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
