DETERMINING THE POTABILITY OF WATER USING MACHINE LEARNING BASED ON DATA PREPROCESSING

In this article, the problem of water composition analysis was studied based on a machine learning decision tree algorithm. Access to clean and safe drinking water is one of the basic human needs, but millions of people around the world still do not have access to potable water. Water quality testing is important in ensuring that it is safe for consumption. The study analyzed a data set based on several physicochemical parameters of water and built a machine learning model based on them. This model allows for a preliminary assessment of the potability of water. The main task of the study is to develop a method that allows for quick, reliable monitoring of water quality, which will be especially useful for regions with limited access to water testing.