Author(s) Name:  Johannes Ledolter

About the Book:

   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.
   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.

Table of Contents

   1. Introduction
   2. Processing the Information and Getting to Know Your Data
   3. Standard Linear Regression
   4. Local Polynomial Regression: a Nonparametric Regression Approach
   5. Importance of Parsimony in Statistical Modeling
   6. Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO)
   7. Logistic Regression
   8. Binary Classification, Probabilities, and Evaluating Classification Performance
   9. Classification Using a Nearest Neighbor Analysis
   10. The Na¨ýve Bayesian Analysis: a Model for Predicting a Categorical Response from Mostly Categorical
   11. Multinomial Logistic Regression
   12. More on Classification and a Discussion on Discriminant Analysis
   13. Decision Trees
   14. Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods
   15. Clustering
   16. Market Basket Analysis: Association Rules and Lift
   17. Dimension Reduction: Factor Models and Principal Components
   18. Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares
   19. Text as Data: Text Mining and Sentiment Analysis
   20. Network Data

ISBN:  9781118447147

Publisher:  John Wiley & Sons

Year of Publication:  2013

Book Link:  Home Page Url