DescriptionInterest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.What is new in the Third EditionThe current chapters have been completely rewritten.The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops.Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP).Includes SAS subroutines which can be easily converted to other languages.As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.Table of ContentsPreface to Third EditionPreface of Second EditionAcknowledgmentsAuthorIntroductionScience Dealing with Data: Statistics and Data ScienceTwo Basic Data Mining Methods for Variable AssessmentCHAID-Based Data Mining for Paired-Variable AssessmentThe Importance of Straight Data Simplicity and Desirability for Good Model-Building PracticeSymmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of DataPrincipal Component Analysis: A Statistical Data Mining Method for Many-Variable AssessmentMarket Share Estimation: Data Mining for an Exceptional CaseThe Correlation Coefficient: Its Values Range between Plus and Minus 1, or Do They?Logistic Regression: The Workhorse of Response ModelingPredicting Share of Wallet without Survey DataOrdinary Regression: The Workhorse of Profit ModelingVariable Selection Methods in Regression: Ignorable Problem, Notable SolutionCHAID for Interpreting a Logistic Regression ModelThe Importance of the Regression CoefficientThe Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor VariablesCHAID for Specifying a Model with Interaction VariablesMarket Segmentation Classification Modeling with Logistic RegressionMarket Segmentation Based on Time-Series Data Using Latent Class AnalysisMarket Segmentation: An Easy Way to Understand the SegmentsThe Statistical Regression Model: An Easy Way to Understand the ModelCHAID as a Method for Filling in Missing ValuesModel Building with Big Complete and Incomplete DataArt, Science, Numbers, and PoetryIdentifying Your Best Customers: Descriptive, Predictive, and Look-Alike ProfilingAssessment of Marketing ModelsDecile Analysis: Perspective and PerformanceNet T-C Lift Model: Assessing the Net Effects of Test and Control CampaignsBootstrapping in Marketing: A New Approach for Validating ModelsValidating the Logistic Regression Model: Try BootstrappingVisualization of Marketing Models: Data Mining to Uncover Innards of a ModelThe Predictive Contribution Coefficient: A Measure of Predictive ImportanceRegression Modeling Involves Art, Science, and Poetry, TooOpening the Dataset: A Twelve-Step Program for DataholicsGenetic and Statistic Regression Models: A ComparisonData Reuse: A Powerful Data Mining Effect of the GenIQ ModelA Data Mining Method for Moderating Outliers Instead of Discarding ThemOverfitting: Old Problem, New SolutionThe Importance of Straight Data: RevisitedThe GenIQ Model: Its Definition and an ApplicationFinding the Best Variables for Marketing ModelsInterpretation of Coefficient-Free ModelsText Mining: Primer, Illustration, and TXTDM SoftwareSome of My Favorite Statistical SubroutinesIndexAuthor BiographyBruce Ratner, The Significant Statistician TM, is President and Founder of DM STAT-1 Consulting, the ensample for Statistical Modeling, Analysis and Data Mining, and Machine-learning Data Mining in the DM Space. DM STAT-1 specializes in all standard statistical techniques, and methods using machine-learning/statistics algorithms, such as its patented GenIQ Model, to achieve its clients’ goals – across industries including Direct and Database Marketing, Banking, Insurance, Finance, Retail, Telecommunications, Healthcare, Pharmaceutical, Publication & Circulation, Mass & Direct Advertising, Catalog Marketing, e-Commerce, Web-mining, B2B, Human Capital Management, Risk Management, and Nonprofit Fundraising. Bruce holds a doctorate in mathematics and statistics, with a concentration in multivariate statistics and response model simulation. His research interests include developing hybrid-modeling techniques, which combine traditional statistics and machine learning methods. He holds a patent for a unique application in solving the two-group classification problem with genetic programming.
