Question 1 Support Vector Machine via SciKit Learn (5 points) Scikit-Learn is a free machine learning library for data mining and data analysis. It was built upon NumPy, SciPy, and Matplotlib. It supports the following methods: support vector machine, decision trees, random forests, k-nearest neighbors, k-means clustering, etc. Conduct the following tasks: (1) Load the built-in Iris dataset from sklearn (2) Split the dataset into training data and test data based on a 6:4 ratio (3) Import svm from sklearn (4) Create a svm model based on svm.SVC() call (5) Train the svm model by using the training data (6) Run an evaluation by using score( ) of the svm model over the test data (7) Use the predict( ) function of the svm model to predict the first and second images in the Iris dataset. Compare the predictions and ground truths. Note: The basic information of the Iris dataset is as follows: Scikit Learning • Iris Data Set sepal petal ⚫The data set contains three species of iris (iris setosa, virginica, and versicolor) with 50 samples for each species; four measured features of each sample are the length and width of sepals and petals in centimeters. Iris versicolor this virginice 151 The prediction results will be an integer between 0 and 2, representing three categories of the flowers. Question 2 Tensor Flow and Keras via Google Colab (5 points) (a) Visit https://colab.research.google.com/ to open an account at Google Colab (b) Visit a Keras Website: https://keras.io/examples/vision/image_classification_from_scratch/ (c) Try to run the code at Google Colab and train the model (d) Test the trained model with two your own images about dog or cat (e) Create screenshots for outputs If your free time of using gpu exceeds the limit at Google Colab, then try to run your code another day. You should create screenshots for all the task.