subject: machine learning

using python

Task 5: Run the example codes of "Building Your First Model: K-Nearest Neighbors" and "Making predictions sections on the Juryter notebook at once as follows: from sklearn.neighbors import Kneighborsclassifier knn = KNeighborsClassifier (n_neighbors=1) knn.fit(x_train, y_train) X_new = np.array([[5, 2.9, 1, 0.2]]) print("x_new.shape: {}".Format(x_new.shape)) prediction = knn.predict(X_new) print("Prediction: {}".Format(prediction)) print("Predicted target name: {}".format( iris_dataset['target_names '][prediction])) a. Above example code predicts the input (X new) as class setosa. Change the values in X new.to observe when the model output different predictions (versicolor, or virginica). Find out which value has the highest impact on prediction. Include your code and results