About

Zerve User Success Analysis
By: Mahimna Trivedi


Objective
Analyze user behavior patterns to identify factors predicting high credit usage.

Success Definition
Users in the top 25% of total credit consumption were labeled as successful.

Methodology
Event-level data was cleaned and aggregated into user-level features.
A balanced Random Forest model was trained.

Results
The model achieved **75% accuracy** and **67% recall** on high-value users.

Key Insights
- Activity frequency is the strongest predictor of success
- Retention duration strongly influences credit usage
- Early credit usage predicts long-term value
- Tool diversity has limited impact

Conclusion
Consistent engagement is more important than experimentation.