Plant breeders and agricultural researchers can increase crop productivity by
identifying desirable features, disease resistance, and nutritional content by
analysing the Dry Bean dataset. This study analyses and compares different
Support Vector Machine (SVM) classification algorithms, namely linear,
polynomial, and radial basis function (RBF), along with other popular
classification algorithms. The analysis is performed on the Dry Bean Dataset,
with PCA (Principal Component Analysis) conducted as a preprocessing step for
dimensionality reduction. The primary evaluation metric used is accuracy, and
the RBF SVM kernel algorithm achieves the highest Accuracy of 93.34%, Precision
of 92.61%, Recall of 92.35% and F1 Score as 91.40%. Along with adept
visualization and empirical analysis, this study offers valuable guidance by
emphasizing the importance of considering different SVM algorithms for complex
and non-linear structured datasets.

通过对 Dry Bean 数据集的分析，支持向量机（SVM）的径向基函数（RBF）算法在准确率、精度、召回率和 F1 得分等方面均表现最佳，这为复杂和非线性结构数据集中考虑不同 SVM 算法的重要性提供了宝贵的指导。