This dataset collects information from 100k medical appointments in Brazil and is focused on the question of whether or not patients show up for their appointment. A number of characteristics about the patient are included in each row.

1. PatientId: Uniqe identification of each patient
2. AppointmentID: Unique identification of each appointment
3. Gender: Male or Female
4. ScheduledDay: The day the patient set up their appointment.
5. Age: How old the patient is.
6. Neighborhood: The location of the hospital.
7. Scholarship: Indicates whether or not the patient is enrolled in Brasilian welfare program Bolsa Família.
8. Hypertension: Whether a patient has it(True or False).
9. Diabetes: Whether a patient has it(True or False).
10. Alcoholism: Whether a patient has it(True or False).
11. Handicap; Whether a patient is(True or False).
12. SmsReceived: Messages sent to the patient.
13. NoShow: Shows whether a patient showed up or not. Says ‘No’ if the patient showed up to their appointment, and ‘Yes’ if they did not show up.

Questions for Analysis My questions for this dataset are split into two main areas of concern. How TIME and POPULATION play in this dataset.

• Were there patients who missed appointments?
• What was the distribution like for appointment waiting times?
• What areas had the shortest and longest wait times?
• Timeframes with the highest and lowest appointments?
• Is there a correlation between the waiting time and no-show ups?
• What gender was most likely to miss and of what age?
• Which area was hit headest by patients who didn't show up?
• Which areas had the most and least appointments?
• Did the patients who received an sms(es) more likely show up?
• What factors correlate?