Evaluation of Machine Learning Algorithms on Finding Drinking Water Quality Based on Feature Selection Methodologies

Production of wastage plastics, heavy metals, pesticides from the variety of the domestic and non-domestic organizations like industries, offices, people, hospitals, agriculture etc., nearer to the surface and groundwater bodies destroyed the natural resources useful to public health. Polluted water leads to the ill health to the people and effected with several diseases. Timely monitoring of quality of the water is required for the public health. Quality of the drinking water is based on several factors like pH value, hardness, solids, chloramines. Here, analyzed the factors that decide the quality of the water using 11 AI based classifiers with feature selection techniques and evaluated performance metrics. Results shows that ensemble based random forest classifier with gradient boost feature selector produced the highest accuracy to decide the potability of the drinking water.