SmartShopper: An AI-Based Price Tracking and Prediction System for Online Deals

With SmartShopper, one can easily develop an efficient price prediction mechanism without being weighed down by elaborate frameworks or the use of headless browsers. With Node.js for backend development, Cheerio for web scraping, and user-agent switching, prices and alerts are in real-time. Google Gemini’s artificial intelligence technology not only alerts the shoppers, but also offers them insights into price predictions for several months to come. In addition, through hourly checks and real-time notifications, the users will know when the ideal buying time comes. Next is the development of a mobile application that will incorporate push notifications, advanced review analysis (both pricing and quality rating), shareable deal vaults for group monitoring, and ultimately, automation, whereby SmartShopper will automatically make purchases at the ideal prices.

[1]     N. Kaur, "Price Prediction in E-Commerce Using Machine Learning Models: A Comparative Study " ShodhKosh: Journal of Visual and Performing Arts, vol. 4 No. 1 January to June 2023. [2]     A. Abudureheman, Y. Zhao and A. Nilupaer "Design of E-Commerce Product Price Prediction Model Based on Generative Adversarial Network with Adaptive Weight Adjustment " Scientific Reports, vol. 15 Article 25126 July 2025. [3]     M. Gupta, "Dynamic Pricing Optimization in E-Commerce Using AI and Machine Learning: A Comprehensive Framework for Demand Prediction and Revenue Maximization " SSRN, November 2024. You can find it here: https://ssrn.com/abstract=5075196 [4]    M. Nowak, "Dynamic Pricing Method in the E-Commerce Industry " Applied Sciences, vol. 14 No. 24 2024. [5]    G.. Y. Horbunova, "Modeling and Forecasting Dynamic Factors of Pricing in E-Commerce " in Proc. CEUR Workshop. I-2020, Kyiv, Ukraine December 2020. [6]     K. J. Ferreira, B. H. A. Lee and D. Simchi-Levi "Analytics for an Online Retailer: Demand Forecasting and Price Optimization " Manufacturing & Service Operations Management vol. 18 No. 1 2016. This study focuses on E-Commerce. [7]    Y. J. Zhang and W. Wang, "Dynamic Pricing in E-Commerce Using Reinforcement Learning " IEEE Access, 2020. The authors explore E-Commerce. [8]     A. A. Bukhari and S. Ahmad "Time Series Forecasting for Product Prices Using LSTM Networks " Journal of AI Research, 2021. They look at E-Commerce product prices. [9]     T. Dhulipalla, N. Karumanchi, M. Munji, K. Harshitha and S. Kunchala "Price Comparison Application for E-Commerce Using Web Scraping " International Journal of Engineering Research 2025. The application is for E-Commerce. [10]    H. Chen and K. Liu "Competitor-Aware Price Forecasting for E-Commerce Using Gradient Boosting Models " Expert Systems with Applications, 2022. This study is about E-Commerce.