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, train a KNeighborsClassifier model, make predictions on the test set, and print the confusion matrix.
  assert iris_classification()==None
  assert iris_classification()==None
  assert iris_classification()==None
  assert iris_classification()==None
  assert iris_classification()==None
output: 
  from sklearn.datasets import load_iris
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.metrics import confusion_matrix
  
  def iris_classification():
      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)
      
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      
      knn = KNeighborsClassifier(n_neighbors=3)
      knn.fit(X_train, y_train)
      
      y_pred = knn.predict(X_test)
      
      print(confusion_matrix(y_test, y_pred))
  