Volume 20 No 9 (2022)
 Download PDF
Human Resource Data Analysis & predictionusing Decision Tree Algorithm and Random Forest
Durga S,Prathusha Perugu,Nidhi Sree D, Venkateswararao Podile ,Mahesh Manohar Bhanushali, Revathi R
Abstract
Due to the complexity and unpredictability of human behaviour in contrastto equipment or other physical assets, allocating human capital efficiently has always been a challenging task. Because of this issue, human resource analytics has lately gained prominence. Employee attrition is a fascinating data set to investigate. Apartfrom the reasons mentioned in the dataset, other factors, such as distance from work, further education, establishing one’s own business, politics, or relocation, may occur. However, let us do some analysis and prediction using the dataset that has been provided to us. Before we begin, let us clarify why we are doing this analysis: discover why a small percentage of workers leave the company and their satisfaction. The prediction is used to determine which workers will depart from the business. Future research may concentrate on developing validated models of the suggested theoretical frameworks and assessing the impact of Evidence-Based Management methods on human resources and organizational performance
Keywords
.
Copyright
Copyright © Neuroquantology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Articles published in the Neuroquantology are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJECSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.