Streaming logistic regression for an on-line classifier

In this recipe, we will be using the Pima Diabetes dataset we downloaded in the previous recipe and Spark's streaming logistic regression algorithm with SGD to predict whether a Pima with various features will test positive as a diabetic. It is an on-line classifier that learns and predicts based on the streamed data.

How to do it...

1. Start a new project in IntelliJ or in an IDE of your choice. Make sure that the necessary JAR files are included.

2. Set up the package location where the program will reside:

package spark.ml.cookbook.chapter13

3. Import the necessary packages:

import org.apache.log4j.{Level, Logger}
import org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import scala.collection...