Skip to content

The following project predicts the likes of youtube videos between July and November 2023 using a dataset curated using YouTube's API

Notifications You must be signed in to change notification settings

Devansh-Gutti/Topics-With-Deep-Learning-Youtube-likes-prediction-

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

1 Commit
 
 
 
 
 
 
 
 

Repository files navigation

TOPICS WITH DEEP LEARNING

TEAM : BENCH ODYSSEY TEAM NO: 23

  • PES2UG21CS148 Choppa Rasagnya
  • PES2UG21CS158 Deepika Indran
  • PES2UG21CS160 Devansh Guttikonda

Predicting the Likes Count of a YouTube Video

Most popular Indian Youtube videos from June to November 2023 were scraped using Youtube's API and the following were the attributes obtained-

  • Video_id
  • Title
  • PublishedAt
  • ChannelId
  • ChannelTitle
  • Trending_Date
  • Tags
  • View Count
  • Likes
  • Comment Count
  • Thumbnail_Link etc

The following features was used to predict the likes -

  • view_count
  • comment_count
  • Sentiment_Scores
  • time_till_trend
  • Subscriber_Count

The model which we used was the XGBoost (Extreme Gradient Boosting). XGBoost is an ensemble learning technique that combines multiple decision trees to improve predictive performance.

  • RMSE: 110538.93938925382
  • R-squared value: 0.9597375701889096
  • MAE: 31216.7363869918
  • MSE: 12218857121.30113

How to Use

  1. git clone TDL_PROJECT_CODE_Team23.ipynb

Limitations

Predictions need to be periodically re-evaluated as the analysis isn't based on real-time data.

Conclusion and Future Work

  • Market Analysis: Analyze trends in video popularity, viewership, and engagement to understand audience preferences and behavior in the Indian market.

  • Thumbnail Analysis: Identify the influence of a thumbnail of a video and how it performs using CNN’s and other Deep learning techniques.

  • Sentiment Analysis: Utilize comments data to gauge audience sentiment towards specific topics or creators, aiding in reputation management and content optimization strategies.

About

The following project predicts the likes of youtube videos between July and November 2023 using a dataset curated using YouTube's API

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published