Machine Learning for Business Analytics Concepts Techniques and Applications in RapidMiner 1st Edition by Galit Shmueli, Peter Bruce, Amit Deokar, Nitin Patel ISBN 9781119828815 1119828813

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Product details:
ISBN 10: 1119828813
ISBN 13: 9781119828815
Author: Galit Shmueli, Peter Bruce, Amit Deokar, Nitin Patel 

Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner 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 seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes:

• A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner
• Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years
• 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.

Machine Learning for Business Analytics Concepts Techniques and Applications in RapidMiner 1st Edition Table of contents:

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

1.9 USING RAPIDMINER STUDIO

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 WITH RAPIDMINER

2.7 USING RAPIDMINER FOR MACHINE LEARNING

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 INTRODUCTION

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 ATTRIBUTES

4.7 CONVERTING A CATEGORICAL ATTRIBUTE TO A NUMERICAL ATTRIBUTE

4.8 PRINCIPAL COMPONENT ANALYSIS

4.9 DIMENSION REDUCTION USING REGRESSION MODELS

4.10 DIMENSION REDUCTION USING CLASSIFICATION AND REGRESSION TREES

PROBLEMS

NOTES

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 LABEL)

7.2 k‐NN FOR A NUMERICAL LABEL

7.3 ADVANTAGES AND SHORTCOMINGS OF k‐NN ALGORITHMS

APPENDIX: COMPUTING DISTANCES BETWEEN RECORDS IN RAPIDMINER

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

NOTE

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 IMPROVING PREDICTION: RANDOM FORESTS AND BOOSTED TREES

9.9 ADVANTAGES AND WEAKNESSES OF A TREE

PROBLEMS

NOTES

CHAPTER 10: Logistic Regression

10.1 INTRODUCTION

10.2 THE LOGISTIC REGRESSION MODEL

10.3 EXAMPLE: ACCEPTANCE OF PERSONAL LOAN

10.4 LOGISTIC REGRESSION FOR MULTI‐CLASS CLASSIFICATION

10.5 EXAMPLE OF COMPLETE ANALYSIS: PREDICTING DELAYED FLIGHTS

APPENDIX: LOGISTIC REGRESSION FOR ORDINAL CLASSES

PROBLEMS

NOTES

CHAPTER 11: Neural Networks

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 TARGET ATTRIBUTE

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 AUTOMATED MACHINE LEARNING (AUTOML)

13.2 EXPLAINING MODEL PREDICTIONS

13.3 ENSEMBLES

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 k‐MEANS ALGORITHM

PROBLEMS

NOTES

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 RAPIDMINER

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

Data Files Used in the Book

Index

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