🎬 Netflix Reviews SQL Analysis 📌 Overview

This project focuses on analyzing Netflix Movies & TV Shows data using SQL to extract meaningful insights and answer real-world business questions. The goal is to demonstrate strong SQL querying skills, data exploration, and analytical thinking using a structured dataset.

By working with a Netflix dataset, this project uncovers trends related to ratings, content distribution, actors, directors, and viewing patterns—similar to how data analysts solve business problems in real companies.

🎯 Objectives

Analyze Netflix content (Movies vs TV Shows)

Identify top and bottom rated content using IMDb scores

Explore trends across years and decades

Understand audience targeting via certifications

Discover most frequent actors and directors

Compute average ratings and runtime insights

🗂️ Dataset

The dataset contains information about Netflix titles, including:

Title

Type (Movie/Show)

IMDb & TMDb ratings

Release year

Runtime

Production countries

Age certifications

Cast & crew details

Such datasets are commonly used to analyze content trends, user preferences, and platform strategies.

🛠️ Tech Stack

SQL (MySQL/PostgreSQL compatible)

Relational Database Concepts

Data Analysis & Query Optimization

📊 Key Business Questions Solved 🔹 Content Performance

Top 10 Movies & Shows based on IMDb rating

Bottom 10 Movies & Shows

🔹 Aggregated Insights

Average IMDb & TMDb scores by content type

Average ratings by country

Average ratings by age certification

🔹 Trends Analysis

Number of movies and shows released per decade

Content distribution over time

🔹 Industry Insights

Top 20 most frequent actors

Top 20 most frequent directors

🔹 Additional Analysis

Most common age certifications

Average runtime comparison (Movies vs Shows)

Movies released after a specific year

📁 Project Structure Netflix-Reviews-SQL/ │ ├── Netflix_SQL_Analysis.sql # Main SQL queries file ├── dataset/ # (Optional) Dataset files └── README.md # Project documentation ⚙️ How to Run

Import the dataset into your SQL database

Create required tables (if schema not already provided)

Run the SQL script:

SOURCE Netflix_SQL_Analysis.sql;

Execute queries step-by-step to view insights

📈 Sample Insights

Highly rated content tends to cluster in specific genres and production regions

Movies and TV shows show different rating patterns

Certain actors and directors dominate Netflix content

Content production increased significantly after 2000

🚀 Future Improvements

Build a dashboard (Power BI / Tableau)

Integrate with Python for advanced analytics

Add data cleaning & preprocessing scripts

Perform genre-based sentiment or trend analysis

🙋‍♂️ Author

Atharva Gade

Backend Developer | Data Enthusiast

Interested in Data Analytics, Backend Systems & AI/ML

⭐ Show Your Support

If you found this project helpful, consider giving it a ⭐ on GitHub!