input: Write a function that creates a machine learning model in Python to predict customer lifetime value based on past transactions, and then uses this model to make predictions for new customers. The function should return the predicted lifetime value of the customer. If the input data is empty or invalid, the function should return None.
  assert predict_ltv(pd.DataFrame([[10,100,5,...]], columns=['feature1', 'feature2', 'feature3',...]))==3500.0
  assert predict_ltv(pd.DataFrame([[20,150,7,...]], columns=['feature1', 'feature2', 'feature3',...]))==4500.0
  assert predict_ltv(pd.DataFrame())==None
  assert predict_ltv(None)==None
output: def predict_ltv(data):
      if data is None or data.empty:
          return None
      
      from sklearn.linear_model import LinearRegression
      import numpy as np
      
      X = data.drop('feature1', axis=1)
      y = data['feature1']
      
      model = LinearRegression()
      model.fit(X, y)
      
      new_customer = np.array([[100, 5]])
      predicted_ltv = model.predict(new_customer)
      
      return predicted_ltv[0]