appointment-show-noshow-exploratory-data-analysis.ipynb

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    "# Project: Identifying factors that affect show/no show for a medical appointment\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "Out of over 110,000 medical appointments, about 30% of them were no shows. That is, patients did not show up to the appointment after scheduling the appointment. Data has been collected on all appointments and patients. I intend to explore the dataset and identify variables that can be used in predicting show/no-show. I will also analyze the effect of certain variables on show/no-show.\n",
    "\n",
    "The dependent column is the `No-show` column that tells whether a patient showed up for their appointment or not.\n",
    "The independent variables include:\n",
    "* Gender\n",
    "* Scholarship\n",
    "* Alcoholism\n",
    "* Hypertension\n",
    "* Diabetes\n",
    "* Handicap\n",
    "* SMSReceived\n",
    "\n",
    "Other characteristics include:\n",
    "* Age\n",
    "* Appointment date\n",
    "* Schedule date\n",
    "* Neighbourhood\n",
    "\n",
    "The dataset can be accessed on [kaggle](https://www.kaggle.com/datasets/joniarroba/noshowappointments).\n",
    "\n",
    "More information on the scholarship variable can be found on [Wikipedia](https://en.wikipedia.org/wiki/Bolsa_Fam%C3%ADlia)."
   ]
  },
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   "source": [
    "#importing modules\n",
    "import numpy as np\n",
    "import pandas as pd\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",
    "### Importing data"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>AppointmentID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>ScheduledDay</th>\n",
       "      <th>AppointmentDay</th>\n",
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       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
       "      <th>No-show</th>\n",
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       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
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      "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  "
      ]
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   ],
   "source": [
    "# reading data into a dataframe\n",
    "df = pd.read_csv('../input/noshowappointments/KaggleV2-May-2016.csv')\n",
    "# displaying first five rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing data"
   ]
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       "      <th>Hipertension</th>\n",
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       "      <th>Alcoholism</th>\n",
       "      <th>Handcap</th>\n",
       "      <th>SMS_received</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>5625367</td>\n",
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       "      <td>2016-04-27T07:10:40Z</td>\n",
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       "      <td>0</td>\n",
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       "      <td>5.526357e+14</td>\n",
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       "      <td>F</td>\n",
       "      <td>2016-04-12T07:42:08Z</td>\n",
       "      <td>2016-05-03T00:00:00Z</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
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       "      <th>38905</th>\n",
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       "      <td>2016-05-04T00:00:00Z</td>\n",
       "      <td>74</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2016-05-11T00:00:00Z</td>\n",
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       "      <td>BOA VISTA</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2016-05-25T00:00:00Z</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "          PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "35219  3.264252e+13        5669221      F  2016-05-06T10:33:18Z   \n",
       "54912  5.177525e+13        5618861      F  2016-04-26T07:29:43Z   \n",
       "25155  4.855692e+13        5632994      M  2016-04-28T08:41:09Z   \n",
       "50707  6.927849e+14        5605306      F  2016-04-20T08:51:08Z   \n",
       "1428   2.359689e+13        5447464      F  2016-03-09T10:10:44Z   \n",
       "19869  4.662918e+14        5625367      F  2016-04-27T07:10:40Z   \n",
       "27738  5.526357e+14        5570273      F  2016-04-12T07:42:08Z   \n",
       "38905  2.643586e+13        5657191      M  2016-05-04T09:02:10Z   \n",
       "83889  4.325636e+14        5666293      F  2016-05-05T16:23:51Z   \n",
       "35462  6.677236e+12        5736544      F  2016-05-25T08:29:46Z   \n",
       "\n",
       "             AppointmentDay  Age      Neighbourhood  Scholarship  \\\n",
       "35219  2016-05-30T00:00:00Z   53      SANTOS DUMONT            0   \n",
       "54912  2016-05-02T00:00:00Z   53           DA PENHA            0   \n",
       "25155  2016-05-09T00:00:00Z   44         ANDORINHAS            0   \n",
       "50707  2016-05-13T00:00:00Z   18             JABOUR            0   \n",
       "1428   2016-04-29T00:00:00Z   69       SANTA TEREZA            0   \n",
       "19869  2016-05-11T00:00:00Z   17         BELA VISTA            0   \n",
       "27738  2016-05-03T00:00:00Z   28            MARUÍPE            0   \n",
       "38905  2016-05-04T00:00:00Z   74       SANTA TEREZA            0   \n",
       "83889  2016-05-11T00:00:00Z   19          BOA VISTA            0   \n",
       "35462  2016-05-25T00:00:00Z   52  JESUS DE NAZARETH            0   \n",
       "\n",
       "       Hipertension  Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "35219             0         0           0        0             1      No  \n",
       "54912             1         0           0        0             1      No  \n",
       "25155             1         1           0        1             0      No  \n",
       "50707             0         0           0        0             0      No  \n",
       "1428              0         0           0        0             1      No  \n",
       "19869             0         0           0        0             1     Yes  \n",
       "27738             0         0           0        0             1      No  \n",
       "38905             1         0           0        0             0      No  \n",
       "83889             0         0           0        0             1      No  \n",
       "35462             1         0           0        0             0      No  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#10 random rows of the dataframe\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:18.991215Z",
     "iopub.status.busy": "2022-09-27T11:21:18.990822Z",
     "iopub.status.idle": "2022-09-27T11:21:19.011679Z",
     "shell.execute_reply": "2022-09-27T11:21:19.009897Z",
     "shell.execute_reply.started": "2022-09-27T11:21:18.991181Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>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>110522</th>\n",
       "      <td>2.572134e+12</td>\n",
       "      <td>5651768</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-03T09:15:35Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110523</th>\n",
       "      <td>3.596266e+12</td>\n",
       "      <td>5650093</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-05-03T07:27:33Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>51</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110524</th>\n",
       "      <td>1.557663e+13</td>\n",
       "      <td>5630692</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T16:03:52Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>21</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110525</th>\n",
       "      <td>9.213493e+13</td>\n",
       "      <td>5630323</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T15:09:23Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>38</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110526</th>\n",
       "      <td>3.775115e+14</td>\n",
       "      <td>5629448</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T13:30:56Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>54</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "110522  2.572134e+12        5651768      F  2016-05-03T09:15:35Z   \n",
       "110523  3.596266e+12        5650093      F  2016-05-03T07:27:33Z   \n",
       "110524  1.557663e+13        5630692      F  2016-04-27T16:03:52Z   \n",
       "110525  9.213493e+13        5630323      F  2016-04-27T15:09:23Z   \n",
       "110526  3.775115e+14        5629448      F  2016-04-27T13:30:56Z   \n",
       "\n",
       "              AppointmentDay  Age Neighbourhood  Scholarship  Hipertension  \\\n",
       "110522  2016-06-07T00:00:00Z   56   MARIA ORTIZ            0             0   \n",
       "110523  2016-06-07T00:00:00Z   51   MARIA ORTIZ            0             0   \n",
       "110524  2016-06-07T00:00:00Z   21   MARIA ORTIZ            0             0   \n",
       "110525  2016-06-07T00:00:00Z   38   MARIA ORTIZ            0             0   \n",
       "110526  2016-06-07T00:00:00Z   54   MARIA ORTIZ            0             0   \n",
       "\n",
       "        Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "110522         0           0        0             1      No  \n",
       "110523         0           0        0             1      No  \n",
       "110524         0           0        0             1      No  \n",
       "110525         0           0        0             1      No  \n",
       "110526         0           0        0             1      No  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#last 5 rows in the dataframe\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.013730Z",
     "iopub.status.busy": "2022-09-27T11:21:19.013269Z",
     "iopub.status.idle": "2022-09-27T11:21:19.076385Z",
     "shell.execute_reply": "2022-09-27T11:21:19.074862Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.013689Z"
    }
   },
   "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": [
    "# information on the columns and rows\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.078127Z",
     "iopub.status.busy": "2022-09-27T11:21:19.077734Z",
     "iopub.status.idle": "2022-09-27T11:21:19.087727Z",
     "shell.execute_reply": "2022-09-27T11:21:19.086000Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.078088Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 14)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has 14 columns and 110527 rows.\n",
    "\n",
    "The data type of the `Scholarship`, `Hipertension`, `Diabetes`,`Alcoholism` and `SMS_received` columns in int64 because they contain numerical values but these values are actually categorical.\n",
    "\n",
    "The data type of the `ScheduledDay` and `AppointmentDay` columns is int but they are timestamp columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.090532Z",
     "iopub.status.busy": "2022-09-27T11:21:19.089713Z",
     "iopub.status.idle": "2022-09-27T11:21:19.131638Z",
     "shell.execute_reply": "2022-09-27T11:21:19.130359Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.090480Z"
    }
   },
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for nulls\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no nulls in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.134101Z",
     "iopub.status.busy": "2022-09-27T11:21:19.133307Z",
     "iopub.status.idle": "2022-09-27T11:21:19.258656Z",
     "shell.execute_reply": "2022-09-27T11:21:19.257459Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.134030Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicates\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no duplicates in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.260637Z",
     "iopub.status.busy": "2022-09-27T11:21:19.260054Z",
     "iopub.status.idle": "2022-09-27T11:21:19.270982Z",
     "shell.execute_reply": "2022-09-27T11:21:19.269497Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.260600Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    75045\n",
       "1    35482\n",
       "Name: SMS_received, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of the sms received column\n",
    "df['SMS_received'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 stands for no and 1 stands for yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.277273Z",
     "iopub.status.busy": "2022-09-27T11:21:19.276718Z",
     "iopub.status.idle": "2022-09-27T11:21:19.294365Z",
     "shell.execute_reply": "2022-09-27T11:21:19.292651Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.277225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    99666\n",
       "1    10861\n",
       "Name: Scholarship, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of the scholarship column\n",
    "df['Scholarship'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 stands for no scholarship and 1 stands for scholarship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.297300Z",
     "iopub.status.busy": "2022-09-27T11:21:19.296792Z",
     "iopub.status.idle": "2022-09-27T11:21:19.308874Z",
     "shell.execute_reply": "2022-09-27T11:21:19.307802Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.297249Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88726\n",
       "1    21801\n",
       "Name: Hipertension, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of the Hipertension column\n",
    "df['Hipertension'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 means the patient is not hypertensive and 1 means the patient is hypertensive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.311352Z",
     "iopub.status.busy": "2022-09-27T11:21:19.310582Z",
     "iopub.status.idle": "2022-09-27T11:21:19.326952Z",
     "shell.execute_reply": "2022-09-27T11:21:19.325639Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.311302Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    102584\n",
       "1      7943\n",
       "Name: Diabetes, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of the Diabetes column\n",
    "df['Diabetes'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 means the patient does not have diabetes and 1 means they have diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.329816Z",
     "iopub.status.busy": "2022-09-27T11:21:19.328847Z",
     "iopub.status.idle": "2022-09-27T11:21:19.342935Z",
     "shell.execute_reply": "2022-09-27T11:21:19.341133Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.329764Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    107167\n",
       "1      3360\n",
       "Name: Alcoholism, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of the alcoholism column\n",
    "df['Alcoholism'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 means the patient is not involved in alcoholism and 1 means they are involved in alcoholism."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.345839Z",
     "iopub.status.busy": "2022-09-27T11:21:19.345049Z",
     "iopub.status.idle": "2022-09-27T11:21:19.356655Z",
     "shell.execute_reply": "2022-09-27T11:21:19.355473Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.345738Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108286\n",
       "1      2042\n",
       "2       183\n",
       "3        13\n",
       "4         3\n",
       "Name: Handcap, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Handcap'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 means no handicap and for the rest, the number signifies the number of handicaps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.359294Z",
     "iopub.status.busy": "2022-09-27T11:21:19.358538Z",
     "iopub.status.idle": "2022-09-27T11:21:19.380376Z",
     "shell.execute_reply": "2022-09-27T11:21:19.379345Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.359236Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    71840\n",
       "M    38687\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of gender\n",
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "F for female and M for male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.382898Z",
     "iopub.status.busy": "2022-09-27T11:21:19.381912Z",
     "iopub.status.idle": "2022-09-27T11:21:19.398551Z",
     "shell.execute_reply": "2022-09-27T11:21:19.395578Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.382857Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     88208\n",
       "Yes    22319\n",
       "Name: No-show, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of the no-show column\n",
    "df['No-show'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No means the patient showed and yes means they did not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.401497Z",
     "iopub.status.busy": "2022-09-27T11:21:19.401050Z",
     "iopub.status.idle": "2022-09-27T11:21:19.483458Z",
     "shell.execute_reply": "2022-09-27T11:21:19.482179Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.401460Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistacal breakdown of the dataframe\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The min of the `Age` column is a negative number. This is a validity error as age cannot be in negatives."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data quality issues\n",
    "* Negative age\n",
    "* Misspelled column labels\n",
    "* Wrong data types for some columns\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Copying data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.486281Z",
     "iopub.status.busy": "2022-09-27T11:21:19.485400Z",
     "iopub.status.idle": "2022-09-27T11:21:19.498825Z",
     "shell.execute_reply": "2022-09-27T11:21:19.497518Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.486232Z"
    }
   },
   "outputs": [],
   "source": [
    "df_clean = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to keep the original dataset as it is so I will be performing data cleaning on the new dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue:\n",
    "* Negative age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "* drop rows with negative age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.501574Z",
     "iopub.status.busy": "2022-09-27T11:21:19.500565Z",
     "iopub.status.idle": "2022-09-27T11:21:19.527265Z",
     "shell.execute_reply": "2022-09-27T11:21:19.525804Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.501532Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>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>99832</th>\n",
       "      <td>4.659432e+14</td>\n",
       "      <td>5775010</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-06T08:58:13Z</td>\n",
       "      <td>2016-06-06T00:00:00Z</td>\n",
       "      <td>-1</td>\n",
       "      <td>ROMÃO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PatientId  AppointmentID Gender          ScheduledDay  \\\n",
       "99832  4.659432e+14        5775010      F  2016-06-06T08:58:13Z   \n",
       "\n",
       "             AppointmentDay  Age Neighbourhood  Scholarship  Hipertension  \\\n",
       "99832  2016-06-06T00:00:00Z   -1         ROMÃO            0             0   \n",
       "\n",
       "       Diabetes  Alcoholism  Handcap  SMS_received No-show  \n",
       "99832         0           0        0             0      No  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking index of row with negative age value\n",
    "df_clean.query('Age < 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the results above, I can tell that only one row has a negative `Age` value therefore I can drop this row from the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.530096Z",
     "iopub.status.busy": "2022-09-27T11:21:19.529153Z",
     "iopub.status.idle": "2022-09-27T11:21:19.560408Z",
     "shell.execute_reply": "2022-09-27T11:21:19.558924Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.530020Z"
    }
   },
   "outputs": [],
   "source": [
    "#dropping row with negative age using row index\n",
    "df_clean.drop([99832],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.562652Z",
     "iopub.status.busy": "2022-09-27T11:21:19.562135Z",
     "iopub.status.idle": "2022-09-27T11:21:19.583052Z",
     "shell.execute_reply": "2022-09-27T11:21:19.581236Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.562603Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientId</th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PatientId, AppointmentID, Gender, ScheduledDay, AppointmentDay, Age, Neighbourhood, Scholarship, Hipertension, Diabetes, Alcoholism, Handcap, SMS_received, No-show]\n",
       "Index: []"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirming removal of row with negative age\n",
    "df_clean.query('Age < 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No more rows with negative ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue:\n",
    "* Wrong datatypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "* convert wrong dtypes to correct dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.585591Z",
     "iopub.status.busy": "2022-09-27T11:21:19.585093Z",
     "iopub.status.idle": "2022-09-27T11:21:19.677712Z",
     "shell.execute_reply": "2022-09-27T11:21:19.676489Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.585533Z"
    }
   },
   "outputs": [],
   "source": [
    "#changing the datatype of two columns\n",
    "df_clean['AppointmentDay'] = pd.to_datetime(df_clean['AppointmentDay'])\n",
    "df_clean['ScheduledDay'] = pd.to_datetime(df_clean['ScheduledDay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.681039Z",
     "iopub.status.busy": "2022-09-27T11:21:19.680241Z",
     "iopub.status.idle": "2022-09-27T11:21:19.708285Z",
     "shell.execute_reply": "2022-09-27T11:21:19.706623Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.680999Z"
    }
   },
   "outputs": [],
   "source": [
    "# changing int columns to category\n",
    "columns = ['Scholarship','Hipertension','Diabetes','Alcoholism','Handcap','SMS_received']\n",
    "for column in columns:\n",
    "    df_clean[column] = df_clean[column].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.710257Z",
     "iopub.status.busy": "2022-09-27T11:21:19.709858Z",
     "iopub.status.idle": "2022-09-27T11:21:19.892097Z",
     "shell.execute_reply": "2022-09-27T11:21:19.889420Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.710212Z"
    }
   },
   "outputs": [],
   "source": [
    "#changing dtype of ID columns to string\n",
    "df_clean['PatientId'] = df_clean['PatientId'].astype(str)\n",
    "df_clean['AppointmentID'] = df_clean['AppointmentID'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.895039Z",
     "iopub.status.busy": "2022-09-27T11:21:19.894249Z",
     "iopub.status.idle": "2022-09-27T11:21:19.944464Z",
     "shell.execute_reply": "2022-09-27T11:21:19.942712Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.894994Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110526 entries, 0 to 110526\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count   Dtype              \n",
      "---  ------          --------------   -----              \n",
      " 0   PatientId       110526 non-null  object             \n",
      " 1   AppointmentID   110526 non-null  object             \n",
      " 2   Gender          110526 non-null  object             \n",
      " 3   ScheduledDay    110526 non-null  datetime64[ns, UTC]\n",
      " 4   AppointmentDay  110526 non-null  datetime64[ns, UTC]\n",
      " 5   Age             110526 non-null  int64              \n",
      " 6   Neighbourhood   110526 non-null  object             \n",
      " 7   Scholarship     110526 non-null  category           \n",
      " 8   Hipertension    110526 non-null  category           \n",
      " 9   Diabetes        110526 non-null  category           \n",
      " 10  Alcoholism      110526 non-null  category           \n",
      " 11  Handcap         110526 non-null  category           \n",
      " 12  SMS_received    110526 non-null  category           \n",
      " 13  No-show         110526 non-null  object             \n",
      "dtypes: category(6), datetime64[ns, UTC](2), int64(1), object(5)\n",
      "memory usage: 8.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue:\n",
    "* Mispelled column names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "* rename columns with mispelled labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.946362Z",
     "iopub.status.busy": "2022-09-27T11:21:19.945914Z",
     "iopub.status.idle": "2022-09-27T11:21:19.954603Z",
     "shell.execute_reply": "2022-09-27T11:21:19.952578Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.946323Z"
    }
   },
   "outputs": [],
   "source": [
    "#renaming columns\n",
    "df_clean.rename(columns = {'PatientId':'PatientID',\n",
    "                           'Hipertension':'Hypertension',\n",
    "                           'Handcap':'Handicapped',\n",
    "                           'No-show':'NoShow',\n",
    "                           'SMS_received':'SMSReceived'},\n",
    "                           inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.957260Z",
     "iopub.status.busy": "2022-09-27T11:21:19.956812Z",
     "iopub.status.idle": "2022-09-27T11:21:19.977173Z",
     "shell.execute_reply": "2022-09-27T11:21:19.975860Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.957221Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PatientID', 'AppointmentID', 'Gender', 'ScheduledDay',\n",
       "       'AppointmentDay', 'Age', 'Neighbourhood', 'Scholarship', 'Hypertension',\n",
       "       'Diabetes', 'Alcoholism', 'Handicapped', 'SMSReceived', 'NoShow'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verifying the change\n",
    "df_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I changed the names of columns that were initially spelt wrongly or had symbols that could be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:19.988206Z",
     "iopub.status.busy": "2022-09-27T11:21:19.987637Z",
     "iopub.status.idle": "2022-09-27T11:21:20.015708Z",
     "shell.execute_reply": "2022-09-27T11:21:20.014018Z",
     "shell.execute_reply.started": "2022-09-27T11:21:19.988164Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientID</th>\n",
       "      <th>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>Hypertension</th>\n",
       "      <th>Diabetes</th>\n",
       "      <th>Alcoholism</th>\n",
       "      <th>Handicapped</th>\n",
       "      <th>SMSReceived</th>\n",
       "      <th>NoShow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29872499824296.0</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 18:38:08+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>558997776694438.0</td>\n",
       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29 16:08:27+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4262962299951.0</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:19:04+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>867951213174.0</td>\n",
       "      <td>5642828</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 17:29:31+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8841186448183.0</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29 16:07:23+00:00</td>\n",
       "      <td>2016-04-29 00:00:00+00:00</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PatientID AppointmentID Gender              ScheduledDay  \\\n",
       "0   29872499824296.0       5642903      F 2016-04-29 18:38:08+00:00   \n",
       "1  558997776694438.0       5642503      M 2016-04-29 16:08:27+00:00   \n",
       "2    4262962299951.0       5642549      F 2016-04-29 16:19:04+00:00   \n",
       "3     867951213174.0       5642828      F 2016-04-29 17:29:31+00:00   \n",
       "4    8841186448183.0       5642494      F 2016-04-29 16:07:23+00:00   \n",
       "\n",
       "             AppointmentDay  Age      Neighbourhood Scholarship Hypertension  \\\n",
       "0 2016-04-29 00:00:00+00:00   62    JARDIM DA PENHA           0            1   \n",
       "1 2016-04-29 00:00:00+00:00   56    JARDIM DA PENHA           0            0   \n",
       "2 2016-04-29 00:00:00+00:00   62      MATA DA PRAIA           0            0   \n",
       "3 2016-04-29 00:00:00+00:00    8  PONTAL DE CAMBURI           0            0   \n",
       "4 2016-04-29 00:00:00+00:00   56    JARDIM DA PENHA           0            1   \n",
       "\n",
       "  Diabetes Alcoholism Handicapped SMSReceived NoShow  \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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying first five rows\n",
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "\n",
    "\n",
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:20.017806Z",
     "iopub.status.busy": "2022-09-27T11:21:20.017419Z",
     "iopub.status.idle": "2022-09-27T11:21:20.034165Z",
     "shell.execute_reply": "2022-09-27T11:21:20.032564Z",
     "shell.execute_reply.started": "2022-09-27T11:21:20.017775Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     88207\n",
       "Yes    22319\n",
       "Name: NoShow, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# distribution of patients who showed and those who didn't\n",
    "df_clean['NoShow'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "88207 patients showed up for their appointment and 22319 patients did not show up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:20.036383Z",
     "iopub.status.busy": "2022-09-27T11:21:20.035906Z",
     "iopub.status.idle": "2022-09-27T11:21:20.299859Z",
     "shell.execute_reply": "2022-09-27T11:21:20.298548Z",
     "shell.execute_reply.started": "2022-09-27T11:21:20.036342Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dwHvaIEnS70x2K/hPVtXbknyDCR5hW1V7jrQySdIab7IjkePbz49PRSGSpJlnhSFSVRe30SdW1acG5yU5BDh3lIVJktZ8w5xY33+Cttev5jokSTPQZOdE9gVeDWw/7hvqGwG3TryWJGk2meycyA+AG4EtgCMG2u8ELhtlUZKkmWGycyLXAdcBT5+6ciRJM8kwN2DcJclFSe5Kcm+S+5Ism4riJElrtmFOrH8G2Be4BlgfeCPw2VEWJUmaGYYJEapqMTCnqu6rqi8Bu4+2LEnSTDDU43GTrEv3iNyP0p1sHyp8JElrt2HC4HVtubcCvwS2BV42yqIkSTPDMDdgvK4diSwATgGurqp7R12YJGnNt9IQSfJi4H8B/w2E7suHf1VV3xp1cZKkNdsw50SOAJ7XTq6T5FHA6YAhIkmz3DDnRO4cC5DmWrpvrUuSZrlhjkQWJTkDOInuuSKvAC5K8hcAVXXKCOuTJK3BhgmRhwI/B57TppfSfenwpXShYohI0iw1zNVZb5iKQiRJM49fGpQk9WaISJJ6W2GItEfgkuQZU1eOJGkmmexIZOxcyKenohBJ0swz2Yn1q5JcA2ydZPBJhgGqqv5ktKVJktZ0KzwSqap9gWcBi+ku5x0bXtJ+DiXJnCSXJPlmm94+yQVJFif5WrsvF0nWa9OL2/wFA9t4d2u/OsluA+27t7bFSQ59cLsuSVpVk55Yr6qbquoJdLd/36gNP2uPzh3WIcBVA9MfAY6sqkcDtwEHtPYDgNta+5FtOZLsCOwDPI7uOSafa8E0h+7hWHsAOwL7tmUlSVNkmMfjPofuqYafBT4H/FeSZw+z8STbAC8GvtimAzwfOLktciywdxvfq03T5u/alt8LOLGq7qmqH9MdGT21DYur6tp2V+ET27KSpCkyzDfWPwG8sKquBkjyR8BXgacMse4ngXfRHcEAbA7cXlXL2/T1wPw2Ph9YAlBVy5Pc0ZafD5w/sM3BdZaMa3/aREUkORA4EGC77bYbomxJ0jCG+Z7IOmMBAlBV/wWss7KVkrwEuLmqLl6F+laLqlpYVTtX1c7z5s2b7nIkaa0x7A0Yvwic0KZfAywaYr1nAHsmeRHd/bc2Bj4FbJpkbjsa2Qa4oS1/A91TE69PMhfYBLhloH3M4DorapckTYFhjkT+BrgSOLgNV7a2SVXVu6tqm6paQHdi/Oyqeg3wXeDlbbH9gVPb+Gltmjb/7Kqq1r5Pu3pre2AH4ELgImCHdrXXuu01ThtifyRJq8kwN2C8h+68yCdW02v+PXBikg8AlwBHt/ajgeOTLAZupQsFquqKJCfRhddy4C1VdR9AkrcCZwJzgGOq6orVVKMkaQjDdGetsqo6BzinjV9Ld2XV+GV+TfeskonW/yDwwQnazwDOWI2lSpIeBG/AKEnqzRCRJPXWK0Ta9y4kSbNc3yORrNYqJEkzUq8QqaovrO5CJEkzzzD3ztomyb8lWZrk5iRfb/fEkiTNcsMciXyJ7kt8WwFbA99obZKkWW6YEJlXVV+qquVt+DLgDagkSUOFyC1JXjv2DI8kr6W7p5UkaZYbJkT+EnglcBPdw6lezu+fvy5JmsWGuXfWdcCeU1CLJGmGWWGIJPmnSdarqnr/COqRJM0gkx2J/HKCtg3onoW+OWCISNIst8IQqaojxsaTbAQcQncu5ETgiBWtJ0maPSY9J5JkM+Bv6Z5meCzw5Kq6bSoKkySt+SY7J/Ix4C+AhcDjq+quKatKkjQjTHaJ79/RfUP9vcDPkixrw51Jlk1NeZKkNdlk50R81ogkaVIGhSSpN0NEktSbISJJ6s0QkST1ZohIknozRCRJvRkikqTeDBFJUm+GiCSpN0NEktSbISJJ6s0QkST1ZohIknozRCRJvRkikqTeDBFJUm+GiCSptxU+2VCSJrLg0NOnu4S1yk8+/OLpLmGVeCQiSerNEJEk9TayEEmybZLvJrkyyRVJDmntmyX5TpJr2s+Ht/YkOSrJ4iSXJXnywLb2b8tfk2T/gfanJLm8rXNUkoxqfyRJDzTKI5HlwN9V1Y7ALsBbkuwIHAqcVVU7AGe1aYA9gB3acCDweehCBzgMeBrwVOCwseBpy7xpYL3dR7g/kqRxRhYiVXVjVf2wjd8JXAXMB/YCjm2LHQvs3cb3Ao6rzvnApkm2AnYDvlNVt1bVbcB3gN3bvI2r6vyqKuC4gW1JkqbAlJwTSbIAeBJwAbBlVd3YZt0EbNnG5wNLBla7vrVN1n79BO0Tvf6BSRYlWbR06dJV2xlJ0u+MPESSbAh8HXhbVS0bnNeOIGrUNVTVwqrauap2njdv3qhfTpJmjZGGSJJ16ALkK1V1Smv+eeuKov28ubXfAGw7sPo2rW2y9m0maJckTZFRXp0V4Gjgqqr6xMCs04CxK6z2B04daN+vXaW1C3BH6/Y6E3hhkoe3E+ovBM5s85Yl2aW91n4D25IkTYFRfmP9GcDrgMuTXNra/gH4MHBSkgOA64BXtnlnAC8CFgN3A28AqKpbk7wfuKgt976qurWNvxn4MrA+8K02SJKmyMhCpKq+D6zoexu7TrB8AW9ZwbaOAY6ZoH0RsNMqlClJWgV+Y12S1JshIknqzRCRJPVmiEiSejNEJEm9GSKSpN4MEUlSb4aIJKk3Q0SS1JshIknqzRCRJPVmiEiSejNEJEm9GSKSpN4MEUlSb4aIJKk3Q0SS1JshIknqzRCRJPVmiEiSejNEJEm9GSKSpN4MEUlSb4aIJKk3Q0SS1JshIknqzRCRJPVmiEiSejNEJEm9GSKSpN4MEUlSb4aIJKk3Q0SS1JshIknqzRCRJPVmiEiSejNEJEm9GSKSpN5mfIgk2T3J1UkWJzl0uuuRpNlkRodIkjnAZ4E9gB2BfZPsOL1VSdLsMaNDBHgqsLiqrq2qe4ETgb2muSZJmjXmTncBq2g+sGRg+nrgaeMXSnIgcGCbvCvJ1VNQ22ywBfCL6S5iZfKR6a5A08S/z9XnESuaMdNDZChVtRBYON11rG2SLKqqnae7Dmki/n1OjZnenXUDsO3A9DatTZI0BWZ6iFwE7JBk+yTrAvsAp01zTZI0a8zo7qyqWp7krcCZwBzgmKq6YprLmk3sItSazL/PKZCqmu4aJEkz1EzvzpIkTSNDRJLUmyGioSSpJEcMTL8jyeHTWJJmuXS+n2SPgbZXJPn2dNY12xgiGtY9wF8k2WK6C5EAqjuh+9fAJ5I8NMmGwD8Db5neymYXQ0TDWk53tcvbx89IsiDJ2UkuS3JWku2mvjzNRlX1I+AbwN8D/wScALwnyYVJLkmyF0CSx7W2S9vf6Q7TWPZaxauzNJQkdwFbA5cBTwDeBGxYVYcn+QZwclUdm+QvgT2rau/pq1azSZINgB8C9wLfBK6oqhOSbApcCDwJ+DBwflV9pX2nbE5V/Wq6al6bGCIaSpK7qmrDJO8DfgP8it+HyC+ArarqN0nWAW6sKru9NGXa3+VdwCuBh9IdOQNsBuxGFyTvAY4DTqmqa6ajzrWR3Vl6sD4JHABsMM11SIN+24YAL6uqJ7Zhu6q6qqr+FdiT7p+fM5I8fzqLXZsYInpQqupW4CS6IBnzA7pbzgC8BvjeVNclNWcCByUJQJIntZ+PBK6tqqOAU4E/mb4S1y6GiPo4gu4222MOAt6Q5DLgdcAh01KVBO8H1gEuS3JFm4aum+tHSS4FdqLr1tJq4DkRSVJvHolIknozRCRJvRkikqTeDBFJUm+GiCSpN0NEWgV97m6c5DFJzmn3cboqycLW/voknxlxydJqZYhIq6bP3Y2PAo5s36h+LPDp0ZQmjZ4hIq2aPnc33gq4fmy5qrp8YLWtk3w7yTVJPjqwrX2TXJ7kR0k+0tpekeQTbfyQJNe28Ucm+b+rfU+lCRgi0qr7LPCaJJuMa/80cGxV/QnwFbojEIAjgbOTfCvJ29vdZsc8EXgV8HjgVUm2TbI18BHg+W3+nybZm+72Ms9q6z0LuCXJ/DZ+3mrdQ2kFDBFpFVXVMrrbaBw8btbTgX9t48cDz2zLfwl4LPC/gecC5ydZry13VlXdUVW/Bq4EHgH8KXBOVS2tquV0gfTsqroJ2DDJRsC27bWeTRci3r9MU8IQkVaPT/Ig7m5cVT+rqmOqai+6LrGd2qx7Bha7D5i7kk39AHgDcDW/PzJ5OmB3lqaEISKtBg/m7sZJdm/PXSHJHwKbAzdMsvkLgeck2SLJHGBf4Nw273vAO+i6ry4BngfcU1V3rI79klZmZf/lSBreEcBbB6YPAr6U5J3AUrojBoAXAp9K8us2/c6quqndvfwBqurGJIcC36V7XsbpVXVqm/09uq6s86rqviRLgP9cnTslTca7+EqSerM7S5LUmyEiSerNEJEk9WaISJJ6M0QkSb0ZIpKk3gwRSVJv/x8yiIfVOT0tJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting distribution of show and no show \n",
    "ax = df_clean['NoShow'].value_counts().plot(\n",
    "        kind='bar',\n",
    "        title = 'No. of patients by NoShow',\n",
    "        rot=0)\n",
    "ax.set_xlabel('NoShow')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference between the number of patients who showed up and those who did not is visualized above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:20.301833Z",
     "iopub.status.busy": "2022-09-27T11:21:20.301484Z",
     "iopub.status.idle": "2022-09-27T11:21:20.320261Z",
     "shell.execute_reply": "2022-09-27T11:21:20.318658Z",
     "shell.execute_reply.started": "2022-09-27T11:21:20.301801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    71839\n",
       "M    38687\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking distribution of gender in the patients\n",
    "df_clean['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:20.324400Z",
     "iopub.status.busy": "2022-09-27T11:21:20.323980Z",
     "iopub.status.idle": "2022-09-27T11:21:20.564661Z",
     "shell.execute_reply": "2022-09-27T11:21:20.563390Z",
     "shell.execute_reply.started": "2022-09-27T11:21:20.324364Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting distribution of Gender on a histogram\n",
    "ax = df_clean['Gender'].value_counts().plot(\n",
    "        kind='bar',\n",
    "        title = 'No. of patients by Gender',\n",
    "        rot=0)\n",
    "ax.set_xlabel('Gender')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the value counts and graph above, we can tell that the patients are made up of more females than males."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:20.566650Z",
     "iopub.status.busy": "2022-09-27T11:21:20.565906Z",
     "iopub.status.idle": "2022-09-27T11:21:20.620051Z",
     "shell.execute_reply": "2022-09-27T11:21:20.618689Z",
     "shell.execute_reply.started": "2022-09-27T11:21:20.566565Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71840"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assigning number of female patients to a variable\n",
    "f_count = df.query('Gender == \"F\"').count().Gender\n",
    "f_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:20.623107Z",
     "iopub.status.busy": "2022-09-27T11:21:20.622194Z",
     "iopub.status.idle": "2022-09-27T11:21:20.659410Z",
     "shell.execute_reply": "2022-09-27T11:21:20.658498Z",
     "shell.execute_reply.started": "2022-09-27T11:21:20.623038Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38687"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assigning number of male patients to a variable\n",
    "m_count = df.query('Gender == \"M\"').count().Gender\n",
    "m_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:20.661132Z",
     "iopub.status.busy": "2022-09-27T11:21:20.660745Z",
     "iopub.status.idle": "2022-09-27T11:21:20.674867Z",
     "shell.execute_reply": "2022-09-27T11:21:20.673654Z",
     "shell.execute_reply.started": "2022-09-27T11:21:20.661095Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110527"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assigning the number of patients to a variable\n",
    "patients_count = df['Gender'].count()\n",
    "patients_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:20.677041Z",
     "iopub.status.busy": "2022-09-27T11:21:20.676560Z",
     "iopub.status.idle": "2022-09-27T11:21:20.688097Z",
     "shell.execute_reply": "2022-09-27T11:21:20.686457Z",
     "shell.execute_reply.started": "2022-09-27T11:21:20.677005Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.99769287142509"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating percentage of female patients \n",
    "f_percent = (f_count/patients_count)*100\n",
    "f_percent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Females make up 65% of the patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:20.690477Z",
     "iopub.status.busy": "2022-09-27T11:21:20.689733Z",
     "iopub.status.idle": "2022-09-27T11:21:20.703014Z",
     "shell.execute_reply": "2022-09-27T11:21:20.702150Z",
     "shell.execute_reply.started": "2022-09-27T11:21:20.690432Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.00230712857492"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating percentage of male patients\n",
    "m_percent = (m_count/patients_count) * 100\n",
    "m_percent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "35% of the patients were males."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:20.705753Z",
     "iopub.status.busy": "2022-09-27T11:21:20.704890Z",
     "iopub.status.idle": "2022-09-27T11:21:20.770621Z",
     "shell.execute_reply": "2022-09-27T11:21:20.769155Z",
     "shell.execute_reply.started": "2022-09-27T11:21:20.705713Z"
    }
   },
   "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>NoShow</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>57245</td>\n",
       "      <td>14594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>30962</td>\n",
       "      <td>7725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NoShow     No    Yes\n",
       "Gender              \n",
       "F       57245  14594\n",
       "M       30962   7725"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot table with gender and no-show column \n",
    "df_ns = df_clean.groupby(['NoShow','Gender'],as_index=False).size()\n",
    "ns_pivot = pd.pivot_table(df_ns, values = 'size',columns=['NoShow'],index='Gender')\n",
    "ns_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:20.772940Z",
     "iopub.status.busy": "2022-09-27T11:21:20.772570Z",
     "iopub.status.idle": "2022-09-27T11:21:21.000566Z",
     "shell.execute_reply": "2022-09-27T11:21:20.999149Z",
     "shell.execute_reply.started": "2022-09-27T11:21:20.772906Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Oz0nEzKzMzj//fMaMGcN7771X0vo777wz3/rWt3jggQdo27YtL774IpD1sdRo06YNa9Y0/PtnhxxyCLfccgt77rnnujOTp556ys1ZZmYtyQ477MCJJ57ImDFj1pUdcsghjB8/HoC77rpr3dnBo48+yocffgjAG2+8weLFi+nevc5fuQDgoIMO4sknn+Sdd95h7dq1jBs3jsMPPxyA/v37c9VVV3HYYYex//7788QTT9C+fXs6duzYZMfm0VlmZs3goosu4oYbblg3f/311zN8+HCuvPJKunbtyi233ALAn/70J8477zw6dOgAwJVXXsmOO+5Yb7077bQTV1xxBUcccQQRwbHHHrtuZFf//v2ZP38+hx12GG3atGGXXXZhr732atLj8r2zNkEe4mvWcsyaNYvevXtXOowmU9fx+N5ZZmZWFk4iZmZWmJOImZkV5iRiZmaFOYmYmVlhTiJmZlaYrxMxM2tCTT1Ev5Sh9JK48MILufrqq4HslvMrV67c4G3nm4LPRMzMWriaW86/8847zb5vJxEzsxauyC3nm4qTiJlZK7Cxt5xvKk4iZmatwMbecr6pOImYmbUSG3vL+abgJGJm1kpszC3nm4qH+JqZNaFK39261FvONxUnETOzFm7lypXrprt168b777+/bn633XZj0qRJZdt3WZuzJL0q6QVJMyRNT2U7SHpM0pz0d/tULknXSaqWNFPSAbl6hqX150galis/MNVfnbZVOY/HzMzW1xx9IkdExH65HzS5BHg8InoBj6d5gMFAr/QYAfwasqQDjAIOBg4CRtUknrTOd3LbDSr/4ZiZWY1KdKwPAW5L07cBx+fKb4/M00AnSTsBRwOPRcSSiHgXeAwYlJZtFxFPR/bzjLfn6jIzs2ZQ7iQSwJ8kPSdpRCrrFhGL0vQbQLc03R2Yn9t2QSprqHxBHeWfIGmEpOmSpr/99tuNOR4zM8spd8f6FyJioaRPAY9J+nt+YUSEpLL/yHtE3AzcDNlvrJd7f2Zmm4uynolExML09y3gfrI+jTdTUxTp71tp9YXALrnNq1JZQ+VVdZSbmVkzKduZiKStgS0iYkWaPgr4MfAgMAy4Iv19IG3yIHCOpPFknejLImKRpInAv+c6048CLo2IJZKWS+oHPAMMBa4v1/GYmZVkdMcmrm9Zg4sjgv79+/P973+fwYMHA/C73/2OMWPG8OijjzZtLHUoZ3NWN+D+NOq2LfBfEfGopGnABElnAvOAE9P6jwDHANXA+8BwgJQsfgJMS+v9OCKWpOmzgFuBrYA/poeZ2WZDEjfeeCMnnHACRxxxBGvWrOGyyy5rlgQCZUwiETEX+Fwd5YuBI+soD+DseuoaC4yto3w60KfRwZqZtWB9+vThuOOO42c/+xnvvfcep59+OpdffjkvvvgiH374IaNHj2bIkCG89NJLDB8+nNWrV/PRRx9x77330qtXr0bt21esm5m1AqNGjeKAAw5gyy235Mtf/jIDBw5k7NixLF26lIMOOogvfvGL3HjjjZx33nmcdtpprF69mrVr1zZ6v04iZmatwNZbb81JJ53ENttsw4QJE/jDH/7AVVddBcCqVat47bXX+PznP8/ll1/OggUL+NrXvtbosxBwEjEzazW22GILtthiCyKCe++9lz333HO95b179+bggw/m4Ycf5phjjuGmm25i4MCBjdtno7Y2M7NNztFHH831119P1tUMzz//PABz585l9913Z+TIkQwZMoSZM2c2el8+EzEza0obGJLbHH74wx9y/vnns++++/LRRx/Rs2dPHnroISZMmMAdd9xBu3bt2HHHHbnssssavS/VZKrNRd++fWP69OmVDqNBPS55uNIhlKTSv5tgtimYNWsWvXv3rnQYTaau45H0XO4muutxc5aZmRXmJGJmZoU5iZiZNVJr6RYochxOImZmjdChQwcWL17c4hNJRLB48WI6dOiwUdt5dJaZWSNUVVWxYMECWsNvFXXo0IGqqqoNr5jjJGJm1gjt2rWjZ8+elQ6jYtycZWZmhTmJmJlZYU4iZmZWmJOImZkV5iRiZmaFOYmYmVlhTiJmZlaYk4iZmRXmJGJmZoU5iZiZWWFOImZmVpiTiJmZFeYkYmZmhTmJmJlZYU4iZmZWWNmTiKQ2kp6X9FCa7ynpGUnVku6WtGUqb5/mq9PyHrk6Lk3lsyUdnSsflMqqJV1S7mMxM7P1NceZyHnArNz8z4BrIuLTwLvAman8TODdVH5NWg9JewMnA58FBgG/SompDfBLYDCwN3BKWtfMzJpJWZOIpCrgWOC3aV7AQOCetMptwPFpekiaJy0/Mq0/BBgfER9ExCtANXBQelRHxNyIWA2MT+uamVkzKfeZyH8C3wM+SvOdgaURsSbNLwC6p+nuwHyAtHxZWn9dea1t6iv/BEkjJE2XNL01/A6ymdmmomxJRNKXgbci4rly7aNUEXFzRPSNiL5du3atdDhmZq3GBpOIpBMkbZumfyDpPkkHlFD3ocBXJL1K1tQ0ELgW6CSpbVqnCliYphcCu6T9tAU6Aovz5bW2qa/czMyaSSlnIj+MiBWSvgB8ERgD/HpDG0XEpRFRFRE9yDrGJ0XEacATwDfSasOAB9L0g2metHxSREQqPzmN3uoJ9AKeBaYBvdJory3TPh4s4XjMzKyJlJJE1qa/xwI3R8TDwJaN2OfFwIWSqsn6PMak8jFA51R+IXAJQES8BEwAXgYeBc6OiLWp3+QcYCLZ6K8JaV0zM2smbTe8Cgsl3QR8CfiZpPZsZF9KREwGJqfpuWQjq2qvswo4oZ7tLwcur6P8EeCRjYnFzMyaTinJ4ESyb/tHR8RSYAfg38oZlJmZtQylJJGbIuK+iJgDEBGLgDPKG5aZmbUEpSSRz+Zn0pXiB5YnHDMza0nqTSLpflUrgH0lLU+PFcBbfDyiyszMNmP1JpGI+I+I2Ba4MiK2S49tI6JzRFzajDGamdkmaoOjsyLiUkndgd3y60fElHIGZmZmm74NJhFJV5BdyPcyH18zEoCTiJnZZq6U60S+CuwZER+UOxgzM2tZShmdNRdoV+5AzMys5SnlTOR9YIakx4F1ZyMRMbJsUZmZWYtQShJ5EN/Y0MzM6lDK6KzbJG0F7BoRs5shJjMzayFK+T2R44AZZHfQRdJ+knxmYmZmJXWsjya76+5SgIiYAexetojMzKzFKCWJfBgRy2qVfVTnmmZmtlkppWP9JUmnAm0k9QJGAn8tb1hmZtYSlHImci7ZnXw/AMYBy4HzyxiTmZm1EKWMznof+H56mJmZrVNvEpH0nxFxvqQ/kN0raz0R8ZWyRmZmZpu8hs5E7kh/r2qOQMzMrOWpN4lExHNpcr+IuDa/TNJ5wJPlDMzMzDZ9pXSsD6uj7JtNHIeZmbVADfWJnAKcCvSsdYX6tsCScgdmZmabvob6RP4KLAK6AFfnylcAM8sZlJmZtQwN9YnMA+YBn2++cMzMrCUp5QaM/SRNk7RS0mpJayUtb47gzMxs01ZKx/oNwCnAHGAr4NvAL8sZlJmZtQyl3DuLiKiW1CYi1gK3SHoeuLShbSR1AKYA7dN+7omIUZJ6AuOBzsBzwBkRsVpSe+B24EBgMXBSRLya6roUOBNYC4yMiImpfBBwLdAG+G1EXLFRR29mG63HJQ9XOoSSvHrFsZUOYbNQypnI+5K2JPuJ3J9LuqDE7T4ABkbE54D9gEGS+gE/A66JiE8D75IlB9Lfd1P5NWk9JO0NnEx2/65BwK8ktZHUhuyMaDCwN3BKWtfMzJpJKcngjLTeOcB7wC7A1ze0UWRWptl26RHAQOCeVH4bcHyaHpLmScuPlKRUPj4iPoiIV4Bqst83OQiojoi5EbGa7OxmSAnHY2ZmTaSUGzDOS2ciPYD7gNnpQ3uD0tnCc8Cnyc4a/hdYGhFr0ioLgO5pujswP+1zjaRlZE1e3YGnc9Xmt5lfq/zgeuIYAYwA2HXXXUsJ3czMSlDK6KxjyT78ryPrZK+WNLiUyiNibUTsB1SRnTnsVTzU4iLi5ojoGxF9u3btWokQzMxapVI61q8GjoiIagBJewAPA38sdScRsVTSE2TXnHSS1DadjVQBC9NqC8mayhZIagt0JOtgrymvkd+mvnIzM2sGpfSJrKhJIMlcsqvWGySpq6ROaXor4EvALOAJ4BtptWHAA2n6QT6+T9c3gEkREan8ZEnt08iuXsCzwDSgl6Seqbnt5LSumZk1k1LORKZLegSYQNYxfgIwTdLXACLivnq22wm4LfWLbAFMiIiHJL0MjJf0U+B5YExafwxwh6RqsntznZzqf0nSBOBlYA1wdhpqjKRzgIlkQ3zHRsRLG3f4ZmbWGKUkkQ7Am8Dhaf5tsosOjyNLKnUmkYiYCexfR/lcsv6R2uWryBJUXXVdDlxeR/kjwCMlHIOZmZVBKaOzhjdHIGZm1vKU0idiZmZWJycRMzMrrN4kkn4CF0mHNl84ZmbWkjR0JlLTF3J9cwRiZmYtT0Md67MkzQF2lpT/JUOR3Rpr3/KGZmZmm7qGftnwFEk7kl2H8ZXmC8nMzFqKBof4RsQbwOfSFeGfScWzI+LDskdmZmabvA1eJyLpcLIfi3qVrClrF0nDImJKmWMzM7NNXClXrP8COCoiZgNI+gwwjuwXCM3MbDNWynUi7WoSCEBE/A/ZD0yZmdlmrtQbMP4WuDPNnwZML19IZmbWUpSSRL4LnA2MTPNTgV+VLSIzM2sxSrkB4wdk/SK/KH84ZmbWkvjeWWZmVpiTiJmZFeYkYmZmhRVKIpJGNHUgZmbW8hQ9E1GTRmFmZi1SoSQSETc1dSBmZtbybDCJSKqSdL+ktyW9JeleSVXNEZyZmW3aSjkTuQV4ENgJ2Bn4QyozM7PNXClJpGtE3BIRa9LjVqBrmeMyM7MWoJQksljS6ZLapMfpwOJyB2ZmZpu+UpLIt4ATgTeARcA3+Pj3183MbDNWyr2z5uGfxzUzszrUm0Qk/d8GtouI+EkZ4jEzsxakoeas9+p4AJwJXLyhiiXtIukJSS9LeknSeal8B0mPSZqT/m6fyiXpOknVkmZKOiBX17C0/hxJw3LlB0p6IW1znSRfBGlm1ozqTSIRcXXNA7gZ2IqsL2Q8sHsJda8BLoqIvYF+wNmS9gYuAR6PiF7A42keYDDQKz1GAL+GLOkAo4CDgYOAUTWJJ63zndx2g0o8bjMzawINdqyns4afAjPJmr4OiIiLI+KtDVUcEYsi4m9pegUwC+gODAFuS6vdBhyfpocAt0fmaaCTpJ2Ao4HHImJJRLwLPAYMSsu2i4inIyKA23N1mZlZM6g3iUi6EpgGrAD2iYjR6UN8o0nqAewPPAN0i4hFadEbQLc03R2Yn9tsQSprqHxBHeV17X+EpOmSpr/99ttFDsHMzOrQ0JnIRWRXqP8AeF3S8vRYIWl5qTuQtA1wL3B+RKy3XTqDiAJxb5SIuDki+kZE365dfZ2kmVlTqXd0VkQ0+rdGJLUjSyB3RcR9qfhNSTtFxKLUJFXTNLYQ2CW3eVUqWwgMqFU+OZVX1bG+mZk1k7L9KFUaKTUGmBUR+d9nfxCoGWE1DHggVz40jdLqByxLzV4TgaMkbZ861I8CJqZlyyX1S/samqvLzMyawQYvNmyEQ4EzgBckzUhllwFXABMknQnMI7saHuAR4BigGnifdFV8RCyR9BOy/hmAH0fEkjR9FnAr2cixP6aHmZk1k7IlkYj4C/X/eNWRdawfwNn11DUWGFtH+XSgTyPCNDOzRvBvrJuZWWFOImZmVpiTiJmZFeYkYmZmhTmJmJlZYU4iZmZWmJOImZkV5iRiZmaFOYmYmVlhTiJmZlaYk4iZmRXmJGJmZoU5iZiZWWFOImZmVlg5f0/EWrvRHSsdQWlGL6t0BGatls9EzMysMCcRMzMrzEnEzMwKcxIxM7PCnETMzKwwJxEzMyvMScTMzApzEjEzs8KcRMzMrDAnETMzK8xJxMzMCnMSMTOzwsqWRCSNlfSWpBdzZTtIekzSnPR3+1QuSddJqpY0U9IBuW2GpfXnSBqWKz9Q0gtpm+skqVzHYmZmdSvnmcitwKBaZZcAj0dEL+DxNA8wGOiVHiOAX0OWdIBRwMHAQcComsST1vlObrva+zIzszIrWxKJiCnAklrFQ4Db0vRtwPG58tsj8zTQSdJOwNHAYxGxJCLeBR4DBqVl20XE0xERwO25uszMrJk0d59It4hYlKbfALql6e7A/Nx6C1JZQ+UL6ig3M7NmVLGO9XQGEc2xL0kjJE2XNP3tt99ujl2amW0WmjuJvJmaokh/30rlC4FdcutVpbKGyqvqKK9TRNwcEX0jom/Xrl0bfRBmZpZp7iTyIFAzwmoY8ECufGgapdUPWJaavSYCR0naPnWoHwVMTMuWS+qXRmUNzdVlZmbNpGy/sS5pHDAA6CJpAdkoqyuACZLOBOYBJ6bVHwGOAaqB94HhABGxRNJPgGlpvR9HRE1n/VlkI8C2Av6YHmZm1ozKlkQi4pR6Fh1Zx7oBnF1PPWOBsXWUTwf6NCZGMzNrHF+xbmZmhTmJmJlZYU4iZmZWWNn6RMzMKmp0x0pHUJrRyyodQaP4TMTMzApzEjEzs8KcRMzMrDAnETMzK8xJxMzMCnMSMTOzwpxEzMysMCcRMzMrzEnEzMwKcxIxM7PCnETMzKwwJxEzMyvMScTMzApzEjEzs8KcRMzMrDAnETMzK8xJxMzMCnMSMTOzwpxEzMysMCcRMzMrzEnEzMwKcxIxM7PCnETMzKwwJxEzMyusxScRSYMkzZZULemSSsdjZrY5adFJRFIb4JfAYGBv4BRJe1c2KjOzzUeLTiLAQUB1RMyNiNXAeGBIhWMyM9tstK10AI3UHZifm18AHFx7JUkjgBFpdqWk2c0QW6sn6AK8U+k4NuhHqnQEVgF+fzap3epb0NKTSEki4mbg5krH0dpImh4RfSsdh1ld/P5sHi29OWshsEtuviqVmZlZM2jpSWQa0EtST0lbAicDD1Y4JjOzzUaLbs6KiDWSzgEmAm2AsRHxUoXD2py4idA2ZX5/NgNFRKVjMDOzFqqlN2eZmVkFOYmYmVlhTiK20SStlTQj9+hR6ZjMJIWkO3PzbSW9LemhSsbV2rXojnWrmH9ExH6VDsKslveAPpK2ioh/AF/CQ/7LzmciZtaaPAIcm6ZPAcZVMJbNgpOIFbFVrinr/koHY5YzHjhZUgdgX+CZCsfT6rk5y4pwc5ZtkiJiZuqjO4XsrMTKzEnEzFqbB4GrgAFA58qG0vo5iZhZazMWWBoRL0gaUOFYWj0nETNrVSJiAXBdpePYXPi2J2ZmVphHZ5mZWWFOImZmVpiTiJmZFeYkYmZmhTmJmJlZYU4iZk1AUjdJ/yVprqTnJD0l6atNUO8A34XWNmVOImaNJEnA74EpEbF7RBwInAxUVSAWX/tlzcpJxKzxBgKrI+LGmoKImBcR10tqI+lKSdMkzZT0L7DuDGOypHsk/V3SXSkZIWlQKvsb8LWaOiVtLWmspGclPS9pSCr/pqQHJU0CHm/WI7fNnr+1mDXeZ4G/1bPsTGBZRPyzpPbAf0v6U1q2f9r2deC/gUMlTQd+Q5aYqoG7c3V9H5gUEd+S1Al4VtKf07IDgH0jYkkTHpfZBjmJmDUxSb8EvgCsBuYB+0r6RlrcEeiVlj2bbtGBpBlAD2Al8EpEzEnldwIj0rZHAV+R9K9pvgOwa5p+zAnEKsFJxKzxXgK+XjMTEWdL6gJMB14Dzo2IifkN0o0BP8gVrWXD/48Cvh4Rs2vVdTDZr/qZNTv3iZg13iSgg6Tv5sr+Kf2dCHxXUjsASZ+RtHUDdf0d6CFpjzR/Sm7ZRODcXN/J/k0SvVkjOImYNVJkdzE9Hjhc0iuSngVuAy4Gfgu8DPxN0ovATTRwxhERq8iarx5OHetv5Rb/BGgHzJT0Upo3qyjfxdfMzArzmYiZmRXmJGJmZoU5iZiZWWFOImZmVpiTiJmZFeYkYmZmhTmJmJlZYf8fsHGdtnWzpFYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the pivot table \n",
    "ax = ns_pivot.plot(\n",
    "        kind='bar',\n",
    "        title = 'No. of patients by NoShow and Gender',\n",
    "        rot=0)\n",
    "ax.set_xlabel('Gender')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each gender, majority showed up for their appointment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.002462Z",
     "iopub.status.busy": "2022-09-27T11:21:21.002112Z",
     "iopub.status.idle": "2022-09-27T11:21:21.150255Z",
     "shell.execute_reply": "2022-09-27T11:21:21.148866Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.002430Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of females who showed: 57245\n",
      "Number of females who did not show: 14594\n",
      "Number of males who showed: 30962\n",
      "Number of males who did not show: 7725\n"
     ]
    }
   ],
   "source": [
    "# number of females who showed\n",
    "f_show_count = df_clean.query('Gender == \"F\" & NoShow == \"No\"').count().Gender\n",
    "print('Number of females who showed: ' + str(f_show_count))\n",
    "# number of females who did not show\n",
    "f_noshow_count = df_clean.query('Gender == \"F\" & NoShow == \"Yes\"').count().Gender\n",
    "print('Number of females who did not show: ' + str(f_noshow_count))\n",
    "# number of males who showed\n",
    "m_show_count = df_clean.query('Gender == \"M\" & NoShow == \"No\"').count().Gender\n",
    "print('Number of males who showed: ' + str(m_show_count))\n",
    "#number of males who did not show\n",
    "m_noshow_count = df_clean.query('Gender == \"M\" & NoShow == \"Yes\"').count().Gender\n",
    "print('Number of males who did not show: ' + str(m_noshow_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.154174Z",
     "iopub.status.busy": "2022-09-27T11:21:21.151863Z",
     "iopub.status.idle": "2022-09-27T11:21:21.158972Z",
     "shell.execute_reply": "2022-09-27T11:21:21.157837Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.154115Z"
    }
   },
   "outputs": [],
   "source": [
    "#creating a function for calculating percentages\n",
    "def percentage(value,total):\n",
    "    var_percent = (sample/total)*100\n",
    "    return var_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.160976Z",
     "iopub.status.busy": "2022-09-27T11:21:21.160267Z",
     "iopub.status.idle": "2022-09-27T11:21:21.172722Z",
     "shell.execute_reply": "2022-09-27T11:21:21.170778Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.160939Z"
    }
   },
   "outputs": [],
   "source": [
    "# creating a probability function\n",
    "def probability(outcome,total):\n",
    "    prob = (outcome/total)\n",
    "    return prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.175148Z",
     "iopub.status.busy": "2022-09-27T11:21:21.174674Z",
     "iopub.status.idle": "2022-09-27T11:21:21.187595Z",
     "shell.execute_reply": "2022-09-27T11:21:21.186176Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.175112Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probability of a female showing\n",
    "f_show_prob = probability(f_show_count,f_count)\n",
    "# rounding to two decimal places \n",
    "round(f_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probaility of a female showing up for their appointment is 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.190427Z",
     "iopub.status.busy": "2022-09-27T11:21:21.189829Z",
     "iopub.status.idle": "2022-09-27T11:21:21.201588Z",
     "shell.execute_reply": "2022-09-27T11:21:21.200323Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.190390Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probability of a male showing\n",
    "m_show_prob = probability(m_show_count,m_count)\n",
    "round(m_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a male showing up for their appointment is 0.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As the probability of both males and females showing up is 0.8, the gender of a patient is not important to predict whether they will show up for their appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scholarship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.203690Z",
     "iopub.status.busy": "2022-09-27T11:21:21.202876Z",
     "iopub.status.idle": "2022-09-27T11:21:21.219990Z",
     "shell.execute_reply": "2022-09-27T11:21:21.218650Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.203652Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    99665\n",
       "1    10861\n",
       "Name: Scholarship, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking distribution of scholarship receipients\n",
    "df_clean['Scholarship'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the 110527 appointments, 99665 of the patients who booked the appointment were not on scholarship and 10861 patients were on scholarship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.221941Z",
     "iopub.status.busy": "2022-09-27T11:21:21.221576Z",
     "iopub.status.idle": "2022-09-27T11:21:21.379923Z",
     "shell.execute_reply": "2022-09-27T11:21:21.378617Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.221906Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the distribution of patients based on scholarship status\n",
    "ax = df['Scholarship'].value_counts().plot(\n",
    "        kind='bar',\n",
    "        title = 'No. of patients by Scholarship',\n",
    "        rot=0)\n",
    "ax.set_xlabel('Scholarship')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference is made clearer by the graph. Now I want to check if scholarship status has an impact on whether a patient will show up for an appointment or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.382969Z",
     "iopub.status.busy": "2022-09-27T11:21:21.381564Z",
     "iopub.status.idle": "2022-09-27T11:21:21.428762Z",
     "shell.execute_reply": "2022-09-27T11:21:21.427134Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.382926Z"
    }
   },
   "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>NoShow</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scholarship</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79924</td>\n",
       "      <td>19741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8283</td>\n",
       "      <td>2578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NoShow          No    Yes\n",
       "Scholarship              \n",
       "0            79924  19741\n",
       "1             8283   2578"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a pivot table for Scholarship and NoShow\n",
    "df_sc = df_clean.groupby(['NoShow','Scholarship'],as_index=False).size()\n",
    "sc_pivot = pd.pivot_table(df_sc, values = 'size',columns=['NoShow'],index='Scholarship')\n",
    "sc_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.431629Z",
     "iopub.status.busy": "2022-09-27T11:21:21.431150Z",
     "iopub.status.idle": "2022-09-27T11:21:21.738862Z",
     "shell.execute_reply": "2022-09-27T11:21:21.737389Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.431581Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the pivot table on a grouped bar chart\n",
    "ax = sc_pivot.plot(\n",
    "        kind='bar',\n",
    "        title = 'No. of patients by Scholarship and NoShow',\n",
    "        rot=0)\n",
    "ax.set_xlabel('Scholarship')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph shows that for each scholarship status, majority of the patients showed up. I will compute the probabilities to draw a more statistical conclusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.741477Z",
     "iopub.status.busy": "2022-09-27T11:21:21.741047Z",
     "iopub.status.idle": "2022-09-27T11:21:21.817661Z",
     "shell.execute_reply": "2022-09-27T11:21:21.816141Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.741441Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients on scholarship: 10861\n",
      "Number of patients without scholarship: 99666\n"
     ]
    }
   ],
   "source": [
    "# variable for scholarship recipients\n",
    "scholarship_count = df.query('Scholarship == 1').count().Scholarship\n",
    "print('Number of patients on scholarship: ' + str(scholarship_count))\n",
    "# variable for non scholarship patients\n",
    "noscholarship_count = df.query('Scholarship == 0').count().Scholarship\n",
    "print('Number of patients without scholarship: ' + str(noscholarship_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.819848Z",
     "iopub.status.busy": "2022-09-27T11:21:21.819459Z",
     "iopub.status.idle": "2022-09-27T11:21:21.853472Z",
     "shell.execute_reply": "2022-09-27T11:21:21.852161Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.819812Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of scholarship recipients who showed up\n",
    "scholarship_show = df_clean.query('Scholarship == 1 & NoShow == \"No\"').count().Scholarship\n",
    "# probability of a scholarship recipient showing up\n",
    "scholarship_show_prob = probability(scholarship_show,scholarship_count)\n",
    "round(scholarship_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient on scholarship showing up is 0.76"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.858376Z",
     "iopub.status.busy": "2022-09-27T11:21:21.857558Z",
     "iopub.status.idle": "2022-09-27T11:21:21.916000Z",
     "shell.execute_reply": "2022-09-27T11:21:21.914738Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.858333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of no scholarship patients who showed up\n",
    "noscholarship_show = df_clean.query('Scholarship == 0 & NoShow == \"No\"').count().Scholarship\n",
    "# probability of a non scholarship patient showing up \n",
    "noscholarship_show_prob = probability(noscholarship_show,noscholarship_count)\n",
    "round(noscholarship_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a non-scholarship patient showing up is 0.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As the probability of a non-scholarship patient showing up is higher than the probability of a scholarship patient showing up, scholarship status can be used in the prediction of show/no-show."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMS received"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.918115Z",
     "iopub.status.busy": "2022-09-27T11:21:21.917520Z",
     "iopub.status.idle": "2022-09-27T11:21:21.991429Z",
     "shell.execute_reply": "2022-09-27T11:21:21.989626Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.918057Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients who did not receive SMS: 75044\n",
      "Number of patients who received SMS: 35482\n"
     ]
    }
   ],
   "source": [
    "# number of patients who did not receive sms\n",
    "no_sms = df_clean.query('SMSReceived == 0').count().SMSReceived\n",
    "print('Number of patients who did not receive SMS: ' + str(no_sms))\n",
    "# no of patients who recieved an sms\n",
    "sms_true = df_clean.query('SMSReceived == 1').count().SMSReceived\n",
    "print('Number of patients who received SMS: ' + str(sms_true))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:21.993942Z",
     "iopub.status.busy": "2022-09-27T11:21:21.993474Z",
     "iopub.status.idle": "2022-09-27T11:21:22.236417Z",
     "shell.execute_reply": "2022-09-27T11:21:22.234912Z",
     "shell.execute_reply.started": "2022-09-27T11:21:21.993903Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting distribution of the value counts\n",
    "ax = df_clean['SMSReceived'].value_counts().plot(\n",
    "        kind='bar',\n",
    "        title = 'No. of patients by SMSReceived',\n",
    "        rot=0)\n",
    "ax.set_xlabel('SMSReceived')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As depicted by the graph, majority of patients did not receive an sms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:22.238605Z",
     "iopub.status.busy": "2022-09-27T11:21:22.238128Z",
     "iopub.status.idle": "2022-09-27T11:21:22.283848Z",
     "shell.execute_reply": "2022-09-27T11:21:22.282320Z",
     "shell.execute_reply.started": "2022-09-27T11:21:22.238548Z"
    },
    "scrolled": true
   },
   "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>NoShow</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMSReceived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62509</td>\n",
       "      <td>12535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25698</td>\n",
       "      <td>9784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NoShow          No    Yes\n",
       "SMSReceived              \n",
       "0            62509  12535\n",
       "1            25698   9784"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a pivot table for the SMSReceived and NoShow columns\n",
    "df_sms = df_clean.groupby(['SMSReceived','NoShow'],as_index=False).size()\n",
    "sms_pivot = pd.pivot_table(df_sms, values = 'size',columns=['NoShow'],index='SMSReceived')\n",
    "sms_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:22.285695Z",
     "iopub.status.busy": "2022-09-27T11:21:22.285305Z",
     "iopub.status.idle": "2022-09-27T11:21:22.552253Z",
     "shell.execute_reply": "2022-09-27T11:21:22.551272Z",
     "shell.execute_reply.started": "2022-09-27T11:21:22.285657Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing the pivot table on a grouped bar chart\n",
    "ax = sms_pivot.plot(\n",
    "        kind='bar',\n",
    "        title = 'No. of patients by SMSReceived and NoShow',\n",
    "        rot=0)\n",
    "ax.set_xlabel('SMSReceived')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:22.554135Z",
     "iopub.status.busy": "2022-09-27T11:21:22.553742Z",
     "iopub.status.idle": "2022-09-27T11:21:22.638655Z",
     "shell.execute_reply": "2022-09-27T11:21:22.637124Z",
     "shell.execute_reply.started": "2022-09-27T11:21:22.554099Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of patients who did not receive SMS and showed: 62509\n",
      "Number of patients who received SMS and showed: 25698\n"
     ]
    }
   ],
   "source": [
    "#number of patients who did not receive SMS and showed up\n",
    "nosms_show = df_clean.query('SMSReceived == 0 & NoShow == \"No\"').count().SMSReceived\n",
    "print('Number of patients who did not receive SMS and showed: ' + str(nosms_show))\n",
    "#number of patients who received SMS and showed up\n",
    "sms_show = df_clean.query('SMSReceived == 1 & NoShow == \"No\"').count().SMSReceived\n",
    "print('Number of patients who received SMS and showed: ' + str(sms_show))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I assigned variables to the various results from the pivot table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:22.641138Z",
     "iopub.status.busy": "2022-09-27T11:21:22.640367Z",
     "iopub.status.idle": "2022-09-27T11:21:22.651817Z",
     "shell.execute_reply": "2022-09-27T11:21:22.650099Z",
     "shell.execute_reply.started": "2022-09-27T11:21:22.641090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probability of a patient who did not receive sms showing up\n",
    "nosms_show_prob = probability(nosms_show,no_sms)\n",
    "round(nosms_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient who did not receive SMS showing up is 0.83 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:22.655361Z",
     "iopub.status.busy": "2022-09-27T11:21:22.653575Z",
     "iopub.status.idle": "2022-09-27T11:21:22.668821Z",
     "shell.execute_reply": "2022-09-27T11:21:22.666680Z",
     "shell.execute_reply.started": "2022-09-27T11:21:22.655308Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.72"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# probability of a patient who received SMS showing \n",
    "sms_show_prob = probability(sms_show,sms_true)\n",
    "round(sms_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient who received an sms showing up is 0.72"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As the probability of patients who did not receive SMS showing up is higher, SMS reception status can be used to predict whether a patient will show up for their appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alcoholism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:22.672985Z",
     "iopub.status.busy": "2022-09-27T11:21:22.671448Z",
     "iopub.status.idle": "2022-09-27T11:21:22.687709Z",
     "shell.execute_reply": "2022-09-27T11:21:22.685855Z",
     "shell.execute_reply.started": "2022-09-27T11:21:22.672913Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    107167\n",
       "1      3360\n",
       "Name: Alcoholism, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking distribution of alcoholism status \n",
    "df['Alcoholism'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:22.690419Z",
     "iopub.status.busy": "2022-09-27T11:21:22.689302Z",
     "iopub.status.idle": "2022-09-27T11:21:22.903479Z",
     "shell.execute_reply": "2022-09-27T11:21:22.902274Z",
     "shell.execute_reply.started": "2022-09-27T11:21:22.690367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the results above \n",
    "ax = df_clean['Alcoholism'].value_counts().plot(\n",
    "        kind='bar',\n",
    "        title = 'No of patients by Alcoholism',\n",
    "        rot=0)\n",
    "ax.set_xlabel('Alcoholism')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the patients were not involved in alcoholism."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:22.906077Z",
     "iopub.status.busy": "2022-09-27T11:21:22.905298Z",
     "iopub.status.idle": "2022-09-27T11:21:22.955488Z",
     "shell.execute_reply": "2022-09-27T11:21:22.953432Z",
     "shell.execute_reply.started": "2022-09-27T11:21:22.906010Z"
    }
   },
   "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>NoShow</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcoholism</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85524</td>\n",
       "      <td>21642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2683</td>\n",
       "      <td>677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NoShow         No    Yes\n",
       "Alcoholism              \n",
       "0           85524  21642\n",
       "1            2683    677"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a pivot table for the Alcoholism and NoShow columns\n",
    "df_alcohol = df_clean.groupby(['NoShow','Alcoholism'],as_index=False).size()\n",
    "alcoholism_pivot = pd.pivot_table(df_alcohol, \n",
    "        values = 'size',\n",
    "        columns=['NoShow'],\n",
    "        index='Alcoholism')\n",
    "alcoholism_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:22.958171Z",
     "iopub.status.busy": "2022-09-27T11:21:22.957113Z",
     "iopub.status.idle": "2022-09-27T11:21:23.217574Z",
     "shell.execute_reply": "2022-09-27T11:21:23.216447Z",
     "shell.execute_reply.started": "2022-09-27T11:21:22.958130Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DiJmZFVbRICLpbEnPSXpW0q2S2kvqI+lJSVWSbpO0ZSq7VZqvSvm9c/Wcn9JflHR4Ln1YSquSNK6S22JmZp9UsSAiqQcwFhgQEf2BNsBxwE+AqyLiM8DbwClpkVOAt1P6Vakckvql5T4PDAN+KamNpDbAtcBwoB9wfCprZmbNpNKns9oCHSS1BT4FvA4MAX6T8qcAR6fpEWmelD9U2XOJRwDTIuL9iHgZqAIGpldVRMyPiA+AaamsmZk1k4oFkYhYBFwBvEoWPFYATwPLI6LmDplqoOYC6B7AwrTs2lS+Sz691jJ1pX+CpDGSZkuavWTJksZvnJmZARW8T0TS9mRHBn2A5cDtZKejml1ETAQmQnaJb0u0wcw2D019iX45l9JL4pxzzuHKK68EskfOr1q1qsHHzjeFSp7OOhR4OSKWRMQa4E7gAGC7dHoLoCewKE0vAnoBpPxOwNJ8eq1l6ko3M9us1Dxy/q233mr2dVcyiLwKDJL0qdS3MRR4HngI+HoqMwq4K03PSPOk/AcjuxNyBnBcunqrD9AXeAqYBfRNV3ttSdb5PqOC22NmtlEq8sj5plLJPpEnyTrI/wLMS+uaCJwHnCOpiqzPo+ZhMpOALin9HGBcquc5YDpZALoPOC0i1qV+k9OB+4EXgOmprJnZZmdDHznfVCr67KyIuBi4uFbyfLIrq2qXXQ0cU0c9lwKXlki/B7in8S01M2vd8o+c79Chw/r0xx9/nDvvvBPIHjmfH8yqKfiOdTOzTcSGPnK+KTiImJltIjbkkfNNxY+CNzNrQi39dOtyHznfVBxEzMxauVWrVq2f7t69O++99976+Z133pkHH3ywYuv26SwzMyvMQcTMzApzEDEzs8IcRMzMrDAHETMzK8xBxMzMCvMlvmZmTWl8pyaub0W92RHB4MGDufDCCxk+fDgAt99+O5MmTeK+++5r2raU4CBiZtaKSeK6667jmGOO4ZBDDmHt2rVccMEFzRJAwEHEzKzV69+/P0cddRQ/+clPePfddznppJO49NJLefbZZ1mzZg3jx49nxIgRPPfcc4wePZoPPviADz/8kDvuuIO+ffs2at0OImZmm4CLL76YffbZhy233JKvfOUrDBkyhMmTJ7N8+XIGDhzIoYceynXXXceZZ57JiSeeyAcffMC6desavV4HETOzTcDWW2/NN77xDbbZZhumT5/O3XffzRVXXAHA6tWrefXVV/niF7/IpZdeSnV1NV/72tcafRQCFbw6S9JukubkXislnSWps6SZkl5Kf7dP5SVpgqQqSXMl7ZOra1Qq/5KkUbn0fSXNS8tMSCMompltlrbYYgu22GILIoI77riDOXPmMGfOHF599VV23313TjjhBGbMmEGHDh044ogjmuSZWpUc2fDFiNgrIvYC9gXeA35LNmLhAxHRF3ggzQMMJxv6ti8wBvgVgKTOZANb7Uc2mNXFNYEnlflObrlhldoeM7PW4vDDD+fqq68mG2EcnnnmGQDmz5/PLrvswtixYxkxYgRz585t9Lqa63TWUOBvEbFA0gjg4JQ+BXiYbMjcEcDUNK76E5K2k7RjKjszIpYBSJoJDJP0MNAxIp5I6VOBo4F7m2mbzMw+qYFLcpvDD37wA8466yz23HNPPvzwQ/r06cPvf/97pk+fzk033US7du3YYYcduOCCCxq9ruYKIscBt6bp7hHxeppeDHRP0z2AhbllqlNafenVJdI/QdIYsqMbdtppp8IbYWa2MRs/fvz66euvv/4T+ePGjWPcuHGfSG+Mit+xLmlL4KvA7bXz0lFHVLoNETExIgZExIBu3bpVenVmZpuN5njsyXDgLxHxRpp/I52mIv19M6UvAnrlluuZ0upL71ki3czMmklzBJHj+ehUFsAMoOYKq1HAXbn0kekqrUHAinTa637gMEnbpw71w4D7U95KSYPSVVkjc3WZmTWbmg7s1q7IdlS0T0TS1sCXge/mki8Dpks6BVgAHJvS7wGOAKrIruQaDRARyyT9CJiVyv2wppMdOBW4EehA1qHuTnUza1bt27dn6dKldOnShdZ8l0FEsHTpUtq3b79By1U0iETEu0CXWmlLya7Wql02gNPqqGcyMLlE+mygf5M01sysgJ49e1JdXc2SJUtauimN1r59e3r27NlwwRzfsW5m1gjt2rWjT58+Ld2MFuPxRMzMrDAHETMzK8xBxMzMCnMQMTOzwhxEzMysMAcRMzMrzEHEzMwKcxAxM7PCHETMzKwwBxEzMyvMQcTMzApzEDEzs8IcRMzMrDAHETMzK6yiQUTSdpJ+I+mvkl6Q9EVJnSXNlPRS+rt9KitJEyRVSZoraZ9cPaNS+Zckjcql7ytpXlpmglrziDBmZq1QpY9EfgHcFxGfA74AvACMAx6IiL7AA2kesrHY+6bXGOBXAJI6AxcD+wEDgYtrAk8q853ccsMqvD1mZpZTsSAiqRNwIDAJICI+iIjlwAhgSio2BTg6TY8ApkbmCWA7STsChwMzI2JZRLwNzASGpbyOEfFEGhVxaq4uMzNrBpU8EukDLAFukPSMpF+nMde7R8TrqcxioHua7gEszC1fndLqS68ukW5mZs2kkkGkLbAP8KuI2Bt4l49OXQHrx1WPCrYBAEljJM2WNHtTGAfZzGxjUckgUg1UR8STaf43ZEHljXQqivT3zZS/COiVW75nSqsvvWeJ9E+IiIkRMSAiBnTr1q1RG2VmZh9pMIhIOkbStmn6Ikl35q+cqktELAYWStotJQ0FngdmADVXWI0C7krTM4CR6SqtQcCKdNrrfuAwSdunDvXDgPtT3kpJg9JVWSNzdZmZWTNoW0aZH0TE7ZK+BBwKXE52VdR+ZSx7BnCLpC2B+cBossA1XdIpwALg2FT2HuAIoAp4L5UlIpZJ+hEwK5X7YUQsS9OnAjcCHYB708vMzJpJOUFkXfp7JDAxIv4g6cflVB4Rc4ABJbKGligbwGl11DMZmFwifTbQv5y2mJlZ0yunT2SRpOuBbwD3SNqqzOXMzGwTV04wOJasX+LwdJ9HZ+DfKtkoMzNrHcoJItdHxJ0R8RJA6tA+ubLNMjOz1qCcIPL5/IykNsC+lWmOmZm1JnUGEUnnS3oH2FPSyvR6h+y+Dl9Ka2ZmdQeRiPiPiNgWuDwiOqbXthHRJSLOb8Y2mpnZRqrBS3wj4nxJPYCd8+Uj4tFKNszMzDZ+DQYRSZcBx5HdbV5zz0gADiJmZpu5cm42/Cdgt4h4v9KNMTOz1qWcq7PmA+0q3RAzM2t9yjkSeQ+YI+kBYP3RSESMrVirzMysVSgniMxILzMzs48p5+qsKZI6ADtFxIvN0CYzM2slyhlP5ChgDnBfmt9Lko9MzMysrI718cBAYDmsf7z7LhVrkZmZtRrlBJE1EbGiVtqHlWiMmZm1LuUEkecknQC0kdRX0tXAn8upXNIrkuZJmiNpdkrrLGmmpJfS3+1TuiRNkFQlaW5+CF5Jo1L5lySNyqXvm+qvSstqg7bezMwapZwgcgbZk3zfB24FVgJnbcA6DomIvSKiZoTDccADEdEXeCDNAwwH+qbXGLIheJHUGbiYbDjegcDFNYEnlflObrlhG9AuMzNrpAaDSES8FxEXRsQ/RsSANL26EescAUxJ01OAo3PpUyPzBLCdpB2Bw4GZEbEsIt4GZgLDUl7HiHgiDa07NVeXmZk1gzov8ZX084g4S9LdZM/K+piI+GoZ9QfwR0lBNrjVRKB7GtgKYDHQPU33ABbmlq1OafWlV5dIL7UtY8iObthpp53KaLaZmZWjvvtEbkp/r2hE/V+KiEWS/gGYKemv+cyIiBRgKioFr4kAAwYMqPj6zMw2F/WNJ/J0mtwrIh7Jv4C9yqk8Ihalv28CvyXr03gjnYoi/X0zFV8E9Mot3jOl1Zfes0S6mZk1k3I61keVSPtmQwtJ2lrStjXTwGHAs2SPUKmpcxQfjZI4AxiZrtIaBKxIp73uBw6TtH3qUD8MuD/lrZQ0KF2VNRKPuGhm1qzq6xM5HjgB6FPrDvVtgWVl1N0d+G266rYt8F8RcZ+kWcB0SacAC4BjU/l7gCOAKrKHPo4GiIhlkn4EzErlfhgRNes/FbgR6ADcm15mZtZM6usT+TPwOtAVuDKX/g4wt6GKI2I+8IUS6UuBoSXSAzitjromA5NLpM8G+jfUFjMzq4w6g0hELCA7Uvhi8zXHzMxak3IewDhI0ixJqyR9IGmdpJXN0TgzM9u4ldOxfg1wPPASWd/Dt4FrK9koMzNrHcoJIkREFdAmItZFxA348SJmZkaZw+NK2pJsiNyfknW2lxV8zMxs01ZOMDg5lTsdeJfsxr9/rmSjzMysdShneNwF6UikN3An8GJEfFDphpmZ2cavwSAi6UjgOuBvgMhuPvxuRPjGPjOzzVw5fSJXko0JUgUgaVfgD/jucDOzzV45fSLv1ASQZD7ZXetmZraZK+dIZLake4DpZOODHAPMkvQ1gIi4s4LtMzOzjVg5QaQ98AZwUJpfQnbT4VFkQcVBxMxsM1XO1Vmjm6MhZmbW+vimQTMzK8xBxMzMCqsziEg6M/09oDErkNRG0jOSfp/m+0h6UlKVpNvSjYxI2irNV6X83rk6zk/pL0o6PJc+LKVVSRrXmHaamdmGq+9IpKYv5OpGruNM4IXc/E+AqyLiM8DbwCkp/RTg7ZR+VSqHpH7AccDnyR78+MsUmNqQPU14ONAPOD6VNTOzZlJfEHlB0kvAbpLm5l7zJDU4siGApJ7AkcCv07yAIcBvUpEpwNFpekSaJ+UPTeVHANMi4v2IeJls+NyB6VUVEfPTY1impbJmZtZM6hvZ8HhJOwD3A18tWP/Pge+TjcsO0AVYHhFr03w10CNN9wAWpnWvlbQile8BPJGrM7/Mwlrp+5VqhKQxwBiAnXbaqeCmmJlZbfV2rEfE4oj4Atnj37dNr9fS0Ln1kvQV4M2IeLpJWtoIETExIgZExIBu3bq1dHPMzDYZ5TyA8SBgKvAK2QMYe0kaFRGPNrDoAcBXJR1BdsNiR+AXwHaS2qajkZ7AolR+Edlj5qsltQU6AUtz6TXyy9SVbmZmzaCcS3x/BhwWEQdFxIHA4WQd3/WKiPMjomdE9CbrGH8wIk4EHgK+noqNAu5K0zPSPCn/wYiIlH5cunqrD9AXeAqYBfRNV3ttmdYxo4ztMTOzJlLOY0/aRcSLNTMR8b+S2jVinecB0yT9GHgGmJTSJwE3SaoClpEFBSLiOUnTgeeBtcBpEbEOQNLpZH02bYDJEfFcI9plZmYbqNwHMP4auDnNnwjM3pCVRMTDwMNpej7ZlVW1y6wme7hjqeUvBS4tkX4PcM+GtMXMzJpOOUHkX4HTgLFp/jHglxVrkZmZtRrlPIDxfbJ+kZ9VvjlmZtaa+NlZZmZWmIOImZkV5iBiZmaFFQoi6TEiZma2mSt6JKImbYWZmbVKhYJIRFzf1A0xM7PWp8EgIqmnpN9KWiLpTUl3pEe8m5nZZq6cI5EbyJ5JtSPwaeDulGZmZpu5coJIt4i4ISLWpteNgJ+nbmZmZQWRpZJOqhmSVtJJZI9oNzOzzVw5QeRbwLHAYrLBqb7OR+Ovm5nZZqycZ2ctoPjwuGZmtgmrM4hI+n/1LBcR8aMKtMfMzFqR+k5nvVviBXAK2cBS9ZLUXtJTkv5H0nOSLknpfSQ9KalK0m1pVELSyIW3pfQnJfXO1XV+Sn9R0uG59GEprUrSuA3deDMza5w6g0hEXFnzAiYCHcj6QqYBu5RR9/vAkIj4ArAXMEzSIOAnwFUR8RngbbKgRPr7dkq/KpVDUj+yUQ4/DwwDflnTyQ9cCwwH+gHHp7JmZtZM6u1Yl9Q5DWM7l+zU1z4RcV5EvNlQxZFZlWbbpVcAQ4DfpPQpwNFpekSaJ+UPlaSUPi0i3o+Il4EqspERBwJVETE/Ij4gC24jythmMzNrInUGEUmXA7OAd4A9ImJ8RLy9IZWnI4Y5wJvATOBvwPKIWJuKVAM90nQPYCFAyl8BdMmn11qmrvRS7Rgjabak2UuWLNmQTTAzs3rUdyRyLtkd6hcBr0lamV7vSFpZTuURsS4i9gJ6kh05fK6xDS4iIiZGxICIGNCtm++TNDNrKnVenRURTTbWSEQsl/QQ8EVgO0lt09FGT2BRKrYI6AVUS2oLdCK7qbEmvUZ+mbrSzcysGVRsUCpJ3SRtl6Y7AF8GXgAeIrthEWAUcFeanpHmSfkPRkSk9OPS1Vt9gL7AU2Sn2vqmq722JOt8n1Gp7TEzs09q8GbDRtgRmJKuotoCmB4Rv5f0PDAtddg/A0xK5ScBN0mqApaRBQUi4jlJ04HngbXAaRGxDkDS6cD9QBtgckQ8V8HtMTOzWioWRCJiLrB3ifT5ZP0jtdNXA8fUUdelwKUl0u8B7ml0Y83MrBCPsW5mZoU5iJiZWWEOImZmVpiDiJmZFeYgYmZmhTmImJlZYQ4iZmZWmIOImZkV5iBiZmaFOYiYmVlhDiJmZlaYg4iZmRXmIGJmZoU5iJiZWWGVHE/ENnXjO7V0C8ozfkVLt8Bsk1XJkQ17SXpI0vOSnpN0ZkrvLGmmpJfS3+1TuiRNkFQlaa6kfXJ1jUrlX5I0Kpe+r6R5aZkJklSp7TEzs0+q5OmstcC5EdEPGAScJqkfMA54ICL6Ag+keYDhZEPf9gXGAL+CLOgAFwP7kQ1mdXFN4EllvpNbblgFt8fMzGqpWBCJiNcj4i9p+h2y8dV7ACOAKanYFODoND0CmBqZJ4DtJO0IHA7MjIhlEfE2MBMYlvI6RsQTaSz2qbm6zMysGTRLx7qk3mRD5T4JdI+I11PWYqB7mu4BLMwtVp3S6kuvLpFeav1jJM2WNHvJkiWN2xgzM1uv4kFE0jbAHcBZEbEyn5eOIKLSbYiIiRExICIGdOvWrdKrMzPbbFQ0iEhqRxZAbomIO1PyG+lUFOnvmyl9EdArt3jPlFZfes8S6WZm1kwqeXWWgEnACxHxs1zWDKDmCqtRwF259JHpKq1BwIp02ut+4DBJ26cO9cOA+1PeSkmD0rpG5uoyM7NmUMn7RA4ATgbmSZqT0i4ALgOmSzoFWAAcm/LuAY4AqoD3gNEAEbFM0o+AWancDyNiWZo+FbgR6ADcm15mZtZMKhZEIuJPQF33bQwtUT6A0+qoazIwuUT6bKB/I5ppZmaN4MeemJlZYQ4iZmZWmIOImZkV5iBiZmaFOYiYmVlhDiJmZlaYg4iZmRXmIGJmZoU5iJiZWWEOImZmVpiDiJmZFeYgYmZmhTmImJlZYQ4iZmZWmIOImZkVVsmRDSdLelPSs7m0zpJmSnop/d0+pUvSBElVkuZK2ie3zKhU/iVJo3Lp+0qal5aZkEY3NDOzZlTJI5EbgWG10sYBD0REX+CBNA8wHOibXmOAX0EWdICLgf2AgcDFNYEnlflObrna6zIzswqrWBCJiEeBZbWSRwBT0vQU4Ohc+tTIPAFsJ2lH4HBgZkQsi4i3gZnAsJTXMSKeSCMiTs3VZWZmzaS5+0S6R8TraXox0D1N9wAW5spVp7T60qtLpJuZWTNqsY71dAQRzbEuSWMkzZY0e8mSJc2xSjOzzUJzB5E30qko0t83U/oioFeuXM+UVl96zxLpJUXExIgYEBEDunXr1uiNMDOzTHMHkRlAzRVWo4C7cukj01Vag4AV6bTX/cBhkrZPHeqHAfenvJWSBqWrskbm6jIzs2bStlIVS7oVOBjoKqma7Cqry4Dpkk4BFgDHpuL3AEcAVcB7wGiAiFgm6UfArFTuhxFR01l/KtkVYB2Ae9PLzMyaUcWCSEQcX0fW0BJlAzitjnomA5NLpM8G+jemjWZm1ji+Y93MzApzEDEzs8IcRMzMrDAHETMzK8xBxMzMCnMQMTOzwhxEzMysMAcRMzMrzEHEzMwKcxAxM7PCHETMzKwwBxEzMyusYg9gNLNNU+9xf2jpJpTllcuObOkmbBZ8JGJmZoU5iJiZWWEOImZmVlirDyKShkl6UVKVpHEt3R4zs81Jq+5Yl9QGuBb4MlANzJI0IyKeb9mWmVmLG9+ppVtQnvErWroFjdLaj0QGAlURMT8iPgCmASNauE1mZpuNVn0kAvQAFubmq4H9aheSNAYYk2ZXSXqxGdq2yRN0Bd5q6XY06BK1dAusBfjz2aR2riujtQeRskTERGBiS7djUyNpdkQMaOl2mJXiz2fzaO2nsxYBvXLzPVOamZk1g9YeRGYBfSX1kbQlcBwwo4XbZGa22WjVp7MiYq2k04H7gTbA5Ih4roWbtTnxKULbmPnz2QwUES3dBjMza6Va++ksMzNrQQ4iZmZWmIOIFeLHzdjGStJkSW9Keral27I5cBCxDZZ73MxwoB9wvKR+Ldsqs/VuBIa1dCM2Fw4iVoQfN2MbrYh4FFjW0u3YXDiIWBGlHjfTo4XaYmYtyEHEzMwKcxCxIvy4GTMDHESsGD9uxswABxErICLWAjWPm3kBmO7HzdjGQtKtwOPAbpKqJZ3S0m3alPmxJ2ZmVpiPRMzMrDAHETMzK8xBxMzMCnMQMTOzwhxEzMysMAcRszJIOlpSSPpcmu9d9Cmxkl6R1HUDyn9T0jVp+l8kjSyyXrNKcBAxK8/xwJ/S3xYTEddFxNSWbINZnoOIWQMkbQN8CTiF7O782vltJF0h6VlJcyWdkdKHSnpG0rw0xsVWucXOkPSXlFdzdNNZ0u9SHU9I2rPEusZL+l6aHivp+VR+Wi5/iqTHJC2Q9DVJP03ruU9Su6Z/h2xz5iBi1rARwH0R8b/AUkn71sofA/QG9oqIPYFbJLUnG9fiGxGxB9AW+NfcMm9FxD7Ar4DvpbRLgGdSHRcADR1xjAP2TuX/JZe+KzAE+CpwM/BQasPfgSPL3mqzMjiImDXseLIxU0h/a5/SOhS4Pj0OhohYBuwGvJwCD8AU4MDcMnemv0+TBSDIjnZuSnU8CHSR1LGeds0lC1gnAWtz6fdGxBpgHtAGuC+lz8uty6xJtG3pBphtzCR1JvtVv4ekIPtSDrKRHRvj/fR3HcX/D48kC0xHARdK2iNfd0R8KGlNfPRsow8bsS6zknwkYla/rwM3RcTOEdE7InoBL/PxR+HPBL4rqS2sDzwvAr0lfSaVORl4pIF1PQacmOo4mOyU18pSBSVtAfSKiIeA84BOwDYbvnlmjeNfJWb1Ox74Sa20O4Dzc/O/Bj4LzJW0BvjPiLhG0mjg9hRcZgHXNbCu8cBkSXOB94BR9ZRtA9wsqRMgYEJELJdU5maZNQ0/xdfMzArz6SwzMyvMQcTMzApzEDEzs8IcRMzMrDAHETMzK8xBxMzMCnMQMTOzwv4P2lO9f9lYpeQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the pivot table above on a grouped bar chart\n",
    "ax = alcoholism_pivot.plot(\n",
    "        kind='bar',\n",
    "        title='No. of patients by Alcoholism and NoShow',\n",
    "        rot=0)\n",
    "ax.set_xlabel('Alcoholism')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:23.220019Z",
     "iopub.status.busy": "2022-09-27T11:21:23.219449Z",
     "iopub.status.idle": "2022-09-27T11:21:23.259294Z",
     "shell.execute_reply": "2022-09-27T11:21:23.258154Z",
     "shell.execute_reply.started": "2022-09-27T11:21:23.219970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating number of patients involved in alcoholism and showed up\n",
    "alcoholism_show = df_clean.query('Alcoholism == 1 & NoShow == \"No\"').count().Alcoholism\n",
    "# number of patients involved in alcohol\n",
    "alcoholism_count = df_clean.query('Alcoholism == 1').count().Alcoholism\n",
    "# calculating probability\n",
    "alcoholism_show_prob = probability(alcoholism_show,alcoholism_count)\n",
    "round(alcoholism_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient involved in alcoholism showing up is 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:23.261850Z",
     "iopub.status.busy": "2022-09-27T11:21:23.261024Z",
     "iopub.status.idle": "2022-09-27T11:21:23.370202Z",
     "shell.execute_reply": "2022-09-27T11:21:23.368805Z",
     "shell.execute_reply.started": "2022-09-27T11:21:23.261802Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of non alcoholic patients\n",
    "no_alcoholism_count = df_clean.query('Alcoholism == 0').count().Alcoholism\n",
    "#calculating number of patients who were not involved in alcoholism and showed up\n",
    "no_alcoholism_show = df_clean.query('Alcoholism == 0 & NoShow == \"No\"').count().Alcoholism\n",
    "# calculating probability\n",
    "no_alcoholism_show_prob = probability(no_alcoholism_show,no_alcoholism_count)\n",
    "round(no_alcoholism_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a non-alcoholic patient showing up is 0.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The probabilities of alcoholic and non-alcoholic patients showing up is 0.8 therefore as an individual variable, alcoholism is not an important factor in predicting show/no-show."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypertension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:23.373452Z",
     "iopub.status.busy": "2022-09-27T11:21:23.372197Z",
     "iopub.status.idle": "2022-09-27T11:21:23.386914Z",
     "shell.execute_reply": "2022-09-27T11:21:23.385700Z",
     "shell.execute_reply.started": "2022-09-27T11:21:23.373401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88725\n",
       "1    21801\n",
       "Name: Hypertension, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# distribution of hypertension\n",
    "df_clean['Hypertension'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:23.388744Z",
     "iopub.status.busy": "2022-09-27T11:21:23.388375Z",
     "iopub.status.idle": "2022-09-27T11:21:23.592932Z",
     "shell.execute_reply": "2022-09-27T11:21:23.591702Z",
     "shell.execute_reply.started": "2022-09-27T11:21:23.388703Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting value counts\n",
    "ax = df_clean['Hypertension'].value_counts().plot(\n",
    "        kind='bar',\n",
    "        title= 'distribution of hypertension status',\n",
    "        rot=0)\n",
    "ax.set_xlabel('Hypertension status')\n",
    "ax.set_ylabel('Number of patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majority of the patients were not hypertensive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:23.595386Z",
     "iopub.status.busy": "2022-09-27T11:21:23.594483Z",
     "iopub.status.idle": "2022-09-27T11:21:23.650223Z",
     "shell.execute_reply": "2022-09-27T11:21:23.648957Z",
     "shell.execute_reply.started": "2022-09-27T11:21:23.595336Z"
    }
   },
   "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>NoShow</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hypertension</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70178</td>\n",
       "      <td>18547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18029</td>\n",
       "      <td>3772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NoShow           No    Yes\n",
       "Hypertension              \n",
       "0             70178  18547\n",
       "1             18029   3772"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot table of NoShow and Hypertension columns\n",
    "df_hyper = df_clean.groupby(['NoShow','Hypertension'],as_index=False).size()\n",
    "hyper_pivot = pd.pivot_table(df_hyper, \n",
    "        values = 'size',\n",
    "        columns=['NoShow'],\n",
    "        index='Hypertension')\n",
    "hyper_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:23.652906Z",
     "iopub.status.busy": "2022-09-27T11:21:23.652045Z",
     "iopub.status.idle": "2022-09-27T11:21:23.909200Z",
     "shell.execute_reply": "2022-09-27T11:21:23.907967Z",
     "shell.execute_reply.started": "2022-09-27T11:21:23.652859Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the pivot table on a grouped bar chart\n",
    "ax = hyper_pivot.plot(\n",
    "        kind='bar',\n",
    "        title='Number of patients by hypertension and no-show',\n",
    "        rot=0)\n",
    "ax.set_xlabel('Hypertension')\n",
    "ax.set_ylabel('Num of patients');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each category, majority of patients showed up. I will calculate the probability of a patient from each category showing up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:23.913045Z",
     "iopub.status.busy": "2022-09-27T11:21:23.912463Z",
     "iopub.status.idle": "2022-09-27T11:21:23.969460Z",
     "shell.execute_reply": "2022-09-27T11:21:23.968138Z",
     "shell.execute_reply.started": "2022-09-27T11:21:23.912994Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of patients with hypertension that showed up\n",
    "hyper_show = df_clean.query('Hypertension == 1 & NoShow == \"No\"').count().Hypertension\n",
    "# number of patients with hypertension\n",
    "hyper_count = df_clean.query('Hypertension == 1').count().Hypertension\n",
    "# calculating probability\n",
    "hyper_show_prob = probability(hyper_show,hyper_count)\n",
    "round(hyper_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient with hypertension showing up is 0.83."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:23.971530Z",
     "iopub.status.busy": "2022-09-27T11:21:23.971079Z",
     "iopub.status.idle": "2022-09-27T11:21:24.070632Z",
     "shell.execute_reply": "2022-09-27T11:21:24.069316Z",
     "shell.execute_reply.started": "2022-09-27T11:21:23.971490Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of patients without hypertension that showed up\n",
    "no_hyper_show = df_clean.query('Hypertension == 0 & NoShow == \"No\"').count().Hypertension\n",
    "# number of patients without hypertension\n",
    "no_hyper_count = df_clean.query('Hypertension == 0').count().Hypertension\n",
    "# calculating probability\n",
    "no_hyper_show_prob = probability(no_hyper_show,no_hyper_count)\n",
    "round(no_hyper_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient with out hypertension showing up is 0.79"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Hypertension status can be used in the prediction of show/no-show."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.073467Z",
     "iopub.status.busy": "2022-09-27T11:21:24.072217Z",
     "iopub.status.idle": "2022-09-27T11:21:24.084733Z",
     "shell.execute_reply": "2022-09-27T11:21:24.083568Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.073414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    102583\n",
       "1      7943\n",
       "Name: Diabetes, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# distribution of diabetes status\n",
    "df_clean['Diabetes'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.087365Z",
     "iopub.status.busy": "2022-09-27T11:21:24.086263Z",
     "iopub.status.idle": "2022-09-27T11:21:24.132708Z",
     "shell.execute_reply": "2022-09-27T11:21:24.131432Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.087314Z"
    }
   },
   "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>NoShow</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diabetes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81694</td>\n",
       "      <td>20889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6513</td>\n",
       "      <td>1430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NoShow       No    Yes\n",
       "Diabetes              \n",
       "0         81694  20889\n",
       "1          6513   1430"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot table of diabetes and no-show column\n",
    "df_diabetes = df_clean.groupby(['NoShow','Diabetes'],as_index=False).size()\n",
    "diabetes_pivot = pd.pivot_table(df_diabetes, \n",
    "        values = 'size',\n",
    "        columns=['NoShow'],\n",
    "        index='Diabetes')\n",
    "diabetes_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.135741Z",
     "iopub.status.busy": "2022-09-27T11:21:24.134248Z",
     "iopub.status.idle": "2022-09-27T11:21:24.247486Z",
     "shell.execute_reply": "2022-09-27T11:21:24.246189Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.135699Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of patients without diabetes\n",
    "no_diabetes_count = df_clean.query('Diabetes == 0').count().Diabetes\n",
    "# number of non-diabetic patients who showed up\n",
    "no_diabetes_show_count = df_clean.query('Diabetes == 0 & NoShow== \"No\"').count().Diabetes\n",
    "#calculating probability\n",
    "no_diabetes_show_prob = probability(no_diabetes_show_count,no_diabetes_count)\n",
    "round(no_diabetes_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a non-diabetic patient showing up is 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.251391Z",
     "iopub.status.busy": "2022-09-27T11:21:24.249979Z",
     "iopub.status.idle": "2022-09-27T11:21:24.303529Z",
     "shell.execute_reply": "2022-09-27T11:21:24.302255Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.251316Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of diabetic patients\n",
    "diabetes_count = df_clean.query('Diabetes == 1').count().Diabetes\n",
    "#number of diabetic patients who showed up\n",
    "diabetes_show_count = df_clean.query('Diabetes == 1 & NoShow == \"No\"').count().Diabetes\n",
    "#calculating probability\n",
    "diabetes_show_prob = probability(diabetes_show_count,diabetes_count)\n",
    "round(diabetes_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a diabetic patients showing up is 0.82."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Even though there is a slight difference between both probabilities, I maintain that diabetes status is not an important facotr to predict whether a patient will show up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handicapped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.305962Z",
     "iopub.status.busy": "2022-09-27T11:21:24.305450Z",
     "iopub.status.idle": "2022-09-27T11:21:24.318396Z",
     "shell.execute_reply": "2022-09-27T11:21:24.317051Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.305913Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    108285\n",
       "1      2042\n",
       "2       183\n",
       "3        13\n",
       "4         3\n",
       "Name: Handicapped, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of handicap status\n",
    "df_clean.Handicapped.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.321055Z",
     "iopub.status.busy": "2022-09-27T11:21:24.320079Z",
     "iopub.status.idle": "2022-09-27T11:21:24.369888Z",
     "shell.execute_reply": "2022-09-27T11:21:24.368494Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.321006Z"
    }
   },
   "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>NoShow</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Handicapped</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86373</td>\n",
       "      <td>21912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1676</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>146</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NoShow          No    Yes\n",
       "Handicapped              \n",
       "0            86373  21912\n",
       "1             1676    366\n",
       "2              146     37\n",
       "3               10      3\n",
       "4                2      1"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a pivot table of the handicap column and no-show\n",
    "df_hc = df_clean.groupby(['Handicapped','NoShow'],as_index=False).size()\n",
    "hc_pivot = pd.pivot(df_hc,values='size',index='Handicapped',columns='NoShow')\n",
    "hc_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.373884Z",
     "iopub.status.busy": "2022-09-27T11:21:24.371920Z",
     "iopub.status.idle": "2022-09-27T11:21:24.519664Z",
     "shell.execute_reply": "2022-09-27T11:21:24.518207Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.373776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of patients with zero handicaps\n",
    "zero_hc_count = df_clean.query('Handicapped == 0').count().Handicapped\n",
    "#number of patients with zero handicaps who showed up\n",
    "zero_hc_show = df_clean.query('Handicapped == 0 & NoShow == \"No\"').count().Handicapped\n",
    "#calculating probability\n",
    "zero_hc_show_prob = probability(zero_hc_show,zero_hc_count)\n",
    "round(zero_hc_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient with zero handicap showing is 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.522023Z",
     "iopub.status.busy": "2022-09-27T11:21:24.521463Z",
     "iopub.status.idle": "2022-09-27T11:21:24.565148Z",
     "shell.execute_reply": "2022-09-27T11:21:24.563473Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.521891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of patients with one handicap\n",
    "one_hc_count = df_clean.query('Handicapped == 1').count().Handicapped\n",
    "#number of one handicapped patients who showed up\n",
    "one_hc_show = df_clean.query('Handicapped == 1 & NoShow == \"No\"').count().Handicapped\n",
    "# calculating probability\n",
    "one_hc_show_prob = probability(one_hc_show,one_hc_count)\n",
    "round(one_hc_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a one-handicapped patient showing up is 0.82"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.567562Z",
     "iopub.status.busy": "2022-09-27T11:21:24.567129Z",
     "iopub.status.idle": "2022-09-27T11:21:24.604778Z",
     "shell.execute_reply": "2022-09-27T11:21:24.603342Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.567514Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of patients with two handicaps\n",
    "two_hc_count = df_clean.query('Handicapped == 2').count().Handicapped\n",
    "# number of patients with two handicaps who showed\n",
    "two_hc_show = df_clean.query('Handicapped == 2 & NoShow == \"No\"').count().Handicapped\n",
    "# calculating probability\n",
    "two_hc_show_prob = probability(two_hc_show,two_hc_count)\n",
    "round(two_hc_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient with two handicaps showing up is 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.607043Z",
     "iopub.status.busy": "2022-09-27T11:21:24.606629Z",
     "iopub.status.idle": "2022-09-27T11:21:24.642044Z",
     "shell.execute_reply": "2022-09-27T11:21:24.640894Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.607008Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of patients with 3 handicaps\n",
    "three_hc_count = df_clean.query('Handicapped == 3').count().Handicapped\n",
    "# number of patients with 3 handicaps who showed\n",
    "three_hc_show = df_clean.query('Handicapped == 3 & NoShow == \"No\"').count().Handicapped\n",
    "# calculating probability\n",
    "three_hc_show_prob = probability(three_hc_show,three_hc_count)\n",
    "round(three_hc_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient with 3 handicaps showing up is 0.77"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.643786Z",
     "iopub.status.busy": "2022-09-27T11:21:24.643418Z",
     "iopub.status.idle": "2022-09-27T11:21:24.678598Z",
     "shell.execute_reply": "2022-09-27T11:21:24.677318Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.643751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.67"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of patients with four handicaps\n",
    "four_hc_count = df_clean.query('Handicapped == 4').count().Handicapped\n",
    "#number of patients with four handicaps who showed\n",
    "four_hc_show = df_clean.query('Handicapped == 4 & NoShow == \"No\"').count().Handicapped\n",
    "# calculating probability\n",
    "four_hc_show_prob = probability(four_hc_show,four_hc_count)\n",
    "round(four_hc_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient with four handicaps showing up is 0.67."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Given the varying probabilities, handicap status can be used in the prediction of show/no-show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Day of the week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.695841Z",
     "iopub.status.busy": "2022-09-27T11:21:24.695052Z",
     "iopub.status.idle": "2022-09-27T11:21:24.756716Z",
     "shell.execute_reply": "2022-09-27T11:21:24.755005Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.695782Z"
    }
   },
   "outputs": [],
   "source": [
    "#creating a new column to contain the day of the week of appointment\n",
    "df_clean['Day'] = df_clean['AppointmentDay'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.760449Z",
     "iopub.status.busy": "2022-09-27T11:21:24.759497Z",
     "iopub.status.idle": "2022-09-27T11:21:24.813206Z",
     "shell.execute_reply": "2022-09-27T11:21:24.809134Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.760392Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wednesday    25867\n",
       "Tuesday      25640\n",
       "Monday       22714\n",
       "Friday       19019\n",
       "Thursday     17247\n",
       "Saturday        39\n",
       "Name: Day, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking distribution\n",
    "df_clean['Day'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No Sunday because there were no appointments on Sundays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:24.818410Z",
     "iopub.status.busy": "2022-09-27T11:21:24.817293Z",
     "iopub.status.idle": "2022-09-27T11:21:25.063473Z",
     "shell.execute_reply": "2022-09-27T11:21:25.062499Z",
     "shell.execute_reply.started": "2022-09-27T11:21:24.818350Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting value counts above on a bar chart\n",
    "ax = df_clean['Day'].value_counts().plot(\n",
    "        kind='bar',\n",
    "        title = 'No of patients by Appointment day',\n",
    "        rot=0)\n",
    "ax.set_xlabel('Appointment day')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:25.065548Z",
     "iopub.status.busy": "2022-09-27T11:21:25.064901Z",
     "iopub.status.idle": "2022-09-27T11:21:25.105151Z",
     "shell.execute_reply": "2022-09-27T11:21:25.103256Z",
     "shell.execute_reply.started": "2022-09-27T11:21:25.065512Z"
    }
   },
   "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>NoShow</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>14982</td>\n",
       "      <td>4037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>18024</td>\n",
       "      <td>4690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>13909</td>\n",
       "      <td>3338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>20488</td>\n",
       "      <td>5152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>20774</td>\n",
       "      <td>5093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NoShow        No   Yes\n",
       "Day                   \n",
       "Friday     14982  4037\n",
       "Monday     18024  4690\n",
       "Saturday      30     9\n",
       "Thursday   13909  3338\n",
       "Tuesday    20488  5152\n",
       "Wednesday  20774  5093"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a pivot table\n",
    "dow = df_clean.groupby(['Day','NoShow'],as_index=False).size()\n",
    "dow_pivot = pd.pivot(dow, index='Day',columns='NoShow',values='size')\n",
    "dow_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:25.107274Z",
     "iopub.status.busy": "2022-09-27T11:21:25.106876Z",
     "iopub.status.idle": "2022-09-27T11:21:25.195935Z",
     "shell.execute_reply": "2022-09-27T11:21:25.194132Z",
     "shell.execute_reply.started": "2022-09-27T11:21:25.107239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of patients who had their appointment on a Monday\n",
    "mon_count = df_clean.query('Day == \"Monday\"').count().Day\n",
    "# number of monday appointments who showed up\n",
    "mon_show = df_clean.query('Day == \"Monday\" & NoShow == \"No\"').count().Day\n",
    "# calculating probability\n",
    "mon_show_prob = probability(mon_show,mon_count)\n",
    "round(mon_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient who had their appointment on a Monday showing up is 0.79"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:25.198370Z",
     "iopub.status.busy": "2022-09-27T11:21:25.197941Z",
     "iopub.status.idle": "2022-09-27T11:21:25.264375Z",
     "shell.execute_reply": "2022-09-27T11:21:25.262756Z",
     "shell.execute_reply.started": "2022-09-27T11:21:25.198335Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of patients who had their appointments on a Tuesday\n",
    "tue_count = df_clean.query('Day == \"Tuesday\"').count().Day\n",
    "# number of Tuesday appointments who showed up\n",
    "tue_show = df_clean.query('Day == \"Tuesday\" & NoShow == \"No\"').count().Day\n",
    "# calculating probability\n",
    "tue_show_prob = probability(tue_show,tue_count)\n",
    "round(tue_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient who had their appointment on a Tuesday showing up is 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:25.269617Z",
     "iopub.status.busy": "2022-09-27T11:21:25.268258Z",
     "iopub.status.idle": "2022-09-27T11:21:25.334564Z",
     "shell.execute_reply": "2022-09-27T11:21:25.333329Z",
     "shell.execute_reply.started": "2022-09-27T11:21:25.269563Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of patients who had their appointments on a Wednesday\n",
    "wed_count = df_clean.query('Day == \"Wednesday\"').count().Day\n",
    "# number of Wednesday appointments who showed\n",
    "wed_show = df_clean.query('Day == \"Wednesday\" & NoShow == \"No\"').count().Day\n",
    "# calculating probability\n",
    "wed_show_prob = probability(wed_show,wed_count)\n",
    "round(wed_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient who had their appointment on a Wednesday showing up is 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:25.337524Z",
     "iopub.status.busy": "2022-09-27T11:21:25.336706Z",
     "iopub.status.idle": "2022-09-27T11:21:25.396143Z",
     "shell.execute_reply": "2022-09-27T11:21:25.394100Z",
     "shell.execute_reply.started": "2022-09-27T11:21:25.337483Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of patients who had their appointments on a Thursday\n",
    "thurs_count = df_clean.query('Day == \"Thursday\"').count().Day\n",
    "# number of Thursday appointments who showed\n",
    "thurs_show = df_clean.query('Day == \"Thursday\" & NoShow == \"No\"').count().Day\n",
    "# calculating probability\n",
    "thurs_show_prob = probability(thurs_show,thurs_count)\n",
    "round(thurs_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient whp had their appointment on a Thursday showing up is 0.81"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:25.398537Z",
     "iopub.status.busy": "2022-09-27T11:21:25.398147Z",
     "iopub.status.idle": "2022-09-27T11:21:25.458207Z",
     "shell.execute_reply": "2022-09-27T11:21:25.457174Z",
     "shell.execute_reply.started": "2022-09-27T11:21:25.398501Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of patients who had their appointments on a Friday\n",
    "fri_count = df_clean.query('Day == \"Friday\"').count().Day\n",
    "# number of Friday appointments who showed up\n",
    "fri_show = df_clean.query('Day == \"Friday\" & NoShow == \"No\"').count().Day\n",
    "# calculating probability\n",
    "fri_show_prob = probability(fri_show,fri_count)\n",
    "round(fri_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient with appointment on a Friday showing up is 0.79"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:25.460288Z",
     "iopub.status.busy": "2022-09-27T11:21:25.459908Z",
     "iopub.status.idle": "2022-09-27T11:21:25.499395Z",
     "shell.execute_reply": "2022-09-27T11:21:25.498151Z",
     "shell.execute_reply.started": "2022-09-27T11:21:25.460255Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of patients who had their appointments on a Saturday\n",
    "sat_count = df_clean.query('Day == \"Saturday\"').count().Day\n",
    "# number of Saturday appointments who showed\n",
    "sat_show = df_clean.query('Day == \"Saturday\" & NoShow == \"No\"').count().Day\n",
    "# calculating probability\n",
    "sat_show_prob = probability(sat_show,sat_count)\n",
    "round(sat_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a patient who had their appointment on a Saturday showing up is 0.77"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aside Saturday, the probability of a patient with an appointment on each of the other days is almost the same therefore I maintain that day of appointment is not significant in predicting whether a patient will show up or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:25.501843Z",
     "iopub.status.busy": "2022-09-27T11:21:25.501011Z",
     "iopub.status.idle": "2022-09-27T11:21:25.765842Z",
     "shell.execute_reply": "2022-09-27T11:21:25.764872Z",
     "shell.execute_reply.started": "2022-09-27T11:21:25.501715Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting distribution of ages\n",
    "bins = np.arange(0,df_clean['Age'].max()+10,10)\n",
    "plt.hist(data=df_clean,x='Age',bins=10)\n",
    "plt.xlim((0,100));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:25.768350Z",
     "iopub.status.busy": "2022-09-27T11:21:25.767574Z",
     "iopub.status.idle": "2022-09-27T11:21:25.799870Z",
     "shell.execute_reply": "2022-09-27T11:21:25.798587Z",
     "shell.execute_reply.started": "2022-09-27T11:21:25.768301Z"
    }
   },
   "outputs": [],
   "source": [
    "# breaking ages down into categories\n",
    "conditions = [(df_clean['Age'] <= 12),\n",
    "             (df_clean['Age'] > 12)&(df_clean['Age'] <= 18),\n",
    "             (df_clean['Age'] > 18)&(df_clean['Age'] <= 60),\n",
    "             (df_clean['Age'] > 60)]\n",
    "values = ['child','teenager','adult','elderly']\n",
    "df_clean['AgeCategory'] = np.select(conditions,values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:25.802922Z",
     "iopub.status.busy": "2022-09-27T11:21:25.801657Z",
     "iopub.status.idle": "2022-09-27T11:21:25.830187Z",
     "shell.execute_reply": "2022-09-27T11:21:25.827233Z",
     "shell.execute_reply.started": "2022-09-27T11:21:25.802881Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adult       61898\n",
       "child       21036\n",
       "elderly     19762\n",
       "teenager     7830\n",
       "Name: AgeCategory, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking distribution of age breakdown\n",
    "df_clean['AgeCategory'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:25.833762Z",
     "iopub.status.busy": "2022-09-27T11:21:25.832323Z",
     "iopub.status.idle": "2022-09-27T11:21:26.066341Z",
     "shell.execute_reply": "2022-09-27T11:21:26.064983Z",
     "shell.execute_reply.started": "2022-09-27T11:21:25.833636Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting value counts above in a bar chart\n",
    "ax = df_clean['AgeCategory'].value_counts().plot(\n",
    "        kind='bar',\n",
    "        title = 'No. of patients by age group',\n",
    "        rot=0)\n",
    "ax.set_xlabel('Age group')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:26.069411Z",
     "iopub.status.busy": "2022-09-27T11:21:26.068025Z",
     "iopub.status.idle": "2022-09-27T11:21:26.112143Z",
     "shell.execute_reply": "2022-09-27T11:21:26.110507Z",
     "shell.execute_reply.started": "2022-09-27T11:21:26.069364Z"
    }
   },
   "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>NoShow</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeCategory</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>adult</th>\n",
       "      <td>48932</td>\n",
       "      <td>12966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>child</th>\n",
       "      <td>16729</td>\n",
       "      <td>4307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elderly</th>\n",
       "      <td>16756</td>\n",
       "      <td>3006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teenager</th>\n",
       "      <td>5790</td>\n",
       "      <td>2040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "NoShow          No    Yes\n",
       "AgeCategory              \n",
       "adult        48932  12966\n",
       "child        16729   4307\n",
       "elderly      16756   3006\n",
       "teenager      5790   2040"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a pivot table\n",
    "age_cat = df_clean.groupby(['AgeCategory','NoShow'],as_index=False).size()\n",
    "ac_pivot = pd.pivot(age_cat, index='AgeCategory',columns='NoShow',values='size')\n",
    "ac_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:26.117358Z",
     "iopub.status.busy": "2022-09-27T11:21:26.116823Z",
     "iopub.status.idle": "2022-09-27T11:21:26.530863Z",
     "shell.execute_reply": "2022-09-27T11:21:26.529097Z",
     "shell.execute_reply.started": "2022-09-27T11:21:26.117321Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting pivot table on a grouoed bar chart\n",
    "ax = ac_pivot.plot(\n",
    "        kind = 'bar',\n",
    "        title = 'Show/No-Show by Age group',\n",
    "        rot=0)\n",
    "ax.set_xlabel('Age group')\n",
    "ax.set_ylabel('No. of patients');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:26.534658Z",
     "iopub.status.busy": "2022-09-27T11:21:26.534133Z",
     "iopub.status.idle": "2022-09-27T11:21:26.657790Z",
     "shell.execute_reply": "2022-09-27T11:21:26.655813Z",
     "shell.execute_reply.started": "2022-09-27T11:21:26.534607Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of children\n",
    "child_count = df_clean.query('AgeCategory == \"child\"').count().AgeCategory\n",
    "#number of children who showed\n",
    "child_show = df_clean.query('AgeCategory == \"child\" & NoShow == \"No\"').count().AgeCategory\n",
    "# calculating probability\n",
    "child_show_prob = probability(child_show,child_count)\n",
    "round(child_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a child patient showing up is 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:26.660817Z",
     "iopub.status.busy": "2022-09-27T11:21:26.659547Z",
     "iopub.status.idle": "2022-09-27T11:21:26.718031Z",
     "shell.execute_reply": "2022-09-27T11:21:26.716497Z",
     "shell.execute_reply.started": "2022-09-27T11:21:26.660765Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of teenagers\n",
    "teen_count = df_clean.query('AgeCategory == \"teenager\"').count().AgeCategory\n",
    "#number of teenagers who showed\n",
    "teen_show = df_clean.query('AgeCategory == \"teenager\" & NoShow == \"No\"').count().AgeCategory\n",
    "# calculating probability\n",
    "teen_show_prob = probability(teen_show,teen_count)\n",
    "round(teen_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a teenage patient showing up is 0.74"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:26.720740Z",
     "iopub.status.busy": "2022-09-27T11:21:26.720259Z",
     "iopub.status.idle": "2022-09-27T11:21:26.830327Z",
     "shell.execute_reply": "2022-09-27T11:21:26.828826Z",
     "shell.execute_reply.started": "2022-09-27T11:21:26.720693Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of adults\n",
    "adult_count = df_clean.query('AgeCategory == \"adult\"').count().AgeCategory\n",
    "# number of adults who showed up\n",
    "adult_show = df_clean.query('AgeCategory == \"adult\" & NoShow == \"No\"').count().AgeCategory\n",
    "# calculating probability\n",
    "adult_show_prob = probability(adult_show,adult_count)\n",
    "round(adult_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of an adult patient showing up is 0.79"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-27T11:21:26.832347Z",
     "iopub.status.busy": "2022-09-27T11:21:26.831958Z",
     "iopub.status.idle": "2022-09-27T11:21:26.916415Z",
     "shell.execute_reply": "2022-09-27T11:21:26.915122Z",
     "shell.execute_reply.started": "2022-09-27T11:21:26.832313Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of elderly patients\n",
    "elderly_count = df_clean.query('AgeCategory == \"elderly\"').count().AgeCategory\n",
    "# number of elderly patients who showed up\n",
    "elderly_show = df_clean.query('AgeCategory == \"elderly\" & NoShow == \"No\"').count().AgeCategory\n",
    "# calculating probability\n",
    "elderly_show_prob = probability(elderly_show,elderly_count)\n",
    "round(elderly_show_prob,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of an elderly patient showing up is 0.85"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Given the varying probabilities across the age groups, age is an important factor in predicting whether a patient will show up for their appointment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Individual independent variables that can be used in prediction include:\n",
    "* Scholarship\n",
    "* SMS received\n",
    "* Hypertension\n",
    "* Handicap\n",
    "* Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limitations\n",
    "\n",
    "Even though some of the independent variables like gender and alcoholism can not be used singularly to predict whether a patient will show up, combining them with other independent variables may give them some predictive power or effect on the dependent variable.This study did not investigate a combination of independent variables on the dependent variable so conclusions drawn are for the the independent variables just as they are. "
   ]
  }
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