Issue 18/08/2011 Revision Date 11/06/2013 Form No. ECT/AC/F.05.02 Case Study on KNN Classification Date Time: Total Mark: 10 Student’s Name Student’s ID Course Name Business Intelligence Course Code BIT405 Semester Spring 2017 Instructor’s Name Dr. Myriam Bounhas, Dr. Bilel Elayeb, Dr. Wala Saber Scope and Focus: ● Classification Methods ● The Naive Rule ● Naive Bayes ● k-Nearest Neighbors Contributing to the following CLOs: CLO #1 Describe the role of Business Intelligence in an organization. CLO #2 Understand the data mining process and its related issues. CLO #3 Create, evaluate and apply different intelligence models. Case study: KNN classification Consider the following data concerning credit default (cf. Figure 1). Age and Loan are two numerical variables (predictors) and Default is the target. Figure 1: Loan vs. Age 1). Regular Distance (a) We can now use the training set to classify an unknown case (Age=48 and Loan=$142,000) using Euclidean distance. Complete the calculus of distances by filling Table 1. Table 1: Euclidean distance calculus (b) Determine the prediction for the unknown case if K= 1. Provide the detail of calculus for the first nearest neighbor. ............................................................................................................................................................. ............................................................................................................................................................. ............................................................................................................................................................. ............................................................................................................................................................. ............................................................................................................................................................. ............................................................................................................................................................. (c) Determine the prediction for the unknown case if K= 3. Provide the detail of calculus for each nearest neighbor. ............................................................................................................................................................. ............................................................................................................................................................. ............................................................................................................................................................. ............................................................................................................................................................. ............................................................................................................................................................. ............................................................................................................................................................. ............................................................................................................................................................. ............................................................................................................................................................. ............................................................................................................................................................. .............................................................................................................................................................