input: Write a function to implement a supervised learning algorithm for a classification task using the iris dataset. The function should load and preprocess the data, split it into training and test sets, define a K-Nearest Neighbors classifier, fit the model to the training data, make predictions on the test data, and evaluate the model's performance by printing the confusion matrix.
  
  def iris_classification_knn():
      # 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('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)
      print(cm)
  
  assert iris_classification_knn()  # Assertion syntax adjusted for this context
output: def iris_classification_knn():
      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('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)
      print(cm)