Abstract

Human resources (HR) of various groups (e.g., ethnic minority or majority) and their quality play a crucial role in developing and promoting economic and social progress in every region. However, current methods of quality assessment, e.g., surveys, have not provided data-driven insight for policymakers to design targeted interventions. Machine learning is one of the emerging technologies that could analyze complex datasets to support data insight for policymakers in sustainable economic development. This study proposes a framework to predict the quality of HR from ethnic minority community by using various machine learning techniques (K-nearest neighbors, multilayer perceptron, gradient boosting, and voting classifier). To achieve the best model, two techniques for feature selection (recursive feature elimination and extra trees) are employed. In the experiments, the ethnic minority HR data has been used to conduct. Experimental results show that the gradient boosting consistently outperformed other models across feature selection techniques (≥0.99). The findings from this study enhance prediction methods for HR and provide valuable insights for policymakers to develop effective policies for ethnic minority communities.