“F.Q.A.M” Feyn-QLattice Automation Modelling: Python Module of Machine Learning for Data Classification in Water Potability

Abstract

Water potability is a very important material for human life. The water potability indicator is can be used to detect the water that can be consumed as drinking water. We know that if in the water has many minerals, especially pH Value, Sulfate and Chloramines which has the strongest relationship with Water Potability. In data analytics, we can classification water potability using data mining methods. FEYN and Q-Lattice are one of the methods of part machine learning to solve classification data. We try to combining the dataset with machine learning methods to find the best mathematical model in the classification process. Q-Lattice methods bringing out the results increased until 68% accuracy level. For the simple analytics to classification data using combine between FEYN and Q-Lattice present best model.