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Abstract
The existence of hazardous chemicals and pollutants in water is one of many societal concerns. This water when consumed or used for some specific domestic or industrial purposes brings about apprehension regarding health-related consequences. Thisworkexplores the capability ofa machine learning-based systemin detecting the presence of heavy metals like Mercury (Hg) in water. It demonstratesnear-accurate concentration predictionin the range of 0.001mg/liter to 100mg/liter using a voting regressor. Implementation of the system is done by integrating a camera with Raspberry-pi, programmed to detect the presence of metals with its concentration and usability.Datasetwas prepared for training our model considering the different concentrations of reagents and water samples.Thissystem demonstrates a good capability to predict the concentration of heavy metals along with its usage recommendations
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