Python code for the Data Science Bowl 2019 competition (top 7%) hosted by Kaggle
In this challenge, anonymous gameplay data was used, including knowledge of videos watched and games played, from the PBS KIDS Measure Up! app, a game-based learning tool developed as a part of the CPB-PBS Ready To Learn Initiative with funding from the U.S. Department of Education. Competitors were challenged to predict scores on in-game assessments and create an algorithm that will lead to better-designed games and improved learning outcomes.
The intent of the competition is was to use the gameplay data to forecast how many attempts a child will take to pass a given
assessment (an incorrect answer is counted as an attempt). The outcomes in this competition are grouped into 4 groups
(labeled accuracy_group in the data):
3: the assessment was solved on the first attempt
2: the assessment was solved on the second attempt
1: the assessment was solved after 3 or more attempts
0: the assessment was never solved
https://www.kaggle.com/c/data-science-bowl-2019
My solution involved using a regression approach to this task where the evaluation was based on kappa metric. Conisdering the ordinal form of the target feature, a regression approach with optimised thresholds were implemented.