A Machine Learning Approach to Predict Quality of Drinkable Water.

Abstract

Ensuring access to clean and safe drinking water is vital for both human health and environmental balance. This study focus into the application of computer algorithms to predict the safety of drinking water. The primary aim is to enhance water management strategies and mitigate risks associated with contaminants. By utilizing advanced computer programs to analyze vast amounts of data, this research seeks to identify the factors that influence water quality and propose effective measures to maintain its safety.In our study, we implement various sophisticated computer programs such as Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), XGB Classifier, and Random Forest Classifier to predict water quality. These programs are instrumental in providing a comprehensive evaluation of the multiple facets that contribute to water safety. Polluted water may contain harmful microorganisms like bacteria, viruses, and parasites, which can lead to waterborne diseases. For instance, water contaminated with bacteria from fecal matter can cause severe health issues such as stomach infections, cholera, typhoid fever, and dysentery.The research aims to harness the power of these computer programs to predict the safety of drinking water by examining the diverse factors that impact water quality. This involves analyzing various physical, chemical, and biological parameters that determine water safety. By doing so, the study seeks to provide a more accurate and reliable assessment of water quality, thereby empowering communities to make informed decisions about the safety of their water sources.Ultimately, the goal of this research is to develop a dependable tool that communities can use to ensure the well- being of their members. By providing actionable insights into water quality, this tool can help reduce the incidence of waterborne diseases and promote better public health outcomes. The findings of this study have the potential to significantly improve water management practices and contribute to the overall sustainability and safety of water