Feature Engineering: One-Hot Encoding

In this lesson, we’ll start applying feature engineering techniques to our telecom customer dataset. We’ll create, format, and encode features for the supervised machine learning algorithm that will predict customer churn.

Encoding

We have 15 categorical features to encode because the machine learning algorithm we’ll use expects all parameters to be numeric. We will use one-hot encoding for this. The encoding will be performed as follows.