Healthcare analytics case study

Why do patients miss medical appointments?

Python code, visualizations, findings, and recommendations from 110,527 public medical appointment records.

View code on GitHub Download Python script View original notebook Read the full Python analysis walkthrough

Bar chart of missed appointments by weekday — **Weekday pattern**Tuesday and Wednesday had the highest missed-appointment counts in this descriptive view.

110,527appointment records

20.19%missed appointments

79.81%attended appointments

6visual views

Problem

Which patient and scheduling factors are associated with missed visits?

The analysis explores appointment attendance patterns across age, gender, SMS reminder status, scholarship status, and weekday. The analysis is descriptive; it identifies patterns, not causes, and is not a clinical tool.

The public dataset is not redistributed here. The charts and the full Python code are available below so the analysis can be reproduced.

Visual analysis

Six views of appointment behavior.

Appointment attendance comparison — **Attendance balance**Most appointments were attended.

Average age by attendance outcome — **Average age**Age patterns were compared across outcomes.

Attendance by gender — **Gender**Attendance patterns were reviewed by gender.

SMS reminder attendance comparison — **SMS reminders**Reminder behavior was examined descriptively.

Scholarship status attendance comparison — **Support status**Scholarship status did not show a large attendance difference in this view.

Missed appointments by weekday — **Weekday**Early-week outreach may be worth testing.

Code and recommendation

Transparent, reproducible analysis.

Python analysis fileThe complete Python script used for this analysis.Download code

Project reflection

Lessons Learned

This project strengthened my ability to transform public healthcare data into clear visualizations, reproducible Python analysis, and operational recommendations while respecting model and data limitations.

More of my work.

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