Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 6 Issue 6
November-December 2025

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MLOps-Driven Intelligent Platform for Government Procurement Price Benchmarking Using Regional and Financial Attributes

Author(s) Ms. Gnanika G, Ms. Sreehitha K, Ms. Keerthana C S, Ms. Shirley J, Dr. Anil Kumar B
Country India
Abstract This project presents an MLOps-driven platform designed to automate government procurement price benchmarking using real-time web-scraped data, intelligent data processing pipelines, and hybrid machine-learning models. The system supports procurement departments in identifying fair prices, detecting anomalies, and ensuring transparency in public spending.

Data is collected from online platforms such as JustDial, Sulekha, and Google Shopping, then cleaned, standardized, and analyzed through automated ML pipelines. Regression-based prediction models and statistical anomaly detectors estimate fair pricing and flag abnormal vendor quotations.

The platform is built with React.js, Node.js, MongoDB, and Python-based ML APIs. It implements baseline MLOps practices such as automated data workflows, version control, model performance tracking, and API-based deployment. The result is a scalable, reliable, and data-driven decision-support system that enhances transparency and reduces procurement fraud and overpricing in government operations.
Keywords Government procurement, MLOps, price benchmarking, anomaly detection, web scraping, automated pipelines, machine learning, public transparency, decision support system
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2025
Published On 2025-11-23
DOI https://doi.org/10.63363/aijfr.2025.v06i06.1868
Short DOI https://doi.org/hbdswz
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