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.
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