Feature Engineering Made Easy

Encoding at the nominal level

Let's begin with data at the nominal level. The main method we have is to transform our categorical data into dummy variables. We have two options to do this:

• Utilize pandas to automatically find the categorical variables and dummy code them
• Create our own custom transformer using dummy variables to work in a pipeline 

Before we delve into these options, let's go over exactly what dummy variables are. 

Dummy variables take the value zero or one to indicate the absence or presence of a category. They are proxy variables, or numerical stand-ins, for qualitative data.

Consider a simple regression analysis for wage determination. Say we are given gender, which is qualitative, and years of education, which is quantitative. ...