Data-Driven Models for Water Potability: A Machine Learning Approach

Water is an essential component in our daily lives. Water is used for different purposes, such as drinking, industrial, and irrigation. Many problems can be seen nowadays because of containment water. Water is being polluted, and as a result, the environment is experiencing many negative effects, such as global warming and different types of diseases spreading in the environment. Different ML models are used to predict water quality because it's very important for the environment and surroundings. Water Potability can be predicted by machine learning using parameters such as PH, Conductivity, Turbidity, Solids, Hardness, silver, nitrates, cadmium, lead, etc. As there is a huge increase in the contamination of water by industries from urban to rural areas, it is affecting the environment and our way of life. So good accessibility and better quality of water are needed. The point of this work is to get goodquality of water by using machine learning techniques. In recent years, numerous contaminants have put water quality at risk. Forecasting and model development for assessing water quality have become essential to control water pollution. The level of quality is measured using variables like water quality indexing (WQI) and water quality categorization.