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QBUS6850 Group Assignment
Rationale
This assignment has been designed to allow students to apply state-of-the-art machine learning
techniques to real-world problems, and to help students develop essential communication and
collaboration skills when working in a team setting.
• Carefully read the requirements for each part of the assignment and follow any further
instructions (if any) announced on Canvas.
• Failure to read information and follow instructions may lead to a loss of marks.
• You must use Python for this assignment.
2024S1 QBUS6850 Group Assignment 2
• Reproducibility is crucial in machine learning, so you are required to submit your code
files that can generate the results presented in your report. Failure to submit your code
will result in a 50% deduction from your assignment mark.
• The University of Sydney takes plagiarism very seriously. Please be warned that
plagiarism between individuals/groups is always obvious to the markers and can be
easily detected by Turnitin.
• Each group will be awarded a group mark as per the marking criteria. Each group is
required to record at least 3 meeting minutes. In case of a dispute in a group, the unit
coordinator will request minutes of the previous meetings. Individual marks may be
applied if there is a dispute and the quality or quantity of contributions made by
individuals are significantly different within a group.
Project Description
In the modern digital landscape, the volume of user-generated review data on social media
platforms has surged significantly. This data is now crucial for businesses to gain deep insights
into customer experiences, improving their offerings, and strengthen their brand image.
Concurrently, the trends and sentiments expressed in online user review data provide a strong
signal about a company’s standing, prospects, and value to shareholders and investors.
In this group assignment, suppose you are working in a Machine Learning Team within a
private equity firm. Your objective is to build a rating prediction system that automatically
predicts customers’ preferences across a variety of restaurants. Your analyses and findings
will assist the firm’s decision making in selecting prospective restaurants for investment.