For this quarter's final lab, you will be using the knowledge of machine learning that you have gained this quarter to training models to predict dissolved inorganic carbon in water samples collected by the [California Cooperative Oceanic Fisheries Investigations](https://calcofi.org/) program (CalCOFI).  

## Your Task

Acquire domain knowledge. (Dr. Satterthwaite's presentation)
Explore the data.
Preprocessing.
Choose a model algorithm.
Tune relevant parameters (Cross validation).
Submit your prediction.

##Submitting your lab and predictions

You will first need to create an online notebook on Kaggle.  You can set it up to be .Rmd form and then paste code from an .Rmd in RStudio into the notebook.  This is where your lab will be graded, to make sure you have proper documentation of your code for each of the steps in the model process.

If you'd like to work as a team you can.  Each team member will create their own account but you will have a joint submission.

Kaggle's [competition documentation](https://www.kaggle.com/docs/competitions)

Instructions on [submitting predictions](https://www.kaggle.com/docs/competitions#making-a-submission).

Once you have created your online notebook and used it to submit your predictions for the competition, please share (via share button at the top right of the notebook screen) your notebook with both Brian (user name: bhyleee) and I (Mateo Robbins) so we can view and grade your code.  Don't forget to annotate you code and guide the viewer through the ML steps you went through and the decisions you made along the way.

Kaggle has the test data that it will use to evaluate your submission's performance.  You will have both a public and private score, based on different subsets of the test data.  The public score will be visible to everyone so we can see who is winning.  The private score will be used to determine the winner.
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## Acknowledgements

We thank CalCOFI and Dr. Erin Satterthwaite for providing this dataset.