In explaining support and confidence percentages in this chapter, the classic example of shopping basket analysis was used. For this exercise, you will do a shopping basket association rule analysis. Complete the following steps:

$1)$ Using the Internet, locate a sample shopping basket data set. Search terms such as $$association rule data set$$ or $$shopping basket data set$$ will yield a number of downloadable examples. With a little effort, you will be able to find a suitable example.

$2)$ If necessary, convert your data set to CSV format and either connect to it via a Read CSV operator, or import it into your RapidMiner repository. If you do the latter, give it a descriptive name and drag it into a new process window.

$3)$ As necessary, conduct your Data Understanding and Data Preparation activities on your data set. Ensure that all of your variables have consistent data and that their data types are appropriate for the FP$-$Growth operator.

$4)$ Generate association rules for your data set. Modify your confidence and support values in order to identify their most ideal levels such that you will have some interesting rules with reasonable confidence and support. Look at the other measures of rule strength such as LaPlace or Conviction. Take Screenshot and copy to the Word.

$5)$ Document your findings. What rules did you find? What attributes are most strongly associated with one another. Are there products that are frequently connected that surprise you? Why do you think this might be$?$ How much did you have to test different support and confidence values before you found some association rules? Were any of your association rules good enough that you would base decisions on them? Why or why not?