A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over9% success. This has encouraged the retail marketing department to devise smarter campaigns with better target marketing. The goal is to use

k-NN to predict whether a new customer will accept a loan offer. This will serve as the basis for the design of a new campaign.

The dataset UniversalBank.csv below contains data on 5000 customers. The data include customer demographics information (age, income, etc.), the customer's relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (PersonalLoan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign.

MUST USE R

Consider the following customer:

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.

Classify the above customer using the best k.

Repartition the data, this time into training, validation, and test sets (50% : 30% : 20%).

Apply the k-NNN method with the k chosen above.

Compare the confusion matrix of the test set with that of the training and validation sets.

What are the differences and their reason

dataset-https://github.com/MyGitHub2120/UniversalBank