Suggesting Product Prices in Automotive E-Commerce: A Study Assessing Regression Models and Explicability

André Regino, Gilson Yuuji Shimizu, Fernando Rezende Zagatti, Fernando Rezende Zagatti, Filipe Loyola Lopes, Rodrigo Bonacin, Julio Reis, Cristina Dutra de Aguiar

2025

Abstract

E-commerce pricing may involve complex processes, including various factors such as cost, perceived value, and market demand. Exploring machine learning (ML) for informing pricing in the automotive sector presents significant open research challenges that require innovative solutions. This investigation examines a real-world Brazilian e-commerce dataset to train, test, and compare several state-of-the-art regression models to understand their applicability. Our study originally includes how SHapley Additive exPlanations (SHAP) help to interpret the most influential features for price prediction. Results indicate that Light GBM and XGBoost performed best, combining high predictive accuracy with computational efficiency, and reveal features such as product weight, stock levels, and physical dimensions as the most influential on final pricing. This study outcome paves the way for novel data-driven pricing strategies in Brazilian automotive e-commerce.

Paper Citation

in Harvard Style

Regino A., Shimizu G., Zagatti F., Lopes F., Bonacin R., Reis J. and Dutra de Aguiar C. (2025). Suggesting Product Prices in Automotive E-Commerce: A Study Assessing Regression Models and Explicability. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 147-158. DOI: 10.5220/0013830400004000

in Bibtex Style

@conference{kdir25,
author={André Regino and Gilson Shimizu and Fernando Zagatti and Filipe Lopes and Rodrigo Bonacin and Julio Reis and Cristina Dutra de Aguiar},
title={Suggesting Product Prices in Automotive E-Commerce: A Study Assessing Regression Models and Explicability},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={147-158},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013830400004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Suggesting Product Prices in Automotive E-Commerce: A Study Assessing Regression Models and Explicability
SN -
AU - Regino A.
AU - Shimizu G.
AU - Zagatti F.
AU - Lopes F.
AU - Bonacin R.
AU - Reis J.
AU - Dutra de Aguiar C.
PY - 2025
SP - 147
EP - 158
DO - 10.5220/0013830400004000
PB - SciTePress