Use the given data to classify the record below using the k-NN algorithm for k = 1 to 5. Loan Purpose Education Checking 680 Savings 600 Months Months Customer Employed 50 18 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 Tie 4 High 5 Low (Round to four decimal places as needed.) J9 JX A B D E F Checking 0 0 0 237 3565 0 0 Savings 533 17545 10723 236 0 109 789 763 1366 0 0 399 0 0 166 8357 922 904 0 0 1 Loan Purpose 2 Business 3 Furniture 4 New Car 5 Small Appliance 6 Small Appliance 7 Used Car 8 Used Car 9 Small Appliance 10 New Car 11 Used Car 12 Furniture 13 New Car 14 New Car 15 New Car 16 Small Appliance 17 Small Appliance 18 Furniture 19 Furniture 20 Education 21 Used Car 22 Repairs 23 New Car 24 Small Appliance 25 New Car 26 Furniture 27 Business 28 Small Appliance 29 Small Appliance 20 Ponnir Months Customer Months Employed Credit Risk 14 2 Low 34 16 High 11 15 Low 37 24 Low 31 32 Low 25 26 Low 25 28 Low 13 46 Low 19 17 Low 31 o High 25 5 High 13 2 High 12 6 Low 16 59 High 46 13 High 7 18 Low 31 21 High 25 5 High 37 9 High 37 1 High 25 19 Low 22 96 Low 13 4 High 25 19 High 13 4 Low 31 90 High 13 15 High 19 6 High 19 Aliah 0 759 192 583 1533 199 922 540 772 886 829 876 192 0 109 0 0 0 0 352 8948 0 483 3273 0 7525 110 552 415 710 B Checking 0 498 0 0 Savings 718 598 508 11838 836 0 6628 214 823 0 425 0 8636 911 0 1 Loan Purpose 30 Repairs 31 Business 32 New Car 33 Small Appliance 34 Furniture 35 New Car 36 Small Appliance 37 Furniture 38 Small Appliance 39 Other 40 New Car 41 Business 42 Furniture 43 Furniture 44 Business 45 Furniture 46 Used Car 47 Furniture 48 New Car 49 Furniture 50 New Car 51 Furniture 52 D E F Months Customer Months Employed Credit Risk 19 O High 37 14 High 13 3 High 7 70 Low 16 4 Low 19 7 High 37 65 Low 11 3 Low 46 4 High 37 0 Low 19 13 High 49 9 Low 37 101 High 25 59 High 9 1 Low 25 46 High 49 73 High 19 16 High 13 27 High 13 65 High 23 37 Low 31 53 High 0 177 0 523 541 0 987 538 124 146 396 435 19811 0 0 0 0 929 0 0 0 10417 642 3880 0 Use the given data to classify the record below using the k-NN algorithm for k = 1 to 5. Loan Purpose Education Checking 680 Savings 600 Months Months Customer Employed 50 18 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 Tie 4 High 5 Low (Round to four decimal places as needed.) J9 JX A B D E F Checking 0 0 0 237 3565 0 0 Savings 533 17545 10723 236 0 109 789 763 1366 0 0 399 0 0 166 8357 922 904 0 0 1 Loan Purpose 2 Business 3 Furniture 4 New Car 5 Small Appliance 6 Small Appliance 7 Used Car 8 Used Car 9 Small Appliance 10 New Car 11 Used Car 12 Furniture 13 New Car 14 New Car 15 New Car 16 Small Appliance 17 Small Appliance 18 Furniture 19 Furniture 20 Education 21 Used Car 22 Repairs 23 New Car 24 Small Appliance 25 New Car 26 Furniture 27 Business 28 Small Appliance 29 Small Appliance 20 Ponnir Months Customer Months Employed Credit Risk 14 2 Low 34 16 High 11 15 Low 37 24 Low 31 32 Low 25 26 Low 25 28 Low 13 46 Low 19 17 Low 31 o High 25 5 High 13 2 High 12 6 Low 16 59 High 46 13 High 7 18 Low 31 21 High 25 5 High 37 9 High 37 1 High 25 19 Low 22 96 Low 13 4 High 25 19 High 13 4 Low 31 90 High 13 15 High 19 6 High 19 Aliah 0 759 192 583 1533 199 922 540 772 886 829 876 192 0 109 0 0 0 0 352 8948 0 483 3273 0 7525 110 552 415 710 B Checking 0 498 0 0 Savings 718 598 508 11838 836 0 6628 214 823 0 425 0 8636 911 0 1 Loan Purpose 30 Repairs 31 Business 32 New Car 33 Small Appliance 34 Furniture 35 New Car 36 Small Appliance 37 Furniture 38 Small Appliance 39 Other 40 New Car 41 Business 42 Furniture 43 Furniture 44 Business 45 Furniture 46 Used Car 47 Furniture 48 New Car 49 Furniture 50 New Car 51 Furniture 52 D E F Months Customer Months Employed Credit Risk 19 O High 37 14 High 13 3 High 7 70 Low 16 4 Low 19 7 High 37 65 Low 11 3 Low 46 4 High 37 0 Low 19 13 High 49 9 Low 37 101 High 25 59 High 9 1 Low 25 46 High 49 73 High 19 16 High 13 27 High 13 65 High 23 37 Low 31 53 High 0 177 0 523 541 0 987 538 124 146 396 435 19811 0 0 0 0 929 0 0 0 10417 642 3880 0