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

ABSTRACT: In recent years, China's e-commerce industry has developed at a high speed, and the scale of various industries has continued to expand. Service-oriented enterprises such as e-commerce transactions and information technology came into being. This paper analyzes the shortcomings and challenges of traditional online shopping behavior prediction methods, and proposes an online shopping behavior analysis and prediction system. The paper chooses linear model logistic regression and decision tree based XGBoost model. After optimizing the model, it is found that the nonlinear model can make better use of these features and get better prediction results. In this paper, we first combine the single model, and then use the model fusion algorithm to fuse the prediction results of the single model. The

SUBMITTER: Liu CJ 

PROVIDER: S-EPMC7714352 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature