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

Saving your work

Now that we have produced our final Spark data frame, we can write it to disk. Then, from the next chapter onwards, we will read it back into the workspace rather than have to recreate it from scratch. If you are proceeding directly to the next chapter, you can skip this step for now:

  • We will save in Parquet file format, which is a very efficient format for Spark and SQL. The %fs (file system) directive allows you to issue a directory (or file listing) command using the ls operating system command.
  • Once the file is saved, you can validate the integrity of the file by reading it back in and assigning it to the out_sd dataframe (again).
  • Use the head command to verify that the data was read back in:
        saveAsParquetFile(out_sd, "/tmp/temp.parquet") 
        %fs ls  
        out_sd <- parquetFile(sqlContext, "/tmp/temp.parquet")