In this project, we’ll build a churn prevention system that uses machine learning to identify customers likely to churn. Customer churn is a major challenge for subscription-based businesses, and we address it by designing an end-to-end pipeline that turns raw customer behavior data into retention signals teams can act on. We’ll use Python data science tools, including pandas, scikit-learn, SMOTE, and FastAPI, to build a machine learning workflow that covers data preparation, model training, evaluation, and deployment.
We’ll start by generating and preprocessing customer data, where we clean features, scale numerical values, and handle class imbalance to improve model learning. We then train a Random Forest classifier to detect churn patterns and evaluate its performance using standard classification metrics. Finally, we’ll deploy the trained model using FastAPI, allowing real-time predictions through a REST API that returns both churn probability and business-driven retention strategies. This project gives us hands-on experience in building production-ready machine learning systems that directly support data-driven decision-making.