Abstract
This article explores the application of machine learning techniques to analyze and evaluate water quality. In particular, the article focuses on the use of logistic regression to identify and analyze key parameters affecting the potability of water. The application of logistic regression in water quality analysis not only allows us to build models for prediction, but also to formulate recommendations for improving water treatment and monitoring processes. As a result, the resulting data and models can be used to develop strategies to provide safe drinking water, which is important for the health and well-being of the community. Thus, the article proposes a modern approach to analyzing water quality using logistic regression, which allows for a deeper understanding of the relationships between water parameters and its potability, as well as the development of effective methods for water quality management.
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