Comparison of machine learning techniques to predict the attrition rate of the employees

Rohit Hebbar, A and Patil, SH and Rajeshwari, SB and Saqquaf, SSM (2018) Comparison of machine learning techniques to predict the attrition rate of the employees. In: 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 18-19 May 2018, Institute at Sri Venkateshwara College of Engineering (SVCE), Bangalore, India, pp. 934-938.

PDF IEEE_INT_CON_REC_TRE_ELE_INF_COM_TEC_934-938_2018.pdf - Published Version Restricted to Registered users only Download (3MB) | Request a copy

Abstract

In most of the organizations, Employee Attrition has been one of the greatest concerns in today's world. The reason behind this can be due to personal or company related issues such as long-distance travelling, no work life balance, less salary hike, no job satisfaction etc. According to a study done by Businessdictonary, employee attrition results from resigning from their post, retirement, illness, or demise. Considering these issues, the project aims to find the employees who are most likely to attrite from the organization using pre-processing techniques such as exploratory data Analysis (EDA), feature selection techniques and utilizing various machine learning techniques such as Logistic Regression, Support Vector Machine (SVM) and Random Forest. According to which several programs can be incorporated by the organizations to minimize the attrition rate and also help in building and maintaining a robust relationship between the employees and the organization. © 2018 IEEE.

Item Type:	Conference Paper
Publication:	2018 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018 - Proceedings
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	cited By 0; Conference of 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018 ; Conference Date: 18 May 2018 Through 19 May 2018; Conference Code:158096
Keywords:	Classification (of information); Compensation (personnel); Data handling; Data reduction; Decision trees; Feature extraction; Job satisfaction; Logistic regression; Random forests; Support vector machines; Support vector regression, Attrition; Attrition rate; Exploratory data analysis; In-buildings; Machine learning techniques; Pre-processing; Selection techniques; Work-life balance, Learning systems
Department/Centre:	Centres under the Director > Digital Campus and IT Services Office
Date Deposited:	04 Sep 2020 06:28
Last Modified:	04 Sep 2020 06:28
URI:	http://eprints.iisc.ac.in/id/eprint/65021

Actions (login required)

View Item