Forecasting Salary Using a Machine Learning System

DOI

10.2991/978-94-6463-787-8_13How to use a DOI?

Keywords

Machine learning; Random Forest regressor; K-Nearest Neighbours (KNN); statistical models; Decision Tree; Deep Learning & Support vector machine (SVM)

Abstract

The employment sector has been significantly influenced by the Internet’s growth. Nowadays, technology that predicts with human-like accuracy is increasingly popular and aids in resolving the majority of prediction and detection issues. This pay forecasting method is aimed at providing better encouragement to both employers and employees to make informed decisions about job offers and their career paths. One of the major decisions would be salary; this algorithm is programmed to guide them regarding the salary that they can aspect based on several factors. Now that candidates have more skills, knowledge, and experience but are still ready to work for a lower salary, they are underestimating their talent and skills. However, this model will help them design their career path accordingly. This study proposes a model that uses relevant variables and a suitable algorithm to forecast employee pay.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Preeti Raj
AU  - Abhishek Kumar
AU  - Ravi Kumar Burman
AU  - Laxmi Kumari
PY  - 2025
DA  - 2025/07/17
TI  - Forecasting Salary Using a Machine Learning System
BT  - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
PB  - Atlantis Press
SP  - 131
EP  - 146
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-787-8_13
DO  - 10.2991/978-94-6463-787-8_13
ID  - Raj2025
ER  -