Overview
Linear regression is one of the most basic and widely used prediction techniques in machine learning.
It models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the data.
Key Concepts
- Simple vs. Multiple Linear Regression
- Ordinary Least Squares method
- Assumptions of Linear Regression
- Evaluation metrics (R², MSE, RMSE, MAE)
- Regularization techniques (Ridge, Lasso)
Practice Exercise
Exercise: Boston Housing Price Prediction
Using the Boston Housing dataset:
- Split the data into training and testing sets
- Build a simple linear regression model
- Evaluate the model using appropriate metrics
- Improve the model by adding more features
- Compare the performance of different regularization techniques
Resources
StatQuest: Linear Regression
Main resource for today
Linear Regression in Python
Step-by-step guide with scikit-learn
Assumptions of Linear Regression
Understanding and testing assumptions
Regularization Techniques
Ridge, Lasso, and Elastic Net
Complete Today's Task
Mark today's task as complete to track your progress and earn achievements.