Exploring the Influence of Country, Industry, and Gender Features in Machine Learning-Based Rich List Prediction

Juming Zhang

2024

Abstract

In the context of economic globalization, the global rich list is also undergoing subtle changes year by year. From a macro perspective, the impact of countries, industries, and gender on personal assets cannot be ignored. This study will use learning models to predict the 2025 rich list, and use information visualization to draw images and machine learning to explore the impact of different features on the list, so as to understand the global wealth distribution, analyze market and industry trends, and infer whether gender has an impact on the rich industry. The study found that when using a linear model for prediction, the best prediction effect is to build a separate model for each rich person. At present, the concentration of wealth in the United States is relatively high, far exceeding other countries among the rich, but China, Russia, India, Germany, the United Kingdom and other countries also have certain advantages on the rich list, showing their importance in their respective fields. Technology, finance and fashion have a significant impact on the wealth accumulation of the rich. Gender currently has little impact, reflecting gender equality among the rich.

Paper Citation

in Harvard Style

Zhang J. (2024). Exploring the Influence of Country, Industry, and Gender Features in Machine Learning-Based Rich List Prediction. In Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM; ISBN 978-989-758-738-2, SciTePress, pages 66-72. DOI: 10.5220/0013231500004558

in Bibtex Style

@conference{mlscm24,
author={Juming Zhang},
title={Exploring the Influence of Country, Industry, and Gender Features in Machine Learning-Based Rich List Prediction},
booktitle={Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM},
year={2024},
pages={66-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013231500004558},
isbn={978-989-758-738-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM
TI - Exploring the Influence of Country, Industry, and Gender Features in Machine Learning-Based Rich List Prediction
SN - 978-989-758-738-2
AU - Zhang J.
PY - 2024
SP - 66
EP - 72
DO - 10.5220/0013231500004558
PB - SciTePress