The workflow begins by loading the housing dataset and splitting it into training and testing subsets using the Split node. The H2O Distributed Random Forest node trains a regression model to estimate housing prices based on the dataset’s features.

Predictions are generated with the H2O Score node and previewed using Print N Rows. The trained model is saved through the H2O ML Model Save node for future prediction or deployment.

This workflow provides an efficient, scalable approach for housing price prediction using distributed H2O machine learning.