This example demonstrates an end-to-end machine-learning workflow that processes transaction data, runs a forecast model, and uploads the results to Amazon S3. It shows the orchestration of distinct stages—ETL, forecasting, and cloud storage—within a single bash script. The workflow invokes Apache Spark via `spark-submit` for the `etl_transaction.py` ETL job, uses `Rscript` to execute `forecast.R` for prediction, and relies on the AWS CLI (`aws s3 cp`) for uploading output directories. The script also handles parameter validation and logs all activity to `xgb-workflow.log`.