Famous-Paintings-Insights-SQL

Overview

This project explores the Famous Paintings & Museum dataset from Kaggle, aiming to gain insights using SQL queries. The dataset includes information about paintings, museums, artists, and related details.

Dataset Source

The dataset was obtained from Kaggle and consists of CSV files containing information about famous paintings, museums, and more.

Project Structure

The project includes Python scripts for loading CSV files into a SQL database and SQL queries for analyzing the dataset. The queries are designed to answer various questions related to paintings, museums, and artists.

Project Files

• loadDataToSQL.py: Python script to load CSV files into the SQL database.
• queriesSolved.sql: SQL Script for each query.

Running the Code

1. Load CSV files to SQL Database:

   • Execute the loadDataToSQL.py script to load CSV files into your SQL database.

2. Run SQL Queries:

   • Execute SQL queries from the queriesSolved.sql to gain insights from the dataset.

Query Difficulty Levels

• Basic Level: Simple queries involving selections and filtering.
• Intermediate Level: Queries with aggregations, joins, and conditional filtering.
• Advanced Level: Complex queries requiring subqueries, aggregations, and data manipulation.

Contributions

Contributions, issues, and feature requests are welcome. Feel free to open an issue for discussions or submit a pull request.

License

This project is licensed under the MIT License.

Acknowledgements

Special thanks to Kaggle for providing the Famous Paintings dataset.