A Prediction of Water Quality Analysis Using Machine Learning
S Suma, R. Moon, Mohammed Umer, K. Srujan Raju, Nuthanakanti Bhaskar, Rakshita Okali
Abstract
Data on water quality in Kenya is analyzed using a decision tree classification model. Using data mining techniques based on parameters related to water quality, the decision tree algorithm helps predict clean water. A predictive model was developed to identify water samples requiring further analysis in order to streamline the work of laboratory technologists. WEKA software was used to implement the model based on secondary data collected from the Kenya Water Institute. Water samples were classified into clean and contaminated categories using the decision tree algorithm. A crucial factor for evaluating water quality is its alkalinity and conductivity. Public health and safety depend on access to clean drinking water. Researchers used five decision tree classifiers to evaluate the model’s accuracy: J48, LMT, Random Forest, Hoeffding Tree, and Decision Stump