(c) Consider the following data concerning credit default: Default D1 Age 25 Loan D2 102 000...

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(c) Consider the following data concerning credit default: Default D1 Age 25 Loan D2 102 000 82 000 62 000 122 000 22 000 124 000 47 000 80 000 40 000 N 104 35 60 000 69 45 80 000 N 92 20 20 000 N 28 35 120 000 N 125 52 18 000 N 55 23 95 000 Y 98 40 62 000 Y 74 60 100 000 Y 42 000 117 78 000 8 000 48 220 000 Y 225 33 150 000 Y 154 Age and Loan are two numerical variables (predictors) and Default is the target. Use the k-NN algorithm with k-3 and the given training set to classify an unknown case (Age-48 and Loan=142 000) using Euclidean distance, and without Normalisation for the following cases: (i) (ii) Use the loan amount as is and Use the loan amount in 1000 units. You are also required to comment on your result. The calculation for both part (i) and (ii) are included in the data table: columns D1 and D2 [10 marks] (c) Consider the following data concerning credit default: Default D1 Age 25 Loan D2 102 000 82 000 62 000 122 000 22 000 124 000 47 000 80 000 40 000 N 104 35 60 000 69 45 80 000 N 92 20 20 000 N 28 35 120 000 N 125 52 18 000 N 55 23 95 000 Y 98 40 62 000 Y 74 60 100 000 Y 42 000 117 78 000 8 000 48 220 000 Y 225 33 150 000 Y 154 Age and Loan are two numerical variables (predictors) and Default is the target. Use the k-NN algorithm with k-3 and the given training set to classify an unknown case (Age-48 and Loan=142 000) using Euclidean distance, and without Normalisation for the following cases: (i) (ii) Use the loan amount as is and Use the loan amount in 1000 units. You are also required to comment on your result. The calculation for both part (i) and (ii) are included in the data table: columns D1 and D2 [10 marks]