代做6001CMD Machine Learning调试Python程序

Assignment Information

Module Name: Machine Learning

Module Code: 6001CMD

Assignment Title: Coursework

Assignment Due: 6 pm Monday 1^st December 2025

If you do not pass this assessment, you may have an opportunity to resit it. If you do need to resit, you will be asked to use the feedback provided to revise your original submission, so that it meets the pass requirements for the module. You must clearly indicate the changes you have made in the new submission. Please check your SOLAR results and the submission links on your Aula module page to see when the resit is due.

Assignment Credit: 20 credits

Word Count (or equivalent): 2500 words

Assignment Type:

Percentage Grade (Applied Core Assessment). You will be provided with an overall grade between 0% and 100%. To pass the assignment you must achieve a grade of 40% or above.

Assignment Task

Scenario

For this coursework, you are required to select a real-world problem of your choice and apply various machine learning algorithms and methods to solve the selected problem. This problem could be a classification, regression, or clustering problem.

Task 1

Your first task comprises the following:

1. Select a real-world problem.

2. Select suitable dataset(s) for the chosen problem. You can combine more than one dataset.

3. Analyse the dataset and pre-process it

4. Select more than one (at least three) appropriate Machine Learning algorithm for implementing the models.

5. Tune the models to achieve better performance.

6. Critically evaluate the results from applying the selected models on the chosen dataset.

Note:

· You are advised to choose a dataset that allows you to demonstrate your ability to perform. data analysis and pre-processing techniques such as, but not limited to, handling missing, categorical, non-numeric and duplicate values; outliers; scaling; etc. The selected dataset must contain at least 2500 samples, after pre-processing. The dataset cannot be one of the scikit-learn or synthetic datasets. Also, the dataset cannot be picked from www.kaggle.com.

· If you are not sure where to start, you may find a list of suggested resources with numerous problems and datasets in the “Open Data Repositories” section on Aula.

· You can use existing machine learning algorithms or a combination of some of them or even come up with a new algorithm of your own.

· The required programming language is Python 3 (others are not accepted).

Task 2

For the second task, you are required to submit a demonstration video recording the execution and performance of your implementation.

Note:

· The maximum length of the demonstration video is five minutes.

· You are NOT required to walk through every line of the source code, but it is important to demonstrate the execution of all stages and the corresponding outputs of the source code.

· Voice over the video must be used to describe what is happening and some of the reasoning throughout the video.

· Ensure that all texts, tables, graphs, etc. are of an appropriate size to view, free from noise and not blurred. Also, ensure that the audio is clear.

· You are required to use either Jupyter Notebook on a browser, Colab Notebook on a browser, Spyder or Visual Studio Code when recording the demonstration video.

Task 3

Write a report (2500 words) based on a literature review and the technical work. This should include:

Task 3a

1. A literature review on the application of machine learning to one of the following tasks: product recommendation, facial recognition, language translation, or text/image/video generation.

Task 3b: This is a write-up of the work done in Task 1

1. A specification of the chosen problem area.

2. Comparing the approaches and results of other existing pieces of work on the same problem

3. Analysing and pre-processing the data.

4. Applying different algorithms and methods to build learning models.

5. Making appropriate adjustments to improve the models’ performances.

6. Critically evaluating the performance of the models.

Note:

· The first part of your report should review literature on the application of machine learning to one of the following tasks: product recommendation, facial recognition, language translation, or text/image/video generation. This task will be different from the problem area you are working on.

· The second part of your report should focus on how algorithms/methods/techniques are actually applied or developments that are novel and specific to your work rather than how they work theoretically.

· Your report should include appropriate outcomes such as data analysis diagrams, outcomes from the models, code snippets, etc. to support your text. Include the source of your dataset.

· Include all your source code as text in Appendix B at the end of the report. Do not use screenshots of your code in Appendix B. Your code muse be presented as text (see coursework template).

· Ensure you use comments to demonstrate an understanding of all parts of your code.

· A course work template is provided as a guide in the “Assessment” section on Aula

· The word limit includes quotations, but excludes the (GitHub, datasets, OneDrive) URLs, bibliography, reference list, and appendices (see coursework template)

AI Use Policy (Amber Category):

Students are permitted to use AI as an assistive tool under the following conditions:

Citing AI Contributions: Students must identify and cite any portions of their work generated or assisted by AI.

Submission Instructions:

The submission of your coursework must be in the form. of ONE MS Word, or pdf, file through the indicated Aula submission link. Other formats (other than MS Word or pdf) will not be accepted.

The submission of the implementation and demonstration video must be in the form. of:

· A URL of Coventry GitHub Repository, OR

· A URL of Coventry OneDrive shared folder

The URL must be included at the beginning of your report (submitted on Aula).

**Examiners will not check for the required URLs in other places.

The shared folder or repository must be accessible by examiners, and should include the following:

· The URL to the selected dataset(s) in README or a separate file.

· The dataset(s) that are used for your problem.

· The source code with appropriate comments, and

· The demonstration video.

Note:

· No other platform. is accepted. Please ensure that it is COVENTRY GitHub or COVENTRY OneDrive.

· Make sure that you add [email protected], [email protected] and [email protected] as Collaborators to facilitate marking

· The submitted source code must be in the form. of a (Jupyter) notebook (.ipynb)

· You must ensure that you commit your work appropriately (with the corresponding outputs of all cells – if applicable – clearly present)

· Only include the notebook for your final submission (i.e., remove all draft notebooks)

· The following naming convention must be used for your repository or shared folder:

StudentID-Initials-s1

For example, a student Stanley Bassey whose student ID is 12345678 would name their repository or shared folder as 12345678-SB-s1

· A failure to use this naming convention may delay the release of marks and feedback for your coursework.

Marking and Feedback

How will my assignment be marked?

Your assignment will be marked by the module team.

How will I receive my grades and feedback?

Provisional marks will be released once all submissions, including extensions, have been marked and internally moderated.

Feedback will be provided by the module team on Aula alongside the release of grades.

Your provisional marks and feedback should be available within 2 weeks (10 working days) after the extended deadline.

What will I be marked against?

Details of the marking criteria for this task can be found at the bottom of this assignment brief.

Assessed Module Learning Outcomes

The Learning Outcomes for this module align to the marking criteria which can be found at the end of this brief. Ensure you understand the marking criteria to ensure successful achievement of the assessment task. The following module learning outcomes are assessed in this task:

1. Compare supervised or unsupervised approaches to machine learning problems.

2. Select, apply and justify different pre-processing methods for a given dataset.

3. Select, apply and compare appropriate machine learning algorithms, techniques and methods to create and optimise different learning models for machine learning problems.

4. Communicate and critically evaluate the results from applying machine learning models, and recommend the most appropriate models for a given task.