Handling sparse categorial variables with Word2Vec

This workflow shows how to compute word embedding on a set of categorical variables with the granularity which allows them to be used as input of predictive models.
A PCA Analysis is directly applied inside the component. I kept only a portion of embedding dimensions which caputures most of the variation, by doing so you can monitor model complexity.

py package required:
pandas
gensim
numpy
nltk