BiteBase Restaurant Analytics Dashboard

A comprehensive analytics dashboard for restaurant location analysis and business intelligence.

Features

• Interactive map visualization with Kepler.gl
• Real-time location change predictions
• Business analytics with PyGWalker
• Historical trend analysis
• Dynamic risk assessment
• Multi-factor location scoring

Project Structure

.
├── RAW/                      # Raw data files
│   ├── Dynamics/            # Dynamic data sources
│   │   └── LMWN/           # Restaurant data
│   └── Statics/            # Static reference data
├── src/                     # Source code
│   ├── data/               # Data processing modules
│   ├── models/             # ML models and predictors
│   │   ├── combined_location_model.py
│   │   ├── location_change_prediction_model.py
│   │   ├── realtime_location_change_model.py
│   │   └── saved_model.pkl
│   ├── utils/              # Utility functions
│   │   └── mock_data_generator.py
│   └── restaurant_dashboard.py  # Main dashboard application
├── requirements.txt         # Project dependencies
└── README.md               # Project documentation

Installation

1. Clone the repository:

git clone https://github.com/yourusername/map-data-visulization.git
cd map-data-visulization

2. Install dependencies:

pip install -r requirements.txt

Usage

1. Run the dashboard:

streamlit run src/restaurant_dashboard.py

2. Use the interactive map:

• Click on any location to get real-time analysis
• Use sidebar filters to customize the view
• Explore different analytics tabs for detailed insights

3. View analytics:

• Product Analytics: Menu performance and sales trends
• Place Analytics: Geographic and competitive analysis
• Price Analytics: Revenue and profitability metrics
• Promotion Analytics: Marketing and customer engagement

Models

Combined Location Model

• Integrates static and real-time predictions
• Uses historical data for trend analysis
• Provides explainable AI insights

Real-time Location Change Model

• Dynamic risk assessment
• Time-sensitive predictions
• Trend monitoring and alerts

Data Sources

• Restaurant information from LMWN API
• Geographic data for location analysis
• Historical performance metrics
• Real-time business indicators

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Acknowledgments

• Streamlit for the web framework
• Kepler.gl for map visualization
• PyGWalker for analytics tools