INFS4203/7203 Project
Semester 2, 2021

Due dates:
16:00 on 17th September 2021 for project proposal (Phase 1, 15%)
16:00 on 29th October 2021 for project report (Phase 2, 20%)
All assignments should be submitted to UQ Blackboard only. If any assignment is failed to be submitted
appropriately before due, penalty will be applied according to ECP. It is your responsibility to ensure your
submission is successful before due time. Email submission will not be accepted.
Overview
The assignment is designed to test the ability to apply data mining techniques to solve real-world
problems. This is an individual assignment. The completion of the assignment should be based on your
own design.
In this assignment, you will be asked to individually complete a project proposal and implement your
proposal to have data mining models which could be applied to test data. You need to choose either
? a data-oriented project, or
? a competition-oriented project.
To complete the project, you need to submit a proposal in the 1st phase describing clearly and thoroughly
the data pre-processing, model training and evaluation techniques you plan to apply, and based on the
above proposal in the 2nd phase a project implementation and a report on the final test result.
Track 1: Data-oriented project
In this project, you will be provided with a dataset named Ecoli.csv. Except for the first row, each row in
the data file corresponds to one data point. There are 1500 data points in this dataset, formed by micro-
array expression data and functional data of 1500 genes of E. coli, a bacterium that is commonly found in
the lower intestine of warm-blooded organisms. The first 103 columns are numerical features describing
their expression level. The following 13 columns (from Column 104 to Column 116) are nominal features
describing the gene functional. If the gene has a special functional, it will be denoted 1; otherwise, 0. NaN
denotes that the feature value is missing at the position. The final column “Target” (Column 117) is the
label for the gene indicating whether the gene has the function “Cell communication”. In this column, the
positive class is denoted as 1, and the negative class is 0.
Based on the provided labelled data, the overall objective is to design a classifier with good generalization
to differentiate whether a given gene has the function “Cell communication”. Note that the test data
(without ground truth) for measuring the generalization ability will be released in Week 9.
V1.0 2

Phase 1: project proposal (15 marks)
In the first phase of the project, a proposal is needed to be submitted by 16:00, 17th September 2021.
The proposal takes 15 marks in total. 12 marks could be earned by describing clearly and
comprehensively the following aspects and how to make use of them to achieve the best
generalization performance
1. (3 marks) Based on your analysis of the dataset, discuss whether the following pre-processing
techniques should be considered: outlier detection, normalization, imputation, etc. Describe how
to determine appropriate techniques by cross validation, and how to apply them to the current
data.
2. (5 marks) Based on the above pre-processed data, describe the procedure of applying the four
classification techniques learned in lectures (decision tree, random forest, k-nearest neighbor and
na?ve bayes) to the data, including necessary model selection and hypermeter tuning by cross
validation. You also need to consider an ensemble of the classification results from different
classifiers at the end of learning.
3. (3 marks) Describe the process of evaluating the model given the current dataset using cross
validation. Based on your analysis of the data, answer explicitly which metric is the most
appropriate for measuring the classification performance of the current dataset.
4. (1 mark) Give your timeline for the implementation of your project in the 2nd phase. The timeline
should include a justified, comprehensive and feasible list of milestones.
The final 3 marks will be given to the presentation of the proposal. We expect the proposal to have
good structure, which helps comprehension. The presentation should be neat and professional, with
bibliography, which is correctly formatted following the examples in the provided template and
appropriately referenced. Marks will be deduced if there are formatting, spelling, grammar,
bibliography, referencing or punctuation errors which impact the understanding of the proposal.
Format
The proposal should follow the style of Proposal_Template.doc. The submission should be within four
pages, including all references.
Submission
The proposal should be submitted
? in PDF or Doc (Docx) format, other formats are not acceptable, and
? through the “Proposal submission” Turnitin link provided at Blackboard -> Assessment ->
Project -> Proposal submission before the deadline.
Only your submitted version will be marked. A penalty will be applied to the late submission (see ECP).
Phase 2: Project report (20 marks)
In this phase, you will need to implement the ideas in your proposal and use them to classify the test data
which will be provided in Week 9. Details on format, marking standard and submission will be released in
Week 9 as well.
V1.0 3


Track 2: Competition-oriented project
In this project, you will need to complete a data mining-related online competition and achieve
satisfactory test performance. The competition should end no later than Oct. 1st, 2021 and be related to
the learning object of this course. After you have successfully targeted a competition with a minimum of
ten competitors, please Express of Interest (EOI) by this link (or http://forms.gle/BMJcCAXNkivSfg4B7 ).
There are limited spots for this project of up to 20 students, determined by the time you submit your EOI
and whether the project fits the learning objective of this course.

Phase 1: project proposal (15 marks)
In the first phase of the project, you need to submit a proposal. The proposal takes 15 marks in
total. 12 marks could be earned by describing clearly and comprehensively the following aspects and
how to make use of them to achieve the best generalization performance
1. (2 marks) Describe the task of the competition and the basic statistics of the provided dataset.
2. (3 marks) Based on your analysis of the dataset, discuss whether the following pre-processing
techniques should be considered: outlier detection, normalization, imputation, etc. Describe how
to determine appropriate techniques by cross validation, and how to apply them to the current
data.
3. (5 marks) Based on the above pre-processed data, describe the procedure of applying the four
classification techniques learned in lectures (decision tree, random forest, k-nearest neighbor
and na?ve bayes) or beyond (SVM, logistic regression, neural networks, boosting etc.) to the
data, including necessary model selection and hypermeter tuning by cross validation. You also
need to consider an ensemble of the classification results from different classifiers at the end of
learning.
4. (1 mark) Describe the process of evaluating the model given the current dataset using cross
validation.
5. (1 mark) Give your timeline for the implementation of your project in the second phase. The
timeline should include a justified, comprehensive and feasible list of milestones.
The final 3 marks will be given to the presentation of the proposal. We expect the proposal to have
good structure, which helps comprehension. The presentation should be neat and professional, with
bibliography, which is correctly formatted following the examples in the provided template and
appropriately referenced. Marks will be deduced if there are formatting, spelling, grammar,
bibliography, referencing or punctuation errors which impact the understanding of the proposal.

Format
The proposal should follow the style of Proposal_Template.doc. The submission should be within six
pages, including all references.
V1.0 4

Submission
The proposal should be submitted
? in PDF or Doc (Docx) format, other formats are not acceptable, and
? through the “Proposal submission” Turnitin link provided at Blackboard -> Assessment ->
Project -> Proposal submission before the deadline.
Only your submitted version will be marked. A penalty will be applied to the late submission (see ECP).

Phase 2: Project report (20 marks)
In this phase, you will need to implement the ideas in your proposal and use the implemented
models to achieve a good position in the competition’s public leading board.
Format and submission details will be released in Week 9.
Marking standard
You need to submit the evidence of your achievements in the public leading board by the end of
the project deadline to earn your marks. Your username in the public leading board must be
your student username (sxxxxxxx, each x represents a digit).
If your targeted competition ends before the project deadline, you could show by cross validation
that you have achieved comparable performance to a particular competitor on the public leading
board before the project deadline. Your project could then be assessed by the competitor’s
corresponding rank percentage on the public leading board.
1. For three entry-level Kaggle competitions
a. Titanic - Machine Learning from Disaster
http://www.kaggle.com/c/titanic
You need to get an accuracy through the Public Leader Board by the project deadline.
Earned marks = min (20 – (1 - PublicLeaderBoardAccuracy) *200, 0)
That is, you earn 20 marks when having Public Leader Board Accuracy 1.0.

b. House Prices - Advanced Regression Techniques
http://www.kaggle.com/c/house-prices-advanced-regression-techniques
Note that this project is not related to the learning objective of the course. It is not
eligible as a competition-based project.

c. Digit Recognizer
http://www.kaggle.com/c/digit-recognizer
You need to get an accuracy through the Public Leader Board by the project deadline
V1.0 5

Earned marks = min (20 – (1.0-PublicLeaderBoardAccuracy) *200, 0)
That is, you earn 20 marks when having Public Leader Board Accuracy 1.0.

2. For other prized competitions
You have to earn a public Leader Board top ranking index (your rank divided by the total
number of competitors) by the project deadline
Earned marks = min (20 – max (public_LB_top_ranking_index – 0.5, 0)*40, 0)
That is, you earn 20 marks when having Public Leader Board top ranking to be within
50% of all competitors.


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