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Automated Machine Learning for Business$
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Kai R. Larsen and Daniel S. Becker

Print publication date: 2021

Print ISBN-13: 9780190941659

Published to Oxford Scholarship Online: July 2021

DOI: 10.1093/oso/9780190941659.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use.date: 03 July 2022

Acquire and Integrate Data

Acquire and Integrate Data

Chapter:
(p.47) Section III Acquire and Integrate Data
Source:
Automated Machine Learning for Business
Author(s):

Kai R. Larsen

Daniel S. Becker

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190941659.003.0003

Access to additional and relevant data will lead to better predictions from algorithms until we reach the point where more observations (cases) are no longer helpful to detect the signal, the feature(s), or conditions that inform the target. In addition to obtaining more observations, we can also look for additional features of interest that we do not currently have, at which point it will invariably be necessary to integrate data from different sources. This section introduces this process of data integration, starting with an introduction of two methods: “joins” (to access more features) and “unions” (to access more observations) and continues on to cover regular expressions, data summarization, crosstabs, data reduction and splitting, and data wrangling in all its flavors.

Keywords:   data purchase, data cleaning, joins, unions, regular expressions (RegEx), data summarization, cross-tabulation, data reduction, data splitting, data wrangling

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