This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

Overview

An-Introduction-to-Statistical-Learning

This repository contains the exercises and its solution contained in the book An Introduction to Statistical Learning

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An-Introduction-to-Statistical-Learning is one of the most popular books among data scientists to learn the conepts and intuitions behind machine learning algorithms, however, the exercises are implemented in R language, which is a hinderence for all those who are using python language. To overcome this i have tried solving all the questions in practical exerices in Python language, so people using python language can also get the most our of this amazing book. Along with that i have also provided the solutions for conceptual questions. I had tried my best to write the correct solutions to the problem, It was a challenge, and i need to learn to do a lot of research. I do not gurantee that all the solutions are absoletely correct. I have commented the notebooks. If you find any query, do send a feedback about the same. Suggestions and corrections are welcome. this is my email - [email protected] Happy Learning!

An Introduction to Statistical Learning

REFERENCES

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Karma Yogi
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