Single hidden layer MLP to classify Wine tables
Goal- is to design a single hidden layer MLP to classify Wine tables using a set of features.
These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars (3 classes). The analysis determined the quantities of 13 constituents found in each of the three types of wines.
The attributes are :1) Alcohol; 2) Malic acid; 3) Ash; 4) Alcalinity; 5) Magnesium 6) Total phenols; 7) Flavanoids; 8) Nonflavanoid phenols; 9) Proanthocyanins; 10) Color intensity; 11)Hue; 12)OD280/OD315 of diluted wines; 13)Proline.
The data set and further information is available at. https://archive.ics.uci.edu/ml/datasets/Wine