input: Write a function to create a machine learning model in Python that predicts customer lifetime value given a customer's past transactions. The function should load transaction data from a CSV file, preprocess the data, train a RandomForestRegressor, and then perform prediction on new customer data. The function should return the predicted customer lifetime value. If the input data is empty, the function should return 0.
  assert predict_customer_ltv([10,100,5, ...])==250.75
  assert predict_customer_ltv([5,50,2, ...])==150.30
  assert predict_customer_ltv([])==0
output: def predict_customer_ltv(transactions):
      if not transactions:
          return 0
      from sklearn.ensemble import RandomForestRegressor
      import numpy as np
      model = RandomForestRegressor()
      X = np.array(transactions).reshape(-1, 1)
      model.fit(X, transactions)
      new_data = np.array([transactions[-1]]).reshape(-1, 1)
      return model.predict(new_data)[0]