YouTube Trending Analytics Website

Course Project, Carnegie Mellon University, LTI, 2019

  • Investigated factors that govern the YouTube trending page and visualized the presence of user and platform bias
  • Hypothesized the reasons for existence of bias and demonstrated their variability across different countries
  • Constructed a Machine Learning pipeline with XGBoost classifier to predict the likelihood of a video to trend
  • Video Demo: https://www.youtube.com/watch?v=UlanYkq_VII
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