# An-Introduction-to-Statistical-Learning

**Repository Path**: agan06/An-Introduction-to-Statistical-Learning

## Basic Information

- **Project Name**: An-Introduction-to-Statistical-Learning
- **Description**: This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-08-29
- **Last Updated**: 2021-09-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# [An-Introduction-to-Statistical-Learning](https://static1.squarespace.com/static/5ff2adbe3fe4fe33db902812/t/6062a083acbfe82c7195b27d/1617076404560/ISLR%2BSeventh%2BPrinting.pdf)

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

![alt text](https://images.springer.com/sgw/books/medium/9781461471370.jpg)

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 - hardikkamboj1@gmail.com
Happy Learning!

## An Introduction to Statistical Learning

- [Chapter_2_Statistical_Learning](/Chapter_2/)
- [Chapter_3_Linear_Regression](/Chapter_3/)
- [Chapter_4_Classification](/Chapter_4/)
- [Chapter_5_Resampling_Methods](/Chapter_5/)
- [Chapter_6_Linear_Model_Selection_and_Regularization](/Chapter_6/)
- [Chapter_7_Moving_Beyond_Linearity](/Chapter_7/)
- [Chapter_8_Tree_Based_Methods](/Chapter_8/)
- [Chapter_9_Support_Vector_Machines](/Chapter_9/)
- [Chapter_10_Unsupervised_Learning](/Chapter_10/)

[<img height="31" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://deepnote.com/launch?template=deepnote&url=https%3A%2F%2Fgithub.com%2Fhardikkamboj%2FAn-Introduction-to-Statistical-Learning%2Fblob%2Fmaster%2FChapter_2%2FApplied%2520Questions.ipynb)

REFERENCES

- https://botlnec.github.io/islp/
- https://github.com/a-martyn/ISL-python
- https://github.com/mscaudill/IntroStatLearn