An Integrated Machine Learning Framework for Effective Classification of Water

Water is an essential need for all living things. The problem people are facing especially in urban areas is the diseases that have been caused by water which is mainly Dengue or malaria because there are many metals such as ammonium aluminum silver and other bacterial or viral things present in water. The first step toward a healthy lifestyle is to drink purified water. In this paper, the methodology that has been used here is to detect whether the water people are using for drinking purposes is safe enough to use or not in the data set and the specific methodology is binomial type as yes or no, 912 for positive states and 7084 negative states which means that 0.8864 for 0 and 0.114 for 1. The ratio for negative is far higher than the positive one. We tested the model in two ways first with simple feature extraction, smote Upsampling and with vote ensemble. In smote Upsampling accuracy with the random forest is 88.21 % (highest). The classification error of random forest is 11.7% and with the highest rate is 94.73% which is recall and specificity is 81.71% which is the lowest in the random forest whereas with vote ensemble the combinations of two algorithms have been used and the highest accuracy is from naive Bayes and KNN and the accuracy from them is 96.00% with the classification error of 6% only its precision rate is higher which is 98.89% and lowest specificity of 75.00%.