input: def predict_customer_lifetime_value_machine_learning():
      """This function leverages a machine learning model in Python to predict a customer's lifetime
      value given their past transactions. The model uses a RandomForestRegressor and assumes the
      data has been preprocessed using StandardScaler. The provided example shows how to train the 
      model on a set of transactions and use the trained model to predict the lifetime value of a
      hypothetical customer based on their past transactions.
      Example:
      >>> predict_customer_lifetime_value_machine_learning()
      Predicted customer lifetime value 2385.0
      """
output:     from sklearn.ensemble import RandomForestRegressor
      from sklearn.preprocessing import StandardScaler
      import numpy as np
  
      # Example data: past transactions
      transactions = np.array([
          [100, 200, 150, 300, 250],  # Customer 1
          [50, 100, 75, 150, 125],    # Customer 2
          [200, 400, 300, 600, 500]   # Customer 3
      ])
  
      # Preprocess the data
      scaler = StandardScaler()
      transactions_scaled = scaler.fit_transform(transactions)
  
      # Train the model
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(transactions_scaled[:-1], transactions_scaled[-1])
  
      # Hypothetical customer's past transactions
      hypothetical_customer = np.array([[150, 300, 200, 400, 350]])
      hypothetical_customer_scaled = scaler.transform(hypothetical_customer)
  
      # Predict the lifetime value
      predicted_value = model.predict(hypothetical_customer_scaled)
  
      # Output the result
      print(f"Predicted customer lifetime value {predicted_value[0]}")