Data Mining & BI Report

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Unit
Assessment Type
Assessment Number
Assessment Name
Weighting
Alignment
with Unit
and Course
Due Date and Time
Group Assignment
A4
Data Mining & BI Report
25%
ULO1, ULO2, ULO3, ULO4
Assessment Description In this assessment, the students will extend their previous work from assessment A3
Business case understanding. Here, the students have to submit a report of the data
mining process on a real-world scenario and a presentation and QA Session will be held
based on the report written. The report will consist of the details of every step followed by the
students.
Detailed Submission
Requirements
Cover Page
• Title
• Group members
Introduction
• Importance of the chosen area
• Why this data set is interesting
• What has been done so far
• Which can be done
• Description of the present experiment
1. Data preparation and Feature extraction:
1.1 Select data
o Task Select data
1.2 Clean data
o Task Clean data
o Output Data cleaning report
1.3 Construct data/ feature extraction
o Task Construct data
o Output Derived attributes
o Activities: Derived attributes
o Add new attributes to the accessed data
o Activities Single-attribute transformations
o Output Generated records
Report (10%): Week 11, Friday, 04 June 2021, 11:59 pm via
Moodle. Presentation and QA Session (15%): Week 12 In Class.
2 Modeling
2.1 Select modeling technique
o Task – Select Modelling Technique
2.2 Output Modeling technique
o Record the actual modeling technique that is used.
2.3 Output Modeling assumption
o Activities Define any built-in assumptions made by the technique about
the data (e.g. quality, format, distribution). Compare these assumptions
with those in the Data Description Report. Make sure that these
assumptions hold and step back to the Data Preparation Phase if
necessary. You can explain the data file here, even when it is pre
prepared.
3 Generate test design
3.1 Task Generate test design
o Activities Check existing test designs for each data mining goal
separately. Decide on necessary steps (number of iterations, number of
folds etc.). Prepare data required for test. (You can use 66% of records
for model Building and rest for Testing)
3.2 Build model
o Task – Build model
Run the modeling tool on the prepared dataset to create one or more
models. (Using Knime Tool as shown in the lab).
3.3 Output Parameter settings
o Activities – Set initial parameters. Document reasons for choosing those
values.
o Activities – Run the selected technique on the input dataset to produce
the model. Post-process data mining results (e.g. editing rules, display
trees).
3.4 Output Model description
o Activities – Describe any characteristics of the current model that may
be useful for the future. Give a detailed description of the model and
any special features.
o Activities – State conclusions regarding patterns in the data (if any);
sometimes the model reveals important facts about the data without a
separate Assessment process (e.g. that the output or conclusion is
duplicated in one of the inputs).
4 Evaluation and Conclusion
Previous evaluation steps dealt with factors such as the accuracy and generality of the
model. This step assesses the degree to which the model meets the business
objectives and seeks to determine if there is some business reason why this model is
deficient. It compares results with the evaluation criteria defined at the start of the
project. A good way of defining the total outputs of a data mining project is to use the
equation:
RESULTS = MODELS + FINDINGS
In this equation we are defining that the total output of the data mining project is not
just the models (although they are, of course, important) but also findings which we
define as anything (apart from the model) that is important in meeting objectives of the
business (or important in leading to new questions, line of approach or side effects
(e.g. data quality problems uncovered by the data mining exercise).
Note: although the model is directly connected to the business questions, the findings
need not be related to any questions or objective, but are important to the initiator of
the project.
~ End of Assessment Details ~
Marking Criteria
Activities Rank the possible actions. Select one of the possible actions. Document
reasons for the choice.
Content
Marks
Cover Page
Table of contents
0.5
Executive Summary
0.5
Introduction
0.5
Data Pre-processing and feature extraction
2.5
Experiment
3
Result analysis
2.5
Conclusion
0.5
Presentation and QA
15
Rubrics
Marking criteria
HD
D
C
P
F
ULO1: Demonstrate broad
understanding of data
mining and business
intelligence and their
benefits to business
practice
ULO 2: Choose and apply
models and key methods
for classification,
prediction, reduction,
exploration, affinity
analysis, and customer
segmentation that can be
applied to data mining as
part of a business
intelligence strategy
ULO3: Analyse appropriate
models and methods for
classification, prediction,
reduction, exploration,
affinity analysis, and
customer segmentation to
data mining
ULO4: Propose a data
mining approach using real
business cases as part of a
business intelligence
strategy
Report,
presentation
and QA
outcome
address all the
tasks.
Report
consists of
no/minor
mistakes.
(21-25 marks)
Report,
presentation
and QA
outcome
address all the
tasks.
Report consists
of a few number
of mistakes.
(18-20 marks)
Report,
presentation
and QA
outcome
address most of
the contents.
Report consists
of a few number
of mistakes.
(15-17 marks)
Report,
presentation
and QA
outcome
address a few of
the contents.
Report consists
of a good
number of
mistakes.
(13-14 marks)
Incomplete
report.
Unable to
perform the
experiment/dat
a pre
processing/
conclude result.
Unable to
answer to the
question of QA
Session and
Unable to
present the
work that has
been done.
(0-12.5 marks)
Misconduct • Engaging someone else to write any part of your assessment for you is classified as
misconduct.
• To avoid being charged with Misconduct, students need to submit their own work.
• Remember that this is a Turnitin assignment and plagiarism will be subject to severe
penalties.
• The AIH misconduct policy and procedure can be read on the AIH website
(https://aih.nsw.edu.au/about-us/policies-procedures/).
Late Submission • Late submission is not permitted, practical submission link will close after 1 hour.
Special consideration • Students whose ability to submit or attend an assessment item is affected by sickness,
misadventure or other circumstances beyond their control, may be eligible for special
consideration. No consideration is given when the condition or event is unrelated to the
student’s performance in a component of the assessment, or when it is considered not
to be serious.
• Students applying for special consideration must submit the form within 3 days of the
due date of the assessment item or exam.
• The form can be obtained from the AIH website (https://aih.nsw.edu.au/currentstudents/student-forms/) or on-campus at Reception.
• The request form must be submitted to Student Services. Supporting evidence should
be attached. For further information please refer to the Student Assessment Policy and
associated Procedure available on
(https://aih.nsw.edu.au/about-us/policies-procedures/).

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