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E-Commerce User Purchase Prediction Analysis Based on Data Mining

Qixian Rui, Xinyu Tan


In the Internet era, various e-commerce platforms are gradually rising, people's consumption has gradually changed from offline to online. How to predict users' purchasing behavior based on big data has become an important challenge for e-commerce platform. Based on the knowledge of e-commerce, this paper constructs a funnel model to analyze the user conversion from each link, so as to locate the link with low conversion rate. The data mining method is used to process the user history data of an international e-commerce company. And through the importance of features,we can know from which aspects to promote the transformation of users, so as to take targeted measures to users.


Data Mining; Funnel Analysis; Logistic Regression; Decision Tree; Random Forest; Feature Importance

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Included Database


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DOI: http://dx.doi.org/10.18686/fm.v7i3.4531