Implementation of Job Classification for Accounting Field using Decision Tree Algorithm

Abstract

Data mining is seen as an increasingly important tool by modern business to transform data into an informational advantage. Data mining could also be described as trying to create a simplified model of the complex world described in the database. Data mining is a way of dealing with large amounts of information, and it is helpful for finding useful information faster than any human. Decision tree is mainly used for classification purposes. Decision tree is a classifier in the form of a tree structure. Rules can be easily extracted from the decision tree. The main task performed in this system is using inductive methods to the given values of attributes of an unknown object to determine appropriate classification according to decision tree rules. This paper examines the decision tree learning algorithm for classifying job related accounting field. This paper implements the decision tree using ID3 and gives advice to users about the types of job in accounting field. This system uses 900 training data set and 300 testing data set. This paper calculates the system accuracy by using Hold_Out Method and provides 84.75% after reviewing 300 testing data set.