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Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst Paperback – 23 May 2014

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

Review

This book provides an excellent background to predictive analytics (BCS, December 2014)

From the Back Cover

APPLY THE RIGHT ANALYTIC TECHNIQUE

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.

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.

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.

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

  • Choose the proper analytics technique for various scenarios
  • Avoid common mistakes and identify the weaknesses of various techniques
  • Mitigate outliers and fill in missing data when necessary
  • Interpret predictive models often considered black boxes, including model ensembles
  • Learn how to assess model performance so the best model is selected
  • Apply the appropriate sampling techniques for building and updating models

See all Description for Applied Predictive Analytics: Principles and Techniques for the Professional Data

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Product details

  • Paperback: 456 pages
  • Publisher: John Wiley & Sons (23 May 2014)
  • Language: English
  • ISBN-10: 1118727967
  • ISBN-13: 978-1118727966
  • Product Dimensions: 18.8 x 2.2 x 23.6 cm
  • Average Customer Review: 4.1 out of 5 stars 3 customer reviews
  • Amazon Bestsellers Rank: #10,47,099 in Books (See Top 100 in Books)
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Customer reviews

4.1 out of 5 stars
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See all 3 customer reviews

Top customer reviews

11 May 2018
Format: Paperback|Verified Purchase
17 December 2014
Format: Paperback|Verified Purchase
One person found this helpful
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25 May 2017
Format: Kindle Edition|Verified Purchase

Most helpful customer reviews on Amazon.com

Amazon.com: 4.0 out of 5 stars 23 reviews
Keith McCormick
5.0 out of 5 starsComprehensive Approach
29 May 2014 - Published on Amazon.com
Format: Kindle Edition|Verified Purchase
26 people found this helpful.
William
5.0 out of 5 starsA must for any student or predictive analytics professional. ...
2 September 2016 - Published on Amazon.com
Format: Kindle Edition|Verified Purchase
3 people found this helpful.
Earl D Yoder
5.0 out of 5 starsTerrific Read
11 October 2016 - Published on Amazon.com
Format: Kindle Edition|Verified Purchase
2 people found this helpful.
David K
4.0 out of 5 starsThe right text for useful tips on using Predictive Analytics.
2 April 2015 - Published on Amazon.com
Verified Purchase
3 people found this helpful.
John
5.0 out of 5 starsVery useful book!
22 July 2015 - Published on Amazon.com
Verified Purchase
3 people found this helpful.

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