Nowadays there are many risks related to bank loans, especially for the banks so as to reducetheir capital loss. The analysis of risks and assessment of default becomes crucial thereafter. Banks holdhuge volumes of customer behaviour related data from which they are unable to arrive at a judgement ifan applicant can be defaulter or not. Data Mining is a promising area of data analysis which aims toextract useful knowledge from tremendous amount of complex data sets. In this paper we aim to design amodel and prototype the same using a data set available in the UCI repository. The model is a decisiontree based classification model that uses the functions available in the R Package. Prior to building themodel, the dataset is pre-processed, reduced and made ready to provide efficient predictions. The finalmodel is used for prediction with the test dataset and the experimental results prove the efficiency of thebuilt model.
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