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Full-stack ML analysis of 409,287 behavioral events. This project identifies a critical 86.7% drop-off before first block creation and delivers a 98.2% accurate Random Forest model to predict user activation. Key insight: AI Agent engagement is 8.8x more predictive than onboarding skips. Includes a real-time API scoring architecture to trigger contextual help guides, projected to lift conversion by 30-60%.