Open Government Data for Machine Learning Tax Recommendation

Abstract

Taxpayers may be interested in overpayment and which group of taxpayers he or she belongs to. Government officials may be concerned with underpaying taxpayers for auditing purposes and group taxpayers in the rapidly changing society. Machine learning and data mining techniques have been applied to provide solutions to these taxation related queries. Classification algorithms allow predicting the tax bracket based on the taxpayers' attributes. The regression model allows to predict the tax estimate so that the overpayment or underpayment can be determined. Clustering algorithms group taxpayers so that they can be compared to the past year tax brackets. Finally, feature selection allows finding salient attributes to predict the tax and tax bracket. In this article, New York State's Open Tax Data is used to demonstrate the machine learning and data mining algorithms and identify issues of using them. Furthermore, various visualization techniques are to present the discovered information to both taxpayers and government officials.