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Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies from the fields of statistics, data mining, information systems, and economics, Modeling Online Auctions intro

Internet auctions.

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"This book supplies insightful, detailed guidance on fundamental data mining techniques. The book guides readers through the use of Python software for developing predictive models and techniques in order to describe and find patterns in data. The authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, with a focus on analytics rather than programming. The book includes discussions of Python subroutines, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Topics covered include time series, text mining, and dimension reduction. Each chapter concludes with exercises that allow readers to expand their comprehension of the presented material. Over a dozen cases that require use of the different data mining techniques are introduced, and a related Web site features over two dozen data sets, exercise solutions, PowerPoint slides, and case solutions"--

Business --- Python (Computer program language) --- Data processing

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"This book provides comprehensive information on data analytics. It begins with a preliminary introduction and overview of the data mining process in Part I. In Part II the authors move on to discuss data exploration and dimension reduction. Part III covers performance evaluation and Part IV goes into prediction and classification methods. Part V shows how mining relationships among records are determined before the book moves to forecasting time series in Part VI. The book closes with a more in-depth look at data analytics in Part VII and case studies in Part VIII"--

Quantitative methods in social research --- Artificial intelligence. Robotics. Simulation. Graphics --- Business --- Data mining --- Data processing --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Electronic data processing --- 681.3*H28 --- 681.3*H28 Database applications --- Database applications