Assignment Prediction Model

Graded 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.

Requirements

• explain the dataset in 1 or 2 paragraphs
• use tidyverse
• 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: 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!

Grading

Make sure to check the rubric that we are going to use for grading the assignments.

• The different aspects get the following weights:
  • Explanation of the data set (15%)
  • Correctness of code and output (30%)
  • Interpretation/explanation of approach and results (30%)
  • Clarity of code and reproducibility (15%)
  • Layout and overall appearance (10%)