Assessment 4 : applied project

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Assessment 4 : applied project


Assessment Brief: BIS3001 Data Analytics for Business Trimester-1 2022 Assessment Overview Assessment Task Type Weighting Due Length ULO Assessment 1: Report Write a report to discuss the techniques and tools used to analyse the growing volume, velocity and variety of data. Individual 30% Week 6 2500 words ULO-1 ULO-2 Assessment 2: Quizzes Quizzes assess students’ ability to understand theoretical materials. The quiz will be either multiple choice questions or short questions which are relevant to the lecture materials. Individual Invigilated 30% Week 3, 4, 6, 8, 10 15 mins (Equiv. 1250 words) ULO-1 ULO-2 ULO-3 ULO-4 Assessment 3: Laboratory Practicum weekly lab activities and exercises assess students’ ability to understand theoretical materials. Individual 10% Weekly equiv. 2300 words ULO-1 ULO-2 ULO-3 ULO-4 Assessment 4: Applied Project Analyse set of data related to a selected organisation to extract useful information and use different techniques for virtualisation Group 30% Week 12 2500 words ULO-1 ULO-2 ULO-3 ULO-4 equiv. – equivalent word count based on the Assessment Load Equivalence Guide. It means this assessment is equivalent to the normally expected time requirement for a written submission containing the specified number of words. Assessment 1: Report Due date: Week 6 Group/individual: individual Word count/Time provided: 2000 words Weighting: 30% Unit Learning Outcomes: ULO-1, ULO-2 Assessment 1 Detail Task 1: Descriptive Analysis Report In this task, you are required to read the following journal articles via APIC library (https://ecalibrary.on.worldcat.org/discovery ) and write a discussion report based on the points below: Wang, Y. (2021). Artificial intelligence in educational leadership: a symbiotic role of human-artificial intelligence decision-making. Journal of Educational Administration, 59(3), 256–270. https://doi.org/10.1108/JEA-10-2020-0216 Silver, M. S. (1988). Descriptive analysis for computer-based decision support. Operations Research, 36(6), 904–916. https://doi.org/10.1287/opre.36.6.904 Seydel, J. (2006). Data envelopment analysis for decision support. Industrial Management & Data Systems, 106(1), 81–95. https://doi.org/10.1108/02635570610641004 • Investigating and discuss the theoretic foundations of decision support systems (DSS). • Identify and discuss the major issue of descriptive analysis for DSS. • Identify and discuss the major process of descriptive analysis development with a means for describing and differentiating DSS. • Identify and discuss some of prescriptive decision support tools. • Support your response with proper examples and references from at least three journal papers. • The report follows a referencing style that complies with the APA style and the in-text citations. The recommended word length for this task is 1700 to 2000 words. Assessment 1 Marking Criteria and Rubric The assessment will be marked out of 30 and weighted 100% of the total unit mark. The marking criteria and rubric are shown on the following page. Assessment 1 Marking Criteria and Rubric Marking Criteria Not Satisfactory (0-49% of the criterion mark) Satisfactory (50-64% of the criterion mark) Good (65-74% of the criterion mark) Very Good (75-84% of the criterion mark) Excellent (85-100% of the criterion mark) Task 1: Depth of analysis of descriptive analysis for DSS systems [30%] Demonstrates incomplete/insufficient research in decision support systems with incomplete responses supported by no or irrelevant examples, incorrect terminologies and poor/inadequate references. Demonstrate an ability to analyse, reason and discuss most concepts to draw justified conclusions that are generally logically supported by examples and best practice. The answers are partially structured into loosely linked introductory sentences to create a comprehensive, descriptive analysis using correct big data and decision support systems terminologies. Demonstrate an ability to analyse, reason and discuss the concepts to draw justified conclusions that are generally logically supported by examples and best practice. The answers are usually logically structured to create a comprehensive, mainly descriptive piece of Analysis. Some use of correct big data and decision support systems terminologies. Demonstrate an ability to analyse, reason and discuss the concepts to draw justified conclusions logically supported by examples and best practice. The answers are logically structured to create a cohesive and coherent piece of Analysis that consistently use correct big data and decision support systems terminologies. Demonstrate an ability to analyse, reason and discuss the concepts to draw justified conclusions logically supported by examples and best practice. Answers succinctly integrate and link information into a cohesive and coherent piece of Analysis and consistently use correct big data and decision support systems terminologies and sophisticated language. Task 1: Context setting in the report [30%] The report does not have answered all task 1 questions. All need to be better structured and developed with further details. The report has answered all task 1 questions. Some of them should be better structured...and/or developed with further details. The report has answered all task 1 questions. All elements in the questions are structured and developed with enough details but not well connected. The report has answered all task 1 questions. All elements in the questions are structured and developed with well selected details. The report has answered all task 1 questions. All elements in the questions are structured and developed with well selected details. All components are connected to form a narrative with the specific purpose of the report. The organisation of report and Quality of writing [20%] The report is neither organised logically nor formatted as a report. The tone and accuracy of the language used in writing are not understandable at times. The report is organised logically but not formatted as a report. The writing is understandable. The tone is not appropriate or consistent. The report is organised logically and formatted as a report, though not specifically for its purpose. The writing is most articulate. The tone could be more appropriate and/or consistent. The report is organised logically and formatted as a report, though not specifically for its purpose. The writing is most articulate. The tone is appropriate and mostly consistent. The report is organised logically and formatted as a report, specifically for its purpose. The writing is articulate. The tone is appropriate and consistent. Appropriate citation of sources using the APA style. [20%] The report does not include any citations and/or a reference list. The report follows a referencing style that does not comply with the APA style or includes either the in-text citations or the reference list. The report follows a referencing style that mostly complies with the APA style. However, the in-text citations are not made purposefully. The report follows a referencing style that complies with the APA style, and the in-text citations are mostly purposeful. The report follows a referencing style that complies with the APA style, and the in-text citations are made purposefully. Assessment 2: Quizzes Due date: Week 3, 4, 6, 8, 10 Group/individual: individual Word count/Time provided: 15 mins Weighting: 30% Unit Learning Outcomes: ULO-1, ULO-2, ULO-3, ULO-4 Assessment 2 Detail Quizzes assess students’ ability to understand theoretical materials. The quiz will be either multiple choice questions or short questions which are relevant to the lecture materials. There will be five (5) online quizzes on Week 3, 4, 6, 8, and 10. The online quizzes must be attempted by the students individually using the subject site. Each quiz is weighted 6%. Thus in total, the online quizzes are worth 30% of the subject grade. There will be no practice attempt. When you start the quiz, you will need to complete it. Assessments 2 Marking Criteria and Rubric The assessment will be marked out of 100 and weighted 30% of the total unit mark. Assessment 3: Laboratory Practicum Due date: Weekly Group/individual: individual Word count/Time provided: equiv. 2300 words Weighting: 10% Unit Learning Outcomes: ULO-1, ULO-2, ULO-3, ULO-4 Assessment 4 Detail Weekly lab activities and exercises assess students’ ability to understand theoretical materials. The weekly lab activities must be attempted by the students individually and submit it using the subject site. Each weekly lab activities are weighted 1%. Thus in total, the lab activities are worth 10% of the subject grade. Assessments 3 Marking Criteria and Rubric The assessment will be marked out of 100 and weighted 10% of the total unit mark. Assessment 4: Applied Project Due date: Week 12 Group/individual: Group Wordcount/Time provided: 2500 words Weighting: 30% Unit Learning Outcomes: ULO-1, ULO-2, ULO-3, ULO-4 Assessment 4 Detail For this assessment, you are required to use Weka software and a text editor such as WordPad, Notepad++ for windows system or Textedit for Mac. You can download Weka from https://www.cs.waikato.ac.nz/ml/weka/downloading.html). Task 1: Create and explore Weka data file of type ARFF Download a text file called data.csv from the subject site (Canvas) and open it using a text editor such as WordPad, Notepad++ etc., for windows system or Textedit for Mac. You need to explore and convert this file into an ARFF file for Weka. The text file you will be using contains a sample of real- life data related to customers. The data.csv file is not entirely formatted as a Weka file (ARFF). This file has some formatting errors, and your task is to find these errors and fix them to have a valid
Answered Same DayMay 04, 2022

Answer To: Assessment 4 : applied project

Mohd answered on May 05 2022
93 Votes
Analysis Summary:
Take a screenshot of your corrected ARFF file.
Which attribute in the dataset do you think is useless and did not provide useful information for prediction?
A. ID attribute in the dataset is useless and did not provide useful information for prediction.
How many attributes the dataset has?
A. 12
How many instances the dataset has?
A. 500
What is the class attribute in the dat
a.arff dataset?
Pep attribute( by default) is the class attribute in the data.arff dataset.
What proportion of customers who has a mortgage and living in Inner City?
A. 15.8 percent of total customers
What proportion of customers who has a mortgage and their income is between $8000 and $29000?
A. 40.8 percent of total customers
How many customers are married and has no mortgage?
A. 43.2 percent of total customers
How many customers have not owned a car and has a mortgage?
A. 18.2 percent of total customers.
Comparative Analysis of classifiers:
    Classifiers
    Test Option
    Accuracy
    Precision
    Recall
    Naive Bayes (weka.classifiers.NaiveBayes, default parameters)
    10-fold cross validation
    0.638
    0.636
    0.638
    Decision tree (weka.classifiers.j48.J48, default parameters)
    
    0.884
    0.884
    0.884
    HoeffdingTree (weka.classifiers.trees.HoeffdingTree)
    
    0.638
    0.636
    0.638
    SMO(weka.classifiers.functions.SMO)
    
    0.588
    0.584
    0.588
Classifier’s summary:
As we can see from above table, Decision tree J48 classifier has highest accuracy (correctly classified instances) and SMO has lowest accuracy among all four classifiers.
Decision tree J48 is the best performing classifiers for the given data.
The incorrectly classified instances are two types false positive and false negative. Decision tree classifier has lowest number false positive and false negative 33 and 25 respectively. There is a cost associated with each false positive and false negative.
We must set priority to our cost related to incorrectly classified instances. SMO classifier has highest number false positive and false negative 125 and 81 respectively.
    Classifiers
    Test Option
    Accuracy
    False Positive
    False negative
    Naive Bayes (weka.classifiers.NaiveBayes, default parameters)
    10-fold cross validation
    0.638
    107
    74
    Decision tree (weka.classifiers.j48.J48, default parameters)
    
    0.884
    33
    25
    HoeffdingTree (weka.classifiers.trees.HoeffdingTree)
    
    0.638
    107
    74
    SMO(weka.classifiers.functions.SMO)
    
    0.588
    125
    81
Classifiers Output:
Naïve Bayes result:
=== Run information ===
Scheme: weka.classifiers.bayes.NaiveBayes
Relation: data-weka.filters.unsupervised.attribute.Remove-R1-weka.filters.AllFilter-weka.filters.MultiFilter-Fweka.filters.AllFilter-S1
Instances: 500
Attributes: 11
age
sex
region
income
married
children
car
save_act
current_act
mortgage
pep
Test mode: 10-fold cross-validation
=== Classifier model (full training set) ===
Naive Bayes Classifier
Class
Attribute YES NO
(0.46) (0.54)
=====================================
age
mean 45.25 40.5882
std. dev. 14.4108 14.2682
weight sum 228 272
precision 1 1
sex
FEMALE 107.0 140.0
MALE 123.0 134.0
[total] 230.0 274.0
region
INNER_CITY 104.0 127.0
TOWN 63.0 83.0
RURAL 41.0 43.0
SUBURBAN 24.0 23.0
[total] 232.0 276.0
income
mean 30952.2495 25085.045
std. dev. 13540.6053 11637.7974
weight sum 228 272
precision 116.6986 116.6986
married
NO 102.0 72.0
YES 128.0 202.0
[total] 230.0 ...
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