Exercise

# Applying Zhang's rule

In Chapter 2, we learned that Zhang's rule is a continuous measure of association between two items that takes values in the [-1,+1] interval. A -1 value indicates a perfectly negative association and a +1 value indicates a perfectly positive association. In this exercise, you'll determine whether Zhang's rule can be used to refine a set of rules a gift store is currently using to promote products.

Note that the frequent itemsets have been computed for you and are available as `frequent_itemsets`

. Additionally, `zhangs_rule()`

has been defined and `association_rules()`

have been imported from `mlxtend`

. You will start by re-computing the original set of rules. After that, you will apply Zhang's metric to select only those rules with a high and positive association.

Instructions

**100 XP**

- Generate the set of association rules with a lift value of at least 1.00.
- Set the antecedent support threshold to 0.005.
- Compute Zhang's rule and assign the output to the column
`zhang`

in`rules`

. - Select the rules that have a Zhang's metric that is greater than 0.98.