• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/
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The Case Study for Childcare Service Demand Forecasting Using Bigdata Reference Analysis Model


Chung-Sik Yun, Seung Ryul Jeong, Journal of Internet Computing and Services, Vol. 23, No. 6, pp. 87-96, Dec. 2022
10.7472/jksii.2022.23.6.87, Full Text:  HTML
Keywords: BigData, Reference Analysis Model, Effectiveness

Abstract

This paper is an empirical analysis as a reference model that can predict up to the maximum number of elementary school student care needs in local governments across the country. This study analyzed and predicted the characteristics of the region based on machine learning to predict the demand for elementary care in a new apartment complex. For this purpose, a total of 292 variables were used, including data related to apartment structure, such as number of parking spaces per household, and building-to-land ratio, environmental data around apartments such as distance to elementary schools, and population data of administrative districts. The use of various variables is of great significance, and it is meaningful in complex analysis. It is also an empirical case study that increased the reliability of the model through comparison with the actual value of the basic local government.


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Cite this article
[APA Style]
Yun, C. & Jeong, S. (2022). The Case Study for Childcare Service Demand Forecasting Using Bigdata Reference Analysis Model. Journal of Internet Computing and Services, 23(6), 87-96. DOI: 10.7472/jksii.2022.23.6.87.

[IEEE Style]
C. Yun and S. R. Jeong, "The Case Study for Childcare Service Demand Forecasting Using Bigdata Reference Analysis Model," Journal of Internet Computing and Services, vol. 23, no. 6, pp. 87-96, 2022. DOI: 10.7472/jksii.2022.23.6.87.

[ACM Style]
Chung-Sik Yun and Seung Ryul Jeong. 2022. The Case Study for Childcare Service Demand Forecasting Using Bigdata Reference Analysis Model. Journal of Internet Computing and Services, 23, 6, (2022), 87-96. DOI: 10.7472/jksii.2022.23.6.87.