A consumer advocacy agency, Equitable Ernest, is interested in providing a service in which an individual can estimate their own credit score {a continuous measure used by banks, insurance companies, and other businesses when granting loans, quoting premiums, and issuing credit). The DATAIe CreditScore contains data on an individual's credit score and other variables. Create a standard partition of the data with all the tracked variables and 50% of observations in the training set, 30% in the validation set, and 20% in the test set. Predict the individuals' credit scores using kNearest Neighbors Iwith up to k = 10. Use CreditScore as the output variable and all the other variables as input variables. In Step 2 of k-nearest neighbors Prediction procedure, be sure to Normalize Input data and to Score on best It between 1 and specified value. Generate a Detailed Report for all three sets of data. Refer to the Appendix for instructions on how to perform k-nearest neighbors using the Analytic Solver Platform. In the Parameters tab, Partllion Data and Use parlitlon variable named Partition, Reseale Data with the Standardlzatlon type, enter 10 for the # Neighbors (K), and select Search 1..l( for value of k that achieves the minimum classication error. Click on the datale logo to reference the data. DATAIie {a} What value of it minimizes the RHSE on the validation data? (b) How does the RMSE on the test set compare to the RMSE on the validation set? If needed, round your answers to one decimal digit. Set RHS Error Validation Test