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

Running the ets function iteratively over all of the categories

Now that we have run an ets model on one category, we can construct some code to automate model construction over all of the categories.

In the process, we will also save some of the accuracy measures so that we can see how our models performed:

  1. First, sort the dataframe by category, and then by year.

  2. Then, initialize a new dataframe (onestep.df) that we will use to store the accuracy results for each moving window prediction of test and training data.

  3. Then, process each of the groups, all of which have 14 time periods, as an iteration in a for loop.

  4. For each iteration, extract a test and training dataframe.

  5. Fit a simple exponential smoothed model for the training dataset.

  6. Apply a model fit to the test dataset.

  7. Apply the accuracy function in order to extract the validation statistics.

  8. Store each of them in the onestep.df dataframe that was initialized in the previous step:

df <- x2 %>% arrange(cat, Year.1)

# create results data...