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@article{186062,
        author = {Mrs Prachi Fuskele and Mr Anurag Jain and Mr Rajneesh Pachouri},
        title = {A Machine Learning Approach for Water Quality Index Prediction and Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {197-202},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186062},
        abstract = {Water quality assessment plays a vital role in ensuring safe water for drinking, agriculture, and industrial usage. Traditional laboratory-based testing methods are often costly, time-consuming, and unsuitable for real-time monitoring. To address these challenges, this study proposes a machine learning-based approach for water quality classification using the Gradient Boosting Classifier. The system utilizes key physicochemical parameters such as pH, dissolved oxygen (DO), conductivity, biological oxygen demand (BOD), nitrate, fecal coliform, and total coliform to calculate the Water Quality Index (WQI). The dataset, sourced from a government-based repository, is pre-processed and analyzed to train the model effectively. The proposed model achieves a training accuracy of 98% and a testing accuracy of 94.1%, successfully classifying water into four categories: Excellent, Good, Poor, and Very Poor. The results demonstrate the robustness and efficiency of the model, highlighting its potential for real-time water quality monitoring, environmental management, and decision-making in water treatment applications.},
        keywords = {Water Quality Index (WQI), Gradient Boosting, Machine Learning, Classification, Environmental Monitoring, Prediction.},
        month = {October},
        }