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Paper Title : PREDICTING AND CLASSIFYING BANK CUSTOMERS BY APPLYING CLASSIFICATION METHODS OF DATA MINING

ISSN : 2395-1303
Year of Publication : 2022
10.5281/zenodo.6659874

Authors: B.PRIYANKA

         


Abstract
Data mining is a significant area for various commercial organizations comprising banking sector. It is a procedure of analysing the data from numerous viewpoint and précising it into valued information. Classification method is one of the most important techniques in data mining. In classification method is instinctively learns the possessions of classes. In this paper, banking customer dataset is used to predict and classify the accuracy of the customer’s using a transaction process, using of internet banking and the customer’s subscribed a term deposits. Classification techniques such as Bayes net, Naive Bayes, Logistic Regression, K-nearest neighbours and J48 is applied for the banking customer dataset. Finally, the research shows that the performance measures of customer’s transaction process: J48 provides the best accuracy rate is 86.75%, internet banking of the customer’s:J48 provides the best accuracy rate is 92.25% and the customer’s subscribed a term deposits: Bayes net provides the best accuracy rate is 90.25%.

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Keywords
- K-nearest neighbours, Logistic Regression, Naive Bayes, J48.