Market Basket Analysis Using Apriori algorithm in python Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy. Association Rules are widely used to analyze retail basket or transaction data, and are intended to identify strong rules discovered in transaction data using measures of interestingness Association rules The Apriori algorithm generates association rules for a given data set. An association rule implies that if an item A occurs, then item B also occurs with a certain probability. Let’s see an example, In the table above we can see seven transactions from a clothing store. Each transaction shows items bought in that transaction. We can represent our items as an item set ...