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QBUS3600 Group Project
Value: 40%
Length: maximum of 25 pages excluding appendices
Notes to Students
1. The assignment MUST be submitted, by the team leader, electronically to Turnitin
through QBUS3600 Canvas site. Please do NOT submit a zipped file.
2. The main assignment document is due on Monday 23 October and others like
meeting agendas/minutes are at various dates as indicated on Canvas. The late
penalty for the assignment is 5% of the assigned mark per day, starting after 5:00pm
on the due date. The closing date is the 7th day after the due date which is the last
date on which an assessment will be accepted for marking, normally one week after
the announced due date.
3. The data sets for this assignment can be downloaded from Canvas. The dataset is
highly confidential, and you have responsibility to keep it secure and for it to be used
only for your QBUS3600 coursework.
4. Presentation of the assignment is part of the assignment. Marks are assigned for
clarity of writing and presentation.
5. 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.
6. Numbers with decimals should be reported to the second decimal point.
2023S2 QBUS3600 Group Assignment - Big W 1
Background and Task
During the individual assignment, you identified several potential insights into identifying
the top behaviours and attributes that are likely to be useful for predicting total store
sales. Now, your task as a group is to synthesise your potential insights and construct a
model which can perform this prediction task.
You will need to build a model with whatever machine learning approaches you feel
appropriate. You should evaluate your model/s on a range of metrics, however, the RMSE
(defined below) will be used to evaluate the performance of your final model on the test
data. You should follow an industry recognised approach to Data Science problems (e.g.
CRISP-DM) and include a justification for your selected model. You will be required to show
the methods you used to prioritise your potential insights and defend the models and results
with supporting evidence. You will also be required to submit your retention predictions on
the test data.
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Important:
1) Please use the pre-splitted training and test set that has already been provided.
Your evaluation metrics on the test set are important.
2) Please consider which variables are not available at the time of predictors, and
exclude those as predictor variables (because in real life, your model won’t have
them available when making predictions!).
The Woolworths Group Team will be available for a Q&A session with the class (date and
time TBA). This session will run for 60 minutes; groups will have the opportunity questions of
the management team. Please use this session to ask questions following your engagement
with the problem and data through individual assignment 1. This Q&A session will be
recorded and shared with students who are unable to attend, however each group is
required to have at least one member in attendance.