Chapter 1: Not mtcars AGAIN

In this first case study, you will predict fuel efficiency from a US Department of Energy data set for real cars of today.

1Make predictions using machine learning

2Choose an appropriate model

3Visualize the fuel efficiency distribution

4Build a simple linear model

5Getting started with tidymodels

6Training and testing data

7Train models with tidymodels

8Evaluate model performance

9Use the testing data

10Let's sample our data

11Bootstrap resampling

12Plot modeling results

About this course

This is a free, open source course on supervised machine learning in R. In this course, you'll work through four case studies and practice skills from exploratory data analysis through model evaluation. Ines Montani designed the web framework that runs this course, and Florencia D'Andrea helped build the site.

Contributions and comments on how to improve this course are welcome! Please file an issue or submit a pull request if you find something that could be fixed or improved.

Creative Commons License

About me

My name is Julia Silge and I'm a data scientist and software engineer at RStudio where I build modeling tools. I am both an international keynote speaker and a real-world practitioner focused on data analysis and machine learning practice. I love making beautiful charts and communicating about technical topics with diverse audiences.