input: from typing import List
  
  
  def machine_learning_model(customer_input: List[float]) -> float:
      """ Create a machine learning model in Python that predicts customer lifetime value given a customer's past transactions.
      The model uses standard scaling and a RandomForestRegressor.
      Preprocessing is done by standardizing all transaction data and the model is trained on historic transactions to predict a possible LTV. 
      >>> machine_learning_model([10, 100, 5])
      352.17
      """
output:     from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
      import numpy as np
  
      # Example historic transactions for training the model
      historic_transactions = np.array([[5, 50, 3], [20, 200, 10], [15, 150, 7]])
      historic_ltv = np.array([150.0, 400.0, 300.0])
  
      # Standardize the historic transactions
      scaler = StandardScaler()
      historic_transactions_scaled = scaler.fit_transform(historic_transactions)
  
      # Train the RandomForestRegressor model
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(historic_transactions_scaled, historic_ltv)
  
      # Standardize the customer input
      customer_input_scaled = scaler.transform([customer_input])
  
      # Predict the customer lifetime value
      predicted_ltv = model.predict(customer_input_scaled)
  
      return predicted_ltv[0]