## Trending YouTube Video Statistics for unique Video IDs with its Subscriber data ## Context Original dataset was adopted from below URL : [Trending YouTube Video Statistics][1] That data was collected for several countries : US(United states of America), GB(Great Britain), DE(Germany), CA(Canada), and FR(France). I chose the data for USA country only. I modified the dataset to analyze some hidden information. Such as, I removed duplicate video_id's and make use of them to retrieve some meaningful data. I removed some unrelated attributes.As per my requirement,I changed type/class of few attributes too. I derived some new attributes from existing once.And many other minor modifications. All the modifications are done by R-Programming. I also added a new feature called "subscriber" to the dataset. I collected all subscriber information from youtube.com ,process was automatically done by a python script,written by me. Content YouTube (the world-famous video sharing website) maintains a list of the top trending videos on the platform. According to Variety magazine, To determine the year's top-trending videos, YouTube uses a combination of factors including measuring users interactions (number of views, shares, comments and likes).Note that they’re not the most-viewed videos overall for the calendar year. This dataset is the daily record from the top trending YouTube videos. Top 200 trending videos of a given day. Original Data was collected during 14th November 2017 & 5th March 2018(though, data for January 10th & 11th of 2017 is missing) Original dataset was collected by Youtube API. Subscriber column data scrapped by me on 13th March of 2018, through a automated python script. NA introduced in the column for videos those are removed by the Youtube due to copyright or other issue. Acknowledgements https://www.kaggle.com/datasnaek/ Inspiration Analyzing what factors affect how popular a YouTube video will be. [1]: https://www.kaggle.com/datasnaek/youtube-new