📺 Netflix SQL Project – Movies & TV Shows Data Analysis

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🌟 Overview

The Netflix SQL Project is a comprehensive data analysis project on Netflix’s movies and TV shows dataset. The project uses PostgreSQL to explore patterns, trends, and insights across various aspects of Netflix content.

Key highlights:

• Analyze content distribution: Movies vs TV Shows
• Explore popular genres, ratings, actors, and directors
• Identify top producing countries and content trends
• Investigate content release trends over the last 5–10 years
• Categorize content as family-friendly or violent using keyword analysis

This project demonstrates practical SQL skills including aggregate functions, string and date manipulation, conditional logic, and analytical queries.

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🎯 Objectives

1. Content Analysis – Understand distribution by type, genre, and country
2. Popularity Analysis – Find common ratings, top actors, and directors
3. Trend Analysis – Explore content added over the years and release patterns
4. Content Classification – Categorize movies and TV shows based on description keywords

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🛠️ Tools & Technologies

• Database: PostgreSQL
• SQL Concepts Used:
  • Aggregate functions (COUNT, MAX, AVG)
  • Conditional queries (CASE WHEN)
  • String manipulation (STRING_TO_ARRAY, UNNEST, TRIM, SPLIT_PART)
  • Date handling (TO_DATE, EXTRACT)
  • Ranking & window functions (RANK() OVER(PARTITION BY ...))
  • Filtering with WHERE, ILIKE, AND, OR

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📊 Dataset Description

The dataset contains information about movies and TV shows on Netflix.

Column Name	Description
show_id	Unique ID of the content
type	Movie or TV Show
title	Name of the content
director	Director name
cast	Actors in the content
country	Country of production
date_added	Date added to Netflix
release_year	Year of release
rating	Content rating (PG, R, etc.)
duration	Movie duration or number of seasons
listed_in	Genres
description	Brief summary of content

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Dataset Download Link: Netflix Shows Dataset on Kaggle

💡 Business Problems & Solutions

Here’s an interactive summary of the queries and insights generated:

1️⃣ Movies vs TV Shows Count

SELECT type, COUNT(*) AS number_of_movies
FROM netflix
GROUP BY type;

✅ Insight: Understand the distribution of movies and TV shows on Netflix.

2️⃣ Most Common Rating per Type

SELECT type, rating
FROM (
    SELECT type, rating, COUNT(*),
    RANK() OVER(PARTITION BY type ORDER BY COUNT(*) DESC) AS ranking
    FROM netflix
    GROUP BY type, rating
) AS t1
WHERE ranking = 1;

✅ Insight: Find the most popular ratings (PG, TV-MA, etc.) in movies vs TV shows.

3️⃣ Movies Released in a Specific Year (e.g., 2020)

SELECT title
FROM netflix
WHERE type = 'Movie' 
  AND release_year = 2020;

✅ Insight: Filter movies released in a specific year.

4️⃣ Top 5 Countries by Content Count

SELECT TRIM(UNNEST(STRING_TO_ARRAY(country, ','))) AS country,
       COUNT(show_id) AS total_content
FROM netflix
GROUP BY 1
ORDER BY total_content DESC
LIMIT 5;

✅ Insight: Identify which countries produce the most Netflix content (handles multiple countries per row).

5️⃣ Identify the Longest Movie

SELECT *
FROM netflix
WHERE type = 'Movie'
  AND duration = (
    SELECT MAX(duration) FROM netflix WHERE type = 'Movie'
);

✅ Insight: Find the movie with the longest runtime.

6️⃣ Find Content Added in Last 5 Years

SELECT *
FROM netflix
WHERE TO_DATE(date_added, 'Month DD, YYYY') >= CURRENT_DATE - INTERVAL '5 years';

✅ Insight: Retrieve content that was added to Netflix in the last 5 years.

7️⃣ Movies / TV Shows by Director 'Rajiv Chilaka'

SELECT *
FROM netflix
WHERE director ILIKE '%Rajiv Chilaka%';

✅ Insight: List all content (movies & TV shows) directed by Rajiv Chilaka.

8️⃣ TV Shows with More than 5 Seasons

SELECT *
FROM netflix
WHERE type = 'TV Show'
  AND SPLIT_PART(TRIM(duration), ' ', 1)::numeric > 5;

✅ Insight: List TV shows having more than 5 seasons (uses TRIM to avoid spacing issues).

9️⃣ Count the Number of Content Items in Each Genre

SELECT TRIM(UNNEST(STRING_TO_ARRAY(listed_in, ','))) AS genre,
       COUNT(show_id) AS total_count
FROM netflix
GROUP BY 1
ORDER BY total_count DESC;

✅ Insight: Get how many items belong to each genre (splits multi-genre rows).

🔟 Yearly Content Percentage in India

SELECT EXTRACT(YEAR FROM TO_DATE(date_added, 'Month DD, YYYY')) AS year,
       COUNT(*) AS yearly_content,
       ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM netflix WHERE country ILIKE '%India%'), 2) AS percentage_content
FROM netflix
WHERE country ILIKE '%India%'
  AND date_added IS NOT NULL
GROUP BY 1
ORDER BY 1;

✅ Insight: For each year, show total Indian content and that year’s percentage share of all Indian content.

1️⃣1️⃣ List All Movies That Are Documentaries

SELECT *
FROM netflix
WHERE listed_in ILIKE '%documentaries%';

✅ Insight: Find movies that belong to documentary genre(s).

1️⃣2️⃣ Find All Content Without a Director

SELECT *
FROM netflix
WHERE director IS NULL;

✅ Insight: Detect rows with missing director information (NULL or empty string).

1️⃣3️⃣ Movies with 'Salman Khan' in Last 10 Years

SELECT *
FROM netflix
WHERE casts ILIKE '%Salman Khan%'
  AND type = 'Movie'
  AND release_year >= EXTRACT(YEAR FROM CURRENT_DATE) - 10;

✅ Insight: Count/list movies that include Salman Khan released in the last 10 years (inclusive).

1️⃣4️⃣ Top 10 Actors in Indian Movies

SELECT TRIM(UNNEST(STRING_TO_ARRAY(casts, ','))) AS actor,
       COUNT(show_id) AS movies
FROM netflix
WHERE country ILIKE '%India%'
GROUP BY 1
ORDER BY movies DESC
LIMIT 10;

✅ Insight: Identify actors who appear most frequently in Indian movies on Netflix.

1️⃣5️⃣ Categorize Content as Bad or Good Based on Keywords

SELECT CASE
         WHEN description ILIKE '%kill%' OR description ILIKE '%violence%' THEN 'Bad_Content'
         ELSE 'Good_Content'
       END AS category,
       COUNT(*) AS total_count
FROM netflix
GROUP BY category;

✅ Insight: Classify content by presence of violent keywords in descriptions and count per category.

🧠 Key Skills Demonstrated

• Writing complex SQL queries with subqueries, aggregates, and window functions
• Performing data cleaning and transformation using string and date functions
• Categorizing data dynamically using CASE expressions
• Handling multi-valued fields via STRING_TO_ARRAY + UNNEST (and TRIM)
• Using PostgreSQL features like RANK(), TO_DATE(), and window functions

✅ Conclusion

This project demonstrates practical SQL-based data analysis on a real-world dataset (Netflix).
It is suitable for:

• Data analysts learning SQL
• Candidates who want project examples for portfolios or GitHub
• Anyone interested in exploring streaming-platform data using SQL

You can copy these queries into your PostgreSQL environment to reproduce results and refine them further for presentations, dashboards, or visualizations.