A bank is interested in identifying different attributes of its customers, and below is the sample data of 150 customers. In the data table for the dummy variable Gender, 0 represents Male and 1 represents Female. And for the dummy variable Personal loan. 0 represents a customer who has not taken a personal loan and 1 represents a customer who has taken a personai loan. Partition the data into training [50 percent], validation [30 percent], and test {20 percent} sets. Classify the data using it- nearest neighbors with up to k = 10. Use Age. Gender. Work experience. Income {in $10005). and Family size as input variables and Personai loan as the output variable. Be sure to Normalize input data and to Score on best it: between 1 and specied value. Generate lift charts for both the validation data and test data. 1. For the cutoff probability value 0.5, what value of it minimizes the overall error rate on the validation data? Explain the difference in the overall error rate on the training. validation. and test data. 2. Examine the decilewise lift chart on the test data. Identify and interpret the rst decile lift. 3. For cutoff probability values of 0.5. 0.4, 0.3, and 0.2, what are the corresponding Class 1 error rates and Class 0 error rates on the validation data