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

Chapter 4. Introduction to Regression Algorithms

"It ain't ignorance causes so much trouble; it's folks knowing so much that ain't so."


Every day, the number of predictive analytics techniques seems to be increasing. There is a constant debate about whether we need better algorithms, or whether more or better data is needed in order to attack a predictive analytics problem.

Whatever the case, it is always good to keep abreast of all of the available algorithms at your disposal. It is equally important to hone your skills on the top three or four algorithms that can tackle 90% of the problems that you will face, and be able to understand the situations in which it makes sense to use them.

This chapter is the first of two chapters which will present four basic algorithms which cover a lot of typical business situations that you will encounter. In fact, many of the data science surveys which poll analytics practitioners will often include these techniques in the top techniques that all...