Generally, Water pollution refers to the release of pollutants into the water that is detrimental to human health and the planet as whole. The aim is to investigate machine learning-based techniques for water quality forecasting by predicting results with the best accuracy. The analysis of the data set by supervised machine learning technique(SMLT) to capture information like variable identification, uni-variate analysis, bi-variate and multivariate analysis, missing value treatments and analysis data validation, data cleaning/preparation, and data visualization will be done on the entire given data set. Our analysis provides a comprehensive guide to sensitivity analysis of model parameters with regard to performance in the prediction of water quality pollution by accuracy calculation. To propose a machine learning-based method to accurately predict the Water Quality Index value by prediction results in the form of best accuracy from comparing supervised classification machine learning algorithms. . Additionally, to compare and discuss the performance of various machine learning algorithms from the given transport traffic department data set with evaluation classification report, identify the confusion matrix and categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with the best accuracy with precision, Recall and F1 Score.
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