IFN509 Assignment 2: Project
Assignment type: Project (applied)
Topics: Data preparation (cleaning, integration, transformation), data analysis and mining.
Weight: 25%
Group or Individual: You will work on this assignment in groups of two or three. Note that
only a single submission is required from each group, however make sure the report submitted
contains the name and student number of each student in the group.
Due date: Sunday June 2nd, 23:50pm (end of week 13)
Driving question
How does weather affect air quality in Brisbane, Australia?
Data provided :
southbrisbane-aq-2018: Daily weather observations in various cities in Australia from July
2008 until March 20191
.IFN509留学生作业代做、Data preparation作业代写、代写SQL程序语言作业
weatherAUS.csv: South Brisbane (South East Queensland) 2018 hourly air quality and
meteorological data2
.
Analysis required:
a) Investigate whether there are any direct correlations between air quality indicators
and either rain, humidity, wind, or temperatures. Explain what the correlations
mean (you may use visualization if you wish).
b) Use decision trees to see if at least one of the average daily air quality indicators can
be predicted on the basis of any or all of the weather indicators provided. Explain
what patterns the best decision tree highlights.
c) Cluster the days and demonstrate through visualization how the clusters are
organized. Explain what patterns they reveal.

1 Observations were drawn from numerous weather stations. The daily observations are available
from http://www.bom.gov.au/climate/data. Copyright Commonwealth of Australia 2010, Bureau of
Meteorology. Available as a package at https://rattle.togaware.com/weatherAUS.csv
2 Environment and Science, Queensland Government, South Brisbane (South East Queensland) 2018
hourly air quality and meteorological data API, licensed under Creative Commons Attribution
4.0 sourced on 16 May 2019. Available at https://data.qld.gov.au/dataset/air-quality-monitoring-
2018/resource/f28488d1-44fc-4fda-aeff-291039d30f70
Preparing the data:
a) Investigate the quality of the datasets, clean the data where required (address
missing data and outliers). Explain what decisions you made for cleaning the data
and why.
b) Integrate the two provided datasets. You may use manual methods, or external
sources to do so. Explain your approach3
.
c) Provide the final, clean dataset you have used.
Software:
Explain which technology/ies you have chosen to use for this project. Explain the limitations
and benefits of your approach. If you are not using MySQL, R, please consult with the unit
coordinator for approval.
Submission Requirements
You are to submit the following files:
1. Through the Blackboard link: a) CSV files containing your clean data that you used for
analysis (import1.csv, import2.csv etc.) b) Source code. You will need to compress
several files into a .zip file.
2. Through the Turnitin link: An 8 pages (maximum) report which contains the following
sections (report.pdf):
a. Title, Group members (including student numbers)
b. Description of how software was used (1/2 page)
c. Data Summary (quality, cleaning, preparation, integration) (2 pages)
d. Correlations (1 page)
e. Decision Tree (1.5 pages)
f. Clusters (1.5 pages)

3 You can proceed with the rest of the assignment without integrating the two datasets, and only work with
the air quality dataset. If you chose to do so, you will lose marks in the data quality analysis/and data
preparation sections of the marking scheme, but not in other sections.
Marking Scheme
Marking is based only on the report, code and dataset are for verification purposes only. ?
Task Marks Criteria
Explain choice of
technology
3 - The student described the software they used in
the report : The student described how they used
software in the report, and this evidences that the
packages were used effectively (e.g. MySQL might
be used for complex joins, Excel for particular
graphs, R for association analysis, another program
for a bubble plot etc.)
- The student critically reflected on the benefits and
limitations of their chosen approach
Data quality analysis 3 - The student explains and illustrates the steps
required and applied to prepare the dataset.
- Data quality issues are all considered and discussed
appropriately.
Data preparation 4 - The student correctly cleans the data provided so
that the entire set can be imported into a statistical
package for analysis
- The student organizes data in a way that is sensible
for the software they use
- Redundant data is removed
- Data types are correctly applied
- Outliers are removed using a commonly accepted
rule
- Methods for cleaning and outlier exclusion are
described in the report
Visuals/summary for
correlations
2 The student was able to represent the effectively data
to explain.
Correlations 2 The student appropriately identified trends in the data.
Decision tree 2 The decision tree produced supports the analysis
required
Analysis of decision
tree
2 The interpretation of the decision tree is correct and
well explained
Visualisation of
clusters
2 - The visualization provided is effective to illustrate
the nature of the clusters.
- A variety of layers are used appropriately.
Analysis of clusters 2 Appropriate conclusions were drawn based on the
data set.
Presentation 3 - No spelling or grammatical errors were found in
the report
- The report is presented professionally
- Files are appropriately titled

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