COMPARATIVE STUDY AND ANALYSIS OF AUDIT DATA USING DATAMINING TOOLS

Abstract

machine learning for spam classification is an important research issue at present. Classification of Audit dataset is implemented by using Weka, Tanagra, and Rapid Miner. This classification creates a global model, which is used for predicting the class label of unknown data. Thus, it is useful in many decision problems, where for a given data item a decision is to be made. This paper explores and identifies the use of different Machine learning algorithms for classifying the missing and erroneous data in the dataset. Different classification algorithms are experimented and performance is analysed