International Journal of Mathematical Sciences and Computing (IJMSC)
IJMSC Vol. 9, No. 1, 8 Feb. 2023
Cover page and Table of Contents: PDF (size: 629KB)
Predictive Analytics of Employee Attrition using K-Fold Methodologies
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Author(s)
V. Kakulapati
1,*
Shaik Subhani
1
Index Terms
Machine learning, Gradient Boosting Algorithms, K-Fold approaches, Light GBM Boost, and XGBoost
Abstract
Currently, every company is concerned about the retention of their staff. They are nevertheless unable to recognize the genuine reasons for their job resignations due to various circumstances. Each business has its approach to treating employees and ensuring their pleasure. As a result, many employees abruptly terminate their employment for no apparent reason. Machine learning (ML) approaches have grown in popularity among researchers in recent decades. It is capable of proposing answers to a wide range of issues. Then, using machine learning, you may generate predictions about staff attrition. In this research, distinct methods are compared to identify which workers are most likely to leave their organization. It uses two approaches to divide the dataset into train and test data: the 70 percent train, the 30 percent test split, and the K-Fold approaches. Cat Boost, LightGBM Boost, and XGBoost are three methods employed for accuracy comparison. These three approaches are accurately generated by using Gradient Boosting Algorithms.
Cite This Paper
V. Kakulapati, Shaik Subhani, "Predictive Analytics of Employee Attrition using K-Fold Methodologies ", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.9, No.1, pp. 23-36, 2023. DOI: 10.5815/ijmsc.2023.01.03
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