Please show me step by step how to create all the necessary charts that the following prompt asks for in Microsoft Excel. Attached at the bottom is an example of the first portion of the data set.

Grey Code Corporation (GCC) is a media and marketing company involved in magazine and book publishing and in television broadcasting. GCCs portfolio of home and family magazines has been a long-running strength, but it has expanded to become a provider of a spectrum of services (market research, communications planning, web site advertising, etc.) that can enhance its clients brands. GCCs relational database contains over a terabyte of data encompassing 75 million customers. GCC uses the data in its database to develop campaigns for new customer acquisition, customer reactivation, and identification of cross-selling opportunities for products. For example, GCC will generate separate versions of a monthly issue of a magazine that will differ only by the advertisements they contain. It will mail a subscribing customer the version with the print ads identified by its database as being of most interest to that customer. 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. GCCs 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:

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

2. 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.

3. 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 industrys average response rate?