This project is an AI-powered Flight Delay Prediction System that predicts the expected delay duration of flights using machine learning regression models.
The system enables airlines to:
- Estimate potential delays in advance
- Optimize flight scheduling and reduce cancellations
- Improve passenger satisfaction with proactive communication & rebooking strategies
- Regression-based Predictions for flight delay duration
- Data-driven Modeling with historical flight, weather & operational data
- API Integration using FastAPI for predictions
- Frontend Dashboard (React + Tailwind) for users & airline staff
- Dockerized Microservices for easy deployment
Data Sources
- Historical flight datasets
- Real-time weather feeds
- Operational & airport-level information
Processing Pipeline
- Data cleaning & preprocessing
- Feature engineering (weather, congestion, seasonal patterns, etc.)
- Regression model training & validation
Prediction Layer
- FastAPI-based ML microservice (
ml_service) - Predicts expected delay (in minutes)
- Provides APIs for integration
Frontend
- Built with React + Tailwind CSS
- Displays predictions, insights, and dashboards
Deployment
- Docker & Docker Compose for containerization
- Optionally extendable to Kubernetes
- Reduce flight disruptions & cancellations
- Improve customer satisfaction with accurate delay forecasts
- Optimize airline resources → higher ROI
- Languages: Python, SQL, JavaScript (React)
- ML Libraries: Scikit-learn, XGBoost/LightGBM, Pandas, NumPy
- Backend (ML Service): FastAPI
- Frontend: React + Tailwind CSS
- Deployment: Docker, Docker Compose
- Database (optional): PostgreSQL / SQLite
📦 flight-insight
┣ 📂 backend_dev # (optional backend experiments / services)
┣ 📂 ml_service # Machine Learning microservice
┃ ┣ 📂 app # FastAPI app
┃ ┃ ┣ 📄 main.py # Main FastAPI entrypoint
┃ ┃ ┣ 📄 __init__.py
┃ ┣ 📂 databases # DB-related configs (if used)
┃ ┣ 📂 model # ML models / training scripts
┃ ┣ 📄 requirements.txt # Python dependencies
┃ ┣ 📄 Dockerfile # Dockerfile for ML service
┃ ┣ 📄 alembic.ini # DB migrations (if using Alembic)
┃ ┗ 📄 init_db.py # DB initialization script
┣ 📂 frontend # React frontend
┃ ┣ 📂 src # Components, pages, hooks
┃ ┣ 📂 public # Static assets
┃ ┣ 📄 package.json # Frontend dependencies
┃ ┣ 📄 Dockerfile # Dockerfile for frontend
┣ 📂 docs # Documentation files
┣ 📄 .env.example # Example environment variables
┣ 📄 docker-compose.yml # (optional) service orchestration
┣ 📄 README.md # Project documentation
┗ 📄 LICENSE # License information
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We welcome contributions! Please fork this repo, create a feature branch, and submit a pull request.
For queries, collaborations, or feedback:
Team CTS NPN Hackathon Project – [Your contact info or team email]