Machine Learning For Business Analytics: Concepts, Techniques, and Applications in R

✍ Scribed by Galit Shmeuli, Peter C. Bruce, Peter Gedeck, Inbal Yahav, Nitin R. Patel

• Publisher: Wiley
• Year: 2023
• Tongue: English
• Leaves: 870
• Edition: 2
• Category: Library

✦ Synopsis

MACHINE LEARNING FOR BUSINESS ANALYTICS

Machine learning ―also known as data mining or data 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 in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, 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 is the second R edition of Machine Learning for Business Analytics. This edition also includes:

A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R
An expanded chapter focused on discussion of deep learning techniques
A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning
A new chapter on responsible data science
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
A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques
End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions

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.

✦ Table of Contents

Cover
Title Page
Copyright
Dedication
Foreword by Ravi Bapna
Foreword by Gareth James
Preface to the Second R Edition
Acknowledgments
PART I: Preliminaries
CHAPTER 1: Introduction
1.1 WHAT IS BUSINESS ANALYTICS?
1.2 WHAT IS MACHINE LEARNING?
1.3 MACHINE LEARNING, AI, AND RELATED TERMS
1.4 BIG DATA
1.5 DATA SCIENCE
1.6 WHY ARE THERE SO MANY DIFFERENT METHODS?
1.7 TERMINOLOGY AND NOTATION
1.8 ROAD MAPS TO THIS BOOK
CHAPTER 2: Overview of the Machine Learning Process
2.1 INTRODUCTION
2.2 CORE IDEAS IN MACHINE LEARNING
2.3 THE STEPS IN A MACHINE LEARNING PROJECT
2.4 PRELIMINARY STEPS
2.5 PREDICTIVE POWER AND OVERFITTING
2.6 BUILDING A PREDICTIVE MODEL
2.7 USING R FOR MACHINE LEARNING ON A LOCAL MACHINE
2.8 AUTOMATING MACHINE LEARNING SOLUTIONS
2.9 ETHICAL PRACTICE IN MACHINE LEARNING
PROBLEMS
NOTES
PART II: Data Exploration and Dimension Reduction
CHAPTER 3: Data Visualization
3.1 USES OF DATA VISUALIZATION
3.2 DATA EXAMPLES
3.3 BASIC CHARTS: BAR CHARTS, LINE CHARTS, AND SCATTER PLOTS
3.4 MULTIDIMENSIONAL VISUALIZATION
3.5 SPECIALIZED VISUALIZATIONS
3.6 SUMMARY: MAJOR VISUALIZATIONS AND OPERATIONS, BY MACHINE LEARNING GOAL
PROBLEMS
NOTES
CHAPTER 4: Dimension Reduction
4.1 INTRODUCTION
4.2 CURSE OF DIMENSIONALITY
4.3 PRACTICAL CONSIDERATIONS
4.4 DATA SUMMARIES
4.5 CORRELATION ANALYSIS
4.6 REDUCING THE NUMBER OF CATEGORIES IN CATEGORICAL VARIABLES
4.7 CONVERTING A CATEGORICAL VARIABLE TO A NUMERICAL VARIABLE
4.8 PRINCIPAL COMPONENT ANALYSIS
4.9 DIMENSION REDUCTION USING REGRESSION MODELS
4.10 DIMENSION REDUCTION USING CLASSIFICATION AND REGRESSION TREES
PROBLEMS
Note
PART III: Performance Evaluation
CHAPTER 5: Evaluating Predictive Performance
5.1 INTRODUCTION
5.2 EVALUATING PREDICTIVE PERFORMANCE
5.3 JUDGING CLASSIFIER PERFORMANCE
5.4 JUDGING RANKING PERFORMANCE
5.5 OVERSAMPLING
PROBLEMS
NOTES
PART IV: Prediction and Classification Methods
CHAPTER 6: Multiple Linear Regression
6.1 INTRODUCTION
6.2 EXPLANATORY VS. PREDICTIVE MODELING
6.3 ESTIMATING THE REGRESSION EQUATION AND PREDICTION
6.4 VARIABLE SELECTION IN LINEAR REGRESSION
PROBLEMS
NOTES
CHAPTER 7: k ‐Nearest Neighbors ( k ‐NN)
7.1 THE K ‐NN CLASSIFIER (CATEGORICAL OUTCOME)
7.2 K ‐NN FOR A NUMERICAL OUTCOME
7.3 ADVANTAGES AND SHORTCOMINGS OF K ‐NN ALGORITHMS
PROBLEMS
NOTES
CHAPTER 8: The Naive Bayes Classifier
8.1 INTRODUCTION
8.2 APPLYING THE FULL (EXACT) BAYESIAN CLASSIFIER
8.3 SOLUTION: NAIVE BAYES
8.4 ADVANTAGES AND SHORTCOMINGS OF THE NAIVE BAYES CLASSIFIER
PROBLEMS
CHAPTER 9: Classification and Regression Trees
9.1 INTRODUCTION
9.2 CLASSIFICATION TREES
9.3 EVALUATING THE PERFORMANCE OF A CLASSIFICATION TREE
9.4 AVOIDING OVERFITTING
9.5 CLASSIFICATION RULES FROM TREES
9.6 CLASSIFICATION TREES FOR MORE THAN TWO CLASSES
9.7 REGRESSION TREES
9.8 ADVANTAGES AND WEAKNESSES OF A TREE
9.9 IMPROVING PREDICTION: RANDOM FORESTS AND BOOSTED TREES
PROBLEMS
NOTES
CHAPTER 10: Logistic Regression
10.1 INTRODUCTION
10.2 THE LOGISTIC REGRESSION MODEL
10.3 EXAMPLE: ACCEPTANCE OF PERSONAL LOAN
10.4 EVALUATING CLASSIFICATION PERFORMANCE
10.5 VARIABLE SELECTION
10.6 LOGISTIC REGRESSION FOR MULTI‐CLASS CLASSIFICATION
10.7 EXAMPLE OF COMPLETE ANALYSIS: PREDICTING DELAYED FLIGHTS
PROBLEMS
NOTES
CHAPTER 11: Neural Nets
11.1 INTRODUCTION
11.2 CONCEPT AND STRUCTURE OF A NEURAL NETWORK
11.3 FITTING A NETWORK TO DATA
11.4 REQUIRED USER INPUT
11.5 EXPLORING THE RELATIONSHIP BETWEEN PREDICTORS AND OUTCOME
11.6 DEEP LEARNING
11.7 ADVANTAGES AND WEAKNESSES OF NEURAL NETWORKS
PROBLEMS
NOTES
CHAPTER 12: Discriminant Analysis
12.1 INTRODUCTION
12.2 DISTANCE OF A RECORD FROM A CLASS
12.3 FISHER'S LINEAR CLASSIFICATION FUNCTIONS
12.4 CLASSIFICATION PERFORMANCE OF DISCRIMINANT ANALYSIS
12.5 PRIOR PROBABILITIES
12.6 UNEQUAL MISCLASSIFICATION COSTS
12.7 CLASSIFYING MORE THAN TWO CLASSES
12.8 ADVANTAGES AND WEAKNESSES
PROBLEMS
NOTES
CHAPTER 13: Generating, Comparing, and Combining Multiple Models
13.1 ENSEMBLES
13.2 AUTOMATED MACHINE LEARNING (AUTOML)
13.3 EXPLAINING MODEL PREDICTIONS
13.4 SUMMARY
PROBLEMS
NOTES
PART V: Intervention and User Feedback
CHAPTER 14: Interventions: Experiments, Uplift Models, and Reinforcement Learning
14.1 A/B TESTING
14.2 UPLIFT (PERSUASION) MODELING
14.3 REINFORCEMENT LEARNING
14.4 SUMMARY
PROBLEMS
NOTES
PART VI: Mining Relationships Among Records
CHAPTER 15: Association Rules and Collaborative Filtering
15.1 ASSOCIATION RULES
15.2 COLLABORATIVE FILTERING
15.3 SUMMARY
PROBLEMS
NOTES
CHAPTER 16: Cluster Analysis
16.1 INTRODUCTION
16.2 MEASURING DISTANCE BETWEEN TWO RECORDS
16.3 MEASURING DISTANCE BETWEEN TWO CLUSTERS
16.4 HIERARCHICAL (AGGLOMERATIVE) CLUSTERING
16.5 NON‐HIERARCHICAL CLUSTERING: THE ‐MEANS ALGORITHM
PROBLEMS
PART VII: Forecasting Time Series
CHAPTER 17: Handling Time Series
17.1 INTRODUCTION
17.2 DESCRIPTIVE VS. PREDICTIVE MODELING
17.3 POPULAR FORECASTING METHODS IN BUSINESS
17.4 TIME SERIES COMPONENTS
17.5 DATA PARTITIONING AND PERFORMANCE EVALUATION
PROBLEMS
NOTES
CHAPTER 18: Regression‐Based Forecasting
18.1 A MODEL WITH TREND
18.2 A MODEL WITH SEASONALITY
18.3 A MODEL WITH TREND AND SEASONALITY
18.4 AUTOCORRELATION AND ARIMA MODELS
PROBLEMS
NOTES
CHAPTER 19: Smoothing and Deep Learning Methods for Forecasting
19.1 SMOOTHING METHODS: INTRODUCTION
19.2 MOVING AVERAGE
19.3 SIMPLE EXPONENTIAL SMOOTHING
19.4 ADVANCED EXPONENTIAL SMOOTHING
19.5 DEEP LEARNING FOR FORECASTING
PROBLEMS
NOTES
PART VIII: Data Analytics
CHAPTER 20: Social Network Analytics
20.1 INTRODUCTION
20.2 DIRECTED VS. UNDIRECTED NETWORKS
20.3 VISUALIZING AND ANALYZING NETWORKS
20.4 SOCIAL DATA METRICS AND TAXONOMY
20.5 USING NETWORK METRICS IN PREDICTION AND CLASSIFICATION
20.6 COLLECTING SOCIAL NETWORK DATA WITH R
20.7 ADVANTAGES AND DISADVANTAGES
PROBLEMS
NOTES
CHAPTER 21: Text Mining
21.1 INTRODUCTION
21.2 THE TABULAR REPRESENTATION OF TEXT: TERM–DOCUMENT MATRIX AND “BAG‐OF‐WORDS”
21.3 BAG‐OF‐WORDS VS. MEANING EXTRACTION AT DOCUMENT LEVEL
21.4 PREPROCESSING THE TEXT
21.5 IMPLEMENTING MACHINE LEARNING METHODS
21.6 EXAMPLE: ONLINE DISCUSSIONS ON AUTOS AND ELECTRONICS
21.7 EXAMPLE: SENTIMENT ANALYSIS OF MOVIE REVIEWS
21.8 SUMMARY
PROBLEMS
NOTES
CHAPTER 22: Responsible Data Science
22.1 INTRODUCTION
22.2 UNINTENTIONAL HARM
22.3 LEGAL CONSIDERATIONS
22.4 PRINCIPLES OF RESPONSIBLE DATA SCIENCE
22.5 A RESPONSIBLE DATA SCIENCE FRAMEWORK
22.6 DOCUMENTATION TOOLS
22.7 EXAMPLE: APPLYING THE RDS FRAMEWORK TO THE COMPAS EXAMPLE
22.8 SUMMARY
PROBLEMS
NOTES
PART IX: Cases
CHAPTER 23: Cases
23.1 CHARLES BOOK CLUB
23.2 GERMAN CREDIT
23.3 TAYKO SOFTWARE CATALOGER
23.4 POLITICAL PERSUASION
23.5 TAXI CANCELLATIONS
23.6 SEGMENTING CONSUMERS OF BATH SOAP
23.7 DIRECT‐MAIL FUNDRAISING
23.8 CATALOG CROSS‐SELLING
23.9 TIME SERIES CASE: FORECASTING PUBLIC TRANSPORTATION DEMAND
23.10 LOAN APPROVAL
NOTES
References
R Packages Used in the Book
Data Files Used in the Book
Index
End User License Agreement