Predicting Water Potability: Leveraging Machine Learning Techniques

In recent times, the spotlight has been on understanding and forecasting water quality, owing to the variety of pollutants that pose potential harm. This research aims to advance strategies for managing and minimizing water pollution risks by examining the application of machine learning models for water quality classification. The algorithms under scrutiny include Logistic Regression, K-Nearest Neighbors Classifier, Support Vector Machine, Decision Tree, and Random Forest Classifier. These models underwent testing on a wate_potability dataset with 10 features, and their performance was assessed using various accuracy metrics, precision, fl_score and recall, where parameter tuning is also applied lastly for increasing better performace. The findings indicate that the proposed models effectively categorize water quality, with the Random Forest Classifier emerging as the most accurate in making predictions with 80% before parameter tuning and with 81% after parameter tuning. The. pkl pickle library has been employed to save the random forest classifier, ensuring its accessibility and usefulness in the future. Through this process, the ultimate model has been stored, facilitating its application in forecasting outcomes on unseen datasets in upcoming scenarios.This research where the prediction is made on the dataset to categorize the water as potable or non-potable.