Water Quality Data Analyzer

Water pollution has become a major environmental concern due to rapid industrialization, agricultural runoff, and domestic sewage discharge. Continuous monitoring of water quality is essential to ensure safe drinking water and protect aquatic ecosystems. Traditional water quality assessment methods are laboratory-based, time-consuming, expensive, and require skilled personnel, making real-time monitoring difficult. This project presents a Water Quality Analyzer, a data-driven software system designed to evaluate and classify water quality using key physicochemical parameters such as pH, ammonia, Biological Oxygen Demand (BOD), dissolved oxygen (DO), nutrients, temperature, and CCME Water Quality Index values. The system integrates data preprocessing techniques including validation, cleaning, normalization, and handling of missing values to improve analysis accuracy. Machine learning models are trained using historical datasets to predict water quality categories efficiently. Based on the prediction results, water is classified into categories such as Excellent, Good, Moderate, and Poor. The system is implemented as an interactive web application using Streamlit, enabling users to input parameter values and receive real-time analysis results with graphical visualizations.