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🥇1st place out of 57 teams, which included 350 graduate and undergraduate students, in a 11.5-hour dataset challenge sprint. Provided two random forest classification models for Valassis to predict user conversion, depending on the data available to them. AUROC values were 0.64 and 0.86 respectively.

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Duke-Datathon-2019

🥇1st place out of 57 teams, which included 350 graduate and undergraduate students, in a 11.5-hour dataset challenge sprint. Provided two random forest classification models for Valassis to predict user conversion, depending on the data available to them.

View code under the code folder. Unfortunately, the dataset is too big to uploaded. If you wish to run the code, download the dataset @ https://drive.google.com/file/d/1I0M9_sDo8yi3DJ_MXcv-f6442VO6TaI-/view?fbclid=IwAR0kSIe06Xw1nJ2I2AWkmftieW1JomaXwmir64ZayDa1qZHZmO-02TwOZDk with password: Datathon_2019Duke!

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🥇1st place out of 57 teams, which included 350 graduate and undergraduate students, in a 11.5-hour dataset challenge sprint. Provided two random forest classification models for Valassis to predict user conversion, depending on the data available to them. AUROC values were 0.64 and 0.86 respectively.

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