Predictive modeling of return occurrence in e-commerce apparel market: a comparative study of logistic regression, LASSO, XGBoost and random forest techniques

Abstract

This study focuses on the development of a predictive model for return occurrence in the apparel segment of an e-commerce company based in Turkey. Leveraging data provided by the company, the research employs various machine learning techniques to explore the impact of various factors on return. Models are developed, incorporating predictor variables related to product, supplier, customer and shopping information with the final model also including interaction of these variables. LASSO is applied to simplify the final model and select the most relevant variables. Performance metrics; AUC score, accuracy, precision, and recall are evaluated for the models, with comparisons made between logistic regression, LASSO, XGBoost, and Random Forest. Findings indicate that logistic regression models outperform XGBoost and Random Forest in terms of AUC score.