A Comprehensive Analysis of the Effectiveness of Machine Learning Algorithms for Predicting Water Quality

Abstract

This study provides a comprehensive analysis of the effectiveness of eight different machine learning algorithms for predicting water quality. The algorithms, which include Gaussian Naive Bayes, Extreme Gradient Boost Classifier, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression, Random Forest, and Decision Tree, were tested using the water potability dataset. This study's main goals were to identify the best accurate machine learning algorithm for predicting water quality and to present a thorough comparison of these methods. Algorithm's effectiveness. The study's findings demonstrated that one algorithm performed better than the others, with the lowest mean squared error and maximum accuracy. The results of this study may be used as a guide for future research in this area and offer a strong foundation for selecting the best machine learning algorithm for predicting water quality, Predicting water quality is often hampered by a lack of data, especially in developing or rural areas. Machine learning techniques may be used to predict water quality. This study highlights how crucial it is to use a suitable machine learning algorithm for predicting water quality since the precision and efficiency of these algorithms may have a big influence on the outcomes. Organizations that manage and monitor water quality, as well as academics and experts in the field of water quality forecasting, can benefit from the study's findings.