input: def predict_customer_ltv(transactions_data: pd.DataFrame) -> float:
      """ Given a pandas DataFrame containing customer transactions, this function preprocesses the data, trains a RandomForestRegressor model, and predicts the customer's lifetime value (LTV) based on given past transactions.
      >>> data = pd.DataFrame({
      ...    'Transaction1': [10, 20, 15],
      ...    'Transaction2': [100, 200, 150],
      ...    'Transaction3': [5, 10, 7],
      ...    'LTV': [1000, 2000, 1500]
      ... })
      >>> predicted_ltv = predict_customer_ltv(data)
      >>> print(predicted_ltv)
      1000.0
      """
      X = transactions_data.iloc[:,:-1].values
      y = transactions_data.iloc[:,-1].values
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
  
      model = RandomForestRegressor()
      model.fit(X, y)
  
      customer_input = np.array([X[0]]) # Predict for the first customer in the dataset
      trans_scaled = scaler.transform(customer_input)
      predicted_LTV = model.predict(trans_scaled)[0]
      return predicted_LTV
output:     import pandas as pd
      import numpy as np
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor