Real data, real problems, real people. Every project here started with a question I actually wanted answered.
AI tools are everywhere, but not everyone is using them the same way — or at all. I wanted to understand how moms specifically engage with AI in their real lives. So I went and asked them. Fifty-two real survey responses later, I built an end-to-end machine learning pipeline that turns messy, unstructured text into a behavior-based persona framework that can actually predict who someone is.
This one is personal. As the founder of Melanated Mamas Golden Crescent, I know firsthand that the tools we build should reflect the people using them. This project is my attempt to do that with data.
Healthcare data holds patterns that can save lives. Using 303 anonymous patient records and 13 clinical features — from cholesterol to chest pain type — I built and compared two classification models to predict heart disease risk. Logistic Regression came out on top, hitting 90% accuracy and a 0.94 ROC-AUC score. The real story is in why: heart disease risk follows predictable, linear patterns that logistic regression is built for.
One limitation I noted: no lifestyle data was available. Factors like diet, smoking, and activity level would paint a fuller picture — a real direction for future work.