input: def predict_customer_lifetime_value(transactions: pd.DataFrame, customer_input: np.ndarray) -> float:
      """ Given a DataFrame of customers past transactions and a new customer's transaction data, 
      predict the customer lifetime value using a RandomForest regressor.
      >>> df = pd.DataFrame({
          'feature1': [1, 2, 3, 4, 5],
          'feature2': [100, 200, 300, 400, 500],
          'LTV': [1000, 2000, 3000, 4000, 5000]
      })
      >>> customer_input = np.array([[1, 100]])
      >>> predict_customer_lifetime_value(df, customer_input)
      1000.0
      """
      X = transactions.iloc[:,:-1].values
      y = transactions.iloc[:,-1].values
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
      model = RandomForestRegressor()
      model.fit(X, y)
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV
output:     from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor