Enhancing Employee Performance Management

Abstract

Human resource management (HRM) plays a crucial role in the effective functioning of modern businesses. However, As the volume of data continues to increase, HR professionals are facing growing challenges in objectively gathering, measuring, and interpreting human resources data. The research problem addressed in this study is the need to improve methods for the objective classification of teams based on the most relevant performance factors considering the subjectivity of current tools. To tackle this issue, the research questions focus on the possibility of developing an efficient model for team classification using supervised machine learning algorithms. This study consists of developing and validating three team classification models using the support vector machine (SVM), the K-nearest neighbor (KNN) algorithm, and the multiple linear regression algorithm (MLR) after using PCA for data reduction. Following extensive validation, the module based on MLR was identified as the most effective, achieving an accuracy of 87.5% in Predicting employee performance, which makes it possible to anticipate and fill employee skills gaps and optimize recruiting efforts. This work provides human resources professionals with a data-driven decision support to enhance Human Resources Management using Machine Learning.