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

Regression techniques

Regression analysis (which is considered a supervised learning algorithm) dates back to the early 1800s when Gauss and Legendre utilized these techniques to measure the trajectories of the planets around the sun ( The regression algorithm's usage is still going strong within the predictive analytics community due to its large base of literature and its ability to adapt to a wide range of problems.

Linear regression is the basic regression technique to use when your target variable is continuous. Linear regression is built upon the concept of ordinary least squares, and the functional form of the model is:

The preceding formula illustrates that linear regression models are additive, that is, the results of the prediction are calculated by summing the cross-product values of all of the independent variables, and then adding an intercept (first term of the formula), and error term (last term of the formula). That calculation...