input: from typing import List, Tuple
  
  def predict_customer_lifetime_value(transactions: List[Tuple[float]], past_transactions_file: str) -> float:
      """Predict the customer lifetime value (LTV) given an array of transactions.
      The input transactions should be a list of tuples where each tuple contains the features related to past transactions.
      The function will read the csv file containing all past transaction data, preprocess and scale the data, 
      and then use a RandomForestRegressor to predict the customer's LTV. 
      
      >>> predict_customer_lifetime_value([(10, 100, 5)])
      1200.5
      >>> predict_customer_lifetime_value([(20, 200, 10)])
      2400.5
      """
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      # Read past transactions data
      df = pd.read_csv(past_transactions_file)
      X = df.iloc[:,:-1].values
      y = df.iloc[:,-1].values
      
      # Preprocess and scale the data
      scaler = StandardScaler()
      X_scaled = scaler.fit_transform(X)
  
      # Initialize and train the model
      model = RandomForestRegressor()
      model.fit(X_scaled, y)
  
      # Preprocess input transactions
      customer_input = np.array(transactions)
      trans_scaled = scaler.transform(customer_input)
      
      # Predict LTV
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV
output:     import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      # Read past transactions data
      df = pd.read_csv(past_transactions_file)
      X = df.iloc[:,:-1].values
      y = df.iloc[:,-1].values
      
      # Preprocess and scale the data
      scaler = StandardScaler()
      X_scaled = scaler.fit_transform(X)
  
      # Initialize and train the model
      model = RandomForestRegressor()
      model.fit(X_scaled, y)
  
      # Preprocess input transactions
      customer_input = np.array(transactions)
      trans_scaled = scaler.transform(customer_input)
      
      # Predict LTV
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV