input: Write a function to implement a supervised learning algorithm 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 data, make predictions on the test set, and evaluate the model's performance using a confusion matrix. If the dataset is not found at 'iris.csv', the function should return an error message.
  def iris_classification():
      try:
          # 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)
          return cm
      except FileNotFoundError:
          return "Dataset 'iris.csv' not found."
  assert np.array_equal(iris_classification(), np.array([[11, 0, 0], [0, 13, 0], [0, 0, 6]])) == True
  assert isinstance(iris_classification(), str) == False
  assert isinstance(iris_classification(), np.ndarray) == True
output: def iris_classification():
      try:
          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)
          return cm
      except FileNotFoundError:
          return "Dataset 'iris.csv' not found."