Modeling of user profiles of financial products and comparison of purchase prediction models: Based on machine learning

Rouyu Yu; Dongming Li; Yining Liu

doi:10.54254/2755-2721/2/20220545

Research Article

Open access

Published on 22 March 2023

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Yu,R.;Li,D.;Liu,Y. (2023). Modeling of user profiles of financial products and comparison of purchase prediction models: Based on machine learning. Applied and Computational Engineering,2,64-77.

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Modeling of user profiles of financial products and comparison of purchase prediction models: Based on machine learning

Rouyu Yu ¹, Dongming Li *^,2, Yining Liu ³

¹ Oberlin College, Oberlin 44074, United States
² School of Science in Mathematics and Applied Mathematics, Xiamen University Malaysia, Negeri Selangor,43900, Malaysia
³ Ulink College of Shanghai, Shanghai,201615, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2/20220545

Abstract

In this paperwork, the Apriori algorithm can be used to describe the association between data in a database. This paper will use the demographic and financial information of the users in the data set to analyze the user profile as the basic information. This work will fit the model by optimizing the Apriori algorithm, generating a combination of two or more associated variables, and then using the decision tree to learn and output the final model. The main contribution of this paper is to explore the optimization of Apriori and the ef-fect of Apriori's combined model with the decision tree in predicting user buying behavior. Therefore, the conclusion can be drawn that in the sale of deposit products, if the custom-er has the above characteristics, such as no credit fault, no loan, housing loan, with a col-lege degree, marital status is single, and act as an administrator. The people mentioned above may be more interested in the product. In other words, it is more likely to sell suc-cessfully. In future work, in addition to deposit products, more customer buying infor-mation is highly needed to extend predictable results to broader anger.

Keywords

Apriori, decision tree, association rule, methodology.

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Cite this article

Yu,R.;Li,D.;Liu,Y. (2023). Modeling of user profiles of financial products and comparison of purchase prediction models: Based on machine learning. Applied and Computational Engineering,2,64-77.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

Conference website: https://www.confcds.org/

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)

Conference date: 16 July 2022

Editor：Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.2

ISSN：2755-2721(Print) / 2755-273X(Online)

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