This repository is based on the exercises from the excellent book An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.
The book focuses on the R programming language which means the included labs as well as the exercises are given in R. Because I prefer Python this repository captures my attempts at solving the given exercises using Python with pandas, matplotlib and numpy.
The exercise texts are included to simplify understanding the solutions. I hope this reproduction is within the given license from the book.
Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work.
The datasets can be obtained from google and won't be distributed here. However those are the files I used:
The preprocessing in the notebooks targets those exact files.
Each chapter contains conceptual and applied exercises. The notebook files are split accordingly.
02_Statistical_Learning_Applied.ipynb
^ ^ ^
| | |- Exercise type (Applied|Conceptual)
| - Name of the chapter
- Number of the chapter
Corrections and contributions are welcome. Probably there are some mistakes in my solutions :)