input: Write a function to implement a supervised learning algorithm for a classification task using the iris dataset. The function should: import necessary libraries, load and explore the data, split the dataset into training and test sets, preprocess the data, define a K-Nearest Neighbors classifier, fit the model to the training set, make predictions on the test set, and evaluate the model's performance by printing the confusion matrix.
  
  
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  from sklearn.metrics import confusion_matrix
  import pandas as pd
  
  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)
      print(cm)
  
  assert iris_classification() == None # Function has no return value but prints the confusion matrix
  
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)
      print(cm)