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has been cited by the following article:

• TITLE: Prediction of After-Sales Behavior in E-Commerce Using Machine Learning Models

  AUTHORS: Yulin Cai, Fengqing Chen, Jingyang Zhang

  KEYWORDS: E-Commerce, Consumer Behavior, Machine Learning Algorithms, Random Forest

  JOURNAL NAME: Open Journal of Statistics, Vol.14 No.6, December 24, 2024

  ABSTRACT: With the rapid growth of e-commerce and online transactions, e-commerce platforms face a critical challenge: predicting consumer behavior after purchase. This study aimed to forecast such after-sales behavior within the digital retail environment. We utilized four machine learning models: logistic regression, decision tree, random forest, and XGBoost, employing SMOTE oversampling and class weighting techniques to address class imbalance. To bolster the models’ predictive capabilities, we executed pivotal data processing steps, including feature derivation and one-hot encoding. Upon rigorous evaluation of the models’ performance through the 5-fold cross-validation method, the random forest model was identified as the superior performer, excelling in accuracy, F1 score, and AUC value, and was thus deemed the most effective model for anticipating consumer after-sales behavior. The findings from this research offer actionable strategies for e-commerce platforms to refine their after-sales services and enhance customer satisfaction.