$9\mathrm{.}8$ LAB: Support Vector Machines $($SVM$)$ for classification

The Moons dataset consists of two features and one class label for $1000$ instances of data points that form two interleaving half circles $($moons$).$ The features are related to the $x$ and $y$ coordinates of the points, while the class label indicates the corresponding half circle each point belongs to$.$ In this lab, you will use Support Vector Machine $($SVM$)$ to classify the instances based on these two features.

Your task is to follow the steps outlined below, using sklearn's SVC function, to classify the data points and evaluate the performance of the SVM model. You will also experiment with different SVM kernels and tune hyperparameters to find the best model.

Import the necessary libraries and generate the Moons dataset using the make$\_$moons function from sklearn.

Convert the dataset into DataFrames for the features and target labels to facilitate data manipulation.

Split the dataset into training and testing sets $(80\%$ training, $20\%$ testing$).$

Initialize an SVM model with a linear kernel and fit the model on the training data.

Predict the classes for the test set and evaluate the model by calculating accuracy and confusion matrix.

Experiment with different SVM kernels $($linear$,$ polynomial, RBF$)$ by initializing models and comparing their performance based on accuracy.

Tune hyperparameters $($C and gamma$)$ for the RBF kernel using a defined function to find the best combination for model performance. Visualize the decision boundary for the best model using the plotdecisionboundary function to understand how the SVM classifier separates the classes.