1. Objective: To build predictive model, which help in predicting views of video uploaded on TEDX website.
Number of TED talks: 4,005
Number of speakers: 3,274
Number of events: 459
Timespan: Jun 2006 - Apr 2020
Attributes: 19
Task is to predict views of TED talk videos => Regression problem
- MAE,MSE,RMSE
- R2_Score
- Multiple Speakers in Ted Talk we have converted this feature with speakers_avg_views, Mutiple events converted to event_avg_views etc etc
- Year feature has been converted to Age of video with (2023-(Year when video was published)) to understand how much older the video is.
- Used One hot encoding on Orignal langauge in which Ted Talk was released to compare Videos langauge wise.
4.1 f_regression to get feature importance, Dropping features with higher P-value with threshold > 0.4
After comparing all the models w.r.t.o their R2 scores even simple models like linear & Ridge can perform better than ensemble models like Random Forest and XGB with this type of use cases.