Unveiling Salary Trends: Exploring Machine Learning Models for Predicting Data Science Job Salaries

DOI

10.2991/978-94-6463-540-9_20How to use a DOI?

Keywords

Employment Landscape; Salary Prediction; Machine Learning; Data Science

Abstract

The challenging employment landscape is characterized by a significant disparity between high job expectations and intense competition, often resulting in a discrepancy between applicants’ self-assessment and enterprise standards. Within the vast array of job information available, salary emerges as a crucial concern for job seekers. This paper delves into the Kaggle salary prediction dataset, a rich repository that serves as a valuable resource for understanding trends and patterns in salary expectations across various industries, with a specific focus on data science job predictions. This paper systematically introduces three prominent models of machine learning—deep learning, decision trees, and random forests—and elucidate their applications in the context of salary prediction. By providing a detailed overview of each model’s workings and methodologies, author aims to offer readers a comprehensive understanding of their potential utility in predicting salary outcomes. Through rigorous analysis, this paper meticulously evaluates the strengths and weaknesses inherent in each model, shedding light on their respective performance metrics and predictive capabilities. In addition, this article outlines the future prospects of machine learning in the field of salary prediction and emphasizes trends and potential avenues for further research. The importance of a comprehensive approach is emphasized, which combines the insights of multiple models and can significantly improve the accuracy and effectiveness of predictions in real-world scenarios.

Copyright

© 2024 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  - Jiayi Zhu
PY  - 2024
DA  - 2024/10/16
TI  - Unveiling Salary Trends: Exploring Machine Learning Models for Predicting Data Science Job Salaries
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 173
EP  - 182
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_20
DO  - 10.2991/978-94-6463-540-9_20
ID  - Zhu2024
ER  -