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Unlocking High-Value Usage: Predicting Power Users from Day 2

This project answers the critical question: What user behaviors best predict long-term success and monetization? By analyzing the Zerve dataset, we defined "Success" as users demonstrating payment intent (visiting checkout). We built a Random Forest classifier (AUC=0.87) that identified "Day 2 Retention" and "AI Agent Interaction" as the strongest predictors of value.

Our analysis reveals that users who engage with the AI agent to refactor code in their first week are 3x more likely to convert. This submission provides a reproducible notebook, a clear feature engineering pipeline, and actionable insights for driving product growth. It demonstrates the power of Zerve's environment for end-to-end data science, from raw log ingestion to predictive modeling.