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Zomato-Database-Insights-SQL

Zomato Sales Analysis – SQL Project This project focuses on performing sales and operations analysis on a simulated Zomato database using SQL. The dataset includes information on customers, restaurants, orders, employees, food items, and ratings. It is designed to help learners and aspiring data professionals strengthen their SQL skills through practical, business-driven query exercises.

🔍 Project Highlights: Identify top customers, restaurants, and food items based on various performance metrics.

Perform revenue analysis and delivery performance evaluation.

Use aggregate functions, joins, subqueries, ordering, and filtering techniques.

Gain insights into customer behavior, restaurant performance, and operational efficiency.

🗃️ Database File: Run the provided SQL file zomatodf.sql in your MySQL Workbench (or any SQL-compatible platform) to create and populate the database.

✅ Key SQL Tasks: Top 3 customers with the most orders

Restaurant with the highest average rating

Orders delivered under 30 minutes

Total revenue per food item

Second highest revenue-generating restaurant

Top 5 most popular food items by quantity

Top 3 employees with highest delivery ratings

Month with the most orders

Average order amount per customer

Most frequent customer per restaurant

Orders placed on weekends

Average delivery time: weekdays vs weekends

Top 5 most expensive food items

Restaurant with the most diverse menu

Total payment amount per payment type

Skills Used

SQL Querying

Joins (INNER, LEFT, RIGHT, SELF)

Subqueries

Aggregate Functions (SUM, AVG, COUNT, MAX, MIN)

GROUP BY & ORDER BY

HAVING Clauses

Window Functions (if used)

Date & Time Functions

Conditional Statements (CASE WHEN)

Data Filtering and Sorting

Data Analysis & Business Insight Generation

Relational Database Understanding

Performance-Oriented Query Writing

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This project focuses on performing sales and operations analysis on a simulated Zomato database using SQL. The dataset includes information on customers, restaurants, orders, employees, food items, and ratings.

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