Classifying auto-MPG data set using neural network

Data mining has been used in several studies to uncover hidden information within dataset and to predict outcomes after application of algorithms. In this study, neural connection was adapted as data mining tool where supervised multilayer perceptron which is the architecture with back propagation learning algorithms was used on a new automobile data after application of clustering to remove outliers in the original auto mpg data set. The purpose was to classify the city fuel consumption of different car automobiles as efficient, averagely efficient and less efficient..