Authors:
André Regino
1
;
Gilson Yuuji Shimizu
1
;
Fernando Rezende Zagatti
1
;
2
;
Filipe Loyola Lopes
1
;
Rodrigo Bonacin
1
;
Julio Reis
3
and
Cristina Dutra de Aguiar
4
Affiliations:
1
DIMEC, Center for Technology Information Renato Archer, Campinas, Brazil
;
2
Department of Computing, UFScar, São Carlos, Brazil
;
3
Institute of Computing, UNICAMP, Campinas, Brazil
;
4
Institute of Mathematical and Computer Sciences, USP, São Carlos, Brazil
Keyword(s):
Pricing, E-Commerce Analysis, Machine Learning.
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.