Regular paper
Journal of information and communication convergence engineering 2024; 22(4): 310-315
Published online December 31, 2024
https://doi.org/10.56977/jicce.2024.22.4.310
© Korea Institute of Information and Communication Engineering
Utilization of XGBoost for Behavior Analysis of Lottery Purchasers
Esther Kim 
1, Yunjun Park 2, Gwanghyun Jo 2*, and Seong-Yoon Shin3*
Correspondence to : Seong-Yoon Shin (E-mail: s3397220@kunsan.ac.kr) Department of Computer Science and Engineering, Kunsan National University
Gwanghyun Jo (E-mail: gwanghyun@hanyang.ac.kr) Department of Mathematical Data Science, Hanyang University, ERICA
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
In this study, we conducted a data-driven analysis of lottery purchase behavior by using the XGBoost algorithm to predict future lottery purchase amounts based on purchase patterns of the previous four weeks. We began by judiciously defining key features including the weekly average purchase amount and variance in purchase amount. Subsequently, we evaluated the proposed method’s performance, finding the predicted future purchase amounts to match the actual purchase amounts. A key strength of this study was the interpretability of feature variables. Through the feature importance score from XGBoost, we found that features that capture impulsive patterns in purchases (e.g., variability in purchase amount) are strongly correlated with future spending, which agrees with conventional behavior analysis. Our study can be extended to the development of early warning systems designed to identify at-risk and potentially addicted purchasers on online lottery platforms.
Keywords XGBoost, Lottery Purchase, Behavior Analysis, Feature Importance
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Regular paper
Journal of information and communication convergence engineering 2024; 22(4): 310-315
Published online December 31, 2024 https://doi.org/10.56977/jicce.2024.22.4.310
Copyright © Korea Institute of Information and Communication Engineering.
Utilization of XGBoost for Behavior Analysis of Lottery Purchasers
Esther Kim 1, Yunjun Park 2, Gwanghyun Jo 2*, and Seong-Yoon Shin3*
1Department of Counselling Psychology, Korea Baptist Theological University/Seminary
2Department of mathematical data science, Hanyang university ERICA, Ansan, Republic of Korea
3Department of Computer Science and Engineering, Kunsan National University, Guansan-si, Republic of Korea
Correspondence to:Seong-Yoon Shin (E-mail: s3397220@kunsan.ac.kr) Department of Computer Science and Engineering, Kunsan National University
Gwanghyun Jo (E-mail: gwanghyun@hanyang.ac.kr) Department of Mathematical Data Science, Hanyang University, ERICA
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In this study, we conducted a data-driven analysis of lottery purchase behavior by using the XGBoost algorithm to predict future lottery purchase amounts based on purchase patterns of the previous four weeks. We began by judiciously defining key features including the weekly average purchase amount and variance in purchase amount. Subsequently, we evaluated the proposed method’s performance, finding the predicted future purchase amounts to match the actual purchase amounts. A key strength of this study was the interpretability of feature variables. Through the feature importance score from XGBoost, we found that features that capture impulsive patterns in purchases (e.g., variability in purchase amount) are strongly correlated with future spending, which agrees with conventional behavior analysis. Our study can be extended to the development of early warning systems designed to identify at-risk and potentially addicted purchasers on online lottery platforms.
Keywords: XGBoost, Lottery Purchase, Behavior Analysis, Feature Importance
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