Applied Predictive Analytics: Principles and Techniques for by Dean Abbott

By Dean Abbott

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

Predictive analytics is what interprets large facts into significant, usable enterprise details. Written by means of a number one professional within the box, this consultant examines the technology of the underlying algorithms in addition to the rules and top practices that govern the artwork of predictive analytics. It sincerely explains the idea at the back of predictive analytics, teaches the tools, rules, and strategies for accomplishing predictive analytics tasks, and provides assistance and methods which are crucial for profitable predictive modeling. Hands-on examples and case experiences are included.

  • The skill to effectively follow predictive analytics allows companies to successfully interpret mammoth facts; crucial for festival today
  • This consultant teaches not just the rules of predictive analytics, but additionally tips to observe them to accomplish actual, pragmatic solutions
  • Explains tools, rules, and strategies for accomplishing predictive analytics tasks from begin to finish
  • Illustrates each one method with hands-on examples and contains as sequence of in-depth case stories that practice predictive analytics to universal company scenarios
  • A significant other site offers the entire info units used to generate the examples in addition to a unfastened trial model of software

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

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

Sample text

The advantage of parametric models is that if the distributions are known, extensive properties of the data are also known and therefore algorithms can be proven to have very specific properties related to errors, convergence, and certainty of learned coefficients. Because of the assumptions, however, the analyst often spends considerable time transforming the data so that these advantages can be realized. Non-parametric models are far more flexible because they do not have underlying assumptions about the distribution of the data, saving the analyst considerable time in preparing data.

Predictive models were built to do exactly this. The system was called Optimal Path-to-Go guidance. These two programs designed by two different companies seemingly could not be more different. One program knows characteristics of people, such as demographics and their level of engagement with a brand, and tries to predict a human decision. The second program knows locations of a bomb in space and tries to predict the best physical action for it to hit a target. But they share something in common: They both need to estimate values that are unknown but tremendously useful.

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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