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

Data input

Data by itself is just a pure stream of a numerical something. It is the analytics process that turns this something into knowledge, but before we start understanding it, we have to be able to obtain it. The number of ways in which we can now generate data has grown exponentially. Progressing from fixed length format, through HTML, and then to free form unstructured input, and then to today's schema on read technologies, there are so many different data formats today that there is a very good chance that you haven't, and will never work with a few of them. Reading data in and understanding the variables and what the data represents can also be incredibly frustrating. Integrating the data with other sources, both internal and external, can seem like a jigsaw puzzle at times. At times, the data will not seem to fit as nicely as you would hope.

However, with regard to raw input, most people will work with a couple of common formats in the course of their work, and it will be useful...