Data Mining and Business Analytics with R

Name: Data Mining and Business Analytics with R
Availability: InStock
ISBN: 9781118447147

ISBN-10: 111844714X

ISBN-13: 9781118447147

Edition: 2013

Authors: Johannes Ledolter

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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 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…

Book details

List price: $233.95
Copyright year: 2013
Publisher: John Wiley & Sons, Limited
Publication date: 5/17/2013
Binding: Hardcover
Pages: 368
Size: 6.20" wide x 9.30" long x 1.00" tall
Weight: 1.870
Language: English



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 Naï¿½ve 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