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Smart Campus Digital Twin
A comprehensive Smart Campus Digital Twin aimed at ingesting, processing, and analyzing real-time sensor data. It features robust Python data pipelines orchestrated through Apache Kafka and InfluxDB to handle environmental, occupancy, and energy metrics. The platform leverages MLflow for managing anomaly detection models that identify unusual operational patterns, which are then visualized using Grafana and Three.js.
Project Overview
Project Type
S3- Project
Year
2026
Focus
Data Engineering & ML
My Role
Data & ML Engineer
Technologies Used
Python
Apache Kafka
InfluxDB
MLflow
Grafana
Three.js
IoT
My Contributions
- Built Python data pipelines to ingest and process real-time environmental, occupancy, and energy sensor streams through Kafka and InfluxDB.
- Built machine-learning anomaly detection models to identify unusual sensor readings and abnormal campus operational patterns.