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

Step 6 deployment

Deployment of a model is the process by which you put your models into a real-world production setting. This can depend on many factors, such as the environment in which it was developed, the algorithm that was chosen, assumptions concerning the data that was made when the model was developed, and of course, the level of the developer. Often a model is unable to scale up to the demands of a production environment and knowing your possible production environment in advance will dictate what problems or techniques are feasible.

Model scoring

Model scoring makes the model actionable. If you develop a model and you are unable to apply the results to new data, then you will be unable to do any prediction on an ongoing basis. New model scoring often involves outputing the development model outputs to a real-time scoring engine. That engine is often Java or C++. How that is performed varies vastly depending upon the modeling technique. Sometimes the scoring is performed separately...