An Approach to Human Resource Demand Forecasting Based on Machine Learning Techniques

Human resource is a key factor which strongly contributes to evolution of developing country. Forecasting human resource demand is conducted in both private and public section in order to make specific-level strategy more adaptable. Furthermore, this frequent work also consolidates macro-economic sustainability and generates motivation for long-term growth. In this work, we propose an approach to forecast human resource demand based on machine learning techniques. We apply regression algorithms such as random forest regression (RFR), linear regression (LR), K-nearest neighbors regression (KNNR) and decision tree regression (DTR) to explore predictably human resource demand from dataset of Binh Duong Career Service Center (BDCSC). Then we benchmark this model based on actual data. The experimental results demonstrate that random forest achieves highest accuracy which is over 90%.