ISL-R-Notebooks

R Notebooks on the Introduction to Statistical Learning

language: R status: WIP

A collection of R Markdown Notebooks going through the chapters of the Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, specifically the 7th edition. The book is freely available in the above link.

Briefly,

This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

As such, I will be mainly using R for programming as I go along these lessons. As a budding Data Scientist, it will be interesting to see how my analyses and ability to glean insights from the book will change over time.

Please do not hesitate to message me for suggestions and tips on how to improve this repository!

Chapter Status Updates

I hopefully get to fill out most of these in a year.

Chapter Study Labs Exercises
2 - Statistical Learning Done! Coming Soon! Coming Soon!
3 - Linear Regression Coming Soon! Coming Soon! Coming Soon!
4 - Classification Coming Soon! Coming Soon! Coming Soon!
5 - Resampling Methods Coming Soon! Coming Soon! Coming Soon!
6 - Linear Model Selection and Regularization Coming Soon! Coming Soon! Coming Soon!
7 - Moving Beyond Linearity Coming Soon! Coming Soon! Coming Soon!
8 - Tree-Based Methods Coming Soon! Coming Soon! Coming Soon!
9 - Support Vector Machines Coming Soon! Coming Soon! Coming Soon!
10 - Unsupervised Learning Coming Soon! Coming Soon! Coming Soon!

References

James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R, Springer Science+Business Media, New York. http://www-bcf.usc.edu/~gareth/ISL/index.html