![]() ![]() For this case study, I used a simplified version of the original dataset that only has 10 variables: EDA exposes trends, patterns, and relationships within the dataset that may not be apparent. Exploratory Data AnalysisĮxploratory Data Analysis (EDA) is used to make a thorough examination of a dataset. The task requires exploring the data and building a predictive model to provide a bank manager guidance for making a decision on whether to approve a loan to a prospective applicant based on his/her profile. ![]() The German Credit Data contains data on 20 variables and the classification of whether an applicant is considered a Good or Bad credit risk for 1000 loan applicants. The German Credit data set is a publically available data set downloaded from the UCI Machine Learning Repository. An applicant’s demographic and socio-economic profiles are considered by loan managers before a decision is taken regarding his/her loan application. The objective of the German Credit Data is to minimize the chances of issuing risky loans to applicants while maximizing the chances of profiting from good loans. is not likely to repay the loan, then approving the loan to the person results in a financial loss to the bank
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