[Mandatory: You can do the calculation on paper, but please attach the calculations for all three questions along with your submission, without the calculation, the assignment will not be graded] Please answer the following questions: Q1. (Item similarity calculation) 4 Points The following table represents the attributes and their values for three Persons (PersonA, PersonB, and PersconC) Attribute Person A Person B Person C Age 35 50 55 Credit Score 730 820 680 Income 95K 120K 75K Loan 100K 40K 100K Using Euclidean distance, calculate the similarities between the three persons (PersonA, PersonB, and PersonC) and complete the following table with their similarity scores: PersonA PersonB PersonC PersonA PersonB PersonC Now, use the table to find the most similar person to PersonA? Q2. (Classification using k-NN) 7 Points The following 7- customers have applied for credit cards to a credit card company. The column 'Approved' shows the credit card application status: 'No' means the application was not approved, and 'Yes' means the application was approved. Customer Age Income Loan Credit Score Approved John 35 95K 300K 730 No Rachel 22 50K 10K 830 Yes Hannah 63 180K 20K 800 Yes Tom 59 50K 80K 650 No Nellie 25 60K 250K 680 No James 45 120K 40K 850 Yes Robin 42 70K 40K 780 Yes Now, a new customer 'David' applies for a credit card, and David has the following profile: Age: 37 Income: 85K Loan: 45K Credit Score: 790 Apply the k-NN algorithm to determine whether David's application will be Approved or Not! Complete the following table with the approval status (Yes/No) for the given values of k: k Approve 1 2 3 4 5 6 7 Q3. (Confusion matrix) 14 Points Given this confusion matrix for machine learning classifier, answer the following questions: 3.1 how many classes are there in this data set? 3.2 What is the data set size? 3.3 How many samples where misclassified? 3.4 How many cases belong to class (C1)? 3.5 The accuracy of the classifier is ___________ 3.6 Compute the precision of the classifier and interpret it. 3.7 Compute the recall of the classifier and interpret it. Actual labels Predicted labels C1 C2 C3 C1 25 5 1 C2 6 24 3 C3 2 4 26