Porting the R code in Introduction to Statistical Learning to Python.
Inspired by and sometimes borrowed from Jordi Warmenhoven's and Hyun Bong Lee's excellent repos.
Mainly Labs and some exercises are ported. The jupyter notebooks are in labs and exercises folders respectively.
I'm trying to update the code, as I learn new tricks with scikit-learn and other libraries.
- Chapter 2 [Labs] [Exercises] - Introduction
- Chapter 3 [Labs] [Exercises] - Linear Regression
- Chapter 4 [Labs] [Exercises] - Classification
- Chapter 5 [Labs] [Exercises] - Resampling Methods
- Chapter 6 [Labs 1] [Labs 2] [Labs 3] [Exercises] - Linear Model Selection and Regularization
- Chapter 7 [Labs] [Exercises] - Moving Beyond Linearity
- Chapter 8 [Labs] [Exercises] - Tree-Based Methods
- Chapter 9 [Labs] [Exercises] - Support Vector Machines
- Chapter 10 [Labs 1] [Labs 2] [Labs 3] [Exercises] - Unsupervised Learning