input: 
  from typing import Dict, List
  
  def supervised_learning_classification(dataset_path: str, test_size: float, random_state: int, n_neighbors: int) -> Dict[str, List[int]]:
      """ Implements a supervised learning algorithm for a classification task using the iris dataset.
      The function loads the dataset, splits it into training and test sets, preprocesses the data, 
      defines a K-Nearest Neighbors classifier, fits the model to the training data, makes predictions 
      on the test data, and evaluates the model's performance using a confusion matrix.
      >>> supervised_learning_classification('iris.csv', 0.2, 0, 5)
      {'confusion_matrix': [[11, 0, 0], [0, 13, 0], [0, 0, 6]]}
      """
      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
  
      dataset = pd.read_csv(dataset_path)
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, -1].values
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      clf = KNeighborsClassifier(n_neighbors=n_neighbors)
      clf.fit(X_train, y_train)
  
      y_pred = clf.predict(X_test)
      cm = confusion_matrix(y_test, y_pred).tolist()
      
      return {'confusion_matrix': cm}
  
output:     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
  
      dataset = pd.read_csv(dataset_path)
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, -1].values
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      clf = KNeighborsClassifier(n_neighbors=n_neighbors)
      clf.fit(X_train, y_train)
  
      y_pred = clf.predict(X_test)
      cm = confusion_matrix(y_test, y_pred).tolist()
      
      return {'confusion_matrix': cm}