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MITS5509 Assignment 3

MITS5509

Intelligent Systems for Analytics

Assignment 3

MITS5509 Assignment 3

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

MITS5509 Assignment 3

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

Firm WC Category

1 309.577 1

2 363.79 1

3 341.399 1

4 363.616 1

5 323.673 1

6 323.353 1

7 350.371 1

8 240.602 1

9 220.057 1

10 287.837 1

11 274.6 1

12 278.494 1

13 234.267 1

14 284.923 1

15 190.62 1

16 327.76 1

17 211.94 1

18 373.571 1

19 219.891 1

20 193.489 1

21 204.333 1

22 205.657 1

23 362.361 1

24 285.562 1

25 352.649 1

26 400.44 1

27 307.301 1

28 240.314 1

29 322.995 1

30 408.197 1

31 209.027 1

32 198.979 1

33 340.418 1

34 320.154 1

35 3338.61 0

36 3801.72 0

37 2818.817 0

38 1250.953 0

39 2444.406 0

MITS5509 Assignment 3

40 937.917 0

41 1600.792 0

42 3128.813 0

43 2486.803 0

44 4220.996 0

45 2585.41 0

46 3512.085 0

47 4170.333 0

48 938.879 0

49 1437.695 0

50 627.985 0

51 4430.049 0

52 989.568 0

53 3275.474 0

54 1500.437 0

55 848.989 0

56 1386.494 0

57 1554.257 0

58 2228.338 0

59 2568.391 0

60 1720.128 0

61 4106.106 0

62 3500.883 0

63 1217.846 0

64 3544.406 0

65 2082.873 0

66 709.01 0

67 2523.939 0

68 2781.307 0

MITS5509 Assignment 3

Firm WC

1 367.325

2 347.513

3 330.226

4 178.106

5 378.899

6 257.212

7 333.088

8 182.324

9 238.099

10 329.643

11 4204.066

12 1411.733

13 4197.206

14 1121.866

15 820.683

16 1349.887

17 3128.736

18 2551.433

19 809.115

20 2866.623

21 294.644

22 281.666

23 308.086

24 317.079

25 245.139

26 354.662

27 292.256

28 306.79

29 222.396

30 367.628

31 1193.951

32 2014.445

33 4400.268

34 1781.718

35 3711.358

36 2030.189

37 845.019

38 1925.183

39 1549.089

40 1953.371

41 342.115

MITS5509 Assignment 3

42 353.326

43 336.39

44 298.008

45 266.396

46 243.554

47 172.184

48 362.479

49 249.981

50 327.877

51 286.696

52 182.762

53 338.347

54 302.57

55 1058.649

56 956.021

57 2089.824

58 2198.033

59 4538.527

60 3137.934

61 2002.459

62 2136.376

63 932.5

64 924.554

65 2386.011

66 2112.875

67 3568.877

68 4104.984

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:

MITS5509 Assignment 3

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,

MITS5509 Assignment 3

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