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Medical_No_Shows.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Projcet2 - Predicting Medical Appointment No Shows\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",
"I choose the 'Medical Appointment No Shows' dataset in Kaggle. This dataset collects information from 100k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment. A number of characteristics about the patient are included in each row. There are 14 features in this dataset. The explanation for each feature is as follows. \n",
"- PatientId: Identification of a patient \n",
"- AppointmentID: Identification of each appointment \n",
"- Gender: Male or Female; Female is the greater proportion, woman takes way more care of they health in comparison to man. \n",
"- ScheduledDay: The day someone called or registered the appointment, this is before appointment of course.\n",
"- AppointmentDay: The day of the actuall appointment, when they have to visit the doctor. \n",
"- Age: How old is the patient. \n",
"- Neighbourhood: Where the appointment takes place. \n",
"- Scholarship: Ture of False. (This is a broad topic, consider reading this article https://en.wikipedia.org/wiki/Bolsa_Fam%C3%ADlia)\n",
"- Hipertension: True or False \n",
"- Diabetes: True or False \n",
"- Alcoholism: = True or False \n",
"- Handcap: True or False \n",
"- SMS_received: 1 or more messages sent to the patient. \n",
"- No-show: True or False. (‘No’if the patient showed up to their appointment, and ‘Yes’ if they didn't show up.)\n",
"\n",
"The purpose of this analysis is that what factors are important for us to know in order to predict if a patient will show up for their scheduled appointment. \n",
"\n",
"Independent variables for analysis.\n",
"1. Gender\n",
"2. Period between the scheduled day and appointment day.\n",
"3. Age\n",
"4. Scholarship\n",
"5. Disease (hipertension, diabetes, alcoholism) - each element is counted as 1, sum of all\n",
"6. SMS-received\n",
"\n",
"Dependent variable is the 'No-show'\n",
"\n",
"The proper question is **\"what factors are important for us to know in order to predict if a patient will show up for their scheduled appointment?\".**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, import pandas, numpy and matplotlib,pyplot. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"% matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='wrangling'></a>\n",
"## Data Wrangling\n",
"\n",
"> In this section of the report, I will load in the data, check for cleanliness, and then trim and clean my dataset for analysis.\n",
"\n",
"### Load a dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"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></th>\n",
" <th>PatientId</th>\n",
" <th>AppointmentID</th>\n",
" <th>Gender</th>\n",
" <th>ScheduledDay</th>\n",
" <th>AppointmentDay</th>\n",
" <th>Age</th>\n",
" <th>Neighbourhood</th>\n",
" <th>Scholarship</th>\n",
" <th>Hipertension</th>\n",
" <th>Diabetes</th>\n",
" <th>Alcoholism</th>\n",
" <th>Handcap</th>\n",
" <th>SMS_received</th>\n",
" <th>No-show</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.987250e+13</td>\n",
" <td>5642903</td>\n",
" <td>F</td>\n",
" <td>2016-04-29T18:38:08Z</td>\n",
" <td>2016-04-29T00:00:00Z</td>\n",
" <td>62</td>\n",
" <td>JARDIM DA PENHA</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.589980e+14</td>\n",
" <td>5642503</td>\n",
" <td>M</td>\n",
" <td>2016-04-29T16:08:27Z</td>\n",
" <td>2016-04-29T00:00:00Z</td>\n",
" <td>56</td>\n",
" <td>JARDIM DA PENHA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.262960e+12</td>\n",
" <td>5642549</td>\n",
" <td>F</td>\n",
" <td>2016-04-29T16:19:04Z</td>\n",
" <td>2016-04-29T00:00:00Z</td>\n",
" <td>62</td>\n",
" <td>MATA DA PRAIA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8.679510e+11</td>\n",
" <td>5642828</td>\n",
" <td>F</td>\n",
" <td>2016-04-29T17:29:31Z</td>\n",
" <td>2016-04-29T00:00:00Z</td>\n",
" <td>8</td>\n",
" <td>PONTAL DE CAMBURI</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8.841190e+12</td>\n",
" <td>5642494</td>\n",
" <td>F</td>\n",
" <td>2016-04-29T16:07:23Z</td>\n",
" <td>2016-04-29T00:00:00Z</td>\n",
" <td>56</td>\n",
" <td>JARDIM DA PENHA</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PatientId AppointmentID Gender ScheduledDay \\\n",
"0 2.987250e+13 5642903 F 2016-04-29T18:38:08Z \n",
"1 5.589980e+14 5642503 M 2016-04-29T16:08:27Z \n",
"2 4.262960e+12 5642549 F 2016-04-29T16:19:04Z \n",
"3 8.679510e+11 5642828 F 2016-04-29T17:29:31Z \n",
"4 8.841190e+12 5642494 F 2016-04-29T16:07:23Z \n",
"\n",
" AppointmentDay Age Neighbourhood Scholarship Hipertension \\\n",
"0 2016-04-29T00:00:00Z 62 JARDIM DA PENHA 0 1 \n",
"1 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 0 \n",
"2 2016-04-29T00:00:00Z 62 MATA DA PRAIA 0 0 \n",
"3 2016-04-29T00:00:00Z 8 PONTAL DE CAMBURI 0 0 \n",
"4 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 1 \n",
"\n",
" Diabetes Alcoholism Handcap SMS_received No-show \n",
"0 0 0 0 0 No \n",
"1 0 0 0 0 No \n",
"2 0 0 0 0 No \n",
"3 0 0 0 0 No \n",
"4 1 0 0 0 No "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"noshowappointments-kagglev2-may-2016.csv\")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check the type of each element and the number of elements"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 110527 entries, 0 to 110526\n",
"Data columns (total 14 columns):\n",
"PatientId 110527 non-null float64\n",
"AppointmentID 110527 non-null int64\n",
"Gender 110527 non-null object\n",
"ScheduledDay 110527 non-null object\n",
"AppointmentDay 110527 non-null object\n",
"Age 110527 non-null int64\n",
"Neighbourhood 110527 non-null object\n",
"Scholarship 110527 non-null int64\n",
"Hipertension 110527 non-null int64\n",
"Diabetes 110527 non-null int64\n",
"Alcoholism 110527 non-null int64\n",
"Handcap 110527 non-null int64\n",
"SMS_received 110527 non-null int64\n",
"No-show 110527 non-null object\n",
"dtypes: float64(1), int64(8), object(5)\n",
"memory usage: 11.8+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There isn't null value in this dataset."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> **Tip**: You should _not_ perform too many operations in each cell. Create cells freely to explore your data. One option that you can take with this project is to do a lot of explorations in an initial notebook. These don't have to be organized, but make sure you use enough comments to understand the purpose of each code cell. Then, after you're done with your analysis, create a duplicate notebook where you will trim the excess and organize your steps so that you have a flowing, cohesive report.\n",
"\n",
"> **Tip**: Make sure that you keep your reader informed on the steps that you are taking in your investigation. Follow every code cell, or every set of related code cells, with a markdown cell to describe to the reader what was found in the preceding cell(s). Try to make it so that the reader can then understand what they will be seeing in the following cell(s).\n",
"\n",
"### Remove duplicated elements\n",
"Check if there are any duplicated elements."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.duplicated().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is no duplicated raw so that we don't need to remove duplicated raw\n",
"### Fix the typo\n",
"Let's fix 'handcap' to 'handicap'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df.rename(columns={\"Handcap\": \"Handicap\"}, inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Change the data type\n",
"Change the current date-time type to only date type in order to extract only the date part of timestamps without timezone information"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df.AppointmentDay = pd.to_datetime(df.AppointmentDay).dt.date\n",
"df.ScheduledDay = pd.to_datetime(df.ScheduledDay).dt.date"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Subtract the appointment day to scheduled day, then add the difference of the two values into dataframe in order to anaylze that the difference affects No-show rates"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PatientId</th>\n",
" <th>AppointmentID</th>\n",
" <th>Gender</th>\n",
" <th>ScheduledDay</th>\n",
" <th>AppointmentDay</th>\n",
" <th>Age</th>\n",
" <th>Neighbourhood</th>\n",
" <th>Scholarship</th>\n",
" <th>Hipertension</th>\n",
" <th>Diabetes</th>\n",
" <th>Alcoholism</th>\n",
" <th>Handicap</th>\n",
" <th>SMS_received</th>\n",
" <th>No-show</th>\n",
" <th>PeriodBetween</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.987250e+13</td>\n",
" <td>5642903</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</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",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.589980e+14</td>\n",
" <td>5642503</td>\n",
" <td>M</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>56</td>\n",
" <td>JARDIM DA PENHA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.262960e+12</td>\n",
" <td>5642549</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>62</td>\n",
" <td>MATA DA PRAIA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8.679510e+11</td>\n",
" <td>5642828</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>8</td>\n",
" <td>PONTAL DE CAMBURI</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8.841190e+12</td>\n",
" <td>5642494</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>56</td>\n",
" <td>JARDIM DA PENHA</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PatientId AppointmentID Gender ScheduledDay AppointmentDay Age \\\n",
"0 2.987250e+13 5642903 F 2016-04-29 2016-04-29 62 \n",
"1 5.589980e+14 5642503 M 2016-04-29 2016-04-29 56 \n",
"2 4.262960e+12 5642549 F 2016-04-29 2016-04-29 62 \n",
"3 8.679510e+11 5642828 F 2016-04-29 2016-04-29 8 \n",
"4 8.841190e+12 5642494 F 2016-04-29 2016-04-29 56 \n",
"\n",
" Neighbourhood Scholarship Hipertension Diabetes Alcoholism \\\n",
"0 JARDIM DA PENHA 0 1 0 0 \n",
"1 JARDIM DA PENHA 0 0 0 0 \n",
"2 MATA DA PRAIA 0 0 0 0 \n",
"3 PONTAL DE CAMBURI 0 0 0 0 \n",
"4 JARDIM DA PENHA 0 1 1 0 \n",
"\n",
" Handicap SMS_received No-show PeriodBetween \n",
"0 0 0 No 0 \n",
"1 0 0 No 0 \n",
"2 0 0 No 0 \n",
"3 0 0 No 0 \n",
"4 0 0 No 0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['PeriodBetween'] = df.AppointmentDay - df.ScheduledDay\n",
"\n",
"# convert derived datetime to int\n",
"df['PeriodBetween'] = df['PeriodBetween'].dt.days\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Change 'No-show' column name to 'Show'. Also, change 'No' value to 1, 'yes' value to 0. (1 if the patient showed up to their appointment, and 0 if they didn't show up.)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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" <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",
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" <td>2.987250e+13</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <th>1</th>\n",
" <td>5.589980e+14</td>\n",
" <td>5642503</td>\n",
" <td>M</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>56</td>\n",
" <td>JARDIM DA PENHA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>4.262960e+12</td>\n",
" <td>5642549</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>62</td>\n",
" <td>MATA DA PRAIA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8.679510e+11</td>\n",
" <td>5642828</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>8</td>\n",
" <td>PONTAL DE CAMBURI</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8.841190e+12</td>\n",
" <td>5642494</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>56</td>\n",
" <td>JARDIM DA PENHA</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PatientId AppointmentID Gender ScheduledDay AppointmentDay Age \\\n",
"0 2.987250e+13 5642903 F 2016-04-29 2016-04-29 62 \n",
"1 5.589980e+14 5642503 M 2016-04-29 2016-04-29 56 \n",
"2 4.262960e+12 5642549 F 2016-04-29 2016-04-29 62 \n",
"3 8.679510e+11 5642828 F 2016-04-29 2016-04-29 8 \n",
"4 8.841190e+12 5642494 F 2016-04-29 2016-04-29 56 \n",
"\n",
" Neighbourhood Scholarship Hipertension Diabetes Alcoholism \\\n",
"0 JARDIM DA PENHA 0 1 0 0 \n",
"1 JARDIM DA PENHA 0 0 0 0 \n",
"2 MATA DA PRAIA 0 0 0 0 \n",
"3 PONTAL DE CAMBURI 0 0 0 0 \n",
"4 JARDIM DA PENHA 0 1 1 0 \n",
"\n",
" Handicap SMS_received Show PeriodBetween \n",
"0 0 0 1 0 \n",
"1 0 0 1 0 \n",
"2 0 0 1 0 \n",
"3 0 0 1 0 \n",
"4 0 0 1 0 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.rename(columns={\"No-show\": \"Show\"}, inplace=True)\n",
"df.replace({'Show': {'No': 1, 'Yes': 0}}, inplace = True)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Modify nonsense values\n",
"Check the detail of 'PeriodBetween' column. It has minimum value of -6, which means applintmemnt day is ahead of scheduled day. It is nonsense, so the negative 'PeriodBetween' value can be replaced by 0. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 110527.000000\n",
"mean 10.183702\n",
"std 15.254996\n",
"min -6.000000\n",
"25% 0.000000\n",
"50% 4.000000\n",
"75% 15.000000\n",
"max 179.000000\n",
"Name: PeriodBetween, dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.PeriodBetween.describe()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 110527.000000\n",
"mean 10.183792\n",
"std 15.254924\n",
"min 0.000000\n",
"25% 0.000000\n",
"50% 4.000000\n",
"75% 15.000000\n",
"max 179.000000\n",
"Name: PeriodBetween, dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['PeriodBetween'] < 0] = 0\n",
"df.PeriodBetween.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check if 'Age' column has any nonsense values. It has -1 value, which means age is -1. It can be replace by 0"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 110527.000000\n",
"mean 37.087363\n",
"std 23.110888\n",
"min -1.000000\n",
"25% 18.000000\n",
"50% 37.000000\n",
"75% 55.000000\n",
"max 115.000000\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.Age.describe()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 110527.000000\n",
"mean 37.087372\n",
"std 23.110873\n",
"min 0.000000\n",
"25% 18.000000\n",
"50% 37.000000\n",
"75% 55.000000\n",
"max 115.000000\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.replace({'Age': {-1: 0}}, inplace = True)\n",
"df.Age.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In 'Gender' column, 'F' means female and 'M' means male. But there is 0 value so we can remove it."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PatientId</th>\n",
" <th>AppointmentID</th>\n",
" <th>Age</th>\n",
" <th>Scholarship</th>\n",
" <th>Hipertension</th>\n",
" <th>Diabetes</th>\n",
" <th>Alcoholism</th>\n",
" <th>Handicap</th>\n",
" <th>SMS_received</th>\n",
" <th>Show</th>\n",
" <th>PeriodBetween</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Gender</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F</th>\n",
" <td>1.460053e+14</td>\n",
" <td>5.674337e+06</td>\n",
" <td>38.893927</td>\n",
" <td>0.123237</td>\n",
" <td>0.213525</td>\n",
" <td>0.078038</td>\n",
" <td>0.017025</td>\n",
" <td>0.019530</td>\n",
" <td>0.336902</td>\n",
" <td>0.796887</td>\n",
" <td>10.502248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M</th>\n",
" <td>1.502570e+14</td>\n",
" <td>5.677103e+06</td>\n",
" <td>33.737443</td>\n",
" <td>0.051906</td>\n",
" <td>0.167041</td>\n",
" <td>0.060411</td>\n",
" <td>0.055241</td>\n",
" <td>0.027246</td>\n",
" <td>0.291586</td>\n",
" <td>0.800362</td>\n",
" <td>9.593744</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PatientId AppointmentID Age Scholarship Hipertension \\\n",
"Gender \n",
"0 0.000000e+00 0.000000e+00 0.000000 0.000000 0.000000 \n",
"F 1.460053e+14 5.674337e+06 38.893927 0.123237 0.213525 \n",
"M 1.502570e+14 5.677103e+06 33.737443 0.051906 0.167041 \n",
"\n",
" Diabetes Alcoholism Handicap SMS_received Show PeriodBetween \n",
"Gender \n",
"0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"F 0.078038 0.017025 0.019530 0.336902 0.796887 10.502248 \n",
"M 0.060411 0.055241 0.027246 0.291586 0.800362 9.593744 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('Gender').mean()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 110522\n",
"unique 2\n",
"top F\n",
"freq 71837\n",
"Name: Gender, dtype: object"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"null_containing_rows_index = df[df.Gender == 0].index\n",
"df.drop(index = null_containing_rows_index, inplace = True)\n",
"df.Gender.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Segment PeriodBetween values into bins\n",
"The maximum value of the 'PeriodBetween' is 179 and minimum is 0. Variation of the 'PeriodBetween' values are so huge that we have to segment the values into bins like below"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"bins = pd.IntervalIndex.from_tuples([(-1, 1), (1, 5), (5, 10), (10, 200)])\n",
"labels=[\"rightNow\", \"fewDaysAgo\", \"severalDaysAgo\", \"longAgo\"]\n",
"df['PeriodBetween'] = pd.cut(df['PeriodBetween'], bins=bins, labels=labels)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\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>Handicap</th>\n",
" <th>SMS_received</th>\n",
" <th>Show</th>\n",
" <th>PeriodBetween</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.987250e+13</td>\n",
" <td>5642903</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</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>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.589980e+14</td>\n",
" <td>5642503</td>\n",
" <td>M</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>56</td>\n",
" <td>JARDIM DA PENHA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.262960e+12</td>\n",
" <td>5642549</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>62</td>\n",
" <td>MATA DA PRAIA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8.679510e+11</td>\n",
" <td>5642828</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>8</td>\n",
" <td>PONTAL DE CAMBURI</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8.841190e+12</td>\n",
" <td>5642494</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>56</td>\n",
" <td>JARDIM DA PENHA</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PatientId AppointmentID Gender ScheduledDay AppointmentDay Age \\\n",
"0 2.987250e+13 5642903 F 2016-04-29 2016-04-29 62 \n",
"1 5.589980e+14 5642503 M 2016-04-29 2016-04-29 56 \n",
"2 4.262960e+12 5642549 F 2016-04-29 2016-04-29 62 \n",
"3 8.679510e+11 5642828 F 2016-04-29 2016-04-29 8 \n",
"4 8.841190e+12 5642494 F 2016-04-29 2016-04-29 56 \n",
"\n",
" Neighbourhood Scholarship Hipertension Diabetes Alcoholism \\\n",
"0 JARDIM DA PENHA 0 1 0 0 \n",
"1 JARDIM DA PENHA 0 0 0 0 \n",
"2 MATA DA PRAIA 0 0 0 0 \n",
"3 PONTAL DE CAMBURI 0 0 0 0 \n",
"4 JARDIM DA PENHA 0 1 1 0 \n",
"\n",
" Handicap SMS_received Show PeriodBetween \n",
"0 0 0 1 (-1, 1] \n",
"1 0 0 1 (-1, 1] \n",
"2 0 0 1 (-1, 1] \n",
"3 0 0 1 (-1, 1] \n",
"4 0 0 1 (-1, 1] "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Segment Age values into bins"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 110522.00000\n",
"mean 37.08905\n",
"std 23.11005\n",
"min 0.00000\n",
"25% 18.00000\n",
"50% 37.00000\n",
"75% 55.00000\n",
"max 115.00000\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.Age.describe()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"bins = pd.IntervalIndex.from_tuples([(0, 18), (18, 37), (37, 55), (55, 115)])\n",
"\n",
"df['Age'] = pd.cut(df['Age'], bins)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PatientId</th>\n",
" <th>AppointmentID</th>\n",
" <th>Gender</th>\n",
" <th>ScheduledDay</th>\n",
" <th>AppointmentDay</th>\n",
" <th>Age</th>\n",
" <th>Neighbourhood</th>\n",
" <th>Scholarship</th>\n",
" <th>Hipertension</th>\n",
" <th>Diabetes</th>\n",
" <th>Alcoholism</th>\n",
" <th>Handicap</th>\n",
" <th>SMS_received</th>\n",
" <th>Show</th>\n",
" <th>PeriodBetween</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.987250e+13</td>\n",
" <td>5642903</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>(55, 115]</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>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.589980e+14</td>\n",
" <td>5642503</td>\n",
" <td>M</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>(55, 115]</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>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.262960e+12</td>\n",
" <td>5642549</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>(55, 115]</td>\n",
" <td>MATA DA PRAIA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8.679510e+11</td>\n",
" <td>5642828</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>(0, 18]</td>\n",
" <td>PONTAL DE CAMBURI</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8.841190e+12</td>\n",
" <td>5642494</td>\n",
" <td>F</td>\n",
" <td>2016-04-29</td>\n",
" <td>2016-04-29</td>\n",
" <td>(55, 115]</td>\n",
" <td>JARDIM DA PENHA</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PatientId AppointmentID Gender ScheduledDay AppointmentDay Age \\\n",
"0 2.987250e+13 5642903 F 2016-04-29 2016-04-29 (55, 115] \n",
"1 5.589980e+14 5642503 M 2016-04-29 2016-04-29 (55, 115] \n",
"2 4.262960e+12 5642549 F 2016-04-29 2016-04-29 (55, 115] \n",
"3 8.679510e+11 5642828 F 2016-04-29 2016-04-29 (0, 18] \n",
"4 8.841190e+12 5642494 F 2016-04-29 2016-04-29 (55, 115] \n",
"\n",
" Neighbourhood Scholarship Hipertension Diabetes Alcoholism \\\n",
"0 JARDIM DA PENHA 0 1 0 0 \n",
"1 JARDIM DA PENHA 0 0 0 0 \n",
"2 MATA DA PRAIA 0 0 0 0 \n",
"3 PONTAL DE CAMBURI 0 0 0 0 \n",
"4 JARDIM DA PENHA 0 1 1 0 \n",
"\n",
" Handicap SMS_received Show PeriodBetween \n",
"0 0 0 1 (-1, 1] \n",
"1 0 0 1 (-1, 1] \n",
"2 0 0 1 (-1, 1] \n",
"3 0 0 1 (-1, 1] \n",
"4 0 0 1 (-1, 1] "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Remove unnecessary columns\n",
"We don't need 'Neighbourhood', 'ScheduledDay', 'AppointmentDay' columns for the analsis. We've got the 'PeriodBetween' column from the 'ScheduledDay' and 'ApplintmentDay'. So we only need the 'PeriodBetween' column. 'Neighbourhood' is not necessary for us to get some information."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PatientId</th>\n",
" <th>AppointmentID</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Scholarship</th>\n",
" <th>Hipertension</th>\n",
" <th>Diabetes</th>\n",
" <th>Alcoholism</th>\n",
" <th>Handicap</th>\n",
" <th>SMS_received</th>\n",
" <th>Show</th>\n",
" <th>PeriodBetween</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.987250e+13</td>\n",
" <td>5642903</td>\n",
" <td>F</td>\n",
" <td>(55, 115]</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>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.589980e+14</td>\n",
" <td>5642503</td>\n",
" <td>M</td>\n",
" <td>(55, 115]</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.262960e+12</td>\n",
" <td>5642549</td>\n",
" <td>F</td>\n",
" <td>(55, 115]</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8.679510e+11</td>\n",
" <td>5642828</td>\n",
" <td>F</td>\n",
" <td>(0, 18]</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8.841190e+12</td>\n",
" <td>5642494</td>\n",
" <td>F</td>\n",
" <td>(55, 115]</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>(-1, 1]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PatientId AppointmentID Gender Age Scholarship Hipertension \\\n",
"0 2.987250e+13 5642903 F (55, 115] 0 1 \n",
"1 5.589980e+14 5642503 M (55, 115] 0 0 \n",
"2 4.262960e+12 5642549 F (55, 115] 0 0 \n",
"3 8.679510e+11 5642828 F (0, 18] 0 0 \n",
"4 8.841190e+12 5642494 F (55, 115] 0 1 \n",
"\n",
" Diabetes Alcoholism Handicap SMS_received Show PeriodBetween \n",
"0 0 0 0 0 1 (-1, 1] \n",
"1 0 0 0 0 1 (-1, 1] \n",
"2 0 0 0 0 1 (-1, 1] \n",
"3 0 0 0 0 1 (-1, 1] \n",
"4 1 0 0 0 1 (-1, 1] "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.drop(columns=['Neighbourhood', 'ScheduledDay', 'AppointmentDay'], inplace=True)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='eda'></a>\n",
"## Exploratory Data Analysis\n",
"\n",
"\n",
"### Question 1 - Does the difference bewtween Appointment day and Scheduled day affect No-show rate?\n",
"Groupby 'PeriodBetween' columns to check if it affects the rate of No-show"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodBetween\n",
"(-1, 1] 0.933639\n",
"(1, 5] 0.758778\n",
"(5, 10] 0.729521\n",
"(10, 200] 0.674853\n",
"Name: Show, dtype: float64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_PeriodBetween = df.groupby('PeriodBetween').mean()['Show']\n",
"df_PeriodBetween.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Patients scheduled in the day of appointment or the day before appointment are more likely to show up than patients scheduled long ago. To visualize, we can plot the bar chart like below. Also, we can make a function for plotting."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def draw_plot(locations, heights, labels, chartTitle, chartXlabel, chartYlabel):\n",
" plt.bar(locations, heights, tick_label=labels)\n",
" plt.title(chartTitle)\n",
" plt.xlabel(chartXlabel)\n",
" plt.ylabel(chartYlabel);"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7a01e450f0>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"locations = [1, 2, 3, 4]\n",
"heights = df_PeriodBetween\n",
"labels=[\"rightNow\", \"fewDaysAgo\", \"severalDaysAgo\", \"longAgo\"]\n",
"chartTitle = 'Probability of showing up'\n",
"chartXlabel = 'PeriodBetween'\n",
"chartYlabel = 'Show Rate'\n",
"\n",
"draw_plot(locations, heights, labels, chartTitle, chartXlabel, chartYlabel);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The shorter the 'PeriodBetween', the more likely to show up.\n",
"\n",
"### Question 2 - Does the gender affect No-show rate?\n",
"Similar to above, check if there is a different showing rate by gender."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Gender\n",
"F 0.796887\n",
"M 0.800362\n",
"Name: Show, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_gender = df.groupby('Gender').mean()['Show']\n",
"df_gender"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7a02c3cc88>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"locations = [1, 2]\n",
"heights = df_gender\n",
"labels = [\"Female\", \"Male\"]\n",
"chartXlabel = 'Gender'\n",
"\n",
"draw_plot(locations, heights, labels, chartTitle, chartXlabel, chartYlabel);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It has almost the same rate, so we can conclude gender cannot affect the showing rate."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3 - Does the age affect No-show rate?\n",
"Similar to above, check if there is a different showing rate by age."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Age\n",
"(0, 18] 0.774658\n",
"(18, 37] 0.764680\n",
"(37, 55] 0.804720\n",
"(55, 115] 0.843581\n",
"Name: Show, dtype: float64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_Age = df.groupby('Age').mean()['Show']\n",
"df_Age"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7a016f9128>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"locations = [1, 2, 3, 4]\n",
"heights = df_Age\n",
"labels = [\"very young\", \"young\", \"old\", \"very old\"]\n",
"chartXlabel = 'Age'\n",
"\n",
"draw_plot(locations, heights, labels, chartTitle, chartXlabel, chartYlabel);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The older the patient are, the more likely to show up.\n",
"### Question 4 - Does the scholarship affect No-show rate?"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Scholarship\n",
"0 0.801969\n",
"1 0.762637\n",
"Name: Show, dtype: float64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_scholarship = df.groupby('Scholarship').mean()['Show']\n",
"df_scholarship"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7a02718128>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"locations = [1, 2]\n",
"heights = df_scholarship\n",
"labels = [\"don't get a Bolsa Família\", \"get a Bolsa Família\"]\n",
"chartXlabel = 'Sholarship'\n",
"\n",
"draw_plot(locations, heights, labels, chartTitle, chartXlabel, chartYlabel);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The patients who didn't get a sholarship (also known as Bolsa Familia) are a little more likely to make an appointment.\n",
"### Question 5 - Does the disease affect No-show rate?\n",
"Let Disease be the sum of Hipertension, Diabetes and Alcoholism."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"df['Disease'] = df['Hipertension'] + df['Diabetes'] + df['Alcoholism']"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Disease\n",
"0 0.790955\n",
"1 0.822034\n",
"2 0.822963\n",
"3 0.820312\n",
"Name: Show, dtype: float64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_disease = df.groupby('Disease').mean()['Show']\n",
"df_disease"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7a02710668>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"locations = [1, 2, 3, 4]\n",
"heights = df_disease\n",
"labels = [0, 1, 2, 3]\n",
"chartXlabel = 'The Number of Disease'\n",
"\n",
"draw_plot(locations, heights, labels, chartTitle, chartXlabel, chartYlabel);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The more diseases the patients have, the more likely to show up.\n",
"### Question 6 - Does the handicap affect No-show rate?\n",
"Let's do the same way."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Handicap\n",
"0 0.797669\n",
"1 0.821569\n",
"2 0.797814\n",
"3 0.769231\n",
"4 0.666667\n",
"Name: Show, dtype: float64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_handicap = df.groupby('Handicap').mean()['Show']\n",
"df_handicap"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Handicap\n",
"0 0.797669\n",
"1 0.821569\n",
"2 0.797814\n",
"3 0.769231\n",
"4 0.666667\n",
"Name: Show, dtype: float64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('Handicap').mean()['Show']"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7a026bd8d0>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"locations = [1, 2, 3, 4, 5]\n",
"heights = df_handicap\n",
"labels = [0, 1, 2, 3, 4]\n",
"chartXlabel = 'Severity of Handicap'\n",
"\n",
"draw_plot(locations, heights, labels, chartTitle, chartXlabel, chartYlabel);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Showing rate of 0 handicap is less than that of 1 handicap. So, we cannot definitely conclude that the higher severity of handicap, the less likely to show up."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='conclusions'></a>\n",
"## Conclusions\n",
"\n",
"I've analyzed 6 dependent variables (one for 2D, five for 1D) that affect No-show rate which is a independent variable. The higher or more 'Age', 'Number of disease', the more likely to show up. The less 'PeriodBetween', 'Scholarship', the more likely to show up. I'm not sure 'Gender' and 'Handicap' are the factors that affect the showing rate. I didn't use any statistics in this analysis so that I can't say it was statistical significant. So, I could say 'more likely' not 'statistical significant'. The purpose of this analysis is to see the factors that seem to affect the No-show rate."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from subprocess import call\n",
"call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
]
}
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https://nbviewer.jupyter.org/gist/BaekKyunShin/399c16ed6f2284763e9256e9c9db46af

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