The major goal of this project is to use machine learning techniques to measure water quality. A potability is a numerical phrase that is used to assess the quality of a body of water. The following water quality parameters were utilised to assess the overall water quality in terms of potability in this study. ph, Hardness, Solids, Chloromines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes, Turbidity were the parameters. To depict the water quality, these parameters are used as a feature vector. To estimate the water quality class, the paper used two types of classification algorithms: Decision Tree (DT) and K- Nearest Neighbor (KNN). Experiments were carried out utilising a real dataset containing information from various locations around Andhra Pradesh, as well as a synthetic dataset generated at random using parameters. Based on the results of two different types of classifiers, it was discovered that the KNN classifier outperforms other classifiers. According to the findings, machine learning approaches are capable of accurately predicting the potability. Potability, Water Quality Parameters, Data Mining, and Classification are all index terms.



Software And Hardware