Vehicle Insurance Eligibility Prediction & MLOps Pipeline
Project Type
Objective
Built an end-to-end MLOps pipeline to predict whether a client should be offered vehicle insurance based on personal details, vehicle attributes, and historical claim data.
Tools & Technologies
Project Details
Ingested and transformed raw client and insurance data using MongoDB, Python, and Pandas to create clean, structured datasets for model training and analysis.
Built a classification workflow to predict vehicle insurance eligibility from customer, vehicle, and claim-related features.
Managed datasets, model versioning, and experiment tracking with DVC to support reproducible machine learning workflows.
Deployed the trained model as a FastAPI service on AWS EC2, stored artifacts in AWS S3, and automated deployment using GitHub Actions.