Optimizing Prediction of YouTube Video Popularity Using XGBoost

Meher UN Nisa, Danish Mahmood*, Ghufran Ahmed, Suleman Khan, Mazin Abed Mohammed, Robertas Damaševičius*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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YouTube is a source of income for many people, and therefore a video’s popularity ultimately becomes the top priority for sustaining a steady income, provided that the popularity of videos remains the highest. Analysts and researchers use different algorithms and models to predict the maximum viewership of popular videos. This study predicts the popularity of such videos using the XGBoost model, considering features selection, fusion, min-max normalization and some precision parameters such as gamma, eta, learning_rate etc. The XGBoost gives 86% accuracy and 64% precision. Moreover, the Tuned XGboost also shows enhanced accuracy and precision. We have also analyzed the classification of unpopular videos for a comparison with our results. Finally, cross-validation methods are also used to evaluate certain combination of parameter’s values to validate our claims. Based on the obtained results, it can be said that our proposed models and techniques are very useful and can precisely and accurately predict the popularity of YouTube videos.
Original languageEnglish
Article number2962
Number of pages16
Issue number23
Publication statusPublished - 28 Nov 2021


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