Water Quality Classification Using SVM And XGBoost Method

This project focuses on the classification of water quality using machine learning methods—Support Vector Machine (SVM) and XGBoost. The system uses various chemical indicators like pH, dissolved oxygen, turbidity, and conductivity to predict the water quality status. The dataset is preprocessed and important features are extracted before being passed into the models. After evaluating multiple models, XGBoost showed higher accuracy and robustness compared to SVM. The system aims to help environmental authorities monitor and improve water resources more effectively.