Reference no: EM132663980

MITS5509 Intelligent Systems for Analytics - Victorian Institute of Technology

Carefully read the following two questions and provide the appropriate answer.

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 two ratios that have been computed from the financial statements of real-world firms. These two ratios have 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

The attached tables show the training sample and test data you should use for this exercise.

From the above data set, the group has to prepare a report which include the following:
1. List the values (40 values) in the Table used for Training set
2. List the values (40 values) in the Table used for Testing set
3. The output results of each classifier for the testing set in Table form
4. Snapshot or Screenshot of each of the steps

Note: Students can use any open source free data mining software such as Statistica Data Miner, Weka, RapidMiner, KNIME and MATLAB etc.

Question 2. Create a DASHBOARD. For creating a dashboard, the group can use the above database or any other database. The group have to prepare a report which include the following:
1. List of the values in the Table used for creating the dashboard
2. A Snapshot or Screenshot of each of the steps

The above list of documents is not necessarily in any order. The chronological order we cover these topics in lectures is not meant to dictate the order in which you collate these into one coherent document for your assignment.

Your report must include a Title Page with the title of the Assignment and the name and ID numbers of all group members. A contents page showing page numbers and titles of all major sections of the report. All Figures included must have captions and Figure numbers and be referenced within the document.

Captions for figures placed below the figure, captions for tables placed above the table. Include a footer with the page number. Your report should use 1.5 spacing with a 12 point Times New Roman font.

Include references where appropriate. Citation of sources (if using any ) is mandatory and must be in the Harvard style.

Only one submission is to be made per group. The group should select a member to submit the assignment by the due date and time. All members of the group will receive the same grade unless special arrangement is made due to group conflicts. Any conflict should be resolved by the group, but failing that, please contact your lecture who will then resolve any issues which may involve specific assignment of work tasks, or removal of group members.

Attachment:- Intelligent Systems for Analytics.rar