Online shops often sell tons of different items and this can become very messy very quickly!
Data science can be extremely useful to automatically organize the products in categories so that they can be easily found by the customers.
The goal of this challenge is to look at user purchase history and create categories of items that are likely to be bought together and, therefore, should belong to the same section.
Company XYZ is an online grocery store. In the current version of the website, they have manually grouped the items into a few categories based on their experience.
However, they now have a lot of data about user purchase history. Therefore, they would like to put the data into use!
This is what they asked you to do:
- The company founder wants to meet with some of the best customers to go through a focus group with them. You are asked to send the ID of the following customers to the founder:
a. The customer who bought the most items overall in her lifetime
b. For each item, the customer who bought that product the most
- Cluster items based on user co-purchase history. That is, create clusters of products that have the highest probability of being bought together. The goal of this is to replace the old/manually created categories with these new ones. Each item can belong to just one cluster.