Survey on Water Quality Prediction

Physio-chemical characteristics, including heat, pH, EC, hardness, chlorine ions, alkalinity, phosphate, and sulfur of water samples from various sampling points are the main focus of the current study. The increase in pollution concentration indicates a growth in the amount of pollution in the air as a result of human activity, including the disposal of wastes into rivers, the discharge of residential sewage, wastewaters, and other pollutants. Water serves a variety of functions, thus it must be suitable before usage. Water sources must also be routinely examined to determine whether they are healthy or not. The goal is to research machine learning-based approaches to achieve the most accurate prediction of wastewater improved outcomes. Several pieces of information are captured all through the analysis of the dataset using the supervised machine learning approach (SMLT), such as the classification of variables and results from uni-variate, bi-variate, and multivariate analyses. The effectiveness of different machine learning techniques from the provided dataset is compared and debated using evaluation techniques.