Smart Prediction of Water Quality System for Aquaculture using Machine Learning Algorithms

This article focuses on the importance of the continuous collection of water parameters data from the sensors and also the prediction of water quality using the latest different Machine learning algorithms like Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, K-nearest Neighbour, XGBoost, Gradient Boosting and Naive Bayes. These Machine learning models are implemented and tested to validate and achieve a satisfactory result of water quality prediction in terms of different attributes like pH, hardness, Solids, Chloramines, Sulfate, Conductivity, organic carbon, trihalomethanes, Turbidity and potability.