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@article{198646,
        author = {Akash Raj and Jyoti Mandal and K Nandini and Kanishka Verma and Palak Yadav},
        title = {EstiCheck - A ML Powered Price Estimation and Reasonability Website for Mobile Phones},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15854-15858},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198646},
        abstract = {Online marketplaces feature thousands of mobile devices at various prices. This variation can confuse customers who want to know if a deal is fair, overpriced, or underpriced. Manually comparing specifications, past prices, and market trends takes time and can be inaccurate because device demand and availability change. This paper introduces Esti Check, a machine learning system that predicts fair market prices for mobile phones and checks if user-entered prices are reasonable using real-time market data. The system uses structured product details like brand, model, storage, RAM, processor type, and condition. These details are processed and input into machine learning models, including Random Forest Regression. It also performs real-time market scraping from sites like Amazon and Croma to provide up-to-date comparisons. Esti Check gives a predicted fair price and categorizes listings as Underpriced, Fair, or Overpriced, along with explanations based on different factors. A simple web interface built with React and Express.js allows users to input device details and quickly see predictions. The system’s modular and scalable design, along with its capacity to incorporate continuous market data, makes it useful for e-commerce customers, refurbishes, and price-comparison tools.},
        keywords = {—Price Prediction, Machine Learning, Mobile Devices, Random Forest, Market Scraping, FastAPI, Price Reasonability, E-commerce Analytics.},
        month = {April},
        }