“Comparative Analysis of Machine Learning Techniques for Water Quality assessment”

Abstract: Water quality assessment and prediction are crucial for environmental management and public health. This research delves into a comprehensive analysis of a water quality dataset, employing standard methodologies for Water Quality Index (WQI) calculation and leveraging advanced machine learning techniques for predictive modeling. The study meticulously details the data loading, preprocessing, WQI computation, and data labeling processes. A comparative analysis of four prominent classification algorithms.Random Forest (0.9718 accuracy), Support Vector Machine (SVM) (0.8250 accuracy), XGBoost (0.9750 accuracy), and Logistic Regression (0.8843 accuracy) is presented, highlighting their performance in classifying water quality into distinct categories. The findings reveal the exceptional predictive capability of the XGBoost model on this dataset, achieving perfect evaluation scores. Visualizations are included to illustrate the distribution of water quality and the comparative performance of the models. This research contributes to the application of machine learning in environmental monitoring and provides a robust framework for predicting water quality

Keywords: Water Quality Prediction, Machine Learning, Random Forest, XGBoost, Support Vector Machine, Logistic Regression.