Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{173303,
        author = {P Nisha Priya and V Priya and Sindhumathi G},
        title = {WATER QUALITY PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2735-2741},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173303},
        abstract = {The preservation of water quality is vital to human well-being since it is an essential and necessary resource for maintaining human life. The water contamination presents serious health dangers, such as illnesses. In such as cholera, diarrhea, and other watery illnesses. Therefore, maintaining clean and safe water becomes essential to advancing public health. According to recent research, it is estimated that water-related ailments claim the lives of 3,575,000 people annually. As a result, precise water quality forecasting could significantly lower the prevalence of these illnesses. Algorithms for machine learning have become extremely good at forecasting water quality, allowing for accurate and timely monitoring of water resources. In this project, we used Machine Learning models including the Random Forest Classifier, Decision Tree, Support Vector Machine, and K-Nearest Neighbor Classifier. A dataset comprising parameters such as pH, hardness, total dissolved solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity is used to train the models. Using measures like precision and recall, the algorithms are evaluated to determine the Water Quality Index with accuracy. When compared to other models, the Random},
        keywords = {},
        month = {February},
        }