Reference no: EM132375937

Intelligent Systems for Analytics

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

Assignment :-

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 five classifiers.
1. Neural networks
2. Support vector machines
3. Nearest neighbor algorithms
4. Decision trees
5. Naive Bayes

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
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

	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
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 of each of the steps
5. A Calculation of the accuracy of the results for each classifier used

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 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.

Attachment:- Intelligent Systems for Analytics.rar