Data Classification and Prediction Using SVM
The wine dataset originates from the UCI repository and contains chemical analysis results of three different wine cultivars from the same region in Italy. The dataset comprises 178 samples, each with 13 feature components (chemical properties), along with predefined class labels. We allocate 50% of the samples (89 instances) as the training set and the remaining 50% as the test set. By training an SVM classifier on the training data, we generate a classification model that can predict class labels for the test set. The implementation typically involves feature scaling, model training with kernel selection, and performance evaluation using metrics like accuracy.