Hotel Booking Cancellation Prediction
Consists of a full-stack MLOps pipeline for real-time predictions and automation. The model is uploaded as a container to heroku which serves real time predictions and is scheduled using Airflow DAGs.
Machine Learning & MLOps
Model Development: XGBoost classifier trained on real-world booking data, optimized for accuracy and fairness.
Data Pipeline: Automated DataOps & MLOps workflows using Apache Airflow for ingestion, transformation, and model retraining.
Monitoring & Governance: SHAP values for explainability, drift detection to ensure model relevance, and a feedback loop for continuous learning.
Flowchart
Impact
Increased revenue by proactively managing overbookings.
Enhanced resource planning with real-time insights.
Improved customer retention through smarter cancellation policies.
Deployment & Hosting
Containerization: Dockerized model packaged for portability and scalability.
CI/CD Pipeline: GitHub Actions automates testing and deployment.
Hosting: The FastAPI model API and Flask dashboard are deployed on Heroku, with separate staging and production environments.
Daily Predictions: Integrated with a MySQL database and scheduled via Airflow DAGs, triggering batch predictions and logging insights for hotel managers.