Frequently Bought Together Product Suggestions via Association Rule Mining

This experiment demonstrates how the Discover Association Rules custom module can be leveraged to deliver product suggestions based on the current contents of an online shopping cart. Sales transaction data are simulated for a collection of products such that some items are more likely to be bought together (e.g. phones and phone chargers). A portion of these transactions are used as a training set for association rule mining. For each transaction in the test set, the learned association rules are applied to generate product suggestions based on all products in the shopping cart *except* the last product added. The suggestion "accuracy" is assessed by calculating how often the last product purchased is among the suggested products.