  This paper attempts multi-label classification by extending the idea of
independent binary classification models for each output label, and exploring
how the inherent correlation between output labels can be used to improve
predictions. Logistic Regression, Naive Bayes, Random Forest, and SVM models
were constructed, with SVM giving the best results: an improvement of 12.9\%
over binary models was achieved for hold out cross validation by augmenting
with pairwise correlation probabilities of the labels.
