Download e-book for kindle: Applied Predictive Analytics: Principles and Techniques for by Dean Abbott

By Dean Abbott

ISBN-10: 1118727967

ISBN-13: 9781118727966

Learn the paintings and technological know-how of predictive analytics — innovations that get results

Predictive analytics is what interprets enormous information into significant, usable enterprise info. Written by means of a number one specialist within the box, this consultant examines the technology of the underlying algorithms in addition to the foundations and most sensible practices that govern the paintings of predictive analytics. It essentially explains the idea in the back of predictive analytics, teaches the equipment, rules, and methods for accomplishing predictive analytics tasks, and provides counsel and methods which are crucial for winning predictive modeling. Hands-on examples and case reviews are included.

  • The skill to effectively follow predictive analytics allows companies to successfully interpret enormous info; crucial for pageant today
  • This advisor teaches not just the rules of predictive analytics, but in addition easy methods to practice them to accomplish actual, pragmatic solutions
  • Explains tools, rules, and strategies for undertaking predictive analytics tasks from begin to finish
  • Illustrates each one procedure with hands-on examples and contains as sequence of in-depth case experiences that follow predictive analytics to universal enterprise scenarios
  • A better half web site offers the entire facts units used to generate the examples in addition to a loose trial model of software

Applied Predictive Analytics hands info and company analysts and enterprise managers with the instruments they should interpret and capitalize on significant data.

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

Example text

Predictive modelers frequently approach problems in this more unstructured, even casual manner. The data, in whatever form it is found, drives the models. This isn't a problem as long as the data continues to be collected in a manner consistent with the data as it was used in the models; consistency in the data will increase the likelihood that there will be consistency in the model's predictions, and therefore how well the model affects decisions. Predictive Analytics vs. Data Mining Predictive analytics has much in common with its immediate predecessor, data mining; the algorithms and approaches are generally the same.

Predictive analytics doesn't do anything that any analyst couldn't accomplish with pencil and paper or a spreadsheet if given enough time; the algorithms, while powerful, have no common sense. Consider a supervised learning data set with 50 inputs and a single binary target variable with values 0 and 1. One way to try to identify which of the inputs is most related to the target variable is to plot each variable, one at a time, in a histogram. 1. With 50 inputs, you need to look at 50 histograms.

Predictive Analytics vs. Data Mining Predictive analytics has much in common with its immediate predecessor, data mining; the algorithms and approaches are generally the same. Data mining has a history of applications in a wide variety of fields, including finance, engineering, manufacturing, biotechnology, customer relationship management, and marketing. I have treated the two fields as generally synonymous since “predictive analytics” became a popular term. This general overlap between the two fields is further emphasized by how software vendors brand their products, using both data mining and predictive analytics (some emphasizing one term more than the other).

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


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