A Comparative Study of Machine Learning Techniques for Water Potability Classification

Water is an essential natural resource for life on Earth, and it is the foundation of all living things. However, water pollution is a growing environmental concern caused by human activities, such as improper waste disposal and the discharge of untreated sewage. The consequences of this problem on human health and aquatic life highlight the need for effective supervision and administration of water reserves. This research paper aims to utilize a machine learning approach to predict water quality and identify the most influential features affecting water potability. These features were obtained from three methods, namely Univariate Selection, Recursive Feature Elimination, and Feature Importance, to identify the most influential features. The study compares the performance of various classification algorithms, including K-Nearest Neighbor, Decision Tree, Random Forest, AdaBoost, XGBoost, Linear Discriminant Analysis, Gaussian Naïve Bayes, Logistic Regression, MLPClassifier, and ExtraTree Classifier, using evaluation criteria such as accuracy, precision, recall, F1 score, and computational efficiency. After conducting all these processes, ExtraTree Classifier achieved the highest accuracy of 89 % among the compared machine learning models. Overall, the results of this research may contribute to better public health outcomes and improved management of water resources.