Food Delivery App Database Analysis -- SQL Project

This project focuses on leveraging SQL to perform comprehensive data analysis on a simulated Zomato database. The goal is to extract actionable insights that can help enhance customer experience, optimize restaurant operations, and drive business growth.

1. Analysis of a Food
Delivery App Database
Mayank’s
Kitchen

2. PROJECT
SUMMARY
To extract actionable insights that can help enhance customer
experience, optimize restaurant operations, and drive business
growth.
01
Objective
About the Dataset
CTE,WindowFunctions, Joins, Sub Query.
02
Features of the Project
03
This project focuses on leveraging SQL to perform comprehensive
data analysis on a simulated Zomato database.
CheckthenextslideforDatabaseSchema.

3. Interactive Delivery Experiences
Pizza-Making Kits: DIY pizza kits delivered with fresh
ingredients for a fun cooking experience
Virtual Reality Pizza Delivery: Immersive VR experience
where you deliver pizzas in a virtual world
Mystery Pizza Delivery: Surprise pizzas with secret
toppings for adventurous eaters
Database Schema

4. BASIC
QUESTIONS

5. Find the list of customers who have
never ordered.

6. Find the avg rating for each partner ID
and restaurant.

7. The same food items may be served at
different restaurant. Find the avg
price/dish.

8. Find top 10 restaurants in terms of no.
of order for a given month.

9. Restaurants with monthly sales > X.

10. Show all orders from a particular
customer in a particular date range.

11. ADVANCED
QUESTIONS

12. Find restaurants with max repeated
customers (loyal customers)

13. Month over month revenue growth
of Mayank’s Kitchen.

14. Find the top 2 spenders for all the 3
months.

15. Find customer -wise favorite food.

16. Find most loyal customers for all
restaurants.

17. Find the most paired products
(food items ordered together).

18. THANKS!
Do you have any questions?
❑https://www.linkedin.com/in/mayank-srivastava-6a84211054
❑https://github.com/Mayank4694
❑[email protected]