INT3095代做、代写Python语言编程

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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
Group report (80% of total project marks)
0 1 2 3 4
Introduction and
theoretical
background
(20 marks)
Background
information is
missing or
contains major
gaps or
inaccuracies.
Background
information is
accurate, but
irrelevant or too
disjointed to
make relevance
clear.
Background
information is
accurate but has
omissions that
detract from the
theme of the
project.
Background
information may
contain minor
omissions or
inaccuracies but
does not detract
from the major
theme of the
project.
Background
information is
accurate and has
the appropriate
level of
specificity to
provide concise
and useful
context to aid the
reader's
understanding.
Data preparation
(10 marks)
The dataset is
incomplete or is
not loaded into
the notebook.
The dataset is
loaded into the
notebook, but its
key properties
and sample
records are not
adequately
illustrated.
The dataset is
loaded and wellillustrated, but it
is not
appropriately
split into training
set and testing
set.
The dataset is
correctly loaded,
illustrated, and
appropriately
split, but lacking
a detailed
description.
The dataset is
correctly loaded,
illustrated, and
appropriately
split, with
detailed
description.
Data mining
(30 marks)
The data mining
is incomplete.
The data mining
is complete but
contains major
errors.
The data mining
is done but
contains minor
errors.
The data mining
is correctly done
but not clearly
explained.
The data mining
is correctly done
and clearly
explained.
Evaluation and
discussions
(20 marks)
No evaluation or
discussion is
performed or
discussed.
Evaluation is
performed but
with omissions or
errors, or not
discussed in
sufficient detail.
Evaluation is
properly
performed but
only briefly
discussed.
Evaluation is
properly
performed and
reasonably well
explained or
discussed.
Evaluation is
appropriate,
correct, and
clearly explained
or discussed.
Some good
points are made.
Conclusion,
limitations, and
recommendation
s (10 marks)
The conclusions
and
recommendation
s section is
incomplete or
non-existent.
The conclusions
and
recommendation
s have major
omissions.
The conclusion
relates
appropriately to
the project
objectives but
the limitations
and
recommendation
s were not well
discussed.
The conclusion
relates
appropriately to
the project
objectives. The
limitations and
recommendation
s are written but
not always well
justified.
The conclusion
relates
appropriately to
the project
objectives and
contains welljustified
limitations or
recommendation
s.
Clarity of
presentation
(10 marks)
The report has
terrible spelling
or grammar.
Sections are not
put under
appropriate
headings.
The report has
significant errors
in spelling,
formatting, or
grammars.
Sections are not
put under
appropriate
headings.
The report has
some spelling,
formatting or
grammatical
errors. Sections
are put under
appropriate
headings.
The report has
some minor
spelling,
formatting or
grammatical
errors. Sections
are put under
appropriate
headings.
The report is
written with good
spelling,
formatting, and
grammar.
Sections are put
under
appropriate
headings.