I've been given a youtube trending dataset with the assignment to make a predictive model which outputs the probability of a video getting into trending with at least 60% accuracy.
I have the title, channel, thumbnail_link, views, likes, dislikes, comments, date, ...
I've done some analyses and go figure the important columns are
category, tags(a "|" separated list)
The problem is that it's assumed all videos have trended so I can't use a classifier and fit it with training data to predict a trending yes/no column or use a regression algorithm without changing the goal to "predict how liked will it be" or something.
So it sounds like what I'm looking for is a clustering alg, I've looked into KMeans but as far as I can tell it won't do the trick
I'm thinking that I could compare video by video which categories and tags it contains and score it by the popularity of them or make a distance calculating similarity function but the implication is that I should use scikit