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Assessment 4 : applied project

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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 li
ary
(https:
ecali
ary.on.worldcat.org/discovery ) and write a discussion report based on the points
elow:
Wang, Y XXXXXXXXXXArtificial 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 XXXXXXXXXX
Silver, M. S XXXXXXXXXXDescriptive analysis for computer-based decision support. Operations Research,
36(6), 904–916. https:
doi.org/10.1287/opre XXXXXXXXXX
Seydel, J XXXXXXXXXXData envelopment analysis for decision support. Industrial Management & Data
Systems, 106(1), 81–95. https:
doi.org/10.1108/ XXXXXXXXXX
• 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 Ru
ic
The assessment will be marked out of 30 and weighted 100% of the total unit mark. The marking
criteria and ru
ic are shown on the following page.

Assessment 1 Marking Criteria and Ru
ic
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
esearch in decision support
systems with incomplete
esponses supported by no
or i
elevant examples,
inco
ect terminologies and
poo
inadequate
eferences.
Demonstrate an ability to
analyse, reason and discuss
most concepts to draw
justified conclusions that are
generally logically supported
y examples and best
practice. The answers are
partially structured into
loosely linked introductory
sentences to create a
comprehensive, descriptive
analysis using co
ect 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
y examples and best
practice. The answers are
usually logically structured
to create a comprehensive,
mainly descriptive piece of
Analysis. Some use of
co
ect 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 co
ect 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
est practice. Answers
succinctly integrate and link
information into a cohesive
and coherent piece of
Analysis and consistently
use co
ect 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
etter 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 na
ative with the
specific purpose of the
eport.
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
eport, 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
eport, 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
eport, 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
eference list.
The report follows a
eferencing style that does
not comply with the APA
style or includes either the
in-text citations or the
eference list.
The report follows a
eferencing style that mostly
complies with the APA style.
However, the in-text
citations are not made
purposefully.
The report follows a
eferencing style that
complies with the APA style,
and the in-text citations are
mostly purposeful.
The report follows a
eferencing 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
y 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 Ru
ic
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 Ru
ic
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 e
ors, and your task is to find these e
ors and fix them to have a valid
Answered Same Day May 04, 2022

Solution

Mohd answered on May 05 2022
109 Votes
Analysis Summary:
Take a screenshot of your co
ected 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 data.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 ma
ied 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 (co
ectly classified instances) and SMO has lowest accuracy among all four classifiers.
Decision tree J48 is the best performing classifiers for the given data.
The inco
ectly 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 inco
ectly 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
ma
ied
children
ca
save_act
cu
ent_act
mortgage
pep
Test mode: 10-fold cross-validation
=== Classifier model (full training set) ===
Naive Bayes Classifie
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
egion
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
ma
ied
NO 102.0 72.0
YES 128.0 202.0
[total] 230.0 ...
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