{ "cells": [ { "cell_type": "code", "execution_count": 6, "id": "65bdc4ab", "metadata": { "id": "65bdc4ab" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "#import pyforest" ] }, { "cell_type": "markdown", "id": "7400f98e", "metadata": { "id": "7400f98e" }, "source": [ "Reading the dataset." ] }, { "cell_type": "code", "execution_count": 7, "id": "612f49cf", "metadata": { "scrolled": true, "colab": { "base_uri": "https://localhost:8080/", "height": 488 }, "id": "612f49cf", "outputId": "df05dc35-d855-46ea-da27-a55d547e49f1" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Column Description\n", "0 MemberID Member ID\n", "1 AppointmentID Appointment ID\n", "2 Gender M = Male, F = Female\n", "3 ScheduledDay Date the appointment was scheduled\n", "4 AppointmentDay Actual appointment date\n", "5 Age Age\n", "6 LocationID Patient Geography ID\n", "7 MedicaidIND 1 = Medicaid patient, 0 = Non-Medicaid patient\n", "8 Hypertension Hypertension indicator 1 = Yes, 0 = No\n", "9 Diabetes Diabetes indicator 1 = Yes, 0 = No\n", "10 Alcoholism Alcoholism indicator 1 = Yes, 0 = No\n", "11 Disability Disability indicator 1 = Yes, 0 = No\n", "12 SMS_received Text was sent to patient as an appointment rem...\n", "13 No-show Yes = Did not attend the appointment, No = App..." ], "text/html": [ "\n", "
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ColumnDescription
0MemberIDMember ID
1AppointmentIDAppointment ID
2GenderM = Male, F = Female
3ScheduledDayDate the appointment was scheduled
4AppointmentDayActual appointment date
5AgeAge
6LocationIDPatient Geography ID
7MedicaidIND1 = Medicaid patient, 0 = Non-Medicaid patient
8HypertensionHypertension indicator 1 = Yes, 0 = No
9DiabetesDiabetes indicator 1 = Yes, 0 = No
10AlcoholismAlcoholism indicator 1 = Yes, 0 = No
11DisabilityDisability indicator 1 = Yes, 0 = No
12SMS_receivedText was sent to patient as an appointment rem...
13No-showYes = Did not attend the appointment, No = App...
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PatientIDAppointmentIDGenderScheduledDayAppointmentDayAgeLocationIDMedicaidINDHypertensionDiabetesAlcoholismDisabilitySMS_receivedNo-show
0#298724998242965642903F2016-04-29T18:38:08Z2016-04-29T00:00:00Z6240010000No
1#5589977766944385642503M2016-04-29T16:08:27Z2016-04-29T00:00:00Z5640000000No
2#42629622999515642549F2016-04-29T16:19:04Z2016-04-29T00:00:00Z6247000000No
3#8679512131745642828F2016-04-29T17:29:31Z2016-04-29T00:00:00Z855000000No
4#88411864481835642494F2016-04-29T16:07:23Z2016-04-29T00:00:00Z5640011000No
.............................................
110522#25721343692935651768F2016-05-03T09:15:35Z2016-06-07T00:00:00Z5645000001No
110523#35962663287355650093F2016-05-03T07:27:33Z2016-06-07T00:00:00Z5145000001No
110524#155766317298935630692F2016-04-27T16:03:52Z2016-06-07T00:00:00Z2145000001No
110525#921349314355575630323F2016-04-27T15:09:23Z2016-06-07T00:00:00Z3845000001No
110526#3775115181211275629448F2016-04-27T13:30:56Z2016-06-07T00:00:00Z5445000001No
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110527 rows × 14 columns

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\n", " " ] }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "markdown", "id": "d1f9132f", "metadata": { "id": "d1f9132f" }, "source": [ "# Processing" ] }, { "cell_type": "markdown", "id": "e2866800", "metadata": { "id": "e2866800" }, "source": [ "Making Some Columns Easier to Use.\n", "Basically making dummy variables so I dont have to later on." ] }, { "cell_type": "code", "execution_count": 9, "id": "fdc76fec", "metadata": { "id": "fdc76fec" }, "outputs": [], "source": [ "df['Showed_up'] = df['No-show'].map(\n", " {'Yes':0 ,'No':1})\n", "\n", "df['sum_missed'] = df['No-show'].map(\n", " {'Yes':1 ,'No':0})\n", "\n", "df['Gender'] = df['Gender'].map(\n", " {'M':1 ,'F':0})" ] }, { "cell_type": "markdown", "id": "c5144772", "metadata": { "id": "c5144772" }, "source": [ "Grouping Data set by PatientID to make a new column of the number of missed appointments by each patient and checking the correlation with the missing an appointment." ] }, { "cell_type": "code", "execution_count": 10, "id": "ace6d3a5", "metadata": { "scrolled": true, "colab": { "base_uri": "https://localhost:8080/" }, "id": "ace6d3a5", "outputId": "66bc93ef-430d-4a64-e0df-d2481f1a94a3" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "-0.4625076926719454" ] }, "metadata": {}, "execution_count": 10 } ], "source": [ "missed_prior = df.groupby('PatientID')['sum_missed'].sum()\n", "df.drop(['sum_missed'], axis=1, inplace=True)\n", "df.drop(['No-show'], axis=1, inplace=True)\n", "missed_prior = pd.DataFrame(missed_prior)\n", "df = pd.merge(df, missed_prior, on=\"PatientID\")\n", "df['sum_missed'].corr(df['Showed_up'])" ] }, { "cell_type": "markdown", "id": "6b71645b", "metadata": { "id": "6b71645b" }, "source": [ "Grouping Data set by PatientID to make a new column showing if patient has missed before." ] }, { "cell_type": "code", "execution_count": 11, "id": "391a10be", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "391a10be", "outputId": "9035c4f1-a10f-4d8b-df65-25991e6c0caa" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0.6098312772100071" ] }, "metadata": {}, "execution_count": 11 } ], "source": [ "missed_appointment = df.groupby('PatientID')['Showed_up'].sum()\n", "missed_appointment = missed_appointment.to_dict()\n", "df['missed_appointment_before'] = df.PatientID.map(lambda x: 1 if missed_appointment[x]>0 else 0)\n", "df['Showed_up'].corr(df['missed_appointment_before'])" ] }, { "cell_type": "markdown", "id": "9c8d97de", "metadata": { "id": "9c8d97de" }, "source": [ "Extracting different time variables for further analysis later" ] }, { "cell_type": "code", "execution_count": 12, "id": "88aa9246", "metadata": { "id": "88aa9246" }, "outputs": [], "source": [ "import datetime\n", "df[\"ScheduledDayofweek\"] = pd.to_datetime(df['ScheduledDay']).dt.day_name()\n", "df[\"Scheduledmonth\"] = pd.to_datetime(df['ScheduledDay']).dt.month_name()\n", "df[\"Scheduledhour\"] = pd.to_datetime(df['ScheduledDay']).dt.hour\n", "\n", "df[\"AppointmentDayofweek\"] = pd.to_datetime(df['AppointmentDay']).dt.day_name()\n", "df[\"Appointmentmonth\"] = pd.to_datetime(df['AppointmentDay']).dt.month_name()" ] }, { "cell_type": "markdown", "id": "c00d6cbe", "metadata": { "id": "c00d6cbe" }, "source": [ "Dropping unwanted columns" ] }, { "cell_type": "code", "execution_count": 13, "id": "4116cf4b", "metadata": { "id": "4116cf4b" }, "outputs": [], "source": [ "df.drop([\"PatientID\", \"AppointmentID\",'ScheduledDay','AppointmentDay', \"LocationID\"], axis=1, inplace=True)\n", "# \"Gender\"" ] }, { "cell_type": "code", "execution_count": 14, "id": "ca94bc61", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ca94bc61", "outputId": "38708baa-95a0-451d-c99b-fb4daefc3b39" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Index(['Gender', 'Age', 'MedicaidIND', 'Hypertension', 'Diabetes',\n", " 'Alcoholism', 'Disability', 'SMS_received', 'Showed_up', 'sum_missed',\n", " 'missed_appointment_before', 'ScheduledDayofweek', 'Scheduledmonth',\n", " 'Scheduledhour', 'AppointmentDayofweek', 'Appointmentmonth'],\n", " dtype='object')" ] }, "metadata": {}, "execution_count": 14 } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "id": "baf12a94", "metadata": { "id": "baf12a94" }, "source": [ "On an average most people do show up. Im sensing the dataset must be imbalanced so Ill need handle this prior to modeling.\n", "We have someone/people who've missed 18 times. That's quite unfortunate for the doctors." ] }, { "cell_type": "code", "execution_count": 15, "id": "38b3e962", "metadata": { "scrolled": true, "colab": { "base_uri": "https://localhost:8080/", "height": 364 }, "id": "38b3e962", "outputId": "ab6d6ffe-dae3-44b8-93c9-475f278be634" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Gender Age MedicaidIND Hypertension \\\n", "count 110527.000000 110527.000000 110527.000000 110527.000000 \n", "mean 0.350023 37.088874 0.098266 0.197246 \n", "std 0.476979 23.110205 0.297675 0.397921 \n", "min 0.000000 -1.000000 0.000000 0.000000 \n", "25% 0.000000 18.000000 0.000000 0.000000 \n", "50% 0.000000 37.000000 0.000000 0.000000 \n", "75% 1.000000 55.000000 0.000000 0.000000 \n", "max 1.000000 115.000000 1.000000 1.000000 \n", "\n", " Diabetes Alcoholism Disability SMS_received \\\n", "count 110527.000000 110527.000000 110527.000000 110527.000000 \n", "mean 0.071865 0.030400 0.022248 0.321026 \n", "std 0.258265 0.171686 0.161543 0.466873 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 1.000000 \n", "max 1.000000 1.000000 4.000000 1.000000 \n", "\n", " Showed_up sum_missed missed_appointment_before Scheduledhour \n", "count 110527.000000 110527.000000 110527.000000 110527.000000 \n", "mean 0.798067 0.632796 0.913994 10.774517 \n", "std 0.401444 1.145807 0.280374 3.216189 \n", "min 0.000000 0.000000 0.000000 6.000000 \n", "25% 1.000000 0.000000 1.000000 8.000000 \n", "50% 1.000000 0.000000 1.000000 10.000000 \n", "75% 1.000000 1.000000 1.000000 13.000000 \n", "max 1.000000 18.000000 1.000000 21.000000 " ], "text/html": [ "\n", "
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GenderAgeMedicaidINDHypertensionDiabetesAlcoholismDisabilitySMS_receivedShowed_upsum_missedmissed_appointment_beforeScheduledhour
count110527.000000110527.000000110527.000000110527.000000110527.000000110527.000000110527.000000110527.000000110527.000000110527.000000110527.000000110527.000000
mean0.35002337.0888740.0982660.1972460.0718650.0304000.0222480.3210260.7980670.6327960.91399410.774517
std0.47697923.1102050.2976750.3979210.2582650.1716860.1615430.4668730.4014441.1458070.2803743.216189
min0.000000-1.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000006.000000
25%0.00000018.0000000.0000000.0000000.0000000.0000000.0000000.0000001.0000000.0000001.0000008.000000
50%0.00000037.0000000.0000000.0000000.0000000.0000000.0000000.0000001.0000000.0000001.00000010.000000
75%1.00000055.0000000.0000000.0000000.0000000.0000000.0000001.0000001.0000001.0000001.00000013.000000
max1.000000115.0000001.0000001.0000001.0000001.0000004.0000001.0000001.00000018.0000001.00000021.000000
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 GenderAgeMedicaidINDHypertensionDiabetesAlcoholismDisabilitySMS_receivedShowed_upsum_missedmissed_appointment_beforeScheduledhour
Gender1.000000-0.089898-0.122643-0.052455-0.0317500.1076070.024807-0.0468770.0061720.006974-0.0141440.000063
Age-0.0898981.000000-0.1227600.5010150.2909500.0856280.074033-0.0382510.106571-0.0881410.081525-0.019296
MedicaidIND-0.122643-0.1227601.000000-0.033131-0.0333250.030996-0.013361-0.016568-0.0139150.039385-0.000075-0.036376
Hypertension-0.0524550.501015-0.0331311.0000000.4274910.0820010.077579-0.0387130.063945-0.0533960.052116-0.052622
Diabetes-0.0317500.290950-0.0333250.4274911.0000000.0145160.055605-0.0370400.032903-0.0297650.028443-0.028557
Alcoholism0.1076070.0856280.0309960.0820010.0145161.0000000.000701-0.0383600.0092100.0070600.008809-0.015566
Disability0.0248070.074033-0.0133610.0775790.0556050.0007011.000000-0.0373800.0195830.0046450.019667-0.007222
SMS_received-0.046877-0.038251-0.016568-0.038713-0.037040-0.038360-0.0373801.000000-0.090447-0.011530-0.0591790.024449
Showed_up0.0061720.106571-0.0139150.0639450.0329030.0092100.019583-0.0904471.000000-0.4548920.604063-0.029399
sum_missed0.006974-0.0881410.039385-0.053396-0.0297650.0070600.004645-0.011530-0.4548921.000000-0.2090660.044835
missed_appointment_before-0.0141440.081525-0.0000750.0521160.0284430.0088090.019667-0.0591790.604063-0.2090661.000000-0.014312
Scheduledhour0.000063-0.019296-0.036376-0.052622-0.028557-0.015566-0.0072220.024449-0.0293990.044835-0.0143121.000000
\n" ] }, "metadata": {}, "execution_count": 17 } ], "source": [ "corr = df.corr()\n", "corr.style.background_gradient(cmap='Purples')" ] }, { "cell_type": "code", "execution_count": 18, "id": "90be347c", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 239 }, "id": "90be347c", "outputId": "a372fc3e-9767-4810-904f-ddac17deac88" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Gender Age MedicaidIND Hypertension Diabetes Alcoholism Disability \\\n", "count 91402 91402 91402 91402 91402 91402 91402 \n", "unique 1 1 1 1 1 1 1 \n", "top False False False False False False False \n", "freq 91402 91402 91402 91402 91402 91402 91402 \n", "\n", " SMS_received Showed_up sum_missed missed_appointment_before \\\n", "count 91402 91402 91402 91402 \n", "unique 1 1 1 1 \n", "top False False False False \n", "freq 91402 91402 91402 91402 \n", "\n", " ScheduledDayofweek Scheduledmonth Scheduledhour AppointmentDayofweek \\\n", "count 91402 91402 91402 91402 \n", "unique 1 1 1 1 \n", "top False False False False \n", "freq 91402 91402 91402 91402 \n", "\n", " Appointmentmonth \n", "count 91402 \n", "unique 1 \n", "top False \n", "freq 91402 " ], "text/html": [ "\n", "
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GenderAgeMedicaidINDHypertensionDiabetesAlcoholismDisabilitySMS_receivedShowed_upsum_missedmissed_appointment_beforeScheduledDayofweekScheduledmonthScheduledhourAppointmentDayofweekAppointmentmonth
count91402914029140291402914029140291402914029140291402914029140291402914029140291402
unique1111111111111111
topFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
freq91402914029140291402914029140291402914029140291402914029140291402914029140291402
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\n", " " ] }, "metadata": {}, "execution_count": 18 } ], "source": [ "df.isna().describe()" ] }, { "cell_type": "code", "execution_count": 19, "id": "7f5d3fe1", "metadata": { "scrolled": true, "colab": { "base_uri": "https://localhost:8080/" }, "id": "7f5d3fe1", "outputId": "f3f16814-20e8-43cf-a3f6-95fef665ac3a" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "count 91402\n", "unique 1\n", "top False\n", "freq 91402\n", "dtype: object" ] }, "metadata": {}, "execution_count": 19 } ], "source": [ "df.duplicated().describe()" ] }, { "cell_type": "markdown", "id": "be26bad3", "metadata": { "id": "be26bad3" }, "source": [ "## EDA" ] }, { "cell_type": "code", "execution_count": 21, "id": "064de5a6", "metadata": { "scrolled": false, "id": "064de5a6" }, "outputs": [], "source": [ "# #Creates automated visualizations \n", "# %matplotlib inline\n", "# from autovizwidget.widget.utils import display_dataframe\n", "\n", "# display_dataframe(df)" ] }, { "cell_type": "markdown", "id": "81432584", "metadata": { "id": "81432584" }, "source": [ "Exporting Automated Visuals to html" ] }, { "cell_type": "code", "execution_count": 22, "id": "b0739480", "metadata": { "id": "b0739480" }, "outputs": [], "source": [ "#from pandas_profiling import ProfileReport\n", "#design_report = ProfileReport(df)\n", "#design_report.to_file(output_file='no_showreport.html')\n", "#design_report" ] }, { "cell_type": "code", "execution_count": 23, "id": "792debc7", "metadata": { "scrolled": true, "id": "792debc7", "outputId": "ddf0dc75-cff8-44b5-d698-cf2e5b25954d", "colab": { "base_uri": "https://localhost:8080/", "height": 279 } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 23 }, { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": { "needs_background": "light" } } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set_palette(\"RdBu\")\n", "\n", "df[\"Showed_up\"].value_counts().plot(kind='bar')\n", "# Showed = 1\n", "# Missed = 0" ] }, { "cell_type": "markdown", "id": "211ac0e5", "metadata": { "id": "211ac0e5" }, "source": [ "Gender doesnt seem to have much visible effect." ] }, { "cell_type": "code", "execution_count": 24, "id": "9bfbe8b1", "metadata": { "id": "9bfbe8b1", "outputId": "d237dd79-f9d3-4990-9d5c-e64efea61bcd", "colab": { "base_uri": "https://localhost:8080/", "height": 279 } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "ax = sns.countplot(x=\"Gender\", hue=\"Showed_up\", data=df)\n", "# Men = 1\n", "# Women = 0" ] }, { "cell_type": "markdown", "id": "7ce67ac7", "metadata": { "id": "7ce67ac7" }, "source": [ "Sending texts seems to help only a little. Perhaps switching to calls would be better." ] }, { "cell_type": "code", "execution_count": 25, "id": "75d987ff", "metadata": { "id": "75d987ff", "outputId": "7f6d7ffa-49a8-4f15-b297-2790eb53ad6c", "colab": { "base_uri": "https://localhost:8080/", "height": 280 } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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ct2iPiLguIlZGxNKIGF21rfOL/isi4vyq9iMj4tlineuKNwxLkuqkx2/xzczlmXlD8Vneg1W2AP8lM0dSeW38pRExErgCeCwzRwCPFfMAJwMjis/FwE2w9dTZNGAscDQwrerU2k3A31StN6mnxyNJ2n7v+lXwPZWZ6zLz6WL6X4HngSFUXtw4q+g2Czi9mJ4MzC5eNf9TYJ+IOIDKK+fnZ+am4uL+fGBSsWzvzPxpZiYwu2pbkqQ6qFmIVIuIYVTu5voZsH9mrisW/Q7Yv5geAqypWq21aNtWe2sX7V3t/+KIaImIlg0bNmzXsUiS/qTmIRIRewH3An+Xma9ULytGENnlijtQZs7MzDGZOWbQIN/WIkk7Sk1DJCJ2pRIg38vMHxTNvy9ORVH8XF+0rwWGVq3eXLRtq725i3ZJUp3ULESKO6VuBZ7PzH+sWvQA0HGH1fnA/VXt5xV3aY0D/lCc9noEOCki9i0uqJ8EPFIseyUixhX7Oq9qW5KkOqjlA4PHAlOAZyPimaLtvwFfA+6OiKnAb6i8kwvgQeAUYCXwb8BnofJ0fER8GVhc9LumeGIe4HPA7cAewEPFR5JUJzULkcx8EujuuY2PddE/gUu72dZtwG1dtLcAf7YdZUqStkNd7s6SJL03GSKSpNIMEUlSaYaIJKk0Q0SSVJohIkkqzRCRJJVmiEiSSjNEJEmlGSKSpNIMEUlSaYaIJKk0Q0SSVJohIkkqzRCRJJVmiEiSSjNEJEmlGSKSpNIMEUlSaYaIJKk0Q0SSVJohIkkqzRCRJJVmiEiSSjNEJEmlGSKSpNIMEUlSaYaIJKk0Q0SSVJohIkkqzRCRJJVmiEiSSuvf6AJ6m/smfaLRJew0Rn/6LxpdgqQGcyQiSSrNEJEklWaISJJKq1mIRMRtEbE+Ip6ratsvIuZHxIri575Fe0TEdRGxMiKWRsToqnXOL/qviIjzq9qPjIhni3Wui4io1bFIkrpWy5HI7cCkTm1XAI9l5gjgsWIe4GRgRPG5GLgJKqEDTAPGAkcD0zqCp+jzN1Xrdd6XJKnGahYimbkQ2NSpeTIwq5ieBZxe1T47K34K7BMRBwATgfmZuSkzXwLmA5OKZXtn5k8zM4HZVduSJNVJva+J7J+Z64rp3wH7F9NDgDVV/VqLtm21t3bRLkmqo4ZdWC9GEFmPfUXExRHREhEtGzZsqMcuJalPqPfDhr+PiAMyc11xSmp90b4WGFrVr7loWwtM6NS+oGhv7qJ/lzJzJjATYMyYMXUJLqkRfBj2T3wYtj7qPRJ5AOi4w+p84P6q9vOKu7TGAX8oTns9ApwUEfsWF9RPAh4plr0SEeOKu7LOq9qWJKlOajYSiYg7qYwimiKilcpdVl8D7o6IqcBvgL8uuj8InAKsBP4N+CxAZm6KiC8Di4t+12Rmx8X6z1G5A2wP4KHiI0mqo5qFSGae282ij3XRN4FLu9nObcBtXbS3AH+2PTVKkraPT6xLkkozRCRJpRkikqTSDBFJUmmGiCSpNENEklSaISJJKs0QkSSVZohIkkozRCRJpRkikqTSDBFJUmmGiCSpNENEklSaISJJKs0QkSSVZohIkkozRCRJpRkikqTSDBFJUmmGiCSpNENEklSaISJJKs0QkSSVZohIkkozRCRJpRkikqTSDBFJUmmGiCSpNENEklSaISJJKs0QkSSVZohIkkozRCRJpRkikqTSDBFJUmmGiCSptF4fIhExKSJeiIiVEXFFo+uRpL6kV4dIRPQDbgROBkYC50bEyMZWJUl9R68OEeBoYGVm/jozXwfmAJMbXJMk9RmRmY2uobSIOAuYlJkXFfNTgLGZeVmnfhcDFxezhwAv1LXQ964mYGOji5C64b/PHedDmTmoqwX9611JI2TmTGBmo+t4r4mIlswc0+g6pK7477M+evvprLXA0Kr55qJNklQHvT1EFgMjImJ4ROwGnAM80OCaJKnP6NWnszJzS0RcBjwC9ANuy8xlDS6rL/EUoXZm/vusg159YV2S1Fi9/XSWJKmBDBFJUmmGiErxdTPaWUXEbRGxPiKea3QtfYEhonfN181oJ3c7MKnRRfQVhojK8HUz2mll5kJgU6Pr6CsMEZUxBFhTNd9atEnqYwwRSVJphojK8HUzkgBDROX4uhlJgCGiEjJzC9Dxupnngbt93Yx2FhFxJ/AUcEhEtEbE1EbX9F7ma08kSaU5EpEklWaISJJKM0QkSaUZIpKk0gwRSVJphogkqTRDRAIi4u8jYllELI2IZyJibEQsiIgXIyKq+t0XEa8W07tExHUR8VxEPBsRiyNieJ3rHhMR1+2gba2OiKYdsS31Hb36O9alHSEijgFOBUZn5mvFH9LdisUvA8cCT0bEPsABVaueDQwGRmXmmxHRDPyxh/vsl5nt21t7ZrYALdu7HaksRyJSJRg2ZuZrAJm5MTN/WyybQ+W1LgBnAj/otN66zHyzWK81M1/qbicR8WpEfDMifgEcExGfiYhFxcjn5uJ7Wjq+8OvpiPhFRDxWtO1ZfNnSooj4eURMLtonRMS8YlS0ugi6jv2tiIj9I2JQRNxbjJQWR8SxxfKBEfFoMQL7DhBvr1raNkNEgkeBoRHxq4j4p4j4i6pljwHjiz/w5wB3VS27G/hkEQLfjIgj3mE/ewI/y8w/B9qojGSOzczDgXbg0xExCLgF+Kui36eKdf8eeDwzjwZOAP5nROzZseEiyO4HzgCIiLHAbzLz98C3gW9l5lHAXwHfKVabBjyZmYcCc4EDe/brkv7E01nq8zLz1Yg4Ejieyh/ou6q+8rcdeJJKgOyRmas7LpFkZmtEHAKcWHwei4hPZeZj3eyqHbi3mP4YcCSwuNjeHsB6YBywMDNXFfvo+HKlk4DTIuILxfwA3v5H/y7gfwDf5a2B93FgZNWlnb0jYi9gPJXRFZn5LxHR7ShK6o4hIgHF9YkFwIKIeBY4v2rxHCr/U5/exXqvAQ8BD0XE74HTqYxeurK56jpIALMy88rqDhHxyW7WDSqjkxc69d+/avYp4OBiNHM68JWifRdgXGZu7rRuN7uSes7TWerzIuKQiBhR1XQ48Juq+R8B/wDc2Wm90RExuJjeBRjVab1teQw4KyI+WKy/X0R8CPgpldNnwzvai/6PAJd33CnW1amzrLxNdS7wj8DzmdlWLHoUuLyq7sOLyYXAfyzaTgb27WHt0laGiAR7AbMiYnlELAVGUjXqyIprM3Njp/U+CPxzRDwHLAW2ADf0ZIeZuRz478CjxT7nAwdk5gbgYuAHxQX4jlNSXwZ2BZZGxLJivit3AZ/hrddu/hYYU9y+vBz4z0X71VQCaxmV01ov9qR2qZqvgpckleZIRJJUmhfWpR0sIn4G7N6peUpmPtuIeqRa8nSWJKk0T2dJkkozRCRJpRkikqTSDBFJUmn/H/3u55H9kWXLAAAAAElFTkSuQmCC\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "ax = sns.countplot(x=\"SMS_received\", hue=\"Showed_up\", data=df)\n", "# Texted = 1\n", "# Not-texted = 0" ] }, { "cell_type": "markdown", "id": "a2ba3b14", "metadata": { "id": "a2ba3b14" }, "source": [ "Now lets see how time(month, days, hour) affects cancelations." ] }, { "cell_type": "code", "execution_count": 26, "id": "f0627046", "metadata": { "id": "f0627046", "outputId": "77b11d03-9a72-4b88-f82d-e3ca1750a8a0", "colab": { "base_uri": "https://localhost:8080/", "height": 279 } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "ax = sns.countplot(x=\"Scheduledmonth\", hue=\"Showed_up\", data=df)\n", "# Showed = 1\n", "# Missed = 0" ] }, { "cell_type": "code", "execution_count": 27, "id": "e814f06f", "metadata": { "id": "e814f06f", "outputId": "101d4b8e-4731-44cc-a8e6-a1e2835a8c48", "colab": { "base_uri": "https://localhost:8080/", "height": 279 } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "ax = sns.countplot(x=\"Appointmentmonth\", hue=\"Showed_up\", data=df)\n", "# Showed = 1\n", "# Missed = 0" ] }, { "cell_type": "markdown", "id": "8e59b88f", "metadata": { "id": "8e59b88f" }, "source": [ "We can disregard months since theres not enough data for it or people simple dont go to hospitals outside of the summertime. The later is extremely unlikely." ] }, { "cell_type": "code", "execution_count": 28, "id": "1f7013b2", "metadata": { "id": "1f7013b2", "outputId": "da7dcc62-60b3-441f-a75b-c03cfb5e2029", "colab": { "base_uri": "https://localhost:8080/", "height": 279 } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "ax = sns.countplot(x=\"ScheduledDayofweek\", hue=\"Showed_up\", data=df)\n", "# Showed = 1\n", "# Missed = 0" ] }, { "cell_type": "markdown", "id": "f8a8f270", "metadata": { "id": "f8a8f270" }, "source": [ "Thursdays and Fridays have the lowest cancellations and lowest appointments. Doctors should encourage patients for these days as people generally have more flexibility towards the end of the week." ] }, { "cell_type": "code", "execution_count": 29, "id": "71e736a6", "metadata": { "id": "71e736a6", "outputId": "736a52cc-d9d9-4f46-dd50-4d8c89ed29b1", "colab": { "base_uri": "https://localhost:8080/", "height": 279 } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "ax = sns.countplot(x=\"AppointmentDayofweek\", hue=\"Showed_up\", data=df)\n", "# Showed = 1\n", "# Missed = 0" ] }, { "cell_type": "markdown", "id": "e43b23e6", "metadata": { "id": "e43b23e6" }, "source": [ "Less cancelations for people who called to set the apt during mid-day and evenings. Probably due to the working class being less pressured during lunch breaks and after working hours to be sure if they'll make the commitment.\n" ] }, { "cell_type": "markdown", "id": "58c88426", "metadata": { "id": "58c88426" }, "source": [ "Evenings and mid-afternoon seem to be the best time to call to set an appointment. Dataset had no time for when the appointment will actually be held but it be nice to see the correlation with it. " ] }, { "cell_type": "code", "execution_count": 30, "id": "6c18796f", "metadata": { "id": "6c18796f", "outputId": "3c03c803-66a9-47e3-dc8a-bc1f3a02519c", "colab": { "base_uri": "https://localhost:8080/", "height": 279 } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "ax = sns.countplot(x=\"Scheduledhour\", hue=\"Showed_up\", data=df)\n", "# Showed = 1\n", "# Missed = 0" ] }, { "cell_type": "code", "source": [ "ax = sns.countplot(x=\"sum_missed\", hue=\"Showed_up\", data=df)\n", "# Showed = 1\n", "# Missed = 0" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 280 }, "id": "k84wCtJdCfyi", "outputId": "e259ae93-cc31-41a0-96e5-90d0f32da9d5" }, "id": "k84wCtJdCfyi", "execution_count": 33, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "df[\"condition\"] = df['Hypertension'] + df['Diabetes']" ], "metadata": { "id": "rPbEI6D2CgAS" }, "id": "rPbEI6D2CgAS", "execution_count": 34, "outputs": [] }, { "cell_type": "code", "source": [ "ax = sns.countplot(x=\"condition\", hue=\"Showed_up\", data=df)\n", "# Showed = 1\n", "# Missed = 0" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 279 }, "id": "wQyvauzLDdSd", "outputId": "08155042-2b64-48f7-f45c-1ea2628a9bf0" }, "id": "wQyvauzLDdSd", "execution_count": 35, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "id": "54a68292", "metadata": { "id": "54a68292" }, "source": [ "# Model" ] }, { "cell_type": "markdown", "id": "62aac8c9", "metadata": { "id": "62aac8c9" }, "source": [ "Lets handle the imbalance issue" ] }, { "cell_type": "code", "execution_count": 36, "id": "51104f61", "metadata": { "scrolled": false, "id": "51104f61", "outputId": "b70fc080-3bb0-499f-b094-c75b1a693ba5", "colab": { "base_uri": "https://localhost:8080/", "height": 352 } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "(70047, 17)\n", "(21355, 17)\n", "1 70047\n", "0 42710\n", "Name: Showed_up, dtype: int64\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 36 }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ], "source": [ "showed = df[df[\"Showed_up\"] == 1]\n", "missed = df[df[\"Showed_up\"] == 0]\n", "print(showed.shape)\n", "print(missed.shape)\n", "df = pd.concat([df, missed])\n", "\n", "print(df[\"Showed_up\"].value_counts())\n", "\n", "df.groupby('Showed_up').size().plot(kind='pie',\n", " y = \"Showed_up\",\n", " label = \"Type\",\n", " autopct='%1.1f%%')" ] }, { "cell_type": "markdown", "id": "8364cbf7", "metadata": { "id": "8364cbf7" }, "source": [ "Ill try downsampling, if it doesnt achieve a somewhat decent F1 score I'll opt for upsampling using SMOTE." ] }, { "cell_type": "code", "execution_count": 37, "id": "d72be7ad", "metadata": { "id": "d72be7ad", "outputId": "3378e387-dbb3-4054-f42e-f8d09b4deca0", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "(21355, 17)\n" ] } ], "source": [ "from sklearn.utils import resample\n", "df_resampled = resample(showed,\n", " replace=True,\n", " n_samples=len(missed),\n", " random_state=42)\n", "\n", "print(df_resampled.shape)" ] }, { "cell_type": "code", "execution_count": 38, "id": "6b39956b", "metadata": { "scrolled": true, "id": "6b39956b", "outputId": "650cebdd-d45e-4377-b50b-b63ae98d5e06", "colab": { "base_uri": "https://localhost:8080/", "height": 317 } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "1 21355\n", "0 21355\n", "Name: Showed_up, dtype: int64\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 38 }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ], "source": [ "df_resampled = pd.concat([df_resampled, missed])\n", "\n", "print(df_resampled[\"Showed_up\"].value_counts())\n", "\n", "df_resampled.groupby('Showed_up').size().plot(kind='pie',\n", " y = \"Showed_up\",\n", " label = \"Type\",\n", " autopct='%1.1f%%')" ] }, { "cell_type": "markdown", "id": "b8dccc75", "metadata": { "id": "b8dccc75" }, "source": [ "Lets analyze these features one more time" ] }, { "cell_type": "code", "source": [ "ax = sns.countplot(x=\"condition\", hue=\"Showed_up\", data=df_resampled)\n", "# Showed = 1\n", "# Missed = 0" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 279 }, "id": "HEAQe4NGDz3F", "outputId": "39ebea3c-b22e-4f1a-a2b1-8c40c9b228a5" }, "id": "HEAQe4NGDz3F", "execution_count": 39, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "Chances of missing when there's no pre-existing conditions are high but when there's 1 or 2 conditions then chances of missing reduces." ], "metadata": { "id": "93_5-OjcFkJM" }, "id": "93_5-OjcFkJM" }, { "cell_type": "code", "execution_count": 43, "id": "cd4d029f", "metadata": { "scrolled": true, "id": "cd4d029f", "outputId": "244a378d-7950-4c13-81bf-cdda27e0f7b4", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[{'Correlation': 0.5221551562049078, 'Feature': 'missed_appointment_before'},\n", " {'Correlation': 0.12602838004041125, 'Feature': 'Age'},\n", " {'Correlation': 0.07542836038507394, 'Feature': 'Hypertension'},\n", " {'Correlation': 0.06867140655649014, 'Feature': 'condition'},\n", " {'Correlation': 0.03288910570793788, 'Feature': 'Diabetes'},\n", " {'Correlation': 0.023093822097828173, 'Feature': 'Disability'},\n", " {'Correlation': 0.00874532827670649, 'Feature': 'Alcoholism'},\n", " {'Correlation': 0.004724813996662372, 'Feature': 'Gender'},\n", " {'Correlation': -0.011934077638705375, 'Feature': 'MedicaidIND'},\n", " {'Correlation': -0.03717159317752768, 'Feature': 'Scheduledhour'},\n", " {'Correlation': -0.10538677555673681, 'Feature': 'SMS_received'},\n", " {'Correlation': -0.47840224582944235, 'Feature': 'sum_missed'}]" ] }, "metadata": {}, "execution_count": 43 } ], "source": [ "#Feature Importance\n", "abscorrshowed = []\n", "df = df_resampled\n", "y = df['Showed_up']\n", "features = df.drop(['Showed_up', 'ScheduledDayofweek', 'Scheduledmonth','AppointmentDayofweek', 'Appointmentmonth'], axis = 1)\n", "for x in features.columns:\n", " abscorrshowed.append({\"Feature\": x, \"Correlation\" : (df[x].corr(y))})\n", "abssorted_list = sorted(abscorrshowed,key=lambda x:x['Correlation'],reverse=True)\n", "abssorted_list" ] }, { "cell_type": "code", "execution_count": 44, "id": "131b55d5", "metadata": { "id": "131b55d5", "outputId": "3789ba22-b820-46e4-f4bb-7bc078811a43", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "######### By Absolute ########\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[{'Correlation': 0.5221551562049078, 'Feature': 'missed_appointment_before'},\n", " {'Correlation': 0.47840224582944235, 'Feature': 'sum_missed'},\n", " {'Correlation': 0.12602838004041125, 'Feature': 'Age'},\n", " {'Correlation': 0.10538677555673681, 'Feature': 'SMS_received'},\n", " {'Correlation': 0.07542836038507394, 'Feature': 'Hypertension'},\n", " {'Correlation': 0.06867140655649014, 'Feature': 'condition'},\n", " {'Correlation': 0.03717159317752768, 'Feature': 'Scheduledhour'},\n", " {'Correlation': 0.03288910570793788, 'Feature': 'Diabetes'},\n", " {'Correlation': 0.023093822097828173, 'Feature': 'Disability'},\n", " {'Correlation': 0.011934077638705375, 'Feature': 'MedicaidIND'},\n", " {'Correlation': 0.00874532827670649, 'Feature': 'Alcoholism'},\n", " {'Correlation': 0.004724813996662372, 'Feature': 'Gender'}]" ] }, "metadata": {}, "execution_count": 44 } ], "source": [ "print(\"######### By Absolute ########\")\n", "abscorrshowed = []\n", "y = df['Showed_up']\n", "features = df.drop(['Showed_up', 'ScheduledDayofweek', 'Scheduledmonth','AppointmentDayofweek', 'Appointmentmonth'], axis = 1)\n", "for x in features.columns:\n", " abscorrshowed.append({\"Feature\": x, \"Correlation\" : abs(df[x].corr(y))})\n", "abssorted_list = sorted(abscorrshowed,key=lambda x:x['Correlation'],reverse=True)\n", "abssorted_list" ] }, { "cell_type": "code", "execution_count": 45, "id": "e9af982b", "metadata": { "id": "e9af982b", "outputId": "4928022b-8e65-49f0-e0c1-2bfec74ec4a9", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "-0.1584175923864393" ] }, "metadata": {}, "execution_count": 45 } ], "source": [ "# Both are somewhat correlated to each other, hope this wont be too much of an issue if I use both while modeling\n", "df['missed_appointment_before'].corr(df['sum_missed'])" ] }, { "cell_type": "markdown", "id": "431fe8ec", "metadata": { "id": "431fe8ec" }, "source": [ "A function for our metrics: F1 Score, Accuracy & a confusion matrix for all the models" ] }, { "cell_type": "code", "execution_count": 46, "id": "833e8bbb", "metadata": { "id": "833e8bbb" }, "outputs": [], "source": [ "# Defining metrics\n", "import itertools\n", "def plot_confusion_matrix(y_test,y_pred,\n", " normalize=False,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues):\n", " \"\"\"\n", " This function prints and plots the confusion matrix.\n", " Normalization can be applied by setting `normalize=True`.\n", " \"\"\"\n", " classes = ['Missed', 'Showed']\n", " from sklearn.metrics import confusion_matrix\n", " cm = confusion_matrix(y_test,y_pred)\n", " \n", " if normalize:\n", " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", " print(\"Normalized confusion matrix\")\n", " else:\n", " print('Confusion Matrix, without normalization')\n", "\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(classes))\n", " plt.xticks(tick_marks, classes, rotation=45)\n", " plt.yticks(tick_marks, classes)\n", "\n", " fmt = '.2f' if normalize else 'd'\n", " thresh = cm.max() / 2.\n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " plt.text(j, i, format(cm[i, j], fmt),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", " plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label')\n", " \n", " # obtian Accuracy & f1 score\n", " print(\"Accuracy :\" , accuracy_score(y_test, y_pred))\n", " print(\"F1 Score:\" , f1_score(y_test, y_pred))" ] }, { "cell_type": "markdown", "id": "7c16b54b", "metadata": { "id": "7c16b54b" }, "source": [ "### Logistic Regression" ] }, { "cell_type": "code", "execution_count": 56, "id": "d7c48b05", "metadata": { "scrolled": true, "id": "d7c48b05", "outputId": "836b2549-0efe-4294-be5e-469e737c2b26", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Normalized confusion matrix\n", "Accuracy : 0.8114024818543667\n", "F1 Score: 0.8276083467094704\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.metrics import accuracy_score,f1_score,roc_auc_score\n", "\n", "\n", "#important = ['missed_appointment_before','sum_missed']\n", "important = ['missed_appointment_before','sum_missed','Age','SMS_received', 'Hypertension','Diabetes']\n", "#important = ['missed_appointment_before','sum_missed','Age','SMS_received', 'Hypertension','Diabetes','Scheduledhour']\n", "\n", "\n", "X = df[important]\n", "Y = df['Showed_up']\n", "\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, Y, random_state=42, test_size=0.2)\n", "\n", "pipe = make_pipeline(StandardScaler(), LogisticRegression(random_state=42, max_iter=200))\n", "pipe.fit(X_train, y_train)\n", "y_pred = pipe.predict(X_test)\n", "plot_confusion_matrix(y_test,y_pred,\n", " normalize=True,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues)\n", "\n", "# Saving our basic model that performed pretty well\n", "import pickle\n", "olsmodel = \"olsmodel.sav\"\n", "\n", "# save\n", "pickle.dump(pipe, open(olsmodel, \"wb\"))" ] }, { "cell_type": "markdown", "id": "102730cc", "metadata": { "id": "102730cc" }, "source": [ "Time didnt seem to affect it so I just removed it to prevent over-generalizing. \n", "\n", "Removing either missed before (yes or no) OR Total Sum of Missed dropped our models accuracy by 5% so I choose to keep them while ignoring their multicollinearity.\n", "\n", "The diseases and notification also made no impact on the models performance but I choose to leave them still for the doctors themselves to decide the right course of action. For instance there might be an emergency so getting a hold of patient might be life saving. \n" ] }, { "cell_type": "code", "execution_count": 48, "id": "62505b55", "metadata": { "scrolled": true, "id": "62505b55", "outputId": "dfa54ed0-7277-40a9-c948-43bc3d790512", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Might Show With A Probability of: 54.05%\n" ] } ], "source": [ "# Function for spitting out the predictions and its probability after done modeling\n", "def analysis(missedprior,totalmissed,reminded,age,hyper,diabetes):\n", "\n", " column = ['missed_appointment_before', 'sum_missed', 'Age', 'SMS_received',\n", " 'Hypertension', 'Diabetes']\n", " \n", " serInput = [missedprior,totalmissed,age,reminded,hyper,diabetes]\n", " \n", " data = pd.DataFrame([serInput], columns=column)\n", " \n", " filename = 'olsmodel.sav'\n", " model = pickle.load(open(filename, 'rb'))\n", "\n", " #from joblib import dump, load\n", " #scaler = load('std_scaler.bin')\n", "\n", " #data = scaler.transform(data)\n", "\n", " r = model.predict(data)\n", " for i in r:\n", " if r == 0:\n", " result = str(\"Might Miss\")\n", " else: \n", " result = str(\"Might Show\")\n", " proba = np.max(model.predict_proba(data)*100, axis=1)\n", " pred = str( (result) + ' With A Probability of: ' +'%.2f' % (proba) +'%')\n", " \n", "\n", " return pred\n", "\n", "missedprior,totalmissed,age,reminded,hyper,diabetes = 1,1,50,1,0,0\n", "result = analysis(missedprior,totalmissed,reminded,age,hyper,diabetes)\n", "\n", "print(result)" ] }, { "cell_type": "markdown", "id": "fa5b83c0", "metadata": { "id": "fa5b83c0" }, "source": [ " " ] }, { "cell_type": "markdown", "id": "a5f5b8fd", "metadata": { "id": "a5f5b8fd" }, "source": [ "### Xgboost Classifier" ] }, { "cell_type": "markdown", "id": "33c198b6", "metadata": { "id": "33c198b6" }, "source": [ "Lets see how much better a gradient boosted ensemble model will perform." ] }, { "cell_type": "code", "execution_count": 55, "id": "a2ae6fc3", "metadata": { "scrolled": true, "id": "a2ae6fc3", "outputId": "b53d00a8-2d4f-4356-993c-818912e69bab", "colab": { "base_uri": "https://localhost:8080/", "height": 363 } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Normalized confusion matrix\n", "Accuracy : 0.8686490283306018\n", "F1 Score: 0.8496381667113375\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "import xgboost as xgb\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, Y, random_state=42, test_size=0.2)\n", "\n", "pipe = make_pipeline(StandardScaler(), xgb.XGBClassifier(n_jobs=-1, eval_metric='auc', use_label_encoder=False,\n", " random_state=42))\n", "pipe.fit(X_train, y_train.astype(int))\n", "y_pred = pipe.predict(X_test)\n", "# obtian Accuracy & f1 score\n", "plot_confusion_matrix(y_test,y_pred,\n", " normalize=True,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues)\n", "\n", "# save\n", "xgbmodel = \"xgb.sav\"\n", "pickle.dump(pipe, open(xgbmodel, \"wb\"))\n", "# load\n", "xgb_model_loaded = pickle.load(open(xgbmodel, \"rb\"))" ] }, { "cell_type": "markdown", "id": "5e319fa6", "metadata": { "id": "5e319fa6" }, "source": [ "Not so much better unfortunately so in an actual production environment, it'll be better to just use to the base model so as to not incure computational cost overtime for just a 2% increase. Still I'll use xgboost while deploying this for now just because I already have an environment set up for it and I dont have much time to reinvent the wheel. Hence why I'll also be performing a grid search with just so few parameters below:" ] }, { "cell_type": "markdown", "id": "2b884be6", "metadata": { "id": "2b884be6" }, "source": [ "### Hyper-Parameter Tuning Using Randomized Search" ] }, { "cell_type": "code", "execution_count": null, "id": "e3ea60d8", "metadata": { "scrolled": false, "id": "e3ea60d8", "outputId": "cc63845d-8f73-4a3e-ab1d-adb4d81fa9c3" }, "outputs": [ { "data": { "application/javascript": [ "\n", " if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import RandomizedSearchCV'); }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "RandomizedSearchCV(cv=20,\n", " estimator=XGBClassifier(base_score=None, booster=None,\n", " colsample_bylevel=None,\n", " colsample_bynode=None,\n", " colsample_bytree=None,\n", " enable_categorical=False,\n", " eval_metric='auc', gamma=None,\n", " gpu_id=None, importance_type=None,\n", " interaction_constraints=None,\n", " learning_rate=None,\n", " max_delta_step=None, max_depth=None,\n", " min_child_weight=None, missing=nan,\n", " monotone_constraints=None,\n", " n_estimators=100, n_jobs=-1,\n", " num_parallel_tree=None,\n", " predictor=None, random_state=42,\n", " reg_alpha=None, reg_lambda=None,\n", " scale_pos_weight=None,\n", " subsample=None, tree_method=None,\n", " use_label_encoder=False,\n", " validate_parameters=None,\n", " verbosity=None),\n", " n_iter=6,\n", " param_distributions={'max_depth': [3, 4, 10],\n", " 'n_estimators': [3, 5, 6]},\n", " scoring='f1')" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# given parameters different values\n", "Random_Grid={\n", " 'n_estimators':[3,5,6],\n", " 'max_depth':[3,4,10]\n", " }\n", "# xgboost model\n", "gb = xgb.XGBClassifier( n_jobs=-1, eval_metric='auc', use_label_encoder=False,\n", " random_state=42)\n", "# randommized searchCV\n", "rs=RandomizedSearchCV(gb,Random_Grid,cv=20,scoring=\"f1\", n_iter = 6)\n", "\n", "# fit the train data\n", "rs.fit(X_train,y_train)" ] }, { "cell_type": "code", "execution_count": null, "id": "9a7037fd", "metadata": { "id": "9a7037fd", "outputId": "828d279e-5a45-4503-aa2e-9967261bc10a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy : 0.8455569350833629\n", "F1 Score: 0.8715850016591086\n", "Normalized confusion matrix\n", "Accuracy : 0.8455569350833629\n", "F1 Score: 0.8715850016591086\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gs = xgb.XGBClassifier( n_estimators=rs.best_params_[\"n_estimators\"],\n", " max_depth=rs.best_params_[\"max_depth\"],\n", " n_jobs=-1, eval_metric='auc', use_label_encoder=False,\n", " random_state=42)\n", "# fit the model \n", "gs.fit(X_train, y_train)\n", "\n", "y_pred = gs.predict(X_test)\n", "# obtian Accuracy & f1 score\n", "print(\"Accuracy :\" , accuracy_score(y_test, y_pred))\n", "print(\"F1 Score:\" , f1_score(y_test, y_pred))\n", "plot_confusion_matrix(y_test,y_pred,\n", " normalize=True,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues)\n", "\n", "import pickle\n", "gsmodel = \"gs.pkl\"\n", "\n", "# save\n", "pickle.dump(gs, open(gsmodel, \"wb\"))\n", "# load\n", "#xgb_model_loaded = pickle.load(open(xgbmodel, \"rb\"))" ] }, { "cell_type": "markdown", "id": "82db7dff", "metadata": { "id": "82db7dff" }, "source": [ "### Neural Network" ] }, { "cell_type": "markdown", "id": "5679fd49", "metadata": { "id": "5679fd49" }, "source": [ "Finally Ill like to see how much better a simple neural network might perform in contrast to prior models. " ] }, { "cell_type": "code", "execution_count": 60, "id": "b8b5d6ab", "metadata": { "scrolled": false, "id": "b8b5d6ab", "outputId": "1d5110c4-ade1-4214-840d-11fe50d24127", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"sequential_1\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_4 (Dense) (None, 64) 448 \n", " \n", " dropout_3 (Dropout) (None, 64) 0 \n", " \n", " dense_5 (Dense) (None, 128) 8320 \n", " \n", " dropout_4 (Dropout) (None, 128) 0 \n", " \n", " dense_6 (Dense) (None, 128) 16512 \n", " \n", " dropout_5 (Dropout) (None, 128) 0 \n", " \n", " dense_7 (Dense) (None, 1) 129 \n", " \n", "=================================================================\n", "Total params: 25,409\n", "Trainable params: 25,409\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "Epoch 1/10\n", "855/855 [==============================] - 5s 4ms/step - loss: 0.3244 - accuracy: 0.8354 - val_loss: 0.2470 - val_accuracy: 0.8709\n", "Epoch 2/10\n", "855/855 [==============================] - 5s 6ms/step - loss: 0.2627 - accuracy: 0.8655 - val_loss: 0.2480 - val_accuracy: 0.8709\n", "Epoch 3/10\n", "855/855 [==============================] - 5s 6ms/step - loss: 0.2597 - accuracy: 0.8672 - val_loss: 0.2503 - val_accuracy: 0.8709\n", "Epoch 4/10\n", "855/855 [==============================] - 2s 3ms/step - loss: 0.2600 - accuracy: 0.8676 - val_loss: 0.2475 - val_accuracy: 0.8709\n", "Epoch 5/10\n", "855/855 [==============================] - 3s 3ms/step - loss: 0.2580 - accuracy: 0.8679 - val_loss: 0.2462 - val_accuracy: 0.8709\n", "Epoch 6/10\n", "855/855 [==============================] - 2s 3ms/step - loss: 0.2583 - accuracy: 0.8675 - val_loss: 0.2467 - val_accuracy: 0.8709\n", "Epoch 7/10\n", "855/855 [==============================] - 2s 3ms/step - loss: 0.2580 - accuracy: 0.8674 - val_loss: 0.2487 - val_accuracy: 0.8709\n", "Epoch 8/10\n", "855/855 [==============================] - 2s 3ms/step - loss: 0.2580 - accuracy: 0.8677 - val_loss: 0.2475 - val_accuracy: 0.8709\n", "Epoch 9/10\n", "855/855 [==============================] - 2s 3ms/step - loss: 0.2581 - accuracy: 0.8678 - val_loss: 0.2468 - val_accuracy: 0.8709\n", "Epoch 10/10\n", "855/855 [==============================] - 2s 3ms/step - loss: 0.2574 - accuracy: 0.8679 - val_loss: 0.2473 - val_accuracy: 0.8709\n", "Normalized confusion matrix\n", "Accuracy : 0.8727464294076329\n", "F1 Score: 0.8537208989368861\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "import tensorflow \n", "from tensorflow.keras.layers import Dense, Dropout, LSTM, Embedding,MaxPooling1D,ConvLSTM2D,RNN\n", "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "from tensorflow.keras.models import Sequential\n", "\n", "#important = ['missed_appointment_before','sum_missed']\n", "important = ['missed_appointment_before','sum_missed','Age','SMS_received', 'Hypertension','Diabetes']\n", "#important = ['missed_appointment_before','sum_missed','Age','SMS_received', 'Hypertension','Diabetes','Scheduledhour']\n", "\n", "\n", "X = df[important]\n", "Y = df['Showed_up']\n", "\n", "\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)\n", "\n", "standardScaler = StandardScaler()\n", "X_train = standardScaler.fit_transform(X_train)\n", "X_test = standardScaler.transform(X_test)\n", "\n", "classifier = Sequential()\n", "classifier.add(Dense(units = 64, activation = 'relu', input_dim = len(important)))\n", "classifier.add(Dropout(rate = 0.5))\n", "classifier.add(Dense(units = 128, activation = 'relu'))\n", "classifier.add(Dropout(rate = 0.5))\n", "classifier.add(Dense(units = 128, activation = 'relu'))\n", "classifier.add(Dropout(rate = 0.5))\n", "classifier.add(Dense(units = 1, activation = 'sigmoid'))\n", "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n", "classifier.summary()\n", "\n", "history = classifier.fit(X_train, y_train, epochs = 10, validation_split = 0.2)\n", "\n", "y_pred = classifier.predict(X_test)\n", "y_pred = y_pred > 0.5\n", "\n", "\n", "plot_confusion_matrix(y_test,y_pred,\n", " normalize=True,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues)" ] }, { "cell_type": "markdown", "id": "ae0d8b95", "metadata": { "id": "ae0d8b95" }, "source": [ "# Conlusion:" ] }, { "cell_type": "markdown", "id": "d53b7eec", "metadata": { "id": "d53b7eec" }, "source": [ "Adding 3 dense layers and dropout layers after each dense layer to reduce over generalizing, the model didnt impove by much if any at all so definitely shouldnt use the neural network model as it wont be cost efficient in contrast to simpler models. I did just use 10 epochs in training so chances are that if I had used more it would have outperformed the rest by more but I want to build a users interface right now so that would be work for another day.\n" ] }, { "cell_type": "markdown", "id": "41369289", "metadata": { "id": "41369289" }, "source": [ "# Recommendations:" ] }, { "cell_type": "markdown", "id": "f5343113", "metadata": { "id": "f5343113" }, "source": [ "#### Operation Unit\n", "\n", "Send text notifications to those who scheduled during in high cancelation time periods i.e [between 7am - 11am & 2pm -3pm]. \n", "\n", "Encourage Friday and Thursday bookings more.\n", "\n", "Switch to phone call reminders a day prior to the appointment.\n", "\n", "Use the app built to view a patients probability of canceling a day prior, if its > 70%, call to suggest reschedulling before the patient even thinks of canceling.\n", "\n", "#### Model:\n", "Try upsampling for better accuracy of model.\n", "\n", "Try a more indepth grid search approach using more parameters.\n", "\n", "\n", "Get more data especially for time variables.\n", "\n", "Experiment with label encoding for the time variables made in the begining i.e ['ScheduledDayofweek', 'Scheduledmonth','AppointmentDayofweek', 'Appointmentmonth'].\n" ] }, { "cell_type": "code", "source": [ "%%shell\n", "jupyter nbconvert --to html code.ipynb" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lF9ZH3Fb5L6f", "outputId": "29aba7bb-74f9-46c0-a145-9cb92469904c" }, "id": "lF9ZH3Fb5L6f", "execution_count": 59, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[NbConvertApp] Converting notebook code.ipynb to html\n", "[NbConvertApp] Writing 607126 bytes to code.html\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 59 } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "8Yd5vS2C7B8E" }, "id": "8Yd5vS2C7B8E", "execution_count": null, "outputs": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10" }, "colab": { "name": "code.ipynb", "provenance": [] } }, "nbformat": 4, "nbformat_minor": 5 }