A relatively young bank is growing rapidly in terms of overall customer acquisition Majority of these are Liability customers with varying sizes of relationship with the bank The customer base of Asset customers is quite small, and the bank WANTS to grow this base rapidly to bring in more loan business Specifically, it wants to explore ways of converting its liability customers to Personal Loan customers A campaign the bank ran for liability customers last year showed a healthy conversion rate of over 9 successes This has encouraged the Retail Marketing department to design a new model of customer behavior to analyze what combination of parameters make a customer more likely to accept a personal loan Data Description ID Customer ID Age Customer's age in completed years Experience years of professional experience Income Annual income of the customer ($000) Family Family size of the customer CCAvg Avg spending on credit cards per month ($000) Education Education Level 0 Undergrad 1 Advanced Professional Mortgage Value of house mortgage if any ($000) Securities Account Does the customer have a securities account with the bank CD Account Does the customer have a CD account with the bank Online Does the customer use internet banking facilities CreditCard Does the customer use a credit card issued by the bank Personal Loan Did this customer accept the personal loan offered in the last campaign The entire data set is given in Sheet1 Use XL Miner for this problem Bin the continuous variables as follows Age 5 bins of equal width Experience 5 bins of equal width Income 6 bins of equal width CCAvg 3 bins of equal width Mortgage 5 bins of equal count Partition the data into 60 training and 40 validation set Run Nave Bayes with detailed report only for validation set Compute the probability of the two classes (0 Not accept personal loan, 1 accept personal loan) for the first case in the validation data set (see Sheet1)

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Posted on May 22, 2021

A relatively young bank is growing rapidly in terms of overall customer acquisition. Majority of these are Liability customers with varying sizes of relationship with

A relatively young bank is growing rapidly in terms of overall customer acquisition. Majority of these are Liability customers with varying sizes of relationship with the bank. The customer base of Asset customers is quite small, and the bank WANTS to grow this base rapidly to bring in more loan business. Specifically, it wants to explore ways of converting its liability customers to Personal Loan customers.

A campaign the bank ran for liability customers last year showed a healthy conversion rate of over 9% successes. This has encouraged the Retail Marketing department to design a new model of customer behavior to analyze what combination of parameters make a customer more likely to accept a personal loan?

Data Description

ID	Customer ID
Age	Customer's age in completed years
Experience	#years of professional experience
Income	Annual income of the customer ($000)
Family	Family size of the customer
CCAvg	Avg. spending on credit cards per month ($000)
Education	Education Level. 0: Undergrad; 1: Advanced/Professional
Mortgage	Value of house mortgage if any. ($000)
Securities Account	Does the customer have a securities account with the bank?
CD Account	Does the customer have a CD account with the bank?
Online	Does the customer use internet banking facilities?
CreditCard	Does the customer use a credit card issued by the bank?
Personal Loan	Did this customer accept the personal loan offered in the last campaign?

The entire data set is given in Sheet1. Use XL Miner for this problem.

Bin the continuous variables as follows:

Age	5 bins of equal width
Experience	5 bins of equal width
Income	6 bins of equal width
CCAvg	3 bins of equal width
Mortgage	5 bins of equal count

Partition the data into 60% training and 40% validation set.
Run Naïve Bayes’ with detailed report only for validation set.
Compute the probability of the two classes (0 = Not accept personal loan, 1 = accept personal loan) for the first case in the validation data set. (see Sheet1)