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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

The forecast (fit) method


The forecast method contains many objects that you can display, such as the fitted value, original values, confidence intervals, and residuals. Use str(forecast(fit)) to see which objects are available.

We will use cbind to print out the original data point, fitted data point, and model fitting method.

cbind(forecast(fit)$method,forecast(fit)$x,forecast(fit)$fitted,forecast(fit)$residuals) 
Time Series: 
Start = 1999  
End = 2012  
Frequency = 1  
     forecast(fit)$method   forecast(fit)$x forecast(fit)$fitted forecast(fit)$residuals 
1999           ETS(A,N,N)  0.15412470117969    0.154120663632029    4.03754766081788e-06 
2000           ETS(A,N,N) 0.157413125646824    0.154124700770241     0.00328842487658335 
2001           ETS(A,N,N) 0.162942355969924    0.157412792166205     0.00552956380371911 
2002           ETS(A,N,N) 0.160986044554207    0.162941795214416      -0.001955750660209 
2003           ETS(A,N,N) 0.148533847659868    0.160986242887746     -0.0124523952278778...