Attrition Prediction and Retention of High Risk Employees Using Machine Learning and Explainable Artificial Intelligence

Author

Sidharth Nigade, Amit Shinde, Nikhil Dethe, Ganesh Chormale, Vaishali Kandekar


Abstract

The Indian Express’ reports that the attrition rate in the IT industry has surpassed 25%. The majority of Fortune 500 firms are currently looking for new employees. On the one hand, there are more opportunities, but according to a report by Employee Benefits News, companies are also losing a significant amount of money - about 33% of their annual income. The current era in which the IT industry serves as the foundation for the economies of the world's leading nations. It's interesting to note that 36% of these individuals don't even have their next job secured. The cost of attrition includes both monetary and non-monetary costs, like halted billing, delayed product launches, lack of reliability, etc. Our objective is to determine the causes of attrition and implement preventative actions as a result. The ideal answer was one-on-one interaction with HR, but as we are well aware, it is not scalable. The only option left to us is to use the Data to replicate this ideal state. Use the machine learning model to reliably predict the attrition rate based on all of the available data. Consequently, in this forthcoming modern period where the economics of the world's top-tier countries are centered on the IT industry, recognizing attrition is crucial. While discussing attrition, Bill Gates once said, "If the top 1% of skilled employees leave Microsoft, the company declines from an extraordinary to an average company. As a result, the goal of this paper is to provide customized software to predict the employee retention rate based on supervised and unsupervised machine learning algorithms by analyzing Glassdoor reviews as well as churn dataset to reduce the cost of Employee turnover, to find the potential factors responsible for voluntarily increase in attrition rate, to provide better work life for the employees, etc. Lime which is a library of XAI is used to predict the top 10 important features for the particular employees leaving the organization. Algorithms like Backward selection, logistic regression, decision trees and Random forest regressor were used. Along with this measures were taken to fill the positions or retain the employees using XAI methods.


Keywords

Machine Learning, React, Node, Random forest, Logistic Regression, eXplainaible Artificial Intelligence



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References


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