Analysing Employee Management Using Machine Learning Techniques and Solutions in Human Resource Management

In the contemporary landscape of Human Resource Management (HRM), organizations are increasingly turning to advanced technologies to streamline employee management processes. This study explores the integration of machine learning (ML) techniques as a transformative solution for optimizing HRM practices, with a specific focus on employee management. By leveraging the power of ML algorithms, this research aims to enhance decision-making, efficiency, and overall effectiveness in HRM. The study encompasses a comprehensive analysis of existing HRM challenges, such as talent acquisition, performance evaluation, and employee retention, and proposes ML-based solutions to address these issues. By applying natural language processing, pattern identification, and predictive analytics, businesses may learn a great deal about employee behavior, performance patterns, and possible areas for development. HR professionals are more equipped to make well-informed choices, customize employee experiences, and put proactive talent development initiatives into action thanks to this data-driven approach. Additionally, the study examines the moral issues and difficulties surrounding the use of ML in HRM, stressing the significance of openness, justice, and privacy. By understanding and mitigating these concerns, organizations can successfully harness the transformative potential of ML in employee management, fostering a more dynamic and adaptive HRM framework. The study’s conclusions add to the growing body of knowledge on the relationship between technology and HRM and offer useful advice to businesses looking to use cutting-edge approaches to improve labor management procedures.