Abstract: Water is crucial for public health and environmental management. T his study looks into the prediction of water quality using machine learning algorithms based on different physical and chemical factors. We implemented several algorithms, including Gradient Boosting, Random Forest, and Support Vector Machines, to identify the most precise model. The Gradient Boosting model achieved the highest accuracy of 85%. This paper presents the methodology, results, and implications of using machine learning for water quality prediction, providing a scalable and efficient solution for real-time water quality assessment.

Keywords: Water quality, Machine Learning, Gradient Boosting, Prediction Model, Environment Monitoring

| DOI: 10.17148/IARJSET.2024.11724