Projects

The Silent Emergency - Predicting Preterm Birth

Capstone Project for Data Science Boot Camp

Preterm birth is a primary cause of infant mortality and morbidity in the United States, affecting approximately 1 in 10 births. The rates are notably higher among Black women (14.6%), compared to White (9.4%) and Hispanic women (10.1%). Despite its prevalence, predicting preterm birth remains challenging due to its multifaceted etiology rooted in environmental, biological, genetic, and behavioral interactions. Our project harnesses machine learning techniques to predict preterm birth using electronic health records. This data intersects with social determinants of health, reflecting some of the interactions contributing to preterm birth. Recognizing that under-representation in healthcare research perpetuates racial and ethnic health disparities, we take care to use diverse data to ensure equitable model performance across underrepresented populations.

Project page link (includes 5 min summary video)   GitHub link

Classifying 2024 MLB Pitches

No two pitchers truly throw the same pitch the same way. For example, Tim Hill threw his four-seam fastball with an average of 17.9in of arm-side movement, 4.4in of vertical movement, and at 90.7 MPH. Mason Miller, on the other hand, threw his four-seam fastball with 9.7in of arm-side movement, 16.6in of vertical movement, and at 100.9 MPH. In this project, we classify pitch types using observable data, paying special attention to the inclusion/exclusion of pitcher IDs and the differences between classical machine learning models and a simple neural network. 

GitHub link