input: Write a function to implement a supervised learning algorithm in Python for a classification task using the iris dataset. The function should load the dataset, preprocess it, split it into training and test sets, train a KNeighborsClassifier, make predictions on the test set, and return the confusion matrix of the model's performance.
  
  
  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
  
  def iris_classification():
      # Load and explore the data
      dataset = pd.read_csv('iris.csv')
      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
  
  
  assert (np.array_equal(iris_classification(), np.array([[11,  0,  0], [ 0, 13,  0], [ 0,  0,  6]]))) == True
  assert (np.array_equal(iris_classification(), np.array([[ 0, 11,  0], [ 0, 13,  0], [ 0,  0,  6]]))) == False
  assert (np.array_equal(iris_classification(), np.array([[11,  0,  0], [ 0, 13,  0], [ 0,  1,  5]]))) == False
  assert (np.array_equal(iris_classification(), np.array([[11,  0,  0], [ 0, 12,  1], [ 0,  0,  6]]))) == False
  assert (np.array_equal(iris_classification(), np.array([]))) == False
output: def iris_classification():
      dataset = pd.read_csv('iris.csv')
      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