Cryptic Species Classification Using SVM

Developed a model using Support Vector Machines (SVMs) to classify two cryptic species of shellfish, achieving 97% accuracy. Explained SVM concepts and mathematical formulations, including handling complex data with slack variables and kernel tricks. Highlighted the effectiveness of SVMs in species classification without genetic analysis.