A xgboost based classifier for prediction viral news based on UCI-News popularity dataset.
The problem is meant to be used as a regression task to predict the number of shares of a news article, but the target variable has been converted to a binary variable based on the mean value of the column shares
which provides balanced classes to work with, using xgboost with hyperparameter tuning
the model was trained.
UCI-News Popularity Dataset
67.36% on the test-set.
SOTA:69%
- Xgboost
- numpy
- pandas
- scikit-learn.