input: Write a function to create a machine learning model in Python that predicts customer lifetime value (LTV) based on a customer's past transactions. The function should preprocess the data, train a RandomForestRegressor model, and return the predicted LTV for a given customer input related to their past transactions. If the input data is not applicable, the function should raise a ValueError.
  assert create_ltv_model() == ValueError("Input data is not applicable")
  assert create_ltv_model(pd.DataFrame({'transactions': [10, 100, 5]})) == 500.0
  assert create_ltv_model(pd.DataFrame({'transactions': [5, 50, 2]})) == 250.0
  assert create_ltv_model(pd.DataFrame({'transactions': [20, 200, 10]})) == 1000.0
output: def create_ltv_model(data=None):
      if data is None or 'transactions' not in data.columns:
          raise ValueError("Input data is not applicable")
      
      X = data[['transactions']]
      y = data['transactions'].mean() * 10
      
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(X, y)
      
      return model.predict(X)[0]