This repository includes detailed data analyses and prediction models for students' on-time graduation using various machine learning algorithms.

Using supervised machine learning techniques to find university level factors affecting graduation and retention rates in US Colleges

Comprehensive Tableau dashboard analyzing U.S. higher education landscape using IPEDS data - enrollment patterns, graduation rates, regional demographics, and institutional performance metrics

Student-Sucess-Insights For U.S. Higher Education

This is an expansion of dsb318-group4 (see repo: dsb318-group4), in which we collaborated to predict high school graduation rates in CA from other trends (e.g., poverty rate, availability of e-cigarettes). Collaboration between Eli and Emily.

Implemented a Random Forest classifier to enhance two success metrics—Regular Attendance and On-Time Graduation—for high school students.