Amazon Logistics Intelligence Dashboard

A comprehensive logistics analytics platform built with Streamlit that provides real-time insights into Amazon delivery operations, cost analysis, and predictive analytics for delivery delays.

🚀 Features

📊 Dashboard Sections

• Prime Performance: Comprehensive delivery metrics and carrier analysis
• Last-Mile Operations: Real-time route optimization and vehicle efficiency
• Cost Efficiency Analysis: Cost analysis and optimization insights
• Amazon Purchase Trends: Product sales and category analysis
• ML Prediction Demo: Real-time delivery delay prediction
• AI Dispatcher Assistant: Intelligent logistics advisor with guided questions

🤖 AI & ML Capabilities

• Delivery Delay Prediction: Machine learning model for predicting delivery delays
• Natural Language Queries: Ask questions about logistics data in plain English
• Proactive Alerts: Real-time monitoring of internal and external factors
• Route Optimization: Google Routes API integration for traffic-aware routing

📈 Data Integration

• Real-time APIs: Weather alerts, news alerts, traffic data
• Historical Data: Amazon purchase history and simulated logistics data
• External Sources: OpenWeatherMap, NewsAPI, Google Maps APIs

🛠️ Technology Stack

• Frontend: Streamlit
• Backend: Python, FastAPI
• Data Processing: Pandas, NumPy
• Visualization: Altair, Plotly
• Machine Learning: Scikit-learn, SHAP
• APIs: Google Routes, OpenWeatherMap, NewsAPI
• Database: PostgreSQL (optional)

📋 Prerequisites

• Python 3.8+
• pip package manager
• Google Routes API key (optional, for route optimization)
• OpenWeatherMap API key (optional, for weather data)
• NewsAPI key (optional, for news alerts)

🔧 Installation

1. Clone the repository

   git clone <repository-url>
   cd AWS_Project

2. Create and activate virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables Create a .env file in the project root:

   # API Keys (optional - app works without them)
   GOOGLE_ROUTES_API_KEY=your_google_routes_api_key
   OPENWEATHERMAP_API_KEY=your_openweathermap_api_key
   NEWSAPI_API_KEY=your_newsapi_key
   MAPS_API_KEY=your_google_maps_api_key
   
   # Database (optional)
   DB_HOST=localhost
   DB_PORT=5432
   DB_NAME=amazon_delivery_db
   DB_USER=postgres
   DB_PASSWORD=your_database_password

5. Set up Streamlit secrets (alternative to .env) Create .streamlit/secrets.toml:

   GOOGLE_ROUTES_API_KEY = "your_google_routes_api_key"
   OPENWEATHERMAP_API_KEY = "your_openweathermap_api_key"
   NEWSAPI_API_KEY = "your_newsapi_key"
   MAPS_API_KEY = "your_google_maps_api_key"

🚀 Usage

Start the Dashboard

streamlit run scripts/web_app.py

Start the ML Prediction API (optional)

cd scripts
uvicorn ml_prediction_api:app --reload --port 8000

Run Data Pipeline

python scripts/run_pipeline.py

📁 Project Structure

AWS_Project/
├── scripts/
│   ├── web_app.py                 # Main Streamlit application
│   ├── pages/                     # Dashboard page modules
│   │   ├── prime_performance.py
│   │   ├── last_mile_operations.py
│   │   └── cost_efficiency_analysis.py
│   ├── utils/                     # Utility functions
│   ├── config/                    # Configuration files
│   ├── ml_prediction_api.py       # FastAPI for ML predictions
│   ├── data_generator.py          # Simulated data generation
│   └── api_integrator.py          # External API integrations
├── data/                          # Data files (not in git)
├── models/                        # ML models (not in git)
├── .streamlit/                    # Streamlit configuration
├── config/                        # Project configuration
└── requirements.txt               # Python dependencies

🔒 Security Considerations

API Keys

• Never commit API keys to version control
• Use environment variables or Streamlit secrets
• Rotate keys regularly
• Use least-privilege access

Data Privacy

• All data is simulated or anonymized
• No real customer data is used
• External APIs are optional

Database Security

• Use strong passwords
• Enable SSL connections
• Restrict network access
• Regular security updates

🧪 Testing

Run Basic Tests

python -c "import sys; sys.path.append('.'); from scripts.pages.cost_efficiency_analysis import render_page; print('✅ Cost Analysis page works')"

Test API Endpoints

curl http://localhost:8000/health

📊 Data Sources

Simulated Data

• Orders: 15,000+ simulated Amazon orders
• Carriers: AMZL, UPS, FedEx, USPS
• Cities: 15 major US cities
• Time Period: 6 months of historical data

External APIs

• Weather: OpenWeatherMap API
• News: NewsAPI
• Traffic: Google Maps Directions API
• Routing: Google Routes API

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📝 License

This project is for educational and demonstration purposes.

🆘 Troubleshooting

Common Issues

API Key Errors

• Ensure API keys are set in environment variables or Streamlit secrets
• Check API key permissions and quotas
• Verify API key format

Database Connection Issues

• Check database credentials
• Ensure PostgreSQL is running
• Verify network connectivity

Missing Dependencies

• Reinstall requirements: pip install -r requirements.txt
• Check Python version compatibility

Getting Help

• Check the logs in app.log and data_processing.log
• Review the IMPROVEMENTS_GUIDE.md for known issues
• Open an issue with detailed error information

🔄 Updates

Recent Changes

• ✅ Modular code structure with separate page modules
• ✅ Enhanced security with proper API key management
• ✅ Simplified cost efficiency analysis
• ✅ Improved error handling and logging
• ✅ Comprehensive documentation

Roadmap

• 🔄 Advanced filtering and search
• 🔄 Real-time data streaming
• 🔄 Custom dashboard builder
• 🔄 Mobile-responsive design
• 🔄 Export functionality

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Note: This is a demonstration project. For production use, implement proper security measures, error handling, and monitoring.