Water Quality Analysis Using Machine Learning Techniques

The most crucial resource that must be preserved by humans is water. Due to numerous environmental and societal causes, the quality of the water is deteriorating day by day. In order to take remedial action in the best way possible, it is essential to regularly evaluate the quality of the water we consume. The purpose of this study is to use supervised machine learning techniques to examine the quality of water. Many supervised machine learning methods are employed in these applications. The relationship between the features and their related labels, the size of the datasets, and the applications all affect how well the machine learning technique performs. This study considers the K Nearest Neighbor method, Support Vector Machine algorithm, Logistic Regression technique, and Random Forest Classifier algorithm when evaluating the water quality. The Random Forest method performs better in terms of training and testing accuracy than other algorithms among the several supervised machine learning approaches used in our work.