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, split it into training and test sets, preprocess the data, define a KNeighborsClassifier model, fit the model to the training set, make predictions on the test set, and evaluate the model's performance by printing the confusion matrix. The function should accept no input parameters and return nothing.
  
  def supervised_learning_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 supervised_learning_iris_classification.__code__.co_argcount == 0
  assert "iris.csv" in supervised_learning_iris_classification.__code__.co_consts
output: def supervised_learning_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)