Water Quality Classification Using SVM And XGBoost Method

Water Quality Classification Using SVM And XGBoost Method

Beldar Faijan Shaikh Akil1, Lohar Bhavesh Kantilal2, Pawar  Darshan Madhukar 3, Patil Rushikesh Sanjay4

Mr. Aakash . B. Kholi. (Asst. Prof)

Department of Computer Science & Engineering, D.N.Patel College Of Engineering, Shahada

Faijan Shaikh Akil Beldar, Computer Science & Engineering, D.N.Patel College Of Engineering, Shahada, Lohar Bhavesh Kantilal, Computer Science & Engineering, D.N.Patel College Of Engineering, Shahada, Pawar Darshan Madhukar, Computer Science & Engineering, D.N.Patel College Of Engineering, Shahada, Patil Rushikesh Sanjay, Computer Science & Engineering, D.N.Patel College Of Engineering, Shahada.

Abstract – 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.

Key Words:  Water Quality, Machine Learning, SVM, XGBoost, Classification