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

Using the ets function

While moving averages re extremely useful, they are only one component of what is known as an exponential smoothed state space model, which has many options to define the optimal smoothing factor, as well as enabling you to define the type of trend and seasonality via the parameters. To implement this model we will use the ets() function from the forecast package to model the Not-Covered Percent variable for the "ALL AGES" category.

The ets() function is flexible in that it can also incorporate trend, as well as seasonality for its forecasts.

We will just be illustrating a simple exponentially smoothed model (ANN). However, for completeness, you should know that you specify three letters when calling the ets() function, and you should be aware of what each letter represents. Otherwise, it will model based upon the default parameters.

Here is the description as specified by the package author, Hydman:

  • The first letter denotes the error type ("A", "M", or "Z")

  • The second...