Titlebook: Applying Predictive Analytics; Finding Value in Dat Richard V. McCarthy,Mary M. McCarthy,Leila Halawi Textbook 20191st edition Springer Nat

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,Know Your Data—Data Preparation,and prepare the data for predictive modeling. Most raw data is considered “dirty” or “noisy” because the data may have incomplete information, redundant information, outliers, or errors. Therefore, the data should be analyzed and “cleaned” prior to model development. Chap. . outlines the entire data

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What Do Descriptive Statistics Tell Us,e of understanding what if anything will need to be done to the data to prepare it for analysis. There are many statistical tests that can be utilized. This chapter focuses on a review of descriptive statistical analysis that is used to prepare and support predictive analytics. It reviews methods to

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Predictive Models Using Regression,ive model. Popular regression models include linear regression, logistic regression, principal component regression, and partial least squares. This chapter defines these techniques and when it is appropriate to use the various regression models. Regression assumptions for each type are discussed. E

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Predictive Models Using Decision Trees,ble to provide an easy method to determine which input variables have an important impact on a target variable. In this chapter, decision trees are defined and then demonstrated to show how they can be used as an important predictive modeling tool. Both classification and regression decision trees w

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