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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

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Someone on Overflow pointed me to the right direction and I got it working, This is a one class classifier problem, algorithms such as OneClassSVM will solve it. Outlier and novelty detection

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