Book Image

Apache Spark for Data Science Cookbook

By : Padma Priya Chitturi
Book Image

Apache Spark for Data Science Cookbook

By: Padma Priya Chitturi

Overview of this book

Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.
Table of Contents (17 chapters)
Apache Spark for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Variable identification


In this recipe, we will see how to identify predictor (input) and target (output) variables for data at scale in Spark. Then the next step is to identify the category of the variables.

Getting ready

To step through this recipe, you will need Ubuntu 14.04 (Linux flavor) installed on the machine. Also, you need to have Apache Hadoop 2.6 and Apache Spark 1.6.0 installed.

How to do it…

  1. Let's take an example of student's data, using which we want to predict whether a student will play cricket or not. Here is what the sample data looks like:

  2. The preceding data resides in HDFS and load the data into Spark as follows:

          import org.apache.spark._ 
          import org.apache.spark.sql._ 
            object tricky_Stats { 
             def main(args:Array[String]): Unit = { 
                val conf = new SparkConf() 
                    .setMaster("spark://master:7077") 
                    .setAppName("Variable_Identification") 
                val sc = new SparkContext...