input: 
  from sklearn.ensemble import RandomForestRegressor
  
  def predict_ltv(transaction_data):
      """ Given customer's past transaction data, predict the customer lifetime value (LTV).
      The transaction data should be provided as a list of lists, where each sublist represents 
      a customer's transactions. The model is trained using RandomForestRegressor.
      >>> predict_ltv([[10,100,5], [20,200,10], [30,300,15]])
      'Predicted customer lifetime value 0'
      """
      ...
  
output:     model = RandomForestRegressor()
      X = [[t[0], t[2]] for t in transaction_data]
      y = [t[1] for t in transaction_data]
      model.fit(X, y)
      predicted_ltv = model.predict([X[0]])[0]
      return f'Predicted customer lifetime value {int(predicted_ltv)}'