COMP 3027J代做、代写Python/Java程序

日期：2024-04-09 09:01

Data Mining and Machine Learning COMP 3027J

ASSIGNMENT 1

Weight: 40%

Submissions: A report (PDF), and a zip file (including code and datasets) on Brightspace.

The purpose of this assignment is to practice how to use data mining and machine learning to

solve real-world problems. You will need to identify the target problem yourself. You can

choose any project, but it must be a classification task and includes visual analytics in the

report. (Note: Do not related to or use the dataset in Assignment 2; Do not related to your

FYP project.) as long as it is legal. This assignment is a group project, and each group should

have four members. Each group only needs to submit one solution.

Your pdf report should clearly detail how you carried out the experiment to address your

targeted problem and show the results you got.

1. Your report should be written in Overleaf, and use the provided template:

https://www.overleaf.com/latex/templates/acm-journals-primary-articletemplate/cpkjqttwbshg.

2. It should be a human-readable document (e.g. do not include code)

3. The final report is expected to be 4-6 pages including references.

4. You should provide your UCD student number instead of institution in the provided

template.

5. Use clear headings for each section.

6. Include tables and figures if needed appropriately, such as giving captions, describing

your figures or analysing the results provided in your tables in your text etc.

7. The final report filename should be “Comp3027J_GroupXX” (e.g.

Comp3027J_Group01)

In your report, it is recommended to discuss the following essential topics, but not limited to

these topics:

1. What is the real-world problem addressed and why it is important.

2. Dataset selection (collection) and Data pre-processing.

Where you find your data (or how do you collect the data and create your dataset)?

How do you analyze your data?

how to pre-process your data to fit your solution?

Any challenges with your dataset?

etc.

3. Methodology

Any machine learning algorithm can be used (not limited to the algorithm we have

learned).

Creativity is encouraged.

Be careful, a sophisticated approach with little description and explanation will

receive little credit.

4. Evaluation

Elaborate your experiment, such as splitting dataset, K-fold;

Compare your solution with benchmarks in literature;

Evaluation metrics for your task;

Analysing your results etc.

You should submit a pdf file and a zip file. In your zip file, you should include your code and

dataset. Please make sure to clean up your code to make the results reproducible. If its size

exceeds the Brightspace limit, it needs to be submitted via a USB key. Note your pdf report

must be submitted as an individual file, which should not be compressed into the zip file.

There will be an interview at the end of the term, and you will be asked about the methodology

adopted.

2

? Grading

Problem Literature Methodolgy Evaluation Code+Reproducibility

5% 5% 15% 10% 5%