Overview of my solution in the WSDM 2018 Cup Challenge, a data science competition hosted by Kaggle. This solution placed 1st out of 575 teams. I entered the competition about 6 weeks prior to completion as a solo competitor. (Bryan Gregory) -----Tools Used----- -Microsoft SQL Server 2016, Linux mode (Azure VM) -LightGBM Python Library – 2.0.11 -XGBoost Python Library – 0.6 -SKLearn – 0.19.1 -Pandas – 0.22.0 -NumPy – 1.14.0 rc1 --------Links--------- White paper: https://arxiv.org/abs/1802.03396 Article: https://medium.com/@bryan.gregory1/predicting-customer-churn-extreme-gradient-boosting-with-temporal-data-332c0d9f32bf Recorded Presentation: https://www.youtube.com/watch?v=OEDUzVH1aDI Competition Overview: https://www.kaggle.com/c/kkbox-churn-prediction-challenge/ Final standings: https://www.kaggle.com/c/kkbox-churn-prediction-challenge/leaderboard WSDM 2018 Cup Challenge: https://wsdm-cup-2018.kkbox.events/ My Blog: http://bryangregory.com