Individual Assignment AM11
1. Project Selection: Choose a problem where you will use at least one out of the 5 
topics that you have learnt to help solve a problem of your choice (CNN, SVM, Text Mining, 
PCA, Recommendation Systems). 
Þ The project should have a well-defined goal, such as classification, clustering, 
recommendation etc.
Þ Plagiarism will result in 0 marks (e.g. replication of an existing Kaggle notebook). 
Your work must be original and well documented to explain your workings.
Þ The complexity of your project should match the time available for submission.
Þ The complexity of your work will reflect your grade (e.g. if you decide to work with a 
dataset that requires PCA pre-processing before classifying with SVM, thus utilising 
two out of five algorithms that you have learnt).
2. Dataset: Use an open dataset (e.g., from Kaggle, UCI ML Repository, etc.) or collect 
your own, ensuring it has enough samples but that it is not too large (you should be able to 
run your analysis on your laptop). For classification problems, ensure to properly balance 
your classes. 
3. Methodology:
• Explain why the chosen technique is suitable for the problem.
• Preprocess the data appropriately.
• Train and evaluate the model using appropriate performance metrics.
• Compare with at least one baseline model
4. Implementation (.py or .ipynb):
• Use Python (with libraries like TensorFlow, Scikit-learn, Pandas, etc.).
• Ensure reproducibility (seed the random number generator where 
appropriate, provide a Jupyter Notebook (and its knitted output) or a well-documented .py 
script).
5. Report (pdf):
• Introduction: Explain the problem and dataset. Ensure to supply references. If 
you can produce your how to use TeX Studio and LaTeX.
• Methodology: Describe preprocessing, model selection, and training.
• Results & Discussion: Present evaluation metrics, visualizations, and insights.
• Conclusion: Summarize the findings and suggest future improvements.
Your report should be a maximum of 3 pages long, in an Arial 11 font with standard margins.
Demonstrate the art of concise writing (brevity, economy of words, clarity and precision). 
Ensure your figure axes labelling and tickers are legible.
6. Grading Criteria:
You will be evaluated on both the technical execution and on your ability to communicate 
your findings. 
Category Weight Description
Problem clarity & justification 20% Clearly defines the problem, explains its 
relevance, and justifies the chosen ML 
technique.
Data preprocessing & exploratory 
analysis
20% Properly cleans, preprocesses, and 
visualizes the data; identifies key patterns 
and challenges.
Model selection, training, and 
evaluation
30% Implements an appropriate model, explains 
parameter choices, evaluates performance 
with meaningful metrics, and compares with 
a baseline.
Interpretation & discussion of 
results
20% Provides insightful analysis, interprets 
results, discusses limitations, and suggests 
improvements.
Code quality & reproducibility 10% Code is well-documented, structured, and 
reproducible; submission includes a Jupyter 
Notebook or well-commented script.

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