Practical Exercises with SVMs

In this final section, we will engage in practical exercises that involve building, tuning, and evaluating SVM models on various datasets. These exercises are designed to reinforce the concepts learned throughout the chapter and demonstrate how to effectively apply SVMs in different scenarios. By the end of this section, we will have hands-on experience that can be leveraged in our own ML projects.

Exercise 1: Building a Simple SVM Classifier

In this exercise, we will build a simple SVM classifier using the Iris dataset.

Implementation steps:

1. Load libraries.
2. Load the dataset.
3. Split the data.
4. Create and train an SVM classifier.
5. Make predictions.
6. Evaluate performance.

Exercise 2: Tuning SVM Parameters with Grid Search

In this exercise, we will tune SVM parameters using grid search on the Breast Cancer dataset.

Implementation steps:

1. Load libraries.
2. Load the dataset.
3. Split the data.
4. Define a hyperparameter for grid search.
5. Perform grid search with cross...