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

Performing some automated forecasting using the ets function

So far, we have looked at ways in which we can explore any linear trends which may be inherent in our data. That provided a solid foundation for the next step, prediction. Now we will begin to look at how we can perform some actual forecasting.

Converting the dataframe to a time series object

As a preparation step, we will use the ts function to convert our dataframe to a time series object. It is important that the time series be equally spaced before converting to a ts object. At a minimum, you supply the time series variable, and start and end dates as arguments to the ts function.

After creating a new object, x, run a str() function to verify that all of the 14 time series from 1999 to 2012 have been created:

# only extract the 'ALL' timeseries
x <- ts(x2$Not.Covered.Pct[1:14], start = c(1999), end = c(2012), frequency = 1)

>  Time-Series [1:14] from 1999 to 2012: 0.154 0.157 0.163 0c.161 0.149 ...