Data Set:

Loan Purpose Checking Savings Months Customer Months Employed Credit Risk New Car 0 389 19 119 High New Car 0 3305 11 15 Low New Car 396 228 13 26 Low Used Car 0 0 19 58 High Small Appliance 237 236 37 24 Low Small Appliance 0 490 5 41 Low Furniture 0 162 25 1 High Small Appliance 0 138 7 119 Low Furniture 216 0 19 3 High Furniture 4256 0 16 36 Low New Car 0 1366 19 17 Low Furniture 513 442 7 0 Low New Car 166 922 13 2 High Repairs 273 904 7 2 Low Education 0 127 13 22 High Furniture 0 836 25 99 Low Small Appliance 12974 19568 13 7 Low New Car 0 500 28 7 High Used Car 0 260 25 78 Low Furniture 0 948 19 2 Low Small Appliance 0 762 10 1 High Used Car 7885 6330 16 14 Low New Car 0 886 22 96 Low Small Appliance 0 6490 19 85 Low Small Appliance 734 348 7 100 Low Furniture 0 835 19 42 High Small Appliance 0 823 25 47 Low New Car 585 2223 16 0 High Business 8948 110 31 90 High Education 0 3105 16 19 Low Repairs 0 718 19 0 High Small Appliance 0 296 16 8 Low Business 778 861 49 21 High New Car 1613 0 25 118 Low Retraining 0 603 13 35 High Business 0 859 19 23 High Furniture 0 13970 13 24 High New Car 0 483 19 90 High Small Appliance 0 813 43 28 High Small Appliance 0 596 13 67 Low New Car 0 541 19 13 High Furniture 0 7710 25 114 Low Small Appliance 0 798 25 42 High Small Appliance 8249 0 31 77 Low Education 842 0 37 9 Low Business 929 124 9 1 Low New Car 0 14190 37 92 Low Furniture 0 435 19 16 High Small Appliance 0 17653 22 4 Low Used Car 0 674 37 69 Low

10.3.7 Use the given data to classify the record below using the k-NN algorithm for k = 1 to 5. Loan Purpose Retraining Checking 644 Savings 2000 Months Customer 20 Months Employed 60 Click the icon to view the table of classified records. Complete the table below for the nearest neighbors and for the classification of the new record for each value of k. k Distance Classification 1 2 3 7 4 7 5 (Round to four decimal places as needed.)