Encoding categorical features with medium or high cardinality

When we are working with a categorical feature that has many unique values, say 10 or more, it can be impractical to create a dummy variable for each value. When there is high cardinality, that is, a very large number of unique values, there might be too few observations with certain values to provide much information for our models. At the extreme, with an ID variable, there is just one observation for each value.

There are a couple of ways in which to handle medium or high cardinality. One way is to create dummies for the top k categories and group the remaining values into an other category. Another way is to use feature hashing, also known as the hashing trick. In this section, we will explore both strategies. We will be using the COVID-19 dataset for this example:

1. Let's create training and testing DataFrames from COVID-19 data, and import the feature_engine and category_encoders libraries:

   import pandas...