https://github.com/karimabousselham/islp-applied-solutions
Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023
https://github.com/karimabousselham/islp-applied-solutions
applied-exercises classification clustering deep-learning exercise islp machine-learning multiple-testing python python-codes regression resampling-methods self-learning shrinkage-approaches statistical-learning support-vector-machines survival-analysis tree-based-methods
Last synced: 6 months ago
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Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023
- Host: GitHub
- URL: https://github.com/karimabousselham/islp-applied-solutions
- Owner: KarimABOUSSELHAM
- License: mit
- Created: 2023-10-29T13:32:16.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-30T17:00:52.000Z (almost 2 years ago)
- Last Synced: 2025-03-25T18:25:31.681Z (7 months ago)
- Topics: applied-exercises, classification, clustering, deep-learning, exercise, islp, machine-learning, multiple-testing, python, python-codes, regression, resampling-methods, self-learning, shrinkage-approaches, statistical-learning, support-vector-machines, survival-analysis, tree-based-methods
- Language: Jupyter Notebook
- Homepage: https://www.statlearning.com/
- Size: 9.76 MB
- Stars: 23
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ISLP-applied-solutions
Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023
Authors introduction: "As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.
The first edition of this book, with applications in R (ISLR), was released in 2013. A 2nd Edition of ISLR was published in 2021. It has been translated into Chinese, Italian, Japanese, Korean, Mongolian, Russian, and Vietnamese. The Python edition (ISLP) was published in 2023.
Each edition contains a lab at the end of each chapter, which demonstrates the chapter’s concepts in either R or Python."
TOpics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.