Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM29215
Volume:
8 |
3 |
Mar 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Companies are constantly looking for ways to keep their professional workforce engaged to reduce the additional costs of recruitment and training. Predicting whether or not a particular employee will leave will help the company make preventative decisions. Unlike physical systems, human resource problems cannot be explained from a scientific-analytical perspective. Therefore, machine learning is the best tool to do this. Many reasons can cause employee anxiety. In this we can use several machine learning models are developed to automatically and accurately predict employee turnover. This work uses the IBM attrition dataset to train and test machine learning models; namely Logistical Regression, Random Forest and Gradient Boosting, examples. The ultimate goal is to accurately identify attrition among a company’s conservative workforce to help improve retention strategies and increase the satisfaction of those employees.
KEYWORDS
Publication Date
12/31/45363
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
3
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...