Machine Learning-based Water Potability Prediction: Model Evaluation, and Hyperparameter Optimization

Abstract

This research aims to predict water potability, which is of utmost importance for community safety. A comprehensive analysis of a machine learning model is presented here, considering various quality parameters, to achieve this prediction. The model incorporates decision tree, KNN, Random Forest, SGD, SVM, logistic regression, and other algorithms. All steps of the study, including pre-processing, exploratory data analysis, feature scaling, model construction, assessment, and hyperparameter tuning, are thoroughly covered. Performance indicators like accuracy, confusion matrix, and classification report are used to evaluate the effectiveness of each model. Hyperparameter tweaking is implemented in decision trees, random forest algorithms, and K-nearest neighbors through grid search to optimize accuracy. The suggested model demonstrates its capability to forecast water portability accurately. It provides stakeholders with a systematic approach to model construction, evaluation, and optimization, thus ensuring water safety.