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QBUS3600 Individual Assignment
1. The assignment is due at 5:00pm on Monday 3 June 2024. The late penalty for the
assignment is 5% of the assigned mark per day, starting after 5pm on the due date.
The closing date Monday 17 June 2024 is the last date on which an assessment will
be accepted for marking.
2. The assignment MUST be submitted electronically to Turnitin through QBUS3600
Canvas site. Please do NOT submit a zipped file.
3. Your report shall be provided as a word-processed document (Microsoft Word, LaTeX
or equivalent) giving full explanation and interpretation of any aspect you are
discussing.
4. Be warned that plagiarism between individuals is always obvious to the markers of
the assignment and can be easily detected by Turnitin.
5. This report should be considered to be highly confidential as you may discuss the
Mad Paws or Big W project, and you have responsibility to keep it secure and for it to
be used only for your QBUS3600 coursework.
6. Presentation of the assignment is part of the assignment. Marks are assigned for
clarity of writing and presentation.
7. Think about the best and most structured way to present your work, summarise the
procedures implemented, support your results/findings and prove the originality of
your work.
8. Please note that the final mark of this assignment is deemed as the final exam mark,
hence the results will not be released until after grade approval according to the
University policy.
2024S1 QBUS3600 Individual Assignment 2 1
2023S1
Background
Throughout this semester, you have studied and practiced your data analysis and machine
learning skills using a large-scale, real-world dataset. It is now a good time to reflect on what
you have done and learnt from this semester-long project.
It is highly recommended that you prepare for this report early and write it during the
semester, e.g., you may document your critical thinking and reflection on a machine learning
topic you might like, or any technical difficulties you may face when doing your group
project.
If this is the first time you write a reflection statement, please look at a couple of online
resources:
1. https://www.sydney.edu.au/content/dam/students/documents/learning-resources/l
earning-centre/writing/reflective-writing.pdf
2. https://student.unsw.edu.au/examples-reflective-writing
Tasks:
For tasks 1-3, draw on and link insights and real-world learnings from guest lectures
throughout the semester
1. (40 marks) Choose one or two Machine Learning topics that you have studied in
this semester, and critically discuss what aspects should be considered when it is used in a
real-world problem. You can use a publicly available dataset or data from your group
project to support your analysis and writing. Do not simply list the pros and cons of a
model but illustrate them using the chosen dataset. You can describe the steps you use to
understand the data and to train/select/calibrate/evaluate a model.