Probabilistic Matching
Probabilistic matching is a technique used in data science to identify and match records that refer to the same entity across different data sources. It is a process of comparing two or more records that may or may not match exactly, and assigning a probability score to each potential match. This score is based on the degree of similarity between the records, and takes into account factors such as spelling variations, missing or incomplete data, and other types of errors or inconsistencies. Probabilistic matching is particularly useful in situations where there is no unique identifier for the entity being matched, or where the data is of poor quality or has been collected from multiple sources. It is commonly used in applications such as customer relationship management, fraud detection, and healthcare analytics.
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