Pass / Fail assignment
Find a dataset and create and form, build and evaluate a prediction model.
You can use data from anywhere. For example, you may use Google dataset search, Kaggle datasets, a dataset from an
R package, or something you collected yourself.
- explain the dataset in 1 or 2 paragraphs
- clean, legible
R code (preferably following something close to the Google style guide)
- a simple model
- improved by a more complex model
- explain which method you use (regression/classification and what exactly)
- assess your predictions
- interpret the parameters of your method, if applicable.
- if no parameters, interpret the contribution of the features have to the model
- make conclusions about your predictions
- use plots where useful (they are almost always useful)
Format: GitHub submission of an RStudio project folder
- Should have these components:
- the dataset (csv, xlsx, sav, dat, json, or any other common format)
- one .Rmd (R Markdown) file
- a compiled .pdf or .html
- we should be able to compile the .Rmd to the same .pdf or .html. That means no errors!