Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.

Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:

• A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools

• Illustrations of how to use the outlined concepts in real-world situations

• Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials

• Numerous exercises to help readers with computing skills and deepen their understanding of the material

Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

Content:
Chapter 1 Introduction (pages 1–6):
Chapter 2 Processing the Information and Getting to Know Your Data (pages 7–39):
Chapter 3 Standard Linear Regression (pages 40–54):
Chapter 4 Local Polynomial Regression: A Nonparametric Regression Approach (pages 55–66):
Chapter 5 Importance of Parsimony in Statistical Modeling (pages 67–70):
Chapter 6 Penalty?Based Variable Selection in Regression Models with Many Parameters (LASSO) (pages 71–82):
Chapter 7 Logistic Regression (pages 83–107):
Chapter 8 Binary Classification, Probabilities, and Evaluating Classification Performance (pages 108–114):
Chapter 9 Classification Using a Nearest Neighbor Analysis (pages 115–125):
Chapter 10 The Naive Bayesian Analysis: A Model for Predicting a Categorical Response from Mostly Categorical Predictor Variables (pages 126–131):
Chapter 11 Multinomial Logistic Regression (pages 132–149):
Chapter 12 More on Classification and a Discussion on Discriminant Analysis (pages 150–160):
Chapter 13 Decision Trees (pages 161–184):
Chapter 14 Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods (pages 185–195):
Chapter 15 Clustering (pages 196–219):
Chapter 16 Market Basket Analysis: Association Rules and Lift (pages 220–234):
Chapter 17 Dimension Reduction: Factor Models and Principal Components (pages 235–246):
Chapter 18 Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares (pages 247–257):
Chapter 19 Text as Data: Text Mining and Sentiment Analysis (pages 258–271):
Chapter 20 Network Data (pages 272–292):