A Modification of the K-Nearest Neighbor Algorithm in the Assessment of Water Potability

Water potability is crucial for public health, as access to clean and safe drinking water is vital for the prevention of waterborne diseases and promotion of overall well-being. Contaminated water poses significant health hazards, including gastrointestinal infections, chronic diseases, and potential outbreaks of life-threatening ailments, such as cholera. Dependable evaluation techniques are essential for detecting hazardous water sources and facilitating prompt action to reduce the hazards. In recent years, machine learning techniques have been versatile in solving classification problems, as they can analyze and discover hidden patterns in datasets that may be too complex for the human mind. In this study, we applied several machine learning techniques to predict the potability of a water body and attempted to modify one of these methods. The objective is to evaluate the models by testing their accuracy and propose a new model that is more advanced in terms of accurate prediction than the previous models. A dataset composed of nine features of a water body was used to examine the efficiency of the models in assessing water quality. By presenting a detailed comparison of the methods and results, we unlock a path for further modifications in the future, with the aim of further enhancing the performance and accuracy of the model.