About

Analyzing 409K+ Zerve user events to predict long-term success from first 7 days of behavior.

KEY FINDINGS:
- 85% of users leave after Day 1 (The 85% Problem)
- Heavy agent users (20+) are 5.5x more likely to succeed
- High Day-1 activity predicts CHURN, not success (Burnout Effect)
- Users who wait before using agent succeed more (Timing Paradox)

METHODOLOGY:
- Logistic Regression + Random Forest (98% accuracy)
- K-Means user segmentation
- Cohort analysis & dose-response modeling
- 12+ professional visualizations

DELIVERABLES:
- Deployed predict_user_success() function
- 6 actionable product recommendations
- Complete reproducible analysis pipeline