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Full Description
Entity resolution is a key analytic technique that enables you to identify multiple data records that refer to the same real-world entity. With this hands-on guide, product managers, data analysts, and data scientists will learn how to add value to data by cleansing, analyzing, and resolving datasets using open source Python libraries and cloud APIs.
Author Michael Shearer shows you how to scale up your data matching processes and improve the accuracy of your reconciliations. You'll be able to remove duplicate entries within a single source and join disparate data sources together when common keys aren't available. Using real-world data examples, this book helps you gain practical understanding to accelerate the delivery of real business value.
With entity resolution, you'll build rich and comprehensive data assets that reveal relationships for marketing and risk management purposes, key to harnessing the full potential of ML and AI. This book covers:
Challenges in deduplicating and joining datasets
Extracting, cleansing, and preparing datasets for matching
Text matching algorithms to identify equivalent entities
Techniques for deduplicating and joining datasets at scale
Matching datasets containing persons and organizations
Evaluating data matches
Optimizing and tuning data matching algorithms
Entity resolution using cloud APIs
Matching using privacy-enhancing technologies
About the Author
Michael Shearer is the Group Head of Compliance Product Management for HSBC. Since joining HSBC in 2014 he has led the delivery of financial crime risk capabilities for the bank, including industry-leading artificial intelligence and network analytics platforms. Prior to HSBC Michael spent 20 years in UK government service where he led the delivery of international projects to acquire and process large volumes of highly sensitive data. Michael is a Chartered Engineer. He was educated at Queen's University Belfast where he gained a Master's degree in Electrical and Electronic Engineering with distinction.
