There are two main goals of the project:


The final product of this project will be a written report of your analysis. The report will be written using rmarkdown and rendered into a .html file. The YAML header should use the following template:

title: 'A Unique and Interesting Title'
author: "Your Name"
date: 'NetID'
abstract: 'This will be the abstract!'
    theme: simplex

Be sure to include your name and NetID and to give the report an interesting title. (Using the above YAML header will take care of this.)

For the report format we will utilize the IMRD organization structure. See also this helpful IMRD cheat sheet from CMU.

Using the IMRD structure, your report must contain the following sections:


Even though it is the first thing to appear in the report, the abstract should be the last thing that you write. Generally the abstract should serve as a summary of the entire report. Reading only the abstract, the reader should have a good idea about what to expect from the rest of the document. Abstracts can be extremely variable in length, but a good heuristic is to use a sentence for each of the main sections of the IMRD:

  • Why are you doing this analysis? (Introduction)
  • What did you do? (Methods)
  • What did you find? (Results)
  • What does it mean? Why does it matter? (Discussion)

To add an abstract to a report written in rmarkdown, see the use of abstract in the YAML example above.


The introduction should discuss the “why” of your analysis and the “what” of your data. Essentially, you need to motivate why the analysis that you’re about to do should be done. Why does this analysis need to be done? What is the goal of this analysis? The introduction should also provide enough background on the subject area for a reader to understand your analysis. Do not assume your reader knows anything about the subject area that your data comes from. If the reader does not understand your data, there is no way the reader will understand your motivation.

Since we are providing data, but not a scenario, you can create any reasonable scenario that you would like.

You do not need to provide a complete data dictionary in the introduction, but you should include one in the appendix. Often the data would be introduced in the Methods section, but here the data is very closely linked to the motivation of the analysis.

Consider including some exploratory data analysis here, and providing some of it to the reader in the report if you feel it helps present the data.


The methods section should discuss what you did. The methods that you are using are those learned in class. This section should contain the bulk of your “work.” This section will contain most of the R code that is used to generate the results. Your R code is not expected to be perfect idiomatic R, but it is expected to be understood by a reader without too much effort. The majority of your code should be suppressed from the final report, but consider displaying code that helps illustrate the analysis you performed, for example, training of models.

Consider adding subsections in this section. One potential set of subsections could be data and models. The data section would describe your data. How will it be used in performing your analysis? What if any preprocessing have you done to it? The models section would describe the modeling methods that you will consider, as well as strategies for comparison.

Your goal is not to use as many methods as possible. Your task is to use appropriate methods to find a good model that can perform the desired statistical learning task.


The results section should contain numerical or graphical summaries of your results. What are the results of applying your chosen methods? Consider reporting a “final” or “best” model you have chosen. There is not necessarily one, singular correct model, but certainly some methods and models are better than others in certain situations. The results sections is about reporting your results.


The discussion section should contain discussion of your results. That is, the discussion section is used for commenting on your results. This should also frame your results in the context of the data. What do your results mean? Results are often just numbers, here you need to explain what they tell you about the analysis you are performing. The results section tells the reader what the results are. The discussion section tells the reader why those results matter.


The appendix section should contain any additional code, tables, and graphics that are not explicitly referenced in the narrative of the report. The appendix must contain a data dictionary.


The data for this project originates from Kaggle.. We will use the House Sales in King County data.

The data has been re-hosted on the course website. Do not supply the data when you submit your project. Instead, read the file into your .Rmd document by linking to the provided data.


Some background:


The 100 points for the project will be assigned as follows:



How long should the report be?