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The Interactive Python notebooks can be opened using tools like Jupyter Notebooks or by opening .ipynb files in Github/browser

For Python3

Install the required libraries using Pip such as 
pip install sklearn pip install pandas pip install numpy pip install matplotlib

For R

Install the required libraries using install.packages or RStudio
Run the files using 
R -f <filename>

or

Run in RStudio

RBF SVM

SVM Radial Basis Function RBF

Variation with C and Gamma

SVM variations with C and Gamma

Tuning a linear SVM for Purpose

SVC Types

Running algorithms to find the best parameters

Grid Search

Grid Search

Principle Component Analysis

Plot the data

Kaggle Credit Card Fraud

True Positive Rate vs False Positive Rate on Credit Card data

TPR FPR

Precision Recall on Credit Card data

Precision Recall

Sources

1. Dummy data and practise from Coursera - Machine Learning - University of Michigan

2. Kaggle datasets and competitions

Fork this on Github to make changes!

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Learn to use Support Vector Machines in Python(sklearn) and R

Topics

python r anaconda rstudio svm sklearn jupyter-notebook cross-validation ipython-notebook pandas credit-card-fraud kaggle matplotlib support-vector-machines grid-search mushroom-classification pyplot rbf

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