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ISSN 2063-5346
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AN EXPERIMENTAL STUDY ON THE EFFECTIVENESS OF MACHINE LEARNING ALGORITHM AS AN ATTRITION PREDICTOR

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Sriram P , Dr Usha S
» doi: 10.31838/ecb/2023.12.s2.344

Abstract

The issue of employee attrition is a major concern for organisations, as it can lead to a significant loss of valuable resources, including time, money, and knowledge. In recent years, machine learning techniques have been applied to predict employee attrition and help organisations make data-driven decisions to mitigate this problem. Objective: This study aims to analyse how machine learning has been used to predict employee attrition. And when it is so used, can it be used as the final determinant of attrition? The study also aims to find out if such findings will really serve the purpose of retaining employees in the organisation. Methodology: An experimental research methodology was adopted. The employee attrition algorithm using ML was taken from reference papers using secondary sources. Further, using ML, the data was analysed to find the extent of success it has in predicting actual attrition. Findings and implications: It were found that machine learning can be employed to predict employee attrition nut may require further human inference. Such results can be further used by the HR department to make a final decision about the employee. The caveat expressed through the study is that the results of ML cannot be taken as the sole predictor for employee termination , but must be accompanied by personal analysis by the HR. It can serve as a valuable tool to retain engaged employees in the organisation. But sometimes the use of ML to predict employee attrition can also have an adverse impact on employee morale and engagement. Employees may feel that their every move is being analysed and that they are constantly being monitored, which can lead to decreased job satisfaction and even push them towards leaving the organisation.

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