YouTube Data Harvesting Project

Overview

This project focuses on harvesting data from YouTube using the YouTube API. The collected data is processed into a structured format, stored in a MySQL database, and visualized using a Streamlit application. The application provides an interactive interface to explore and analyze the data.

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Features

• YouTube API Integration: Extract detailed video and channel data directly from YouTube.
• Data Processing: Use pandas to clean, structure, and format data into a DataFrame for analysis.
• Database Management: Store the processed data in a MySQL database for efficient retrieval and storage.
• Interactive Visualization: Build a Streamlit app to visualize key insights and trends in the data.

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Technologies Used

• Programming Language: Python
• Libraries:
  • pandas for data manipulation and analysis
  • googleapiclient for interacting with the YouTube API
  • sqlalchemy or mysql-connector for database operations
  • streamlit for app development
• Database: MySQL

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How It Works

1. Data Extraction:
   • Use the YouTube API to fetch video and channel details.
   • Extract data points such as title, views, likes, comments, and channel statistics.
2. Data Processing:
   • Clean and organize the extracted data into a DataFrame.
   • Perform necessary transformations for database storage and visualization.
3. Data Storage:
   • Insert processed data into a MySQL database.
   • Maintain efficient database structure for querying.
4. Data Visualization:
   • Create an interactive Streamlit dashboard.
   • Display metrics such as most-viewed videos, channel performance, and trends.

Future Enhancements

• Add advanced filtering and search capabilities in the Streamlit app.
• Integrate additional APIs for enriched data insights.
• Implement user authentication for secure app access.