• Journal of Internet Computing and Services
    ISSN 2287 - 1136(Online) / ISSN 1598 - 0170 (Print)
    http://jics.or.kr/

Exploring the Predictive Variables of Government Statistical Indicators on Retail sales Using Machine Learning: Focusing on Pharmacy


Gwang-Su Lee, Journal of Internet Computing and Services, Vol. 23, No. 3, pp. 125-135, Jun. 2022
10.7472/jksii.2022.23.3.125, Full Text:
Keywords: Machine Learning, Random Forest, XGBoost, LightGBM, CatBoost, Government Statistical Indicators

Abstract

This study aims to explore variables using machine learning and provide analysis techniques suitable for predicting pharmacy sales whether government statistical indicators built to create an industrial ecosystem based on data, network, and artificial intelligence affect pharmacy sales. Therefore, this study explored predictive variables and performance through machine learning techniques such as Random Forest, XGBoost, LightGBM, and CatBoost using analysis data from January 2016 to December 2021 for 28 government statistical indicators and pharmacies in the retail sector. As a result of the analysis, economic sentiment index, economic accompanying index circulation change, and consumer sentiment index, which are economic indicators, were found to be important variables affecting pharmacy sales. As a result of examining the indicators MAE, MSE, and RMSE for regression performance, random forests showed the best performance than XGBoost, LightGBM, and CatBoost. Therefore, this study presented variables and optimal machine learning techniques that affect pharmacy sales based on machine learning results, and proposed several implications and follow-up studies.


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Cite this article
[APA Style]
Gwang-Su Lee (2022). Exploring the Predictive Variables of Government Statistical Indicators on Retail sales Using Machine Learning: Focusing on Pharmacy. Journal of Internet Computing and Services, 23(3), 125-135. DOI: 10.7472/jksii.2022.23.3.125.

[IEEE Style]
G. Lee, "Exploring the Predictive Variables of Government Statistical Indicators on Retail sales Using Machine Learning: Focusing on Pharmacy," Journal of Internet Computing and Services, vol. 23, no. 3, pp. 125-135, 2022. DOI: 10.7472/jksii.2022.23.3.125.

[ACM Style]
Gwang-Su Lee. 2022. Exploring the Predictive Variables of Government Statistical Indicators on Retail sales Using Machine Learning: Focusing on Pharmacy. Journal of Internet Computing and Services, 23, 3, (2022), 125-135. DOI: 10.7472/jksii.2022.23.3.125.