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.
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