Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst

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9781118727966: Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst
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Learn the art and science of predictive analytics —techniques that get results

Predictive analytics is what translates big data intomeaningful, usable business information. Written by a leadingexpert in the field, this guide examines the science of theunderlying algorithms as well as the principles and best practicesthat govern the art of predictive analytics. It clearly explainsthe theory behind predictive analytics, teaches the methods,principles, and techniques for conducting predictive analyticsprojects, and offers tips and tricks that are essential forsuccessful predictive modeling. Hands-on examples and case studiesare included.

  • The ability to successfully apply predictive analytics enablesbusinesses to effectively interpret big data; essential forcompetition today
  • This guide teaches not only the principles of predictiveanalytics, but also how to apply them to achieve real, pragmaticsolutions
  • Explains methods, principles, and techniques for conductingpredictive analytics projects from start to finish
  • Illustrates each technique with hands-on examples and includesas series of in-depth case studies that apply predictive analyticsto common business scenarios
  • A companion website provides all the data sets used to generatethe examples as well as a free trial version of software

Applied Predictive Analytics arms data and businessanalysts and business managers with the tools they need tointerpret and capitalize on big data.

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From the Back Cover:

APPLY THE RIGHT ANALYTIC TECHNIQUE

Applied Predictive Analytics: Principles and Techniques for theProfessional Data Analyst shows tech-savvy business managers anddata analysts how to use predictive analytics to solve practicalbusiness problems. It teaches readers the methods, principles, andtechniques for conducting predictive analytics projects, from startto finish. Internationally recognized data mining and predictiveanalytics expert Dean Abbott provides a practical and authoritativeguide to best practices for successful predictive modeling,including expert tips and tricks to avoid common pitfalls.

This book explains the theory behind the principles ofpredictive analytics in plain English; readers don’t need anextensive background in math and statistics, which makes it idealfor most tech-savvy business and data analysts. Each of thechapters describes one or more specific techniques and how theyrelate to the overall process model for predictive analytics. Thedepth of the description of a technique will match the complexityof the approach, with the intent to describe the techniques inenough depth for a practitioner to understand the effect of themajor parameters needed to effectively use the technique andinterpret the results.

Each of the techniques is illustrated by examples, either uniqueto the task or as part of predictive modeling competitions. Thecompanion website will provide all of the data sets used togenerate these examples, along with links to open source andcommercial software, so that readers can recreate and explore theexamples.

With detailed descriptions of techniques that get results,Applied Predictive Analytics shows you how to:

  • Choose the proper analytics technique for variousscenarios
  • Avoid common mistakes and identify the weaknesses of varioustechniques
  • Mitigate outliers and fill in missing data whennecessary
  • Interpret predictive models often considered “blackboxes,” including model ensembles
  • Learn how to assess model performance so the best model isselected
  • Apply the appropriate sampling techniques for building andupdating models

About the Author:

DEAN ABBOTT is President of Abbott Analytics, Inc. (SanDiego). He is an internationally recognized data mining andpredictive analytics expert with over two decades experience infraud detection, risk modeling, text mining, personalityassessment, planned giving, toxicology, and other applications. Heis also Chief Scientist of SmarterRemarketer, a company focusing onbehaviorally- and data-driven marketing and web analytics.

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