German credit data set arff download5/25/2023 ![]() ![]() To minimize loss from the bank’s perspective, the bank needs a decision rule regarding who to give approval of the loan and who not to. However, later in another course we will also use this dataset to build a predictive credit risk model. In this section, we will explore the dataset using ggplot2 and create both exploratory as well as explanatory data visualizations. is not likely to repay the loan, then approving the loan to the person results in a financial loss to the bank If the applicant is a bad credit risk, i.e.is likely to repay the loan, then not approving the loan to the person results in a loss of business to the bank ![]() If the applicant is a good credit risk, i.e.Two types of risks are associated with the bank’s decision: ![]() When a bank receives a loan application, based on the applicant’s profile the bank has to make a decision regarding whether to go ahead with the loan approval or not. The German Credit Data contains data on 20 variables and the classification whether an applicant is considered a Good or a Bad credit risk for 1000 loan applicants. However, one interesting dataset that we will be using quite a lot in this section is the German Credit dataset. To learn data visualization with ggplot2 in R, we will be making use of various datasets.
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