Project-Investigate-a-Dataset-No-Show-Appointments

This Project Explore the variable factors To be Considered In Patients showing up for scheduled Appointments

by OLAMIDE QUZEEM O.

Installations

• NumPy
• pandas
• Matplotlib
• Seaborn

Dataset

About the Dataset

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.

Summary of Findings

From the analysis and visualizations, the factors to be considered in predicting no-show appointments are:

• Gender
• Days apart
• Appointment day variables
• Age group categories each patient belongs to

These factors will all be important in predicting no-show appointments of patients.

Note: The neighborhood variable could have been further researched for additional insights.

Project Limitation:

• The data would have been better analyzed if there were variables that indicated the cause of visitations, such as basic check-ups, emergencies, etc. Additionally, a variable indicating patients who missed their appointment and then came back for rescheduling would have been helpful.
• The appointment date variable was recorded as a date format, which differs from the datetime formatting of the scheduling date. Having both date and time information would have provided insights into the time patients tend to show up for their appointments or when they are more likely to default.