About

This project analyzes user behavior to predict long-term success based on engagement patterns. Features such as total events, active days, and events per day were engineered from the dataset. A Random Forest model was used to classify successful users and achieved strong performance. The analysis shows that consistent activity and feature exploration are key drivers of user success. These insights can help improve onboarding, engagement, and retention strategies.