✈️ Flight Delay Predictor

A machine learning-powered web application that predicts flight delays based on historical data, weather conditions, and flight characteristics.

🚀 Features

• Real-time Predictions: Get instant delay predictions for your flights
• Interactive UI: Modern, responsive web interface with beautiful styling
• Time-based Filtering: Filter flights by departure time ranges
• Visual Indicators: Color-coded delay risk categories (Low/Medium/High)
• Route Statistics: View delay probability distributions for specific routes
• Weather Integration: Considers weather conditions in predictions

🛠️ Technology Stack

• Frontend: Streamlit (Python web framework)
• Backend: Python 3.12+
• Machine Learning: XGBoost, Scikit-learn
• Data Processing: Pandas, NumPy
• Visualization: Plotly
• Deployment: Azure App Service

📋 Prerequisites

• Python 3.12 or higher
• Git
• Azure account (for deployment)

🚀 Quick Start

Local Development

1. Clone the repository

   git clone https://github.com/yourusername/flight-delay-predictor.git
   cd flight-delay-predictor

2. Install dependencies

   pip install -r requirements.txt

3. Run the application

   streamlit run app.py

4. Open your browser Navigate to http://localhost:8501

Azure Deployment

1. Create Azure App Service

   az group create --name flight-delay-predictor-rg --location eastus
   az appservice plan create --name flight-delay-plan --resource-group flight-delay-predictor-rg --sku B1
   az webapp create --name flight-delay-predictor --resource-group flight-delay-predictor-rg --plan flight-delay-plan --runtime "PYTHON:3.12"

2. Configure deployment settings

   az webapp config set --name flight-delay-predictor --resource-group flight-delay-predictor-rg --startup-file "streamlit run app.py --server.port 8000 --server.address 0.0.0.0"

3. Deploy from Git

   az webapp deployment source config --name flight-delay-predictor --resource-group flight-delay-predictor-rg --repo-url https://github.com/yourusername/flight-delay-predictor.git --branch main

📁 Project Structure

flight-delay-predictor/
├── app.py                      # Main Streamlit application
├── flight_predictor.py         # ML model prediction class
├── flight_delay_predictor.pkl  # Trained model file
├── test_data.csv              # Test dataset
├── requirements.txt           # Python dependencies
├── README.md                 # Project documentation
├── .gitignore               # Git ignore file
└── azure-deploy.yml         # Azure deployment configuration

🎯 How to Use

1. Select Route: Choose origin and destination airports
2. Pick Date: Select your travel date
3. Filter Time (optional): Choose preferred departure time range
4. Get Predictions: View delay predictions with risk categories

Delay Categories

• 🟢 Low Risk (< 45%): Likely on time
• 🟡 Medium Risk (45-65%): Possible delays
• 🔴 High Risk (> 65%): High chance of delays

🔧 Configuration

Model Thresholds

The application uses configurable thresholds for delay predictions:

• Main Threshold: 0.5 (50% probability for delay classification)
• Low Risk: < 45%
• Medium Risk: 45-65%
• High Risk: > 65%

Customization

You can adjust thresholds in:

• app.py: Line 180 (main threshold)
• flight_predictor.py: Line 42 (model threshold)

📊 Model Information

• Algorithm: XGBoost Classifier
• Features: Weather conditions, flight times, seasonal patterns
• Training Data: Historical flight data with weather information
• Performance: Optimized for realistic delay predictions

🌐 Deployment

Azure App Service

The application is configured for Azure App Service deployment with:

• Runtime: Python 3.12
• Framework: Streamlit
• Port: 8000 (configurable)
• Scaling: Automatic (configurable)

Environment Variables

Set these in Azure App Service Configuration:

PYTHON_VERSION=3.12
SCM_DO_BUILD_DURING_DEPLOYMENT=true

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Flight data sources
• Weather data providers
• Streamlit community
• Azure documentation

Made with ❤️ for better travel planning