Contents of folder:
AWS Output - 5 workers and 10 workers - respective syslog files
Program - src files, Makefile and pom.xml
Report.pdf
JoshiSurana.pdf - Presentation file
JoshiSurana.csv - output file obtained from AWS Run

To run the Program:
1. Go to the Program

2. Change the value of variables to run spark locally, in standalone mode:
	spark.root : Path to the folder where spark is located
	local.input : Path to the folder that has the input files (dataset)
	local.output: Path to the folder where the output is to be stored
	local.code: Path to the folder where the records to be predicted is to be stored

3. To run the program on spark locally in standalone mode, run command:
	make alone jobname=<Classpath>
	Eg:
	make alone jobname=MRProject.App

4. Change the value of variables to deploy on AWS:
	aws.region 	: Region where your emr is to be deployed
	aws.bucket.name	: Name of the bucket in S3, where all files will be stored
	aws.subnet.id	: Subnet ID for your AWS account
	aws.input	: Name of the folder in the bucket where the input files will be stored
	aws.output	: Name of the folder in the bucket where the output will be saved
	aws.code: Name to the folder in the bucket where the records to be predicted is to be stored (unlabeled.csv.bz2)
	aws.log.dir	: Name of the folder in the bucket where the logs will be written
	aws.num.nodes	: Number of worker nodes to be deployed

5. To run the program on AWS, run command:
	make cloud jobname=<Classpath>
    Eg:
    make cloud jobname=MRProject.App

Files that are used in this (located in Program/src/main/scala):
1. App.scala