input: Write a function in Python that creates a machine learning model to predict customer lifetime value (CLV) based on a customer's past transactions. The function should read transaction data from a CSV file, preprocess the data, train a RandomForestRegressor model, and return the predicted CLV for an input array of past transactions. If the input array is provided, the function should also transform the input data using the same scaler used during preprocessing and print the predicted CLV.
  def predict_customer_ltv():
      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)
  
      return model, scaler
  
  assert predict_customer_ltv()[0].predict(predict_customer_ltv()[1].transform(np.array([[10, 100, 5, ...]])))[0] > 0
output: 
  def predict_customer_ltv():
      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)
  
      return model, scaler
  