Water Quality Drinking Classification Using Machine Learning

Water is crucial for all known forms of life. It is required for the biochemical processes that occur within living organisms. Human beings need water to survive. It helps maintain bodily functions, regulate body temperature, and transport nutrients and oxygen to cells. Drinking an adequate amount of water is essential for good health. It helps in digestion, circulation, absorption of nutrients, and the removal of waste products from the body. In this paper, we conduct an analysis and exploration of the nine parameters that are essential for assessing the quality of drinking water. Additionally, we compare the performance accuracy of five different machine learning algorithms including (Random Forest RF, k-Nearest Neighbors KNN, Support Vector Machine SVM, XGBoost, and Decision Tree DT). Our results indicate the Support Vector Machine (SVM) achieved the highest accuracy with a score of 70%, outperforming the other algorithms. Additionally, we discuss and analyze the key factors influencing model accuracy. To engage more with the deployed model, we provide a front-end dashboard for collecting user data input, which is connected to a pre-trained model through a RESTful API.