final report which should be about 1500 words, plus any appendices you would like to include. Use external sources where appropriate, and provide clear citations and bibliography. You should also submit your data file and a working R script which I can run on it.
You will get the most out of the project if you interact with me during the development of your ideas.
Your report should include the information detailed below, in approximately the order given. Your report need not have corresponding sections or bullet points, but I should be able to find the information without searching too hard. Be as precise/specific as you can.
Business Understanding (take this seriously)
·Identify, define, and motivate the business problem that you are addressing.
·How (precisely) will a data mining solution address the business problem?
(NB: I’d like to see a good definition/motivation of the business problem and a precise statement of how a data mining solution will address the problem. It’s not so important that the hands-on results match perfectly. It’s more important that you have the experience of working through a realistic problem definition.)
·Identify and describe the data (and data sources) that will support data mining to address the business problem. Include those aspects of the data that we talk about in class and/or in the quizzes.
·Specify how these data are integrated to produce the format required for data mining.
(NB: data preparation can be time consuming. Get started early. Talk to me if you need advice.)
·Specify the type of model(s) built and/or patterns mined.
·Discuss choices for data mining algorithm: what are alternatives, and what are the pros and cons?
·Discuss why and how this model should “solve” the business problem (i.e., improve along some dimension of interest to the firm).
·Discuss how the result of the data mining is/should be evaluated. How should a business case be developed to project expected improvement? ROI? If this is impossible/very difficult, explain why and identify any viable alternatives.
·Discuss how the result of the data mining will be deployed.
·Discuss any issues the firm should be aware of regarding deployment.
·Are there important ethical considerations?
·Identify the risks associated with your proposed plan and how you would mitigate them.