Investigate_a_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Project 2 :No-show appointments Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introduction:\n",
    "In this project, I’m going to move around all the Data Analysis processes and work with them in more detail inshallah.\n",
    "I selected the Dataset which talks about (No-show appointments) This dataset collects information from 100k medical\n",
    "appointments in Brazil and is focused on the question of whether or not patients show up for their appointment to\n",
    "analyse it. it contains 14 columns: (PatientId, AppointmentID, Gender, scheduledDay, AppointmentDay, Age,\n",
    "Neighbourhood, Scholarship, Hypertension, Diabetes, Alcoholism, Handicap, SMS_received, Not-show) and 110527 rows\n",
    "before any modifications. I have questions that I’ll move around all the Data Analysis processes to answer it:\n",
    "\n",
    "1.what is the appointment rate  of Show-up VS  No Show-up?\n",
    "\n",
    "2.What factors are important for us to know in order to predict if a patient will show up for their \n",
    "scheduled appointment?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import statements for all of the packages that i use\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load  data and print out a few lines\n",
    "df=pd.read_csv('KaggleV2-May-2016.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assess the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The unique value for Gender:  ['F' 'M']\n",
      "The unique value for Age:  [-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 115]\n",
      "The unique value for Neighbourhood:  ['AEROPORTO', 'ANDORINHAS', 'ANTÔNIO HONÓRIO', 'ARIOVALDO FAVALESSA', 'BARRO VERMELHO', 'BELA VISTA', 'BENTO FERREIRA', 'BOA VISTA', 'BONFIM', 'CARATOÍRA', 'CENTRO', 'COMDUSA', 'CONQUISTA', 'CONSOLAÇÃO', 'CRUZAMENTO', 'DA PENHA', 'DE LOURDES', 'DO CABRAL', 'DO MOSCOSO', 'DO QUADRO', 'ENSEADA DO SUÁ', 'ESTRELINHA', 'FONTE GRANDE', 'FORTE SÃO JOÃO', 'FRADINHOS', 'GOIABEIRAS', 'GRANDE VITÓRIA', 'GURIGICA', 'HORTO', 'ILHA DAS CAIEIRAS', 'ILHA DE SANTA MARIA', 'ILHA DO BOI', 'ILHA DO FRADE', 'ILHA DO PRÍNCIPE', 'ILHAS OCEÂNICAS DE TRINDADE', 'INHANGUETÁ', 'ITARARÉ', 'JABOUR', 'JARDIM CAMBURI', 'JARDIM DA PENHA', 'JESUS DE NAZARETH', 'JOANA D´ARC', 'JUCUTUQUARA', 'MARIA ORTIZ', 'MARUÍPE', 'MATA DA PRAIA', 'MONTE BELO', 'MORADA DE CAMBURI', 'MÁRIO CYPRESTE', 'NAZARETH', 'NOVA PALESTINA', 'PARQUE INDUSTRIAL', 'PARQUE MOSCOSO', 'PIEDADE', 'PONTAL DE CAMBURI', 'PRAIA DO CANTO', 'PRAIA DO SUÁ', 'REDENÇÃO', 'REPÚBLICA', 'RESISTÊNCIA', 'ROMÃO', 'SANTA CECÍLIA', 'SANTA CLARA', 'SANTA HELENA', 'SANTA LUÍZA', 'SANTA LÚCIA', 'SANTA MARTHA', 'SANTA TEREZA', 'SANTO ANDRÉ', 'SANTO ANTÔNIO', 'SANTOS DUMONT', 'SANTOS REIS', 'SEGURANÇA DO LAR', 'SOLON BORGES', 'SÃO BENEDITO', 'SÃO CRISTÓVÃO', 'SÃO JOSÉ', 'SÃO PEDRO', 'TABUAZEIRO', 'UNIVERSITÁRIO', 'VILA RUBIM']\n",
      "The unique value for Scholarship:  [0 1]\n",
      "The unique value for Scholarship:  [1 0]\n",
      "The unique value for Diabetes:  [0 1]\n",
      "The unique value for Alcoholism:  [0 1]\n",
      "The unique value for Handicap:  [0 1 2 3 4]\n",
      "The unique value for SMS_received:  [0 1]\n",
      "The unique value for No-show:  ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "#Defind the unique value for each column\n",
    "print(\"The unique value for Gender: \",df.Gender.unique())\n",
    "print(\"The unique value for Age: \",sorted(df.Age.unique()))\n",
    "print(\"The unique value for Neighbourhood: \",sorted(df.Neighbourhood.unique()))\n",
    "print(\"The unique value for Scholarship: \",df.Scholarship.unique())\n",
    "print(\"The unique value for Scholarship: \",df.Hipertension.unique())\n",
    "print(\"The unique value for Diabetes: \",df.Diabetes.unique())\n",
    "print(\"The unique value for Alcoholism: \",df.Alcoholism.unique())\n",
    "print(\"The unique value for Handicap: \",df.Handcap.unique())\n",
    "print(\"The unique value for SMS_received: \",df.SMS_received.unique())\n",
    "print(\"The unique value for No-show: \",df['No-show'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PatientId         0\n",
       "AppointmentID     0\n",
       "Gender            0\n",
       "ScheduledDay      0\n",
       "AppointmentDay    0\n",
       "Age               0\n",
       "Neighbourhood     0\n",
       "Scholarship       0\n",
       "Hipertension      0\n",
       "Diabetes          0\n",
       "Alcoholism        0\n",
       "Handcap           0\n",
       "SMS_received      0\n",
       "No-show           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check and confirm the null values , in our Dataset there aren't null values:\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of dublicated :  0\n"
     ]
    }
   ],
   "source": [
    "#Check, drop, and confirm the duplicate columns, in our dataset there aren't:\n",
    "print(\"Num of dublicated : \", + sum(df.duplicated()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop and confirm columns that will not use:\n",
    "df.drop(['PatientId', 'AppointmentID', 'Hipertension', 'Diabetes'], axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>No</td>\n",
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      ],
      "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  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            0           0        0             0      No  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>110345</th>\n",
       "      <td>F</td>\n",
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       "      <td>2016-06-01T00:00:00Z</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>110346</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-06-06T14:22:34Z</td>\n",
       "      <td>2016-06-08T00:00:00Z</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>110454</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-03T15:18:44Z</td>\n",
       "      <td>2016-06-03T00:00:00Z</td>\n",
       "      <td>0</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110460</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-03T08:56:51Z</td>\n",
       "      <td>2016-06-03T00:00:00Z</td>\n",
       "      <td>0</td>\n",
       "      <td>RESISTÊNCIA</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
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       "</table>\n",
       "<p>3540 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Gender          ScheduledDay        AppointmentDay  Age  \\\n",
       "59          F  2016-04-29T08:08:43Z  2016-04-29T00:00:00Z    0   \n",
       "63          M  2016-04-27T10:46:12Z  2016-04-29T00:00:00Z    0   \n",
       "64          M  2016-04-25T13:28:21Z  2016-04-29T00:00:00Z    0   \n",
       "65          M  2016-04-27T10:48:50Z  2016-04-29T00:00:00Z    0   \n",
       "67          F  2016-04-29T08:53:02Z  2016-04-29T00:00:00Z    0   \n",
       "...       ...                   ...                   ...  ...   \n",
       "110345      F  2016-05-16T12:30:58Z  2016-06-01T00:00:00Z    0   \n",
       "110346      M  2016-06-06T14:22:34Z  2016-06-08T00:00:00Z    0   \n",
       "110454      F  2016-06-03T15:18:44Z  2016-06-03T00:00:00Z    0   \n",
       "110460      F  2016-06-03T08:56:51Z  2016-06-03T00:00:00Z    0   \n",
       "110507      F  2016-06-08T09:04:18Z  2016-06-08T00:00:00Z    0   \n",
       "\n",
       "            Neighbourhood  Scholarship  Alcoholism  Handcap  SMS_received  \\\n",
       "59              CONQUISTA            0           0        0             0   \n",
       "63           SÃO BENEDITO            0           0        0             0   \n",
       "64      ILHA DAS CAIEIRAS            0           0        0             1   \n",
       "65              CONQUISTA            0           0        0             0   \n",
       "67         NOVA PALESTINA            0           0        0             0   \n",
       "...                   ...          ...         ...      ...           ...   \n",
       "110345        RESISTÊNCIA            0           0        0             0   \n",
       "110346        RESISTÊNCIA            0           0        0             0   \n",
       "110454        RESISTÊNCIA            0           0        0             0   \n",
       "110460        RESISTÊNCIA            0           0        0             0   \n",
       "110507        MARIA ORTIZ            0           0        0             0   \n",
       "\n",
       "       No-show  \n",
       "59          No  \n",
       "63          No  \n",
       "64          No  \n",
       "65          No  \n",
       "67          No  \n",
       "...        ...  \n",
       "110345      No  \n",
       "110346      No  \n",
       "110454      No  \n",
       "110460      No  \n",
       "110507      No  \n",
       "\n",
       "[3540 rows x 10 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check if there is any age <= zero :\n",
    "df[df[\"Age\"] <= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the Mean of Age: 37.08887421173107\n"
     ]
    },
    {
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       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T17:29:31Z</td>\n",
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       "      <td>8.0</td>\n",
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       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "  Gender          ScheduledDay        AppointmentDay   Age      Neighbourhood  \\\n",
       "0      F  2016-04-29T18:38:08Z  2016-04-29T00:00:00Z  62.0    JARDIM DA PENHA   \n",
       "1      M  2016-04-29T16:08:27Z  2016-04-29T00:00:00Z  56.0    JARDIM DA PENHA   \n",
       "2      F  2016-04-29T16:19:04Z  2016-04-29T00:00:00Z  62.0      MATA DA PRAIA   \n",
       "3      F  2016-04-29T17:29:31Z  2016-04-29T00:00:00Z   8.0  PONTAL DE CAMBURI   \n",
       "4      F  2016-04-29T16:07:23Z  2016-04-29T00:00:00Z  56.0    JARDIM DA PENHA   \n",
       "\n",
       "   Scholarship  Alcoholism  Handcap  SMS_received No-show  \n",
       "0          0.0         0.0      0.0           0.0      No  \n",
       "1          0.0         0.0      0.0           0.0      No  \n",
       "2          0.0         0.0      0.0           0.0      No  \n",
       "3          0.0         0.0      0.0           0.0      No  \n",
       "4          0.0         0.0      0.0           0.0      No  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fix it by change the zero value with mean to be constant:\n",
    "Age_Mean = df['Age'].mean()\n",
    "print(\"the Mean of Age:\",Age_Mean)\n",
    "df[df['Age'] <= 0] = Age_Mean\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Gender</th>\n",
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       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2016-04-29T00:00:00Z</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>8.0</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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  62.0    JARDIM DA PENHA   \n",
       "1      M  2016-04-29T16:08:27Z  2016-04-29T00:00:00Z  56.0    JARDIM DA PENHA   \n",
       "2      F  2016-04-29T16:19:04Z  2016-04-29T00:00:00Z  62.0      MATA DA PRAIA   \n",
       "3      F  2016-04-29T17:29:31Z  2016-04-29T00:00:00Z   8.0  PONTAL DE CAMBURI   \n",
       "4      F  2016-04-29T16:07:23Z  2016-04-29T00:00:00Z  56.0    JARDIM DA PENHA   \n",
       "\n",
       "   Scholarship  Alcoholism  Handicap  SMS_received No_show  \n",
       "0          0.0         0.0       0.0           0.0      No  \n",
       "1          0.0         0.0       0.0           0.0      No  \n",
       "2          0.0         0.0       0.0           0.0      No  \n",
       "3          0.0         0.0       0.0           0.0      No  \n",
       "4          0.0         0.0       0.0           0.0      No  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fixing the spelling problem in data columns:\n",
    "df.rename(columns = {'Handcap': 'Handicap','No-show':'No_show'}, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "      <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>8.0</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <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>56.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</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  62.0   \n",
       "1      M 2016-04-29 16:08:27+00:00 2016-04-29 00:00:00+00:00  56.0   \n",
       "2      F 2016-04-29 16:19:04+00:00 2016-04-29 00:00:00+00:00  62.0   \n",
       "3      F 2016-04-29 17:29:31+00:00 2016-04-29 00:00:00+00:00   8.0   \n",
       "4      F 2016-04-29 16:07:23+00:00 2016-04-29 00:00:00+00:00  56.0   \n",
       "\n",
       "       Neighbourhood  Scholarship  Alcoholism  Handicap  SMS_received No_show  \n",
       "0    JARDIM DA PENHA          0.0         0.0       0.0           0.0      No  \n",
       "1    JARDIM DA PENHA          0.0         0.0       0.0           0.0      No  \n",
       "2      MATA DA PRAIA          0.0         0.0       0.0           0.0      No  \n",
       "3  PONTAL DE CAMBURI          0.0         0.0       0.0           0.0      No  \n",
       "4    JARDIM DA PENHA          0.0         0.0       0.0           0.0      No  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert the datatype of the columns that has wrong data type:\n",
    "df['ScheduledDay'] = pd.to_datetime(df['ScheduledDay'])\n",
    "df['AppointmentDay'] = pd.to_datetime(df['AppointmentDay'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
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       "      <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>8.0</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>56.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    </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  62.0   \n",
       "1      M 2016-04-29 16:08:27+00:00 2016-04-29 00:00:00+00:00  56.0   \n",
       "2      F 2016-04-29 16:19:04+00:00 2016-04-29 00:00:00+00:00  62.0   \n",
       "3      F 2016-04-29 17:29:31+00:00 2016-04-29 00:00:00+00:00   8.0   \n",
       "4      F 2016-04-29 16:07:23+00:00 2016-04-29 00:00:00+00:00  56.0   \n",
       "\n",
       "       Neighbourhood  Scholarship  Alcoholism  Handicap  SMS_received  No_show  \n",
       "0    JARDIM DA PENHA          0.0         0.0       0.0           0.0      0.0  \n",
       "1    JARDIM DA PENHA          0.0         0.0       0.0           0.0      0.0  \n",
       "2      MATA DA PRAIA          0.0         0.0       0.0           0.0      0.0  \n",
       "3  PONTAL DE CAMBURI          0.0         0.0       0.0           0.0      0.0  \n",
       "4    JARDIM DA PENHA          0.0         0.0       0.0           0.0      0.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert No_show data to 0 and 1 instead of 'yes' and 'no' :\n",
    "df.No_show.replace(['Yes', 'No'], [1, 0], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#conver float to int\n",
    "df['No_show'] =  df[[\"No_show\"]].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
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       "      <td>2016-04-29 17:29:31+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>8.0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>56.0</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</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  62.0   \n",
       "1      M 2016-04-29 16:08:27+00:00 2016-04-29 00:00:00+00:00  56.0   \n",
       "2      F 2016-04-29 16:19:04+00:00 2016-04-29 00:00:00+00:00  62.0   \n",
       "3      F 2016-04-29 17:29:31+00:00 2016-04-29 00:00:00+00:00   8.0   \n",
       "4      F 2016-04-29 16:07:23+00:00 2016-04-29 00:00:00+00:00  56.0   \n",
       "\n",
       "       Neighbourhood  Scholarship  Alcoholism  Handicap  SMS_received  No_show  \n",
       "0    JARDIM DA PENHA          0.0         0.0       0.0           0.0        0  \n",
       "1    JARDIM DA PENHA          0.0         0.0       0.0           0.0        0  \n",
       "2      MATA DA PRAIA          0.0         0.0       0.0           0.0        0  \n",
       "3  PONTAL DE CAMBURI          0.0         0.0       0.0           0.0        0  \n",
       "4    JARDIM DA PENHA          0.0         0.0       0.0           0.0        0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert other columns as boolean to be more consistent:\n",
    "df['Scholarship'] =  df[[\"Scholarship\"]].astype(bool)\n",
    "df['Alcoholism'] =  df[[\"Alcoholism\"]].astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Handicap'] =  df[['Handicap']].astype(bool)\n",
    "df['SMS_received'] =  df[['SMS_received']].astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <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",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62.0</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 16:08:27+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>56.0</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>62.0</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</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>8.0</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2016-04-29 16:07:23+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>56.0</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</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  62.0   \n",
       "1      M 2016-04-29 16:08:27+00:00 2016-04-29 00:00:00+00:00  56.0   \n",
       "2      F 2016-04-29 16:19:04+00:00 2016-04-29 00:00:00+00:00  62.0   \n",
       "3      F 2016-04-29 17:29:31+00:00 2016-04-29 00:00:00+00:00   8.0   \n",
       "4      F 2016-04-29 16:07:23+00:00 2016-04-29 00:00:00+00:00  56.0   \n",
       "\n",
       "       Neighbourhood  Scholarship  Alcoholism  Handicap  SMS_received  No_show  \n",
       "0    JARDIM DA PENHA        False       False     False         False        0  \n",
       "1    JARDIM DA PENHA        False       False     False         False        0  \n",
       "2      MATA DA PRAIA        False       False     False         False        0  \n",
       "3  PONTAL DE CAMBURI        False       False     False         False        0  \n",
       "4    JARDIM DA PENHA        False       False     False         False        0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert age to int:\n",
    "df['Age']=df['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a mask for people who came and Who not:\n",
    "Showed = df['No_show'] == 0\n",
    "Not_showed = df['No_show'] == 1\n",
    "df['Showed'] = Showed\n",
    "df['Not_showed'] = Not_showed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
       "      <th>Age</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Scholarship</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No_show</th>\n",
       "      <th>Showed</th>\n",
       "      <th>Not_showed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 16:08:27+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</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  Alcoholism  Handicap  SMS_received  \\\n",
       "0    JARDIM DA PENHA        False       False     False         False   \n",
       "1    JARDIM DA PENHA        False       False     False         False   \n",
       "2      MATA DA PRAIA        False       False     False         False   \n",
       "3  PONTAL DE CAMBURI        False       False     False         False   \n",
       "4    JARDIM DA PENHA        False       False     False         False   \n",
       "\n",
       "   No_show  Showed  Not_showed  \n",
       "0        0    True       False  \n",
       "1        0    True       False  \n",
       "2        0    True       False  \n",
       "3        0    True       False  \n",
       "4        0    True       False  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110527 entries, 0 to 110526\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count   Dtype              \n",
      "---  ------          --------------   -----              \n",
      " 0   Gender          110527 non-null  object             \n",
      " 1   ScheduledDay    110527 non-null  datetime64[ns, UTC]\n",
      " 2   AppointmentDay  110527 non-null  datetime64[ns, UTC]\n",
      " 3   Age             110527 non-null  int64              \n",
      " 4   Neighbourhood   110527 non-null  object             \n",
      " 5   Scholarship     110527 non-null  bool               \n",
      " 6   Alcoholism      110527 non-null  bool               \n",
      " 7   Handicap        110527 non-null  bool               \n",
      " 8   SMS_received    110527 non-null  bool               \n",
      " 9   No_show         110527 non-null  int64              \n",
      " 10  Showed          110527 non-null  bool               \n",
      " 11  Not_showed      110527 non-null  bool               \n",
      "dtypes: bool(6), datetime64[ns, UTC](2), int64(2), object(2)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the cleaned version of the dataset in another file:\n",
    "df.to_csv('no-showappointments-cleaned.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "### Question 1-what is the appointment rate  of Show-up VS  No Show-up?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True     85307\n",
      "False    25220\n",
      "Name: Showed, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Get the rate of Show-up and No Show-up columns:\n",
    "#1)Get the Showed columns value counts:\n",
    "Show_rate= df['Showed'].value_counts()\n",
    "print(Show_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rate of Showed: 77.18204601590561\n"
     ]
    }
   ],
   "source": [
    "#2)Get the rate of Show-up and No Show-up columns:\n",
    "Rate=Show_rate[1] / Show_rate.sum() * 100\n",
    "print('The rate of Showed:',Rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2)Visualize the rate of Show-up and No Show-up columns:\n",
    "colors=['green', 'red']\n",
    "Rate_Chart =Show_rate.plot.pie(figsize=(5,11), autopct='%1.1f%%', fontsize = 10, colors= colors);\n",
    "Rate_Chart.set_title(\"Rate of Show-up VS No Show-up\" , fontsize = 11);\n",
    "Rate_Chart.legend();\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As we can see rate of  Show-up is highest than no Show-up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2:what are the most features of 'Age', 'being alcoholic', 'Having an SMS', 'Gender', 'Scholarship' in order to predict if a patient will show up for their scheduled appointment?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### visualize the features to see the relation between the features and the people who came to their appointment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the mean of Showed: 39.07518726482\n",
      "the mean of Not Showed: 35.329151291512915\n"
     ]
    }
   ],
   "source": [
    "#The mean age of people who show up and people who Not_showed:\n",
    "Showed_mean=df.Age[Showed].mean()\n",
    "NoShowed_mean=df.Age[Not_showed].mean()\n",
    "print(\"the mean of Showed:\",Showed_mean)\n",
    "print(\"the mean of Not Showed:\",NoShowed_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1ae41990>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KyF09ZmYF48JvZlYwW3Xhb8SXyGztfIzMrLettvDvsMMOrFq1yoWtDxHBSy+91NJfFGNmjbfVvrk7YcIEuru7ef7555uaY/369S1dWNeuXcuBBx7Y7Bhm1kK22sI/fPjwlvhWqc7OTg46aLOvHWgZnZ2dLfu1i2bWHFttV4+ZmW0ZF34zs4Jx4TczKxgXfjOzgnHhNzMrGBd+M7OCceE3MysYF34zs4Jx4TczKxgXfjOzgnHhNzMrmLoVfkkTJc2T9LikRyV9Ki2fJWm5pEXpdmy9MpiZ2ebqOUjbK8A5EbFQ0khggaQ70rpvRMTFdWzbzMwqqFvhj4gVwIo0vUbS48D4erVnZmbVUSO+yETSJKAL2B/4O+B04GVgPtmrghfL3GcmMBNg3LhxU+fOnVv3nFuip6eHtra2ZseoyPlq43y16Svf8tXrBry/8TuPqDXSJlr9+EFtGadPn74gItp7L6974ZfUBtwDXBARN0oaB6wEAvgCsFtEfKyvfbS3t8f8+fPrmnNLdXZ20tHR0ewYFTlfbZyvNn3lO+/GJQPe30UnTq4x0aZa/fhBbRkllS38db2qR9Jw4Abgmoi4ESAifhcRGyPiVWAO8PZ6ZjAzs03V86oeAZcDj0fE13PLd8ttdgLwSL0ymJnZ5up5Vc87gBnAEkmL0rLPAqdKmkLW1bMM+Ks6ZjAzs17qeVXPfYDKrLqtXm2amVn//MldM7OCceE3MysYF34zs4Jx4TczKxgXfjOzgnHhNzMrGBd+M7OCceE3MysYF34zs4Jx4TczKxgXfjOzgnHhNzMrGBd+M7OCceE3MysYF34zs4Jx4TczKxgXfjOzgnHhNzMrGBd+M7OCceE3MysYF34zs4Jx4TczKxgXfjOzghnW7ABm1prOu3HJgLa/6MTJdUpig81n/GZmBePCb2ZWMC78ZmYFU7fCL2mipHmSHpf0qKRPpeW7SrpD0tL0c5d6ZTAzs83V84z/FeCciHgrcCjwSUn7AecCd0XEPsBdad7MzBqkboU/IlZExMI0vQZ4HBgPfAC4Mm12JXB8vTKYmdnmFBH1b0SaBHQB+wNPR8TOuXUvRsRm3T2SZgIzAcaNGzd17ty5dc+5JXp6emhra2t2jIqcrzZFzrd89boBbT9+5xGbLesr30D3X6mNWrT68wu1ZZw+ffqCiGjvvbzu1/FLagNuAD4dES9Lqup+ETEbmA3Q3t4eHR0ddctYi87OTlo1GzhfrYqcb6DX8X+oY/Pr+PvKN9D9V2qjFq3+/EJ9Mtb1qh5Jw8mK/jURcWNa/DtJu6X1uwHP1TODmZltqp5X9Qi4HHg8Ir6eW3ULcFqaPg24uV4ZzMxsc/Xs6nkHMANYImlRWvZZ4EvADyV9HHga+PM6ZjAzs17qVvgj4j6gUof+O+vVrpmZ9c2f3DUzKxgXfjOzgnHhNzMrGBd+M7OCceE3MysYF34zs4Jx4TczKxgXfjOzgnHhNzMrGBd+M7OCceE3MysYF34zs4Jx4TczKxgXfjOzgnHhNzMrGBd+M7OCceE3MysYF34zs4Jx4TczKxgXfjOzgqlY+CW9JTe9fa91h9YzlJmZ1U9fZ/zX5qYf7LXu3+uQxczMGqCvwq8K0+XmzcxsKzGsj3VRYbrcvJkV3Hk3Ltls2f5D1pVdbs3VV+GfIOmbZGf3pWnS/Pi6JzMzs7roq/B/Jjc9v9e63vNmZraVqFj4I+LKRgYxM7PG6OtyzsMlfSQ3f72ku9PtqP52LOl7kp6T9Ehu2SxJyyUtSrdja38IZmY2EH1d1fM5Nu3SeTNZ988s4O+r2PcVwHvKLP9GRExJt9uqzGlmZoOkr8K/U0Q8lptfGhELIqILGNnfjtN2L9Qa0MzMBpciyl+ZKWlpROxTYd2TEbF3vzuXJgG3RsT+aX4WcDrwMtmriXMi4sUK950JzAQYN27c1Llz5/bXXFP09PTQ1tbW7BgVOV9tipxv+ep1Ne9jBBtYx/BBSJMZv/OIQdsXtP7zC7VlnD59+oKIaO+9vK+ren4h6b0R8V/5hZKOA57YohTwbeALZJ8D+ALwNeBj5TaMiNnAbID29vbo6OjYwibrq7Ozk1bNBs5XqyLnG4zr7/cf0s0jr04YhDSZD3VMHrR9Qes/v1CfjH0V/r8F/kvSScDCtGwqcBhw3JY0FhG/K01LmgPcuiX7MTOzLVexjz8ingQOAO4FJgF7APeQnaF/aksak7RbbvYE4JFK25qZWX30dcZPRPwe+J6kg4BTgfOBp4Ab+tuxpOuADmCMpO503w5JU8i6epYBf1VLeDMzG7iKhV/SvsApZAV/FfADsjeDp1ez44g4tcziy7ckpJmZDZ4+39wl6+Z5X+r2QdLfNiSVmZnVTV/X8X8Q+C0wT9IcSe/EwzGbmW31+npz96aIOBl4C9BJdpXPOEnflnR0g/KZmdkg6/c7dyNibURcExHHAROARcC5dU9mZmZ1MaAvW4+IFyLisojod5A2MzNrTQMq/GZmtvVz4TczKxgXfjOzgunzk7tmtu3YFr70fKCP4aITB3dQt22Fz/jNzArGhd/MrGBc+M3MCsaF38ysYFz4zcwKxoXfzKxgXPjNzArGhd/MrGBc+M3MCsaF38ysYFz4zcwKxoXfzKxgXPjNzArGhd/MrGBc+M3MCsaF38ysYFz4zcwKpm6FX9L3JD0n6ZHcsl0l3SFpafq5S73aNzOz8up5xn8F8J5ey84F7oqIfYC70ryZmTVQ3Qp/RHQBL/Ra/AHgyjR9JXB8vdo3M7PyFBH127k0Cbg1IvZP86sjYufc+hcjomx3j6SZwEyAcePGTZ07d27dctaip6eHtra2ZseoyPlqsy3lW756XZ3TbG4EG1jH8Ia3WzJ+5xF9rm/15xdqyzh9+vQFEdHee/mwmlPVSUTMBmYDtLe3R0dHR3MDVdDZ2UmrZgPnq9W2lO+8G5fUN0wZ+w/p5pFXJzS83ZIPdUzuc32rP79Qn4yNvqrnd5J2A0g/n2tw+2Zmhdfown8LcFqaPg24ucHtm5kVXj0v57wOeBB4s6RuSR8HvgS8W9JS4N1p3szMGqhuffwRcWqFVe+sV5tmZtY/f3LXzKxgXPjNzArGhd/MrGBc+M3MCsaF38ysYFz4zcwKxoXfzKxgXPjNzArGhd/MrGBc+M3MCsaF38ysYFz4zcwKxoXfzKxgXPjNzArGhd/MrGBc+M3MCsaF38ysYFz4zcwKxoXfzKxgXPjNzArGhd/MrGBc+M3MCsaF38ysYFz4zcwKxoXfzKxgXPjNzArGhd/MrGCGNaNRScuANcBG4JWIaG9GDjOzImpK4U+mR8TKJrZvZlZI7uoxMysYRUTjG5WeAl4EArgsImaX2WYmMBNg3LhxU+fOndvYkFXq6emhra2t2TEqcr7abEv5lq9eV+c0mxvBBtYxvOHtlozfeUSf61v9+YXaMk6fPn1Bua70ZhX+3SPiWUlvBO4A/joiuipt397eHvPnz29cwAHo7Oyko6Oj2TEqcr7abEv5zrtxSX3DlLH/kG4eeXVCw9stuejEyX2ub/XnF2rLKKls4W9KV09EPJt+PgfcBLy9GTnMzIqo4YVf0o6SRpamgaOBRxqdw8ysqJpxVc844CZJpfavjYgfNyGHmVkhNbzwR8SvgQMb3a6ZmWV8OaeZWcG48JuZFYwLv5lZwbjwm5kVjAu/mVnBuPCbmRWMC7+ZWcG48JuZFUwzx+M3MyuULRko75hdBz+Hz/jNzArGhd/MrGBc+M3MCsaF38ysYFz4zcwKxoXfzKxgXPjNzArG1/GbbaXOu3EJ+w9Z15QvUd9a9Hdseh+//r6cfVvhM34zs4Jx4TczKxgXfjOzgnHhNzMrGBd+M7OCceE3MyuYbf5yzoFe6rYtXM6Vf8zVXO63LTxms2bYWi+l9Rm/mVnBuPCbmRWMC7+ZWcE0pfBLeo+kJyQ9KencZmQwMyuqhhd+SUOBS4E/A/YDTpW0X6NzmJkVVTPO+N8OPBkRv46IPwBzgQ80IYeZWSEpIhrboHQS8J6IOCPNzwAOiYize203E5iZZt8MPNHQoNUbA6xsdog+OF9tnK82zle7WjLuERFjey9sxnX8KrNss/8+ETEbmF3/OLWRND8i2pudoxLnq43z1cb5alePjM3o6ukGJubmJwDPNiGHmVkhNaPwPwzsI2lPSdsBpwC3NCGHmVkhNbyrJyJekXQ28BNgKPC9iHi00TkGUat3RzlfbZyvNs5Xu0HP2PA3d83MrLn8yV0zs4Jx4TczKxgX/ir1N8yEpE9IWiJpkaT7Gv1p5GqHwZB0kqSQ1NBL2Ko4fqdLej4dv0WSzmilfGmb/yPpMUmPSrq2lfJJ+kbu2P1S0uoWy/cmSfMk/UzSYknHtli+PSTdlbJ1SprQ4Hzfk/ScpEcqrJekb6b8iyUdXFODEeFbPzeyN6F/BewFbAf8HNiv1zY75abfD/y4lfKl7UYCXcBDQHsr5QNOB77Vws/vPsDPgF3S/BtbKV+v7f+a7KKJlslH9gblmWl6P2BZi+X7f8Bpafoo4OoG/w4eARwMPFJh/bHAf5N9DupQ4H9qac9n/NXpd5iJiHg5N7sjZT6U1sx8yReArwDrG5gNWn+Yjmry/SVwaUS8CBARz7VYvrxTgesakixTTb4AdkrTo2jsZ3eqybcfcFeanldmfV1FRBfwQh+bfAC4KjIPATtL2m1L23Phr8544JncfHdatglJn5T0K7Li+jcNygZV5JN0EDAxIm5tYK6Sqo4f8MH0MvZ6SRPLrK+XavLtC+wr6X5JD0l6T8PSVX/8kLQHsCdwdwNylVSTbxbwYUndwG1kr0oapZp8Pwc+mKZPAEZKGt2AbNWq+negGi781al2mIlLI+KPgX8A/qnuqV7XZz5JQ4BvAOc0LNGmqjl+/wlMiogDgDuBK+ue6nXV5BtG1t3TQXZG/V1JO9c5V0lVv3/JKcD1EbGxjnl6qybfqcAVETGBrNvi6vR72QjV5Pu/wJGSfgYcCSwHXql3sAEYyO9Av1z4qzPQYSbmAsfXNdGm+ss3Etgf6JS0jKyP8JYGvsHb7/GLiFUR8fs0OweY2qBsUN3z2w3cHBEbIuIpskED92mhfCWn0NhuHqgu38eBHwJExIPADmSDjzVCNb9/z0bEiRFxEPCPadlLDcpXjcEd6qaRb2BsrTeys71fk72ELr059Ce9ttknN/0+YH4r5eu1fSeNfXO3muO3W276BOChFsv3HuDKND2G7GX36FbJl7Z7M7CM9MHMFjt+/w2cnqbfSla0GpKzynxjgCFp+gLg8408hqndSVR+c/e9bPrm7k9raqvRD25rvZG9PP0l2dUB/5iWfR54f5r+N+BRYBHZm0MVC28z8vXatqGFv8rjd1E6fj9Px+8tLZZPwNeBx4AlwCmtlC/NzwK+1MhcAzh++wH3p+d3EXB0i+U7CViatvkusH2D810HrAA2kJ3dfxz4BPCJ3O/fpSn/klr/fj1kg5lZwbiP38ysYFz4zcwKxoXfzKxgXPjNzArGhd/MrGBc+K0wJP1jGllzcRrF8pA0EmN7bptJ+RESJR0u6aeSfpFuM9PynSWtkqQ0/6dp1NMJaX6UpBckDZF0haSncqNnPpC2KY1I+jNJSyX9RNJhjT0qVkQN/+pFs2aQ9KfAccDBEfF7SWPIPszT133+CLgWOD4iFqb7/ETS8oj4L0m/Jfsw0mPAYWSjdx5G9gnV0giKr6b/DZ+JiOvLNPODiDg7tTcduFHS9Ih4fDAet1k5PuO3otgNWBlpWIiIWBkR/X3k/ZNk48ssLN0H+HugNJ77/WSFnvTzG73mHxhIwIiYRzZ88cyB3M9soFz4rShuByamLyn5d0lH5tZdU+qGIRs5suRPgAW99jM/LYessJcK/V5kY7qXuo0OI/vHUPLVXFfPNX3kXAi8pepHZbYF3NVjhRARPZKmAtOA6cAPct/E9KGImA9ZHz9QGrpalB8BsbTsfuBcSXuSfbHI+vRNSW1kg8z9NHefSl09vZUbhdFsULnwW2FENlRxJ9kopUuA0/q5y6NkZ/C35JZNJevTJyKWStqFbFC+B9P6BcBHgaciomcLYh4EuH/f6spdPVYIkt4sKT+M8hTgN/3c7VLgdGYbFX8AAAClSURBVElT0j5GA18m+6KdkgeBT/F64X8Q+DQD7N9P+z+SrH9/zkDvazYQPuO3omgDLklfnvIK8CRZka3Y/RIRKyR9GJgjaSRZN8y/RsR/5ja7n2zkx/lp/kGy/v7ehf+rkvJfzvP29PNkSYcDbwCeAj7oK3qs3jw6p5lZwbirx8ysYFz4zcwKxoXfzKxgXPjNzArGhd/MrGBc+M3MCsaF38ysYP4XxJBzW6V62OUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the average of people who showed :\n",
    "Group_AgeShowed=df.groupby('Age')['Showed'].mean()\n",
    "Group_AgeShowed.hist(alpha=0.6,bins=25,label='Showed');\n",
    "#visualize details:\n",
    "plt.xlabel(\"SHOWED\")\n",
    "plt.ylabel(\"AGE\")\n",
    "plt.title(\"The Age of people who are showed:\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alcoholism\n",
      "False    0.797321\n",
      "True     0.388841\n",
      "Name: Showed, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1df7e610>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the relation between alcoholic people who are showed up and who are not showed up:\n",
    "Group_ChoShowed=df.groupby('Alcoholism')['Showed'].mean()\n",
    "print(Group_ChoShowed)\n",
    "colors=['pink', 'blue']\n",
    "Group_ChoShowed.plot(kind='bar',figsize=(8,8), color= colors);\n",
    "plt.xlabel(\"SHOWED\")\n",
    "plt.ylabel(\"ALCOHOLISM\")\n",
    "plt.title(\"The Relation between alcoholics and patients who are showed\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMS_received\n",
      "False    0.832712\n",
      "True     0.656184\n",
      "Name: Showed, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1d75df10>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the relation between SMS received and people who showed up and who are not:\n",
    "Group_SmsRcvSh=df.groupby('SMS_received')['Showed'].mean()\n",
    "print(Group_SmsRcvSh)\n",
    "colors=['pink', 'blue']\n",
    "Group_SmsRcvSh.plot(kind='bar',figsize=(8,8),color=colors);\n",
    "plt.xlabel(\"SHOWED\")\n",
    "plt.ylabel(\"SMS_RECIEVED\")\n",
    "plt.title(\"The Relation between SMS_recieverd and patients who are Showed\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender\n",
      "37.08887421173107    0.000000\n",
      "F                    0.796415\n",
      "M                    0.799154\n",
      "Name: Showed, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1dc9ce50>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the relation betweenand the Gender and people who are showed up and who are not:\n",
    "Group_GenderdSh=df.groupby('Gender')['Showed'].mean()\n",
    "print(Group_GenderdSh)\n",
    "colors=['pink', 'blue']\n",
    "Group_GenderdSh.plot(kind='bar',figsize=(8,8),color=colors);\n",
    "plt.xlabel(\"SHOWED\")\n",
    "plt.ylabel(\"GENDER\")\n",
    "plt.title(\"The Relation between Gender and patients who showed\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scholarship\n",
      "False    0.801316\n",
      "True     0.574117\n",
      "Name: Showed, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1e875550>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the relation between people who has scholarship and people who are showed up and who are not:\n",
    "Group_schSh=df.groupby('Scholarship')['Showed'].mean()\n",
    "print(Group_schSh)\n",
    "colors=['pink', 'blue']\n",
    "Group_schSh.plot(kind = 'bar',figsize=(8,8),color=colors)\n",
    "plt.xlabel(\"SHOWED\")\n",
    "plt.ylabel(\"SCHOLARSHIP\")\n",
    "plt.title(\"The relation between patients who has scholarship and patients who showed\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "1-people who received SMS have 10% higher probability to show up to their appointments.\n",
    "\n",
    "2-Age is the most important factor that we can take into consideration that decides if a patient would come or not.\n",
    "\n",
    "3-the average age for people who will be most likely to show up is 37, and the average age for people who are not likely to show up is 34.\n",
    "\n",
    "4- As we can see most of the people who have Scholarship are most likely to miss their appointments with a percentage of 76.2% of showing and patients who don't have a scholarship have the percentage 80.1%\n",
    "\n",
    "5-As we can see female and male has close presenting\n",
    "\n",
    "6-The rate of  Show-up is highest than no Show-up\n",
    "## Submitting your Project \n",
    "## Resources:\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html\n",
    "\n",
    "https://stackoverflow.com/questions/38802675/create-bool-mask-from-filter-results-in-pandas\n",
    "\n",
    "https://matplotlib.org/3.1.0/gallery/color/named_colors.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}