,
pages 267-286
HR Analytics: Analysis of Employee Attrition Using Perspectives from Machine Learning
2
Department of Technology Management, Defence Institute of Advanced Technology (DU), Pune, India
|
Publication type: Book Chapter
Publication date: 2024-03-19
SJR: —
CiteScore: —
Impact factor: —
ISSN: 21998493, 21998507
Abstract
Over the past few decades, technology has transformed nearlyEmployee Attrition every phase of our lives. One of the most important transitions has happened in the area of analyticsAnalytics lending unimaginable power in the hands of business people to transform themselves by leveraging information. With the growing interest in machine learning (ML)Machine Learning (ML) technology businesspeople are interested in exploring its use in business practices. This is because they have a singular mission of gaining a competitive advantageCompetitive advantage. Employee attritionAttrition is one of the largest and most unknown costs an organization may have to face. This chapter provides an extensive overview of employee turnover using MLMachine Learning (ML) techniques. The prediction is completed utilizing the information sourced by IBM AnalyticsAnalytics. The real dataset consists of 35 attributes or features and 1470 samples. In this chapter, different classificationClassification techniques are used for predicting employee attritionEmployee Attrition. The results obtained after model selectionModel Selection are expressed in terms of the confusion matrix along with the algorithm that processesPROCESS the optimum results. The random forest algorithm is found to have delivered the optimum results for the provided dataset. The outcome of the research found seven variables as critical driving factors that contributed to employee attritionEmployee Attrition. The results of the algorithm give 100% accuracy, precision, recall rate, and specificity while giving an 85.228% CV score and ROC score of one. This study will give senior-level executives and policymakers a clear viewpoint from which they can decide whether to keep a majority of the employees within the organization. Future studies could enhance the analysis by taking into account additional elements that have a beneficial impact on employee attrition rates, such as poor hiring practices, a hostile workplace environment, as well as a lack of feedback and appreciation.
Found
Nothing found, try to update filter.
Found
Nothing found, try to update filter.
Top-30
Journals
|
1
|
|
|
SA Journal of Human Resource Management
1 publication, 50%
|
|
|
1
|
Publishers
|
1
|
|
|
AOSIS
1 publication, 50%
|
|
|
Institute of Electrical and Electronics Engineers (IEEE)
1 publication, 50%
|
|
|
1
|
- We do not take into account publications without a DOI.
- Statistics recalculated weekly.
Are you a researcher?
Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
2
Total citations:
2
Citations from 2024:
2
(100%)
Cite this
GOST |
RIS |
BibTex
Cite this
GOST
Copy
Krishna S., Sidharth S. HR Analytics: Analysis of Employee Attrition Using Perspectives from Machine Learning // Flexible Systems Management. 2024. pp. 267-286.
GOST all authors (up to 50)
Copy
Krishna S., Sidharth S. HR Analytics: Analysis of Employee Attrition Using Perspectives from Machine Learning // Flexible Systems Management. 2024. pp. 267-286.
Cite this
RIS
Copy
TY - GENERIC
DO - 10.1007/978-981-99-9550-9_15
UR - https://link.springer.com/10.1007/978-981-99-9550-9_15
TI - HR Analytics: Analysis of Employee Attrition Using Perspectives from Machine Learning
T2 - Flexible Systems Management
AU - Krishna, Shobhanam
AU - Sidharth, Sumati
PY - 2024
DA - 2024/03/19
PB - Springer Nature
SP - 267-286
SN - 2199-8493
SN - 2199-8507
ER -
Cite this
BibTex (up to 50 authors)
Copy
@incollection{2024_Krishna,
author = {Shobhanam Krishna and Sumati Sidharth},
title = {HR Analytics: Analysis of Employee Attrition Using Perspectives from Machine Learning},
publisher = {Springer Nature},
year = {2024},
pages = {267--286},
month = {mar}
}