The book : (data Science for Business: author :Foster provost &Tom Fawcett)

1: Choose a problem from a past job, hobby, or interest that would make for a good predictive modeling classification application. Describe it in one page or less, using the vost should be as complete and precise as possible; referring to the concepts introduced in class/in the book. Please do not choose one of the applications we have discussed in detail already (churn, targeted marketing, credit scoring).

Include answers to the following:

1) What exactly is the business decision you want to support with this solution?

2) Describe the use phase.

3) Why did you select this as a good predictive modeling problem?

4) How and where would you get the data?

5) Explain precisely why and how you expect doing the predictive modeling will add value.

6) What exactly is the quantity that you inherently do not know and need to predict?

7) Is this a classification, ranking, or probability estimation problem?

8) What are the features? Provide a list of at least 5 features that you think (a) you can get and (b) you think might be useful.

9) What exactly would be your training data?

2: Try to give your own definition and description of the following problems. You may look up the textbook, Internet, or other resources. Your answer to each question shall not exceed one page.

a) What is the custom churn problem?

b) What is firmographic data? And how it is related to data mining?

c) What is a market basket problem?

d) Can you describe the online recommendation system?

e) What is the link prediction problem for social network? Can you give examples?

f) What is A/B testing? Describe its use in the online advertising setting.

g) What does the term "customer profiling" mean?

h) What is the placebo effect in data mining?

i) What is the OCR recognition problem? What are the major techniques involved?