You are consulting for a bank that currently uses k-nearest neighbours with k = 1 to determine whether a customer will default on a loan or not default. The features used in this model are weekly spending (measured in dollars) and duration in the current job (measured in years) 1. Explain why the data need to be standardised before carry ing out kNN classification? 2. Suppose a customer arrives who has been in their job for 5 years (standardised value 0.75) and a weekly spend of $129.17 (standardised value of 1). Using Figure 1, determine whether the bank predicts that (2 Marks) this customer defaults or does not default? (1 Mark) Training Data for Loan Approval 3- Default Default No Default -2 Weekly Spending (Standardised) Figure 1: Training data used by bank to determine loan approvals. The features are standardised. The bank uses k nearest neighbours with k-1 to predict default Employment Duration (Standardised) - N 3. Suppose the same customer who has been in their job for 5 years (standardised value 0.75) plans to reduce their weekly spend to $101.44 (standardised value of 0.25). Using Figure 1, determine whether the bank predicts that this customer defaults or does not default? (1 Mark) 4. Suppose the same customer who has been in their job for 5 years plans to reduce their weekly spend $92.20 (standardised value of 0). Using Figure 1, determine whether the bank predicts that this customer defaults or does not default? (1 Mark) 5. With respect to Questions 2 to 4 discuss a limitation (s) of the bank's method. 6. How could you address the limitation (s) discussed in your answer to Question 5 while still using k nearest neighbour classification 7. How could linear discriminant analysis overcome the problem discussed in Question 5 (1 Mark) (2 Mark) (2 Mark)