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@article{161008,
        author = {kajal rajendra gavali and A.S.Gundale},
        title = {Water Quality Evaluation Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {2},
        pages = {1017-1024},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=161008},
        abstract = {One of the most significant and serious issues currently affecting mankind is the degradation of natural water resources, such as rivers and lakes. Polluted water has longterm repercussions on all facets of existence. In order to maximise your water quality, it is crucial to manage your water resources. The impacts of water contents can be efficiently managed if data are analysed and water quality can be forecasted.This study’s objective is to develop a model for predicting quality of water is based on measurements of water quality using machine learning. With some data obtained through machine learning, models made of algorithms can be created. The collected data will be preprocessed, divided into training and testing portions, and exposed to machine learning classification techniques for a better assessment of parametric findings. Some of the classification type techniques used in this work are Decision Tree, LinearSVC, Random Forest, GradientBoosting, SGD, and KNeighbour. Each model’s performance indicators are computed and are different from one another. Hyper tuning is a method for raising perfor- mance metrics for models of machine learning},
        keywords = {Machine learning, prediction, monitoring, data mining, classification, water quality index, water quality param- eters .},
        month = {},
        }