Abstract :

YouTube Analysis Using Machine Learning Is A Machine Learning-based System Designed To Analyze YouTube Video And Channel Data To Identify Patterns, Trends, And Factors That Influence Video Performance. The System Collects Relevant Information Such As Video Views, Likes, Comments, Duration, Publication Time, Category, Engagement Rate, And Subscriber-related Statistics. The Collected Data Is Preprocessed To Remove Inconsistencies And Transformed Into Meaningful Features For Analysis. Machine Learning Algorithms Such As Linear Regression, Random Forest, Decision Tree, And K-Means Clustering Can Be Applied To Predict Video Performance, Classify Videos Based On Engagement Levels, And Identify Groups Of Similar Content. The System Can Also Analyze Audience Engagement And Determine Which Factors Contribute To Higher Views And Interactions. The Proposed Approach Helps Content Creators, Marketers, And Researchers Understand YouTube Trends And Make Datadriven Decisions For Improving Content Performance. The System Provides An Efficient Analytical Framework For Extracting Useful Insights From Large-scale YouTube Data.

Published:

23-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

352 - 359

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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