Description
## Objectives
In this project, your group will use what they have learned in COMP9417 to construct a classifier for the specific task
as well as write a detailed report outlining your exploration of the data and approach to modelling. The report is
expected to be 10-12 pages (with a single column, 1.5 line spacing), and easy to read. The body of the report should
contain the main parts of the presentation, and any supplementary material should be deferred to the appendix. For
example, only include a plot if it is important to get your message across. The report is to be read by the client, and
the client cares about the big picture, pretty plots and intuition. The guidelines for the report are as follows:
1. Title Page: tile of the project, name of the group and all group members (names and zIDs).
2. Introduction: a brief summary of the task, the main issues for the task and a short description of how you
approached these issues.
3. Exploratory Data Analysis: this is a crucial aspect of this project and should be done carefully given the lack
of domain information. Some (potential) questions for consideration: are all features relevant? How can we
represent the data graphically in a way that is informative? What is the distribution of the classes? What are
the relationships between the features?
4. Methodology: A detailed explanation and justification of methods developed, method selection, feature selection,
hyper-parameter tuning, evaluation metrics, design choices, etc. State which method has been selected for the
final test and its hyper-parameters.
5. Results: Include the results achieved by the different models implemented in your work, with a focus on the f
score. Be sure to explain how each of the models was trained, and how you chose your final model.
6. Discussion: Compare different models, their features and their performance. What insights have you gained?
7. Conclusion: Give a brief summary of the project and your findings, and what could be improved on if you had
more time.
8. Reference: list of all literature that you have used in your project if any. You are encouraged to go beyond the
scope of the course content for this project.
## Project implementation
Each group must implement a minimum of two classification methods and select the best classifier, which will be
used to generate predictions for the test sets of the respective task. You are free to select the features and tune the
methods for best performance as you see fit, but your approach must be outlined in detail in the report. You may also
make use of any machine learning algorithm, even if it has not been covered in the course, as long as you provide an
explanation of the algorithm in the report, and justify why it is appropriate for the task. You can use any open-source
libraries for the project, as long as they are cited in your work. You can use all the provided features or a subset of
features; however you are expected to give a justification for your choice. You may run some exploratory analysis or
some feature selection techniques to select your features. There is no restriction on how you choose your features as
long as you are able to justify it. In your justification of selecting methods, parameters and features you may refer to
published results of similar experiments.
## Code submission
Code files should be submitted as a separate.zipfile along with the report, which must be.pdfformat. Penalties
will apply if you do not submit a pdf file (do not put the pdf file in the zip).
## Peer review
Individual contribution to the project will be assessed through a peer-review process which will be announced later,
after the reports are submitted. This will be used to scale marks based on contribution. Anyone who does not complete
the peer review by the 5pm Thursday of Week 11 (29 April) will be deemed to have not contributed to the assignment.
Peer review is a confidential process and group members are not allowed to disclose their review to their peers.
## Project help
Consult Python package online documentation for using methods, metrics and scores. There are many other resources
on the Internet and in literature related to classification. When using these resources, please keep in mind the guidance
regarding plagiarism in the course introduction. General questions regarding group project should be posted in the
Group project forum in the course Moodle page. Note: You will now have been added to the TracHack Teams group,
you can post TracHack specific questions there instead of the forum to get direct help from the TracHack people.
