input: def predict_customer_ltv(df: pd.DataFrame) -> float:
      """
      Given a dataset of past transactions, this function will train a machine learning model
      using a RandomForestRegressor to predict the customer lifetime value (LTV) based on
      the customer's historical transactions.
      >>> predict_customer_ltv(pd.DataFrame({'feature1': [10, 20], 'feature2': [100, 200], 'LTV': [500, 1000]}))
      550.0
      """
      import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      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([[10, 100]]) # example input
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV
output:     return predicted_LTV