How deeply does The Well-Grounded Data Analyst cover record linkage?

Explains blocking, field comparisons, match filtering, and reintegration, while deferring underlying algorithm implementation to a library.

Record linkage

As explained in The Well-Grounded Data Analyst

Record linkage is the process of matching records across or within datasets to determine whether they describe the same entity.

Record linkage matches potentially identical records across or within datasets. It supports deduplication when records differ through typos, conflicting fields, or incomplete information. section 3.4.3

A practical linkage workflow:

Reduce comparisons by blocking candidate pairs on a field such as postcode, accepting an accuracy–performance trade-off. section 3.4.3

Customer deduplication

Customer records sharing a postcode become candidate pairs. First name and surname are compared fuzzily at a threshold, while postcode is compared exactly; pairs matching all rules become linked duplicates.

How The Well-Grounded Data Analyst covers it

liveBook manning.com

Read The Well-Grounded Data Analyst on liveBook Get the book at manning.com

Related concepts

Python Record Linkage Toolkitimplemented by · The Python Record Linkage Toolkit, also known as recordlinkage, is a Python library for comparing, deduplicating, and linking records.
fuzzy matchingused by · Fuzzy matching is a similarity-based technique that identifies approximate correspondence between strings despite differences such as spelling errors, inflections, or capitalization.
entity resolutionalternative to · Entity resolution is the process of determining which records or mentions refer to the same real-world entity despite differences in their representations.

This explainer was generated by Manning's knowledge graph from the text of The Well-Grounded Data Analyst. The section links point to the parts of the book it draws on.