Applied Predictive Analytics: Principles and Techniques for by Dean Abbott

By Dean Abbott

Learn the paintings and technology of predictive analytics — innovations that get results

Predictive analytics is what interprets titanic facts into significant, usable company 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 rules and most sensible practices that govern the paintings of predictive analytics. It truly explains the idea in the back of predictive analytics, teaches the equipment, ideas, and strategies for undertaking predictive analytics tasks, and provides counsel and tips 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 significant facts; crucial for pageant today
  • This advisor teaches not just the foundations of predictive analytics, but in addition the right way to follow them to accomplish actual, pragmatic solutions
  • Explains equipment, ideas, and methods for accomplishing predictive analytics tasks from begin to finish
  • Illustrates every one process with hands-on examples and contains as sequence of in-depth case reports that observe predictive analytics to universal company scenarios
  • A significant other site offers all of the info units used to generate the examples in addition to a unfastened trial model of software

Applied Predictive Analytics palms facts and company analysts and enterprise managers with the instruments they should interpret and capitalize on enormous data.

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

Example text

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). On the other hand, data mining has been caught up in the specter of privacy concerns, spam, malware, and unscrupulous marketers.

Data Preparation Fix problems in the data; create derived variables. Modeling Build predictive or descriptive models. Evaluation Assess models; report on the expected effects of models. Deployment Plan for use of models. Note the feedback loops in the figure. These indicate the most common ways the typical process is modified based on findings during the project. For example, if business objectives have been defined during Business Understanding, and then data is examined during Data Understanding, you may find that there is insufficient data quantity or data quality to build predictive models.

The CRISP-DM audience includes both managers and practitioners. For program managers, CRISP-DM describes the steps in the modeling process from a program perspective, revealing the steps analysts will be accomplishing as they build predictive models. Each of the steps can then have its own cost estimates and can be tracked by the manager to ensure the project deliverables and timetables are met. The last step in many of the sub-tasks in CRISP-DM is a report describing what decisions were made and why.

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