A Gentle Introduction on Market Basket Analysis

Data Science, AI and ML
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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, based on the concept of strong rules.
An example of Association Rules
Assume there are 100 customers
10 of them bought milk, 8 bought butter and 6 bought both of them.
bought milk => bought butter
support = P(Milk & Butter) = 6/100 = 0.06
confidence = support/P(Butter) = 0.06/0.08 = 0.75
lift = confidence/P(Milk) = 0.75/0.10 = 7.5
Note: this example is extremely small. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.
Ok, enough for the theory, let’s get to the code.