Application of support vector machines for categorizing biological and medical data

Support Vector Machines (SVM) have gained significant attention in the field of machine learning due to their robust performance in various classification tasks. In this paper, we explore the application of SVM to categorize Iris flowers into three distinct types based on their features. SVM is a supervised learning algorithm that finds a hyperplane in a high-dimensional feature space to separate classes while maximizing the margin between them. The purpose of this study is to demonstrate the effectiveness of SVM in accurately classifying Iris flowers and to showcase its significance in real-world classification problems.