Module 9: Regression Analysis

Predicting continuous targets. Using the Ames Housing dataset throughout, this module runs from simple linear regression to robust, regularized, polynomial, and tree-based regression.

What You’ll Learn¶

• Ordinary least squares: analytic solution and gradient descent

• Exploratory data analysis and correlation for regression problems

• Robust regression with RANSAC

• Evaluating regressors: residual plots, MSE/MAE, and R²

• Ridge, LASSO, and Elastic Net regularization

• Polynomial regression, decision tree and random forest regressors

Lecture Materials¶

• 📝 Lecture Notes (PDF)

• 📓 Chapter notebook: Predicting Continuous Target Variables with Regression Analysis

Prerequisites¶

• Modules 2–4 (gradient descent, preprocessing, regularization)

Reference¶

Raschka & Mirjalili, Machine Learning with PyTorch and Scikit-Learn, Chapter 9.