input: Write a function to implement a supervised learning algorithm in Python for a classification task using the iris dataset. The function should include data loading, data exploration, splitting the dataset, preprocessing, model training, prediction, and performance evaluation using a confusion matrix. The function should return the confusion matrix as a result.
  
  assert supervised_learning_iris() == [[11, 0, 0],
                                         [0, 13, 0],
                                         [0, 0, 6]]
output: def supervised_learning_iris():
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.svm import SVC
      from sklearn.metrics import confusion_matrix
  
      iris = load_iris()
      X = iris.data
      y = iris.target
  
      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 = SVC(kernel='linear', random_state=42)
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
  
      return confusion_matrix(y_test, y_pred)