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MITS5509 Intelligent Systems for Analytics

Objectives
This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to improve student collaborative skills in a team environment and to give students experience in constructing a range of documents as deliverables form different stages of the Intelligent Systems for Analytics

Assignment :- Assignment

In this assignment students will work in group of (3-4 students) to develop components of the Documents discussed in lectures. Student groups should be formed by Session four. Each group needs to complete the group participation form attached to the end of this document. Assignments will not be grades unless a group participation form is completed.

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 willbe using for this problem includes one ratio that have been computed from the financial statements of real-world firms. These 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 did not. 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.)

Students has to use the following classifiers. The selection of the classifiers depends upon the members of the group, e.g. if the group has four members then they will use the four classifiers from the following five classifiers.
1. Neural network
2. Support vector machine
3. Nearest neighbor algorithm
4. Decision tree
5. Naive Bayes

The following tables show the training sample and test data you should use for this major assignment.

From the above data set, the group has to prepare a report which include the followings:
1. Explain the process of building each classifier using the training set (add the screenshots).
2. Explain how did you evaluate the classifier.
3. Create the confusion matrix based on 70% (training) / 30% (testing).
4. Predict the category of the values (any random 40 values) in table used for Testing set.
5. Compare the results between the different classifiers and discuss which one is the best and why.

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 has to prepare a report which include the followings:
1. Write an introduction about the dataset used and add the reference (link).
2. Create at least four figures (different graphs) and add them to dashboard.
3. Add Screenshot of each of the steps.
4. Describe the figures in the dashboard.

The student can use any software to create the dashboard such as Microsoft excel, Tableau, etc. 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.

Attachment:- Intelligent Systems for Analytics.rar