Clustering for customer segmentation

Here, we will now build a program that will use the k-means clustering algorithm and will make five clusters from our transactional dataset.

Before we crunch the data to figure out the clusters, we have made a few important assumptions and deductions regarding the data to preprocess it:

• We are only going to do clustering for the data belonging to the United Kingdom. The reason being, most of the data belongs to the United Kingdom in this dataset.
• For any missing or null values, we will simply discard that row of data. This is to keep things simple, and also because we have a good amount of data available for analysis. Leaving a few rows should not have much impact.

Let's now start our program. We will first build our boilerplate code to build the SparkSession and Spark configuration:

SparkConf conf = ...
SparkSession session = ...

Next, let's load the data from the file into a dataset:

Dataset<Row> rawData = session.read().csv("data/retail...