This end to end machine learning project predicts average flight departure delays using historical flight data. It applies Ridge regression with polynomial features to model delay patterns and uses MLflow for experiment tracking. The trained model is deployed with a FastAPI application, enabling real time delay predictions based on schedule and airport inputs. The project demonstrates scalable MLOps practices from data preparation to API deployment.