In the sixth lesson of the series we'll discuss some methods for data transformation to improve a linear regression model. In the process, we'll learn to simulate data with known properties, review some of the assumptions of linear regression, and continue to practice our Python skills.
Here are some Stack Overflow questions related to the work we did in today's session:
- More efficient way to mean center a subset of columns in a pandas dataframe and retain column names
- How to interpret results of Linear Regression after log-transforming the target variable?
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, June 29 at 11am EDT to discuss methods for comparing regression models. 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!