After the Apriori algorithm has completed, we have a list of frequent itemsets. These aren't exactly association rules, but they are similar to it. A frequent itemset is a set of items with a minimum support, while an association rule has a premise and a conclusion.

We can make an association rule from a frequent itemset by taking one of the movies in the itemset and denoting it as the conclusion. The other movies in the itemset will be the premise. This will form rules of the following form: if a reviewer recommends all of the movies in the premise, they will also recommend the conclusion.

For each itemset, we can generate a number of association rules by setting each movie to be the conclusion and the remaining movies as the premise.

In code, we first generate a list of all of the rules from each of the frequent itemsets, by iterating over each of the discovered frequent itemsets of each length:

candidate_rules = []
for itemset_length, itemset_counts in frequent_itemsets...