A Machine Learning-Based Water Potability Prediction Model by Using Synthetic Minority Oversampling Technique and Explainable AI

Algorithm 1

Proposed Model for the Water Portability Prediction.

	Input Data Water Portability Dataset from Kaggle
	OutputYes (If water is portable), No
	Data preprocessing
	Normalization using Z-score
	Oversampling using SMOTE
	Calculate the WQI using equation (4).
	Visualize and analyze the data
	Correlation analysis
	Data splitting
	Apply different Machine Learning Model for the water quality prediction
	Evaluate the performance of the different model
	Apply hyper parameter tuning to improve the performance of the model