input: def predict_customer_ltv(csv_file_path: str) -> float:
      """ Train a machine learning model to predict customer lifetime value 
      given customer's past transactions from a CSV file. The CSV file should contain
      customer transaction data with the last column being the label (lifetime value).
      The model used here is a RandomForestRegressor.
  
      Example:
      for predict_customer_ltv('transactions.csv')
      the output might be:
      'Predicted customer lifetime value 250.3'
      """
      import pandas as pd
      import numpy as np
  
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      # Preprocessing
      df = pd.read_csv(csv_file_path)
      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([[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:     return f'Predicted customer lifetime value {predicted_LTV:.1f}'