input: 
  from typing import List, Tuple
  import numpy as np
  import pandas as pd
  from sklearn.preprocessing import StandardScaler
  from sklearn.ensemble import RandomForestRegressor
  
  def predict_customer_lifetime_value(transactions_csv_file: str, new_customer_data: List[List[float]]) -> Tuple[float]:
      """Predicts the customer lifetime value (LTV) given past transactions data and new customer data.
      The function loads past transactions from a CSV file, preprocesses it, trains a RandomForestRegressor,
      and then predicts the LTV for a new customer based on their past transaction data.
      
      >>> predict_customer_lifetime_value('transactions.csv', [[10,100,5]])
      Predicted customer lifetime value (e.g., 1200.56)
      """
      # Preprocessing
      df = pd.read_csv(transactions_csv_file)
      X = df.iloc[:,:-1].values
      y = df.iloc[:,-1].values
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
  
      # Model Training
      model = RandomForestRegressor()
      model.fit(X, y)
  
      # Prediction
      trans_scaled = scaler.transform(new_customer_data)
      predicted_LTV = model.predict(trans_scaled)
      return tuple(predicted_LTV)
  
output:     # Preprocessing
      df = pd.read_csv(transactions_csv_file)
      X = df.iloc[:,:-1].values
      y = df.iloc[:,-1].values
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
  
      # Model Training
      model = RandomForestRegressor()
      model.fit(X, y)
  
      # Prediction
      trans_scaled = scaler.transform(new_customer_data)
      predicted_LTV = model.predict(trans_scaled)
      return tuple(predicted_LTV)