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Huberman Lab Podcast Analytics

1. Overview

The Andrew Huberman Podcast Analytics project is an end-to-end data engineering and analytics application designed to provide deep insights into Andrew Huberman's podcast series hosted on YouTube. Leveraging advanced technologies such as Spark, Snowflake, Power BI, React, and Flask, the project extracts, processes, and visualizes data, offering an enhanced understanding of video performance and audience sentiment. A core aspect of this project involves training each user to comment from all videos using Open AI's GPT-4o Mini API for detailed emotion and sentiment analysis. Additionally, it features an AI-powered chatbot, powered by GPT-4o Mini, to assist users in navigating through the podcasts and discovering episodes tailored to their interests.

This project bridges the gap between raw video data and actionable insights, providing users with an engaging and interactive experience to explore content more effectively.

1.1 Objectives

  1. Analyze Video Performance: Extract and evaluate YouTube video metrics, including views, likes, comments, and engagement.
  2. Understand Audience Sentiment: Conduct detailed sentiment and emotion analysis of user comments using advanced GPT-4o Mini API.
  3. Interactive Visualization: Present the data through a visually appealing, interactive Power BI dashboard.
  4. AI Recommendations: Enable users to find podcast episodes suited to their needs with an AI-powered chatbot.
  5. Seamless User Experience: Integrate the dashboard and chatbot into a unified, user-friendly interface.

1.2 Key Features