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 data, and evaluate the model by printing the confusion matrix.
  assert iris_classification_task(pd.read_csv('iris.csv')) is not None
output: def iris_classification_task(data):
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.metrics import confusion_matrix
      
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
      
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      
      model = KNeighborsClassifier(n_neighbors=3)
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      
      print(confusion_matrix(y_test, y_pred))
      
      return None