SQL

This repository contains a collection of SQL projects that I am working on as I advance my skills in data analysis. Each project demonstrates my proficiency in querying, manipulating, and analyzing datasets using SQL.

Project - 1 (Pizza Sales Analysis)

• This project involves analyzing a pizza sales dataset to extract meaningful insights using SQL.
• The dataset includes details on orders, pizzas, their types, sizes, prices, and the ingredients used.
• The project is structured around three levels of questions: Basic, Intermediate, and Advanced.

Dataset Description

Table - 1 (order_details)

Column Name	Description
order_details_id	Unique identifier for each order detail entry.
order_id	Identifier that links the order detail to a specific order.
pizza_id	Identifier that represents the type and size of the pizza ordered.
quantity	The number of pizzas ordered in that specific entry.

Table - 2 (orders)

Column Name	Description
order_id	Unique identifier for each order.
date	The date when the order was placed (YYYY-MM-DD).
time	The time when the order was placed (HH:MM:SS).

Table - 3 (pizza_types)

Column Name	Description
pizza_type_id	Unique identifier representing the specific type of pizza.
name	The name of the pizza.
category	The category of the pizza (e.g., Chicken, Classic, Supreme, Veggie).
ingredients	A list of ingredients used in the pizza.

Table - 4 (pizzas)

Column Name	Description
pizza_id	Unique identifier for each pizza, indicating its type and size.
pizza_type_id	Identifier representing the specific type of pizza.
size	The size of the pizza (e.g., S, M, L, XL, XXL).
price	The price of the pizza based on its size and type.

Questions

Basic:

• Q1. Retrieve the total number of orders placed.
• Q2. Calculate the total revenue generated from pizza sales.
• Q3. Identify the highest-priced pizza.
• Q4. Identify the most common pizza size ordered.
• Q5. List the top 5 most ordered pizza types along with their quantities.

Intermediate:

• Q1. Join the necessary tables to find the total quantity of each pizza category ordered.
• Q2. Determine the distribution of orders by hour of the day.
• Q3. Join relevant tables to find the category-wise distribution of pizzas.
• Q4. Group the orders by date and calculate the average number of pizzas ordered per day.
• Q5. Determine the top 3 most ordered pizza types based on revenue.

Advanced:

• Q1. Calculate the percentage contribution of each pizza type to total revenue.
• Q2. Analyze the cumulative revenue generated over time.
• Q3. Determine the top 3 most ordered pizza types based on revenue for each pizza category.