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https://github.com/mainak-97/netflix-content-analysis-project

SQL-based analysis of Netflix’s movies and TV shows dataset to uncover content trends, popular genres, geographical insights, and audience preferences. Includes data queries, findings, and a presentation of key insights.
https://github.com/mainak-97/netflix-content-analysis-project

data-analysis mysql mysql-workbench powerpoint presentation-slides sql

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SQL-based analysis of Netflix’s movies and TV shows dataset to uncover content trends, popular genres, geographical insights, and audience preferences. Includes data queries, findings, and a presentation of key insights.

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README 

        

# **Netflix Content Analysis**



![Logo](https://i.imgur.com/aDXlIUW.jpeg)

# Project Overview 📃

This project provides an in-depth analysis of Netflix's extensive library of movies and TV shows using SQL. It addresses various business questions and presents insights that can guide content strategy, identify popular trends, and reveal audience preferences on Netflix.

# Objectives 🎯

- Analyze the distribution of content types (Movies vs. TV Shows).

- Identify prevalent ratings and genres for Netflix content.

- Investigate release trends by year, country, and duration.

- Categorize and filter content based on specific keywords and criteria.

# Dataset Source 📀

The dataset for this project is sourced from Kaggle's Netflix Dataset.

# Business Questions Explored 🔎

Below are the primary questions and corresponding SQL queries used to analyze Netflix content:



1. Total Count of Movies and TV Shows.

2. Top 3 Ratings for Movies and TV Shows.

3. List of Movies Released in 2020 and 2021.

4. Top 5 Countries with Most Content.

5. Top 10 Longest-Duration Movies.

6. Content Added in the Last 5 Years.

7. All Content by Animation Director 'Rajiv Chilaka'.

8. TV Shows with More Than 5 Seasons.

9. Average Number of Titles Released in India Per Year.

10. All Documentaries on Netflix.

11. Content Without an Assigned Director.

12. Top 10 Directors by Number of Titles.

13. Yearly Movie Release Trend.

14. Titles Featuring Popular Actors (e.g., Leonardo DiCaprio)

15. Content with the Longest Descriptions.

# Domain 📺

This project aims at analysis of Netflix global OTT platform.

# Project Structure 💻

- Database Creation & Schema Definition: Creation of a structured database to import and query data.

- SQL Queries: Multiple SQL queries designed to extract insights, answer business questions, and explore the Netflix catalog.

- Presentation: Key findings and visual summaries are included in a presentation to effectively communicate insights.

# Key Findings 💡

- Content Distribution: Movies and TV shows exhibit a balanced representation, with varied genres and release years.

- Popular Ratings: Identifying common ratings provides insights into target audience preferences.

- Geographical Trends: Countries like India emerge as major markets, showing significant content production and demand.

- Content Categorization: Categorizing content by keywords reveals trends in popular genres, lengths, and production sources.

# Tools 🛠

- MySQL Workbench: Primary language for data extraction and analysis.

- PowerPoint: For presenting the findings in a concise, visual format.

# Conclusion 🔹

This project highlights Netflix’s diverse content library and provides insights into content preferences, geographical distribution, and audience-targeted content ratings. The findings can aid in understanding Netflix’s distribution strategy, content categorization, and potential gaps in content offerings.



# How to Use This Repository 📍

- SQL Files: Includes SQL queries for each business question.

- Presentation: PPT file that summarizes findings and insights.

- README: Provides an overview of the project.



## Author 🎓



- **Mainak Mukherjee**



- **Email:** [email protected]



- **Linkedin:** www.linkedin.com/in/mainakmukherjee08