Healthcare analytics case study
Why do patients miss medical appointments?
Python code, visualizations, findings, and recommendations from 110,527 public medical appointment records.

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. It is presented as descriptive analytics and project evidence, not as a clinical diagnostic tool.
The dataset is not bundled with the website. Local chart exports and code are provided so employers can quickly understand the workflow.
Visual evidence
Six views of appointment behavior.






Code and recommendation
Transparent, reproducible analysis.
Project reflection
Lessons Learned
This project strengthened my ability to transform public healthcare data into clear visual evidence, reproducible Python analysis, and operational recommendations while respecting model and data limitations.
Related portfolio links
Continue reviewing healthcare analytics evidence.
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