Analysis of ROBECO data by neural networks

Our task was to find a model for classifying ROBECO clients into four classes according to their degree of satisfaction. Each client was represented by a vector of 30 variables, which could be split into two groups: variables related to the specific client and socio-geographical variables characterizing the area in which the client lived. The original set contained 21, 90, 288, and 146 vectors from group 1, 2, 3, and 4 respectively. Additionally, an independent validation set was provided with 3, 12, 48 and 32 vectors from corresponding groups.