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Machine learning-based e-commerce platform repurchase customer prediction model.

Cheng-Ju Liu1, Tien-Shou Huang1, Ping-Tsan Ho2

  • 1Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung City, Taiwan, China.

Plos One
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study enhances online shopping behavior prediction by fusing logistic regression and XGBoost models. The hybrid approach overcomes limitations of single models, improving prediction accuracy and robustness for e-commerce.

Related Experiment Videos

Area of Science:

  • Data Science
  • Machine Learning
  • E-commerce Analytics

Background:

  • China's rapid e-commerce growth necessitates advanced prediction systems.
  • Traditional online shopping behavior prediction methods face limitations like overfitting and underfitting.
  • Service-oriented enterprises in e-commerce require sophisticated analytical tools.

Purpose of the Study:

  • To analyze shortcomings of traditional online shopping behavior prediction.
  • To propose and evaluate an enhanced online shopping behavior analysis and prediction system.
  • To improve prediction accuracy and model robustness in e-commerce.

Main Methods:

  • Utilized logistic regression (linear model) and XGBoost (decision tree-based model).
  • Optimized models, identifying nonlinear models' superior feature utilization.
  • Employed model fusion algorithms to combine single model predictions, mitigating overfitting and underfitting.
  • Implemented feature filtering algorithms to simplify model complexity.

Main Results:

  • The proposed hybrid model demonstrated improved prediction accuracy compared to single models.
  • Nonlinear models showed better performance by effectively utilizing features.
  • Feature filtering simplified model complexity and enhanced machine learning classification accuracy.
  • The XGBoost hybrid model based on p/n samples proved simpler and more robust than individual models.

Conclusions:

  • Model fusion effectively addresses limitations of individual linear and decision tree models in e-commerce prediction.
  • The developed system offers a more robust and accurate solution for online shopping behavior analysis.
  • Feature selection and hybrid modeling are key to improving machine learning model performance and reliability.