Analyzing restaurant order data using SQL to uncover revenue drivers, top-selling dishes, customer spending behavior, and cuisine performance.

Project Overview 

This project analyzes restaurant order data using MySQL to uncover insights about top-selling dishes, customer spending behavior, and cuisine-wise performance.

The analysis demonstrates how SQL queries and structured data exploration can support data-driven decisions in the restaurant and hospitality industry.

Business Problem 

Restaurants generate large volumes of transactional data through daily orders.

However, without structured analysis, it becomes difficult to understand:

• Which dishes generate the highest revenue  

• Which cuisines are most popular  

• How customers spend across orders  

• Which menu items contribute most to profitability  

This project applies SQL-based analysis to transform raw order data into actionable business insights.

Dataset 

Database Name: restaurant_db

Tables:

menu_items

• Contains menu details including item names, cuisine types, and prices.

order_details

• Contains transaction-level order data with over 12,000 records.

SQL Analysis Performed 

Key analysis performed using SQL:

• Average price comparison across cuisines  

• Top 5 most expensive dishes  

• Cuisine-wise menu distribution  

• Top 5 highest spending orders  

• Revenue contribution by cuisine  

• Most frequently ordered dishes

Key Insights 

• Certain cuisines contribute disproportionately to revenue despite fewer menu items.

• A small number of dishes generate a significant portion of total orders.

• High-priced items do not always correspond to high order frequency.

• Customer spending varies significantly across individual orders.

Tools Used 

MySQL – SQL queries and database analysis

Excel – Data export and intermediate analysis

Power BI – Visualization of analytical insights