Team Members:
- M Taimoor Tariq
- Vafa Batool
- Talal Touseef
- Talha Waheed
- Anna Mazhar
Checkpoint3_part1 includes:
-Analysis of regional preferences.
-Impact on trending videos by public events.
-Correlation between likes, shares and views.
Checkpoint3_part2 includes:
-Analysis of similar characteristics in video titles, tags and description.
-Using text mining to see possible use of catchy words in the titles.
Checkpoint4_part1 includes:
-ML model implementation to understand how long a video stays tredning.
-Model was designed and implmented using Scikit-Learn.
Checkpoint4_part2 includes:
-Tested the hypothesis if the YouTube's Algorithm consciously ensures diversity of content within the Trending List of videos.
-Conducted statistical Inference, mainly using permutation test, to understand the role of numerical statistics such as likes, dislikes, views and comments in making the video trending.
All of the Notebooks contain detailed documentation of the task at hand.
Take a look at the in-depth analysis and what did we learn from the data:
https://taimoor-tt10.medium.com/hack-your-way-into-youtube-trending-9659398ee34e