Assignment 2

School: Cleveland State University - Course: EDUCATION 1 - Subject: Accounting

Adding the following to validation set Age = 40, Experience = 10, Income = 84, Family = 2, CCAvg = 2,Education_1 = 0, Education_2 = 1, Education_3 = 0, Mortgage = 0, Securities Account = 0, CD Account =0, Online = 1, and Credit Card = 1. Performing a k-NN classification with all predictors except ID and ZIPcode using k = 1. Specify the success class as 1 (loan acceptance), and use the default cutoff value of 0.5.How would this customer be classified? ClassprobageexperienceIncomefamilyCCAvg 0040108422 Edu1Edu2mortgageSecuritiesacct Cd accountonlineCredit card 1000011 Frome the output the customer can be classified into loan not approved group. For choice of k that balances between over fitting and ignoring the predictor information? value of k% Error training% Error validation 1010 25.8313.75 36.6711.25 47.518.75 56.6712.5 67.516.25 71012.5 89.1712.5 98.3311.25 K=9 balance overfitting and ignoring informationpredictor information. Classification matrix for best k for validation data. Validation data scoring - summary report (for k=1)

Cutoffprobability value for success (UPDATA BLE) 0.5 Confusion Matrix Predit ed class Actualclass 10 134 0469 Error Report class#cases#errors% erro r 17457. 14 % 07345.4 8% Over all80810 To classify customer using best k. Predictable classProb. For1Actual#neaestageexpereinceincomefamilyCC Avg