Problem
Universal Bank is relatively young bank growing rapidly in terms of overall customer acquisition. The majority of these customers are liability customers depositors) with varying sizes of relationship with the bank. The customer base of asset customers (borrowers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business. In particular, it wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors).
A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% 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 file UniversalBank.csv contains data on 100 customers. The data include customer demographic 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 (Personal Loan). Among these 100 customers, only 12 (= 12%) accepted the personal loan that was offered to them in the earlier campaign
Your task is to:
Build K NN classifier models using UniversalBank.csv and UniversalBankNewData.csv files and perform the necessary data cleaning and feature selection processes.
Consider k = 1, 3, and 5, determine which model must be adopted to be used in the bank's website.
What is the prediction on weather the new customer will accept a loan offer?