Predicting Employee Attrition using Machine Learning

Authors(4) :-Ganesh V, Aishwaryalakshmi S, Aksshaya K, Abinaya M

Employee attrition is a major cost to an organization. Some costs are tangible such as training expenses and the time it takes from when an employee starts to when they become a productive member. However, the most important costs are intangible, such as new product ideas, great project management, or customer relationships. Employee attrition control is critical to the long term health and success of any organization. An organization is only as good as its employees, and these people are the true source of its competitive advantage. Accurate predictions enable organizations to take action for the retention of employees. This project aims to use different supervised classifiers to make predictions, and chooses the most accurate one.

Authors and Affiliations

Ganesh V
Department of Computer Science, Saranathan College of Engineering, Tiruchirapalli, Tamil Nadu, India
Aishwaryalakshmi S
Department of Computer Science, Saranathan College of Engineering, Tiruchirapalli, Tamil Nadu, India
Aksshaya K
Department of Computer Science, Saranathan College of Engineering, Tiruchirapalli, Tamil Nadu, India
Abinaya M
Department of Computer Science, Saranathan College of Engineering, Tiruchirapalli, Tamil Nadu, India

Employee attrition, Supervised learning, Logit transformation, Non-parametric, Chi-Square, Gradient descent

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Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 145-149
Manuscript Number : CSEIT1831481
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Ganesh V, Aishwaryalakshmi S, Aksshaya K, Abinaya M , "Predicting Employee Attrition using Machine Learning", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.145-149, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1831481

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