Predicting Event Participation for an Event-Based Social Network

Accurately predicting event attendance is essential for organizers to allocate resources, improve user experience, and ensure success. This study examines user engagement within the Venzi event-based social networking app by analyzing chat interactions, event views, and user applications and approvals. Key factors influencing attendance and turnout rate-the percentage of approved users who attend-are identified and used to build predictive models. We compare the performance of linear regression, XGBoost, and a specialized weighted distance model (Kappa). The analysis highlights the significance of user interaction metrics in forecasting event outcomes and provides actionable insights for organizers to enhance engagement strategies and optimize attendance.