Analysis of Water Quality Using Machine Learning Techniques

The research aims to analyse water quality using Python to determine its potability. In this context, machine learning models: XGBoost and Random Forest, achieving accuracies of up to 80% and 78%, respectively. The process begins by feeding the mod- els a dataset containing various water quality metrics from multiple sources. These models are then trained to predict water potability, containing parameters like phys- ical, chemical and biological. which is crucial for ensuring safe drinking water. The XGBoost model, which is most noted for its great efficiency and efficacy in classifying issues, can handle large datasets that include a lot of varying variables and is there- fore suitable for complicated water quality datasets. On the other hand, the Random Forest model is a robust ensemble technique that can provide higher accuracy through decision tree aggregation, thus enhancing predictive performance. The models are trained to recognize patterns and correlations within the data, enabling them to predict the quality of water with a certain level of confidence. Both models are instrumental in predicting water quality, and their deployment can sig- nificantly aid in monitoring and managing water resources effectively. The Python language, with its extensive libraries and tools, facilitates the implementation and optimization of these models, ensuring a scalable and reliable water quality analysis framework. The success of such a project could lead to improved water management practices and better health outcomes for communities relying on these water sources. The success of these models in water quality analysis underscores the potential of machine learning in environmental monitoring and protection efforts.