Machine Learning for Business Analytics, 2nd Edition

Machine Learning for Business Analytics, 2nd Edition

by Peter C. Bruce, Mia L. Stephens, Galit Shmueli, Muralidhara Anandamurthy, Nitin R. Patel
Released May 2023
Publisher(s): Wiley
ISBN: 9781119903833

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Book description

MACHINE LEARNING FOR BUSINESS ANALYTICS

An up-to-date introduction to a market-leading platform for data analysis and machine learning

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users’ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses.

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. readers will also find:

  • Updated material which improves the book’s usefulness as a reference for professionals beyond the classroom
  • Four new chapters, covering topics including Text Mining and Responsible Data Science
  • An updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbook
  • A guide to JMP Pro®’s new features and enhanced functionality

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro®, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.

Table of contents

  1. COVER
  2. TITLE PAGE
  3. COPYRIGHT
  4. FOREWORD
  5. PREFACE
    1. NOTES
  6. ACKNOWLEDGMENTS
  7. PART I: PRELIMINARIES
    1. 1 INTRODUCTION
      1. 1.1 WHAT IS BUSINESS ANALYTICS?
      2. 1.2 WHAT IS MACHINE LEARNING?
      3. 1.3 MACHINE LEARNING, AI, AND RELATED TERMS
      4. 1.4 BIG DATA
      5. 1.5 DATA SCIENCE
      6. 1.6 WHY ARE THERE SO MANY DIFFERENT METHODS?
      7. 1.7 TERMINOLOGY AND NOTATION
      8. 1.8 ROAD MAPS TO THIS BOOK
    2. 2 OVERVIEW OF THE MACHINE LEARNING PROCESS
      1. 2.1 INTRODUCTION
      2. 2.2 CORE IDEAS IN MACHINE LEARNING
      3. 2.3 THE STEPS IN A MACHINE LEARNING PROJECT
      4. 2.4 PRELIMINARY STEPS
      5. 2.5 PREDICTIVE POWER AND OVERFITTING
      6. 2.6 BUILDING A PREDICTIVE MODEL WITH JMP Pro
      7. 2.7 USING JMP Pro FOR MACHINE LEARNING
      8. 2.8 AUTOMATING MACHINE LEARNING SOLUTIONS
      9. 2.9 ETHICAL PRACTICE IN MACHINE LEARNING
      10. NOTES
  8. PART II: DATA EXPLORATION AND DIMENSION REDUCTION
    1. 3 DATA VISUALIZATION
      1. 3.1 INTRODUCTION
      2. 3.2 DATA EXAMPLES
      3. 3.3 BASIC CHARTS: BAR CHARTS, LINE GRAPHS, AND SCATTER PLOTS
      4. 3.4 MULTIDIMENSIONAL VISUALIZATION
      5. 3.5 SPECIALIZED VISUALIZATIONS
      6. 3.6 SUMMARY: MAJOR VISUALIZATIONS AND OPERATIONS, ACCORDING TO MACHINE LEARNING GOAL
      7. NOTES
    2. 4 DIMENSION REDUCTION
      1. 4.1 INTRODUCTION
      2. 4.2 CURSE OF DIMENSIONALITY
      3. 4.3 PRACTICAL CONSIDERATIONS
      4. 4.4 DATA SUMMARIES
      5. 4.5 CORRELATION ANALYSIS
      6. 4.6 REDUCING THE NUMBER OF CATEGORIES IN CATEGORICAL VARIABLES
      7. 4.7 CONVERTING A CATEGORICAL VARIABLE TO A CONTINUOUS VARIABLE
      8. 4.8 PRINCIPAL COMPONENT ANALYSIS
      9. 4.9 DIMENSION REDUCTION USING REGRESSION MODELS
      10. 4.10 DIMENSION REDUCTION USING CLASSIFICATION AND REGRESSION TREES
      11. NOTES
  9. PART III: PERFORMANCE EVALUATION
    1. 5 EVALUATING PREDICTIVE PERFORMANCE
      1. 5.1 INTRODUCTION
      2. 5.2 EVALUATING PREDICTIVE PERFORMANCE
      3. 5.3 JUDGING CLASSIFIER PERFORMANCE
      4. 5.4 JUDGING RANKING PERFORMANCE
      5. 5.5 OVERSAMPLING
  10. PART IV: PREDICTION AND CLASSIFICATION METHODS
    1. 6 MULTIPLE LINEAR REGRESSION
      1. 6.1 INTRODUCTION
      2. 6.2 EXPLANATORY VS. PREDICTIVE MODELING
      3. 6.3 ESTIMATING THE REGRESSION EQUATION AND PREDICTION
      4. 6.4 VARIABLE SELECTION IN LINEAR REGRESSION
      5. NOTES
    2. 7 k‐NEAREST NEIGHBORS (k‐NN)
      1. 7.1 THE k‐NN CLASSIFIER (CATEGORICAL OUTCOME)
      2. 7.2 K‐NN FOR A NUMERICAL RESPONSE
      3. 7.3 ADVANTAGES AND SHORTCOMINGS OF K‐NN ALGORITHMS
      4. NOTES
    3. 8 THE NAIVE BAYES CLASSIFIER
      1. 8.1 INTRODUCTION
      2. 8.2 APPLYING THE FULL (EXACT) BAYESIAN CLASSIFIER
      3. 8.3 SOLUTION: NAIVE BAYES
      4. 8.4 ADVANTAGES AND SHORTCOMINGS OF THE NAIVE BAYES CLASSIFIER
    4. 9 CLASSIFICATION AND REGRESSION TREES
      1. 9.1 INTRODUCTION
      2. 9.2 CLASSIFICATION TREES
      3. 9.3 GROWING A TREE FOR RIDING MOWERS EXAMPLE
      4. 9.4 EVALUATING THE PERFORMANCE OF A CLASSIFICATION TREE
      5. 9.5 AVOIDING OVERFITTING
      6. 9.6 CLASSIFICATION RULES FROM TREES
      7. 9.7 CLASSIFICATION TREES FOR MORE THAN TWO CLASSES
      8. 9.8 REGRESSION TREES
      9. 9.9 ADVANTAGES AND WEAKNESSES OF A SINGLE TREE
      10. 9.10 IMPROVING PREDICTION: RANDOM FORESTS AND BOOSTED TREES
      11. NOTES
    5. 10 LOGISTIC REGRESSION
      1. 10.1 INTRODUCTION
      2. 10.2 THE LOGISTIC REGRESSION MODEL
      3. 10.3 EXAMPLE: ACCEPTANCE OF PERSONAL LOAN
      4. 10.4 EVALUATING CLASSIFICATION PERFORMANCE
      5. 10.5 VARIABLE SELECTION
      6. 10.6 LOGISTIC REGRESSION FOR MULTI‐CLASS CLASSIFICATION
      7. 10.7 EXAMPLE OF COMPLETE ANALYSIS: PREDICTING DELAYED FLIGHTS
      8. Notes
    6. 11 NEURAL NETS
      1. 11.1 INTRODUCTION
      2. 11.2 CONCEPT AND STRUCTURE OF A NEURAL NETWORK
      3. 11.3 FITTING A NETWORK TO DATA
      4. 11.4 USER INPUT IN JMP Pro
      5. 11.5 EXPLORING THE RELATIONSHIP BETWEEN PREDICTORS AND OUTCOME
      6. 11.6 DEEP LEARNING
      7. 11.7 ADVANTAGES AND WEAKNESSES OF NEURAL NETWORKS
      8. NOTES
    7. 12 DISCRIMINANT ANALYSIS
      1. 12.1 INTRODUCTION
      2. 12.2 DISTANCE OF AN OBSERVATION FROM A CLASS
      3. 12.3 FROM DISTANCES TO PROPENSITIES AND CLASSIFICATIONS
      4. 12.4 CLASSIFICATION PERFORMANCE OF DISCRIMINANT ANALYSIS
      5. 12.5 PRIOR PROBABILITIES
      6. 12.6 CLASSIFYING MORE THAN TWO CLASSES
      7. 12.7 ADVANTAGES AND WEAKNESSES
      8. NOTES
    8. 13 GENERATING, COMPARING, AND COMBINING MULTIPLE MODELS
      1. 13.1 ENSEMBLES
      2. 13.2 AUTOMATED MACHINE LEARNING (AUTOML)
      3. 13.3 SUMMARY
      4. NOTE
  11. PART V: INTERVENTION AND USER FEEDBACK
    1. 14 INTERVENTIONS: EXPERIMENTS, UPLIFT MODELS, AND REINFORCEMENT LEARNING
      1. 14.1 INTRODUCTION
      2. 14.2 A/B TESTING
      3. 14.3 UPLIFT (PERSUASION) MODELING
      4. 14.4 REINFORCEMENT LEARNING
      5. 14.5 SUMMARY
      6. NOTES
  12. PART VI: MINING RELATIONSHIPS AMONG RECORDS
    1. 15 ASSOCIATION RULES AND COLLABORATIVE FILTERING
      1. 15.1 ASSOCIATION RULES
      2. 15.2 COLLABORATIVE FILTERING
      3. 15.3 SUMMARY
      4. NOTES
    2. 16 CLUSTER ANALYSIS
      1. 16.1 INTRODUCTION
      2. 16.2 MEASURING DISTANCE BETWEEN TWO RECORDS
      3. 16.3 MEASURING DISTANCE BETWEEN TWO CLUSTERS
      4. 16.4 HIERARCHICAL (AGGLOMERATIVE) CLUSTERING
      5. 16.5 NONHIERARCHICAL CLUSTERING: THE K‐MEANS ALGORITHM
      6. NOTE
  13. PART VII: FORECASTING TIME SERIES
    1. 17 HANDLING TIME SERIES
      1. 17.1 INTRODUCTION
      2. 17.2 DESCRIPTIVE VS. PREDICTIVE MODELING
      3. 17.3 POPULAR FORECASTING METHODS IN BUSINESS
      4. 17.4 TIME SERIES COMPONENTS
      5. 17.5 DATA PARTITIONING AND PERFORMANCE EVALUATION
      6. NOTES
    2. 18 REGRESSION‐BASED FORECASTING
      1. 18.1 A MODEL WITH TREND
      2. 18.2 A MODEL WITH SEASONALITY
      3. 18.3 A MODEL WITH TREND AND SEASONALITY
      4. 18.4 AUTOCORRELATION AND ARIMA MODELS
      5. Notes
    3. 19 SMOOTHING AND DEEP LEARNING METHODS FOR FORECASTING
      1. 19.1 INTRODUCTION
      2. 19.2 MOVING AVERAGE
      3. 19.3 SIMPLE EXPONENTIAL SMOOTHING
      4. 19.4 ADVANCED EXPONENTIAL SMOOTHING
      5. 19.5 DEEP LEARNING FOR FORECASTING
      6. NOTES
  14. PART VIII: DATA ANALYTICS
    1. 20 TEXT MINING
      1. 20.1 INTRODUCTION
      2. 20.2 THE TABULAR REPRESENTATION OF TEXT: DOCUMENT–TERM MATRIX AND “BAG‐OF‐WORDS”
      3. 20.3 BAG‐OF‐WORDS VS. MEANING EXTRACTION AT DOCUMENT LEVEL
      4. 20.4 PREPROCESSING THE TEXT
      5. 20.5 IMPLEMENTING MACHINE LEARNING METHODS
      6. 20.6 EXAMPLE: ONLINE DISCUSSIONS ON AUTOS AND ELECTRONICS
      7. 20.7 EXAMPLE: SENTIMENT ANALYSIS OF MOVIE REVIEWS
      8. 20.8 SUMMARY
      9. NOTES
    2. 21 RESPONSIBLE DATA SCIENCE
      1. 21.1 INTRODUCTION
      2. 21.2 UNINTENTIONAL HARM
      3. 21.3 LEGAL CONSIDERATIONS
      4. 21.4 PRINCIPLES OF RESPONSIBLE DATA SCIENCE
      5. 21.5 A RESPONSIBLE DATA SCIENCE FRAMEWORK
      6. 21.6 DOCUMENTATION TOOLS
      7. 21.7 EXAMPLE: APPLYING THE RDS FRAMEWORK TO THE COMPAS EXAMPLE
      8. 21.8 SUMMARY
      9. NOTES
  15. PART IX: CASES
    1. 22 CASES
      1. 22.1 CHARLES BOOK CLUB
      2. 22.2 GERMAN CREDIT
      3. 22.3 TAYKO SOFTWARE CATALOGER
      4. 22.4 POLITICAL PERSUASION
      5. 22.5 TAXI CANCELLATIONS
      6. 22.6 SEGMENTING CONSUMERS OF BATH SOAP
      7. 22.7 CATALOG CROSS‐SELLING
      8. 22.8 DIRECT‐MAIL FUNDRAISING
      9. 22.9 TIME SERIES CASE: FORECASTING PUBLIC TRANSPORTATION DEMAND
      10. 22.10 LOAN APPROVAL
      11. NOTES
  16. REFERENCES
  17. DATA FILES USED IN THE BOOK
  18. INDEX
  19. END USER LICENSE AGREEMENT

Product information

  • Title: Machine Learning for Business Analytics, 2nd Edition
  • Author(s): Peter C. Bruce, Mia L. Stephens, Galit Shmueli, Muralidhara Anandamurthy, Nitin R. Patel
  • Release date: May 2023
  • Publisher(s): Wiley
  • ISBN: 9781119903833