Open the Jupyter Notebook file (SVM.ipynb) in your web browser and work through it. As you work through the Notebook, answer the following questions. Q1: Write the code that creates a support vector classifier and train it on the training data and test it on the testing data. (5 marks) Q2: What is the accuracy of your SVM on the training data? What is the accuracy of your SVM on the testing data? (2 marks) Q3: Write the code that creates a support vector classifier using a *linear* kernel and train it on the training data and test it on the testing data. (5 marks) Q4: What is the accuracy of the linear kernel SVM on the training data? What is the accuracy of the linear kernel SVM on the testing data? (2 marks) Q5: Write the code that creates a support vector classifier, train it on the training data, and test it on the testing data. (10 marks) - For parameter C, explore values of 1.0,10.0,100.0, and 1000.0 - For parameter gamma, explore values of 1.0,10.0,100.0, and 1000.0 Q6: What values for parameter C and parameter gamma lead to the highest accuracy on the testing data? Using these two optimal parameter values, what is the accuracy of your SVM on the training data and on the testing data? (6 marks)