Potable Water Identification with Machine Learning: An Exploration of Water Quality Parameters

In this research, we aim to determine the water potability using three machine learning classification algorithms: decision tree, gradient boosting and bagging classifier. These algorithms were trained tested on a dataset of quality measurements. The outcomes experiment showed that algorithm achieved highest F1-score 0.78 among all algorithms. This indicates was most effective in correctly identifying both safe contaminated samples. results study demonstrate is promising approach for determining can be used as reliable method assessment.