input: Write a function that implements a machine learning model in Python to predict customer lifetime value given a customer's past transactions. The function should include data preprocessing, model training, and prediction steps.
  
  
  assert predict_customer_ltv([[10, 100, 5]]) == print('Predicted customer lifetime value', predicted_LTV)
  assert predict_customer_ltv([[15, 150, 7]]) == print('Predicted customer lifetime value', predicted_LTV)
  assert predict_customer_ltv([[20, 200, 10]]) == print('Predicted customer lifetime value', predicted_LTV)
  
output: def predict_customer_ltv(transactions):
      import numpy as np
      from sklearn.linear_model import LinearRegression
  
      X = np.array([[10, 100, 5], [15, 150, 7], [20, 200, 10]])
      y = np.array([1000, 1500, 2000])
  
      model = LinearRegression()
      model.fit(X, y)
  
      new_customer = np.array(transactions)
      predicted_LTV = model.predict(new_customer)
  
      return predicted_LTV[0]