input: from typing import List
  
  def predict_customer_lifetime_value() -> None:
      """ Train a machine learning model that predicts customer lifetime value (LTV) given a customer's past transactions.
      The model uses past transaction data to predict the LTV. The data is preprocessed using StandardScaler and
      the model is trained using RandomForestRegressor. The prediction is done by providing the model with
      relevant past transaction data.
      Example:
      >>> predict_customer_lifetime_value()
      Predicted customer lifetime value <predicted_value>
      """
output:     from sklearn.ensemble import RandomForestRegressor
      from sklearn.preprocessing import StandardScaler
      import numpy as np
  
      # Example past transaction data
      past_transactions = np.array([[100, 200, 150], [50, 75, 100], [200, 300, 250]])
  
      # Preprocess the data using StandardScaler
      scaler = StandardScaler()
      scaled_transactions = scaler.fit_transform(past_transactions)
  
      # Train the model using RandomForestRegressor
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(scaled_transactions[:, :-1], scaled_transactions[:, -1])
  
      # Predict the customer lifetime value
      new_customer_data = np.array([[120, 220]])
      scaled_new_customer_data = scaler.transform(new_customer_data)
      predicted_ltv = model.predict(scaled_new_customer_data)
  
      print(f"Predicted customer lifetime value {predicted_ltv[0]}")