Description Usage Format Details Source Examples
Predicting no-show medical appointments
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A data frame with 110527 rows and 14 variables:
PatientId
double. Identification of a patient.
AppointmentID
double. dentification of each appointment.
Gender
factor. Male, Female
.
ScheduledDay
datatime. The day and time of the actual appointment, when they have to visit the doctor.
AppointmentDay
double. The day someone called or registered the appointment, this is before appointment of course.
Age
double. Age of the patient.
Neighbourhood
character. Where the appointment takes place.
Scholarship
integer. 0=FALSE, 1=TRUE
. Scholarship
is a social welfare program providing financial aid to poor Brazilian families.
Hypertension
integer. 0=FALSE, 1=TRUE
.
Diabetes
integer. 0=FALSE, 1=TRUE
.
Alcoholism
integer. 0=FALSE, 1=TRUE
.
Handcap
integer. 0=FALSE, 1=TRUE
.
SMS_received
integer. 0=FALSE, 1=TRUE
.
1 or more messages sent to the patient.
No_show
factor. Yes, No.
This Kaggle competition was designed to challenge participants to predict office no-shows. It is also a good dataset to practice date and time manipulation.
Joni Hoppen, Kaggle Medical Appointment No Shows https://www.kaggle.com/joniarroba/noshowappointments.
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