Here is the data fast food restaurant. There are 10 distinct transactions (Order:1 Order:9) and each transaction involves between 2 and 4 meal items. There are a total of 5 meal items that are involved in the transactions. For simplicity, we assign the meal items short names (MI1 MI5) rather than have the full descriptive name (e.g. Big Mac)

Meal Item List of Item IDs Meal Item List of Item IDs

Order:1 MI1, MI2, MI5 Order:6 MI2, MI3

Order:2 MI2, MI4 Order:7 MI1, MI3

Order:3 MI2, MI3 Order:8 MI1, MI2, MI3, MI5

Order:4 MI1, MI2, MI4 Order:9 MI1, MI2, MI3

Order:5 MI1, MI3 Order:10 MI1, MI3, MI4, MI5

For all of the parts below, the minimum support is 2/10 (.2) and the minimum confidence is 7/10 (.7).

You only need to achieve this level, not exceed it.

USING PYTHON, answer the following question.

Apply the apriori algorithm to the dataset of transactions and identify all the frequent k-itemsets. Your python program must execute without modification. You must show all the candidates, but indicate the ones that pass the minimum support threshold. Note: if a candidate itemset is pruned because it violates the apriori property, you must indicate that it fails for this reason and not just because it does not achieve the necessary support count (i.e. in these cases there is no need to actually compute the support count). Explicitly tag those items that are pruned due to a violation of the Apriori property. Find all strong association rules of the form X ^ Y -> Z and note their confidence values. Hint: the answer is not zero, so you should have at least one frequent 3-frequent itemset.