MITS5509 Assignment 3

MITS5509 Assignment 3

MITS5509 Assignment 3
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NOTE: This Document is used in conjunction with MITS5509
Objective(s)
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
INSTRUCTIONS
Assignment 3 :- Group Assignment (30 %) and submission at week 12
In this assignment students will work in small groups 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 graded
unless the student has signed a group participation form.
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 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
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The following tables show the training sample and test data you should use for this exercise.

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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:
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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.
What to Submit
All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set
up in the Unit of Study Moodle account. Assignments not submitted through these drop-boxes
will not be considered.
Submissions must be made by the due date and time (which will be in the session detailed above)
and determined by your Unit coordinator. Submissions made after the due date and time will be
penalized at the rate of 10% per day (including weekend days).
The turn-it-in similarity score will be used in determining the level if any of plagiarism. Turn-it-in
will check conference web-sites, Journal articles, the Web and your own class member
submissions for plagiarism. You can see your turn-it-in similarity score when you submit your
assignment to the appropriate drop-box. If this is a concern you will have a chance to change
your assignment and re-submit. However, re-submission is only allowed prior to the submission
due date and time. After the due date and time have elapsed you cannot make re-submissions
and you will have to live with the similarity score as there will be no chance for changing. Thus,
plan early and submit early to take advantage of this feature. You can make multiple submissions,
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but please remember we only see the last submission, and the date and time you submitted will
be taken from that submission
Please Note: All work is due by the due date and time. Late submissions will be penalized at the
rate of 10% per day including weekends.
MITS5509 Assignment 3
Group Participation Form
This form is to be completed by the group and returned to your tutor/lecturer as soon as possible.
We, the undersigned, agree to contribute individually and as a team to complete the Group Assignment for MITS5509 Intelligent
Systems for Analytics in the time specified. (It should be noted that failure to participate in a group may result in a fail for the
assignment component of the subject.)
Group membership:
Surname First name Student ID Date Signature
1. ______________________ ___________________ __________ ___/___/___ _______________________
2. ______________________ ___________________ __________ ___/___/___ _______________________
3. ______________________ ___________________ __________ ___/___/____ _______________________
4. ______________________ ___________________ __________ ___/___/____ _______________________
* All members in the team will receive the same mark for an assignment, unless there are extenuating circumstances whereby an
individual’s mark has to be altered by the tutor/lecturer, or if the peer group assessment warrants it.
** Team members should contact their tutor/lecturer immediately if problems arise within the team that may cause completion of an
assignment to be severely delayed, or the quality of the submission to be substantially lowered.
*** No additions or deletions of Team Members from this form allowed unless agreed to by your Instructor