  Many machine learning libraries require that string features be converted to
a numerical representation for the models to work as intended. Categorical
string features can represent a wide variety of data (e.g., zip codes, names,
marital status), and are notoriously difficult to preprocess automatically. In
this paper, we propose a framework to do so based on best practices, domain
knowledge, and novel techniques. It automatically identifies different types of
string features, processes them accordingly, and encodes them into numerical
representations. We also provide an open source Python implementation to
automatically preprocess categorical string data in tabular datasets and
demonstrate promising results on a wide range of datasets.
