input: Write a python function to predict customer lifetime value using RandomForestRegressor.
  assert abs(predict_LTV(np.array([[10,100,5,...]])) - predicted_LTV) < 1e-5
  assert abs(predict_LTV(np.array([[20,150,10,...]])) - another_predicted_LTV) < 1e-5
  assert abs(predict_LTV(np.array([[5,50,2,...]])) - yet_another_predicted_LTV) < 1e-5
output: def predict_LTV(features):
      from sklearn.ensemble import RandomForestRegressor
      import numpy as np
      
      # Assuming we have training data available
      # X_train: Feature matrix for training
      # y_train: Target variable (Customer Lifetime Value)
      # These should be replaced with the actual training data
      X_train = np.array([[10, 100, 5], [20, 150, 10], [5, 50, 2]])  # Example data
      y_train = np.array([1000, 2000, 500])  # Example target values
      
      # Initialize and train the model
      model = RandomForestRegressor()
      model.fit(X_train, y_train)
      
      # Predict the LTV for the given features
      predicted_LTV = model.predict(features)
      
      return predicted_LTV[0] if len(predicted_LTV) == 1 else predicted_LTV