In recent years, there has been a significant focus on understanding and predicting water quality due to the various pollutants that can negatively impact it. The methods presented in this work aim to assist in controlling and reducing the risks of water pollution. This research examines the use of machine learning models and their applications in classifying water quality. The algorithms we used in this work were Logistic Regression, K-Nearest Neighbors Classifier, Support Vector Machine, Decision Tree, and Random Forest Classifier. We tested these algorithms using a dataset with 10 features and evaluated their performance using various accuracy measures. The results of the study indicate that the proposed models can effectively classify water quality, with the Random Forest Classifier achieving the highest accuracy.
