PyData NYC 2022: Customizable probabilistic record linkage with Name Match

Linking individuals across records or datasets is often a critical prerequisite for building useful data tools and answering interesting research or business questions. But doing it right is difficult and time-consuming, in part because current off-the-shelf tools do not provide a measure of linking accuracy and are too rigid to incorporate the user’s domain knowledge. In this talk, we’ll 1) define high-quality record linkage and discuss why it matters, 2) show how record linkage can be boiled down to a simple prediction problem, and 3) introduce Name Match, a new open source tool for customizable probabilistic record linkage.