CC7184 Data Mining and Machine Learning Coursework Assignment Summer Semester 2025/26

CC7184 Coursework Assignment Detail: 

The coursework is an individual assessment weighted 50% of the marks for the module. It is primarily an exercise in applying data mining knowledge and techniques to a practical problem. This assignment involves the analysis of one of the largest collections of UK and other nations’ social, economic and population data provided by government department and agencies, public bodies and local authorities. You are asked to mine the data to help tackle key challenges that many communities face in the UK and other nations such as the skills gap, poverty and inequality, discrimination, homelessness, crime, pollution, and health and well -being. You need to provide a journal-type report following the CRISP-DM method and a critical evaluation of possible benefits and commercial risks of the project.

CC7184 Coursework Submission details: 

All your work should include the data mining journal-type report in PDF, python codes associated with the coursework, or a link to a Google Colab Notebook with the codes implemented for the coursework. The report also should have a cover page with the module code and title, your data mining project title, student ID and name. The file must be submitted via WebLearn. Please note that plagiarism is a serious academic offence, for which penalties are severe. All suspected cases of plagiarism will be reported. 

Late submission will be penalised according to the University regulation.

CC7184 Detailed Specifications 

The dataset used for the coursework should be based on real-world data sets provided by data published by government departments and agencies, public bodies and local authorities, which are available as mentioned below and also 5 dataset are made available for the students on Weblearn, you can you that as an alternative dataset too.

You are asked to use Python to mine the data to discover some interesting patterns of UK or international data to help tackle key challenges which many communities face in UK such as the skills gap, poverty and inequality, discrimination, homelessness, crime, pollution, and health and well-being. You are required to select 1 supervised and 1 unsupervised machine learning model and demonstrate a comprehensive understanding of data mining and machine learning fundamental concepts and comparing the different models applied. 

The coursework is divided into three parts (Proposal, peer review and the final journal report) which must cover all the stages of the CRISP-DM method:

1. Business Understanding
2. Data understanding
3. Data preparation and exploration
4. Modelling and model assessment
5. Evaluation
6. Plan for deployment

CC7184 Report Structure:

The IEEE format journal must contain the following sections:

1. Abstract (150 words max): a short outline of the project and the main findings.
2. Introduction: a brief summary of the project.
3. Related Work: a literature review on the studied topic and similar projects and critically review their works.
4. Methodology: description of data preparation and modelling methods.
5. Results and discussion: discussion of the results and the evaluation of the models. All plots must be included as figures; a single figure can have more than one plot; all figures must have a caption and must be cited in the text.
6. Conclusions and perspectives: This section should include a critical evaluation of the possible benefits and commercial risks of the project.
7. Code: Link to Google Colab/Jupyter Notebook project. Check that the code runs correctly.
8. References: Add citation and references following IEEE format

A journal-type report with a maximum of 2000 words (excluding bibliography) and a maximum of 6 figures/images. You need to include the mining topic for the coursework, a background on the mining topic, the objectives of the project, and the data sets. This part of the coursework will cover the CRISP-DM method which you will submit a journal-type report providing details on the whole CRISP-DM cycle and an evaluation of possible benefits and commercial risks of the project. The coursework should be submitted via WebLearn.

Learning outcomes of CC7184 :

Aim on achieving the following learning outcomes with the completion of this project:
LO1: Demonstrate a comprehensive understanding of data mining and machine learning fundamental concepts, algorithms and process
LO2: Demonstrate an understanding of the purpose and breadth of areas of application of data mining and machine learning
LO3: Identify machine learning algorithms appropriate for particular classes of problems
LO4: Undertake a comparative evaluation of the strengths and limitations of various data mining techniques
LO5: Comprehensive understanding of the state of the art techniques in data mining and machine  learning
LO6: Demonstrate capacity to perform a self-directed piece of practical work that applies data mining techniques in a real-world problem and considers potential commercial risk.