International Journal of Mechanical and Production Engineering (IJMPE)
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Statistics report
Apr. 2024
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 130
Paper Published : 2388
No. of Authors : 6802
  Journal Paper


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


DOIONLINE NO - IJMPE-IRAJ-DOIONLINE-20309   View Here

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