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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.

|December 3, 2020

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.

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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.