In this assignment, you will refine your data analytic model by revising your code. This will ensure integrity, quality, and reproducibility of the results of your data analytic plan model. Points of revision include data cleaning, data exploration, modeling code, and modularizing code.
Overview: In this assignment, you will refine your data analytic model through revising your code. This will ensure integrity, quality, and reproducibility of the results of your data analytic plan model. Points of revision include data cleaning, data exploration, modeling code, and modularizing code.
Prompt: For this assignment, make clear and frequent comments throughout to explain the process. Include screenshots of your code to demonstrate where the output originated. Be sure to provide titles or footnotes to any sections of the code that produce output. When you are finished, submit this documentation to your instructor for feedback.
Also, for this assignment, modularize and generalize your code into separate but interacting sections. Keep three primary goals in mind:
Ensure that the code is robust (“bullet-proof”) so that future users are less prone to making unseen or hard-to-trace errors
Ensure that the code is flexible enough to deal with potential new sources of data, updated versions of the existing data, and/or scaling for significant
increases in data.
Make the code modular so that all pieces do not have to be run in order to produce a specific piece of output. For instance, a change to the covariates in
the model should necessitate only that the model portion of the code is run, not the initial data cleaning and exploration code.
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