The bankruptcy-prediction problem can be viewed as a
problem of classification. The data set you will be using
for this problem includes five ratios that have been computed
from the financial statements of real-world firms.
These five ratios have been used in studies involving
bankruptcy prediction. The first sample includes data on
firms that went bankrupt and firms that didn’t. This will be
your training sample for the neural network. The second
sample of 10 firms also consists of some bankrupt firms
and some nonbankrupt firms. Your goal is to use neural
networks, support vector machines, and nearest neighbor
algorithms to build a model, using the first 20 data points,
and then test its performance on the other 10 data points.
(Try to analyze the new cases yourself manually before
you run the neural network and see how well you do.)
The following tables show the training sample and test
data you should use for this exercise.
Describe the results of the neural network, support vector
machines, and nearest neighbor model predictions,
including software, architecture, and training information.