Authors : Dr. N. Muthuvairavan Pillai; Bharath Kumar L; Harul Ganesh S B; Jashvanth S R

Volume/Issue : Volume 8 - 2023, Issue 12 - December

Google Scholar : http://tinyurl.com/yu946up9

Scribd : http://tinyurl.com/yrr8w5cs

DOI : https://doi.org/10.5281/zenodo.10370614

Abstract : In the era of digital content, predicting the trends and popularity of videos on platforms like YouTube has become paramount. Our project, titled "YouTube Trend Analysis and Prediction," is a data-driven initiative aimed at providing valuable insights and predictive capabilities to content creators and digital marketers. By leveraging machine learning algorithms, including Decision Trees, Random Forest, and Gradient Boosting, our system can analyze key video attributes such as titles, descriptions, likes, dislikes, comments, views, and more. This analysis allows us to identify patterns and correlations that influence video trends and popularity. With a user-friendly interface, our platform offers a unique opportunity to explore the relationships between these elements, gain content strategy insights, and predict the potential success of YouTube videos. Through a combination of data processing, feature engineering, and the application of machine learning models, our project assists content creators in optimizing their video content strategies, thereby increasing their visibility and reach. In an age where digital content is king, our YouTube Trend Analysis and Prediction system stands as a powerful tool for creators and marketers, aiding them in the quest to produce engaging and popular videos that resonate with their target audience.

Keywords : Machine-learning ,Data-analysis, Sentiment- analysis, User-engagement, Forecasting, Content-creators, Marketers, Researchers, Trend-prediction, Data-sources, Algorithms, Insights, Dynamic-analytics, Video-trends, Modern-techniques, Prediction-models, Feature engineering, Dashboard-analytics, Trend-enhancement.