Evaluating model performance for water quality classification using Databricks. This ROC Curve illustrates the predictive power of a Random Forest model trained to classify water safety based on chemical parameters. The AUC score provides key insights into the model’s ability to distinguish between safe and unsafe water.

Tools Used: Databricks, Python, Random Forest, Matplotlib.

Key Takeaway: A well-performing model can help in assessing water quality and ensuring safer drinking water.