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ISL-python

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Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.

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최종 버전 다운로드 (.zip)

- - Advertising.csv
  - Auto.csv
  - Caravan.csv
  - Carseats.csv
  - Ch10Ex11.csv
  - College.csv
  - Credit.csv
  - Default.csv
  - Heart.csv
  - Hitters.csv
  - Income1.csv
  - Income2.csv
  - khan_test.csv
  - khan_train.csv
  - OJ.csv
  - Wage.csv
  - Weekly.csv
- - 2_3.jpg
  - 3_5.jpg
  - 3_6.jpg
  - 3_7.jpg
  - 3_table3.4.png
  - 4_1.JPG
  - 4_2.JPG
  - 4_3.jpg
  - 4_4abcd.JPG
  - 4_4e.JPG
  - 4_6b.JPG
  - 5_1.jpg
  - 5_2.jpg
  - 6_5.jpg
  - 6_5bi.jpg
  - 6_5bii.jpg
  - 6_5cd.jpg
  - 8_1a.jpg
  - 8_1b.jpg
  - 8_4a.JPG
  - 8_4b.JPG
- ch2_statistical_learning_applied.ipynb
- ch2_statistical_learning_conceptual.ipynb
- ch3_linear_regression_applied.ipynb
- ch3_linear_regression_conceptual.ipynb
- ch4_classification_applied.ipynb
- ch4_classification_conceptual.ipynb
- ch5_resampling_methods_applied.ipynb
- ch5_resampling_methods_conceptual.ipynb
- ch6_linear_model_selection_and_regularisation_applied.ipynb
- ch6_linear_model_selection_and_regularisation_conceptual.ipynb
- ch6_linear_model_selection_and_regularisation_labs.ipynb
- ch7_moving_beyond_linearity_applied.ipynb
- ch7_moving_beyond_linearity_labs.ipynb
- ch8_tree_based_methods_applied.ipynb
- ch8_tree_based_methods_conceptual.ipynb
- ch8_tree_based_methods_labs.ipynb
- ch9_support_vector_machines_applied.ipynb
- ch9_support_vector_machines_conceptual.ipynb
- ch9_support_vector_machines_labs.ipynb
- Source.gv
- Source.gv.pdf
.gitignore
LICENSE
README.md

// repository documentation

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# ISL-python

[*An Introduction to Statistical Learning*](http://www-bcf.usc.edu/~gareth/ISL/) is a textbook by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Conceptual and applied exercises are provided at the end of each chapter covering supervised learning.

<p><img src="https://images-na.ssl-images-amazon.com/images/I/81HYeeBlGmL.jpg" height=20% width=20%></p>
  
This repository contains my solutions to the labs and exercises as Jupyter Notebooks written in Python using:

- Numpy
- Pandas
- Matplotlib
- Seaborn
- Patsy
- StatsModels
- Sklearn

Perhaps of most interest will be the recreation of some functions from the R language that I couldn't find in the Python ecosystem. These took me some time to reproduce but the implementation details are not essential to the concepts taught in the book so please feel free to reuse. For example, a reproduction of R's `lm()` four-way diagnostic plot for linear regression in Chapter 3. Also, a collection of [all required datasets]((./Notebooks/data)) is provided in .csv format.

## To view notebooks

Links to view each notebook below. The code is provided [here](./Notebooks).

[Chapter 2 - Statistical Learning: Conceptual](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch2_statistical_learning_conceptual.ipynb)  
[Chapter 2 - Statistical Learning: Applied](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch2_statistical_learning_applied.ipynb)

[Chapter 3 - Linear Regression: Conceptual](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch3_linear_regression_conceptual.ipynb)  
[Chapter 3 - Linear Regression: Applied](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch3_linear_regression_applied.ipynb)

[Chapter 4 - Classification: Conceptual](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch4_classification_conceptual.ipynb)  
[Chapter 4 - Classification: Applied](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch4_classification_applied.ipynb)

[Chapter 5 - Resampling Methods: Conceptual](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch5_resampling_methods_conceptual.ipynb)  
[Chapter 5 - Resampling Methods: Applied](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch5_resampling_methods_applied.ipynb)

[Chapter 6 - Linear Model Selection and Regularization: Labs](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch6_linear_model_selection_and_regularisation_labs.ipynb)  
[Chapter 6 - Linear Model Selection and Regularization: Conceptual](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch6_linear_model_selection_and_regularisation_conceptual.ipynb)  
[Chapter 6 - Linear Model Selection and Regularization: Applied](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch6_linear_model_selection_and_regularisation_applied.ipynb)

[Chapter 7 - Moving Beyond Linearity: Labs](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch7_moving_beyond_linearity_labs.ipynb)  
[Chapter 7 - Moving Beyond Linearity: Applied](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch7_moving_beyond_linearity_applied.ipynb)

[Chapter 8 - Tree-Based Methods: Labs](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch8_tree_based_methods_labs.ipynb)  
[Chapter 8 - Tree-Based Methods: Conceptual](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch8_tree_based_methods_conceptual.ipynb)  
[Chapter 8 - Tree-Based Methods: Applied](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch8_tree_based_methods_applied.ipynb)

[Chapter 9 - Support Vector Machines: Labs](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch9_support_vector_machines_labs.ipynb)  
[Chapter 9 - Support Vector Machines: Conceptual](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch9_support_vector_machines_conceptual.ipynb)  
[Chapter 9 - Support Vector Machines: Applied](https://nbviewer.jupyter.org/github/a-martyn/ISL-python/blob/master/Notebooks/ch9_support_vector_machines_applied.ipynb)

## To run notebooks

Running the notebooks enables you to execute the code and play around with any interactive features.

To run:

1. [Install Jupyter Notebooks](https://jupyter.readthedocs.io/en/latest/install.html). I recommend doing this via the Annaconda/Conda method to ensure that package versions play nicely together.
2. `cd` to this repo
3. Run `jupyter notebook` to run the Jupyter server locally on your machine. It should launch in your browser.
4. In the Jupyter browser app, navigate to the notebook you'd like to explore.