The support vector machine (SVM) algorithm is a supervised learning technique. To understand this algorithm, take a look at the following diagram for the optimal hyperplane and maximum margin:

In this classification problem, we only have two classes that exist for many possible solutions to a problem. As shown in the preceding diagram, the SVM classifies these objects by calculating an optimal hyperplane and maximizing the margins between the classes. Both of these things will differentiate the classes to the maximum extent. Samples that are placed closest to the margin are known as support vectors. The problem is then treated as an optimization problem and can be solved by optimization techniques, the most common one being the use of Lagrange multipliers.

Even in a separable linear problem, as shown in the preceding diagram, sometimes, it is not always possible to obtain a perfect separation. In these cases, the SVM model is the one that maximizes the margin while minimizing the number of misclassifications. In the real world, the problems are too far apart to be linearly separated, at least without a previous treatment or transformation of data. In the following diagram, the difference between a linear separable problem and a nonlinear separable problem is shown:

To handle nonlinear problems, a kernel function maps the data to different spaces. This means that data is transformed to a higher-dimensional space. This technique is known as the kernel trick, because sometimes it is possible to perform a linear separation between classes, making transformations in the data. 

The following are the advantages of the SVM algorithm:

• SVM is simple
• SVM is a combination of statistical and machine learning techniques
• SVM can be useful in solving financial problems like our problem statement