This project requires that you apply machine learning techniques taught so far to build predictive regression models on current, topical and original data from your chosen domain. You are expected to carry out an entire data science/analytics workflow by: (1) acquiring data from multiple sources, (2) performing data wrangling, (3) integrating the data, (4) conducting analysis to answer some key research questions and finally (5) perform predictive modelling.

- Each student should aim to create a unique and distinctive data-problem to work on that is made original by combinations of different data sources. The goal of the project is to perform prediction analysis.

- Build multiple regression and kNN models and compare their outputs.

- Experiment with models using different features. Which features are most effective? Why?

- Experiment with kNN using different distance metrics and different values of k, and compare the outputs. Which values of k are most robust for the size of your dataset and your problem domain? Are variables in your data having different scales affecting the algorithm’s accuracy? How have you tried to overcome this?

- Experiment with linear, multiple linear and polynomial regression models and compare them. At what point does a regression model become too complex and no longer captures the true relationships in the data?

- How reliable are your prediction models? What do the confidence intervals and prediction bands tell you? Could you recommend this predictive model to a client? Would you expect this model to preserve its accuracy on data beyond the range it was built on?

- Is your evaluation approach robust enough to be able to draw conclusions about the utility of your models?

Do not use absolute paths, but instead use relative paths if you need to. Consider hiding away some of your Python code in your notebook by putting it into a .py files that you can import. This might help the readability of your final notebook by  removing unnecessary python code that can clutter and distract from your actual findings and discussions.

You may install and use any additional Python packages you wish that will help you with this project. When submitting your project, include a README file that specifies what additional python packages you have installed in order to make your project repeatable on my computer, should I need to install extra modules.

Follow the general structure of the Project Notebook Template provided. Make your notebook professional and tidy (avoid large data dumps) and run your text through an IPython Notebook spell-checker extension. You can also pretend that you are a consultant performing an analysis for a client.

Marks will be awarded for different components of the project using the following rubric:

If you have any questions or concerns about this assignment, please ask the lecturer sooner rather than closer to the submission deadline.

In industry, AI and online resources are commonly used to improve efficiency and productivity. However, at university, the primary goal is to develop your understanding, analytical skills, and ability to work through problems independently. Mastering these skills first will allow you to use AI tools more effectively and critically in the future. While AI can be a helpful tool for learning, relying on it to generate answers directly will short-circuit your learning and development.

For this project, you are required to independently select, wrangle, analyze, and interpret datasets from your chosen domain. You will also maintain a reading log, where critical engagement with academic sources is expected and integrated into your analyses where relevant. The use of generative AI is restricted to planning, explanation, and concept development, as outlined below.

You may use AI for conceptual understanding, guidance, and general problem-solving strategies, but NOT for directly completing any part of your assignment. Specifically, AI can be used to:

You must NOT: