input: 
  from typing import List
  
  def predict_customer_lifetime_value(transactions_filepath: str) -> float:
      """ Create a machine learning model in Python that predicts customer lifetime value
      given a customer's past transactions. The function reads the past transactions data
      from a CSV file, preprocesses it, trains a RandomForestRegressor model, and returns
      the predicted lifetime value for a sample past transaction.
  
      Example:
      >>> predict_customer_lifetime_value('transactions.csv')
      3500.25
      """
  
output:     
      import pandas as pd
      from sklearn.ensemble import RandomForestRegressor
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
  
      # Load the data
      data = pd.read_csv(transactions_filepath)
  
      # Preprocess the data
      X = data.drop('lifetime_value', axis=1)
      y = data['lifetime_value']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Scale the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Train the model
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(X_train, y_train)
  
      # Predict the lifetime value for a sample past transaction
      sample_transaction = X_test[0].reshape(1, -1)
      predicted_lifetime_value = model.predict(sample_transaction)
  
      return predicted_lifetime_value[0]
  