/News-Popularity

A xgboost based classifier for prediction viral news based on UCI-News popularity dataset

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News-Popularity

A xgboost based classifier for prediction viral news based on UCI-News popularity dataset.

Approach:

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.

Dataset:

UCI-News Popularity Dataset

Accuracy:

67.36% on the test-set. SOTA:69%

Dependencies:

  1. Xgboost
  2. numpy
  3. pandas
  4. scikit-learn.