Your work on this task is based on the following scenario:

You are working in a team of developers for a grocery store. The store manager noticed that some items are often bought together. The manager wants to find out exactly what items customers buy most often together in one basket $($we call them itemsets$).$ This information will be used to place itemsets close together, so that customers can find them quickly, which in turn may increase sales.

After analysing the problem, your team has discovered that once frequent itemsets are identified, it is also possible to recommend products from these itemsets to customers on the store website.

Your team, being knowledgeable of both frequent itemsets mining and recommendation systems, wants to go even further: you want to test other well$-$known recommendation methods, such as collaborative filtering, to see which recommendation method works better.

The store collects details about customers$$ buying habits through a loyalty programme and your team is given access to the representative dataset. The system you build, however, should scale to around one million customer transactions.

The project has been approved by the store management, so you are ready to start building the system which can help to significantly increase sales.