One particular problem facing GCC is how to boost the customer response rate to renewal offers that it mails to its magazine subscribers. The industry response rate is about $2\%,$ but GCC has historically performed better than that. However, GCC must update its model to correspond to recent changes. GCC$$s director of database marketing, Chris Grey, wants to make sure that GCC maintains its place as one of the top achievers in targeted marketing. The file Grey contains $38$ variables $($columns$)$ and over $40,000$ rows $($distinct customers$).$ The table appended to the end of this case provides a list of the variables and their descriptions.

Play the role of Chris Grey and construct a classification model to identify customers who are likely to respond to a mailing. Write a report that documents the following steps:

Explore the data. Because of the large number of variables, it may be helpful to filter out unnecessary and redundant variables.

Appropriately partition the data set into training, validation, and test sets. Experiment with various classification methods and propose a final model for identifying customers who will respond to the targeted marketing.

Your report should include appropriate charts $($ROC curves, lift charts, etc.$)$ and include a recommendation on how to apply the results of your proposed model. For example, if GCC sends the targeted marketing to the top $10\%$ of the test set that the model believes is most likely to renew, what is the expected response rate? How does that compare to the industry$$s average response rate?