Non-profit and operations analyst learning as much as she can about data science theory and application in hopes to one day use her superpowers for good.
Using binomial classification to predict COVID-19 infection on a large dataset (>618K samples) with extreme imbalance and minority class (.13% of samples) as target. The final iteration is a manually tuned random forsest classifier with >95% accuracy and >64% recall.