AI / Machine Learning
Intelligent Data Deduplication & Clustering Platform
Objective
Build a scalable ML platform that identifies and clusters duplicate records with a feedback-driven retraining loop — eliminating data quality issues across large document corpora.
About the Project
A machine learning-based platform for identifying duplicate records, clustering similar entities, and continuously improving accuracy through user feedback and automated retraining.
Developed a deduplication algorithm to detect potential duplicate records and group them into clusters. Built scalable backend services using FastAPI for data processing and model integration. Designed and developed an internal actionable UI using Next.js for reviewing and validating deduplication results.
Enabled manual review workflows, allowing users to approve/reject matches and improve overall data quality. Implemented feedback-driven retraining pipelines to continuously enhance model accuracy, and established automated learning loops leveraging real user corrections for ongoing model optimization.
