Introduction to Statistical Learning in Python

This repo use Python to re-produce the lab results from the book Introduction to Statistical Learning with Application in R wittern by James, Witten, Hastie and Tibshirani.
It also includes the exercise solutions in Python3

Book Image:

• CHAPTER 2: Statistical Learning
• CHAPTER 3: Linear Regression
• CHAPTER 4: Classification

• Logistic Regerssion
• Linear Discrimnant Analysis
• Quadratic Discrimnant Analysis
• KNN

• CHAPTER 5: Resampling Methods

• Cross Validation
• Bootstrap

• CHAPTER 6: Linear Model Selection and Regularization

• Best subset selection
• Cross Valiation
• Ridge/Lasso Regression
• Principal Components Rregression
• Partial Least Squares

• CHAPTER 7: Moving Beyond Linearity

• Polynomial Regression and Step Function
• Splines
• GAMs

• CHAPTER 8: Tree-based Methods

• Decision Trees
• Bagging and Random Forests
• Boosting

• CHAPTER 9: Support Vector Machines

• Support Vector Classifier
• Support Vectir Machine
• SVM with Multiple Classes

• CHAPETER 10: Unsupervised Learning

• PCA
• Cluster Methods

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References:

• https://github.com/JWarmenhoven/ISLR-python
• http://www.science.smith.edu/~jcrouser/SDS293/labs/2016/lab8/Lab%208%20-%20Subset%20Selection%20in%20Python.pdf
• https://xavierbourretsicotte.github.io/subset_selection.html#Subset-selection-in-python
• https://jss367.github.io/Exploring-Decision-Trees-in-Python.html#decision-trees-for-classification
• contour plot reference http://www.adeveloperdiary.com/data-science/how-to-visualize-gradient-descent-using-contour-plot-in-python/