Analyzing Stanley A Milner Library data to define Classification and Prediction business problems

Using the Stanley A Milner Library data referenced in the assignments, analyze the data available to define one real-world Classification and one prediction business problem to solve.  Here are some examples:

Can library cardholders be categorized from a combination of variables such as income, home ownership, employment status, etc.?

Can we predict the frequency of visits to the Stanley A Milner Library from a combination of variables?  If, so what were the variables and how can they be used to improve the efficient use of library resources?

Can we predict the primary mode of transportation based on a combination of library variables?  If so, we can collaborate with Edmonton Transit Services and share data to improve popular bus routes to the downtown library.

Can we correctly determine if library visitors own a home?  If so, we can have a more efficient marketing campaign to homeowners.

Can we correctly determine if library visitors are newcomers to Canada?  If so, the library can leverage the information to create learning programs for the target demographic.

As illustrated by the bolded words in each problem statement, there will be one specific output for each business problem