Normalizing data with Spark

In this recipe, we normalizing (scaling) the data prior to importing the into an ML algorithm. There are a good number of ML algorithms such as Support Vector Machine (SVM) that work with scaled input vectors rather than with the raw values.

How to do it...

1. Go to the UCI Machine Learning Repository and download the http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data file.

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

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

package spark.ml.cookbook.chapter4

4. Import the necessary packages for the Spark session to gain access to the cluster and log4j.Logger to reduce the amount of output produced by Spark:

import org.apache.spark.sql.SparkSession
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.feature.MinMaxScaler

5. Define a method to parse wine data into a tuple:

def parseWine(str: String): (Int, Vector...