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🍕PIZZA SALES ANALYSIS USING MYSQL

This project showcases a set of SQL queries used to analyze sales data set from a fictional pizza reasturant.The goal is to extract insights such as revenue trends, best-selling pizzas, average pizzas ordered per day and more using mysql.

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📁Project Structure

• Pizza-sales-analysis
  • data_sets ← contains CSV datasets.
    • order_details.csv
    • orders.csv
    • pizza_types.csv
    • pizzas.csv
  • queries ← contains all SQL queries with outputs
    • query01-total_orders
      • 01a-query-total_orders.sql
      • 01b-output_total_orders.md
    • query02-total_revenue
    • query03-highest_priced_pizza
    • query04-best_selling_size
    • query05-top5_pizza_types_size
    • query06-top5_pizza_types
    • query07-pizza_category_quantity
    • query08-orders_per_hour
    • query09-pizzas_distribution
    • query10-top5_large_size_pizzas
    • query11-avg_pizzas_per_day

🚀 How to Run This SQL project

Follow these steps to run and explore the project using MYSQL:

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📁 1. Clone the repository

  git clone
  https://github.com/sakshisree/sql-projects.git
  cd sql-projects

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🗃️ 2. Set up the Database in MYSQL

1. Open MYSQL WORKBENCH or any MYSQL clients.

2. Create the table structures manually before importing the CSV files:

🧱 Table Structure Setup

Before importing large CSV files, run the table definitions from:

📄 table_definitions.sql

This will create all required tables in the pizza_sales database.

3.Import the CSV files found inside the data_sets/ folder:

• order_details.csv
• orders.csv
• pizza_types.csv
• pizzas.csv

You can import them using:

• MySQL Workbench > Table Data Import Wizard

📂 3. Run the SQL Queries

📂Inside the queries/ folder, open and run each .sql file in order

• Start with 01a-query-total_orders.sql
• Then move to 02a-query-Total_revenue.sql, and so on...

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📊Each query solves a specific business question.:

• Total orders
• Revenue analysis
• Top-selling pizzas
• Pizzas per day/hour
• Size/category breakdown

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📄 4. Check the Outputs

Each query folder contains:

• .sql file (the query)
• .md file (sample output for reference) ✅You can compare your output with the sample provided.

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Note: This is a mock dataset used for learning and demonstration.

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💡 Key Learnings

• Writing optimized SQL queries using GROUP BY, ORDER BY, LIMIT, and DATE functions.
• Drawing real business insights from structured data.
• Organizing code and documentation for data analytics projects.

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⚙️ Tech Stack

• MySQL
• SQL Workbench
• CSV Files for data input
• Git & GitHub for version control

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🙋‍♀️ About Me

Hi, I’m Sakshi Sree a BCA student passionate about Data Analytics. I love solving business problems using SQL and Python.

📬 Contact

Reach out to me at :sakshisree24680@gmail.com