Machine learning approaches for water potability prediction: Addressing class imbalance with SMOTE
Elina Stepanova,
Vasiliy Orlov,
Vladislav Kukartsev
et al.
Abstract:Ensuring access to safe drinking water is a fundamental public health priority. Traditional methods for assessing water quality are laborintensive and require specialized equipment, which may not be feasible for continuous monitoring. This study explores the use of machine learning models to predict water potability based on various chemical properties. Specifically, we evaluate the performance of Logistic Regression and Random Forest models in the presence of class imbalance, a common issue in environmental d… Show more
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