Understanding and processing the MovieLens dataset
In this section, we dive into the code for creating our recommendation system. As with most ML projects, it all starts with data. We use the MovieLens dataset to create a movie recommendation system.
The MovieLens dataset is a widely used benchmark dataset in the field of recommender systems. It consists of user ratings and movie metadata, providing a rich source for training and evaluating recommendation algorithms. The dataset includes various versions, with MovieLens 100K, 1M, 10M, and 20M being some of the commonly used subsets, differing in the number of ratings and movies. In this chapter, we use the MovieLens 100K dataset, which contains over 100K movie ratings.
As shown in Figure 18.4, we first begin by downloading the dataset. We then load the dataset files as DataFrames, analyze the different DataFrames, and clean the dataset if needed.
Figure 18.4: Steps in exploring, analyzing, and processing the MovieLens...
