This project is from the Data Analysis Nano Degree from Udacity. The intent is to walk through the data analysis process on a medical patient show/no-show dataset, identifying the relationshp between various dependent variables to the dependent variable - whether a patient will show up or not.
It starts off at data wrangling, then to exploratory analysis and finally drawing conclusions from the findings.
This dataset is from Kaggle
An individual calls to make a doctor's appointment. Their details are noted down and an appointment date is given. Of all these individuals, some show up for their appointments and others don't. Are there certain characteristics of a patient that tend to shor a higher turn up rate?
01 - PatientId • Identification of a patient 02 - AppointmentID • Identification of each appointment 03 - Gender • Male or Female . Female is the greater proportion, woman takes way more care of they health in comparison to man. 04 - DataMarcacaoConsulta • The day of the actuall appointment, when they have to visit the doctor. 05 - DataAgendamento • The day someone called or registered the appointment, this is before appointment of course. 06 - Age • How old is the patient. 07 - Neighbourhood • Where the appointment takes place. 08 - Scholarship • True of False . Observation, this is a broad topic, consider reading this article https://en.wikipedia.org/wiki/Bolsa_Fam%C3%ADlia 09 - Hipertension • True or False 10 - Diabetes • True or False 11 - Alcoholism • True or False 12 - Handcap • True or False 13 - SMS_received • 1 or more messages sent to the patient. 14 - No-show • True or False. 15 - Inspiration