input: def predict_customer_ltv():
      """
      Trains a machine learning model to predict customer lifetime value (LTV) based on a customer's past transactions. The model uses a RandomForestRegressor after preprocessing the transaction data with StandardScaler.
      >>> predict_customer_ltv()
      Predicted customer lifetime value 1200.5
      """
output:     from sklearn.ensemble import RandomForestRegressor
      from sklearn.preprocessing import StandardScaler
      import numpy as np
  
      # Example transaction data
      transactions = np.array([[100, 200, 300], [150, 250, 350], [200, 300, 400]])
  
      # Preprocessing
      scaler = StandardScaler()
      scaled_transactions = scaler.fit_transform(transactions)
  
      # Training the model
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(scaled_transactions[:, :-1], scaled_transactions[:, -1])
  
      # Predicting LTV
      new_customer_transactions = np.array([[120, 220, 320]])
      scaled_new_customer_transactions = scaler.transform(new_customer_transactions[:, :-1])
      predicted_ltv = model.predict(scaled_new_customer_transactions)
  
      print(f"Predicted customer lifetime value {predicted_ltv[0]}")