input: 
  def supervised_learning_classification(dataset_path: str) -> np.ndarray:
      """ Implement a supervised learning algorithm in Python for a classification task using the iris dataset.
      The function will load the dataset from the provided path, split it into training and test sets, preprocess the data, 
      train a K-Nearest Neighbors classifier, make predictions on the test set, and finally evaluate the model's performance 
      by printing the confusion matrix.
      >>> supervised_learning_classification('iris.csv')
      [[11  0  0]
       [ 0 13  0]
       [ 0  0  6]]
      """
      # Import necessary libraries
      import numpy as np
      import pandas as pd
      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 and explore the data
      dataset = pd.read_csv(dataset_path)
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, -1].values
  
      # Split the dataset into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
  
      # Preprocess the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Define the model
      clf = KNeighborsClassifier(n_neighbors=5)
  
      # Fit the model to the training set
      clf.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = clf.predict(X_test)
  
      # Evaluate the model's performance
      cm = confusion_matrix(y_test, y_pred)
      return cm
  
output:     # Print the confusion matrix
      print(cm)