input: def predict_customer_ltv():
      """Predicts customer lifetime value given past transactions by a pre-trained
      machine learning model. The model uses RandomForestRegressor and standardizes
      the input features before prediction.
  
      Input: A customer_input as a 2D numpy array representing the customer's past
             transactions.
  
      Output: Predicted lifetime value of the customer.
  
      Example:
      >>> predict_customer_ltv()
      Predicted customer lifetime value: [some_value]
      """
  
      import pandas as pd
      import numpy as np
  
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      # Preprocessing
      df = pd.read_csv('transactions.csv')
      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 f'Predicted customer lifetime value: {predicted_LTV}'
output:     import pandas as pd
      import numpy as np
  
      from sklearn.preprocessing import StandardScaler
      from sklearn.ensemble import RandomForestRegressor
  
      # Preprocessing
      df = pd.read_csv('transactions.csv')
      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 f'Predicted customer lifetime value: {predicted_LTV}'