1. Suppose the data provided is the last promotional mail-out records
which consist of information about each of the 100000 customers
(name, address, occupation, salary) and whether each individual
customer responded to the mail (i.e., an attribute indicating “yes” or
“no”). You are asked to produce a data mining solution, that is, a
model describing the characteristics of customers who are likely, as
well as unlikely, to respond to the promotional mail-out. The company
could then use this model to target customers who are likely to
respond to the next promotional mail-out for the same product.

Discuss the following issues:
• is this problem suitable for data mining solution?
• whether this is a classification or estimation problem.
• what are the inputs and output?
• what is the alternative to producing a model?
• how you will use the data for training a model and evaluating the
model?


2. Let say you are given a set of training data with 50% class “positive”
and 50% class “negative”, and you have explored several models and
selected the best model. You can now use the best model to do future
prediction. Now, you are informed that the future data you are going
to get is likely to have the following class distribution: 90% class
“positive” and 10% class “negative”.
Would you go ahead to use the best model to do prediction for all
future data? Provide a reason for your answer. In the case that your
answer is no, you shall also provide an alternative solution to do
prediction for the future data.

Looks like an homework. What is your question about it?