An introduction to statistical learning solution how to#
If you're starting out in machine learning with Python (or R!), we recommend you try it! Technical requirements, and How to Install We are strong advocates of the active learning principles, and this project, once more, reinforced them in our minds. Since the book was written with R in mind, it made the use of Python a cool additional challenge. We had done other data science projects with Python, but, as we imagined, we still had a bit more to learn (and still do!). Our main goal was to use the exercises as an excuse to improve our proficiency using Python's data science stack.
We chose ISLR because it is an excellent, clear introduction to statistical learning, that keeps a nice balance between theory, intuition, mathematical rigour and programming. Today there are several good books and other resources from which to learn the material we covered, and we spent some time choosing a good learning project. The main motivation of this project was learning. Note : we will release each chapter's solutions on a monthly basis (at least). The exercises were solved using Python instead of R.
īoth conceptual and applied exercises were solved.Īn effort was made to detail all the answers and to provide a set of bibliographical references that we found useful. 'An Introduction to Statistical Learning with Applications in R' (ISLR) by James, Witten, Hastie and Tibshirani.This page contains the solutions to the exercises proposed in An Introduction to Statistical Learning: with Applications in R.
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