Golden Bank & Trust, Inc. is interested in identifying different attributes of its customers, and below is
Question:
Golden Bank & Trust, Inc. is interested in identifying different attributes of its customers, and below is the sample data of 30 customers. For a Personal loan, 0 represents a customer who has not taken a personal loan and 1 represents a customer who has taken a personal loan.
Obs. | Age | Income (in $1000s) | Personal loan |
1 | 47 | 53 | 1 |
2 | 26 | 22 | 1 |
3 | 38 | 29 | 1 |
4 | 37 | 32 | 1 |
5 | 44 | 32 | 0 |
6 | 55 | 45 | 0 |
7 | 44 | 50 | 0 |
8 | 30 | 22 | 0 |
9 | 63 | 56 | 0 |
10 | 34 | 23 | 0 |
11 | 52 | 29 | 1 |
12 | 55 | 34 | 1 |
13 | 52 | 45 | 1 |
14 | 63 | 23 | 1 |
15 | 51 | 32 | 0 |
16 | 41 | 21 | 1 |
17 | 37 | 43 | 1 |
18 | 46 | 23 | 1 |
19 | 30 | 18 | 1 |
20 | 48 | 34 | 0 |
21 | 50 | 21 | 1 |
22 | 56 | 24 | 0 |
23 | 35 | 23 | 1 |
24 | 39 | 29 | 1 |
25 | 48 | 34 | 0 |
26 | 51 | 39 | 1 |
27 | 27 | 26 | 1 |
28 | 57 | 49 | 1 |
29 | 33 | 39 | 1 |
30 | 58 | 32 | 0 |
Use k-Nearest Neighbors (KNN) approach to classify the data, setting k-nearest neighbors with up to k = 5 (cutoff value = 0.5). Use Age and Income as input variables and Personal loan as the output variable. Be sure to normalize input data (i.e., using z-score) if necessary and classify a new client Billy Lee’s (30 years old, $ 50 k income) personal loan status (i.e., whether has taken a personal loan) based on the similarity to the values of Age and Income of the observations in the training set (the 30 customer sample data). Please use Euclidean distance to assess the nearest neighbor observations
Auditing and Assurance Services
ISBN: 978-0077862343
6th edition
Authors: Timothy Louwers, Robert Ramsay, David Sinason, Jerry Straws