Using personal information and income range data from the 1994 US Census database, applied naive Bayes classifier and linear discriminant analysis classifier to classify personal income ranges. Through examples, explained the implementation process of the income classification model and the segmentation model.
Personal income has always been an important part of the national economy, and the government can implement policies based on changes in income ranges. This project mainly uses personal information and wage range data from the 1994 US Census database, and uses naive Bayes classifiers and linear discriminant classifiers to classify personal income ranges. Through examples, the implementation process of the income classification model and the segmentation model are described, and the effectiveness of the model training is verified. The training results of the two models are 0.80 and 0.84, respectively, which are greater than zero accuracy, indicating good model performance. This provides a theoretical basis and suggestions for relevant organizations to predict personal income based on information.
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