Overview
This module will not have a traditional team-based case study as in other modules, instead, you'll have a chance to demonstrate your skills with a small individual project of your choosing.
This project is not intended to be a large, overwhelming project, but rather a chance for you to demonstrate that you can work with a dataset from start to finish, using the tools and processes discussed in the course.
You may choose any dataset, libraries, and tools you would like, with the exception that you should select a dataset we have not already used in class this semester. You do not have to use theses resources, but the UCI Data Repository and Kaggle have many freely available datasets that you might consider.
Requirements
For this project you need to demonstrate your skills in the following areas:
- Data preprocessing
- Selection, training, and use of a machine learning model
- Interpretation of results (for example, using proper metrics, understanding limitations)
- Communication of results
- Ethical implications
Submission
Please submit a document (.docx or .pdf) with the above requirements as headings. Then, after each heading, briefly describe your work in that area.
At the end, make sure to include:
- A link to your code
- The category that best describes your project along with a brief justification.
The standard categories are:
- All requirements were met and additional work was done to demonstrate creativity and excellence by going above and beyond.
- All requirements were met.
- Some attempt was made, but was slightly deficient in approach or understanding.
- Some attempt was made, but was significantly deficient in approach or understanding.
- Some attempt was made, but was extremely deficient in approach or understanding.
- No attempt was made.
Your entire submission should be less than two pages (not including a title page).