Part $2$: Support Vector Machine $($SVM$)-$ Linear and w$/$ RBF Kernel. $5$pts$.$ each task

Please complete the following tasks to explore Linear Support Vector Machines $($LSVM$)$ and Support Vector Machines $($SVM$)$ with an RBF kernel. You can use Python and libraries such as scikit$-$learn to implement and demonstrate your work.

Task $1$: Linear Support Vector Machine $($LSVM$)$

$1.$Load the Iris dataset.

$2.$Split the data into training and testing sets.

$3.$Implement a Linear Support Vector Machine $($SVM$)$ classifier using scikit$-$learn.

$4.$Train the LSVM model on the training data.

$5.$Evaluate the LSVM model's performance on the test data and report accuracy.

Task $2$: Support Vector Machine $($SVM$)$ with RBF Kernel

$1$ Load the Iris dataset.

$2.$ Split the data into training and testing sets.

$3.$ Implement a Support Vector Machine $($SVM$)$ classifier with an RBF kernel using scikit$-$learn.

$4.$ Train the SVM model with the RBF kernel on the training data.

$5.$ Evaluate the SVM model's performaice on the test data and report accuracy.

Task $3$: Hyperparameter Tuning for SVM with RBF Kernel

$1.$Perform hyperparameter tuning for the SVM with an RBF kernel. Search for optimal values of hyperparameters such as $C$ and $y$ using Random Search.

$2.$Report the best hyperparameters for the SVM with the RBF kernel.

$3.$Train a new SVM model with the best hyperparameters and evaluate its performance on the test data.

Task $4$: Metrics Comparison

$1.$Calculate and compare relevant evaluation metrics $($e$.$g$.,$ accuracy, precision, recall, F$1-$score$)$ for the LSVM from Task $1$ and the SVM with an RBF kernel from Task $2.$

$2.$Not graded: Discuss the differences in performance and characteristics between these models.