input: 
  def predict_customer_lifetime_value():
      """ Create a machine learning model in Python that predicts customer lifetime value given a customer's past transactions.
      The model uses RandomForestRegressor for prediction and StandardScaler for feature scaling.
      Example:
      # Provide the path to transactions data and the customer input for past transactions
      predict_customer_lifetime_value()
      Predicted customer lifetime value 123.45 # This is an example output. Replace with actual output.
      """
      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