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MITS5509

Intelligent Systems for Analytics

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

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


 

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

 


 

 

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

 


 

 

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

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,

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.


 

 

 
 

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

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