Data mining and business analytics with R /

• Main Author: Ledolter, Johannes.
• Format: eBook
• Language: English
• Published: Hoboken, New Jersey : John Wiley & Sons, Inc., [2013]
• Subjects: Data mining.; R (Computer program language); Commercial statistics.; COMPUTERS > Database Management > Data Mining.; Electronic books.
• Online Access: View fulltext via EzAccess

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...

Table of Contents:

• Introduction
• Processing the information and getting to know your data
• Standard linear regression
• Local polynomial regression: a nonparametric regression approach
• Importance of parsimony in statistical modeling
• Penalty-based variable selection in regression models with many parameters (LASSO)
• Logistic regression
• Binary classification, probabilities, and evaluating classification performance
• Classification using a nearest neighbor analysis
• The Naïve Bayesian analysis: a model predicting a categorical response from mostly categorical predictor variables
• Multinomial logistic regression
• More on classification and a discussion on discriminant analysis
• Decision trees
• Further discussion on regression and classification trees, computer software, and other useful classification methods
• Clustering
• Market basket analysis: association rules and lift
• Dimension reduction: factor models and principal components
• Reducing the dimension in regressions with multicollinear inputs: principal components regression and partial least squares
• Text as data: text mining and sentiment analysis
• Network data
• Appendices: A. Exercises
• B. References.