input: 
  def predict_customer_ltv():
      """
      Predict customer lifetime value (LTV) given a customer's past transactions using a machine learning model.
      The model uses past transaction data, preprocesses it using StandardScaler, and trains a RandomForestRegressor.
      
      Example:
      predict_customer_ltv()
      # Output: 'Predicted customer lifetime value [predicted_value]'
      """
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      # Preprocessing
      df = pd.read_csv('transactions.csv')
      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
      customer_input = np.array([[10,100,5,...]])  # provide data related to past transactions
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      print('Predicted customer lifetime value', predicted_LTV)
  
output:     return predicted_LTV