input: def predict_customer_lifetime_value(model, customer_input):
      """Create a machine learning model in Python that predicts customer lifetime value given a customer's past transactions.
      The model should be pre-trained on a dataset of past transactions and uses feature scaling for input normalization.
      >>> model = train_model('transactions.csv') # Assume train_model is a function that returns the trained model
      >>> customer_input = np.array([[10, 100, 5, ...]]) # Provide data related to past transactions
      >>> predicted_LTV = predict_customer_lifetime_value(model, customer_input)
      Predicted customer lifetime value 500.0
      """
      trans_scaled = model['scaler'].transform(customer_input)
      predicted_LTV = model['model'].predict(trans_scaled)[0]
      print('Predicted customer lifetime value', predicted_LTV)
      return predicted_LTV
output:     trans_scaled = model['scaler'].transform(customer_input)
      predicted_LTV = model['model'].predict(trans_scaled)[0]
      print('Predicted customer lifetime value', predicted_LTV)
      return predicted_LTV