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

Analytic process methodologies

There are several analytic process methodologies which are currently practiced; however, I will be discussing only two longstanding methodologies that have been in existence for a while, CRISP-DM and SEMMA, which can help you organize your journey from problem definition to insight.


Cross-Industry Standard process for Data Mining (CRISP-DM) and Sample, Explore, Modify, Model, and Assess (SEMMA) are two standard data mining methodologies that have been utilized for many years and describe a general methodology for implementing analytical projects. There is a good deal of overlap between the methodologies, even though the names for each step are different. All of the listed steps are important to the success of a predictive analytics project. However, it is not necessary that these steps be followed exactly in order. The concepts outlined are more or less an outline of best practices. It helps to be aware of the importance of each of these steps...