Read e-book online Applied Predictive Analytics: Principles and Techniques for PDF

By Dean Abbott

ISBN-10: 1118727967

ISBN-13: 9781118727966

Learn the artwork and technological know-how of predictive analytics — strategies that get results

Predictive analytics is what interprets huge info into significant, usable enterprise details. Written by means of a number one specialist within the box, this advisor examines the technological know-how of the underlying algorithms in addition to the rules and most sensible practices that govern the artwork of predictive analytics. It in actual fact explains the idea in the back of predictive analytics, teaches the tools, ideas, and methods for carrying out predictive analytics tasks, and provides advice and tips which are crucial for profitable predictive modeling. Hands-on examples and case stories are included.

  • The skill to effectively practice predictive analytics allows companies to successfully interpret great facts; crucial for festival today
  • This consultant teaches not just the foundations of predictive analytics, but additionally the best way to observe them to accomplish genuine, pragmatic solutions
  • Explains equipment, ideas, and strategies for undertaking predictive analytics tasks from begin to finish
  • Illustrates each one approach with hands-on examples and contains as sequence of in-depth case experiences that observe predictive analytics to universal company scenarios
  • A significant other site presents all of the facts units used to generate the examples in addition to a loose trial model of software

Applied Predictive Analytics hands facts and company analysts and company managers with the instruments they should interpret and capitalize on sizeable data.

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

Sample text

Are these KPIs and reports actionable decisions in and of themselves? The answer is no, although they can be with small modifications. In the form of the report, you know what happened and can even identify why it happened in some cases. It isn't a great leap, however, to take reports and turn them into predictions. For example, a report that summarizes the response rates for each ZIP code can then use ZIP as a predictor of response rate. If you consider the reports related to a target variable such as response rate, the equivalent machine learning approach is building a decision stump, a single condition rule that predicts the outcome.

The following are typical questions that might be answered by business intelligence for fraud detection and customer analytics: Fraud Detection How many cases were investigated last month? What was the success rate in collecting debts? How much revenue was recovered through collections? What was the ROI for the various collection avenues: letters, calls, agents? What was the close rate of cases in the past month? Past quarter? Past year? For debts that were closed out, how many days did it take on average to close out debts?

Today I am calling on the Director of National Intelligence, the Defense Secretary and the Director of the NSA to explain whether and how the government is using data mining technology, and what authority it claims for doing so. In an interesting déjà vu, in 2013, information about NSA programs that sift through phone records was leaked to the media. As in 2006, concerns about privacy were again raised, but this time the mathematics behind the program, while typically described as data mining in the past, was now often described as predictive analytics.

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


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