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YOUTUBE TRENDING VIDEO ANALYSIS

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dc.contributor.author Muzaffar, Huzaifa Reg # 60010
dc.contributor.author Danish, Muhammad Reg # 60040
dc.contributor.author Habib, Usama Reg # 60041
dc.date.accessioned 2026-07-02T04:57:30Z
dc.date.available 2026-07-02T04:57:30Z
dc.date.issued 2022
dc.identifier.uri http://hdl.handle.net/123456789/21360
dc.description Supervised by Sameena Javaid en_US
dc.description.abstract YouTube is a global online video sharing and social media platform currently owned by Google. Trending videos represent the content that is gaining viewership over-a certain period. While popular videos get a huge number oflikes and views with time. Trending video analysis has not been properly analysed yet despite their importance. The study is conducted to analyse features to determine the importance of variables for the trendiness of a video and focuses on how these features help a video to trend on YouTube. The study also includes learning different models on the dataset but will also conduct a comparative analysis between yearly datasets. Our work also includes classification models known as Hard Voting, Gradient Boosting, Gaussian Naive.- Bayes, Logistic Regression, and Random Forest to determine which model suits better for prediction en_US
dc.language.iso en_US en_US
dc.publisher Bahria University Karachi Campus en_US
dc.relation.ispartofseries BSCS;MFN BSCS 441
dc.title YOUTUBE TRENDING VIDEO ANALYSIS en_US
dc.type Project Reports en_US


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