Water Quality Prediction using Classification techniques on XGBoost, KNeighbors, SVC, Random Forest, AdaBoost, and GaussianNB Classifier

A key environmental problem that is extensively researched for academic purposes is water contamination. To understand the origins, impacts, and potential solutions to water contamination, researchers look into a variety of issues. Environmental science, chemistry, biology, engineering, and policy are essential for study of water pollution. Classifiers as proposed help create early warning systems for probable pollution events by categorising water quality based on previous and current data. These systems aid in prompt reactions to occurrences of pollution prevention or mitigation. It is essential to tackling one of the most urgent issues affecting the wellbeing of ecosystems and human communities. By training models to predict the degree of pollution or the category of pollutants based on input characteristics, machine learning classifiers can classify water contamination. This study focuses on XGBoost, KNeighbour, SVC, Random Forest, Adaboost, and GaussianNB classifiers' various classification methodologies for predicting water quality. The XGBoost classifier, which may be used for future study, emerges as the best classifier out of all of these.