Main Article Content
Employees are the most important assets of an organisation and evaluation of the employee performance is essential to predict how much profit the organisation will gain. Therefore, Human Resource Management recruits and determines employee performance by considering several factors, theories and practices. Traditionally, normal technologies were in use for identifying employee performance. However, in recent days, Artificial Intelligence (AI) and Machine Learning (ML) approaches are taking an interest to reduce time and effort in performance evaluation. This paper is going to analyse factors essential for ML algorithms to monitor for employee performance evaluation; and then, the paper compared the accuracy of 3 ML algorithms. A primary quantitative survey has been carried out in 20 organisations to collect data related to employee performance, academic grade, rewards, experience and so on. The data were converted to numerical form and kept in Microsoft Excel. Therefore, a linear regression analysis has been carried out in IBM SPSS software to understand the factors
that need to be considered by an ML algorithm for performance evaluation. After analysis, ANOVA output, regression co-efficient output and descriptive statistics have been considered for further interpretation. Findings showed that academic grade (performance), feedback, experience and promotion are the major factors for improving employee performance. Thus, the ML tools need to analyse these factors for organisation success.