input: Write a python function to normalize the data using StandardScaler and split the dataset into training and test sets.
  assert len(X_train) == int(0.8 * len(dataset))
  assert len(X_test) == int(0.2 * len(dataset))
  assert len(y_train) == int(0.8 * len(dataset))
  assert len(y_test) == int(0.2 * len(dataset))
output: from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  
  def normalize_and_split(dataset, target, test_size=0.2, random_state=42):
      X = dataset.drop(columns=[target])
      y = dataset[target]
      
      scaler = StandardScaler()
      X_scaled = scaler.fit_transform(X)
      
      X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=test_size, random_state=random_state)
      
      return X_train, X_test, y_train, y_test