In the second lesson of the series, we'll learn how to fit and interpret a simple linear regression with a categorical predictor. We'll use a simulated dataset to predict the amount of time someone will spend on a website based on the browser they are using. We'll also predict the rental prices of NYC apartments based on the borough where they are located. In the process, we'll go over some important graphing skills and continue to practice writing code in Python.
Here are some Stack Overflow questions related to the work we did in today's session:
- Linear regression with dummy/categorical variables
- Linear regression with string/categorical features (variables)?
If you want to ask any questions or provide feedback on the lesson, you are welcome to leave a comment on the YouTube recording of this lesson. If you’d like to watch a session live, follow the Codecademy YouTube channel. We'll be live again on Tuesday at 11am EDT to introduce multiple linear regression. You can join that session here.
Finally, if you want even more linear regression content, you can sign up for the Linear Regression in Python interactive course this series was based on. This course was developed by Sophie and has many more quizzes, projects, and helpful nuggets that we can’t fit into our streams!