Book description
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
Table of contents
- Cover
- Foreword by Gareth James
- Foreword by Ravi Bapna
- Preface to the Python Edition
- Acknowledgments
-
Part I Preliminaries
- Chapter 1 Introduction
-
Chapter 2 Overview of the Data Mining Process
- 2.1 Introduction
- 2.2 Core Ideas in Data Mining
- 2.3 The Steps in Data Mining
- 2.4 Preliminary Steps
- 2.5 Predictive Power and Overfitting
- 2.6 Building a Predictive Model
- 2.7 Using Python for Data Mining on a Local Machine
- 2.8 Automating Data Mining Solutions
- 2.9 Ethical Practice in Data Mining5
- Problems
- Notes
-
Part II Data Exploration and Dimension Reduction
- Chapter 3 Data Visualization
-
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 Components Analysis
- 4.9 Dimension Reduction Using Regression Models
- 4.10 Dimension Reduction Using Classification and Regression Trees
- Problems
- Notes
- Part III Performance Evaluation
-
Part IV Prediction and Classification Methods
- Chapter 6 Multiple Linear Regression
- Chapter 7 k-Nearest Neighbors (k-NN)
- Chapter 8 The Naive Bayes Classifier
-
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
- Chapter 11 Neural Nets
-
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 Combining Methods: Ensembles and Uplift Modeling
- Part V Mining Relationships Among Records
- Part VI Forecasting Time Series
-
PART VII Data Analytics
- Chapter 19 Social Network Analytics1
-
Chapter 20 Text Mining
- 20.1 Introduction1
- 20.2 The Tabular Representation of Text: Term-Document Matrix and “Bag-of-Words”
- 20.3 Bag-of-Words vs. Meaning Extraction at Document Level
- 20.4 Preprocessing the Text
- 20.5 Implementing Data Mining Methods
- 20.6 Example: Online Discussions on Autos and Electronics
- 20.7 Summary
- Problems
- Notes
- PART VIII Cases
- References
- Data Files Used in the Book
- Python Utilities Functions
- Index
- End User License Agreement
Product information
- Title: Data Mining for Business Analytics
- Author(s):
- Release date: November 2019
- Publisher(s): Wiley
- ISBN: 9781119549840
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