Real-Time Network Frequency Anomaly Detection System - Backend Task

This project involves the development of a real-time network frequency anomaly detection system using a message queue (Kafka) for data ingestion, a MongoDB database for data storage, and machine learning models for anomaly detection. The system leverages Prometheus and Grafana for real-time monitoring and visualizations, providing administrators with up-to-the-minute insights into network operations. The system aims to detect network frequency anomalies and trigger alerts in real-time.