Leveraging Machine Learning to Predict Employee Attrition: India

Kumari, Sangeeta (2023) Leveraging Machine Learning to Predict Employee Attrition: India. Masters thesis, Dublin, National College of Ireland.

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Abstract

Employee attrition is an important issue that affects businesses and organizations across the world. This can be attributed to the fact that hiring new employees takes time and consumes a lot of resources. Hence, predicting if an employee is unhappy at an organization can help the organization take necessary steps to avert attrition. Hence, developing a model that can predict employee attrition is very beneficial for the organization and for the employee as well.

This research studied the performances of four machine-learning models viz. decision tree, random forest, SVM, and artificial neural network. The models were implemented through two experiments in which the first experiment involved predicting the attrition for the whole organization whereas the second experiment involved predicting the attrition department-wise. The random forest model of machine learning model implemented in the model achieved the best performance with accuracy of 64.39% and f1-score of 63.96%.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Mulwa, Catherine UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management > Human Resource Management > Employee Retention Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Issues of Labour and Work > Quality of Work Life / Job Satisfaction
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	19 May 2023 15:24
Last Modified:	19 May 2023 15:24
URI:	https://norma.ncirl.ie/id/eprint/6605

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