Аннотация
The study presents a smart system for predicting and assessing water quality in urban environments by using machine learning techniques. The system predicts the contamination levels by analyzing key water quality parameters such as Total Dissolved Solids (TDS), Electrical Conductivity (EC), Chloride and Sodium. On the surface, the system offers actionable insights into sustainable water management in urban areas and also provides an Early Warning System (EWS) that helps in real-time monitoring and predictions, and ultimately leads to quick and effective solutions to water quality issues. Comprehensive Exploratory Data Analysis (EDA) and robust machine learning models achieved a high predictive accuracy (R2> 0.90 for critical parameters). The approach suggested and built is a cost-effective data-driven urban water quality management system.