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Machine Learning for Water Potability Prediction by ragnarlothbrok1218 is a document available to read on EtoBox.

What is Machine Learning for Water Potability Prediction about?

This research explores the use of machine learning techniques to predict water potability based on physicochemical properties, utilizing a dataset of 3,276 water samples. The study compares various models, including Logistic Regression, Random Forest, and XGBoost, with XGBoost achieving the highest performance metrics. The findings suggest that machine learning can effectively enhance water quality monitoring systems.

Author

ragnarlothbrok1218

Language

EN