Aggregation and filtering

In the video, we helped a gift store manager arrange the sections in her physical retail location according to association rules. The layout of the store forced us to group sections into two pairs of product types. After applying advanced filtering techniques, we proposed the floor layout below.

The image shows the store layout that was selected in the video.

The store manager is now asking you to generate another floorplan proposal, but with a different criterion: each pair of sections should contain one high support product and one low support product. The data, aggregated, has been aggregated and one-hot encoded for you. Additionally, apriori() and association_rules() have been imported from mlxtend.

Generate the set of frequent itemsets with a minimum support threshold to 0.0001.
Identify all rules with a minimum support threshold of 0.0001.
Select all rules with an antecedent support greater than 0.35.
Select all rules with a maximum consequent support lower than 0.35.

Exercise

Aggregation and filtering

Instructions