About

This project investigates which user behaviors and workflows most strongly predict long-term success.
Success is defined by sustained engagement, feature diversity, and iterative analytical workflows.
Using event-level data, the analysis combines behavioral metrics, visual exploration, and machine-learning models to show that retention, workflow depth, and balanced execution patterns are stronger predictors of success than raw activity volume alone.