Analyzed the data set on student's performance and develop a model that will predict the likehoold that a given student will pass, quantifying whether an intervention is necessary.
Three classification methods are initially applied and compared: AdaBoost, Naive Bayesian and support vector machine. The AdaBoost model was picked and further fine-tuned with GridSearchCV().
To run the program:
python student_invention.py