MITS5509 Assignment 3 MITS5509 Intelligent Systems for Analytics Assignment 3 MITS5509 Assignment 3 Copyright © XXXXXXXXXXVIT, All Rights Reserved. 2 NOTE: This Document is used in conjunction with...

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please submit the assignment by the deadline and use the links in the assignment that i gave in the picture


MITS5509 Assignment 3 MITS5509 Intelligent Systems for Analytics Assignment 3 MITS5509 Assignment 3 Copyright © 2015-2020 VIT, All Rights Reserved. 2 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 Copyright © 2015-2020 VIT, All Rights Reserved. 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 Copyright © 2015-2020 VIT, All Rights Reserved. 4 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 Copyright © 2015-2020 VIT, All Rights Reserved. 5 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 Copyright © 2015-2020 VIT, All Rights Reserved. 6 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 Copyright © 2015-2020 VIT, All Rights Reserved. 7 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 Copyright © 2015-2020 VIT, All Rights Reserved. 8 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
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Answered Same DayMay 24, 2021MITS5509

Answer To: MITS5509 Assignment 3 MITS5509 Intelligent Systems for Analytics Assignment 3 MITS5509 Assignment 3...

Neha answered on May 30 2021
150 Votes
58769/__MACOSX/._experiments.py
58769/__MACOSX/._results.csv
58769/experiment.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"with open('sample.txt', 'r') as f:\n",
" data = f.read()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data = data.split('\\n')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"firm = [int(i) for i in data[0].split()[1:]]\n",
"wc = list(map(lambda x: float(x), data[2].split()[1:]))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"for i in data[4].split():\n",
" try:\n",
" firm.append(int(i))\n",
" except:\n",
" wc.append(float(i))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"category = [ 1 for i in range(34)] + [0 for i in range(34)]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df_data = [\n",
" {\n",
" 'firm': f,\n",
" 'wc': w,\n",
" 'category': c\n",
" } for f, w, c in zip(firm, wc,category)\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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firmwccategory
01309.5771
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"text/plain": [
" firm wc category\n",
"0 1 309.577 1\n",
"1 2 363.790 1\n",
"2 3 341.399 1\n",
"3 4 363.616 1\n",
"4 5 323.673 1"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"df = pd.DataFrame().from_records(df_data)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"with open('sample_test.txt', 'r') as f:\n",
" data = f.read()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"data = data.split('\\n')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"firm = [int(i) for i in data[0].split()[1:]]\n",
"wc = list(map(lambda x: float(x), data[2].split()[1:]))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"for i in data[4].split():\n",
" try:\n",
" firm.append(int(i))\n",
" except:\n",
" wc.append(float(i))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"category = [ 1 for i in range(34)] + [0 for i in range(34)]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"df_data = [\n",
" {\n",
" 'firm': f,\n",
" 'wc': w,\n",
" 'category': c\n",
" } for f, w, c in zip(firm,...
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