Problem 1 (kNN Classification)Use kNN method (k = 3) to classify the cancer of Patient # 10 as either Malignant or Benign.Patient ID Diagnosis texture perimeter Area1 Malignant 12 151 9542 Benign 13 133 13263 Malignant 27 130 12034 Benign 16 78 3865 Malignant 19 135 12976 Benign 25 83 4777 Malignant 26 120 10408 Malignant 18 90 5789 Malignant 24 88 52010 ? 17 100 560The measurement units are not the same for the 3 variables. Hence, you must STANDARDIZE the data.Euclidean Distance between (a1, b1, c1) and (a2, b2, c2) is

Patient ID Diagnosis texture perimeter Area 1 Malignant 12 151 954 2 Benign 13 133 1326 Malignant 27 130 1203 Benign 16 78 386 5 Malignant 19 135 1297 O Benign 25 83 477 7 Malignant 26 120 1040 8 Malignant 18 90 578 9 Malignant 24 88 520 10 -J 17 100 560