IRO Journals
Journal of Information Technology and Digital World

AdvaSell: An AI-Powered Dynamic Pricing System Using Linear Regression for E-Commerce Platforms

Open Access
Published: 27 August, 2026
Pages: 254-268
  • Sadeesh S. , Sadeesh S.
    Assistant Professor, Department of Artificial Intelligence and Data Science, Saranathan College of Engineering, Trichy, India
    Assistant Professor, Department of Artificial Intelligence and Data Science, Saranathan College of Engineering, Trichy, India
  • Aakash Lingam M. Aakash Lingam M.
    Student, Department of Artificial Intelligence and Data Science, Saranathan College of Engineering, Trichy, India
    Student, Department of Artificial Intelligence and Data Science, Saranathan College of Engineering, Trichy, India
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How to Cite

S., Sadeesh, and Aakash Lingam M. 2026. “AdvaSell: An AI-Powered Dynamic Pricing System Using Linear Regression for E-Commerce Platforms”. Journal of Information Technology and Digital World 8 (3): 254-68. https://doi.org/10.36548/jitdw.2026.3.009.
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Keywords

Dynamic Pricing
Linear Regression
Ridge Regression
Gradient Boosting
E-Commerce
Feature Engineering
Predictive Analytics

Abstract

The development of dynamic pricing that considers the conditions of the market at the time of purchase has turned out to be one of the major tasks for online stores. In this paper, we present the implementation of AdvaSell, a price prediction system, which is based on linear regression and recommends product prices according to its category, brand, stock, history of purchases, and seasonality. On a random test subset of approximately 30,000 records from an e-commerce dataset, the model scored 0.9945 R², suggesting a high fit and stability of the result. We describe the data gathering and preprocessing pipeline, the six features that make up the model, the training and cross-validation process, and the Flask web app that serves predictions. We perform a comparison of our AdvaSell model against ridge regression and gradient boosting models (XGBoost) in terms of R², RMSE, MAE, MAPE and training time in order to evaluate the trade-off between precision, speed and transparency. We use linear regression as the main model, as in business evaluation of a price recommendation, the team usually requires explanation of the price, rather than a mere output from the black box.

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