There are three main methods explored therein:
- Label encoding – encoding a value based on where the label order falls – could be good for rank and non-parametric methods, but tends to be less used with machine learning models
- One hot encoding (dummy variable encoding) – taking a group of categorical labels and assigning them an encoding / dummy binary label of a 1 (belongs in that category) or 0 (doesn’t belong in that category)
3. Manual encoding – this uses a specific condition to assign the numerical 1 or 0 encoding.
Where to get the content?
The supporting code files can be found in my GitHub account. This includes a Jupyter notebook and a Python file. The next tutorial will look at how to do this type of encoding in scikit-learn and other Python libraries, so look out for that.
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