A credit score is a number, based on the analysis of a persons credit files, to represent the creditworthiness of the person. Lenders, such as banks and credit card companies, use credit scores to evaluate the potential risk posed by lending money to consumers. Credit scoring is not limited to banks. Other organizations, such as mobile phone companies, insurance companies, landlords, and government, employ the same techniques. Credit scoring also has much overlap with data mining.

A consumer services agency is interested in providing a service in which an individual can estimate their own credit score. The Excel file CreditScoreData.xlsx contains data on an individuals credit score and other variables. The description of these nine (9) variables can be found in the worksheet Description.

Make a Standard Partition of the data into Training, Validation, and Test sets. Select all the 9 variables to be in the partition, use 12345 as the seed in the randomized sampling, and specify 50% of observations in the training set, 30% in the validation set, and 20% in the test set.

Predict the individuals credit scores using k-Nearest Neighbors with up to k = 20. Use CreditScore as the output variable and all the other variables as input variables. In Step 2 of XLMiners k-Nearest Neighbors Prediction procedure, be sure to Normalize input data and to Score on best k between 1 and specified value. Select Summary Report for Score Training Data, and Score Validation Data. Select Detailed Report, Summary Report, and Lift Charts for Score Test Data.

Based on the results from XLMiner, answer the following questions.

What is the best k chosen? What does it mean?

Compare the RMSE on the test set to the RMSE on the validation set. Please comment.

What is the average error on the test set? What does it suggest?

Predict the CreditScore for two individuals with the following information, using the best k:

BureauInquiries	CreditUsage	TotalCredit	CollectionReports	MissedPayments	HomeOwner	CreditAge	TimeOnJob
2	0.5	14,000	1	2	0	5	3
3	0.2	25,000	0	0	1	7	8

Hint: For your convenience, this table is stored in the worksheet NewData. After the k-Nearest Neighbors prediction procedure is completed, select the worksheet NewData, and click any cell in the range of the data. Click the XLMINER PLATFORM tab on the ribbon. Click Score in the Tools group. In the Data to be Scored area, confirm that the Worksheet is NewData, and the box for First Row Contains Headers is checked. You should see the same variables appear in both the Variables in New Data and Model Variables areas. Click Match By Name. Click OK. A new worksheet KNNP_ModelScore is then generated. Report the predicted values of CreditScore (rounded to the nearest integers) for the two individuals.

CreditScoreData.xlsx