input: def implement_iris_classification():
      """
      Implement a supervised learning algorithm in Python for a classification task using the iris dataset.
      The function will load the iris dataset, split it into training and test sets, preprocess the data, define a K-Nearest Neighbors classifier,
      fit the model to the training set, make predictions on the test set, and evaluate the model's performance using a confusion matrix.
      
      >>> implement_iris_classification()
      [[11  0  0]
       [ 0 13  0]
       [ 0  0  6]]
      """
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
  
      # Load the iris dataset
      iris = load_iris()
      X = iris.data
      y = iris.target
  
      # Split the dataset into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
  
      # Preprocess the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Define a K-Nearest Neighbors classifier
      knn = KNeighborsClassifier(n_neighbors=3)
  
      # Fit the model to the training set
      knn.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = knn.predict(X_test)
  
      # Evaluate the model's performance using a confusion matrix
      cm = confusion_matrix(y_test, y_pred)
      print(cm)
  