Predicting Employee Attrition Using Machine Learning Approaches
Author(s):
Jeevan Ayer .J, Gunasree.M , Immanuel.C, Harish Bala.D, DR. S THAIYALNAYAKI
Keywords:
Artificial Intelligence, Human Resource, Employee Attrition, Knowledge Economy
Abstract
Organizations today have a plethora of technological options at their disposal to bolster decision-making processes, with artificial intelligence (AI) emerging as a frontrunner in innovation. AI is being leveraged across various domains to aid organizations in shaping business strategies, streamlining organizational operations, and optimizing people management practices. Particularly, in recent years, there has been a growing emphasis on the significance of HR as the quality and skills of the workforce increasingly become pivotal for organizational growth and competitive advantage. Initially embraced by sales and marketing departments, AI is now making significant inroads into HR management, aiming to guide decisions pertaining to employees based on objective data analysis rather than subjective assessments. The overarching objective is to delve into how tangible factors impact employee attrition, deciphering the primary drivers behind an employee's decision to depart from a company. By harnessing AI-driven analytics, organizations aspire not only to identify the root causes of attrition but also to forecast the likelihood of individual employees leaving the company. This endeavor represents a paradigm shift towards evidence-based decision-making in HR, empowering organizations to proactively address retention challenges and optimize workforce stability. Through the judicious utilization of AI technologies, organizations can cultivate a deeper understanding of employee dynamics, thereby fostering an environment conducive to talent retention and organizational resilience
Article Details
Unique Paper ID: 163271

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 805 - 811
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