# No Shows By Chase Kregor¶

## Notebook Bookmarks:¶

### - Go to Train Models

In [64]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#import matplotlib.style as style
#style.use('fivethirtyeight')
import seaborn as sb


## Start of Clean and EDA ¶

In [65]:
data = pd.read_csv("../data/KaggleV2-May-2016.csv")

Out[65]:
PatientId AppointmentID Gender ScheduledDay AppointmentDay Age Neighbourhood Scholarship Hipertension Diabetes Alcoholism Handcap SMS_received No-show
0 2.987250e+13 5642903 F 2016-04-29T18:38:08Z 2016-04-29T00:00:00Z 62 JARDIM DA PENHA 0 1 0 0 0 0 No
1 5.589978e+14 5642503 M 2016-04-29T16:08:27Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 0 0 0 0 0 No
2 4.262962e+12 5642549 F 2016-04-29T16:19:04Z 2016-04-29T00:00:00Z 62 MATA DA PRAIA 0 0 0 0 0 0 No
3 8.679512e+11 5642828 F 2016-04-29T17:29:31Z 2016-04-29T00:00:00Z 8 PONTAL DE CAMBURI 0 0 0 0 0 0 No
4 8.841186e+12 5642494 F 2016-04-29T16:07:23Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 1 1 0 0 0 No
In [66]:
data.rename(columns = {'Hipertension':'Hypertension',
'PatientId': 'PatientID',
'Handcap': 'Handicap',
'No-show': 'NoShow',
'Alcoholism':'Alcoholism'
}, inplace = True)

Out[66]:
PatientID AppointmentID Gender ScheduledDay AppointmentDay Age Neighbourhood Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received NoShow
0 2.987250e+13 5642903 F 2016-04-29T18:38:08Z 2016-04-29T00:00:00Z 62 JARDIM DA PENHA 0 1 0 0 0 0 No
1 5.589978e+14 5642503 M 2016-04-29T16:08:27Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 0 0 0 0 0 No
2 4.262962e+12 5642549 F 2016-04-29T16:19:04Z 2016-04-29T00:00:00Z 62 MATA DA PRAIA 0 0 0 0 0 0 No
3 8.679512e+11 5642828 F 2016-04-29T17:29:31Z 2016-04-29T00:00:00Z 8 PONTAL DE CAMBURI 0 0 0 0 0 0 No
4 8.841186e+12 5642494 F 2016-04-29T16:07:23Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 1 1 0 0 0 No

Now trying to understand the data set. It's distributions and unique values. Also attempting to find funky and incorrect data points. I want to understand and check the integrity of the dataset

In [67]:
data.PatientID.value_counts()

Out[67]:
8.221459e+14    88
9.963767e+10    84
2.688613e+13    70
3.353478e+13    65
2.584244e+11    62
7.579746e+13    62
8.713749e+14    62
6.264199e+12    62
6.684488e+13    57
8.722785e+11    55
8.923969e+13    54
8.435224e+09    51
8.534397e+14    50
1.447997e+13    46
6.543360e+13    46
8.189452e+13    42
9.452745e+12    42
1.882323e+14    40
9.496197e+12    38
2.271580e+12    38
1.336493e+13    37
1.484143e+12    35
8.883500e+13    34
9.861628e+14    34
7.124589e+14    33
4.167557e+14    30
6.128878e+12    30
8.121397e+13    29
8.634164e+12    24
3.699499e+13    23
..
6.375629e+12     1
9.369127e+12     1
5.375556e+14     1
1.662184e+11     1
7.234615e+13     1
9.649990e+12     1
6.912783e+10     1
1.954265e+13     1
2.736377e+10     1
5.532694e+11     1
7.149583e+12     1
8.676752e+13     1
7.838359e+13     1
5.962625e+11     1
4.919862e+13     1
3.477350e+14     1
1.626595e+13     1
7.794917e+12     1
1.161950e+13     1
5.615364e+14     1
4.355592e+11     1
1.321328e+12     1
1.751987e+13     1
4.262579e+13     1
3.115681e+13     1
1.222828e+13     1
6.821231e+11     1
7.163981e+14     1
9.798964e+14     1
2.724571e+11     1
Name: PatientID, Length: 62299, dtype: int64

making sure there arent duplicate appointment IDs

In [68]:
data.AppointmentID.value_counts()

Out[68]:
5769215    1
5731652    1
5707080    1
5702986    1
5715276    1
5717325    1
5711182    1
5758289    1
5762391    1
5741913    1
5483871    1
5660001    1
5653858    1
5666148    1
5668197    1
5641576    1
5639531    1
5649772    1
5645678    1
5647727    1
5692785    1
5686642    1
5694838    1
5696887    1
5674360    1
5733701    1
5651786    1
5672315    1
5719362    1
5672187    1
..
5744033    1
5748131    1
5739943    1
5672324    1
5682563    1
5680512    1
5782866    1
5496110    1
5713200    1
5711153    1
5717298    1
5709110    1
5707063    1
5729592    1
5463358    1
5565768    1
5776721    1
5789023    1
5590396    1
5606756    1
5608807    1
5635434    1
5621101    1
5686470    1
5582192    1
5586290    1
5584243    1
5598584    1
5602682    1
5771266    1
Name: AppointmentID, Length: 110527, dtype: int64
In [69]:
data.Gender.unique()

Out[69]:
array(['F', 'M'], dtype=object)
In [70]:
data.Gender.value_counts()

Out[70]:
F    71840
M    38687
Name: Gender, dtype: int64

trying to understand my timeline

In [71]:
data.AppointmentDay.unique()

Out[71]:
array(['2016-04-29T00:00:00Z', '2016-05-03T00:00:00Z',
'2016-05-10T00:00:00Z', '2016-05-17T00:00:00Z',
'2016-05-24T00:00:00Z', '2016-05-31T00:00:00Z',
'2016-05-02T00:00:00Z', '2016-05-30T00:00:00Z',
'2016-05-16T00:00:00Z', '2016-05-04T00:00:00Z',
'2016-05-19T00:00:00Z', '2016-05-12T00:00:00Z',
'2016-05-06T00:00:00Z', '2016-05-20T00:00:00Z',
'2016-05-05T00:00:00Z', '2016-05-13T00:00:00Z',
'2016-05-09T00:00:00Z', '2016-05-25T00:00:00Z',
'2016-05-11T00:00:00Z', '2016-05-18T00:00:00Z',
'2016-05-14T00:00:00Z', '2016-06-02T00:00:00Z',
'2016-06-03T00:00:00Z', '2016-06-06T00:00:00Z',
'2016-06-07T00:00:00Z', '2016-06-01T00:00:00Z',
'2016-06-08T00:00:00Z'], dtype=object)
In [72]:
data.Age.value_counts()

Out[72]:
 0      3539
1      2273
52     1746
49     1652
53     1651
56     1635
38     1629
59     1624
2      1618
50     1613
57     1603
36     1580
51     1567
19     1545
39     1536
37     1533
54     1530
34     1526
33     1524
30     1521
6      1521
3      1513
17     1509
32     1505
5      1489
44     1487
18     1487
58     1469
46     1460
45     1453
...
74      602
76      571
75      544
78      541
77      527
80      511
81      434
82      392
79      390
84      311
83      280
85      275
86      260
87      184
89      173
88      126
90      109
92       86
91       66
93       53
94       33
95       24
96       17
97       11
98        6
115       5
100       4
102       2
99        1
-1         1
Name: Age, Length: 104, dtype: int64

need to get rid of negative value. It is impossible for someone to be -1.

In [73]:
data['Age'][data['Age'] < 0] = 1

/Applications/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
"""Entry point for launching an IPython kernel.

In [74]:
data.Age.value_counts()

Out[74]:
0      3539
1      2274
52     1746
49     1652
53     1651
56     1635
38     1629
59     1624
2      1618
50     1613
57     1603
36     1580
51     1567
19     1545
39     1536
37     1533
54     1530
34     1526
33     1524
30     1521
6      1521
3      1513
17     1509
32     1505
5      1489
44     1487
18     1487
58     1469
46     1460
45     1453
...
72      615
74      602
76      571
75      544
78      541
77      527
80      511
81      434
82      392
79      390
84      311
83      280
85      275
86      260
87      184
89      173
88      126
90      109
92       86
91       66
93       53
94       33
95       24
96       17
97       11
98        6
115       5
100       4
102       2
99        1
Name: Age, Length: 103, dtype: int64
In [75]:
data.Scholarship.value_counts()

Out[75]:
0    99666
1    10861
Name: Scholarship, dtype: int64
In [76]:
data.Hypertension.value_counts()

Out[76]:
0    88726
1    21801
Name: Hypertension, dtype: int64
In [77]:
data.Diabetes.value_counts()

Out[77]:
0    102584
1      7943
Name: Diabetes, dtype: int64
In [78]:
data.Handicap.value_counts()

Out[78]:
0    108286
1      2042
2       183
3        13
4         3
Name: Handicap, dtype: int64
In [79]:
data.SMS_received.value_counts()

Out[79]:
0    75045
1    35482
Name: SMS_received, dtype: int64
In [80]:
data.NoShow.value_counts()

Out[80]:
No     88208
Yes    22319
Name: NoShow, dtype: int64

making sure there aren't any values missing in the dataset

In [81]:
data.isnull().sum()

Out[81]:
PatientID         0
AppointmentID     0
Gender            0
ScheduledDay      0
AppointmentDay    0
Age               0
Neighbourhood     0
Scholarship       0
Hypertension      0
Diabetes          0
Alcoholism        0
Handicap          0
NoShow            0
dtype: int64
In [82]:
print('Age:',sorted(data.Age.unique()))
print('Gender:',data.Gender.unique())
#print('DayOfTheWeek:',data.DayOfTheWeek.unique())
#print('Status:',data.Status.unique())
print('Diabetes:',data.Diabetes.unique())
print('Alchoholism:',data.Alcoholism.unique())
print('Hypertension:',data.Hypertension.unique())
print('Handicap:',data.Handicap.unique())
#print('Smokes:',data.Smokes.unique())
print('Scholarship:',data.Scholarship.unique())
#print('Tuberculosis:',data.Tuberculosis.unique())
#print('AwaitingTime:',sorted(data.AwaitingTime.unique()))
#print('HourOfTheDay:', sorted(data.HourOfTheDay.unique()))

Age: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 115]
Gender: ['F' 'M']
Diabetes: [0 1]
Alchoholism: [0 1]
Hypertension: [1 0]
Handicap: [0 1 2 3 4]
Scholarship: [0 1]

In [83]:
data.head()

Out[83]:
PatientID AppointmentID Gender ScheduledDay AppointmentDay Age Neighbourhood Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received NoShow
0 2.987250e+13 5642903 F 2016-04-29T18:38:08Z 2016-04-29T00:00:00Z 62 JARDIM DA PENHA 0 1 0 0 0 0 No
1 5.589978e+14 5642503 M 2016-04-29T16:08:27Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 0 0 0 0 0 No
2 4.262962e+12 5642549 F 2016-04-29T16:19:04Z 2016-04-29T00:00:00Z 62 MATA DA PRAIA 0 0 0 0 0 0 No
3 8.679512e+11 5642828 F 2016-04-29T17:29:31Z 2016-04-29T00:00:00Z 8 PONTAL DE CAMBURI 0 0 0 0 0 0 No
4 8.841186e+12 5642494 F 2016-04-29T16:07:23Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 1 1 0 0 0 No

## Feature Selection and Creation ¶

In [84]:
data.drop(data.columns[[0]], axis=1)

Out[84]:
PatientID AppointmentID Gender ScheduledDay AppointmentDay Age Neighbourhood Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received NoShow
0 2.987250e+13 5642903 F 2016-04-29T18:38:08Z 2016-04-29T00:00:00Z 62 JARDIM DA PENHA 0 1 0 0 0 0 No
1 5.589978e+14 5642503 M 2016-04-29T16:08:27Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 0 0 0 0 0 No
2 4.262962e+12 5642549 F 2016-04-29T16:19:04Z 2016-04-29T00:00:00Z 62 MATA DA PRAIA 0 0 0 0 0 0 No
3 8.679512e+11 5642828 F 2016-04-29T17:29:31Z 2016-04-29T00:00:00Z 8 PONTAL DE CAMBURI 0 0 0 0 0 0 No
4 8.841186e+12 5642494 F 2016-04-29T16:07:23Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 1 1 0 0 0 No

Creating two binary columns for the gender

In [85]:
data['Male'] = data['Gender'].replace(['F','M'], [0,1])
data['Female'] = data['Gender'].replace(['F','M'], [1,0])

Out[85]:
PatientID AppointmentID Gender ScheduledDay AppointmentDay Age Neighbourhood Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received NoShow Male Female
0 2.987250e+13 5642903 F 2016-04-29T18:38:08Z 2016-04-29T00:00:00Z 62 JARDIM DA PENHA 0 1 0 0 0 0 No 0 1
1 5.589978e+14 5642503 M 2016-04-29T16:08:27Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 0 0 0 0 0 No 1 0
2 4.262962e+12 5642549 F 2016-04-29T16:19:04Z 2016-04-29T00:00:00Z 62 MATA DA PRAIA 0 0 0 0 0 0 No 0 1
3 8.679512e+11 5642828 F 2016-04-29T17:29:31Z 2016-04-29T00:00:00Z 8 PONTAL DE CAMBURI 0 0 0 0 0 0 No 0 1
4 8.841186e+12 5642494 F 2016-04-29T16:07:23Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 1 1 0 0 0 No 0 1

changing NoShow column to be binary

In [86]:
data['NoShow'] = data['NoShow'].replace(['Yes','No'], [1,0])

Out[86]:
PatientID AppointmentID Gender ScheduledDay AppointmentDay Age Neighbourhood Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received NoShow Male Female
0 2.987250e+13 5642903 F 2016-04-29T18:38:08Z 2016-04-29T00:00:00Z 62 JARDIM DA PENHA 0 1 0 0 0 0 0 0 1
1 5.589978e+14 5642503 M 2016-04-29T16:08:27Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 0 0 0 0 0 0 1 0
2 4.262962e+12 5642549 F 2016-04-29T16:19:04Z 2016-04-29T00:00:00Z 62 MATA DA PRAIA 0 0 0 0 0 0 0 0 1
3 8.679512e+11 5642828 F 2016-04-29T17:29:31Z 2016-04-29T00:00:00Z 8 PONTAL DE CAMBURI 0 0 0 0 0 0 0 0 1
4 8.841186e+12 5642494 F 2016-04-29T16:07:23Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 1 1 0 0 0 0 0 1

dropping uneeded columns

In [87]:
data = data[['PatientID','ScheduledDay','AppointmentDay', 'Age','Neighbourhood', 'Scholarship','Hypertension','Diabetes','Alcoholism','Handicap','SMS_received','NoShow','Male','Female']]

Out[87]:
PatientID ScheduledDay AppointmentDay Age Neighbourhood Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received NoShow Male Female
0 2.987250e+13 2016-04-29T18:38:08Z 2016-04-29T00:00:00Z 62 JARDIM DA PENHA 0 1 0 0 0 0 0 0 1
1 5.589978e+14 2016-04-29T16:08:27Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 0 0 0 0 0 0 1 0
2 4.262962e+12 2016-04-29T16:19:04Z 2016-04-29T00:00:00Z 62 MATA DA PRAIA 0 0 0 0 0 0 0 0 1
3 8.679512e+11 2016-04-29T17:29:31Z 2016-04-29T00:00:00Z 8 PONTAL DE CAMBURI 0 0 0 0 0 0 0 0 1
4 8.841186e+12 2016-04-29T16:07:23Z 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 1 1 0 0 0 0 0 1

cleaning up dates

In [88]:
data['ScheduledDay'] = pd.to_datetime(data['ScheduledDay'])
data['AppointmentDay'] = pd.to_datetime(data['AppointmentDay'])

Out[88]:
PatientID ScheduledDay AppointmentDay Age Neighbourhood Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received NoShow Male Female
0 2.987250e+13 2016-04-29 18:38:08 2016-04-29 62 JARDIM DA PENHA 0 1 0 0 0 0 0 0 1
1 5.589978e+14 2016-04-29 16:08:27 2016-04-29 56 JARDIM DA PENHA 0 0 0 0 0 0 0 1 0
2 4.262962e+12 2016-04-29 16:19:04 2016-04-29 62 MATA DA PRAIA 0 0 0 0 0 0 0 0 1
3 8.679512e+11 2016-04-29 17:29:31 2016-04-29 8 PONTAL DE CAMBURI 0 0 0 0 0 0 0 0 1
4 8.841186e+12 2016-04-29 16:07:23 2016-04-29 56 JARDIM DA PENHA 0 1 1 0 0 0 0 0 1
In [89]:
#original features that I was using, got rid of.
"""
data['ScheduledYear'], data['ScheduledMonth'], data['ScheduleDay'] = data['ScheduledDay'].dt.year, data['ScheduledDay'].dt.month, data['ScheduledDay'].dt.day
data['AppointmentYear'], data['AppointmentMonth'], data['AppointmentDayy'] = data['AppointmentDay'].dt.year, data['AppointmentDay'].dt.month, data['AppointmentDay'].dt.day
"""

Out[89]:
"\ndata['ScheduledYear'], data['ScheduledMonth'], data['ScheduleDay'] = data['ScheduledDay'].dt.year, data['ScheduledDay'].dt.month, data['ScheduledDay'].dt.day\ndata['AppointmentYear'], data['AppointmentMonth'], data['AppointmentDayy'] = data['AppointmentDay'].dt.year, data['AppointmentDay'].dt.month, data['AppointmentDay'].dt.day\ndata.head\n"

Probably have to get rid of Neighbourhood column given we don't have the specefic hospital for all these NoShows. If we did we could have used distance from hospital as a feature.

Creating a feature that calculates the wait time of a particular patient from when they schedule the appointment to when they actually have the appointment. I believe this will be a really great feature to have. One would assume that the longer the wait time the more likely people are to no show for their appointments

## Creating Waiting Time¶

In [90]:
data.ScheduledDay = pd.DatetimeIndex(data.ScheduledDay).normalize()
data['WaitingTime'] = data['AppointmentDay'] - data['ScheduledDay']

Out[90]:
PatientID ScheduledDay AppointmentDay Age Neighbourhood Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received NoShow Male Female WaitingTime
0 2.987250e+13 2016-04-29 2016-04-29 62 JARDIM DA PENHA 0 1 0 0 0 0 0 0 1 0 days
1 5.589978e+14 2016-04-29 2016-04-29 56 JARDIM DA PENHA 0 0 0 0 0 0 0 1 0 0 days
2 4.262962e+12 2016-04-29 2016-04-29 62 MATA DA PRAIA 0 0 0 0 0 0 0 0 1 0 days
3 8.679512e+11 2016-04-29 2016-04-29 8 PONTAL DE CAMBURI 0 0 0 0 0 0 0 0 1 0 days
4 8.841186e+12 2016-04-29 2016-04-29 56 JARDIM DA PENHA 0 1 1 0 0 0 0 0 1 0 days
In [91]:
data['WaitingTime'] = data['WaitingTime'].apply(lambda x: x.days)

Out[91]:
PatientID ScheduledDay AppointmentDay Age Neighbourhood Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received NoShow Male Female WaitingTime
0 2.987250e+13 2016-04-29 2016-04-29 62 JARDIM DA PENHA 0 1 0 0 0 0 0 0 1 0
1 5.589978e+14 2016-04-29 2016-04-29 56 JARDIM DA PENHA 0 0 0 0 0 0 0 1 0 0
2 4.262962e+12 2016-04-29 2016-04-29 62 MATA DA PRAIA 0 0 0 0 0 0 0 0 1 0
3 8.679512e+11 2016-04-29 2016-04-29 8 PONTAL DE CAMBURI 0 0 0 0 0 0 0 0 1 0
4 8.841186e+12 2016-04-29 2016-04-29 56 JARDIM DA PENHA 0 1 1 0 0 0 0 0 1 0
5 9.598513e+13 2016-04-27 2016-04-29 76 REPÚBLICA 0 1 0 0 0 0 0 0 1 2
6 7.336882e+14 2016-04-27 2016-04-29 23 GOIABEIRAS 0 0 0 0 0 0 1 0 1 2
7 3.449833e+12 2016-04-27 2016-04-29 39 GOIABEIRAS 0 0 0 0 0 0 1 0 1 2
8 5.639473e+13 2016-04-29 2016-04-29 21 ANDORINHAS 0 0 0 0 0 0 0 0 1 0
9 7.812456e+13 2016-04-27 2016-04-29 19 CONQUISTA 0 0 0 0 0 0 0 0 1 2
10 7.345362e+14 2016-04-27 2016-04-29 30 NOVA PALESTINA 0 0 0 0 0 0 0 0 1 2
11 7.542951e+12 2016-04-26 2016-04-29 29 NOVA PALESTINA 0 0 0 0 0 1 1 1 0 3
12 5.666548e+14 2016-04-28 2016-04-29 22 NOVA PALESTINA 1 0 0 0 0 0 0 0 1 1
13 9.113946e+14 2016-04-28 2016-04-29 28 NOVA PALESTINA 0 0 0 0 0 0 0 1 0 1
14 9.988472e+13 2016-04-28 2016-04-29 54 NOVA PALESTINA 0 0 0 0 0 0 0 0 1 1
15 9.994839e+10 2016-04-26 2016-04-29 15 NOVA PALESTINA 0 0 0 0 0 1 0 0 1 3
16 8.457439e+13 2016-04-28 2016-04-29 50 NOVA PALESTINA 0 0 0 0 0 0 0 1 0 1
17 1.479497e+13 2016-04-28 2016-04-29 40 CONQUISTA 1 0 0 0 0 0 1 0 1 1
18 1.713538e+13 2016-04-26 2016-04-29 30 NOVA PALESTINA 1 0 0 0 0 1 0 0 1 3
19 7.223289e+12 2016-04-29 2016-04-29 46 DA PENHA 0 0 0 0 0 0 0 0 1 0
In [92]:
data['WaitingTime'].mean()

Out[92]:
10.183701719941734

Need to make one last clean dataset picking which features I will actually use in the model.

In [93]:
data = data[['NoShow','ScheduledDay','AppointmentDay','Age','Scholarship','Hypertension','Diabetes','Alcoholism','Handicap','SMS_received','Male','Female','WaitingTime']]

Out[93]:
NoShow ScheduledDay AppointmentDay Age Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received Male Female WaitingTime
0 0 2016-04-29 2016-04-29 62 0 1 0 0 0 0 0 1 0
1 0 2016-04-29 2016-04-29 56 0 0 0 0 0 0 1 0 0
2 0 2016-04-29 2016-04-29 62 0 0 0 0 0 0 0 1 0
3 0 2016-04-29 2016-04-29 8 0 0 0 0 0 0 0 1 0
4 0 2016-04-29 2016-04-29 56 0 1 1 0 0 0 0 1 0

## Train Models ¶

Here is probably the most interesting and complex part to this analysis.

I will run a bunch of different models to see what their various testing/training accuracies are in order to pick the best one and then try and optomize that particular model.

In [94]:
data.head()

Out[94]:
NoShow ScheduledDay AppointmentDay Age Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received Male Female WaitingTime
0 0 2016-04-29 2016-04-29 62 0 1 0 0 0 0 0 1 0
1 0 2016-04-29 2016-04-29 56 0 0 0 0 0 0 1 0 0
2 0 2016-04-29 2016-04-29 62 0 0 0 0 0 0 0 1 0
3 0 2016-04-29 2016-04-29 8 0 0 0 0 0 0 0 1 0
4 0 2016-04-29 2016-04-29 56 0 1 1 0 0 0 0 1 0
In [95]:
#the features we are going to train our model on
showfeatures = data.iloc[:,3:19]

Out[95]:
Age Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received Male Female WaitingTime
0 62 0 1 0 0 0 0 0 1 0
1 56 0 0 0 0 0 0 1 0 0
2 62 0 0 0 0 0 0 0 1 0
3 8 0 0 0 0 0 0 0 1 0
4 56 0 1 1 0 0 0 0 1 0

## Splitting¶

In [96]:
from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.metrics import accuracy_score

In [97]:
# TRAIN/TEST PARTITION
#create the feature vectors and class labels

features = np.array(showfeatures)
labels = np.array(data['NoShow'])

features

Out[97]:
array([[62,  0,  1, ...,  0,  1,  0],
[56,  0,  0, ...,  1,  0,  0],
[62,  0,  0, ...,  0,  1,  0],
...,
[21,  0,  0, ...,  0,  1, 41],
[38,  0,  0, ...,  0,  1, 41],
[54,  0,  0, ...,  0,  1, 41]])
In [98]:
data['NoShow'].value_counts()

Out[98]:
0    88208
1    22319
Name: NoShow, dtype: int64
In [99]:
#split the data into training and testing sets(67% training, 33% into testing)
training_features, testing_features, training_labels, testing_labels = train_test_split(features,labels, test_size = 0.2,random_state = 42 )


## Logistic regression¶

In [100]:
from sklearn import linear_model

lg = linear_model.LogisticRegression()
lg.fit(training_features,training_labels)

Out[100]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False)
In [101]:
# hold out cross validation

#print(accuracy_score(testing_labels, predictions))

score = cross_val_score(lg, training_features,training_labels, cv = 10, scoring= 'accuracy')
print(score)

predictions = lg.predict(testing_features)
print(predictions)

score = accuracy_score(testing_labels, predictions)
print(score)

[ 0.7955445   0.79463983  0.79359873  0.79246777  0.79552138  0.79518209
0.79552138  0.79371183  0.79416422  0.79199186]
[0 0 0 ..., 0 0 0]
0.795304442233


## Multi Layer Perceptron just for fun, takes forever to run¶

In [102]:
"""from sklearn.neural_network import MLPClassifier
mlp = MLPClassifier(alpha=0.01, hidden_layer_sizes = (100,100))
mlp.fit(training_features,training_labels)"""

Out[102]:
'from sklearn.neural_network import MLPClassifier\nmlp = MLPClassifier(alpha=0.01, hidden_layer_sizes = (100,100))\nmlp.fit(training_features,training_labels)'
In [103]:
"""# hold out cross validation

#print(accuracy_score(testing_labels, predictions))

score = cross_val_score(mlp, training_features,training_labels, cv = 10, scoring= 'accuracy')
print(score)

predictions = mlp.predict(testing_features)
print(predictions)

score = accuracy_score(testing_labels, predictions)
print(score)"""

Out[103]:
"# hold out cross validation\n\n#print(accuracy_score(testing_labels, predictions))\n\nscore = cross_val_score(mlp, training_features,training_labels, cv = 10, scoring= 'accuracy')\nprint(score)\n\npredictions = mlp.predict(testing_features)\nprint(predictions)\n\nscore = accuracy_score(testing_labels, predictions)\nprint(score)"

## Random Forrest¶

In [104]:
from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(max_depth=2,n_estimators=100)
rf.fit(training_features,training_labels)

Out[104]:
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=2, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
oob_score=False, random_state=None, verbose=0,
warm_start=False)
In [105]:
# hold out cross validation

#print(accuracy_score(testing_labels, predictions))

score = cross_val_score(rf, training_features,training_labels, cv = 10, scoring= 'accuracy')
print(score)

predictions = rf.predict(testing_features)
print(predictions)

score = accuracy_score(testing_labels, predictions)
print(score)

[ 0.79769309  0.79769309  0.79778331  0.79778331  0.79778331  0.79778331
0.79778331  0.79778331  0.79778331  0.79776043]
[0 0 0 ..., 0 0 0]
0.79928526192


## KNeighbors¶

In [106]:
from sklearn.neighbors import KNeighborsClassifier

In [107]:
#TRAIN/TEST ALGORITHM
#instance the model

kList = range(1,50)

cv_scores = []

neighbors = filter(lambda x: x % 2 != 0, kList)

In [108]:
for i in neighbors:
print(i)
knn = KNeighborsClassifier(n_neighbors=i)

knn.fit(training_features,training_labels)

#test the model
predictions = knn.predict(testing_features)

# hold out cross validation

#print(accuracy_score(testing_labels, predictions))

scores = cross_val_score(knn, training_features,training_labels, cv = 10, scoring= 'accuracy')
#print(scores)
cv_scores.append(scores.mean())

print("done finding")

1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
done finding

In [109]:
optimalk = cv_scores.index(max(cv_scores))

In [110]:
knn = KNeighborsClassifier(n_neighbors=optimalk)
knn.fit(training_features,training_labels)

Out[110]:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=24, p=2,
weights='uniform')
In [111]:
predictions = knn.predict(testing_features)
#data['predictions'] = knn.predict(testing_features)

score = accuracy_score(testing_labels, predictions)
print(score)

0.795711571519


## Predict Whether a Patient Will Show Up: User Input¶

In [112]:
data.head()

Out[112]:
NoShow ScheduledDay AppointmentDay Age Scholarship Hypertension Diabetes Alcoholism Handicap SMS_received Male Female WaitingTime
0 0 2016-04-29 2016-04-29 62 0 1 0 0 0 0 0 1 0
1 0 2016-04-29 2016-04-29 56 0 0 0 0 0 0 1 0 0
2 0 2016-04-29 2016-04-29 62 0 0 0 0 0 0 0 1 0
3 0 2016-04-29 2016-04-29 8 0 0 0 0 0 0 0 1 0
4 0 2016-04-29 2016-04-29 56 0 1 1 0 0 0 0 1 0
In [113]:
age = 0.0
scholarship = 0.0
hypertension = 0.0
diabetes = 0.0
alcoholism = 0.0
handicap = 0.0
sms = 0.0
male = 0.0
female = 0.0
waitingtime = 0

inputage = int(input("Enter the patient's age: "+ "\n" ))
inputscholarship = input("Is the patient on Scholarship(Yes or No): "+ "\n" )
inputhypertension = input("Does the patient have Hypertension?(Yes or No): "+ "\n" )
inputdiabetes = input("Is the patient Diabetic?(Yes or No): "+ "\n" )
inputalcoholism = input("Is the patient an Alcoholic?(Yes or No): "+ "\n" )
inputhandicap = int(input("How many Handicaps does the patient have?: "+ "\n" ))
inputsms = input("Will the patient recieve a text message?(Yes or No): "+ "\n" )
inputgender = input("What is the patient gender?(Male or Female): "+ "\n")

inputwaitingtime = int(input("How many days away is the appointment?: "+ "\n"))

age = inputage
handicap = inputhandicap
waitingtime = inputwaitingtime

if inputscholarship == "Yes" or "yes":
scholarship = 1.0
elif inputscholarship == "No" or "no":
scholarship = 0

if hypertension == "Yes" or "yes":
hypertension = 1.0
elif hypertension == "No" or "no":
hypertension = 0
if inputdiabetes == "Yes" or "yes":
diabetes = 1.0
elif inputdiabetes == "No" or "no":
daibetes = 0
if inputalcoholism == "Yes" or "yes":
alcoholism = 1.0
elif inputalcoholism == "No" or "no":
alcoholism = 0
if inputsms == "Yes" or "yes":
sms = 1.0
elif inputsms == "No" or "no":
sms = 0
if inputgender == "Male" or "M":
male = 1.0
elif inputgender == "Female" or "F":
female = 1.0
else:
print("incorrect gender input")

#the commented out chunk was for testing to not have to type everything in everytime I wanted to test
"""
print(" ")

fixedanswer = [5, 1.0, 1.0 ,1.0, 1.0, 4, 0.0, 1.0, 1.0,10]

"""

print(" ")


Enter the patient's age:
5
Is the patient on Scholarship(Yes or No):
Yes
Does the patient have Hypertension?(Yes or No):
Yes
Is the patient Diabetic?(Yes or No):
Yes
Is the patient an Alcoholic?(Yes or No):
Yes
How many Handicaps does the patient have?:
5
Will the patient recieve a text message?(Yes or No):
Yes
What is the patient gender?(Male or Female):
Female
How many days away is the appointment?:
10

[5, 1.0, 1.0, 1.0, 1.0, 5, 1.0, 1.0, 0.0, 10]
[0]
[0]
[0]


# Esemble Model¶

In [114]:
print("Here we will use an ensemble of our three different models to vote whether this patient will show up or not.")
print("Let's see how our individual models voted.")
noshowcounter = 0.0
showcounter = 0.0

print("")
print("random forrest:")
print(randomresult[0])

if randomresult[0] == 1:
noshowcounter+=1
print("no show")
else:
showcounter+=1
print("show")

print("")
print("logistic regression:")
print(logresult[0])

if logresult[0] == 1:
noshowcounter+=1
print("no show")
else:
showcounter+=1
print("show")

print("")
print("knn:")
print(knnresult[0])

if knnresult[0] == 1:
noshowcounter+=1
print("no show")
else:
showcounter+=1
print("show")

print("")
print("The final vote is %f no shows to %d shows" %(noshowcounter,showcounter))

Here we will use an ensemble of our three different models to vote whether this patient will show up or not.
Let's see how our individual models voted.

random forrest:
0
show

logistic regression:
0
show

knn:
0
show

The final vote is 0.000000 no shows to 3 shows


### Analysis of Ensemble Model:¶

What you can see here is that there needs to be more descrepencies or more features because an esenmble doesn't really help here because they all vote the same

# Result¶

In [115]:
if noshowcounter >= 2:
print("Our Esemble Model predicts the patient is not going to show up for their appointment.")
print("Be advised, intervention may be neccesary or suggested in order for the patient to show up ")

else:
print("Our Model Predicts that this patient will show up to their appointment, intervention is not needed")

Our Model Predicts that this patient will show up to their appointment, intervention is not needed

In [116]:
rfpredictions = rf.predict(testing_features)
lgpredictions = lg.predict(testing_features)
knnpredictions = knn.predict(testing_features)

print(rfpredictions)
print(lgpredictions)
print(knnpredictions)
print("")

lengthoflists = (len(rfpredictions))

ensemblepredictions = []

ensemblescore = 0

for i in range(len(rfpredictions)):

if rfpredictions[i] == 1:
ensemblescore+=1
if lgpredictions[i] == 1:
ensemblescore+=1
if knnpredictions[i] == 1:
ensemblescore+=1

if ensemblescore >= 2:
ensemblescore = 1
else:
ensemblescore = 0

ensemblepredictions.append(ensemblescore)

print("")
print("done with for loop")

[0 0 0 ..., 0 0 0]
[0 0 0 ..., 0 0 0]
[0 0 0 ..., 0 0 0]

done with for loop

In [117]:
print("")
#uncomment if you want to see my ensemble predicting basically all zeros.
#print(ensemblepredictions)

ensemblescore = accuracy_score(testing_labels, ensemblepredictions)
print(ensemblescore)

0.799149552158


## Confusion Matrix¶

In [118]:
from sklearn.metrics import confusion_matrix

confusion_matrix(testing_labels, ensemblepredictions)

Out[118]:
array([[17658,    11],
[ 4429,     8]])

## Error Analysis:¶

I was a bit confused what was going on here and why all of the predictions were zeros but what we learned is that just because you have these features doesn't neccesarily mean you can convert them into a yes or no no show. Rather indiviudal classifiers and ensemble model learned if you just say people are always going to show up you will get an 80% accuracy

That being said, you can still predict the probability at which individuals may or may not show up using predicta proba on logistic regression

## Patient #1: 5 year old female with a lot of medical "problems"¶

In [119]:
age = 0.0
scholarship = 0.0
hypertension = 0.0
diabetes = 0.0
alcoholism = 0.0
handicap = 0.0
sms = 0.0
male = 0.0
female = 0.0
waitingtime = 0

inputage = int(input("Enter the patient's age: "+ "\n" ))
inputscholarship = input("Is the patient on Scholarship(Yes or No): "+ "\n" )
inputhypertension = input("Does the patient have Hypertension?(Yes or No): "+ "\n" )
inputdiabetes = input("Is the patient Diabetic?(Yes or No): "+ "\n" )
inputalcoholism = input("Is the patient an Alcoholic?(Yes or No): "+ "\n" )
inputhandicap = int(input("How many Handicaps does the patient have?: "+ "\n" ))
inputsms = input("Will the patient recieve a text message?(Yes or No): "+ "\n" )
inputgender = input("What is the patient gender?(Male or Female): "+ "\n")

inputwaitingtime = int(input("How many days away is the appointment?: "+ "\n"))

age = inputage
handicap = inputhandicap
waitingtime = inputwaitingtime

if inputscholarship == "Yes" or "yes":
scholarship = 1.0
elif inputscholarship == "No" or "no":
scholarship = 0

if hypertension == "Yes" or "yes":
hypertension = 1.0
elif hypertension == "No" or "no":
hypertension = 0
if inputdiabetes == "Yes" or "yes":
diabetes = 1.0
elif inputdiabetes == "No" or "no":
daibetes = 0
if inputalcoholism == "Yes" or "yes":
alcoholism = 1.0
elif inputalcoholism == "No" or "no":
alcoholism = 0
if inputsms == "Yes" or "yes":
sms = 1.0
elif inputsms == "No" or "no":
sms = 0
if inputgender == "Male" or "M":
male = 1.0
elif inputgender == "Female" or "F":
female = 1.0
else:
print("incorrect gender input")

#the commented out chunk was for testing to not have to type everything in everytime I wanted to test

print(" ")
"""

fixedanswer = [100, 1.0, 1.0 ,1.0, 1.0, 5, 0.0, 1.0, 1.0,10]

"""

print(" ")


Enter the patient's age:
5
Is the patient on Scholarship(Yes or No):
Yes
Does the patient have Hypertension?(Yes or No):
Yes
Is the patient Diabetic?(Yes or No):
Yes
Is the patient an Alcoholic?(Yes or No):
Yes
How many Handicaps does the patient have?:
5
Will the patient recieve a text message?(Yes or No):
Yes
What is the patient gender?(Male or Female):
Female
How many days away is the appointment?:
10

[5, 1.0, 1.0, 1.0, 1.0, 5, 1.0, 1.0, 0.0, 10]

In [120]:
probabilityresult = lg.predict_proba([answer])

showupproba = probabilityresult[0]
showupproba = showupproba[0]

noshowproba = probabilityresult[0]
noshowproba = noshowproba[1]

print("There is a %f percent chance patient #1 does show up and a %f percent chance this person doesn't show up" % (showupproba,noshowproba))


There is a 0.540198 percent chance patient #1 does show up and a 0.459802 percent chance this person doesn't show up


## Patient #2: 100 year old female with the same lot of medical "problems"¶

In [121]:
age = 0.0
scholarship = 0.0
hypertension = 0.0
diabetes = 0.0
alcoholism = 0.0
handicap = 0.0
sms = 0.0
male = 0.0
female = 0.0
waitingtime = 0

inputage = int(input("Enter the patient's age: "+ "\n" ))
inputscholarship = input("Is the patient on Scholarship(Yes or No): "+ "\n" )
inputhypertension = input("Does the patient have Hypertension?(Yes or No): "+ "\n" )
inputdiabetes = input("Is the patient Diabetic?(Yes or No): "+ "\n" )
inputalcoholism = input("Is the patient an Alcoholic?(Yes or No): "+ "\n" )
inputhandicap = int(input("How many Handicaps does the patient have?: "+ "\n" ))
inputsms = input("Will the patient recieve a text message?(Yes or No): "+ "\n" )
inputgender = input("What is the patient gender?(Male or Female): "+ "\n")

inputwaitingtime = int(input("How many days away is the appointment?: "+ "\n"))

age = inputage
handicap = inputhandicap
waitingtime = inputwaitingtime

if inputscholarship == "Yes" or "yes":
scholarship = 1.0
elif inputscholarship == "No" or "no":
scholarship = 0

if hypertension == "Yes" or "yes":
hypertension = 1.0
elif hypertension == "No" or "no":
hypertension = 0
if inputdiabetes == "Yes" or "yes":
diabetes = 1.0
elif inputdiabetes == "No" or "no":
daibetes = 0
if inputalcoholism == "Yes" or "yes":
alcoholism = 1.0
elif inputalcoholism == "No" or "no":
alcoholism = 0
if inputsms == "Yes" or "yes":
sms = 1.0
elif inputsms == "No" or "no":
sms = 0
if inputgender == "Male" or "M":
male = 1.0
elif inputgender == "Female" or "F":
female = 1.0
else:
print("incorrect gender input")

#the commented out chunk was for testing to not have to type everything in everytime I wanted to test

print(" ")
"""

fixedanswer = [100, 1.0, 1.0 ,1.0, 1.0, 5, 0.0, 1.0, 1.0,10]

"""

print(" ")


Enter the patient's age:
100
Is the patient on Scholarship(Yes or No):
Yes
Does the patient have Hypertension?(Yes or No):
Yes
Is the patient Diabetic?(Yes or No):
Yes
Is the patient an Alcoholic?(Yes or No):
Yes
How many Handicaps does the patient have?:
5
Will the patient recieve a text message?(Yes or No):
Yes
What is the patient gender?(Male or Female):
Female
How many days away is the appointment?:
10

[100, 1.0, 1.0, 1.0, 1.0, 5, 1.0, 1.0, 0.0, 10]

In [122]:
probabilityresult = lg.predict_proba([answer])

showupproba = probabilityresult[0]
showupproba = showupproba[0]

noshowproba = probabilityresult[0]
noshowproba = noshowproba[1]

print("There is a %f percent chance patient #1 does show up and a %f percent chance this person doesn't show up" % (showupproba,noshowproba))


There is a 0.707785 percent chance patient #1 does show up and a 0.292215 percent chance this person doesn't show up


## Analysis of different patients¶

Age seems to be a big huge factor which makes sense because children aren't able to get to appointments themselves. Also if a child is 5 and is an alcholic i'm pretty sure that would mean they would have an alcholic and unrealiable parents which would make sense that they would have a way less likelyhood to show up than a 100 year old with the same features.

While a 16% difference in showing up or not may not be the most instiutive or knowledgable thing in the world we can "empirically" say these people are more or less likely to show up than one another.

side note I tried different training testing partitions(10/90 and 70/30) and that didn't really have any affect on my accuracy levels.