Regardless of attributes used to modeling, the overall flow should be the same. Overall process should be: read in data, process into modeling format, split into training and testing, convert to LabeledPoint object, pass to SVM.

• Apache Spark
• NumPy(for Spark's SVM to run)
• YARN(optional)

• Make sure all the CSV files are in the same directory on HDFS
• Changes the code that reads from CSV files to point to the correct directory on HDFS
• Every code chunk has a different level of data granularity. Edit the code depending on what you feel is the correct granularity/set