• Adelin Kiruba S
• Malavika R
• Pavithra R
• Mrs A.Porselvi

Water contamination a general rule alludes to the arrival of toxins into water that are unsafe to human well being and, thus, to the earth overall. It is regularly alluded to as one of the most hazardous dangers that mankind has at any point confronted. It hurts creatures, yields, and woodlands, in addition to other things. To stay away from this issue, AI strategies should be utilized to expect water quality from foreign substances in the transportation area. Subsequently, evaluating and gauging water quality has turned into a basic exploration field. The objective is to explore AI based answers for water quality gauging with the most noteworthy precision through expectation.The administered AI strategy (SMLT) is utilized to examine the dataset to catch a few snippets of data, like variable distinguishing proof, uni-variate examination, bi-variate and multi-variate investigation, missing worth medicines, and information approval, information cleaning/getting ready, and information representation. Our examination gives a definite manual for model boundary awareness investigation in association with execution in water quality contamination expectation by precision computation. To foster an AI based procedure for viably foreseeing the Water Quality Index esteem by forecast, looking at directed characterization AI calculations yields the best precision. To analyse and examine the exhibition of different AI calculations utilizing the given vehicle traffic office data set, recognize the disarray framework, and sort information by need; subsequently, the adequacy of the proposed AI calculation method will be assessed by contrasting and the best exactness with accuracy, Recall, and F1 Score

Water quality, Data set, Machine Learning, Accuracy

"Predicting The Quality Of Drinking Water Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.k629-k633, May-2022, Available :http://www.jetir.org/papers/JETIR2205B81.pdf

"Predicting The Quality Of Drinking Water Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppk629-k633, May-2022, Available at : http://www.jetir.org/papers/JETIR2205B81.pdf