Prediction and Analysis of Water Quality Using Machine Learning Techniques

Water in daily life is an imperative and needful resource to human beings and all other living organisms. Water contamination by adding pollutants leads to noxious effects and is the new way of introducing disease to people. Thus, machine learning plays a vital role in Predicting whether the water is in excellent or imperfect condition. The machine learning algorithm is used to predict the water quality, and statistical accuracy parameters are calculated for each machine learning algorithm. SVM, KNN, Decision Tree classifiers, XG Boost, Ada boost and Feed Forward Neural Networks are used to predict water conditions. Compared to Conventional Machine learning algorithms, Feed Forward Neural network predicts the water quality with 98% accuracy. Precision values and more statistical parameters are measured and compared by evaluating the accuracy values.