input: 
  def iris_classification():
      """ Implemented a supervised learning model using K-Nearest Neighbors algorithm
      for classification task on the iris dataset. The model is trained on the dataset
      which is split into training and test sets. The test set is used to make predictions
      and the model's performance is evaluated using confusion matrix.
      Example:
      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.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)
  
      # Initialize the KNN classifier
      knn = KNeighborsClassifier(n_neighbors=3)
  
      # Train the model
      knn.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = knn.predict(X_test)
  
      # Evaluate the model using confusion matrix
      conf_matrix = confusion_matrix(y_test, y_pred)
      print(conf_matrix)
  