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This canvas builds an end-to-end machine learning pipeline that predicts customer churn for a telecom company using a Random Forest classifier. The workflow loads and analyzes telco customer data, preprocesses and splits it for training, trains a churn prediction model, evaluates its performance with business ROI metrics, and finally creates a reusable prediction function that estimates churn risk based on customer tenure and monthly charges.