This assignment aims for students to learn how to analyse data relating to a business problem and propose artificial intelligence solutions based on machine learning and data mining techniques. The report will discuss and interpret the results.
Students will learn to:
Customer Analytics is a company specializing in gathering and finding meaning in data collected from social channels to support business decisions. As data analysts working at Customer Analytics, you are tasked with analyzing a large dataset containing customer reviews about a theme park company with three major branches in different cities: California, Paris, and Hong Kong. You are provided with a sample of 35,000 reviews (accessible via Cloud Deakin).
Your task is to use Python and Jupyter Notebook on the Google Colab platform to process and explore the provided data.
You are to generate insights and provide answers to these questions of interest:
A.Identify and compare the top 10 groups of visitors at each theme park branch according to their location of origin. Are there differences in the top visitor groups among the branches? If so, why?
B.Compare the peak visiting times at the California branch for domestic and international visitors. Explain any differences in their visiting patterns and discuss potential reasons for such differences.
C.Compare the overall sentiments expressed by customers about the theme parks. Which theme park received the most positive sentiments?
D.Identify the theme park branch that received the most positive sentiment for their “services”.
E.What are the concerns or interests of customers when visiting the theme parks?
F.What are the differences in concerns and interests between visitors from the United States and the United Kingdom when visiting the theme parks? Explain potential reasons for such differences.
NOTE: Write code that is efficient in terms of time and space. Ensure code is clear and easy to understand, using meaningful variable names and comments where necessary. Only print or display essential outputs that are crucial for understanding, minimize unnecessary printing or displaying of large data structures or intermediate outputs.
You are allowed to use any sample code provided in the lab materials or online resources. However, you must modify/customize such sample code to your own assignment (e.g. rename variables, labels, titles; restructure code flow, modify chart types, colour and symbols). References and citations must be provided where appropriate.
This task allows you to demonstrate your achievement towards the Unit Learning Outcomes (ULOs) which have been aligned to the Deakin Graduate Learning Outcomes (GLOs). Deakin GLOs describe the knowledge and capabilities graduates acquire and can demonstrate on completion of their course. This assessment task is an important tool in determining your achievement of the ULOs. If you do not demonstrate achievement of the ULOs you will not be successful in this unit. You are advised to familiarise yourself with these ULOs and GLOs as they will inform you on what you are expected to demonstrate for successful completion of this unit.
The learning outcomes that are aligned to this assessment task are:
|
Unit Learning Outcomes (ULOs) |
Graduate Learning Outcomes (GLOs) |
|
ULO 1: Appraise the suitability of major artificial intelligence and advanced machine learning concepts to solve business problems ULO 2: Design and develop artificial intelligence solutions for multifaceted business problems |
GLO1: Discipline-specific knowledge and capabilities GLO5: Problem solving |
See CloudDeakin for more info about this assignment, especially the assignment template and the assessment rubric.
The assignment must be prepared using the provided assignment template (.ipynb file) using Jupyter Notebook. Your assignment should contain all necessary codes and ready to run. If you use any new python package, ensure that you include installation code in your .ipynb file. All python codes should be ready to execute without any further modification on Google Colab.
Upon completion of the assignment, execute all python codes and then generate a PDF file. Your files should be named as your firstname_lastname_MIS780A1 (e.g. John_Smith_MIS780A1.pdf and John_Smith_MIS780A1.ipynb).
You are to submit your assignment (both the PDF file and the source .ipynb file) in the individual Assignment Dropbox in the MIS780 CloudDeakin unit site on or before the due date. Do NOT zip the files. Any submission contained in a zip file will not be marked.
Submitting a hard copy of this assignment is not required. You must keep a backup copy of every assignment you submit until the marked assignment has been returned to you. In the unlikely event that one of your assignments is misplaced you will need to submit your backup copy.
Any work you submit may be checked by electronic or other means for the purposes of detecting collusion and/or plagiarism and for authenticating work.
When you submit an assignment through your CloudDeakin unit site, you will receive an email to your Deakin email address confirming that it has been submitted. You should check that you can see your assignment in the Submissions view of the Assignment Dropbox folder after uploading and check for, and keep, the email receipt for the submission.
The marking rubric indicates the assessment criteria for this task. It is available in the CloudDeakin unit site in the Assessment folder, under Assessment Resources. Criteria act as a boundary around the task and help specify what assessors are looking for in your submission. The criteria are drawn from the ULOs and align with the GLOs. You should familiarise yourself with the assessment criteria before completing and submitting this task.
Students who submit their work by the due date will receive their marks and feedback on CloudDeakin 15 working days (3 weeks) after the submission date.
Extensions can only be granted for exceptional and/or unavoidable circumstances outside of your control. Requests for extensions must be made by 12 noon on the submission date using the online Extension Request form under the Assessment tab on the unit CloudDeakin site. All requests for extensions should be supported by appropriate evidence (e.g., a medical certificate in the case of ill health).
Applications for extensions after 12 noon on the submission date require University level special consideration and these applications must be must be submitted via StudentConnect in your DeakinSync site.
If you submit an assessment task after the due date without an approved extension or special consideration, 5% will be deducted from the available marks for each day after the due date up to seven days*. Work submitted more than seven days after the due date will not be marked and will receive 0% for the task. The
Unit Chair may refuse to accept a late submission where it is unreasonable or impracticable to assess the task after the due date. *’Day’ means calendar day for electronic submissions and working day for paper submissions.
An example of how the calculation of the late penalty based on an assignment being due on a Thursday at 8:00pm is as follows:
The Division of Student Life provides a range of Study Support resources and services, available throughout the academic year, including Writing Mentor and Maths Mentor online drop ins and the SmartThinking 24 hour writing feedback service at this link. If you would prefer some more in depth and tailored support, make an appointment online with a Language and Learning Adviser.
Deakin takes academic integrity very seriously. It is important that you (and if a group task, your group) complete your own work in every assessment task Any material used in this assignment that is not your original work must be acknowledged as such and appropriately referenced. You can find information about referencing (and avoiding breaching academic integrity) and other study support resources at the following website: Deakin University Study support and resources
As a student you have both rights and responsibilities. Please refer to the document Your rights and responsibilities as a student in the Unit Guide & Information section in the Content area in the CloudDeakin unit site
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