Abstract
Human resources are the first resource for enterprise development, and a reasonable human resource structure will increase the effectiveness of an enterprise’s human resource input and output. Based on a deep understanding of forecasting mining technology, this paper discusses the multiple linear regression method and BP neural network algorithm for human resource demand forecasting. Through modeling, statistical index analysis and significance test, the data mining algorithms are analyzed and compared. The regression equation is obtained, and the demand forecast is made on the number of the enterprise personnel, and the feasibility of the multiple regression model is verified. At the same time, the BP neural network algorithm is described in detail, and an example is given to compare the forecasting results of multiple linear regression method and BP neural network algorithm.
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Acknowledgments
This work was supported in part by National Natural Science Foundation of China (Nos. 62076111 and 61773012), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 15KJB110004), and Postgraduate Research & Practice Innovation Program of Jiangsu Province.
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Wang, W., Zhu, J., Wang, P. (2024). Data Analytics Methods in Human Resource Demand Forecasting. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2023. Communications in Computer and Information Science, vol 2017. Springer, Singapore. https://doi.org/10.1007/978-981-97-0837-6_1
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DOI: https://doi.org/10.1007/978-981-97-0837-6_1
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