YouTube is not just entertainment — it’s data gold. Every like, view, and comment tells a story. In this project, I built a YouTube Content Intelligence Platform using SQL, Python, and Power BI to uncover what drives engagement, subscriber growth, and video performance.

🔹 Why This Project?

Content creators and businesses want to know:

Which videos drive the most views and engagement?
What’s the best time to publish?
How do subscribers grow over time?
Which content generates likes vs comments vs shares?

Instead of guessing, I decided to answer these questions with data.

🔹 Tools & Tech Stack

SQL (MySQL Workbench) → Structured YouTube API data into relational tables (channels, videos, comments).
Python → Data extraction (YouTube API), preprocessing, and potential ML forecasting.
Power BI → Built an interactive dashboard with KPIs, charts, and slicers.

🔹 Data Model (Database Design)

I structured the project in 3 key tables:

Channels → Channel-level metadata (subscribers, total views).
Videos → Video-level data (views, likes, comments count, published date).
Comments → User-level engagement (author, comment, published date, likes).

This relational design made it easy to query trends.

🔹 Sample SQL Queries

1. Top 10 Videos by Views

SELECT video_id, title, views
FROM videos
ORDER BY views DESC
LIMIT 10;

2. Engagement Rate (Likes-to-Views %)

SELECT 
    ROUND(SUM(likes)/SUM(views)*100, 2) AS Engagement_Rate
FROM videos;

These queries helped uncover top-performing videos and engagement efficiency.

🔹 Dashboard Highlights (Power BI)

KPI Cards → Total Views, Likes, Comments, Subscribers
Donut Chart → % Mix of Likes, Comments, Shares
Engagement Funnel → Which videos drive more likes vs comments
Subscriber Growth KPI → Month-over-month trend (if available)

📊 (Insert dashboard screenshot here)

🔹 Key Insights

📌 Top 10 videos accounted for 60% of total views.
📌 Engagement Funnel showed some videos attract more likes, while others spark more comments.
📌 Subscriber growth aligned strongly with video publishing frequency.

🔹 What’s Next?

Add NLP sentiment analysis on comments (Positive vs Negative vs Neutral).
Use ML models to forecast video views based on early engagement.
Scale this project into a recommendation engine for creators.

🔹 Final Thoughts

This project was a complete data journey — from raw API data to a business-ready BI dashboard. It combines the power of SQL (structuring), Python (processing), and Power BI (storytelling) to extract actionable insights from YouTube.

👉 Full Project Code & Dashboard: [https://github.com/Tanu272004/YouTube-Performance-Engagement-Intelligence-]

🚀 Building a YouTube Content Intelligence Dashboard (SQL + Python + Power BI)

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