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Water Potability Prediction App: A Cost-Free, Streamlit-Based Machine Learning System Using Public Environmental Data

Author(s) Pramath Parashar
Country United States
Abstract Access to safe and potable water is a pressing global challenge, particularly in regions lacking advanced environmental monitoring infrastructure. This paper presents a cost-free, open- source, and publicly deployable machine learning system that predicts water potability based on physicochemical attributes. The solution integrates SMOTE for class imbalance correction, standard scaling for feature normalization, and a calibrated XGBoost classifier for reliable probabilistic predictions. The entire pipeline is deployed as an interactive Streamlit web application, enabling real-time predictions with confidence scores. With support for reproducibility and transparency via a public GitHub repository, the system empowers data-driven decision- making for researchers, field personnel, and public health profes- sionals. Experimental results demonstrate balanced accuracy of approximately 65% and strong interpretability through feature importance analysis. This work bridges the gap between aca- demic modeling and field deployment, contributing a practical and scalable tool for environmental health applications.
Keywords Water Potability, Machine Learning, SMOTE, XGBoost, Streamlit, Environmental Monitoring, Confidence Cal- ibration, Public Health
Published In Volume 6, Issue 6, June 2025
Published On 2025-06-14
Cite This Water Potability Prediction App: A Cost-Free, Streamlit-Based Machine Learning System Using Public Environmental Data - Pramath Parashar - IJLRP Volume 6, Issue 6, June 2025. DOI 10.70528/IJLRP.v6.i6.1650
DOI https://doi.org/10.70528/IJLRP.v6.i6.1650
Short DOI https://doi.org/g9t8gm
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