Data preprocessing and featurization

Featurization is the process that we use to transform unstructured data such as text, images, or time-series data into numerical continuous features that are more easily handled by machine and deep learning models. It can be differentiated from featuring engineering from the fact that in featuring engineering the variables are already in the numerical form or have a more defined structure that leads us to the need to refactor or transform these variables into something that makes the machine or deep learning algorithm easier to extract patterns. In featurization, we need to first define a way in which we will extract numerical features from the unstructured data that we have.

We have the need to perform featurization basically because our deep learning models cannot interpret unstructured data directly and therefore, we need not only to extract it but to do this in a computationally efficient manner. This process needs to be incorporated into...