An-Introduction-to-Statistical-Learning

Why we included this project

Anyone who learned machine learning from An Introduction to Statistical Learning will remember that the exercises come in R, which is a real hurdle when your daily work is in Python. This repository reimplements the applied problems as commented Jupyter notebooks and adds written answers for the conceptual questions, arranged chapter by chapter to track the book's progression from linear regression and classification through resampling, model selection, tree-based methods, and support vector machines. It works well as a reference when you want to compare your own approach against a worked solution or double-check your reasoning on a tricky problem. Just keep in mind the answers are community-written rather than officially endorsed, so treat them as a study aid and verify anything you plan to rely on.