Youtube advertisers pay content creators based on adviews and clicks for the goods and services being marketed. They want to estimate the adview based on other metrics like comments, likes etc. In this project I have trained various regression models and estimated the best out of them using error analysis.
The file train.csv contains metrics and other details of about 15000 youtube videos. The metrics include number of views, likes, dislikes, comments and apart from that published date, duration and category are also included. The train.csv file also contains the metric number of adviews which is our target variable for prediction.
'vidid' : Unique Identification ID for each video
'adview' : The number of adviews for each video
'views' : The number of unique views for each video
'likes' : The number of likes for each video
'dislikes' : The number of likes for each video
'comment' : The number of unique comments for each video
'published' : The data of uploading the video
'duration' : The duration of the video (in min. and seconds)
'category' : Category niche of each of the video