Many of us have visited retail shops such as Reliance and Walmart for our household needs. Let's say that we are planning to buy an iPhoneX from Reliance Digital. What we would typically do is search for the model by visiting the mobile section of the store, and then select the product and head toward the billing counter.

But, in today's world, the goal of the organization is to increase revenue. Can this be done by pitching just one product at a time to the customer? The answer is a clear no. Hence, organizations began mining data relating to frequently bought items. They try to find out associations between different items and products that can be sold together, which gives assisting in right product placement. Typically, it figures out what products are being bought together and organizations can place products in a similar manner.

This is what we are going to talk about in this chapter. How do we come up with such rules by means of machine learning? We will discuss number of techniques here.

In this chapter, we will cover the following topics:

• Association rules
• Frequent pattern growth
• Validation