SVM Series

A series of notebooks on Support Vector Machine algorithm

Notebook 1 : Notebook on Support Vector Machine(SVM) Geometric intuition : which is a better classifying plane / line ?

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Notebook 2 : Notebook on applying SVM on the Indian Diabetes Dataset to find the best value of C for which accuracy is the highest.

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Notebook 3 : Notebook to visualize the decision boundary hyperplane of SVM algorithm, for both linearly separable and non-linearly separable data.

LINEARLY SEPARABLE SAMPLE DATA :

NON-LINEARLY SEPARABLE SAMPLE DATA :

1. Linear kernel

2. Polynomial kernel with default degree = 3

3. Polynomial kernel with degree = 2

4. Rbf kernel

5. Gaussian RBF kernel in 3D space

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Notebook 4 : Notebook to find out the best hyperparameters for a SVC working on the very popular Breast Cancer dataset

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