a-martyn/ISL-python
Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
This resource provides practical Python-based solutions for understanding and applying core statistical learning concepts from the textbook "An Introduction to Statistical Learning." It takes the theoretical exercises and labs from the book and translates them into executable Python code, complete with necessary datasets. It's designed for students or practitioners looking to deepen their grasp of statistical modeling.
349 stars. No commits in the last 6 months.
Use this if you are studying statistical learning and want to see how the concepts and methods taught in "An Introduction to Statistical Learning" can be implemented and visualized using Python.
Not ideal if you are looking for a plug-and-play machine learning library for production systems or advanced, niche statistical models not covered in the ISL textbook.
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Jupyter Notebook
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MIT
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Last pushed
Jul 20, 2024
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