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

Cluster analysis

Cluster analysis has many uses. At its very basic level, a cluster is a group of people or objects that share similar characteristics. In the marketing and sales industries, clustering is important, since customers (or potential customers) can be grouped by characteristics such as average spending, frequency of purchase, and recency of purchases, and assigned a cluster that represents one single measure of the different levels contained in all of the attributes that make up that cluster. So, for our RFM example, cluster A might represent frequent purchasers who spend a lot of money, and spend often (every marketers dream). Cluster B could represent people who are just average consumers across all three of those RFM metrics, and there might even be a cluster Z which represents things that seem to be impossible, such as customers who buy Halloween costumes only on Tuesdays.

Data analysts can often get good results by using tools such as SQL, or by having great insights in customers...