Applied Predictive Analytics

Applied Predictive Analytics
ePUB
  • eBook:
    Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst
  • Author:
    Dean Abbott
  • Edition:
    1 edition
  • Categories:
  • Data:
    April 4, 2014
  • ISBN:
    1118727967
  • ISBN-13:
    9781118727966
  • Language:
    English
  • Pages:
    464 pages
  • Format:
    ePUB

Book Description
Learn the art and science of predictive analytics ― techniques that get results
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.
  • The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today
  • This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions
  • Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish
  • Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios
  • A companion website provides all the data sets used to generate the examples as well as a free trial version of software
Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

Content

Chapter 1: Overview of Predictive Analytics
Chapter 2: Setting Up the Problem
Chapter 3: Data Understanding
Chapter 4: Data Preparation
Chapter 5: Itemsets and Association Rules
Chapter 6: Descriptive Modeling
Chapter 7: Interpreting Descriptive Models
Chapter 8: Predictive Modeling
Chapter 9: Assessing Predictive Models
Chapter 10: Model Ensembles
Chapter 11: Text Mining
Chapter 12: Model Deployment
Chapter 13: Case Studies

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