About

This canvas implements a complete end-to-end machine learning pipeline for customer churn prediction, flowing from data loading and preprocessing through exploratory analysis, Gradient Boosting model training, and production-ready deployment with customer risk segmentation. The workflow loads Telco customer data, splits and preprocesses it with one-hot encoding and feature scaling, performs visual analysis of churn patterns and feature distributions, trains a GradientBoostingClassifier achieving high accuracy and ROC-AUC scores, and finally segments customers into risk buckets (Low/Moderate/High/Very High Risk) with specific retention action recommendations—all outputs formatted as structured JSON for CRM and API integration.