split ISL out of my mlsabattical repo

Author: coxy1989

.gitignore

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+.DS_Store

README.md

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+# ISL
+
+[*An Introduction to Statistical Learning with applications in R*](http://www-bcf.usc.edu/~gareth/ISL/) [1] is a classic textbook written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. This repository contains my solutions to the labs and exercises, I follow in Python rather than R, with heavy use of: `numpy`, `pandas`, `sklearn`, `matplotlib`, `seaborn`, `patsy` and `statsmodels`.
+
+<img src='http://www-bcf.usc.edu/~gareth/ISL/Noah.jpg' width=30%, margin-left=auto, margin-right=auto, display=block>
+
+## Quickstart
+
+1. `git clone [email protected]:coxy1989/ISL.git`
+
+2. `cd ISL`
+
+3. `conda env create -f environment.yml`
+
+4. `source activate isl`
+
+5. `jupyter notebook`
+
+### Run the notebooks
+
+- [Chapter 2: Statistical Learning: Conceptual]()
+- [Bonus: The Curse of Dimensionality]()
+- [Chapter 3: Linear Regression: Conceptual]()
+- [Chapter 3: Linear Regression: Applied]()
+- [Chapter 4: Classification: Conceptual]()
+- [Chapter 4: Classification: Applied]()
+- [Chapter 4: Classification: Lab]()
+- [Chapter 5: Resampling Methods: Conceptual]()
+- [Chapter 5: Resampling Methods: Applied]()
+- [Chapter 5: Resampling Methods: Lab]()
+- [Bonus: The Mean of Correlated Quatities]()
+- [Chapter 6: Linear Model Selection & Regularization: Conceptual]()
+- [Chapter 6: Linear Model Selection & Regularization: Applied]()
+- [Chapter 6: Linear Model Selection & Regularization: Lab]()
+- [Chapter 7: Moving Beyond Linearity: Conceptual]()
+- [Chapter 7: Moving Beyond Linearity: Applied]()
+- [Chapter 7: Moving Beyond Linearity: Lab]()
+- [Chapter 8: Tree-Based Methods: Conceptual]()
+- [Chapter 8: Tree-Based Methods: Applied]()
+- [Chapter 8: Tree-Based Methods: Lab]()
+- [Chapter 9: Support Vector Machines: Conceptual]()
+- [Chapter 9: Support Vector Machines: Applied]()
+- [Chapter 9: Support Vector Machines: Lab]()
+- [Chapter 10: Unsupervised Learning: Conceptual]()
+- [Chapter 10: Unsupervised Learning: Applied]()
+- [Chapter 10: Unsupervised Learning: Lab]()
+
+## References
+
+[1] Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. New York Springer, 2013.
+
+
+
+*This repository was split out from [mlsabattical]() on 09/01/2018*

chapter_10/.ipynb_checkpoints/applied-checkpoint.ipynb

@@ -0,0 +1,51 @@
+"","Murder","Assault","UrbanPop","Rape"
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