German Credit/Decision Tree model and ROC CURVE/KNIME скачать в хорошем качестве

German Credit/Decision Tree model and ROC CURVE/KNIME

German credit details and target customers 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. Two types of risks are associated with the bank’s decision: If the applicant is a good 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 bad credit risk, i.e., is not likely to repay the loan, then approving the loan to the person results in a financial loss to the bank. It may be assumed that the second risk is a greater risk, as the bank (or any other institution lending the money to an untrustworthy party) had a higher chance of not being paid back the borrowed amount. So, it’s on the part of the bank or other lending authority to evaluate the risks associated with lending money to a customer. This study aims at addressing this problem by using the applicant’s demographic and socio-economic profiles to assess the risk of lending loan to the customer. In business terms, we try to minimize the risk and maximize of profit for the bank. 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. An applicant’s demographic and socio-economic profiles are considered by loan managers before a decision is taken regarding his/her loan application. GERMAN CREDIT DETAILS STEP 1: Import the data file using an appropriate node STEP 2: Filter only a specific row which has a missing value and show it separate (This, will help as a reference in future to locate the row with missing value without removing the row) STEP 3: Convert number to string STEP 4: Use Colour Manager for the Target Binary classification in this dataset STEP 5: Split the data into (Learning data and Test data) STEP 6: Perform the decision trees STEP 7: Check accuracy and reliability STEP 8: Check how far the model has the capacity to classify the customers into Valuable customers and invaluable customers