input: Write a function to implement a supervised learning algorithm for a classification task using the iris dataset. The function should split the dataset into training and test sets, preprocess the data, and train a K-Nearest Neighbors classifier. It should also print the confusion matrix to evaluate the model's performance. The function should not take any input and should handle the entire process internally.
  def iris_classification():
      # 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() is None
output: def iris_classification():
      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)