input: def predict_customer_ltv(df: pd.DataFrame) -> float:
      """ This function takes a DataFrame with a customer's past transactions as input and returns the predicted customer lifetime value.
      >>> predict_customer_ltv(pd.DataFrame({'transaction_count': [10], 'total_amount': [100], ...}))
      5
      """
      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([[10,100,5,...]]) # provide data related to past transactions
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV
output:     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([[10,100,5,...]]) # provide data related to past transactions
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV