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This project is a full-stack web application that provides real-time monitoring and analysis from data generated by electrical grids . It utilizes machine learning to predict potential failures and a notification system to alert users about critical issues.
- Real-time dashboard visualizing grid metrics
- Predictive analytics for failure rates
- Notification system for critical alerts
- Historical data analysis and reporting
- Python 3.8+
- FastAPI: High-performance web framework
- PySpark: Distributed data processing
- Pandas: Data manipulation and analysis
- Scikit-learn: Machine learning for predictive analytics
- APScheduler: Task scheduling
- Celery: Asynchronous task queue for notifications
- React: UI framework
- D3.js: Data visualization library
- Axios: HTTP client for API requests
- PostgreSQL: Persistent data storage
- Redis: Caching and message broker
- Docker & Docker Compose: Containerization and orchestration
- Nginx: Reverse proxy server
- Gunicorn: WSGI HTTP Server
- Prometheus & Grafana: Application monitoring
- ELK Stack: Centralized logging