4 Pages outlining the business opportunity/opportunities with the trends discovered. Only include content from the best classification and prediction models and how it supports your recommendation. Do not have more than a half-page discussing the mining data results in this section.

FYI: how you will formulate your business problem- put yourselves in the shoes of the organization (the library) that will use your new system. Explain how they will use it, how it will help them and any risks or issues they might want to watch out for. Take time to write out your ideas and thoughts, as well as describe the decisions you make and give evidence of why you did it in a particular way. I'm less interested in your ability to quote the textbook or the internet, and more interested in your personal thoughts and insights about the problem, how the library will use your system you decided the parameters for creating your model, how you evaluated it, what will happen when it makes an incorrect prediction (and that incorrect prediction is used as correct by the library staff). The screenshots and lift charts, etc. are supporting documentation, but they are not the report.

Business Problem Statement: The library needs to identify if library visitors are newcomers to Canada (Edmonton, Canada), to leverage the information to create learning programs for the target demographic.

In the runs performed, we found that the most successful run was Mining Run 6. We found that Mining Run 6 implemented methods that allowed us to first, as in this run, determine if the visitor was a library visitor and how often they visited and then find out whether they were a newcomer to Canada by using the target filter to only display citizens who have only been in (Edmonton, Canada) Canada for less than one year, thus are newcomers to Canada. Also, because the Naive Bayes line is furthest away from the grey dashed line overall, we can conclude that the best model to classify this information would be Nave Bayes.