<h2>Overview:</h2>
<p>probablepeople is a python library that uses advanced NLP methods to parse unstructured romanized name or company strings into components. It is built on the usaddress library for parsing addresses and offers a web interface and API for non-python developers. While the library makes educated guesses in identifying name or corporation components, it cannot guarantee perfect accuracy or validate the correctness/validity of a given name or company. probablepeople learns from a body of training data, and users can contribute by providing examples of names/companies that the parser struggles with.</p>
<h2>Features:</h2>
<ul>
<li><strong>Parsing Unstructured Names and Companies:</strong> probablepeople can parse unstructured romanized name or company strings and identify components.</li>
<li><strong>Probabilistic Model:</strong> The library uses a probabilistic model to make educated guesses in identifying name or corporation components, even in tricky cases where rule-based parsers typically break down.</li>
<li><strong>Training Data:</strong> probablepeople learns how to parse names/companies from a body of training data. Users can contribute by providing examples of names/companies that the parser struggles with to improve its accuracy and performance.</li>
</ul>


<h2>Overview</h2>
<p>The usaddress is a Python library that uses advanced NLP methods to parse unstructured United States address strings into address components. It is capable of making educated guesses to identify address components, even in tricky cases where rule-based parsers typically fail. However, it cannot guarantee perfect accuracy in identifying address components or verify the correctness/validity of a given address. Additionally, it does not normalize the address, but there is a library built on top of usaddress that provides this functionality.</p>
<h2>Features</h2>
<ul>
<li><strong>Probabilistic Model</strong>: The library uses a probabilistic model to make educated guesses in identifying address components.</li>
<li><strong>Parserator API</strong>: A RESTful API built on top of usaddress, which can be used by programmers who do not use Python. It requires an API key, and the first 1,000 parses are free.</li>
<li><strong>Parserator Google Sheets App</strong>: A Google Sheets app called "Parse and Split Addresses" that allows users to easily split addresses into separate columns by street, city, state, zipcode, and more.</li>
<li><strong>Training and Testing</strong>: The library provides tools for training and improving the usaddress parser's model on labeled training data. It also includes a testing suite to ensure the proper functioning of the code.</li>
</ul>


Datamade

Probablepeople

Usaddress