This project aims to leverage Amazon Web Services to create trending Youtube videos analytics service. Project contains different data engineering, data analysis and data science parts.

A comprehensive data analytics project exploring YouTube trending videos using a large multi-country dataset. Includes data cleaning, feature engineering, NLP on titles, category and channel insights, keyword patterns, upload-time analysis, visual storytelling, and ML models to identify factors that drive video virality.

Data Engineering Project on AWS for YouTube Videos Analysis

This project aims to leverage Amazon Web Services to create trending Youtube videos analytics service. Project contains different data engineering, data analysis and data science parts.

This GitHub repository houses a YouTube Data Engineering Analysis project focusing on efficiently managing, analyzing, and visualizing structured and semi-structured YouTube video data to extract valuable insights.

A Data Engineering pipeline for Youtube Videos Data and Analysis of trending Metrics on AWS Quicksight

Exploratory Data Analysis (EDA) of US YouTube Trending Videos dataset. This project performs comprehensive data analysis including feature engineering, data cleaning, statistical analysis, and visualizations to identify key trends in YouTube trending videos.

A comprehensive Data Engineering project for Analysis of YouTube trending videos based on different categories.

The YouTube Trend Analysis Project is a data engineering and data analysis project designed to analyze trending YouTube videos. The goal is to extract insights from trending video data.

The "YouTube Data Engineering and Analysis Project" is an end-to-end data engineering project that focuses on managing, streamlining, and analyzing YouTube video data

Data Engineering YouTube Analysis Project - This project aims to securely manage, streamline, and perform analysis on the structured and semi-structured YouTube videos data based on the video categories and the trending metrics.

AI, Data Science & Data Analysis tutorials on my YouTube channel. Learn about AI, machine learning, data analysis, and prompt engineering. For more videos, visit my [YouTube channel](https://www.youtube.com/watch?v=l5GNllqtE6I&list=PLC6UZnripyEzjZkWXOOzjFIquTqoqjVH1&index=8)

YouTube Trending Video Analysis A complete end-to-end data analytics project that explores trends in YouTube videos across multiple countries. It includes data cleaning, feature engineering, machine learning to predict video views, and natural language processing to analyze video titles.

The projects aims to build a data engineering pipeline on AWS, for analysis of YouTube data based on video categories and trending metrics.

YouTube Analytics ETL Pipeline — A Python-based ETL pipeline that extracts, cleans, transforms, and loads YouTube video data with engagement analysis, saving results to SQLite and CSV files for data engineering projects.

This project analyzes YouTube podcast data to uncover engagement patterns. Using the YouTube API and Python, it performs data extraction, feature engineering, and clustering. Insights from this analysis can help content creators optimize engagement strategies for both short and long videos.

Analysis of 537 YouTube videos exploring what drives views, engagement, and audience behavior. Includes data cleaning, feature engineering, visualizations, category insights, timing patterns, and correlations showing how likes shape engagement across channels and content types.

This project aims to leverage Amazon Web Services to create a youtube trending video analytics service. The project contains different data engineering, data analysis, and data science sections. The whole project is implemented on AWS Cloud.

Predicting YouTube video views using regression models with data analysis and preprocessing on the YouTube Statistics dataset. Involves feature engineering, model selection (Linear Regression, Random Forest), and evaluation using RMSE and R2 metrics to ensure prediction accuracy.

An end-to-end data analytics and machine learning project that analyzes multi-country YouTube trending data to uncover engagement patterns, regional trends, and key factors influencing video reach. The project includes data preprocessing, exploratory analysis, feature engineering, and regression models to predict video views, along with interactive

This project analyzes India’s YouTube Trending dataset to understand what drives video visibility, engagement, and publishing performance. The work includes end-to-end data cleaning, feature engineering, exploratory analysis, and insight generation using Python.

Developed a machine learning–driven analysis to study and predict YouTube revenue and engagement using per-video, per-day analytics data. Applied feature engineering and regression models to identify key monetization drivers, and used Power BI to visualize and validate insights around views, RPM, engagement, and revenue concentration.