About

Early Signals of User Success is a behavioral intelligence system that predicts which users will become high-impact, long-term contributors based solely on their first 7 days of activity. Instead of relying on generative AI, the system uses advanced feature engineering and classical machine learning to extract meaningful patterns from raw event logs.

The project transforms millions of low-level user actions into interpretable behavioural signals such as engagement momentum, workflow diversity, exploration breadth, collaboration intensity, and early success indicators. These signals are then used to train multiple predictive models—including Logistic Regression, Random Forest, and Gradient Boosting—to estimate different dimensions of user success.

By combining these predictions into a unified User Success Score, the system enables product teams, platform builders, and community managers to identify high-potential users early, optimise onboarding strategies, and drive long-term retention and value creation.
This project demonstrates that powerful, explainable intelligence can be built without generative AI—using data, behaviour, and well-designed models as the core engine of insight.