Machine learning and data science techniques for effective government service delivery:

"In our data-rich era, extracting meaningful insights from the vast amount of information has become a crucial challenge, especially in government service delivery where informed decisions are paramount. Traditional approaches struggle with the enormity of data, highlighting the need for a new...

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Bibliographic Details
Other Authors: Ogunleye, Olalekan Samuel 1979- (Editor)
Format: Electronic eBook
Language:English
Published: Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) IGI Global 2024.
Subjects:
Online Access:DE-862
DE-863
Summary:"In our data-rich era, extracting meaningful insights from the vast amount of information has become a crucial challenge, especially in government service delivery where informed decisions are paramount. Traditional approaches struggle with the enormity of data, highlighting the need for a new approach that integrates data science and machine learning. The book, Machine Learning and Data Science Techniques for Effective Government Service Delivery, becomes a vital resource in this transformation, offering a deep understanding of these technologies and their applications. Within the complex landscape of modern governance, this book stands as a solution-oriented guide. Recognizing data's value in the 21st century, it navigates the world of data science and machine learning, enhancing the mechanics of government service. By addressing citizens' evolving needs, these advanced methods counter inefficiencies in traditional systems. Tailored for experts across technology, academia, and government, the book bridges theory and practicality. Covering foundational concepts and innovative applications, it explores the potential of data-driven decision-making for a more efficient and citizen-centric government future."--
Physical Description:17 PDFs (344 Seiten)
Also available in print.
Format:Mode of access: World Wide Web.
Bibliography:Includes bibliographical references and index.
ISBN:9781668497180
Access:Restricted to subscribers or individual electronic text purchasers.

There is no print copy available.

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