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I implement logistic regression to predict if a customer is a business or a non-business customer with the help of TensorFlow in Python. I have several feature candidates like name, street, zip, longitude and latitude. At them moment I am thinking of how to use the name field. The name often has repeating parts like “GmbH” (e.g. “Mustermann GmbH”) which in this context has a similar meaning to Corp. which is an indicator that the customer is a business customer. This information is useless in combination with the other parts of the name because then the name will be unique. So my question is: how should I pre-process this field so that only repeating parts will be used to predict the classification?

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You may want to tokenize the strings, e.g., “Mustermann GmbH” tokenizes into "Mustermann" and "GmbH". Allow for spaces and commas certainly, perhaps also hyphens and other punctuation.

You may want to look into Natural Language Processing (NLP) if you're classifying text, but whatever method you choose should have better luck sniffing out business vs. non-business using tokens of the strings.

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