Book Image

Practical Predictive Analytics

By : Ralph Winters
Book Image

Practical Predictive Analytics

By: Ralph Winters

Overview of this book

This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data.
Table of Contents (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback


Outliers are values in the data that are outside the range of what is to be expected. "What is to be expected?" is of course subjective. Some people will define an outlier as anything beyond three standard deviations of a normal distribution, or anything beyond 1.5 times the interquartile ranges. This, of course, may be good starting points, but there are many examples of real data that defies any statistical explanation. These rules of thumb are also highly dependent upon the form of the data. What might be considered an outlier for a normal distribution would not hold for a lognormal or Poisson distribution.

In addition to potential single variable outliers, outliers can also exist in multivariate form, and are more prevalent as data is examined more closely in a high-dimensional space.

Whenever they appear, outliers should be examined closely since they may be simple errors or provide valuable insight. Again, it is best to consult with other collaborators when you suspect deviation...