This project dives into Netflix’s content catalog to extract key insights using SQL. It explores trends in content types, genres, country distributions, and time-based additions to understand the streaming giant's global strategy.
The dataset contains metadata about Netflix content including:
- Content type (Movie/TV Show)
- Title, Genre, Country
- Date Added to Netflix
- Release Year and Duration
- Rating
- Checked dataset structure and row count
- Listed all unique content types and genres
- Identified top contributing countries
- Content additions per year, month, and weekday
- Most active month-year combinations
- Year-wise trends in content uploads
- Comparison of Movies vs. TV Shows over time
- Most common titles and duplicate entries
- Genre preferences across Movies and TV Shows
- Top-rated content categories
- Short film analysis (< 40 mins)
- Average duration by content type
- Countries with the highest and lowest content availability
- Country-wise content type distribution
- Untapped or underserved markets
- Content saturation by genre
- Seasonal trends in content release
- Movie vs TV Show distribution insights by region
- Database: MySQL
- Tool: MySQL Workbench
- Language: SQL
- Clone the repository
- Open
netflix_analysis.sqlin MySQL Workbench - Load your Netflix dataset into a table named
NETFLIX - Run queries step by step to reproduce the analysis
Musthaq Ahamed
LinkedIn
📧 mustaaquh@gmail.com