Binary classification of water samples as safe or unsafe using 20 chemical parameters. Compares Logistic Regression, Decision Tree, Random Forest, SVC, and KNN with GridSearchCV tuning, achieving 96.37% accuracy. Includes a Flask web app with interactive UI for real-time predictions..
python data-science machine-learning random-forest svm scikit-learn jupyter-notebook kaggle classification water-quality logistic-regression knn decision-tree hyperparameter-tuning binary-classification imbalanced-data gridsearchcv environmental-ml
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Updated
May 20, 2026 - Jupyter Notebook