EMPLOYEE PERFORMANCE ESTIMATION USING MACHINE LEARNING

Abstract

In this research, we explore how machine learning is changing the way traditional HR practices handle employee reviews. The need for better evaluation methods for employees is increasing, and machine learning provides a variety of tools for analyzing data to meet this need. These algorithms can offer comprehensive and impartial assessments of performance, covering everything from productivity metrics to project outcomes and qualitative feedback. This study looks at the pros and cons of integrating this technology into HR practices, with the aim of enhancing the accuracy, fairness, and effectiveness of evaluations. The goal is to find a middle ground between technology and human judgment to improve performance reviews in modern workplaces. In today's fast-paced work environment, where excellence is key, traditional subjective performance assessments are becoming outdated. This research delves into the promising role of artificial intelligence in human resource management, particularly in creating detailed and fair performance evaluations. Picture AI algorithms as skilled musicians, harmonizing various data points – from productivity metrics to project achievements to qualitative input – to create a comprehensive picture of each employee's contributions. However, navigating this data-driven process can be complex. The focus of this study is on examining the positives and negatives of utilizing machine learning in HRM. The goal is to improve performance evaluations, ensuring they are both precise and unbiased. By connecting technology with a deeper human insight, organizations can move towards a future where performance appraisals truly maximize the abilities of their employees. Key Words: Performance, Evaluation, Employee Metrics, Human Resource Management (HRM), Examining