Assignment
1. Describe the expected output and behavior of the Apriori method and what it will accomplish for the credit approval data. Provide a one-paragraph, masters level response.
2. Run the Apriori method with the default parameters and store the generated rules in a variable called ‘rules'. Include the command, the output screenshot, and provide a one-paragraph, masters-level discussion of the returned rules and the default arguments.
3. Apriori Method - Two Runs with Non-Default Parameters using different combinations of confidence, support, and minimum length values. For each run, specify the input parameters used (include the command, output screenshot, and discuss the rules returned) and then run the inspect command to preview the first 10 rules (include the command, output screenshot, and discuss which of the returned rules is the strongest and why)
4. How does changing the confidence, support, and minimum length values affect the returned rules? Provide a one-paragraph, masters-level response.
5. What are the differences between the confidence, support, and lift metrics for identifying the strongest rules?
6. Why do we as data analysts prune the rules returned by the Apriori method?
7. Why do we consider more than one metric to identify the strongest rules?
Provide a one-paragraph, masters-level response.