RELEVANCE OF MACHINE LEARNING ENTROPY TO DETECT FINANCIAL FRAUD

Data mining and discovery of Information have become areas of growing consequence because of the newest growing demand for knowledge prediction, including those used in machine learning, databases storage, statistics, knowledge acquirement, data revelation, and high recital computing. To analysis and predicting fraudulent in data many use Shannon Entropy. Some important prediction technique for fraud analysis and different methods of classification of data mining were measured to manage fraud finding. There subsist a number of algorithms to achieve fraud information as conclusion and here uses Shannon's theorem, by devious Information gain to build Decision tree with sub-roots and leafs of tree .Also train machine learning model to predict fraud if ever occur in Data performed by SVM classification to detect fraud in finance.