input: def predict_customer_lifetime_value(transaction_csv_path: str) -> float:
      """Predicts the customer lifetime value (LTV) based on past transactions data.
      The function reads the transactions from a CSV file, preprocesses the data, 
      trains a RandomForestRegressor model, and predicts the LTV for a new customer.
      
      >>> predict_customer_lifetime_value('transactions.csv')
      Predicted customer lifetime value 500.0
      """
  
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      # Preprocessing
      df = pd.read_csv(transaction_csv_path)
      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)
      return predicted_LTV
output:     customer_input = np.array([[10, 100, 5, 2, 30, 15, 20, 50, 10, 5]])  # Example data
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      print('Predicted customer lifetime value', predicted_LTV)
      return predicted_LTV