input: Write a function to implement a supervised learning algorithm for a classification task using the iris dataset. The function should preprocess the data, define a K-Nearest Neighbors classifier, train the model, make predictions, and evaluate the model's performance by printing the confusion matrix.
  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 classify_iris(dataset_path='iris.csv'):
      # Load and explore the data
      dataset = pd.read_csv(dataset_path)
      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(classify_iris(), np.array([[11, 0, 0],
                                                  [0, 13, 0],
                                                  [0, 0, 6]])) == True
  assert np.array_equal(classify_iris('path_to_invalid_dataset.csv'), None) == True
output: def classify_iris(dataset_path='iris.csv'):
      try:
          dataset = pd.read_csv(dataset_path)
      except FileNotFoundError:
          return None
      
      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