Research on the national income prediction based on Python

Wanping Sun

doi:10.54254/2755-2721/22/20231225

Research Article

Open access

Published on 23 October 2023

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Sun,W. (2023). Research on the national income prediction based on Python. Applied and Computational Engineering,22,53-62.

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Research on the national income prediction based on Python

Wanping Sun *^,1,

¹ Beijing Wuzi University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/22/20231225

Abstract

The national income level has always been a topic of concern, and there are many influences that affect the income. This paper focuses on the national work, age, education, marriage, gender, weekly working hours and other dimensions to explore the types of people with annual income above $50,000. In this paper, we select the data collected from the U.S. Census as the data set, divide the training set and the test set, and then construct logistic regression and decision tree models to predict the national income respectively. The experimental results show that the ACC of the logistic regression model is 0.773 and the AUC is 0.515, and the ACC of the decision tree model is 0.860 and the AUC is 0.900. It is verified that the decision tree has better performance in predicting national income.

Keywords

Income Prediction, Python, Logistic Regression, Decision Trees

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Cite this article

Sun,W. (2023). Research on the national income prediction based on Python. Applied and Computational Engineering,22,53-62.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-035-6(Print) / 978-1-83558-036-3(Online)

Conference date: 14 July 2023

Editor：Alan Wang, Marwan Omar, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.22

ISSN：2755-2721(Print) / 2755-273X(Online)

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