Feature binning: equal width and equal frequency

We sometimes want to convert a feature from continuous to categorical. The process of creating k equally spaced intervals from the minimum to the maximum value of a distribution is called binning, or the somewhat less friendly discretization. Binning can address several important issues with a feature: skew, excessive kurtosis, and the presence of outliers.

Getting ready

Binning might be a good choice with the COVID-19 total cases data. It might also be useful with other variables in the dataset, including total deaths and population, but we will only work with total cases for now. total_cases is the target variable in the following code, so it is a column—the only column—on the y_train DataFrame.

Let’s try equal width and equal frequency binning with the COVID-19 data.

How to do it...

1. We first need to import the EqualFrequencyDiscretiser and EqualWidthDiscretiser from feature_engine....