By Nick Pentreath
While you are a Scala, Java, or Python developer with an curiosity in desktop studying and information research and are desirous to the best way to practice universal desktop studying thoughts at scale utilizing the Spark framework, this is often the e-book for you. whereas it can be beneficial to have a uncomplicated figuring out of Spark, no past adventure is needed.
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Additional resources for Machine Learning with Spark
Count The result should look something like the following console output: 14/01/29 23:28:28 INFO SparkContext: Starting job: count at
This could help MovieStream to better understand the drivers of user behavior and might also allow a broader targeting approach where groups are targeted as opposed to (or more likely, in addition to) direct one-to-one targeting with personalization. These methods can also help when we don’t necessarily have labeled data available (as is the case with certain user and content profile data) but we still wish to perform more focused targeting than a complete one-size-fits-all approach. [ 40 ] Chapter 2 Predictive modeling and analytics A third area where machine learning can be applied is in predictive analytics.
6 and higher installed on your system (for example, most Linux and Mac OS X systems come with Python preinstalled). The example program is included in the sample code for this chapter, in the directory named python-spark-app, which also contains the CSV data file under the data subdirectory. 0)). 2f" % totalRevenue print "Most popular product: %s with %d purchases" % (mostPopular, mostPopular) If you compare the Scala and Python versions of our program, you will see that generally, the syntax looks very similar.