[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-115354-en":3,"doc-seo-115354-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},115354,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Business Analytics with R and Python - AI for Risks - read online free","Business Analytics with R and Python provides an overview of data mining methods for addressing business management challenges in customer service, risk identification, and decision impact analysis. The book distinguishes descriptive analytics from predictive analytics by extending statistical and AI techniques to support forecasting. It surveys core business analytics problems, analytics and knowledge management, data visualization, association rules, clustering, time-series analysis, predictive classification, and prescriptive modeling with AI applications, structured from business understanding through implementation.","AI for Risks  \nSeries Editors  \nDesheng Wu  \nSchool of Economics and Management, University of Chinese Academy of Sciences, Beijing, China  \nJim Lambert  \nUniversity of Virginia, Charlottesville, VA, USA  \nDavid L. Olson  \nDepartment of Management, University of Nebraska–Lincoln, Lincoln, NE, USA  \nRisks are widespread in human society, such as in the financial field (various investment risks), in technical fields (risks brought by emerging technologies), in social fields (political risks), and in all aspects of our lives (health risks, natural environment risks, etc.) At the same time, the inherent links between various risks generate systemic risks. The major challenge today is not to deal with new types of risks, but to focus more on those risks that are difficult to distinguish effectively and timely, or risks that have emerged in a different way from the past.  \nThis series of books aims to strengthen discussions on frontier hot topics across disciplines, that is, using AI technologies to solve risks problems that exist in the environment, healthcare, technology, and financial fields. The scope of it focuses on the implementation of artificial intelligence technology in dealing with risks, such as risk prediction, assessment, and mitigation. It includes monographs, edited volumes, textbooks and proceedings etc. on the application of artificial intelligence in  \nrisk management in the fields of social media, healthcare, the public sector, financial technology, and regulatory technology.  \nDavid L. Olson, Desheng Dash Wu, Cuicui Luo and Majid Nabavi  \nBusiness Analytics with R and Python  \nDavid L. Olson  \nSupply Chain Management and Analytics, University of Nebraska-Lincoln, Lincoln, NE, USA  \nDesheng Dash Wu  \nUniversity of Chinese Academy of Science, Beijing, China  \nCuicui Luo  \nUniversity of Chinese Academy of Science, Beijing, China  \nMajid Nabavi  \nUniversity of Nebraska-Lincoln, Lincoln, NE, USA  \nISSN 2731-6327 e-ISSN 2731-6335  \nAI for Risks  \nISBN 978-981-97-4771-9 e-ISBN 978-981-97-4772-6  \n[https://doi.org/10.1007/978-981-97-4772-6](https://doi.org/10.1007/978-981-97-4772-6)  \n© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024  \nThis work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in anyother physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.  \nThe use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.  \nThe publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nThis Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.  \nThe registered company address is: 152 Beach Road, \\#21-01/04 Gateway East, Singapore 189721, Singapore  \nPreface  \nThis book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, and descriptive analytics f","cbCaiajOzJUyzc3T","https://ap.wps.com/l/cbCaiajOzJUyzc3T","pdf",40989675,6,1,229,"English","en",105,"# Preface\n# Contents\n## Data Mining in Business\n## Data Mining Processes\n## Data Mining Software\n## Association Rules","[{\"question\":\"What types of analytics does the book distinguish?\",\"answer\":\"It differentiates descriptive analytics, which focuses on what has happened, from predictive analytics, which applies statistical methods and artificial intelligence to enable forecasting.\"},{\"question\":\"What topics are covered in the early chapters?\",\"answer\":\"It covers business management problems for analytics, requirements for data mining, business data mining use cases, and initial data visualization tools.\"},{\"question\":\"Which modeling and advanced data mining areas are included later?\",\"answer\":\"It includes cluster analysis, time series analysis, predictive classification data mining tools, and prescriptive modeling in business with applications of artificial intelligence.\"}]","Business Analytics with R and Python - 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