input: Write a function to implement a supervised learning algorithm in Python for a classification task using the iris dataset. The function should load the dataset, preprocess it, split it into training and test sets, fit a KNeighborsClassifier model to the training set, make predictions on the test set, and evaluate the model's performance using a confusion matrix. If the function runs successfully, it should print out the confusion matrix.
  assert iris_classification()==None
output: def iris_classification():
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.metrics import confusion_matrix
  
      iris = load_iris()
      X = iris.data
      y = iris.target
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
  
      knn = KNeighborsClassifier(n_neighbors=3)
      knn.fit(X_train, y_train)
  
      y_pred = knn.predict(X_test)
  
      cm = confusion_matrix(y_test, y_pred)
      print(cm)