Question

Use the K-NN algorithm to classify the new data in the excel file Credit Approval Decisions Coded using only credit score and years of credit history as input variables.

We're only allowed to use excel or analytic solver to answer problems. I don't know what python is.

Homeowner | Credit Score | Years of Credit History | Revolving Balance ($) | Revolving Utilization (%) | Decision |

Y | 592 | 3 | 21000 | 15 | Reject |

N | 653 | 8 | 4000 | 90 | Reject |

Y | 576 | 1 | 8500 | 25 | Reject |

N | 733 | 4 | 16300 | 70 | Approve |

N | 726 | 13 | 2500 | 90 | Approve |

Y | 675 | 14 | 16700 | 18 | Approve |

Answer #1

K nearest neighbors:

Find the euclidean distance between the new values and all of the old values.

Eg. If the new Credit Score = 633

Years of Credit History = 3

You have a set (633,3). This is (X2,Y2)

Euclidean distance = SQRT ((X1-X2)^2+ (Y1-Y2)^2)

So for row 1, Euclidean Distance = SQRT ((592-633)^2 + (3-3)^2) = 41

Look at the screenshot below:

This should be clear Let me know in case you have any questions.

Use the K-NN algorithm to classify the new data in the excel
file Credit Approval Decisions Coded using only credit score and
years of credit history as input variables.
We're only allowed to use excel or analytic solver to answer
problems. I don't know what python is.
Homeowner
Credit Score
Years of Credit History
Revolving Balance ($)
Revolving Utilization (%)
Decision
Y
592
3
21000
15
Reject
N
653
8
4000
90
Reject
Y
576
1
8500
25
Reject
N...

A recent audit report mentions the e?ciency of one of the loan
o?cers in your credit card company. A sample of 50 applications for
credit cards was randomly selected from among the applications that
this o?cer worked on in the last cycle. The data are in credit
approval.xlsx, which contains the following variables: • homeowner:
a binary variable, Y=yes, N=no • ?co: the applicant's credit score
• years: how many years of credit in the applicant's credit history
• balance:...

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