Python-coded Jupyter Notebooks collection with applied exercises taken from 2nd edition of An Introduction to Statistical Learning ( further on referenced as ISLRv2) book by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani - all files structured per folders by chapters of the book.
We started project log from chapter 3, as long as chapters 1 and 2 are introduction of the book and of the subject and overview of main technical ideas, thus the relevant exercises are quite generic and basic (when available) - hence not included in this project.
Inside the chapters the exercises do not start from number 1, because in the structure of ISLRv2 book the exercises contains two parts with continues numbering: conceptual (theoretical) and applied (lab) - only the second part is implemented as Jupyter Notebooks in this project.
Here you are the ready parts:
- chapter 3: a. list of analytical formulas for SLR, b. exercises 8,9,10,11,12,13,14,15.
The ISLRv2 book itself uses R for applied part, which is not mastered by Python-DA/DS people like me, yet we also want to feel practical side of the book's theory.
There is no goal to port the available for download from the ISLRv2 authors' site the R-coded-Notebooks so that Python could have run it - this task can be better addressed by using dedicated Python libraries supporting R-style of syntax.
The goal is to fulfill ISLRv2 applied exercises by using basic DA/DS libraries like NimPy, Pandas, SciPy and Sklearn as close as possible to the level expected by the ISLRv2 authors - to get practical feeling of ISLRv2 theory without any knowledge of R.
This project is for and by those, who do not plan to study R, yet want to get maximum from study of ISLRv2 book. Any comments and contributions are welcome.
the Jupyter Notebook files in this project were run OK in the Anaconda environment - its backup is also provided in this project here.
This project has purely educational purpose. The project owner holds no responsibility for any attempts of its commercial use and its what-ever consequences.
In case the authors of the book An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani would find the form of this project publication on GitHub somehow interfering with their authors' rights - please kindly notify the project owner via GitHub for necessary corrections to be introduced as per your wishes.
The book An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani was legally downloaded by project owner from the link https://www.statlearning.com/ and is supposed to be used in full compliance with free download conditions provided there - as of Dec 15, 2022.