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
15 freelancers are bidding on average $191 for this job
full stack developers, we have worked on many projects using latest tech stack for A.I, Machine Learning and IoT in web and mobile app development. tech we use in our projects:
Ih ave gone through the project description and will be happy to assist you in the project. I have 4+ years of expertise in developing and deploying machine learning and deep learning models.