Data Mining for Business Analytics: Concepts, Techniques, and Applications in R

  • 9h 15m
  • Galit Shmueli, Inbal Yahav, Kenneth C. Lichtendahl, Jr., Nitin R. Patel, Peter C. Bruce
  • John Wiley & Sons (US)
  • 2018

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration

Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities.

This is the fifth version of this successful text, and the first using R. 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:

  • Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government
  • 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 R 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.

In this Book

  • Foreword by Gareth James
  • Foreword by Ravi Bapna
  • Introduction
  • Overview of the Data Mining Process
  • Data Visualization
  • Dimension Reduction
  • Evaluating Predictive Performance
  • Multiple Linear Regression
  • k-Nearest Neighbors (k-NN)
  • The Naive Bayes Classifier
  • Classification and Regression Trees
  • Logistic Regression
  • Neural Nets
  • Discriminant Analysis
  • Combining Methods—Ensembles and Uplift Modeling
  • Association Rules and Collaborative Filtering
  • Cluster Analysis
  • Handling Time Series
  • Regression-Based Forecasting
  • Smoothing Methods
  • Social Network Analytics
  • Text Mining
  • Cases
  • References
  • Data Files Used in the Book


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