(Information systems) Intelligent Systems for Analytics

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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 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

MITS5509 Assignment 3

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

Training set

Firm WC DC Category

1 3338.61 0.56555 1

2 3801.72 0.570567 1

3 2818.817 0.572058 1

4 1250.953 0.568258 1

5 2444.406 0.553276 1

6 937.917 0.561066 1

7 1600.792 0.534662 1

8 3128.813 0.564714 1

9 2486.803 0.564239 1

10 4220.996 0.58465 1

11 2585.41 0.572457 1

12 3512.085 0.550878 1

13 4170.333 0.569516 1

14 938.879 0.545574 1

15 1437.695 0.529922 1

16 627.985 0.51941 1

17 4430.049 0.567547 1

18 989.568 0.534501 1

19 3275.474 0.555306 1

20 1500.437 0.565886 1

21 848.989 0.548603 1

22 1386.494 0.56229 1

23 1554.257 0.562346 1

24 2228.338 0.565556 1

25 2568.391 0.54973 1

26 1720.128 0.568458 1

27 4106.106 0.57767 1

28 3500.883 0.557197 1

29 1217.846 0.525333 1

30 3544.406 0.568735 1

31 2082.873 0.557527 1

32 709.01 0.541673 1

33 2523.939 0.55366 1

34 2781.307 0.569188 1

35 309.577 0.557668 0

36 363.79 0.561751 0

37 341.399 0.550717 0

38 363.616 0.568882 0

MITS5509 Assignment 3

39 323.673 0.554499 0

40 323.353 0.558233 0

41 350.371 0.566447 0

42 240.602 0.5656 0

43 220.057 0.544182 0

44 287.837 0.522119 0

45 274.6 0.551492 0

46 278.494 0.550846 0

47 234.267 0.554828 0

48 284.923 0.533586 0

49 190.62 0.54899 0

50 327.76 0.538896 0

51 211.94 0.551569 0

52 373.571 0.549753 0

53 219.891 0.546936 0

54 193.489 0.56059 0

55 204.333 0.550777 0

56 205.657 0.550677 0

57 362.361 0.551315 0

58 285.562 0.578965 0

59 352.649 0.541763 0

60 400.44 0.557809 0

61 307.301 0.578949 0

62 240.314 0.548355 0

63 322.995 0.569978 0

64 408.197 0.574972 0

65 209.027 0.554203 0

66 198.979 0.559771 0

67 340.418 0.57343 0

68 320.154 0.560661 0

MITS5509 Assignment 3

Testing set

Firm WC DC

1 4204.066 0.578231

2 1411.733 0.560415

3 4197.206 0.565368

4 1121.866 0.540554

5 820.683 0.566067

6 1349.887 0.524683

7 3128.736 0.547596

8 2551.433 0.57368

9 809.115 0.552148

10 2866.623 0.559484

11 1193.951 0.515996

12 2014.445 0.564598

13 4400.268 0.578645

14 266.396 0.550131

15 243.554 0.559966

16 172.184 0.566274

17 362.479 0.553563

18 249.981 0.55274

19 327.877 0.565451

20 286.696 0.572919

21 182.762 0.56313

22 338.347 0.546618

23 302.57 0.551846

24 1781.718 0.564307

25 3711.358 0.570857

26 2030.189 0.564332

27 845.019 0.550468

28 1925.183 0.574114

29 1549.089 0.538726

30 1953.371 0.577015

31 932.5 0.564721

32 924.554 0.554162

33 2386.011 0.545268

34 2112.875 0.560262

35 3568.877 0.561775

36 4104.984 0.570978

37 367.325 0.533232

38 347.513 0.552354

39 330.226 0.549799

MITS5509 Assignment 3

40 178.106 0.574406

41 378.899 0.531441

42 257.212 0.565379

43 333.088 0.54545

44 182.324 0.569686

45 238.099 0.563344

46 329.643 0.558005

47 294.644 0.556574

48 1058.649 0.54729

49 956.021 0.546774

50 2089.824 0.572031

51 2198.033 0.558597

52 4538.527 0.560383

53 3137.934 0.544445

54 2002.459 0.58141

55 2136.376 0.562953

56 281.666 0.553904

57 308.086 0.553646

58 317.079 0.560538

59 245.139 0.567829

60 354.662 0.548939

61 292.256 0.557991

62 306.79 0.57065

63 222.396 0.547811

64 367.628 0.53711

65 342.115 0.562531

66 353.326 0.548094

67 336.39 0.539131

68 298.008 0.562856

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.

MITS5509 Assignment 3

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.

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

MITS5509 Assignment 3

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,

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