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Apache Spark for Data Science Cookbook by Padma Priya Chitturi

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Feature engineering

In this recipe, we'll see how to apply feature engineering on the explored data.

Getting ready

To step through this recipe, you will need a running Spark cluster in any one of the modes, that is, local, standalone, YARN, and Mesos. For installing Spark on a standalone cluster, please refer to http://spark.apache.org/docs/latest/spark-standalone.html. Also, include the Spark MLlib package in the build.sbt file so that it downloads the related libraries and the API can be used. Install Hadoop (optionally), Scala, and Java.

How to do it…

  1. After data exploration, the next step is to perform feature engineering. Let's try to convert nominal variables into numeric types. Here is the code which does encoding for the nominal variables: ...

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