Towards Predictive Vietnamese Human Resource Migration by Machine Learning: A Case Study in Northeast Asian Countries

Labor exports are currently considered among the most important foreign economic sectors, implying that they contribute to a country's economic development and serve as a strategic solution for employment creation. Therefore, with the support of data collected between 1992 and 2020, this paper proposes that labor exports contribute significantly to Vietnam's socio-economic development. This study also aims to employ the Backpropagation Neural Network (BPNN), k-Nearest Neighbor (kNN), and Random Forest Regression (RFR) models to analyze labor migration forecasting in Taiwan, Korea, and Japan. The study results indicate that the BPNN model was able to achieve the highest accuracy regarding the actual labor exports. In terms of these accuracy metrics, this study will aid the Vietnamese government in establishing new legislation for Vietnamese migrant workers in order to improve the nation's economic development.

Automated summary

Labor exports contribute significantly to Vietnam's socio-economic development, and this study analyzes labor migration forecasting in Taiwan, Korea, and Japan using various models. The results show that the BPNN model achieves the highest accuracy, providing valuable insights for the Vietnamese government to improve the nation's economic development.