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


In this chapter, we added three more algorithms to our arsenal, and these 3, along with regression form the core basic algorithms that can cover a lot of ground in terms of the typical problems a predictive analyst will face. We saw that a good knowledge of decision tree methodologies allows you to start developing models quickly, they are easily interpretable, and are the basis for more advanced techniques such as random forests. We then went on to clustering. Clustering allows you to begin to grasp the concepts of similarity and dissimilarity, and we introduced distance measures. We then ended with a basic introduction to support vector machines, which were demonstrated in the context of text mining.

In the next chapter, we will begin to look at some examples of creating models that predict how long a customer will stay with a company, or for predicting how long it will be until a patient develops a certain medical condition.