K-Means Clustering

Source Branch: lab_kmeans
Due Date: Committed and pushed to git before April 20, 2020 at 11:59pm for 105% credit
Regrade Date for COVID-19 assignments: Committed and pushed to git before May 6 at 11:59pm for 100% credit

Step 0: Install scikit-learn

You only need to do this once, but make sure you’ve installed scikit-learn:

conda install -c anaconda scikit-learn

Step 1: Retrieve the lab using git

Using your command line, navigate to your stat107 repository (cd Desktop -> cd stat107 -> cd [NETID]) and fetch the notebook from our release repository by running the following two git commands:

git fetch release
git merge release/lab_kmeans -m "Merging initial files"

ONLY IF you get an error related to unrelated histories, use:

git merge release/lab_kmeans --allow-unrelated-histories -m "Merging initial files"

Step 2: Open the notebook

Open the notebook with the command:

jupyter notebook

Inside of the notebook webpage:

  • Navigate into lab_kmeans
  • Open up the lab_kmeans.ipynb notebook
  • Follow the instructions inside of the notebook

Submitting Your Work

When you have completed working, you should always submit your work (even if you're not quite finished). We will always grade the latest push you made before the due date (and ignore everything else) — submitting multiple times is okay and encouraged!

Inside of Jupyter:

  • Click File -> Save Checkpoint to ensure your notebook is saved.
  • Click File -> Close and Halt to exit your notebook.
  • Click Quit (in the top-right) to close the directory view.

After exiting Jupyter, your command prompt will return to accept new commands. Using your command prompt, run: 

git add -A
git commit -m "submission (or any message here)"
git push origin master

You can verify your submission was made by visiting the web interface to github: