1. Add features and labels into the schema:

Schema.Builder schemaBuilder = new Schema.Builder();
 schemaBuilder.addColumnString("RowNumber")
 schemaBuilder.addColumnInteger("CustomerId")
 schemaBuilder.addColumnString("Surname")
 schemaBuilder.addColumnInteger("CreditScore");

2. Identify and add categorical features to the schema:

schemaBuilder.addColumnCategorical("Geography", Arrays.asList("France","Germany","Spain"))
 schemaBuilder.addColumnCategorical("Gender", Arrays.asList("Male","Female"));

3. Remove noise features from the dataset:

Schema schema = schemaBuilder.build();
 TransformProcess.Builder transformProcessBuilder = new TransformProcess.Builder(schema);
 transformProcessBuilder.removeColumns("RowNumber","CustomerId","Surname");

4. Transform categorical variables:

transformProcessBuilder.categoricalToInteger("Gender");

5. Apply one-hot encoding by calling categoricalToOneHot():

transformProcessBuilder.categoricalToInteger("Gender")
 transformProcessBuilder.categoricalToOneHot("Geography");
 

6. Remove the correlation dependency on the Geography feature by calling removeColumns():

transformProcessBuilder.removeColumns("Geography[France]")

Here, we selected France as the correlation variable.

7. Extract the data and apply the transformation using TransformProcessRecordReader:

TransformProcess transformProcess = transformProcessBuilder.build();
 TransformProcessRecordReader transformProcessRecordReader = new TransformProcessRecordReader(recordReader,transformProcess);

8. Create a dataset iterator to train/test:

DataSetIterator dataSetIterator = new RecordReaderDataSetIterator.Builder(transformProcessRecordReader,batchSize) .classification(labelIndex,numClasses)
 .build();

9. Normalize the dataset:

DataNormalization dataNormalization = new NormalizerStandardize();
 dataNormalization.fit(dataSetIterator);
 dataSetIterator.setPreProcessor(dataNormalization);

10. Split the main dataset iterator to train and test iterators:

DataSetIteratorSplitter dataSetIteratorSplitter = new DataSetIteratorSplitter(dataSetIterator,totalNoOfBatches,ratio);

11. Generate train/test iterators from DataSetIteratorSplitter:

DataSetIterator trainIterator = dataSetIteratorSplitter.getTrainIterator();
 DataSetIterator testIterator = dataSetIteratorSplitter.getTestIterator();