input: Write a function to implement a supervised learning algorithm for a classification task using the iris dataset. This function should load the dataset, preprocess the data, split it into training and test sets, define a KNeighborsClassifier model, fit the model to the training data, make predictions on the test set, and evaluate the model's performance using a confusion matrix.
  # 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
  
  # Function to implement the supervised learning algorithm
  def iris_classification(filename):
      # Load and explore the data
      dataset = pd.read_csv(filename)
      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
  
  # Test cases
  assert np.array_equal(iris_classification('iris.csv'), np.array([[11, 0, 0], [0, 13, 0], [0, 0, 6]]))
output: def iris_classification(filename):
      dataset = pd.read_csv(filename)
      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=0.2, random_state=0)
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      clf = KNeighborsClassifier(n_neighbors=5)
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      cm = confusion_matrix(y_test, y_pred)
      return cm