Data Science, Data Analysis, Machine Learning, Statistics, Data Visualization.
model on the labeled training data. 4. Parameter Tuning: — Fine-tune parameters like C and the choice of the kernel function. 5. Prediction: — Use the trained SVM model to make predictions on new, unseen data. SVMs are widely used in various domains such as image classification, text