Paper Title :Analysis and Prediction of Employee Attrition Using Machine Learning
Author :Alok Agarwal, Cherian Samuel
Article Citation :Alok Agarwal ,Cherian Samuel ,
(2023 ) " Analysis and Prediction of Employee Attrition Using Machine Learning " ,
International Journal of Mechanical and Production Engineering (IJMPE) ,
pp. 72-76,
Volume-11,Issue-11
Abstract : Employee Attrition is a very big problem today that companies are facing especially after the Coronavirus
pandemic forced most employees to work from home.In 2021, in many big IT companies employee attrition rates were close
to 20% which is very high. Therefore, since this is a critical issue, some steps need to be taken in order to reduce it and make
the employees more comfortable in their current workspaces. For this to happen, it is important for the companies to figure
out the top reasons why employees are leaving along with the remedies to such reasons. A real-life dataset is taken from an
Analytics firm ABC which consists of data of 1470 employees distributed over 35 features. Out of these 35 features top
reasons are separated as to why employees have left which were 11. An analysis of these 11 reasons is done. Then model
building is done where 7 machine learning classification algorithms are run in which the highest test data accuracy was given
by Logistic Regression (87.23%). Based on this, a predictive system was built which would tell whether a particular
employee will leave the company or not, thus solving many issues.
Keywords - Employee Attrition, Machine Learning Classification Algorithms, Predictive System
Type : Research paper
Published : Volume-11,Issue-11
Copyright: © Institute of Research and Journals
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Published on 2024-01-20 |
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