day_diff            age    scholarship   hipertension  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean       10.183702      37.088874       0.098266       0.197246   \n",
       "std        15.254996      23.110205       0.297675       0.397921   \n",
       "min        -6.000000      -1.000000       0.000000       0.000000   \n",
       "25%         0.000000      18.000000       0.000000       0.000000   \n",
       "50%         4.000000      37.000000       0.000000       0.000000   \n",
       "75%        15.000000      55.000000       0.000000       0.000000   \n",
       "max       179.000000     115.000000       1.000000       1.000000   \n",
       "\n",
       "            diabetes     alcoholism        handcap   sms_received  \\\n",
       "count  110527.000000  110527.000000  110527.000000  110527.000000   \n",
       "mean        0.071865       0.030400       0.022248       0.321026   \n",
       "std         0.258265       0.171686       0.161543       0.466873   \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       1.000000   \n",
       "max         1.000000       1.000000       4.000000       1.000000   \n",
       "\n",
       "                show  \n",
       "count  110527.000000  \n",
       "mean        0.798067  \n",
       "std         0.401444  \n",
       "min         0.000000  \n",
       "25%         1.000000  \n",
       "50%         1.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get some statistics about our data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of patients who didn't show up for their appointment is 20.193255946510803 %\n"
     ]
    },
    {
     "data": {
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       "      <th>1</th>\n",
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       "       show\n",
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# percentage of show and no show\n",
    "print(f\"percentage of patients who didn't show up for their appointment is { (1-df.show.mean())*100 } %\" )\n",
    "no_show=len(df[df.show==0])/len(df.show)\n",
    "show=len(df[df.show==1])/len(df.show)\n",
    "plt.bar(['show','no show'],[show*100,no_show*100],color=['g','r']);\n",
    "plt.title('Percentage of patients showing up or missing their appointment ');\n",
    "plt.ylabel('Percentage');\n",
    "plt.xlabel('show or no-show');\n",
    "display(df.groupby('show')[['show']].count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create filters for show and no-show\n",
    "show=(df.show == 1)\n",
    "no_show=(df.show == 0)\n",
    "total_miss=len(df[no_show])\n",
    "total=len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is the time gender related to whether a patient will show or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of Females and Males who missed their appointment:\n"
     ]
    },
    {
     "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>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>13.204013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>6.989242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender\n",
       "F  13.204013\n",
       "M   6.989242"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the number of patients missing their appointments by gender\n",
    "no_show_gender=df[no_show]['gender'].value_counts()\n",
    "no_show_gender.plot(kind='pie');\n",
    "plt.title('patients who missed their appointment by gender');\n",
    "print('percentage of Females and Males who missed their appointment:')\n",
    "#get the percentage of patients missing their appointments by gender\n",
    "pd.DataFrame(no_show_gender*100/total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of patients')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['gender','show']).size().unstack('gender').plot(kind='bar').set_ylabel('number of patients')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 style='color:blue'>Finding</h4>\n",
    "\n",
    "#### The percentage of females missing their appointment is nearly two times the number of males. So females are more likely to miss their appointment.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are patients with scholarships more likely to miss their appointment?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>show</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scholarship</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.801928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.762637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 show\n",
       "scholarship          \n",
       "0            0.801928\n",
       "1            0.762637"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ratio of show or no-show by scholarship')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#what is the percentage of patients missing their appointment by scholarship\n",
    "plot_rat(df.scholarship)\n",
    "plt.title('Ratio of show or no-show by scholarship')\n",
    "# df.groupby('scholarship')[['show']].mean()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 style='color:blue'>Finding</h4>\n",
    "\n",
    "#### It seems that patients with no scholarships are actually more likely to miss their appointment\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are patients who don't receive SMS more likely to miss their appointment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>show</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sms_received</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.832967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  show\n",
       "sms_received          \n",
       "0             0.832967\n",
       "1             0.724255"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#what is the percentage of patient who attended their appointment by sms_received\n",
    "plot_rat(df.sms_received)\n",
    "plt.title('Ratio of show or no-show by sms_received');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 style='color:blue'>Finding</h4>\n",
    "\n",
    "#### A strange finding here suggests that patients who received an SMS are more likely to miss their appointment !!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is the time difference between the scheduling and appointment related to whether a patient will show?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the propotion of different time difference for patients who missed their appiontments:\n"
     ]
    },
    {
     "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>day_diff2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>more_than_15</th>\n",
       "      <td>38.460505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>more_than_4</th>\n",
       "      <td>32.922622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fewdays</th>\n",
       "      <td>20.565438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sameday</th>\n",
       "      <td>8.029034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              day_diff2\n",
       "more_than_15  38.460505\n",
       "more_than_4   32.922622\n",
       "fewdays       20.565438\n",
       "sameday        8.029034"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#filter for positive day difference\n",
    "df1=df[df.day_diff>=0]\n",
    "# df1.day_diff.unique()\n",
    "#turn day diff into categorical column Day_diff2\n",
    "bin_edges=[-1,0,4,15,179]\n",
    "names=['sameday','fewdays','more_than_4','more_than_15']\n",
    "df['day_diff2']=pd.cut(df1.day_diff,bin_edges,labels=names)\n",
    "#filter for no-show records and count values for each category of day_diff2\n",
    "\n",
    "no_show_day_diff=df[no_show].day_diff2.value_counts()/len(df[no_show])*100\n",
    "no_show_day_diff.reindex(names).plot(kind='bar');\n",
    "plt.title('propotion of time difference for no_show appointments');\n",
    "plt.xlabel('days difference between scheduling and appointment');\n",
    "plt.ylabel('Ratio of no_show');\n",
    "print('the propotion of different time difference for patients who missed their appiontments:')\n",
    "pd.DataFrame(no_show_day_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 style='color:blue'>Finding</h4>\n",
    "It appears that the longer the period between the scheduling and appointment the more likely the patient won't show up.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Does age affect whether a patient will show up or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22319.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.317667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.965941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age\n",
       "count  22319.000000\n",
       "mean      34.317667\n",
       "std       21.965941\n",
       "min        0.000000\n",
       "25%       16.000000\n",
       "50%       33.000000\n",
       "75%       51.000000\n",
       "max      115.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the histograns of age for patients who showed up and who didn't\n",
    "df[show].age.hist(alpha=0.5,label='show')\n",
    "df[no_show].age.hist(alpha=0.5,label='no_show')\n",
    "plt.legend()\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('ratio')\n",
    "plt.title('Histogram of age values for patients who showed up or missed their appointment')\n",
    "#ger the mean age for patients who showed up and who didn't\n",
    "df[no_show][['age']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4 style='color:blue'>Finding</h4>\n",
    "\n",
    "#### There is no clear relation between the age and whether the patient shows up or not but younger patients are more likely to miss their appointments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the percentage of patients missing their appointments for every neighborhood?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ILHAS OCEÂNICAS DE TRINDADE</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANTOS DUMONT</th>\n",
       "      <td>0.289185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANTA CECÍLIA</th>\n",
       "      <td>0.274554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SANTA CLARA</th>\n",
       "      <td>0.264822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITARARÉ</th>\n",
       "      <td>0.262664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOLON BORGES</th>\n",
       "      <td>0.147122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MÁRIO CYPRESTE</th>\n",
       "      <td>0.145553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AEROPORTO</th>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ILHA DO BOI</th>\n",
       "      <td>0.085714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARQUE INDUSTRIAL</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             neighbourhood\n",
       "ILHAS OCEÂNICAS DE TRINDADE       1.000000\n",
       "SANTOS DUMONT                     0.289185\n",
       "SANTA CECÍLIA                     0.274554\n",
       "SANTA CLARA                       0.264822\n",
       "ITARARÉ                           0.262664\n",
       "...                                    ...\n",
       "SOLON BORGES                      0.147122\n",
       "MÁRIO CYPRESTE                    0.145553\n",
       "AEROPORTO                         0.125000\n",
       "ILHA DO BOI                       0.085714\n",
       "PARQUE INDUSTRIAL                      NaN\n",
       "\n",
       "[81 rows x 1 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the number of records for each neighbourhood\n",
    "rec_neigh=df['neighbourhood'].value_counts()\n",
    "#get the number of records for patients missing their appointments for each neighbourhood\n",
    "rec_neigh_no_show=df[no_show].neighbourhood.value_counts()\n",
    "#percentage of patients missing their appointments for every neighbourhood\n",
    "rec_neigh_no_show_percentage=rec_neigh_no_show/rec_neigh\n",
    "pd.DataFrame(rec_neigh_no_show_percentage.sort_values(axis=0, ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "\n",
    "## Conclusions\n",
    "\n",
    "#### After analyzing the dataset here are some findings:\n",
    "\n",
    "1.  Percentage of patients who didn't show up for their appointment is 20.19%.\n",
    "2.  The percentage of females missing their appointment is nearly two times the number of males. So females are more likely to miss their appointment.\n",
    "3.  It appears that the longer the period between the scheduling and appointment the more likely the patient won't show up.\n",
    "4.  It seems that patients with scholarships are actually more likely to miss their appointment.\n",
    "5.  A strange finding here suggests that patients who received an SMS are more likely to miss their appointment !!\n",
    "6.  There is no clear relation between the age and whether the patients show up or not but younger patients are more likely to miss their appointments.\n",
    "\n",
    "#### Analysis Shortcoming & Data Limitations\n",
    "\n",
    "*   The data doesn't state the exact hour of the appointment which would have been very useful to try to find out which hours have the most missing appointments and which doesn't. It could also be very useful to know the difference between scheduling and the appointment since many of the scheduling are on the same day.\n",
    "*   The data doesn't state if any day is a vacation or not which can indicate if people tend to miss their appointments more on working days.\n",
    "*   The age column had a negative value but according to the data creator, it means a baby not born yet (a pregnant woman).\n",
    "*   When calculating the day difference between the scheduling and appointment days we had some negative value which makes no sense and might mean that the records of questions have wrong data.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thanks to"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Mostafa Abdelaleem](https://www.linkedin.com/in/mostafa-abdelaleem/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkQuickLabsMedicalAppointmentDataAnalysis30426296-2022-01-01)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Mridul Bhandari](https://www.linkedin.com/in/mridul-bhandari/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkQuickLabsMedicalAppointmentDataAnalysis30426296-2022-01-01)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For inspiring me"
   ]
  }
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