Question 1. The bankruptcy-prediction problem can be viewed as a problem of classification. The data set
you will be using for this problem includes one ratio that have been computed from the financial
statements of real-world firms. This one ratio has been used in studies involving bankruptcy
prediction. The first sample (training set) includes 68 data value on firms that went bankrupt and
firms that didn’t. This will be your training sample. The second sample (testing set) of 68 firms also
consists of some bankrupt firms and some non-bankrupt firms. Your goal is to use different classifiers
to build a training model, by randomly selecting the 40 data points (20 points from category 1 and 20
points from category 0), and then test its performance on the testing model by randomly selecting
40 data points from the testing set. (Try to analyze the new cases yourself manually before you run
the neural network and see how well you do). Both Data Sets are provided below:
Students have to use the following classifiers. The selection of the classifiers depend upon the
members of the group. E.g. If the group has four members then they will use the four classifiers from
the following six classifiers.
1. Neural networks
2. Support vector machines
3. Nearest neighbor algorithms
4. Decision trees
5. Naive Bayes
6. Any other classifier