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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