Traditional DSL troubleshooting solutions are reactive, relying mainly on customers to report problems, and tend to be labor-intensive, time consuming, prone to incorrect resolutions and overall can contribute to increased customer dissatisfaction. In this paper, we propose a proactive approach to facilitate troubleshooting customer edge problems and reducing customer tickets. Our system consists of: i) a ticket predictor which predicts future customer tickets; and ii) a trouble locator which helps technicians accelerate the troubleshooting process during field dispatches. Both components infer future tickets and trouble locations based on existing sparse line measurements, and the inference models are constructed automatically using supervised machine learning techniques. We propose several novel techniques to address the operational constraints in DSL networks and to enhance the accuracy of NEVERMIND. Extensive evaluations using an entire year worth of customer tickets and measurement data from a large network show that our method can predict thousands of future customer tickets per week with high accuracy and signifcantly reduce the time and effort for diagnosing these tickets. This is benefcial as it has the effect of both reducing the number of customer care calls and improving customer satisfaction.