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School of Computing and Information Systems
The University of Melbourne
COMP20008 - Elements of Data Processing, Semester 2, 2024
Assignment 2 – Victorian Vibes
Release: Friday 6 September 2024
Due: • Group contract: Friday 13 September at 5 PM
• Code and Report submission: Monday, 7 October at 5 PM
• Slides submission: Friday, 11 October at 5 PM
• Oral presentation: Week 12 (14 to 18 Oct)
• Peer-Review: Friday 18 October at 5 PM
Marks: The Project will be marked out of 35 and will contribute 35% of your
total mark.
Groups: You should work in groups of 3 or 4
Main Contact: Hasti Samadi (<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>)
1. Overview
In this project, you will build upon the dataset from Assignment 1, incorporating additional
datasets that provide valuable insights into various suburbs and regions across Victoria. Your
primary objective is to analyse these datasets to uncover trends, patterns, and key insights that
can inform local government authorities in their planning efforts. By identifying critical areas for
support, investment opportunities, and the unique challenges faced by each region, suburb, or
Local Government Area (LGA), your analysis will contribute to shaping effective decision-making.
This assessment allows you to develop hands-on experience in data wrangling, processing, and
analysis within an open-ended context.
You will produce both a written technical report and a presentation, tailored to a local
government audience, summarizing your findings. The report should be accessible to readers
with a moderate understanding of data analysis while clearly conveying your insights into the
landscape of local government planning in Victoria.
2. Assignment Structure
Group Contract (Due: Friday 13 October at 5 PM)
You must submit a group contract outlining your team's goals, expectations, and policies for
working on the project. A group contract template is provided. You are welcome to work
with the provided template or customize it according to your preference. Submit as a single
PDF file via Canvas (Assignment 2: Group Contract).
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You may change your group contract throughout the semester, but proposed changes
should be agreed to by all members. There are no marks directly allocated to the content of
the Group Contract, but we may refer to it when assessing the relative contribution of each
group member to resolve any dispute.
Failure to submit your Group Contract by the mentioned deadline will result in a deduction
of 2 marks from the final assignment mark for all members of the group.
Code and Report Submission – 20 marks (Monday, 7 October at 5 PM)
1. Report: Your report should consist of ten to twelve single-column A4 pages. Maintain a
line spacing of exactly 1 with normal margins and ensure that the font size is 11pt or above.
Please note that if your report exceeds twelve pages (excluding the cover page), only the
content within the first 12 pages will be reviewed and assessed. Any additional pages will
be disregarded. The page limit includes all the text including references, captions, and any
table or image. Tables and image content should be readable and sensible in size.
The group name W[XX]G[X] and all group members’ names should appear on the first page
after the title of the report. Submit as a single PDF file through Canvas/Turnitin
(Assignment 2: Written Report)
2. Code: One or more programs, written in Python, including all the code necessary to
reproduce the results in your report (model implementation, data processing,
visualization, and evaluation). Your code should be executable and have enough
comments to make it understandable. You should also include a README file that briefly
details your implementation and describes how to run your code to reproduce the results
in the report. Submit as a single zip file through Canvas/Turnitin (Assignment 2: Code and
Comments).
Slides Submission (Due: Friday, 11 October at 5 PM)
Submit the slides you plan to use for your oral presentation. These slides should effectively
illustrate the key insights from your data analysis task. The slides must be submitted as a
single PowerPoint (.pptx) or PDF file via Canvas/Turnitin (Assignment 2: Oral Presentation
Slides). No other formats will be accepted.
Remember, you are required to use the exact slides you submit during your presentation.
Oral Presentation and Assessment – 13 marks (Due: from Monday 14 to Friday 18 October)
During week 12 of the semester (14-18 Oct) all teams should deliver an Oral Presentation of
their work and findings for assignment 2 and answer some Oral Assessment questions. Some
of the presentations will be conducted in the students' usual workshop room and some in
other venues which will be announced shortly. Two markers will assess the oral
presentations. See section 6 for more details.
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Teamwork Evaluation – 2 marks (Due: Friday 24 May at 5 PM)
For this part of the assessment, every team member needs to evaluate both their own
contributions to the assignment and the contributions of their teammates. This evaluation
should align with the expectations you set in your submitted “group contract”.
The evaluation should be delivered via Feedback Fruits available on Canvas (Assignment 2:
Teamwork Evaluation).
Your group members' evaluations will determine individual group member evaluation scores
worth 2 marks. If any member is identified as a non-contributor, these scores may be used
to adjust those individual’s marks for the report and oral presentation (worth 30 marks).
3. Data Sets
The provided files contain detailed community-level data for Victoria, covering demographic,
health, and geographic information. Additionally, the datasets include data on the region’s
housing, expenditure on Electronic Gambling Machines (EGM), and offence record rates. This
information is distributed across Four distinct files, offering a comprehensive view of various
community aspects.
- ‘Communities.csv’ dataset comprises information on 1,080 communities and 226
columns with various details offering a comprehensive overview of community
characteristics.
o Geographic Information: region, location, distance from CBD and Distance to
nearest public hospital
o Demographic Information: density, population, cultural background, language
diversity, education level, economic information, Housing and more
o Health Information: Public Hospitals, Private Hospitals, Community Health
Centres, Allied Health, emergency response information and more
- ‘Houses_by_suburb.csv’ dataset comprises the percentage shift in median prices per
annum from 2013 to 2023. It includes the details of 790 regions across Victoria and 18
columns. You can add more columns if it helps your analysis.
o The year columns contain data about house prices across different suburbs over
the years.
o Changes Percentage: difference in house prices between 2013 and 2023
- ‘LGA_EGM.csv’ dataset includes the total gaming expenditure on ‘Electronic Gambling
Machines (EGMs) for each ‘Local Government Area (LGA)’ in Victoria. It includes the
details of 57 LGA across Victoria and 12 columns. You can add more columns if it helps
your analysis.
- ‘LGA_Offences.xlsx’ dataset includes 6 separate tables:
o Table1: Offences by Region and LGA
o Table2: Offences by Type, LGA and Police Area
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o Table3: Offences by Type, LGA and Suburb
o Table4: Offences by Location Type and LGA
o Table5: Offences by Investigation Status and LGA
o Table6: Drug Offences by Type and LGA
You do not need to use all datasets. Datasets you use will depend on your research question
and the analysis approach your group agrees on. Details about using these files are provided
in the README file.
4. Data Analysis Tasks
4.1. Research Question
The research question clarifies the purpose of your analysis. It identifies the problem or
question being addressed, sets the context, and explains why the analysis is being
conducted.
In your report, you must introduce (at least) ONE research question clearly and explicitly.
We have presented a few examples of possible research questions in the accompanying
video to provide you with some inspiration. However, each team needs to independently
formulate their own research question based on the provided dataset.
While the possibility exists to explore more than one research question, it's important to
note that the pursuit of several questions is not necessarily desirable or likely to lead to
greater marks (i.e. full marks are obtainable for one well-studied research question). We will
primarily evaluate the quality of your work by assessing the depth of your analysis, and the
insights it yields, rather than simply covering a larger quantity of content or material.
4.2. Data Pre-processing
So far in the subject, you've learned various ways to prepare and organize data. These
include techniques like filling in missing data (data imputation), reshaping data (data
manipulation), adjusting data ranges (scaling) and grouping data into categories
(discretizing). You've also explored methods to simplify complex data (dimensionality
reduction) and handle text data (text processing) using tools like text vectorization and
regular expressions.
For this project, you're encouraged to consider applying any of these methods to the
provided datasets. Your objective is to implement a minimum of ONE data pre-processing
technique, though you're welcome to utilize as many data pre-processing techniques as you
see fit. The methods you select should logically support the research question(s) you have
picked, and in your report and presentation, you should explain the reason for your selection
of each method.
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In your report and presentation, ensure you provide justifications and explanations for all
methods you employ (for both pre-processing and supervised/unsupervised models).
Present the results and highlight any interesting discoveries.
Remember, there's no single expected solution here. The more deeply you engage with and
understand your data, the better set-up you will be for subsequent stages of your project.
4.3. Data Analysis
The data analysis and visualization phase are critical for converting the pre-processed data
into valuable insights that can guide local government authorities in decision-making. This
phase involves a thorough exploration of the datasets, revealing trends, patterns, and
relationships that address the research question and provide actionable recommendations.
In this section, we will expand on various analytical methods and visualization techniques,
detailing how they can be applied to support planning, resource allocation, and problem-
solving across regions, suburbs, and LGAs.
Visualization is an essential part of the data analysis process, as it allows complex
information to be presented in an easily understandable form. Various tools and techniques
can be used to create compelling visual narratives. You are encouraged to use a variety of
analytical and visualization tools such as exploratory data analysis, correlation analysis and
trend analysis.
• Exploratory Data Analysis (EDA) is the initial step in the data analysis process. It helps in
understanding the structure of the data, identifying potential relationships, and uncovering
hidden patterns. EDA provides a foundation for more advanced analyses by summarizing the
data and offering a visual representation of its key characteristics. You can use descriptive
statistics such as central tendency measures (mean, median), standard deviation, ranges and
frequency distribution as well as visualisations such as histograms, boxplots, pie charts, bar
charts and tables to conduct this analysis.
• Correlation analysis investigates the relationships between two or more variables, helping to
identify which factors influence or are related to each other. You can use methods such as the
Pearson Correlation Coefficient and visualisation tools such as heatmaps and scatter plots to
conduct this analysis. You are also highly encouraged to use your own intuition and
understanding the perform a deeper and more intuitive analysis in this section.
• Trend analysis is employed to study changes in variables over time. By understanding how
certain factors evolve, you can anticipate changes and provide recommendations more
effectively. You can use tools such as line charts and area charts to facilitate your analysis.
You are expected to develop a minimum of TWO analytic descriptions for your findings and
highlight critical findings that point to a specific trend or understanding using the
information from your visualisations.
4.4. Use of Supervised and Unsupervised Models
In this subject, we explore certain Machine Learning related techniques. These include
predicting outcomes based on known data (supervised models like Decision trees and linear
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regression) and finding clusters and patterns in data without prior labels (unsupervised
methods like k-means and agglomerative clustering). Many other techniques are possible
too.
Feel free to choose any Machine Learning method(s) that are suitable for answering your
research question. Your choices should be substantiated and clarified in both your report
and presentation. The objective is to implement a minimum of TWO Machine Learning
techniques, though you're welcome to utilise more if you so choose. You might opt to
employ two supervised models, two unsupervised methods, or one of each.
In your report and presentation, it's important to articulate your rationale behind the
machine learning methods you chose. Provide a concise overview of your approach and
outline how you assessed the effectiveness of your chosen methods. Equally important is
your interpretation of the results and their implications.
NOTE: You are welcome (and indeed strongly encouraged) to make use of any relevant existing
Python libraries (such as sklearn or scipy) in your work on this assignment.
5. Report
Your primary submission for this assignment is your report. The report should follow the
structure of a technical paper. It should describe your approach and observations, both in data
preparation, data analysis and the machine learning algorithms you tried. Its main aim is to
provide the reader with knowledge about the problem, in particular critical analysis of your
results and discoveries.
The following is the expected structure of the report for this assignment.
• Executive Summary: A concise overview of the entire report, summarizing the objectives,
methods used, key findings, and recommendations. This section provides a high-level
snapshot of your work and its outcomes.
• Introduction: Introduce the purpose of the report, the problem or research question
being addressed, and the data sources used. Set the context for your analysis and explain
the significance of the study.
• Methodology: Offer a detailed explanation of the methods, techniques, and tools used
for data preparation, analysis, and interpretation. Assume the reader has a basic
understanding of technical terms, so focus on the specifics of your approach.
• Data Exploration and Preprocessing: Present the results from your data preprocessing
and data analysis. Describe any preprocessing techniques applied to clean and prepare
the data. This section should include descriptive statistics, visualizations, and insights
gained from exploring the data and analysing the trends and correlations in the provided
datasets. Use charts, graphs, and tables to illustrate patterns, trends, and relationships.
• Data Modelling: In this section, you need to explain your rationale for selecting the
supervised (e.g., regression, classification) or unsupervised (e.g., clustering) models used.
Describe how the models were implemented, the key features selected, and any
preprocessing tailored for them. Specify the evaluation metrics (e.g., accuracy, precision,
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recall) and justify why they were chosen. Discuss the results in relation to the research
question.
• Discussion and Interpretation: Summarize the most important insights obtained from
your data analysis and data segmentation and/or predictive modelling. Focus on
answering the main questions or addressing the problem you have introduced in the
introduction. Provide an in-depth interpretation of your findings. Bullet points or
numbered lists can help highlight these findings. Explain the significance of the patterns
observed. Explain why these findings are interesting and valuable. Discuss any
unexpected or interesting insights that emerged. (This is the most important section of
your report)
Remember we are more interested in seeing evidence that you have thought about the
task and can identify reasons behind your different results in different experiments. You
should think beyond simple numbers to the reasons that underlie them and connect them
back to your research question. You can also add complementary experiments and their
results in this section.
• Limitations and improvement opportunities: Address the limitations of the analysis, such
as data constraints, potential biases, or assumptions made. Suggest potential
improvements or alternative approaches for future analyses.
• Conclusion: Recap the key findings and recommendations from the report. Emphasize the
overall value and potential impact of your analysis, linking back to the objectives outlined
in the introduction.
• References: List any sources, references, or citations used in the report, especially if
you've drawn upon external research or literature to inform your analysis.
We've supplied a template for the report via the assignment page. You are welcome to work with
the provided template or customize it according to your preference.
6. Oral Presentation and Assessment
6.1. Oral Presentation
You need to conduct an oral presentation explaining what you have done for assignment 2.
Your presentation should encompass the key components below:
1. Introduction of Research Question: Begin by introducing the research question that
guided your assignment. Explain briefly why it is relevant to the managers of the
bookstore.
2. Methods, Techniques, and Tools: Elaborate on the methods, techniques, and tools you
employed for both data preparation and data analysis. Explain how you gathered,
cleaned, and structured the data, as well as the analytical techniques

LGA Name	Region	2012	LGA	Personal income <$400/week, persons	Number of families	Female-headed lone parent families	Male-headed lone parent families	Holds degree or higher, persons	Did not complete year 12, persons	Unemployed, persons	Aboriginal or Torres Strait Islander, persons	Born in non-English speaking country, persons	population	2012 ERP age 0-4, persons	2012 ERP age 5-9, persons	2012 ERP age 10-14, persons	2012 ERP age 15-19, persons	2012 ERP age 20-24, persons	2012 ERP age 25-44, persons	2012 ERP age 45-64, persons	2012 ERP age 65-69, persons	2012 ERP age 70-74, persons	2012 ERP age 75-79, persons	2012 ERP age 80-84, persons	2012 ERP age 85+, persons	IRSD (avg)
whittlesea	M	111583555.1	whittlesea	46539	42472.0	5665.0	1065.0	15620.0	53957	4121.0	1118.0	43572.0	166996	13288	11777	10434	10674	11949	54774	37256	5867	4149	3237	2069	1522	991.9865603821360
northern grampians	C	11219410.93	northern grampians	4554	3498.0	403.5	108.0	869.0	6518	257.5	175.5	353.5	13042	589	736	861	798	635	2697	4017	873	628	452	377	379	939.2104764873320
greater geelong	C	118316078.1	greater geelong	75898	64417.0	9350.5	1957.5	30367.0	98744	6444.5	2097.5	23006.0	249716	15386	15055	15180	16646	17059	64828	63627	12452	9465	7577	6361	6080	992.1933176124780
colac-otway	C	10916081.31	colac-otway	6598	5464.0	649.5	145.5	1701.0	10182	409.0	180.0	701.5	21429	1321	1309	1357	1334	1116	4885	5969	1268	924	748	610	588	964.4935612859170
Given this data(which there are 50 rows of), and the following research question and statement, and which is stored at /Users/rahulkulkarni/Downloads/Data/Proportional_Data_CSV.csv

Suggest and write code for a machine learning model to predict EGM per capita based on the above, noting the type of data and that regression is a method that is already taken by another member of the group. Write the code out in full