Assessment Component: Individual Project (individual report to be submitted) You should prepare and submit a written project report along with the Python code as a combined Google Colab / Jupyter Notebook
CO7062 AI for Modern Use-Cases Coursework (Individual Project) Assessment Brief 2026 | Chester
CO7062 Assessment Brief
| Module title | AI for Modern Use-Cases |
| Module code | CO7062 |
| Assessment task title | Coursework (Individual Project) |
| Summary of assessment task and rationale | You should prepare and submit a written project report, in accordance with the information and instructions provided in Appendix-A (pages 4 and 5 of this document). Individual report to be submitted by each student. |
| Word limit and guidance on submission |
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| Weighting | This assessment task comprises 40% of the total assessment for this module. |
| 7-day submission window | YES |
| Submission deadline | By 1:00 PM, Tuesday, 6th June 2026 |
| Deadline for deferral to next assessment point | 23 June 2026 |
| Eligible for in-year reassessment | No |
| FEEDBACK & LEARNING OUTCOMES | |
| Feedback and provisional marks release date | 9th August 2026 |
| Learning outcomes that will be assessed |
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| For Apprenticeships: Knowledge, Skills and Behaviours linked to the assessment | — |
| In-module support opportunities including formative feedback | Your Module Leader should be able to provide formative feedback on your draft. |
| If you require support with your assessment, please contact | Module Leader: Samar Ansari m.ansari@chester.ac.uk |
Guidance on the completion of the assessment task
Submission for this assignment will be online, via the Turnitin box on the ‘Assessment Information C Submission’ tile on Moodle page for CO7062.
You must submit your work as a SINGLE document in .html format.
Documents submitted through other routes (eg. via email) or in other formats (eg. Open Office / .docx / .pdf) will not be marked.
You must ensure you retain a copy of your completed work prior to submission.
Assessment Component: Individual Project (individual report to be submitted)
You should prepare and submit a written project report along with the Python code as a combined Google Colab / Jupyter Notebook, and convert/save it as .html file with all text, comments, code, and results included, in accordance with the information and instructions provided in Appendix-A (pages 4, 5 of this document).
Marking Criteria
Marking Criteria for assessment is based on the generic marking criteria for level 7 detailed in the Programme Handbook and Moodle page.
Specifically, for this coursework component, the submission should demonstrate:
- Clear understanding of the background of, and the methodology adopted for the project work.
- Evidence of competence pertaining to the tasks assigned in the project.
- Clear and concise reporting of the results and inferences.
- High quality of communication skills and overall presentation of the written document including correct referencing.
Use of Artificial Intelligence (AI) Tools
For this assessment task, you [are] permitted to use artificial intelligence tools in accordance with the following guidance:
While preparing this assessment, you may use [Generative AI tools such as ChatGPT, Google Gemini, and other similar tools] in the early phases of planning your submission, but you may not generate the majority of the submission using the tools. You must declare at the start of the submission how you have used the tools to plan any elements of the assessment task and reference your use of these tools appropriately. If you choose to use AI to help you plan, you should provide the prompts that you used to generate the output. AI tools that are linked to spelling, punctuation, and grammar (e.g., Grammarly, Draft Coach) may be used within this assessment.
The University Academic Conduct Policy explains how students are expected to take responsibility for the fair presentation of the contents of any work they present for assessment. This includes acknowledging the use of Artificial Intelligence tools. Breaching the academic conduct policy can have serious penalties.
Exceptional Circumstances and Assessment Regulations
You can find details about what you need to do if you are unable to submit the assessment on time on the Registry Services Exceptional Circumstances Portal page.
You can find out more about University regulations related to assessment on the Registry Services Assessment Regulations page.
Appendix - A
Instructions:
- This project is to be done individually
- Each student must submit their individual report on Turnitin
- Only ONE .html file will be accepted per submission
- Python code must be included, along with the results and text comments in the report.
Project Tasks
S. No. | Task | Marks |
1. | Each student must first select a freely available (open-source) dataset related to one of the following fields: o Business o Finance o Commerce o Management o Social Sciences o Bioinformatics o Healthcare The chosen dataset should not be too small, and should be such that it should have at least 10 features and at least 1000 samples, and the features should be a mix of both numeric and categorical (e.g. if the chosen dataset has 10 features, 4 may be categorical and the remaining 6 may be numeric). As a depiction of the dataset you have chosen, include the first-5 and the last-5 rows of the dataset in the report, along with the header (the names of the columns). This can be done as a ‘screenshot’ taken from the dataset. The rationale for the choice of the dataset, and the URL (link) to download the dataset must be included in the report. Include the Python code for this as well as all the subsequent steps in this assignment. Clear and detailed commenting on the important portions of the code must also be included for all the code(s) in this entire assignment. | 3 |
2. | Using Python codes, identify and report the following from the dataset: o Number of features o Number of samples o Number and labels (categories) of categorical variables o Number of numeric features o Range, Mean, Ǫ1, Ǫ2, Ǫ3, Standard Deviation for all the numeric variables in the dataset (to be calculated and reported separately for each numeric variable) A brief comment about the inferences drawn by the student from the above observations must also be included in the report. | 3 |
3. | For all the numeric features, perform the Min-Max Scaling operation using Python, and append the normalized values of the numeric features in the dataset as new columns. Insert the first-10 and last-10 rows of the new columns only into the report, along with the header row (names of the columns). | 3 |
4. | For all the categorical variables, perform the Label Encoding operation using Python, and append the label-encoded values of the categorical features in the dataset as new columns. Insert the first-10 and last-10 rows of the new columns only into the report, along with the header row (names of the columns). | 3 |
5. | Using Python, generate 5 data visualizations from the dataset which you believe are the most potent ones (in the context of the chosen dataset) for highlighting patterns and trends in data. Include the relevant Python code in your report. | 5 |
6. | Looking only at the numeric variables in the original dataset, identify and perform 2 regression tasks that may be performed on the dataset. Include the results in your report. Comment on the quality of the results obtained. Include the relevant Python code in your report. | 10 |
7. | Looking only at the categorical variables in the original dataset, identify and perform 2 classification tasks that may be performed on the dataset. Include the results in your report. Comment on the quality of the results obtained. Include the relevant Python code in your report. | 10 |
8. | In the context of the specific field/domain of the dataset (e.g. finance / healthcare / etc.), comment on how the dataset may be used by the company or organisation for improving their organization/company |
