For problems involving regression and classification, Support Vector Machines (SVMs) represent a stable and adaptable class of supervised machine learning algorithms. A Support Vector Machine (SVM) is especially useful in situations when there are more features than samples since its main objective is to locate a hyperplane in a high-dimensional space that maximizes the margin between various classes. The data points that are closest to the decision border and affect its position are known as support vectors, and they are essential to its success. Strong Variable Classifiers (SVMs) are useful in a wide range of applications, including bioinformatics, image classification, and handwriting recognition. They perform well in high-dimensional spaces and remain resilient to outliers. The algorithm’s usefulness in non-linear classification and regression issues is further enhanced by its capacity to handle complex relationships thanks to the kernel approach. SVMs can be an effective tool in your machine learning toolbox if you’re working with data where a distinct margin of separation between classes is essential.