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. 

a. Search for missing data. Identify the loan applicants that have missing data by specifying the loan number and the data that is missing. 

b. Search for mis-entered data by sorting each column to look for outliers. List any suspicious data, indicating the loan number, what is suspicious, and if possible, conjecture on the likely true value. 

c. Some analysis requires numerical rather than text data. Add another column to the dataset labeled “Default 0/1” and transform the Yes & No data in the “Default” column to 0’s and 1’s in the new column with a 1 representing Yes and a 0 representing No.