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Data Mining and Business Analytics with R

ISBN-10: 111844714X
ISBN-13: 9781118447147
Edition: 2013
List price: $184.95 Buy it from $116.43
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Description: Careful analysis of data is becoming more and more critical in business. Across large businesses such as Google, Netflix, and Amazon, large amounts of data are gathered on matters such as customer behavior and purchase history. To correctly  More...

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Book details

List price: $184.95
Copyright year: 2013
Publisher: John Wiley & Sons, Incorporated
Publication date: 5/17/2013
Binding: Hardcover
Pages: 368
Size: 6.50" wide x 9.50" long x 0.75" tall
Weight: 1.892

Careful analysis of data is becoming more and more critical in business. Across large businesses such as Google, Netflix, and Amazon, large amounts of data are gathered on matters such as customer behavior and purchase history. To correctly understand and gather valuable information from this data, it is essential that researchers are equipped with the appropriate, easily-accessible computational and analytical tools. Business Analytics and Data Mining with R showcases the powerful computing capabilities of R software for extracting and analyzing information taken from large sets of data to identify meaningful patterns. While most literature on the topic utilizes expensive and complex software, this book utilizes the freely-available R software for the analysis, exploration, and simplification of large high-dimensional datasets. 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 basic underlying concepts and practical computational skills, the book begins with coverage of standard linear regression and importance of parsimony in statistical modeling. Subsequent chapters feature topical coverage of: 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; multinomial logistic regression; more on classification, and a discussion of discriminant analysis; decision trees; clustering; market basket analysis; dimension reduction; principal components regression and partial least squares; text mining and sentiment analysis; and analysis of network data. An extensive related Web site features R sample programs and R code as well as additional data sets and exercises, providing readers with opportunities to work with the discussed material hands-on.

Preface
Acknowledgments
Introduction
Reference
Processing the Information and Getting to Know Your Data
Example 1: 2006 Birth Data
Example 2: Alumni Donations
Example 3: Orange Juice
References
Standard Linear Regression
Estimation in R
Example 1: Fuel Efficiency of Automobiles
Example 2: Toyota Used-Car Prices
The Effects of Model Overfitting on the Average Mean Square Error of the Regression Prediction
References
Local Polynomial Regression: a Nonparametric Regression Approach
Model Selection
Application to Density Estimation and the Smoothing of Histograms
Extension to the Multiple Regression Model
Examples and Software
References
Importance of Parsimony in Statistical Modeling
How Do We Guard Against False Discovery
References
Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO)
Example 1: Prostate Cancer
Example 2: Orange Juice
References
Logistic Regression
Building a Linear Model for Binary Response Data
Interpretation of the Regression Coefficients in a Logistic Regression Model
Statistical Inference
Classification of New Cases
Estimation in R
Example 1: Death Penalty Data
Example 2: Delayed Airplanes
Example 3: Loan Acceptance
Example 4: German Credit Data
References
Binary Classification, Probabilities, and Evaluating Classification Performance
Binary Classification
Using Probabilities to Make Decisions
Sensitivity and Specificity
Example: German Credit Data
Classification Using a Nearest Neighbor Analysis
The k-Nearest Neighbor Algorithm
Example 1: Forensic Glass
Example 2: German Credit Data
Reference
The Na�ve Bayesian Analysis: a Model for Predicting a Categorical Response from Mostly Categorical Predictor Variables
Example: Delayed Airplanes
Reference
Multinomial Logistic Regression
Computer Software
Example 1: Forensic Glass
Example 2: Forensic Glass Revisited
Specification of a Simple Triplet Matrix
References
More on Classification and a Discussion on Discriminant Analysis
Fisher's Linear Discriminant Function
Example 1: German Credit Data
Example 2: Fisher Iris Data
Example 3: Forensic Glass Data
Example 4: MBA Admission Data 159 Reference
Decision Trees
Example 1: Prostate Cancer
Example 2: Motorcycle Acceleration
Example 3: Fisher Iris Data Revisited
Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods
R Packages for Tree Construction
Chi-Square Automatic Interaction Detection (CELAJD)
Ensemble Methods: Bagging, Boosting, and Random Forests
Support Vector Machines (SVM)
Neural Networks
The R Package Rattle: A Useful Graphical User Interface for Data Mining
References
Clustering
k-Means Clustering
Another Way to Look at Clustering: Applying the Expectation-Maximization (EM) Algorithm to Mixtures of Normal Distributions
Hierarchical Clustering Procedures
References
Market Basket Analysis: Association Rules and Lift
Example 1: Online Radio
Example 2: Predicting Income
References
Dimension Reduction: Factor Models and Principal Components
Example 1: European Protein Consumption
Example 2: Monthly US Unemployment Rates
Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares
Three Examples
References
Text as Data: Text Mining and Sentiment Analysis
Inverse Multinomial Logistic Regression
Example 1: Restaurant Reviews
Example 2: Political Sentiment
Relationship Between the Gentzkow Shapiro Estimate of "Slant" and Partial Least Squares
References
Network Data
Example 1: Marriage and Power in Fifteenth Century Florence
Example 2: Connections in a Friendship Network
References
Exercises
References
Index

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