In the fifth lesson of the series we'll learn how to build more flexible linear models by adding interaction and polynomial terms. We'll fit and inspect our models both mathematically and visually to understand how they work. In the process, we'll continue to practice our Python skills and discuss some of the merits (and drawbacks) of added complexity.
Here are some Stack Overflow questions related to the work we did in today's session:
- Polynomial regression using statsmodels.formula.api
- Difference between the interaction : and * term for formulas in StatsModels OLS regression
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 15 at 11am EDT to discuss polynomial and interaction terms, which can be used to build more flexible 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!