input: Write a python function to create and train a machine learning model based on given features and target.
  assert isinstance(relative_prediction_model(), MLPClassifier)
output: def relative_prediction_model():
      from sklearn.neural_network import MLPClassifier
      from sklearn.datasets import make_classification
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
  
      # Create a synthetic dataset
      X, y = make_classification(n_samples=1000, n_features=20, random_state=42)
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Initialize and train the MLPClassifier
      model = MLPClassifier(random_state=42)
      model.fit(X_train, y_train)
  
      return model