Machine Learning

In this work, you will need to present your own code that performs nested cross-validation and the k-nearest neighbour algorithm, build confusion matrices, and estimate distances between data samples.

The purpose of the coursework is to help you:

Get familiar with common python modules / functions used for ML in python

Get practical experience implementing ML methods in python

Get practical experience regarding parameter selection for ML methods

Get practical experience on evaluating ML methods and applying cross-validation


don't use libraries that implement kNN or cross-validation. We want to see your code!

Remember to comment all of your code (see here for tips: [login to view URL]). You can also make use of Jupyter Markdown, where appropriate, to improve the layout of your code and documentation.

Please add docstrings to all of your functions (so that users can get information on inputs/outputs and what each function does by typing SHIFT+TAB over the function name. For more detail on python docstrings, see here: [login to view URL])

When a question allows a free-form answer (e.g. what do you observe?), create a new markdown cell below and answer the question in the notebook.

Always save your notebook when you are done (this is not automatic)! Please save your file as CW_<your name>.ipynb before submitting.

please make sure that the material you submit has been created by you. Any sources you use for code should be properly referenced

Skills: Python, Machine Learning (ML), Software Architecture, Java, C Programming

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