| Summary: | Water quality is crucial as it directly affects the ecosystem and human health. However, current water quality classification methods are inefficient because they do not compare prediction accuracy between machine learning methods. In this regard, the objective of this study is to classify water quality based on the proposed machine learning tools. To fulfill that, a preliminary study was conducted by collecting related information in the research domain through articles, electronic books, and online databases. The data collection for the prototype's dataset was obtained from an electronic book published by the Pakistan Council of Research in Water Resources 2021. Subsequently, the data pre-processing phase was conducted by using WEKA software which includes the crucial steps to transform the data into a cleaner format and make the model more accurate. The model for each technique was developed using Python in Jupyter Notebook. The results of the accuracy score for each model were also conducted in this phase. The findings of this research show that the Decision Tree model performs excellently with an accuracy of 97.37% compared to the Support Vector Machine and K-Nearest Neighbour models, with an accuracy of 95.69% and 74.72%, respectively. Consequently, implementing a multi-class classification system can help future researchers classify more accurately and reduce the misclassification of water quality. © 2023 IEEE.
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