input: 
  def predict_customer_LTV(customers_file_path: str, customer_transaction_data: List[List[float]]) -> float:
      """Predict the customer lifetime value given a customer's past transactions. Use a machine learning model trained on past transactional data.
      >>> predict_customer_LTV('transactions.csv', [[10,100,5,...]])
      3567.89
      """
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      df = pd.read_csv(customers_file_path)
      X = df.iloc[:,:-1].values
      y = df.iloc[:,-1].values
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
  
      model = RandomForestRegressor()
      model.fit(X, y)
  
      customer_input = np.array(customer_transaction_data)
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
  
      return predicted_LTV
  
output:     return predicted_LTV