🎬 Movies Analysis | SQL Project

This project performs in-depth SQL-based analysis on the MovieLens dataset — a popular dataset containing millions of movie ratings, tags, and metadata. The analysis spans beginner to advanced levels, covering SQL queries, views, CTEs, stored procedures, and window functions.

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⭐ Features

• Dataset setup and table creation using PostgreSQL
• Designed 30+ custom SQL questions across beginner, intermediate, and advanced levels
• Dataset setup and table creation using PostgreSQL
• Imported and managed 1.5+ GB of real-world movie rating data from MovieLens
• Performed genre classification, tag relevance analysis, and user behavior insights
• Implemented views, stored procedures, window functions, and recursive CTEs
• Wrote optimized queries with EXPLAIN ANALYZE for performance evaluation
• Simulated ACID transactions and used temporary/materialized views for advanced use cases

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🛠 Technologies Used

Tool/Service	Purpose
Kaggle	Dataset exploration
PostgreSQL	Database management
pgAdmin 4	GUI for database operations

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⚙️ How It Works

1. 📥 Downloaded data from MovieLens
2. 🗃 Created database and tables using PostgreSQL
3. 📌 Imported CSV data (~1.5 GB) via pgAdmin 4
4. 🔎 Executed 30+ SQL queries across 3 difficulty levels
5. 🧠 Created views, functions, materialized views, and CTEs
6. 📈 Extracted insights on movie trends, top genres, user behavior, and tag relevance

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🔍 Preview

📄 Click here to view the full project log (PDF)

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📁 Folder Structure

MoviesAnalysisSQL/
├── Project_log.pdf
├── Questions.md
└── README.md

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💡 Use Cases

• 📊 Analyzing user behavior through ratings and tags
• 🎯 Finding most rated or top-rated movies by genre
• 🧬 Working with tag genome and relevance analysis
• 🏗 Practicing SQL queries from beginner to advanced levels
• 🧪 Demonstrating database normalization and function creation
• 📚 Building a PostgreSQL portfolio project

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🧰 Setup Instructions

1. Install PostgreSQL and pgAdmin 4
2. Download dataset from: MovieLens Dataset
3. Run the SQL scripts from the project log to create tables
4. Import CSV files into respective tables (Format: CSV, Encoding: UTF-8)
5. Run the SQL queries as per your learning phase (Beginner → Advanced)

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📝 Notes

• The dataset includes 33,832,162 ratings and 2,328,315 tag applications for 86,537 movies
• User and movie IDs are anonymized and consistent across all files
• Ratings are made on a 0.5–5.0 star scale; genres are pipe-separated (e.g., Action|Adventure|Sci-Fi)
• All files are encoded in UTF-8 — ensure correct encoding during import in pgAdmin
• Timestamps are stored in UNIX format (seconds since January 1, 1970 UTC)

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🙋‍♀️ Author

Anushka Sharma
🌐 LinkedIn • 🐱 GitHub 🎓 Learning Data Science, Analytics & Machine Learning

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⭐ Show Your Support

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• ⭐ Star this repository
• 🛠️ Fork it to build upon or adapt it for your own use
• 💬 Share feedback or suggestions via Issues/Discussions