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Lab 10 - Ridge Regression and the Lasso in Python.ipynb
Lab 10 - Ridge Regression and the Lasso in Python.ipynb
 
 
Lab 11 - PCR and PLS Regression in Python.ipynb
Lab 11 - PCR and PLS Regression in Python.ipynb
 
 
Lab 12 - Polynomial Regression and Step Functions in Python.ipynb
Lab 12 - Polynomial Regression and Step Functions in Python.ipynb
 
 
Lab 13 - Splines in Python.ipynb
Lab 13 - Splines in Python.ipynb
 
 
Lab 14 - Decision Trees in Python.ipynb
Lab 14 - Decision Trees in Python.ipynb
 
 
Lab 15 - Support Vector Machines in Python.ipynb
Lab 15 - Support Vector Machines in Python.ipynb
 
 
Lab 16 - Multiclass SVMs and Applications to Real Data in Python.ipynb
Lab 16 - Multiclass SVMs and Applications to Real Data in Python.ipynb
 
 
Lab 18 - PCA in Python.ipynb
Lab 18 - PCA in Python.ipynb
 
 
Lab 2 - Linear Regression in Python.ipynb
Lab 2 - Linear Regression in Python.ipynb
 
 
Lab 3 - K-Nearest Neighbors in Python.ipynb
Lab 3 - K-Nearest Neighbors in Python.ipynb
 
 
Lab 4 - Logistic Regression in Python.ipynb
Lab 4 - Logistic Regression in Python.ipynb
 
 
Lab 5 - LDA and QDA in Python.ipynb
Lab 5 - LDA and QDA in Python.ipynb
 
 
Lab 7 - Cross-Validation in Python.ipynb
Lab 7 - Cross-Validation in Python.ipynb
 
 
Lab 8 - Subset Selection in Python.ipynb
Lab 8 - Subset Selection in Python.ipynb
 
 
Lab 9 - Linear Model Selection in Python.ipynb
Lab 9 - Linear Model Selection in Python.ipynb
 
 
README.md
README.md
 
 

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islr-python

This project is a python adaptation of the lab example in "Introduction to Statistical Learning with Applications in R" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Some of the original adaptations were produced by J. Warmenhoven, and updated by R. Jordan Crouser at Smith College for SDS293: Machine Learning (Spring 2016).

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