SVMs for Linearly Separable Data with Python

In our last few articles, we have talked about Support Vector Machines. We have considered them with hard and soft margins, and also how we can use the Kernel Trick when our data is not linearly separable. However, in this article, we will only consider how to implement an SVM when our data is linearly separable. In the next article, we will move on to consider how to implement it when the data is no longer linearly separable. We will implement our models using Jupyter Notebook and various libraries.