Ingesting the movie review data
Recommendation engines require large amounts of training data in order to do a good job which is why they're often relegated to big data projects. However, to build a recommendation engine we must first get the required data into memory and due to the size of the data must do so in a memory-safe and efficient way. Luckily Python has all of the tools to get the job done and this recipe shows you how.
Getting ready
You will need to have the appropriate movie lens dataset downloaded, as specified in the preceding recipe. If you skipped the setup in Chapter 1 , Preparing Your Data Science Environment, you will need to go back and ensure that you have NumPy correctly installed.
How to do it...
The following steps guide you through the creation of the functions that we will need in order to load the datasets into the memory:
- Open your favorite Python editor or IDE. There is a lot of code, so it should be far simpler to enter it directly into a text file rather than...
