International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

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Analysis Study Research Paper | Computer Science & Engineering | India | Volume 12 Issue 6, June 2023

Impact Analysis on Employee Attrition using Machine Learning Techniques

Roopak Krishna | Neeta Singh [5]

Abstract: Employees are the most valuable resources for any organization but Employee attrition is the biggest problem for any organization, it is a rate at which more employees are leaving the organization than the rate at which they are getting hired. It is the primary and most challenging task for any organization regardless of their size. The cost associated with professional training, the developed loyalty over the years and the sensitivity of some organizational positions, all make it very essential to identify who might leave the organization. According to a report, this is the biggest problem in the call centers. It deteriorates the customer experiences. This problem comes under the Human Resource and for growth in the organization it is believed that the employee retention rate is the deciding factor. Attrition brings down HR management morale way down because it is their task to understand the employee needs and how to keep them by accounting all the factors. If an employee leave, team workflow pipeline get disrupted because the pressure comes down on the shoulder of other team members. Many reasons can lead to employee attrition. In this paper, several machine learning models are compared to automatically and accurately predict employee attrition. IBM attrition dataset is used in this work to train and evaluate machine learning models; namely Bagging Classifier, Random Forest, Logistic Regression, and K Nearest Neighbor models. The ultimate goal is to accurately detect attrition to help any company to improve different retention strategies on crucial employees and boost those employee satisfactions.

Keywords: Data Analysis; Predictive Modeling; Machine learning; Employee Attrition

Edition: Volume 12 Issue 6, June 2023,

Pages: 2762 - 2766

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