We will now be implementing the final technique in market basket analysis for finding out association rules between itemsets to detect and predict product purchase patterns which can be used for product recommendations and suggestions. We will be notably using the Apriori algorithm from the arules
package which uses an implementation for generating frequent itemsets first, which we discussed earlier. Once it has the frequent itemsets, the algorithm generates necessary rules based on parameters such as support, confidence, and lift. We will also show how you can visualize and interact with these rules using the arulesViz
package. The code for this implementation is in the ch3_association
rule mining.R
file which you can directly load and follow the book.
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Canada
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