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Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. Spark Models – Rule-Based Learning

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")
...
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