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  • Book Overview & Buying Apache Spark Machine Learning Blueprints
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Apache Spark Machine Learning Blueprints

Apache Spark Machine Learning Blueprints

By : Alex Liu
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Apache Spark Machine Learning Blueprints

Apache Spark Machine Learning Blueprints

1 (1)
By: Alex Liu

Overview of this book

There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.
Table of Contents (13 chapters)
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12
Index

Deployment


As discussed earlier, MLlib supports model export to Predictive Model Markup Language (PMML). Therefore, we do export some developed models to PMML for this project. However, in practice, the users for this project are more interested in rule-based decision making to use some of our insights besides score-based decision making to prevent frauds.

As for this project, the client is interested in applying our results for the following:

  • Deciding what interventions to use for a combination of car products or services with a special customer segment

  • When the company needs to start some interventions depending on the customer churn score

Therefore, we need to produce a customer churn risk score for the client with which the client will start some intervention when the score is above a cutting value. At the same time, we need to use the results from our logistic regression to recommend interventions.

Note

For more on exporting results from MLlib to PMML, please go to https://spark.apache.org...

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