1. Introduction to Market Basket Analysis

Subtitles Enabled

Sign up for a free trial to access more free content.

Free trial

Clustering and Market Basket Analysis

10 lessons , 0 exercises

Preview Course


In the first half of this course, we will focus on Market Basket Analysis. In this lesson, we will learn about the concepts and terminology associated with this topic.


  1. Lesson Goal (00:48)

    The goal of this lesson is to introduce the concept of market basket analysis. Market basket analysis is an example of an unsupervised learning technique. Unsupervised learning techniques do not require training, but cannot be used to make predictions. They are generally used when we want to find relationships and patterns in a dataset.

  2. Understanding Market Basket Analysis (00:56)

    Market basket analysis is a technique used to look for an affinity or association between different products in each basket. It’s commonly used in retail environments, such as supermarkets. In this context, a basket refers to a set of products purchased together.

    The products contained in each receipt are analyzed and broken into groups, or item sets. Each item set is divided into rules based on the direction of the association. A rule indicates that if one product in the item set is bought, another product in the item set will also be bought. As a result, the likelihood of a rule can vary depending on which item is considered the base product.

    Rules are evaluated based on three metrics. Support takes the number of transactions where an item set exists and expresses this as a percentage of the total number of transactions. Confidence is the probability of a receipt containing any two products. It is the proportion of receipts containing the first product that also contain the second product. Finally, lift is the likelihood of a particular rule occurring compared to our expectation if the items were completely independent.

    Theoretically, rules can contain any number of items, but the complexity increases greatly when we add multiple items to a rule. As a result, our analysis will only contain rules involving a small number of products.

  3. Course Case Study (03:16)

    In this course, we consider the example of Cut Price Supermarkets. Our objective is to analyze three months of sales data and use market basket analysis to better target specific customer segments.


Clustering and Market Basket Analysis


My Notes

You can take notes as you view lessons.

Sign in or start a free trial to avail of this feature.

Free Trial

Download our training resources while you learn.

Sign in or start a free trial to avail of this feature.

Free Trial