Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel

Data Mining for Business Analytics (eBook, ePUB)

Concepts, Techniques and Applications in Python

• Format: ePub

Produktbeschreibung

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: * A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process * A new section on ethical issues in data mining * Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students * More than a dozen case studies demonstrating applications for the data mining techniques described * 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, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. "This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject." --Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 608
• Erscheinungstermin: 28. Oktober 2019
• Englisch
• ISBN-13: 9781119549864
• Artikelnr.: 58045454

Autorenporträt

GALIT SHMUELI, PHD, is Distinguished Professor at National Tsing Hua University's Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 100 publications including books. PETER C. BRUCE is President and Founder of the Institute for Statistics Education at Statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O'Reilly). PETER GEDECK, PHD, is a Senior Data Scientist at Collaborative Drug Discovery, where he helps develop cloud-based software to manage the huge amount of data involved in the drug discovery process. He also teaches data mining at Statistics.com. NITIN R. PATEL, PhD, is cofounder and board member of Cytel Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also 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.

Inhaltsangabe

Foreword by Gareth James xix

Foreword by Ravi Bapna xxi

Preface to the Python Edition xxiii

Acknowledgments xxvii

Part I Preliminaries

Chapter 1 Introduction 3

1.1 What is Business Analytics? 3

1.2 What is Data Mining? 5

1.3 Data Mining and Related Terms 5

1.4 Big Data 6

1.5 Data Science 7

1.6 Why are There So Many Different Methods? 8

1.7 Terminology and Notation 9

1.8 Road Maps to This Book 11

Chapter 2 Overview of the Data Mining Process 15

2.1 Introduction 15

2.2 Core Ideas in Data Mining 16

2.3 The Steps in Data Mining 19

2.4 Preliminary Steps 21

2.5 Predictive Power and Overfitting 34

2.6 Building a Predictive Model 40

2.7 Using Python for Data Mining on a Local Machine 44

2.8 Automating Data Mining Solutions 45

2.9 Ethical Practice in Data Mining 47

Problems 56

Part II Data Exploration and Dimension Reduction

Chapter 3 Data Visualization 61

3.1 Introduction 61

3.2 Data Examples 64

3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 65

3.4 Multidimensional Visualization 74

3.5 Specialized Visualizations 88

3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 93

Problems 97

Chapter 4 Dimension Reduction 99

4.1 Introduction 100

4.2 Curse of Dimensionality 100

4.3 Practical Considerations 100

4.4 Data Summaries 102

4.5 Correlation Analysis 105

4.6 Reducing the Number of Categories in Categorical Variables 106

4.7 Converting a Categorical Variable to a Numerical Variable 108

4.8 Principal Components Analysis 108

4.9 Dimension Reduction Using Regression Models 119

4.10 Dimension Reduction Using Classification and Regression Trees 119

Problems 120

Part III Performance Evaluation

Chapter 5 Evaluating Predictive Performance 125

5.1 Introduction 126

5.2 Evaluating Predictive Performance 126

5.3 Judging Classifier Performance 131

5.4 Judging Ranking Performance 144

5.5 Oversampling 149

Problems 155

Part IV Prediction and Classification Methods

Chapter 6 Multiple Linear Regression 161

6.1 Introduction 162

6.2 Explanatory vs. Predictive Modeling 162

6.3 Estimating the Regression Equation and Prediction 164

6.4 Variable Selection in Linear Regression 169

Appendix: Using Statmodels 179

Problems 180

Chapter 7 k-Nearest Neighbors (kNN) 185

7.1 The k-NN Classifier (Categorical Outcome) 185

7.2 k-NN for a Numerical Outcome 193

7.3 Advantages and Shortcomings of k-NN Algorithms 195

Problems 197

Chapter 8 The Naive Bayes Classifier 199

8.1 Introduction 199

Example 1: Predicting Fraudulent Financial Reporting 201

8.2 Applying the Full (Exact) Bayesian Classifier 201

8.3 Advantages and Shortcomings of the Naive Bayes Classifier 210

Problems 214

Chapter 9 Classification and Regression Trees 217

9.1 Introduction 218

9.2 Classification Trees 220

9.3 Evaluating the Performance of a Classification Tree 228

9.4 Avoiding Overfitting 232

9.5 Classification Rules from Trees 238

9.6 Classification Trees for More Than Two Classes 239

9.7 Regression Trees 239

9.8 Improving Prediction: Random Fore