Module 3: Ridge Regression
Module Overview
In this module, you will build on your regression knowledge with ridge regression. You'll learn about one-hot encoding, feature selection, and how regularization can improve model performance. These techniques will help you handle categorical variables and build more effective models with many features.
Learning Objectives
- Implement one-hot encoding of categorical features
- Implement a univariate feature selection process
- Express and explain the intuition and interpretation of ridge regression
- Use sklearn to fit and interpret ridge regression models
Guided Project
Open JDS_SHR_213_guided_project_notes.ipynb in the GitHub repository below to follow along with the guided project:
Guided Project Video
Module Assignment
Complete the Module 3 assignment to practice ridge regression techniques you've learned.
Assignment Solution Video
Resources
Documentation and Tutorials
- Scikit-learn: OneHotEncoder
- Scikit-learn: Feature Selection
- Scikit-learn: Ridge Regression
- Scikit-learn: Ridge Class