Predicting Telecom Customer Churn with scikit-learn, Keras, and Amazon SageMaker Every month, a telecom operator quietly loses thousands of customers to a competitor. They call it churn — and in an industry where acquiring a new customer costs 5–10x more than retaining an existing one, predicting who is about to leave is one of the most valuable problems machine learning can solve. In this tutorial, I'll walk you through a complete churn prediction pipeline I built for a telecom use case. We'll generate a realistic synthetic dataset, train three models (Decision Tree, Random Forest, and a Keras neural network), compare their performance, and deploy the best one to an Amazon SageMaker real-time endpoint. By the end, you'll have a production-ready pipeline you can adapt for any telecoms operator. Full source code : github.com/tsekatm/ml-churn-predictor Why Telecom Churn Is a Hard ML Problem Telecom churn has a few properties that make it interesting: Class imbalance : Typically 20–40% of customers churn.…