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prediction = knn.predict(X_new)
print("Prediction: {}".format(prediction))
print("Predicted target name: {}".format(iris_dataset['target_names'][prediction]))

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X_new = np.array([[5, 2.9, 1, 0.2]])
print("X_new.shape: {}".format(X_new.shape))

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knn.fit(X_train, y_train)

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knn = KNeighborsClassifier(n_neighbors=1)

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print("X_test shape: {}".format(X_test.shape))
print("y_test shape: {}".format(y_test.shape))

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print("X_train shape: {}".format(X_train.shape))
print("y_train shape: {}".format(y_train.shape))

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X_train, X_test, y_train, y_test = train_test_split(iris_dataset['data'],iris_dataset['target'], random_state=0)

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print("Type of target: {}".format(type(iris_dataset['target'])))
print("Shape of target: {}".format(iris_dataset['target'].shape))
print("Target:\n{}".format(iris_dataset['target']))

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print("Shape of data: {}".format(iris_dataset['data'].shape))

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print("Target names: {}".format(iris_dataset['target_names']))
print("Feature names: {}".format(iris_dataset['feature_names']))
print("Type of data: {}".format(type(iris_dataset['data'])))