-Netflix Headquarters, Los Angeles — Sunset Boulevard
This project analyzes Netflix movies and TV shows data using SQL to solve real business problems and extract actionable insights that support content strategy and decision-making.
An end-to-end SQL project exploring Netflix's global content library. This project translates real business questions into analytical queries and surfaces actionable insights for content strategy and decision-making.
| Metric | Value |
|---|---|
| Total SQL Queries | 15+ |
| Records Analyzed | 8,000+ |
| Countries Covered | 190+ |
| Database | PostgreSQL |
- Database: PostgreSQL
- Domain: Entertainment / Streaming Analytics
- Dataset: Netflix Movies and TV Shows (Kaggle)
Unnests the comma-separated cast column and ranks actors by appearance count across India-produced titles.
SELECT
UNNEST(STRING_TO_ARRAY(casts, ',')) AS actor,
COUNT(*) AS appearances
FROM netflix
WHERE country = 'India'
GROUP BY actor
ORDER BY appearances DESC
LIMIT 10;Uses a CASE expression inside a subquery to label titles as Bad or Good based on keywords in the description, then aggregates counts by category.
SELECT
category,
COUNT(*) AS content_count
FROM (
SELECT
CASE
WHEN description ILIKE '%kill%'
OR description ILIKE '%violence%' THEN 'Bad'
ELSE 'Good'
END AS category
FROM netflix
) AS categorized_content
GROUP BY category;This project demonstrates the ability to:
- ✅ Translate business questions into analytical SQL queries
- ✅ Work with real-world, production-scale datasets
- ✅ Apply advanced SQL concepts — window functions, CTEs, array unnesting
- ✅ Communicate insights clearly for non-technical stakeholders
- ✅ Think from a business decision-making perspective
This analysis provides a comprehensive view of Netflix's content library and can help inform content strategy, regional investment, and platform decision-making.
netflix-sql-analysis/
│
├── datasets/
│ └── netflix_titles.csv
│
├── queries/
│ ├── 01_content_type_distribution.sql
│ ├── 02_movies_vs_shows.sql
│ └── ...
│ └── 15_content_classification.sql
│
├── shre.jpg
└── README.md
Your Name
📧 Email: [email protected]
💼 LinkedIn: linkedin.com/in/mihirr51
🐙 GitHub: github.com/mihirr00051
"Data is the new oil — and SQL is the refinery."