Reconsider Problem 2.16. Using the data on the Clean Data worksheet tab, partition the historical records into a training partition (60 percent of the records) and a validation partition (the remaining 40 percent of the records). 

a. Determine the accuracy, sensitivity, and specificity for the KNN model with k = 10 (based on the historical data in the training partition), when it is used to classify applicants as to whether they are likely to default (defined as greater than a 10 percent chance of default). 

b. Generate the lift chart for the algorithm with k = 10.

Data from Problem 2.16.

Friendly Bank is very active with making loans to deserving people in the local community. However, the bank does need to carefully evaluate each loan to make sure that the recipient of the loan will likely repay the loan as scheduled. Therefore, the bank needs to obtain a prediction of whether this is likely and what the probability is. The bank primarily uses the annual income and the credit rating of the person applying for the loan as the predictor variables for obtaining this prediction. The bank has compiled all of the historical records of substantial loans and their outcomes over recent years. This information is provided in the spreadsheet titled Friendly Bank Data available in www.mhhe.com/Hillier7e. Only loans that have concluded (either paid off in full or ending in default) are included, resulting in 4,985 total records. The original data (on the Original Data worksheet tab) needs to be cleaned. Perform the following data cleaning tasks.