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http://dx.doi.org/10.6109/jkiice.2021.25.2.171

Store Sales Prediction Using Gradient Boosting Model  

Choi, Jaeyoung (Library and Information Science, Sungkyunkwan University)
Yang, Heeyoon (Library and Information Science, Sungkyunkwan University)
Oh, Hayoung (College of Computing & Informatics, Sungkyunkwan University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.2, 2021 , pp. 171-177 More about this Journal
Abstract
Through the rapid developments in machine learning, there have been diverse utilization approaches not only in industrial fields but also in daily life. Implementations of machine learning on financial data, also have been of interest. Herein, we employ machine learning algorithms to store sales data and present future applications for fintech enterprises. We utilize diverse missing data processing methods to handle missing data and apply gradient boosting machine learning algorithms; XGBoost, LightGBM, CatBoost to predict the future revenue of individual stores. As a result, we found that using median imputation onto missing data with the appliance of the xgboost algorithm has the best accuracy. By employing the proposed method, fintech enterprises and customers can attain benefits. Stores can benefit by receiving financial assistance beforehand from fintech companies, while these corporations can benefit by offering financial support to these stores with low risk.
Keywords
CatBoost; LightGBM; Machine learning; Sales prediction; XGBoost;
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