Predictive Analysis for User Engagement and Matches

The project aims to leverage predictive regression models to identify key tipping points in user engagement, campus ambassador activity, and social media posts that lead to successful matches on Callisto's platform. The primary goal is to determine the optimal levels of engagement required at different scales—individual schools, clusters of schools, or at the state level—to Increase matches. By analyzing historical data, the project will explore patterns and correlations that can inform strategic decisions for matches. This project provides an opportunity for learners to apply their knowledge of data analysis, statistics, and predictive modeling in a real-world context. The outcome will help Callisto optimize its engagement strategies and improve overall platform effectiveness.