Data Mining Using IBM SPSS : To predict the success of bank telemarketing

a) Introduction:

1. Often, it is evident that banks try to attract customers when they bring a new product in the market.
2. They make use of such marketing campaigns as a specific mechanism wherein they try to attract customers to maximize their profits.
3. The more number of customers they are able to attract, the greater the chances of the maximizing the market share of product.
4. This dataset deals with such a marketing campaign wherein phone calls were made to the customers in order to access if the product i.e bank term deposit would be subscribed by the customers.

b) Problem Statement:

1. Profound Question: In the real world, we encounter frequent calls from banks or different entities to make us invest in their product by offering a lucrative deal. So the real question over here is whether customers have subscribed to a product i.e term deposit on the basis of marketing calls made to them? Also, on what factors do customers heavily depend as to not invest in such products?

2. Goal: To help predict the success of bank telemarketing by using CART, K-Means and Random Forest Algorithm. The classification goal is to predict if the client will subscribe a term deposit

3. Accomplishment: This will help the banks to take an informed decision as to which customers would be interested in their products based on factors such as personal, housing loans and other factors.