input: def predict_customer_lifetime_value(transactions_data: str, new_customer: List[float]) -> float:
      """
      This function aims to predict the customer lifetime value given a customer's past
      transactions. It includes steps for data preprocessing, model training, and
      prediction for a newly provided customer data.
      >>> predict_customer_lifetime_value('transactions.csv', [10.0, 100.0, 5.0, ...])
      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(transactions_data)
      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([new_customer])
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV
output:     return predicted_LTV