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<p/><br></br><p><b> Book Synopsis </b></p></br></br><p><b>Learn the art and science of predictive analytics -- techniques that get results</b></p> <p>Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included.</p> <ul> <li>The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today</li> <li>This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions</li> <li>Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish</li> <li>Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios</li> <li>A companion website provides all the data sets used to generate the examples as well as a free trial version of software</li> </ul> <p><i>Applied Predictive Analytics</i> arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.</p><p/><br></br><p><b> From the Back Cover </b></p></br></br><p><b>APPLY THE RIGHT ANALYTIC TECHNIQUE</b></p> <p>Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst shows tech-savvy business managers and data analysts how to use predictive analytics to solve practical business problems. It teaches readers the methods, principles, and techniques for conducting predictive analytics projects, from start to finish. Internationally recognized data mining and predictive analytics expert Dean Abbott provides a practical and authoritative guide to best practices for successful predictive modeling, including expert tips and tricks to avoid common pitfalls.</p> <p>This book explains the theory behind the principles of predictive analytics in plain English; readers don't need an extensive background in math and statistics, which makes it ideal for most tech-savvy business and data analysts. Each of the chapters describes one or more specific techniques and how they relate to the overall process model for predictive analytics. The depth of the description of a technique will match the complexity of the approach, with the intent to describe the techniques in enough depth for a practitioner to understand the effect of the major parameters needed to effectively use the technique and interpret the results.</p> <p>Each of the techniques is illustrated by examples, either unique to the task or as part of predictive modeling competitions. The companion website will provide all of the data sets used to generate these examples, along with links to open source and commercial software, so that readers can recreate and explore the examples.</p> <p><b>With detailed descriptions of techniques that get results, <i>Applied Predictive Analytics</i> shows you how to: </b></p> <ul> <li><b>Choose the proper analytics technique for various scenarios</b></li> <li><b>Avoid common mistakes and identify the weaknesses of various techniques</b></li> <li><b>Mitigate outliers and fill in missing data when necessary</b></li> <li><b>Interpret predictive models often considered "black boxes," including model ensembles</b></li> <li><b>Learn how to assess model performance so the best model is selected</b></li> <li><b>Apply the appropriate sampling techniques for building and updating models</b></li> </ul><p/><br></br><p><b> About the Author </b></p></br></br><p><b>DEAN ABBOTT</b> is President of Abbott Analytics, Inc. (San Diego). He is an internationally recognized data mining and predictive analytics expert with over two decades experience in fraud detection, risk modeling, text mining, personality assessment, planned giving, toxicology, and other applications. He is also Chief Scientist of SmarterRemarketer, a company focusing on behaviorally- and data-driven marketing and web analytics.</p>

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