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 group of (3-4 students) 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 grades unless a group participation form is completed.
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 willbe using for this problem includes one ratio that have been computed from the financial statements of real-world firms. These 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 did not. 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.)
Students has to use the following classifiers. The selection of the classifiers depends 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 network
2. Support vector machine
3. Nearest neighbor algorithm
4. Decision tree
5. Naive Bayes
The following tables show the training sample and test data you should use for this major assignment. Copyright © 2015-2018 VIT, All Rights Reserved. 2
MITS5509 Assignment 3
Training Sample Data
Firm 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
WC Category 309.577 1 363.79 1 341.399 1 363.616 1 323.673 1 323.353 1 350.371 1 240.602
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