Applied Predictive Analytics: Principles and Techniques for by Dean Abbott

By Dean Abbott

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

Predictive analytics is what interprets large info into significant, usable enterprise info. Written through a number one specialist within the box, this consultant examines the technology of the underlying algorithms in addition to the foundations and top practices that govern the paintings of predictive analytics. It basically explains the speculation at the back of predictive analytics, teaches the equipment, rules, and methods for undertaking predictive analytics initiatives, and provides information and methods which are crucial for profitable predictive modeling. Hands-on examples and case stories are included.

  • The skill to effectively observe predictive analytics permits companies to successfully interpret significant info; crucial for pageant today
  • This advisor teaches not just the rules of predictive analytics, but in addition the right way to observe them to accomplish actual, pragmatic solutions
  • Explains tools, ideas, and methods for carrying out predictive analytics initiatives from begin to finish
  • Illustrates each one procedure with hands-on examples and contains as sequence of in-depth case experiences that practice predictive analytics to universal enterprise scenarios
  • A spouse site presents all of the 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 titanic data.

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

Sample text

1. 1 CRISM-DM Sequence Stage Description Business Understanding Define the project. Data Understanding Examine the data; identify problems in the data. 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.

Put another way, data-driven algorithms induce models from the data. The induction process can include identification of variables to be included in the model, parameters that define the model, weights or coefficients in the model, or model complexity. Second, predictive analytics algorithms automate the process of finding the patterns from the data. Powerful induction algorithms not only discover coefficients or weights for the models, but also the very form of the models. Decision trees algorithms, for example, learn which of the candidate inputs best predict a target variable in addition to identifying which values of the variables to use in building predictions.

1. 1 CRISM-DM Sequence Stage Description Business Understanding Define the project. Data Understanding Examine the data; identify problems in the data. 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.

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