Netflix-Insights

This project dives deep into Netflix's content catalog using PostgreSQL to uncover insights. It includes queries for content categorization, actor analysis, genre exploration, and year-wise release patterns.

Problems Addressed:

-- 1. No. of Movies Vs TV Shows

-- 2. Most common rating for Movies and TV shows

-- 3. Movies released in 2020

-- 4. Top 5 countries with the most content on Netflix

-- 5. Identify the longest movie

-- 6. Content added in the last 5 years

-- 7. Movies/TV Shows by the Director Steven Spielberg

-- 8. TV Shows that lasted longer than 5 seasons

-- 9. Content in each Genre

-- 10. Average amount of content released by US each year on Netflix

-- 11. Movies that are documentaries

-- 12. Content without director

-- 13. Track Meryl Streep movies in the last 10 years

-- 14. Top 10 actors who appeared in the most number of movies in the US

-- 15. Categorize the content based on the presence of the keywords 'kill' and 'violence' in 
-- the description field. Label content containing these keywords as 'Bad' and all other 
-- content as 'Good'. Count how many items fall into each category.

Netflix Data Analysis

About

This project analyzes Netflix data to explore trends in movies and TV shows. It includes insights into content distribution, ratings, popular genres, and various key metrics using SQL queries.

Dataset

The dataset contains information on Netflix shows, including title, director, actors, country, release year, rating, duration, genres, and descriptions.

Table Structure

Column	Description	Data Type
show_id	Unique ID for the show	VARCHAR(6)
type	Movie or TV Show	VARCHAR(10)
title	Name of the show	VARCHAR(150)
director	Director of the show	VARCHAR(250)
actors	Main actors in the show	VARCHAR(1000)
country	Country of production	VARCHAR(150)
date_added	Date when added to Netflix	VARCHAR(75)
release_year	Year of release	INT
rating	Age rating	VARCHAR(10)
duration	Duration (minutes/seasons)	VARCHAR(20)
listed_in	Genres	VARCHAR(100)
description	Brief summary of the show	VARCHAR(300)

Approach Used

1. Database Setup - Created a table and inserted data.
2. Data Cleaning - Handling NULL values and formatting fields.
3. Feature Extraction - Splitting multi-value columns for better analysis.
4. Exploratory Data Analysis (EDA) - Running SQL queries to generate insights.

Tools Used

• PostgreSQL
• SQL Queries for data analysis

Conclusion

This project provides insights into Netflix's content library, trends, and key information using SQL-based analysis.

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You can explore and use the SQL queries for your projects. Contributions are welcome! 🚀