This notebook develops a machine learning model to predict customer churn, achieving a 73.6% ROC AUC score. It identifies key churn drivers, such as engagement, support interactions, and plan types. The analysis then simulates various intervention strategies, like proactive support and free-to-paid conversion programs, to project their impact on churn reduction and customer retention over twelve months.