Understanding Support Vector Machine via Examples

In the previous post on Support Vector Machines (SVM), we looked at the mathematical details of the algorithm. In this post, I will be discussing the practical implementations of SVM for classification as well as regression. I will be using the iris dataset as an example for the classification problem, and a randomly generated data as an example for the regression problem. In Python, scikit-learn is a widely used library for implementing machine learning algorithms, SVM is also available in scikit-learn library and follow the usual structure (Import library, object creation, fitting model and prediction). As pointed out by Admiral deblue in the comments below, all practical implementations of SVMs have strict requirements for training and testing (prediction).