About

This canvas implements an end-to-end customer churn prediction pipeline that generates synthetic subscription data, preprocesses it with encoding and train-test splitting, trains a Random Forest classifier to predict churn, and visualizes feature importance while aggregating predictions for API deployment readiness. The workflow demonstrates a complete ML lifecycle from data generation through model evaluation with results preparation for downstream integration.