Water Quality Prediction Using Hybrid Classification Model

Water quality requirements for commercial use vary depending on the specific industry applications. Among the cheapest sources of freshwater are natural water resources like groundwater and waterways. The water quality is predicted in several stages such as to pre-process the data, extract the features and classify the data. The models which are already proposed for the water quality prediction give low accuracy. To increase accuracy for predicting the water quality, a hybrid model is proposed. The hybrid classification algorithm is the combination of PCA, K-mean and voting classification. Python is executed to simulate the suggested model, and accuracy, precision, recall, are employed to analyze the outcomes.