This question walks you through the typical process of discovering association rules. We will use the market basket data in the Groceries.csv file to discover association rules. Here are the data contents.

1- Customer: Customer Identifier

2- Item: Name of Product Purchased

For your information, we have sorted the observations in ascending order first by Customer and then by Item. Also, we have removed duplicated items for each customer.

A- What is the number of items in the Universal Set? What is the maximum number of itemsets that we can find in theory from the data? What is the maximum number of association rules that we can generate in theory from the data?

B- We are interested in the itemsets that can be found in the market baskets of at least seventy-five (75) customers. How many itemsets did we find? Also, what is the largest number of items, i.e., among these itemsets?

C- We will use up to the largest

value we found in Part (b) and then generate the association rules whose Confidence metrics are greater than or equal to 1%. How many association rules can we find? Next, we plot the Support metrics on the vertical axis against the Confidence metrics on the horizontal axis for these association rules. We will use the Lift metrics to indicate the size of the marker. We will add a color gradient legend to the chart for the Lift metrics.

D- Among the rules that you found in Part (c), list the rules whose Confidence metrics are greater than or equal to 60%. Please show the rules in a table that shows the Antecedent, the Consequent, the Support, the Confidence, the Expected Confidence, and the Lift.