Machine learning: Classifiying taxpayer’s supervising zone based on the street address using Natural Language Processing algorithm

Abstract

Assigning taxpayers into their respective Account Representatives is a crucial step to optimize Taxpayers supervision. However, the large number of registered taxpayers and missing data has been a great challenge. A lot of taxpayers only include their street addresses and no additional information such as RT, RW, etc. This will cause additional work to manually search each taxpayer address in the internet and manually assigned, which is not efficient and takes a lot of time. This study will try to solve this problem using Natural Language Processing algorithm. Efficiency and accuracy are the key on creating machine learning model. Choosing the right classifier is crucial to the accuracy. Other than the classifier, managing text data is also challenging, since it cannot be understood directly by computers. Thus, this study will also include how we could transform the text data into arrays of numbers called Bag of Words.