Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data Review

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(More customer reviews)I predict that Dr. Ratner's Statistical Modeling and Analysis for Database Marketers: Effective Techniques for Mining Big Data will be on every database marketer's bookshelf. Dr Ratner has put together an assembly of chapters that provide an indispensable resource for the daily problems facing data analysts and model builders in the database/direct marketing community. In each of the seveenteen chatpers Dr. Ratner addresses a typical problem and discusses the common solution. He points out unknown working assumptions or weaknesses of the latter, and then offers better solutions, which require basic knowledge of EDA/data mining. Dr. Ratner's writing style is unique as he makes familar concepts new, and new concepts familar. Thus, the book is easy and enjoyable reading. I specially like chapter that blends statistics with the machine learning, such as the introduction of the GenIQ Model.

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Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses.Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data delivers a collection of successful database marketing methodologies for big data. This compendium solves common database marketing problems by applying new hybrid modeling techniques that combine traditional statistical and new machine learning methods. The book delivers a thorough analysis of these cutting-edge techniques, which include non-statistical machine learning and genetic intelligent hybrid models. By following the step-by-step procedures detailed in the text, database marketing professionals can learn how to apply the proper statistical techniques to any database marketing challenge. The practical case studies and examples provided involve real problems and real data, and are taken from a variety of industries, including banking, insurance, finance, retail, and telecommunications.

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