This tutorial describes theory and practical application of Support Vector Machines (SVM) with R code. It's a popular supervised learning algorithm (i.e. classify or predict target variable). It works both for classification and regression problems. It's one of the sought-after machine learning algorithm that is widely used in data science competitions.

**What is Support Vector Machine?**

The main idea of support vector machine is to find the **optimal hyperplane** (line in 2D, plane in 3D and hyperplane in more than 3 dimensions) which **maximizes the margin between two classes**. In this case, two classes are red and blue balls. In layman's term, it is finding the optimal separating boundary to separate two classes (events and non-events).

**Why Hyperplane?**

**Hyperplane** is just a line in 2D and plane in 3D. In higher dimensions (more than 3D), it's called hyperplane. SVM help us to find a hyperplane (or separating boundary) that can separate two classes (red and blue dots).

**What is Margin?**

It is the distance between the hyperplane and the closest data point. If we double it, it would be equal to the margin.

Objective:Maximize the margin between two categories

**How to find the optimal hyperplane?**

In your dataset, select two hyperplanes which separate the data with no points between them and maximize the distance between these two hyperplanes. The distance here is **'margin'**.

Imagine a case - if there is no straight line (or hyperplane) which can separate two classes? In the image shown below, there is a circle in 2D with red and blue data points all over it such that adjacent data points are of different colors.

SVM : Nonlinear Separable Data |

SVM handles the above case by using a **kernel function** to handle non-linear separable data. It is explained in the next section.

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