Modeling MOOC Dropouts
نویسنده
چکیده
In this project, we model MOOC dropouts using user activity data. We have several rounds of feature engineering and generate features like activity counts, percentage of visited course objects, and session counts to model this problem. We apply logistic regression, support vector machine, gradient boosting decision trees, AdaBoost, and random forest to this classification problem. Our best model is GBDT, achieving AUC of 0.8763, about 3% off the KDD winner.
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