You are to build and compare predictive models using the data provided below. You are to
define a machine learning problem, i.e., the input/output, and what they can be used for.
3 Submission and Marking Scheme
3.1 Jupyter Lab/Notebook file (0%)
The file should have the name of [your_Banner_ID].ipynb, and it should include all the codes
you used for the project. Comments should be given when necessary. It is not going to be
marked, but no-submission or not-working code will result in a zero mark for the whole
3.2 PDF report (100%)
The file should have the name of [your_Banner_ID].pdf, max 4 pages (including everything).
3.2.1 Title, Abstract, Keywords, Introduction (12%)
Provide a brief introduction to the project. Precisely define the problem you are solving, i.e.,
formally specify the inputs and outputs. Frame the problem as a machine learning task.
Explain why this task is interesting and important.
3.2.2 Exploratory Data Analysis and Data Preparation (24%)
Summarise the main characteristics of the dataset, using tables and statistical graphics,
and/or other data visualisation methods. Describe how you split the dataset and present
stats such as count, mean, etc. Describe how you constructed and/or transformed the
3.2.3 Learning Algorithm Selection (8%)
Choose three machine learning algorithms, which are not necessarily taught in this
submodule. They cannot be based on deep learning or reinforcement learning. Explain why
the chosen algorithms are appropriate for the project.
3.2.4 Model Training and Evaluation (32%)
Describe the training process, including the parameters involved and how they fit, concerns
about underfitting and/or overfitting, and concerns about the convergence of the
optimisation. Describe the hyperparameter selection and tuning process, including the
hyperparameters involved and how they were selected and tuned, the candidate values that
were considered, and the performance metric that were used for optimisation. Describe the
evaluation metrics and explain why they are appropriate.
3.2.5 Model Comparison (15%)
Describe how you compared the trained models, using tables and statistical graphics and/or
other data visualisation methods.
3.2.6 Conclusion and Discussion (9%)
Summarise the project: what the project was about, what you did, what the results were,
the major limitations of the approach you used, what could have been done to improve the
procedure and the result, the lessons you learned through this project.
3.2.7 References (0%)
List all the external references.
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