There are very few restrictions on this activity. 

Unlike our other assignments, you may use whatever languages and ML packages you would like. We recommend you continue to use Python / Jupyter / SKLearn as that is what we will be supporting in this class / on Piazza.

Collaboration is slightly different than usual:

- You can share methodology / technique insights with your peers or publicly (e.g. on Piazza)
    - Do not post implementation questions publicly on Piazza (if someone does, please ignore it)
        - instead, reach out to your course staff for coding issues (You can post a private question on Piazza) 

Do not go find other copies of this dataset online. You can find and incorporate external data that can provide a benefit (e.g. if somehow Twitter sentiment or stock market analysis were to prove useful, which I doubt, you could add it to your modeling process), but if you find the same dataset, please do not use it in your model training.

Submissions cap of 2 per day is implemented in Kaggle. Your top score will post on the public leaderboard, and you can select two submissions to be used at the end of the competition against the private test set.

Outcomes will be calculated using F1 score, which will then be mapped to grade outcomes out of 100 Points. Your grade will be measured on Max(Public Score, Private Score). Please see the "Overview" page for further details on grading.