Found 5 repositories(showing 5)
The aim of this project is to combine secure data management with insightful analysis of YouTube video categories and trends using an ETL pipeline.
Dhairshah
End to end Data engineering pipeline for Youtube analysis
YESUKRISHNA
An end-to-end Data Engineering pipeline for extracting, processing, and analyzing sentiment from YouTube comments using ETL principles. Built with Python, PySpark, and modern data tools for scalable and insightful sentiment analysis.
This project demonstrates an end-to-end data engineering and analytics pipeline for YouTube channel insights. It automatically harvests data from the YouTube Data API, processes and stores it in a structured SQL data warehouse, and provides an interactive Streamlit dashboard for analysis.
D-393Patel
An end-to-end MLOps pipeline for sentiment analysis of YouTube comments, built with Python, Docker, and AWS. Demonstrates the complete ML lifecycle — from data ingestion and feature engineering to model training, deployment, and monitoring — following production-grade MLOps practices.
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