Details

Machine Learning for Business Analytics

Concepts, Techniques, and Applications with Analytic Solver Data Mining
4. Aufl.

von: Galit Shmueli, Peter C. Bruce, Kuber R. Deokar, Nitin R. Patel
107,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	19.04.2023
ISBN/EAN:	9781119829867
Sprache:	englisch
Anzahl Seiten:	624

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Beschreibungen

Titelbeschreibung

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning—also known as data mining or predictive analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This fourth edition of Machine Learning for Business Analytics also includes: <ul><li> An expanded chapter on deep learning</li> <li> A new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learning</li> <li> A new chapter on responsible data science</li> <li> Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students</li> <li> A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques</li> <li> End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented</li> <li> A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions</li></ul> This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

Inhaltsverzeichnis

Foreword xix Preface to the Fourth Edition xxi Acknowledgments xxv PART I PRELIMINARIES CHAPTER 1 Introduction 3 CHAPTER 2 Overview of the Machine Learning Process 15 PART II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization 59 CHAPTER 4 Dimension Reduction 91 PART III PERFORMANCE EVALUATION CHAPTER 5 Evaluating Predictive Performance 115 PART IV PREDICTION AND CLASSIFICATION METHODS CHAPTER 6 Multiple Linear Regression 151 CHAPTER 7 k-Nearest-Neighbors (k-NN) 169 CHAPTER 8 The Naive Bayes Classifier 181 CHAPTER 9 Classification and Regression Trees 197 CHAPTER 10 Logistic Regression 229 CHAPTER 11 Neural Nets 257 CHAPTER 12 Discriminant Analysis 283 CHAPTER 13 Generating, Comparing, and Combining Multiple Models 303 PART V INTERVENTION AND USER FEEDBACK CHAPTER 14 Experiments, Uplift Modeling, and Reinforcement Learning 319 PART VI MINING RELATIONSHIPS AMONG RECORDS CHAPTER 15 Association Rules and Collaborative Filtering 341 CHAPTER 16 Cluster Analysis 369 PART VII FORECASTING TIME SERIES CHAPTER 17 Handling Time Series 401 CHAPTER 18 Regression-Based Forecasting 415 CHAPTER 19 Smoothing Methods 445 PART VIII DATA ANALYTICS CHAPTER 20 Social Network Analytics 467 CHAPTER 21 Text Mining 487 CHAPTER 22 Responsible Data Science 507 PART IX CASES CHAPTER 23 Cases 537 References 575 Data Files Used in the Book 577 Index 579

Autorenportrait

Galit Shmueli, PhD, is Distinguished Professor and Institute Director at National Tsing Hua University’s Institute of Service Science. She has designed and instructed business analytics courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Peter C. Bruce, is Founder of the Institute for Statistics Education at Statistics.com, and Chief Learning Officer at Elder Research, Inc. Kuber R. Deokar, is the Data Science Team Lead at UpThink Experts, India. He is also a faculty member at Statistics.com. Nitin R. Patel, PhD, is cofounder and lead researcher at Cytel Inc. He was also a co-founder of Tata Consultancy Services. A Fellow of the American Statistical Association, Dr. Patel has served as a visiting professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.

Back cover copy

Machine learning—also known as data mining or predictive analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This fourth edition of Machine Learning for Business Analytics also includes: <ul><li> An expanded chapter on deep learning</li> <li> A new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learning</li> <li> A new chapter on responsible data science</li> <li> Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students</li> <li> A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques</li> <li> End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented</li> <li> A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions</li></ul> This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.