medical-appointment-no-shows.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Medical Appointment No-Shows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load all the required libraries for reading .csv file, exploratory data analysis, visualization and data modeling in the below cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing\n",
    "import os # manipulate paths\n",
    "from datetime import date # supplies classes for manipulating dates\n",
    "from time import strftime # convert time to a string as specified by the format argument\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder # encode target labels with value between 0 and n_classes-1.\n",
    "from sklearn import ensemble # provides methods for both classification and regression\n",
    "from sklearn.ensemble import GradientBoostingClassifier # gradient boosted decision tree classifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score # function that computes the accuracy of correct predictions of the model\n",
    "\n",
    "from sklearn.model_selection import train_test_split # this splits data into training and test set\n",
    "\n",
    "import matplotlib.pyplot as plt # to create visualisations\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load and Read the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the data in a pandas dataframe called df and return the name of each columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/noshowappointments/KaggleV2-May-2016.csv\n"
     ]
    }
   ],
   "source": [
    "# input data file is available in the \"../input/\" directory.\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "# read the data\n",
    "df = pd.read_csv('../input/noshowappointments/KaggleV2-May-2016.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df columns: ['PatientId', 'AppointmentID', 'Gender', 'ScheduledDay', 'AppointmentDay', 'Age', 'Neighbourhood', 'Scholarship', 'Hipertension', 'Diabetes', 'Alcoholism', 'Handcap', 'SMS_received', 'No-show']\n"
     ]
    }
   ],
   "source": [
    "# print the columns names for each dataset\n",
    "print(\"df columns:\",list(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Basic Date Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section will provide basic informtion about the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df shape: (110527, 14)\n"
     ]
    }
   ],
   "source": [
    "# print the number of rows and columns\n",
    "print(\"df shape:\",df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "# print the basic information: missing values and data types\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data has no missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print a summary of the data in df\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the table above, the first number, the count, shows how many rows have non-missing values. In this instance, we have no missing values.\n",
    "\n",
    "The second value is the mean, which is the average. Patients in df are on average 37 years old. Under that, std is the standard deviation, which measures how numerically spread out the values are, in other word it tell how close to the mean the datpoints are.\n",
    "\n",
    "The column Age has a minimum age of -1 which is erronous data, likewise,the maximum age is 115 years old which seems very high as Brazil's life expectancy for 2020 is 77 years old (please see [here](https://www.worldometers.info/demographics/brazil-demographics/#median-age)). We will deal with these errors in next section.\n",
    "\n",
    "The column Handcap should be binary (True or False) but it has a max value of 4. This will need to be investigated,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values in Gender: ['F' 'M']\n",
      "Values in Neighbourhood: ['JARDIM DA PENHA' 'MATA DA PRAIA' 'PONTAL DE CAMBURI' 'REPÚBLICA'\n",
      " 'GOIABEIRAS' 'ANDORINHAS' 'CONQUISTA' 'NOVA PALESTINA' 'DA PENHA'\n",
      " 'TABUAZEIRO' 'BENTO FERREIRA' 'SÃO PEDRO' 'SANTA MARTHA' 'SÃO CRISTÓVÃO'\n",
      " 'MARUÍPE' 'GRANDE VITÓRIA' 'SÃO BENEDITO' 'ILHA DAS CAIEIRAS'\n",
      " 'SANTO ANDRÉ' 'SOLON BORGES' 'BONFIM' 'JARDIM CAMBURI' 'MARIA ORTIZ'\n",
      " 'JABOUR' 'ANTÔNIO HONÓRIO' 'RESISTÊNCIA' 'ILHA DE SANTA MARIA'\n",
      " 'JUCUTUQUARA' 'MONTE BELO' 'MÁRIO CYPRESTE' 'SANTO ANTÔNIO' 'BELA VISTA'\n",
      " 'PRAIA DO SUÁ' 'SANTA HELENA' 'ITARARÉ' 'INHANGUETÁ' 'UNIVERSITÁRIO'\n",
      " 'SÃO JOSÉ' 'REDENÇÃO' 'SANTA CLARA' 'CENTRO' 'PARQUE MOSCOSO'\n",
      " 'DO MOSCOSO' 'SANTOS DUMONT' 'CARATOÍRA' 'ARIOVALDO FAVALESSA'\n",
      " 'ILHA DO FRADE' 'GURIGICA' 'JOANA D´ARC' 'CONSOLAÇÃO' 'PRAIA DO CANTO'\n",
      " 'BOA VISTA' 'MORADA DE CAMBURI' 'SANTA LUÍZA' 'SANTA LÚCIA'\n",
      " 'BARRO VERMELHO' 'ESTRELINHA' 'FORTE SÃO JOÃO' 'FONTE GRANDE'\n",
      " 'ENSEADA DO SUÁ' 'SANTOS REIS' 'PIEDADE' 'JESUS DE NAZARETH'\n",
      " 'SANTA TEREZA' 'CRUZAMENTO' 'ILHA DO PRÍNCIPE' 'ROMÃO' 'COMDUSA'\n",
      " 'SANTA CECÍLIA' 'VILA RUBIM' 'DE LOURDES' 'DO QUADRO' 'DO CABRAL' 'HORTO'\n",
      " 'SEGURANÇA DO LAR' 'ILHA DO BOI' 'FRADINHOS' 'NAZARETH' 'AEROPORTO'\n",
      " 'ILHAS OCEÂNICAS DE TRINDADE' 'PARQUE INDUSTRIAL']\n",
      "Values in Scholarship: [0 1]\n",
      "Values in Hypertension: [1 0]\n",
      "Values in Diabetes: [0 1]\n",
      "Values in Alcoholism: [0 1]\n",
      "Values in Handicap: [0 1 2 3 4]\n",
      "Values in SMS_received: [0 1]\n",
      "Values in No_Show: ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "# print unique values for each columns\n",
    "print('Values in Gender:', df.Gender.unique())\n",
    "print('Values in Neighbourhood:',df.Neighbourhood.unique())\n",
    "print('Values in Scholarship:',df.Scholarship.unique())\n",
    "print('Values in Hypertension:',df.Hipertension.unique())\n",
    "print('Values in Diabetes:',df.Diabetes.unique())\n",
    "print('Values in Alcoholism:',df.Alcoholism.unique())\n",
    "print('Values in Handicap:',df.Handcap.unique())\n",
    "print('Values in SMS_received:',df.SMS_received.unique())\n",
    "print('Values in No_Show:', df['No-show'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique values in Neighbourhood: 81\n"
     ]
    }
   ],
   "source": [
    "# print the number of unique values in Neighbourhood\n",
    "print('Number of unique values in Neighbourhood:',df.Neighbourhood.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data Cleansing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we want to amend some columns in df, such as the data type, misspellings and erronous data:\n",
    "\n",
    "* PatientId is currently a float, it will be converted it into an integer\n",
    "* ScheduledDay and AppointmentDay are currently objects, it will be converted them into datetime\n",
    "* AppointmentDay's time will be dropped (as it is set as 00:00:00)\n",
    "* Misspelled columns are going to be renamed\n",
    "* Erronous data from the Age column will be deleted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
      "Hypertension      110527 non-null int64\n",
      "Diabetes          110527 non-null int64\n",
      "Alcoholism        110527 non-null int64\n",
      "Handicap          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": [
    "# Convert PatientId from Float to Integer\n",
    "df['PatientId'] = df['PatientId'].astype('int64')\n",
    "\n",
    "# Convert ScheduledDay from 'object' type to 'datetime'\n",
    "df['ScheduledDay'] = pd.to_datetime(df['ScheduledDay']).astype('datetime64[ns]')\n",
    "\n",
    "# Convert AppointmentDay from 'object' type to 'datetime' and drop the unecessary times\n",
    "df['AppointmentDay'] = pd.to_datetime(df['AppointmentDay']).dt.date.astype('datetime64[ns]')\n",
    "\n",
    "# rename the columns\n",
    "df = df.rename(columns={'Hipertension': 'Hypertension', 'Handcap': 'Handicap', 'No-show': 'No_Show'})\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values in Handicap: [0 1]\n"
     ]
    }
   ],
   "source": [
    "# change all the Handicap set above 0 as 1\n",
    "df.loc[df.Handicap > 0, 'Handicap'] = 1\n",
    "# check\n",
    "print('Values in Handicap:',df.Handicap.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at the distribution of the Age feature\n",
    "plt.figure(figsize=(16,2))\n",
    "plt.xticks(rotation=90)\n",
    "dist_age = sns.boxplot(x=df.Age)\n",
    "dist_age.set_title(\"Patients distribution by Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most the patients are between 18 and 55 years old. The patients who are 115 years old are outliars, we will therefore drop these rows as well as the row of the patients aged -1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete patients that are over 102\n",
    "df = df.drop(df[df.Age > 102].index)\n",
    "# delete the patients that are negative\n",
    "df = df.drop(df[df.Age < 0].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Overview of No-Show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No_Show</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>79.808362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>20.191638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PatientId\n",
       "No_Show           \n",
       "No       79.808362\n",
       "Yes      20.191638"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the total percentage of no show\n",
    "pd.DataFrame(df.groupby('No_Show')['PatientId'].count()).apply(lambda x : x / sum(x) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missed appointmemts account for 20% of the total appointments in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Finding Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated rows:  0\n",
      "Duplicated patient ids:  48225\n"
     ]
    }
   ],
   "source": [
    "# check for duplicated rows\n",
    "dups = df[df.duplicated() == True].count()\n",
    "\n",
    "# check for duplicated patients\n",
    "dups_id = df['PatientId'][df['PatientId'].duplicated() == True].count()\n",
    "\n",
    "print('Duplicated rows: ', dups.sum())\n",
    "print('Duplicated patient ids: ', dups_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset does not have duplicated appointments but has 48,228 patients that can be considered as returning/known patients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Convert Integer to Boolean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns Scholarship, Hypertension, Diabetes, Alcoholism, Handicap, SMS_received and No_Show should be binary data set as 0 for False and 1 for True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scholarship\n",
    "df['Scholarship'] = df['Scholarship'].apply(lambda x: True if x==1 else False)\n",
    "# Hypertension\n",
    "df['Hypertension'] = df['Hypertension'].apply(lambda x: True if x==1 else False)\n",
    "# Diabetes\n",
    "df['Diabetes'] = df['Diabetes'].apply(lambda x: True if x==1 else False)\n",
    "# Handicap\n",
    "df['Handicap'] = df['Handicap'].apply(lambda x: True if x==1 else False)\n",
    "# Alcoholism\n",
    "df['Alcoholism'] = df['Alcoholism'].apply(lambda x: True if x==1 else False)\n",
    "# SMS_received\n",
    "df['SMS_received'] = df['SMS_received'].apply(lambda x: True if x==1 else False)\n",
    "# No_Show\n",
    "df['No_Show'] = df['No_Show'].apply(lambda x: True if x=='Yes' else False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgeGroup\n",
       "Less than 10    17475\n",
       "10-18           11391\n",
       "19-25            9733\n",
       "26-35           14404\n",
       "36-45           14582\n",
       "46-55           15437\n",
       "56-65           14203\n",
       "More than 65    13296\n",
       "Name: PatientId, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create age groups using the distribution plotted in section 4\n",
    "bins= [0,10,19,26,36,46,56,66,150]\n",
    "labels = ['Less than 10','10-18','19-25','26-35','36-45','46-55','56-65','More than 65']\n",
    "df['AgeGroup'] = pd.cut(df['Age'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "df.groupby(['AgeGroup'])['PatientId'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# appointment attendance by age\n",
    "age = df.groupby('AgeGroup')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(20,5))\n",
    "age.set_title(\"Percentage of Appointments Show/No Show by Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The patients that seems most likely to not show-up for their appointments are between 10 and 35 years old."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5 Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Gender</th>\n",
       "      <th>No_Show</th>\n",
       "      <th>PatientId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>False</td>\n",
       "      <td>57243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>True</td>\n",
       "      <td>14591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>False</td>\n",
       "      <td>30962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>True</td>\n",
       "      <td>7725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender  No_Show  PatientId\n",
       "0      F    False      57243\n",
       "1      F     True      14591\n",
       "2      M    False      30962\n",
       "3      M     True       7725"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table with the gender and the number of patient that missed or attended their appointment\n",
    "gender_tbl = pd.DataFrame(df.groupby(['Gender','No_Show'])['PatientId'].count()).reset_index()\n",
    "gender_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# appointment attendance by gender\n",
    "gender = df.groupby('Gender')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(10,5))\n",
    "gender.set_title(\"Percentage of Appointments Show/No Show by Gender\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table above, we can clearly see that 'Female' patients usually have more appointments that 'Male' patients, they also have about the double number of missed appointment. However, looking at the percentage of missed appointments by gender shows that it is almost the same rate (about 20%). Therefore, gender does not seem to be an important feature. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.6 Scheduled Day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking at the time of the booking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# split ScheduledDay to get the time of the booking stored in Scheduled_Time\n",
    "df['Scheduled_Time'] = pd.to_datetime(df['ScheduledDay']).dt.hour\n",
    "\n",
    "# Look at the distribution of Scheduled_Time\n",
    "plt.figure(figsize=(16,2))\n",
    "plt.xticks(rotation=90)\n",
    "dist_age = sns.boxplot(x=df.Scheduled_Time)\n",
    "dist_age.set_title(\"Patients distribution by Time of the Booking\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The booking are made between 6am and 8pm. There are three bookings that have been taken at 9pm, although these are showed as outliars on the plot above, they will not be dropped as they could be the results of emergencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing percentage of show/ no-show by hours\n",
    "appt_h = df.groupby('Scheduled_Time')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(20,5))\n",
    "appt_h.set_title(\"Percentage of Appointment Show/No Show by Booking Hours\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create three groupes for morning, afternoon and evening appointments\n",
    "bins= [6,11,17,22]\n",
    "labels = ['Morning','Afternoon','Evening']\n",
    "df['Scheduled_Period'] = pd.cut(df['Scheduled_Time'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# barchart showing percentage of show/ no-show by time of the day\n",
    "appt_tod = df.groupby('Scheduled_Period')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(10,5))\n",
    "appt_tod.set_title(\"Percentage of Appointment Show/No Show by Booking Time of the Day\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Morning bookings seem less likely to be missed than afternoon and evening ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking at the day of booking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# split ScheduledDay into days of the week\n",
    "df['Scheduled_DayOfWeek'] = pd.to_datetime(df['ScheduledDay']).dt.day_name()\n",
    "\n",
    "# barchart showing number of show/ no-show by day of the week\n",
    "day_of_week = df.groupby('Scheduled_DayOfWeek')['No_Show'].value_counts(normalize=False).unstack().plot(kind='bar', figsize=(18,5))\n",
    "day_of_week.set_title(\"Appointment Show/No Show by Booking Day of the Week\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing number of show/ no-show by day of the week\n",
    "day_of_week = df.groupby('Scheduled_DayOfWeek')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(18,5))\n",
    "day_of_week.set_title(\"Percentage of Appointment Show/No Show by Booking Day of the Week\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the bookings are at the beginning of the week, this may be explained because the medical centre seems not to be open over the weekends. Althoug Saturdays have the smallest no show rate, they also represents a too small proportion of the data to be significant. Overall, the days of the week do not seem to be an important feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.7 Appointment Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# split ScheduledDay into days of the week\n",
    "df['Booking_DayOfWeek'] = pd.to_datetime(df['AppointmentDay']).dt.day_name()\n",
    "\n",
    "# barchart showing number of show/ no-show by day of the week\n",
    "day_of_week = df.groupby('Booking_DayOfWeek')['No_Show'].value_counts(normalize=False).unstack().plot(kind='bar', figsize=(18,5))\n",
    "day_of_week.set_title(\"Appointment Show/No Show by Appointment Day of the Week\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing number of show/ no-show by day of the week\n",
    "day_of_week = df.groupby('Booking_DayOfWeek')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(18,5))\n",
    "day_of_week.set_title(\"Percentage of Appointment Show/No Show by Appointment Day of the Week\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first graph follows the same patterns as section 5.6 \"Appointment Show/No Show by Booking Day of the Week\" graph. This may be because patient book and have their their appointment on the same day. The percentage of show vs no show dis roughly the same accross the week."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.8 Waiting Time Between Booking and Medical Appointment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the number of days the booking has been taken before the appointmenet\n",
    "df['ScheduledDay'] = pd.to_datetime(df['ScheduledDay']).dt.date\n",
    "df['AppointmentDay'] = pd.to_datetime(df['AppointmentDay']).dt.date\n",
    "\n",
    "df['Waiting_Days'] = (df['AppointmentDay'] - df['ScheduledDay']).dt.days\n",
    "\n",
    "# Look at the distribution of Waiting_Days\n",
    "plt.figure(figsize=(16,2))\n",
    "plt.xticks(rotation=90)\n",
    "dist_age = sns.boxplot(x=df.Waiting_Days)\n",
    "dist_age.set_title(\"Patients distribution by Waiting Time\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the appointments are taken a month in advance. The graph above, highlights erroneous data and outliars. The negative data will be transformed into 'unknown' wating time category while the outliars will be kept as some medical appointment can take up to six months (like small surgeries)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pre_Appt_Period\n",
       "Same day             38561\n",
       "Less than a week     27277\n",
       "A week               14018\n",
       "Two weeks             9925\n",
       "Three weeks           6859\n",
       "A month               6229\n",
       "More than a month     7647\n",
       "unknown                  5\n",
       "Name: PatientId, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create pre-appointment period groups using the distribution above\n",
    "bins= [0,1,7,14,21,28,35,200]\n",
    "labels = ['Same day','Less than a week','A week','Two weeks','Three weeks','A month','More than a month']\n",
    "df['Pre_Appt_Period'] = pd.cut(df['Waiting_Days'], bins=bins, labels=labels, right=False)\n",
    "# replace missing and erronous values with unknown\n",
    "df['Pre_Appt_Period'] = df['Pre_Appt_Period'].cat.add_categories('unknown').fillna('unknown')\n",
    "\n",
    "df.groupby(['Pre_Appt_Period'])['PatientId'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing number of show/ no-show in percentage by the amount of time between booking and appointment\n",
    "psla = df.groupby('Pre_Appt_Period')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(15,5))\n",
    "psla.set_title(\"Percentage of Appointment Show/No Show by Time Between Booking and Appointment\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above suggests that the longer the waiting time is between booking and the appointmnet the more likely the appointment is to be missed. This feature seems to be an important one as it shows a clear distinction for patients show/ no show depending on the number of days ahead of the appontment patients have booked."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.9 Neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing number of show/ no-show by Neighbourhoods\n",
    "day_of_week = df.groupby('Neighbourhood')['No_Show'].value_counts(normalize=False).unstack().plot(kind='bar', figsize=(18,5))\n",
    "day_of_week.set_title(\"Appointment Show/No Show by Neighbourhoods\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing percentage of show/ no-show by Neighbourhoods\n",
    "day_of_week = df.groupby('Neighbourhood')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(18,5))\n",
    "day_of_week.set_title(\"Percentage of Appointment Show/No Show by Neighbourhoods\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the neighbourhouds have a no show rate of about 20%, the significant drops and peaks are because of the porr representation of a particalar neigbourhood in the dataset rather than because it is significant. Therefre, this feature does not seem to be important for the no show prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.10 Scholarship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing number of show/ no-show by Scholarship\n",
    "day_of_week = df.groupby('Scholarship')['No_Show'].value_counts(normalize=False).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Appointment Show/No Show by Scholarship\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing the percentage of show/ no-show by Scholarship\n",
    "day_of_week = df.groupby('Scholarship')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Percentage of Appointment Show/No Show by Scholarship\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphs above shows that 80% of the patients that do not have a scholarship attended their appointent while 75% of the patient with a scholarship attended. This feature could be helpful in dertermining the no show."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.11 Hypertension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing number of show/ no-show by Hypertension\n",
    "day_of_week = df.groupby('Hypertension')['No_Show'].value_counts(normalize=False).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Appointment Show/No Show by Hypertension\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J77W8FChJklQbEytJkqSamFhJkiTVxPtYSZKkpp566im6urp4/PHH+zuUdWbYsGGMHDmSIUOGrNXjTawkSVJTXV1dbLbZZowZM4aI6O9w+lxmsmLFCrq6uhg7duxateGlQEmS1NTjjz/O1ltvvUEkVQARwdZbb/2CeuhMrCRJUo82lKSq2wvdXhMrSZKkmphYSZIk1cTB65K0ARhzyg/7O4S2dMfn3trfIQx4EcGJJ57Il7/8ZQC+9KUv8cgjjzBz5sw1amfRokW8//3v54EHHuCJJ55g8uTJnHXWWZx33nl0dnbyjW98ow+ir589VpIkaa1ttNFGfO973+Pee+99Qe3MmDGDE044gQULFrBw4UI+8pGP1BThumViJUmS1trgwYOZNm0aZ5555vPKlixZwpQpU9h1112ZMmUKd955Z4/t3H333YwcOXLV/Kte9apV08uWLWPq1KmMGzeOk046adXyiy66iFe96lXssssunHzyyQBcfPHFnHjiiQB89atfZccddwTgT3/6E69//etf2Ma2oKXEKiKmRsSiiFgcEac0KR8dET+PiF9HxC0R8Zb6Q5UkSe3owx/+MBdeeCEPPvjgc5ZPnz6dY445hltuuYWjjjqKGTNm9NjGCSecwBvf+EYOPPBAzjzzTB544IFVZQsWLGDOnDn89re/Zc6cOSxdupRly5Zx8skn87Of/YwFCxYwf/58LrvsMvbZZx+uvfZaAK699lq23npr7rrrLq677jomT57cNzugotfEKiI6gFnAgcB44IiIGN9Q7VPAxZm5O3A48J91BypJktrT5ptvzjHHHMPXvva15yz/5S9/yZFHHgnA0UcfzXXXXddjG8cffzwLFy7k0EMP5aqrrmKvvfbiiSeeAGDKlCkMHz6cYcOGMX78eJYsWcL8+fPZd999GTFiBIMHD+aoo47immuuYdttt+WRRx7h4YcfZunSpRx55JFcc801XHvtte2RWAGTgMWZeXtmPgnMBg5uqJPA5uX0cGBZfSFKkqR297GPfYxzzjmHRx99tMc6vd0jarvttuM973kPP/jBDxg8eDC/+93vgGIcV7eOjg5WrlxJZvbYzt577825557Ly1/+ciZPnsy1117LL3/5S173utet4VatuVYSq+2BpZX5rnJZ1UzgHyKiC5gHNB1xFhHTIqIzIjqXL1++FuFKkqR2tNVWW/Hud7+bc845Z9Wy1772tcyePRuACy+8cLVjnH70ox/x1FNPAXDPPfewYsUKtt++Md141p577snVV1/Nvffey9NPP81FF13EG97wBgD22WcfvvSlL7HPPvuw++678/Of/5yNNtqI4cOH17Gpq9VKYtUsvWxME48AzsvMkcBbgPMj4nltZ+ZZmTkxMyeOGDFizaOVJElt6+Mf//hzvh34ta99jXPPPZddd92V888/n69+9as9PvbHP/4xu+yyC69+9as54IAD+OIXv8i2227bY/2XvOQlnHHGGey33368+tWvZsKECRx8cHFBbfLkySxdupR99tmHjo4ORo0atU4GrgPE6rrSACJib2BmZh5Qzv8zQGaeUalzKzA1M5eW87cDe2XmX3tqd+LEidnZ2fnCt0CS1CvvY9Wc97FavYULF7Lzzjv3dxjrXLPtjoibMnNib49t5Qah84FxETEWuIticPqRDXXuBKYA50XEzsAwwGt9a8GTX3Oe/CRJA0GviVVmroyI6cAVQAfwrcy8NSJOAzozcy7wceC/I+IEisuEx2VvXWGSJGmDc/rpp3PJJZc8Z9mhhx7KJz/5yX6KqF4t/aRNZs6jGJReXXZqZfo2oO+H2kuSpAHtk5/85HqTRDXjndclSZJqYmIlSZJUExMrSZKkmrQ0xkqSJKnub6638o3vjo6O5/wg82WXXcaYMWOat3fHHbztbW9bdcf2/mBiJUmS2tbGG2/MggUL+juMlnkpUJIkDSh33HEHkydPZsKECUyYMIFf/OIXz6tz6623MmnSJHbbbTd23XVX/vjHPwJwwQUXrFr+/ve/n6effrrW2EysJElS23rsscfYbbfd2G233XjHO94BwDbbbMNPfvITbr75ZubMmcOMGTOe97hvfvObfPSjH2XBggV0dnYycuRIFi5cyJw5c7j++utZsGABHR0dXHjhhbXG66VASZLUtppdCnzqqaeYPn36quToD3/4w/Met/fee3P66afT1dXFIYccwrhx4/jpT3/KTTfdxB577AEUSds222xTa7wmVpIkaUA588wzefGLX8xvfvMbnnnmGYYNG/a8OkceeSR77rknP/zhDznggAM4++yzyUyOPfZYzjjjjCat1sNLgZIkaUB58MEHeclLXsKgQYM4//zzm46Tuv3229lxxx2ZMWMGBx10ELfccgtTpkzh0ksv5a9//SsA9913H0uWLKk1NnusJElSS1q5PcK68KEPfYh3vvOdXHLJJey3335ssskmz6szZ84cLrjgAoYMGcK2227LqaeeylZbbcVnPvMZ3vzmN/PMM88wZMgQZs2axQ477FBbbNFfv5U8ceLE7Ozs7Jd1t7O67xGyvmiXF7M0UHluac5zy+otXLiQnXfeub/DWOeabXdE3JSZE3t7rJcCJUmSamJiJUmSVBMTK0mSpJqYWEmSJNWkpcQqIqZGxKKIWBwRpzQpPzMiFpR/f4iIB+oPVZIkqb31eruFiOgAZgFvArqA+RExNzNv666TmSdU6n8E2L0PYpUkSWprrdzHahKwODNvB4iI2cDBwG091D8C+Ld6wpMkSW1j5vCa23twtcUrVqxgypQpANxzzz10dHQwYsQIAG688UaGDh1abzw1aCWx2h5YWpnvAvZsVjEidgDGAj/roXwaMA1g9OjRaxSoJEnasGy99darfidw5syZbLrppnziE594Tp3MJDMZNKg9ho23EkU0WdbTXUUPBy7NzOffWx7IzLMyc2JmTuzOOCVJktbE4sWL2WWXXfjABz7AhAkTWLp0KVtsscWq8tmzZ/O+970PgL/85S8ccsghTJw4kUmTJnHDDTf0aWytJFZdwKjK/EhgWQ91DwcueqFBSZIkrc5tt93Ge9/7Xn7961+z/fbb91hvxowZnHTSSXR2dnLxxRevSrj6SiuXAucD4yJiLHAXRfJ0ZGOliHg5sCXwy1ojlCRJarDTTjuxxx579FrvyiuvZNGiRavm77//fh577DE23njjPomr18QqM1dGxHTgCqAD+FZm3hoRpwGdmTm3rHoEMDv768cHJUnSBqP6w8uDBg2imn48/vjjq6Yzc50OdG9ppFdmzsvMl2XmTpl5erns1EpSRWbOzMzn3eNKkiSpLw0aNIgtt9ySP/7xjzzzzDN8//vfX1W2//77M2vWrFXz3YPh+0orlwIlSZJ6vT1Cf/r85z/P1KlTGT16NOPHj+eJJ54AYNasWXzwgx/k3HPPZeXKley3337PSbTqZmIlSZLa3syZM1dNv/SlL31ez9Nhhx3GYYcd9rzHjRgxgksvvbSvw1ulPW76IEmStB4wsZIkSaqJiZUkSerRhvZl/xe6vSZWkiSpqWHDhrFixYoNJrnKTFasWMGwYcPWug0Hr0uSpKZGjhxJV1cXy5cv7+9Q1plhw4YxcuTItX68iZUkSWpqyJAhjB07tr/DGFC8FChJklQTEytJkqSamFhJkiTVxMRKkiSpJiZWkiRJNTGxkiRJqomJlSRJUk1MrCRJkmrSUmIVEVMjYlFELI6IU3qo8+6IuC0ibo2I79YbpiRJUvvr9c7rEdEBzALeBHQB8yNibmbeVqkzDvhn4HWZeX9EbNNXAUuSJLWrVnqsJgGLM/P2zHwSmA0c3FDnH4FZmXk/QGb+td4wJUmS2l8ridX2wNLKfFe5rOplwMsi4vqIuCEipjZrKCKmRURnRHRuSD/oKEmSNgytJFbRZFk2zA8GxgH7AkcAZ0fEFs97UOZZmTkxMyeOGDFiTWOVJElqa60kVl3AqMr8SGBZkzo/yMynMvPPwCKKREuSJGmD0UpiNR8YFxFjI2IocDgwt6HOZcB+ABHxIopLg7fXGagkSVK76zWxysyVwHTgCmAhcHFm3hoRp0XEQWW1K4AVEXEb8HPgnzJzRV8FLUmS1I56vd0CQGbOA+Y1LDu1Mp3AieWfJEnSBsk7r0uSJNXExEqSJKkmJlaSJEk1MbGSJEmqiYmVJElSTUysJEmSamJiJUmSVBMTK0mSpJqYWEmSJNXExEqSJKkmJlaSJEk1MbGSJEmqiYmVJElSTUysJEmSamJiJUmSVBMTK0mSpJq0lFhFxNSIWBQRiyPilCblx0XE8ohYUP69r/5QJUmS2tvg3ipERAcwC3gT0AXMj4i5mXlbQ9U5mTm9D2KUJEkaEFrpsZoELM7M2zPzSWA2cHDfhiVJkjTwtJJYbQ8srcx3lcsavTMibomISyNiVLOGImJaRHRGROfy5cvXIlxJkqT21UpiFU2WZcP85cCYzNwVuBL4drOGMvOszJyYmRNHjBixZpFKkiS1uVYSqy6g2gM1ElhWrZCZKzLziXL2v4HX1BOeJEnSwNFKYjUfGBcRYyNiKHA4MLdaISJeUpk9CFhYX4iSJEkDQ6/fCszMlRExHbgC6AC+lZm3RsRpQGdmzgVmRMRBwErgPuC4PoxZkiSpLfWaWAFk5jxgXsOyUyvT/wz8c72hSZIkDSzeeV2SJKkmJlaSJEk1MbGSJEmqiYmVJElSTUysJEmSamJiJUmSVBMTK0mSpJqYWEmSJNXExEqSJKkmJlaSJEk1MbGSJEmqiYmVJElSTUysJEmSamJiJUmSVBMTK0mSpJqYWEmSJNWkpcQqIqZGxKKIWBwRp6ym3rsiIiNiYn0hSpIkDQy9JlYR0QHMAg4ExgNHRMT4JvU2A2YAv6o7SEmSpIGglR6rScDizLw9M58EZgMHN6n3aeALwOM1xidJkjRgtJJYbQ8srcx3lctWiYjdgVGZ+b81xiZJkjSgtJJYRZNluaowYhBwJvDxXhuKmBYRnRHRuXz58tajlCRJGgBaSay6gFGV+ZHAssr8ZsAuwFURcQewFzC32QD2zDwrMydm5sQRI0asfdSSJEltqJXEaj4wLiLGRsRQ4HBgbndhZj6YmS/KzDGZOQa4ATgoMzv7JGJJkqQ21WtilZkrgenAFcBC4OLMvDUiTouIg/o6QEmSpIFicCuVMnMeMK9h2ak91N33hYclSZI08HjndUmSpJqYWEmSJNXExEqSJKkmJlaSJEk1MbGSJEmqiYmVJElSTUysJEmSamJiJUmSVBMTK0mSpJqYWEmSJNXExEqSJKkmJlaSJEk1MbGSJEmqiYmVJElSTUysJEmSamJiJUmSVBMTK0mSpJq0lFhFxNSIWBQRiyPilCblH4iI30bEgoi4LiLG1x+qJElSe+s1sYqIDmAWcCAwHjiiSeL03cx8VWbuBnwB+I/aI5UkSWpzrfRYTQIWZ+btmfkkMBs4uFohMx+qzG4CZH0hSpIkDQyDW6izPbC0Mt8F7NlYKSI+DJwIDAXe2KyhiJgGTAMYPXr0msYqSZLU1lrpsYomy57XI5WZszJzJ+Bk4FPNGsrMszJzYmZOHDFixJpFKkmS1OZaSay6gFGV+ZHAstXUnw38/QsJSpIkaSBqJbGaD4yLiLERMRQ4HJhbrRAR4yqzbwX+WF+IkiRJA0OvY6wyc2VETAeuADqAb2XmrRFxGtCZmXOB6RGxP/AUcD9wbF8GLUmS1I5aGbxOZs4D5jUsO7Uy/dGa45IkSRpwvPO6JElSTUysJEmSamJiJUmSVBMTK0mSpJqYWEmSJNXExEqSJKkmJlaSJEk1MbGSJEmqiYmVJElSTUysJEmSamJiJUmSVBMTK0mSpJqYWEmSJNXExEqSJKkmJlaSJEk1MbGSJEmqyeBWKkXEVOCrQAdwdmZ+rqH8ROB9wEpgOfCezFxSc6zakM0c3t8RtJ+ZD/Z3BJKkBr32WEVEBzALOBAYDxwREeMbqv0amJiZuwKXAl+oO1BJkqR218qlwEnA4sy8PTOfBGYDB1crZObPM/Nv5ewNwMh6w5QkSWp/rSRW2wNLK/Nd5bKevBf4v2YFETEtIjojonP58uWtRylJkjQAtJJYRZNl2bRixD8AE4EvNitTw/8zAAAJ+UlEQVTPzLMyc2JmThwxYkTrUUqSJA0ArQxe7wJGVeZHAssaK0XE/sAngTdk5hP1hCdJkjRwtNJjNR8YFxFjI2IocDgwt1ohInYH/gs4KDP/Wn+YkiRJ7a/XxCozVwLTgSuAhcDFmXlrRJwWEQeV1b4IbApcEhELImJuD81JkiStt1q6j1VmzgPmNSw7tTK9f81xSZIkDTgtJVaSJK2XvPnw83nz4RfEn7SRJEmqiYmVJElSTUysJEmSamJiJUmSVBMTK0mSpJqYWEmSJNXExEqSJKkmJlaSJEk1MbGSJEmqiYmVJElSTUysJEmSamJiJUmSVBMTK0mSpJqYWEmSJNXExEqSJKkmLSVWETE1IhZFxOKIOKVJ+T4RcXNErIyId9UfpiRJUvvrNbGKiA5gFnAgMB44IiLGN1S7EzgO+G7dAUqSJA0Ug1uoMwlYnJm3A0TEbOBg4LbuCpl5R1n2TB/EKEmSNCC0cilwe2BpZb6rXLbGImJaRHRGROfy5cvXpglJkqS21UpiFU2W5dqsLDPPysyJmTlxxIgRa9OEJElS22olseoCRlXmRwLL+iYcSZKkgauVxGo+MC4ixkbEUOBwYG7fhiVJkjTw9JpYZeZKYDpwBbAQuDgzb42I0yLiIICI2CMiuoBDgf+KiFv7MmhJkqR21Mq3AsnMecC8hmWnVqbnU1wilCRJ2mB553VJkqSamFhJkiTVxMRKkiSpJiZWkiRJNTGxkiRJqomJlSRJUk1MrCRJkmpiYiVJklQTEytJkqSamFhJkiTVxMRKkiSpJiZWkiRJNTGxkiRJqomJlSRJUk1MrCRJkmpiYiVJklSTlhKriJgaEYsiYnFEnNKkfKOImFOW/yoixtQdqCRJUrvrNbGKiA5gFnAgMB44IiLGN1R7L3B/Zr4UOBP4fN2BSpIktbtWeqwmAYsz8/bMfBKYDRzcUOdg4Nvl9KXAlIiI+sKUJElqf4NbqLM9sLQy3wXs2VOdzFwZEQ8CWwP3VitFxDRgWjn7SEQsWpugteEJeBENx9MG79/97CK9UJ5bmvDc0pMdWqnUSmLVbA/nWtQhM88CzmphndJzRERnZk7s7zgkrV88t6hurVwK7AJGVeZHAst6qhMRg4HhwH11BChJkjRQtJJYzQfGRcTYiBgKHA7MbagzFzi2nH4X8LPMfF6PlSRJ0vqs10uB5Zip6cAVQAfwrcy8NSJOAzozcy5wDnB+RCym6Kk6vC+D1gbJS8iS+oLnFtUq7FiSJEmqh3delyRJqomJlSRJUk1MrCRJG5SI2Ki/Y9D6y8RKkrRBiIhJEfFb4I/l/Ksj4uv9HJbWMyZWaltR+IeIOLWcHx0Rk/o7LkkD1teAtwErADLzN8B+/RqR1jsmVmpn/wnsDRxRzj9M8YPgkrQ2BmXmkoZlT/dLJFpvtfKTNlJ/2TMzJ0TErwEy8/7yJrWStDaWlr3eGREdwEeAP/RzTFrP2GOldvZUefJLgIgYATzTvyFJGsA+CJwIjAb+AuxVLpNq4w1C1bYi4ijgMGAC8G2Kn0v6VGZe0q+BSZLUAxMrtbWIeAUwBQjgp5m5sJ9DkjRARcR/U/aAV2XmtH4IR+spx1ipbUXETsCfM3NWROwLvCki7s7MB/o5NEkD05WV6WHAO4Cl/RSL1lP2WKltRcQCYCIwBvgRcDnw8sx8S3/GJWn9EBGDgJ9k5pT+jkXrDwevq509k5krgUOAr2bmCcBL+jkmSeuPscAO/R2E1i9eClQ7eyoijgCOAd5eLhvSj/FIGsAi4n6eHWM1CLgPOKX/ItL6yMRK7ex44APA6Zn554gYC1zQzzFJGoAiIoBXA3eVi55Jx8KoDzjGSpK0QYiImzLzNf0dh9Zv9lip7ZQ/ktpjxp+Zu67DcCStP26MiAmZeXN/B6L1lz1WajsRsdrBpE1+60uSehQRgzNzZfmhbWfgT8CjFPfHy8yc0K8Bar1iYiVJWq9FxM3l747u1Kw8M/+0rmPS+stLgWpbEbEX8HWKT5hDgQ7g0czcvF8DkzTQBJhAad0wsVI7+wZwOHAJxY1CjwFe2q8RSRqIRkTEiT0VZuZ/rMtgtH4zsVJby8zFEdGRmU8D50bEL/o7JkkDTgewKWXPldSXTKzUzv4WEUOBBRHxBeBuYJN+jknSwHN3Zp7W30Fow+BP2qidHU1xjE6n+AbPKOCd/RqRpIHIniqtM34rUG0nIkZn5p39HYek9UNEbJWZ9/V3HNow2GOldnRZ90RE/E9/BiJp4DOp0rpkYqV2VO2237HfopAkaQ2ZWKkdZQ/TkiS1NcdYqe1ExNM8+3MTGwN/6y6i+PkJbxAqSWpLJlaSJEk18VKgJElSTUysJEmSamJiJalWEfFIw/xxEfGNPl7nmIg4sg/a/UBEHFN3u5LWX/6kjaQBLSIGA2OAI4Hv1tl2Zn6zzvYkrf/ssZK0TkTEZhHx54gYUs5vHhF3RMSQiLgqIr4SEb+IiN9FxKSyziYR8a2ImB8Rv46Ig8vlx0XEJRFxOfBj4HPA5IhYEBEnRERHRHyxfNwtEfH+8nH7luu6NCJ+HxEXRkSUZZ+LiNvK+l8ql82MiE+U07tFxA1l+fcjYsty+VUR8fmIuDEi/hARk9fxrpXURuyxklS3jSNiQWV+K2BuZj4cEVcBb6W4u/7hwP9k5lNlbrNJZr42IvYBvgXsAnwS+FlmvicitgBujIgry3b3BnbNzPsiYl/gE5n5NoCImAY8mJl7RMRGwPUR8ePycbsDrwSWAdcDr4uI24B3AK/IzCzX1eg7wEcy8+qIOA34N+BjZdngzJwUEW8pl++/9rtP0kBmj5Wkuj2Wmbt1/wGnVsrOBo4vp48Hzq2UXQSQmdcAm5fJzZuBU8pE7SpgGDC6rP+T1fxUyZuBY8rH/QrYGhhXlt2YmV2Z+QywgOIy4kPA48DZEXEIz947DYCIGA5skZlXl4u+DexTqfK98v9NZXuSNlD2WElaZzLz+nKg+RuAjsz8XbW4sTrFTWHfmZmLqgURsSfFTWR7EhS9S1c0PG5f4InKoqcpeptWlpcfp1D0pE0H3tj6lq1q82k8r0obNHusJK1r36HonTq3YflhABHxeorLeA8CVwAfqYyD2r2HNh8GNqvMXwF8sDKe62URsUlPAUXEpsDwzJxHcXlvt2p5Gcv9lfFTRwNXI0kN/GQlaV27EPgM5aW/ivsj4hfA5sB7ymWfBr4C3FImV3cAb2vS5i3Ayoj4DXAe8FWKS3I3l49bDvz9amLaDPhBRAyj6O06oUmdY4FvRsTfAbfz7CVNSVrFn7SRtE5FxLuAgzPz6MqyqygGn3f2W2CSVAN7rCStMxHxdeBA4C39HYsk9QV7rCRJkmri4HVJkqSamFhJkiTVxMRKkiSpJiZWkiRJNTGxkiRJqsn/ByqJpxDskcJkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing the percentage of show/ no-show by Hypertension\n",
    "day_of_week = df.groupby('Hypertension')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Percentage of Appointment Show/No Show by Hypertension\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The patients suffering from hypertension tend to attend their appointment more often than those who do not have this condition. However, appointment with hypertension patients represent a small pool in our dataset, just under 20% of the total appointments. This feature could be helpful in dertermining the no show."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.12 Diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing number of show/ no-show by Diabetes\n",
    "day_of_week = df.groupby('Diabetes')['No_Show'].value_counts(normalize=False).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Appointment Show/No Show by Diabetes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing the percentage of show/ no-show by Diabetes\n",
    "day_of_week = df.groupby('Diabetes')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Percentage of Appointment Show/No Show by Diabetes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The patients suffering from Diabetes tend to attend their appointment more often than those who do not have this condition. This feature may not be helpful in dertermining the no show."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.13 Alcoholism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing number of show/ no-show by Alcoholism\n",
    "day_of_week = df.groupby('Alcoholism')['No_Show'].value_counts(normalize=False).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Appointment Show/No Show by Alcoholism\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Patients who suffer from alcoholism represent only 3% of all the appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing the percentage of show/ no-show by Alcoholism\n",
    "day_of_week = df.groupby('Alcoholism')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Percentage of Appointment Show/No Show by Alcoholism\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the graph above, there does not seem to be a difference between the patient suffering from alcoholism and the rest of the dataset. This feature may not be helpful in dertermining the no show."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.14 Handicap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing number of show/ no-show by Alcoholism\n",
    "day_of_week = df.groupby('Handicap')['No_Show'].value_counts(normalize=False).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Appointment Show/No Show by Handicap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Patients suffering from a handicap represent 2% of the total appointments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing the percentage of show/ no-show by Alcoholism\n",
    "day_of_week = df.groupby('Handicap')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Percentage of Appointment Show/No Show by Handicap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the graph above, there does not seem to be a difference between the patient suffering from handicap and the rest of the dataset. This feature may not be helpful in dertermining the no show."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.15 SMS Received"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing number of show/ no-show by SMS Received\n",
    "day_of_week = df.groupby('SMS_received')['No_Show'].value_counts(normalize=False).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Appointment Show/No Show by SMS Received\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barchart showing the percentage of show/ no-show by SMS Received\n",
    "day_of_week = df.groupby('SMS_received')['No_Show'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(10,4))\n",
    "day_of_week.set_title(\"Percentage of Appointment Show/No Show by SMS Received\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphs above do not show expected results: 38% appointments for patients that received the sms were missed while 20% of the appointments for patients that did not received a sms. This feature seem to be important in dertermining appointments no show."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Creating a Model for Appointments No Show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 Selecting the Prediction Target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the variable that we are trying to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.No_Show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['Gender', 'Age', 'Neighbourhood', 'Scholarship', 'Hypertension','Diabetes', \n",
    "        'Alcoholism', 'Handicap', 'SMS_received', 'AgeGroup', 'Scheduled_Time', \n",
    "        'Scheduled_Period', 'Waiting_Days', 'Pre_Appt_Period']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dummies\n",
    "dum_Gender = pd.get_dummies(X['Gender'], prefix = 'Gender')\n",
    "dum_AgeGroup = pd.get_dummies(X['AgeGroup'], prefix = 'AgeGroup')\n",
    "dum_Scheduled_Period = pd.get_dummies(X['Scheduled_Period'], prefix = 'Scheduled_Period')\n",
    "dum_Pre_Appt_Period  = pd.get_dummies(X['Pre_Appt_Period'], prefix = 'Pre_Appt_Period')\n",
    "\n",
    "\n",
    "# add the dummy data to the dataframe\n",
    "X = pd.concat([X, dum_Gender, dum_AgeGroup,dum_Scheduled_Period, dum_Pre_Appt_Period], axis = 1)\n",
    "\n",
    "# drop the original columns\n",
    "X = X.drop(columns=['Gender','AgeGroup','Scheduled_Period','Pre_Appt_Period'])\n",
    "\n",
    "# create label encoders\n",
    "le = LabelEncoder()\n",
    "X['Neighbourhood'] = le.fit_transform(X['Neighbourhood'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4 Build the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split de Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(82890, 31) (82890,)\n",
      "(27631, 31) (27631,)\n"
     ]
    }
   ],
   "source": [
    "# split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 101)\n",
    "print (X_train.shape, y_train.shape)\n",
    "print (X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{200: 0.8000434294813796,\n",
       " 400: 0.8000796207158626,\n",
       " 600: 0.8002605768882777,\n",
       " 800: 0.8004415330606927,\n",
       " 1000: 0.8004777242951757,\n",
       " 1200: 0.8006224892331077,\n",
       " 1400: 0.8004415330606927,\n",
       " 1600: 0.8004777242951757,\n",
       " 1800: 0.8001881944193117}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a for loop to test different n-estimators with loss set as ‘deviance’ refering to logistic regression for classification\n",
    "\n",
    "ac_dev_gbc = {}\n",
    "\n",
    "for i in range(200,2000,200):\n",
    "    params = {'n_estimators': i,\n",
    "              'max_depth': 4, \n",
    "              'min_samples_split': 2,\n",
    "              'learning_rate': 0.01,\n",
    "              'loss': 'deviance',\n",
    "              'random_state': 101}\n",
    "    gbc = ensemble.GradientBoostingClassifier(**params)\n",
    "    # fit the model\n",
    "    gbc.fit(X_train, y_train)\n",
    "    # predict no show\n",
    "    gbc_predictions = gbc.predict(X_test)\n",
    "    # calculate the accuracy score\n",
    "    ac_dev_gbc[i] = accuracy_score(y_test, gbc_predictions)\n",
    "\n",
    "ac_dev_gbc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f884b623320>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the results\n",
    "plt.plot(ac_dev_gbc.keys(), ac_dev_gbc.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best score occurs at n_estimators = 1200, therefore we are choosing it as our parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.8006224892331077\n"
     ]
    }
   ],
   "source": [
    "# fit the final model using the best accuracy score produced in the for loop\n",
    "final_params = {'n_estimators': 1200, \n",
    "                'max_depth': 4, \n",
    "                'min_samples_split': 2,\n",
    "                'learning_rate': 0.01, \n",
    "                'loss': 'deviance',\n",
    "                'random_state': 101}\n",
    "final_gbc = ensemble.GradientBoostingClassifier(**final_params)\n",
    "\n",
    "final_gbc.fit(X_train, y_train)\n",
    "\n",
    "# predict no show\n",
    "final_gbc_predictions = final_gbc.predict(X_test)\n",
    "\n",
    "# calculate the accuracy score\n",
    "final_ac_gbc = accuracy_score(y_test, final_gbc_predictions)\n",
    "\n",
    "print(\"Accuracy Score:\" , final_ac_gbc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create an output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Actual</th>\n",
       "      <th>Final Predictions</th>\n",
       "      <th>PatientId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29415</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>543433819342712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65921</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>34852598961971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37876</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>939897224313925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56689</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>112974733865223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76332</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>835157761296283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual  Final Predictions        PatientId\n",
       "29415   False              False  543433819342712\n",
       "65921   False              False   34852598961971\n",
       "37876   False              False  939897224313925\n",
       "56689   False              False  112974733865223\n",
       "76332   False              False  835157761296283"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Link the results back to a patient and save\n",
    "patients = df['PatientId']\n",
    "output = pd.DataFrame({'Actual': y_test,\n",
    "                       'Final Predictions': final_gbc_predictions})\n",
    "output = pd.merge(output, patients, left_index=True, right_index=True)\n",
    "output.head()"
   ]
  }
 ],
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