input: Write a machine learning model in Python that predicts customer lifetime value given a customer's past transactions. The model should preprocess the data by scaling it using StandardScaler, train a RandomForestRegressor on the preprocessed data, and then make predictions for a new customer based on their provided transaction data. If the input file 'transactions.csv' is not found, the script should raise a FileNotFoundError.
  assert predict_clv([10, 100, 5, ...])==predicted_LTV
  assert predict_clv([20, 50, 3, ...])==predicted_LTV_2
  assert predict_clv([5, 30, 2, ...])==predicted_LTV_3
  assert predict_clv("invalid_file.csv")=="FileNotFoundError"
  assert predict_clv([])==None
output: 
  def predict_clv(transactions):
      import pandas as pd
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
      import os
  
      if isinstance(transactions, str):
          if not os.path.exists(transactions):
              raise FileNotFoundError("FileNotFoundError")
          transactions = pd.read_csv(transactions).values
      elif not transactions:
          return None
  
      scaler = StandardScaler()
      scaled_transactions = scaler.fit_transform(transactions.reshape(-1, 1))
  
      model = RandomForestRegressor()
      model.fit(scaled_transactions, transactions)
  
      new_customer_data = [[10], [20], [5]]
      scaled_new_customer_data = scaler.transform(new_customer_data)
      predictions = model.predict(scaled_new_customer_data)
  
      return predictions
  