Multilayer neural network-based wage forecasting in data science industry

Zhenghan Song

doi:10.1117/12.3009236

27 October 2023 Multilayer neural network-based wage forecasting in data science industry

Zhenghan Song

Proceedings Volume 12922, Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023); 129222J (2023) https://doi.org/10.1117/12.3009236
Event: The Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023), 2023, Xiamen, China

Abstract

The data science industry has experienced significant growth, highlighting the need for a comprehensive understanding of global salary dynamics. This paper aims to bridge the gap in recent research by providing valuable insights into wage forecasting in the data science industry while enhancing prediction accuracy. Utilizing the most recent dataset collated in 2023, rigorous preprocessing techniques ensure data integrity and relevance. Descriptive statistics summarize key dataset characteristics, while correlation analysis investigates variable relationships. Visualizations such as bar charts, box plots, and scatter plots effectively depict data distributions and patterns. Employing a multi-layer neural network (MLP) algorithm as the primary predictive model, the performance of neural network is compared with multiple linear regression and gradient boosting algorithms. The experimental results show that the superiority of MLP in terms of fit and effectiveness in predicting data science wages. The main contribution of this research lies in the application of deep learning methods, significantly improving prediction accuracy. This study holds significant value for data science professionals, empowering them with a valuable tool for salary negotiation and career planning.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenghan Song "Multilayer neural network-based wage forecasting in data science industry", Proc. SPIE 12922, Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023), 129222J (27 October 2023); https://doi.org/10.1117/12.3009236

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