Belgium Insurance Claim Severity AI Project

This project is an end-to-end Machine Learning pipeline designed to predict individual motor insurance claim severity using a dataset of over 70,000 Belgian insurance records. I developed a robust system that cleans raw data, performs advanced feature engineering—such as target encoding for high-cardinality vehicle models—and evaluates seven different regression architectures. The final solution utilizes a Random Forest model optimized for Mean Absolute Percentage Error (MAPE) to ensure high accuracy for individual claim predictions. To bridge the gap between development and production, I integrated MLflow for experiment tracking, hosted the versioned model on the Hugging Face Hub, and deployed a real-time prediction interface via Streamlit.