Pattern Recognition Implementation in MATLAB Using SVM Algorithm for Iris Data Classification

In the field of machine learning, pattern recognition represents a fundamental task. This project focuses on classifying the iris dataset to better understand its characteristics and attributes. To achieve this objective, we implement a Support Vector Machine (SVM) algorithm, which is a powerful classification method that separates data based on features and generates accurate results. The MATLAB implementation involves several key steps: data preprocessing using the built-in iris dataset, feature scaling with z-score normalization, and SVM model training through the fitcsvm function with appropriate kernel selection (linear or RBF). The classification process employs the predict function to assign iris specimens to one of three classes: setosa, versicolor, or virginica. By performing pattern recognition and classification on the iris data, we provide valuable information for further analysis and research, including model evaluation through confusion matrices and cross-validation techniques to assess classification accuracy.