Classification for Student Performance
Authors
Wang Ziyi
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Student performance can determine the future path of success of a student . It is essential for us to recognize the factors that influence student performance and predict the student grade level using the existing information. We split the data into the training set and testing set, building up a binary logistic regression to predict the results in the testing set. The results show that our model has good predictability and reaches the AUC value of 0.68. Also, by looking at the coefficient, we figured out several important factors that affect student performance grades.
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Authors
Wang Ziyi
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References:
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