Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{184434,
        author = {Immidisetty Sai Kumar and D.Murali},
        title = {YOUTUBE VIDEO TREND ANALYSIS USING WEB SCRAPING AND PANDAS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {1440-1445},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184434},
        abstract = {The YouTube Video Trend Analysis project is a data analytics application that utilizes web scraping and data processing to analyze trending YouTube videos. The project extracts real-time video data from YouTube’s trending section using web scraping techniques and processes it using Pandas and data visualization libraries. The goal is to identify patterns in trending videos, such as popular content categories, video duration impact, keyword trends, and engagement metrics (likes, comments, views). This project is built using Python, Beautiful Soup, Selenium, Pandas, and Matplotlib/Seaborn for data collection, processing, and visualization. It provides valuable insights for content creators, digital marketers, and data analysts who aim to understand what makes a video trend on YouTube.},
        keywords = {Data Cleaning & Processing, Beautiful Soup, Selenium, Pandas},
        month = {September},
        }