E-commerce Product Price Forecasting using Machine Learning Algorithms

Author(s)	Vaibhav Talkhande, Vaishnavi Pokale, Tanaya Pillewan, Vedant Palandurkar, Umesh Mate, Hemlata Dakhore
Country	India
Abstract	- In today’s competitive e-commerce market, setting the right price is essential for business success. Traditional manual pricing approaches often fall short due to rapidly changing customer preferences, competitor actions, and market trends. To overcome these challenges, this project presents an AI/ML-based Price Prediction Model that analyzes historical sales data, competitor prices, and market trends to generate real-time pricing recommendations. By employing the Gradient Boosting algorithm, the system assists sellers in making informed, data-driven pricing decisions that can improve profitability and market competitiveness. Additionally, the model includes a multi-channel communication feature to deliver personalized pricing suggestions via WhatsApp, SMS, and email, ensuring timely and effective guidance for sellers.
Keywords	Dynamic Pricing, E-commerce, Machine Learning, Gradient Boosting, Price Forecasting.
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-21
DOI	https://doi.org/10.63363/aijfr.2026.v07i02.5168

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