Aniruddha Ghosh

I’m a Machine Learning Intern at the Institute of Engineering & Management, building ML models in Python to solve real-world problems and improve prediction reliability through preprocessing and feature engineering. I focus on turning messy data into dependable, interpretable outcomes.

I’ve developed a fraud detection system using supervised learning to classify anomalous patterns in transactional data. I also handle and analyze structured datasets with SQL to extract meaningful features and support model performance.

Alongside my internship, I build production-minded systems through projects like the Crowd Management System—delivering real-time crowd intelligence, congestion prediction, adaptive routing, and alert-driven decision making. I designed a scalable full-stack architecture using modular REST APIs and dynamic dashboards.

I continue to grow by developing tools such as the Adaptive Planner and the Notion Workspace OS, where I’m working on dynamic scheduling and workflow automation with integrations for real-time updates and orchestration. I enjoy building end-to-end solutions—from backend logic to responsive interfaces.