An Unsupervised Learning and EDA Approach for Specialized High School Admissions

Abstract

This research investigates disparities in access and representation within specialized high school admissions processes, focusing on public middle schools in New York City. Leveraging a dataset by a non-profit organization dedicated to increasing diversity in specialized high school admissions, the study employs exploratory data analysis and unsupervised learning techniques to identify schools with high levels of underrepresentation and academic potential. The analysis reveals significant disparities in access to specialized high schools, with certain demographic groups and schools facing barriers to entry. Through k-means clustering, schools are categorized based on their academic performance and demographic composition, enabling targeted intervention strategies to address disparities in access and representation. The research proposes general use towards education, including on-campus interventions, awareness campaigns, and regional information sessions, aimed at fostering equitable access to specialized high school programs. This study contributes to the broader discourse on educational equity and offers valuable insights for policymakers, educators, and researchers seeking to promote diversity and inclusion within educational systems.