Final Project

Summary of deliverables

Deadline for all deliverables: May 2, Friday, 11:59pm

Deliverables (everything should be in your GitHub repository):

  • 5 minute video presentation (in your GitHub repository)
  • GitHub repo with all data and code
  • A README.md file explaining what you did for your project (2 paragraphs)

You are to submit your link to your GitHub repository to Canvas.

GitHub

  1. Create a GitHub account:
    1. Navigate to https://github.com/.
    2. Click Sign up.
    3. Follow the prompts to create your personal account.
  2. Create a new GitHub repository:
    1. Click “New repository”: Look for the “+ New repository” button in the upper-right corner of the page and click it.
    2. Name your repository: Enter a short, memorable name for your repository.
    3. Add a description (optional): You can add a brief description to explain what the repository is about.
    4. Choose repository visibility: Decide whether your repository should be public (visible to everyone) or private (only accessible to you and collaborators). If you decide on a private repository, make sure you share it with me (my GitHub handle is picoral)
    5. Click “Create repository”: Once you’ve filled in the details, click the “Create repository” button to finalize the process.
  3. Add your files to your repository:
    1. Click to open your repository
    2. Click on Add File (top right of your repo file listing), then choose Upload Files
    3. Drag the files you want to add to your repo
    4. Scroll down and click on Commit changes

If I am unable to access your repository to see your files, your grade will be zero.

Data (data_wrangling.py)

You will choose your your data source. Here are some options where you can find data:

You should have all of your python files in your GitHub repo, including data wrangling/cleaning scripts.

Visualization (plots.ipynb)

Create a python notebook with at least two plots to visualize your raw data.

Modeling (modeling.py)

Create a python script with your modeling code.

Use comments to explain what you did

README (README.md)

Include in your README file information about your data and variables. What modeling approach you used and why. State your performance metrics, what you used, and what results you got.