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INT3095 Practical Programming for Artificial Intelligence
Group Project Specifications (2023-23)
1. Introduction
In this project, you are going to work as a group to demonstrate your knowledge
and skills in conducting data mining with machine learning algorithms in Python.
2. Summary task description
More specifically, you are required to:
− Choose a publicly available dataset from sources such as Kaggle,
data.gov.hk, data.gov, etc.
− Conduct regression, classification, clustering, or association on this
dataset using machine learning algorithm(s) in Python. You should
include the complete dataset as well as the codes so that the marker can
re-run all the results. In case you are fetching from an online dataset
directly, you should submit a backup copy of the dataset to Moodle as
well.
− Evaluate and compare the performance of your machine learning
algorithm(s) with different parameters.
− Discuss and conclude your findings in terms of the insights you obtain
from the data mining, as well as the performance of your machine
learning algorithm(s) under different parameters.
2. Grouping
Maximum of 5 members per group
3. Development
You may use Colab or Thonny, or AI analysis tool to develop this project. If
you use software tool, that means the coding effort will be limited. Therefore,
you need to provide an enhance description on your findings. Please zip all
files related to your project and submit to moodle.
4. Submission schedule
Project Report and Source Code, or files of using AI tools, if any, and the
used data file, and other related files (if any).
Zip all files and submit to Moodle (Only submit one copy is required).
Date: 16 Dec 2023 (week 15, Saturday)
Late Submission Penalty: A 20 marks (out of 100) deduction per day of
late submission without permission may be applied to the total mark of the
project. The project will NOT be accepted if late submitted over 3 days.
[Moodle will set CANNOT submit after 3 days]
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5. Project Report
Word Limit
Around 2500 words, with suitable scree capture photos of testing outcome
of the project. The contents need to directly relate to the project issues, and
is able to fulfill the following general requirements.
Report file format
Word .docx format; or establish a website on your project report (submit all
html file if you use this approach)
Cover Page
Include the Project title, and the full name and student ID of all members, a
contribution table with [Highly Contributed, Contributed, Low Contributed]
identified every member’s contribution.
Content requirements on the group report
The report should consist of (but is not limited to) the following:
− Explain the information provide by your selected dataset. For example,
how it can give value for a real-world application.
− Provide an implementation of the data mining algorithms to analyze the
dataset so as implement your idea on data mining on that selected
dataset.
− Explain the design principles behind your data mining algorithms.
− Report the findings from your outcomes of data-mining algorithm. You
may provide the screenshots of the algorithm testing outcome.
− Evaluation of the performance of your machine learning algorithms
under the data set of parameters
− Discussions on the improvement of your algorithm to carry out mote
insight of your evaluation parameters.
− A summary and conclusion of your findings regarding the data mining
results and the performance evaluation of the algorithms and parameters.
− Reference in APA format
Note:
1. it is not limited to only these features in your report. You may add any
contents that can facilitate your report to get a better outcome.
2. You are also optionally to submit a recording on your project execution to
get your reader more understanding you algorithm design. The recording
needs to be limited on 10 minutes. Provide an access link on the cover page
of the report if any.
6. Plagiarism
Plagiarism is serious matter. Please refer to the Policy on Academic Honesty,
Responsibility and Integrity with the following link:
(https://www.eduhk.hk/re/modules/downloads/visit.php?cid=9&lid=89).
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7. Grading criteria