About

This canvas implements a sophisticated customer churn prediction and prevention system using machine learning and time-series analysis. It generates synthetic usage data, engineers churn indicators (usage drop, feature abandonment, admin inactivity, volatility), trains dual Random Forest models- one on random-split account data and another on time-based validation data, and produces prioritized intervention recommendations mapping at-risk accounts to specific CSM actions like training, executive outreach, and escalation.