https://github.com/ayonika2001/pizza_sales_sql_project
This SQL project models a Pizza Hut database, tracking orders, order details, and pizza types. It includes queries for calculating total orders, revenue, pizza popularity, and category-wise distributions, providing insights into sales trends and pizza preferences.
https://github.com/ayonika2001/pizza_sales_sql_project
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This SQL project models a Pizza Hut database, tracking orders, order details, and pizza types. It includes queries for calculating total orders, revenue, pizza popularity, and category-wise distributions, providing insights into sales trends and pizza preferences.
- Host: GitHub
- URL: https://github.com/ayonika2001/pizza_sales_sql_project
- Owner: Ayonika2001
- Created: 2025-02-05T19:49:00.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-02-05T19:57:05.000Z (5 months ago)
- Last Synced: 2025-04-12T15:13:33.083Z (3 months ago)
- Topics: csv, dbms, mysql, mysqldatabase, mysqlserver, sql
- Homepage:
- Size: 323 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Pizza Hut SQL Analysis Project
### 📌 Project Overview
This project provides an in-depth SQL analysis of a **Pizza Hut** database to extract valuable insights, such as order statistics, revenue generation, pizza popularity, and sales distribution. It consists of various SQL queries categorized into **Basic, Intermediate, and Advanced** levels to explore different aspects of the pizza sales data.
---
### 📂 Database Schema
#### **1. `orders` Table**
Stores details of orders placed.
```sql
CREATE TABLE orders (
order_id INT PRIMARY KEY,
order_date DATE NOT NULL,
order_time TIME NOT NULL
);
```
#### **2. `order_details` Table**
Stores details of individual pizzas ordered.
```sql
CREATE TABLE order_details (
order_details_id INT PRIMARY KEY,
order_id INT NOT NULL,
pizza_id TEXT NOT NULL,
quantity INT NOT NULL
);
```
---
## 🛠SQL Queries
### 🔹 Basic Queries
#### **1. Retrieve the total number of orders placed**
```sql
SELECT COUNT(order_id) AS total_orders FROM orders;
```
#### **2. Calculate the total revenue generated from pizza sales**
```sql
SELECT ROUND(SUM(order_details.quantity * pizzas.price), 0) AS total_revenue
FROM order_details
JOIN pizzas ON order_details.pizza_id = pizzas.pizza_id;
```
#### **3. Identify the highest-priced pizza**
```sql
SELECT pizza_types.name, MAX(pizzas.price) AS highest_priced_pizza
FROM pizza_types
JOIN pizzas ON pizza_types.pizza_type_id = pizzas.pizza_type_id
GROUP BY pizza_types.name
ORDER BY highest_priced_pizza DESC
LIMIT 1;
```
#### **4. Identify the most common pizza size ordered**
```sql
SELECT pizzas.size, COUNT(order_details.order_details_id) AS most_common_pizza_size_ordered
FROM pizzas
JOIN order_details ON pizzas.pizza_id = order_details.pizza_id
GROUP BY pizzas.size
ORDER BY most_common_pizza_size_ordered DESC
LIMIT 1;
```
#### **5. List the top 5 most ordered pizza types along with their quantities**
```sql
SELECT pizza_types.name, SUM(order_details.quantity) AS most_ordered_pizza
FROM pizza_types
JOIN pizzas ON pizza_types.pizza_type_id = pizzas.pizza_type_id
JOIN order_details ON order_details.pizza_id = pizzas.pizza_id
GROUP BY pizza_types.name
ORDER BY most_ordered_pizza DESC
LIMIT 5;
```
---
### 🔹 Intermediate Queries
#### **1. Find the total quantity of each pizza category ordered**
```sql
SELECT pizza_types.category, SUM(order_details.quantity) AS total_quantity
FROM pizza_types
JOIN pizzas ON pizza_types.pizza_type_id = pizzas.pizza_type_id
JOIN order_details ON order_details.pizza_id = pizzas.pizza_id
GROUP BY pizza_types.category;
```
#### **2. Determine the distribution of orders by hour of the day**
```sql
SELECT HOUR(orders.order_time) AS order_hour, COUNT(order_id) AS total_orders
FROM orders
GROUP BY HOUR(orders.order_time);
```
#### **3. Find the category-wise distribution of pizzas**
```sql
SELECT category, COUNT(name) AS pizza_count
FROM pizza_types
GROUP BY category;
```
#### **4. Calculate the average number of pizzas ordered per day**
```sql
SELECT ROUND(AVG(quantity), 0) AS avg_pizzas_per_day
FROM (
SELECT orders.order_date, SUM(order_details.quantity) AS quantity
FROM orders
JOIN order_details ON orders.order_id = order_details.order_id
GROUP BY orders.order_date
) AS order_quantity;
```
#### **5. Determine the top 3 most ordered pizza types based on revenue**
```sql
SELECT pizza_types.name, ROUND(SUM(order_details.quantity * pizzas.price), 0) AS total_revenue
FROM pizza_types
JOIN pizzas ON pizza_types.pizza_type_id = pizzas.pizza_type_id
JOIN order_details ON order_details.pizza_id = pizzas.pizza_id
GROUP BY pizza_types.name
ORDER BY total_revenue DESC
LIMIT 3;
```
---
### 🔹 Advanced Queries
#### **1. Calculate the percentage contribution of each pizza type to total revenue**
```sql
SELECT pizza_types.name,
ROUND(SUM(order_details.quantity * pizzas.price) /
(SELECT ROUND(SUM(order_details.quantity * pizzas.price), 2)
FROM order_details
JOIN pizzas ON order_details.pizza_id = pizzas.pizza_id) * 100, 2) AS revenue_percentage
FROM pizza_types
JOIN pizzas ON pizza_types.pizza_type_id = pizzas.pizza_type_id
JOIN order_details ON order_details.pizza_id = pizzas.pizza_id
GROUP BY pizza_types.name;
```
#### **2. Analyze the cumulative revenue generated over time**
```sql
SELECT order_date,
SUM(total_revenue) OVER (ORDER BY order_date) AS cumulative_revenue
FROM (
SELECT orders.order_date, ROUND(SUM(order_details.quantity * pizzas.price), 0) AS total_revenue
FROM order_details
JOIN pizzas ON order_details.pizza_id = pizzas.pizza_id
JOIN orders ON orders.order_id = order_details.order_id
GROUP BY orders.order_date
) AS total_sales;
```
#### **3. Determine the top 3 most ordered pizza types based on revenue for each pizza category**
```sql
WITH ranked_pizza_types AS (
SELECT pizza_types.name, pizza_types.category,
SUM(order_details.quantity * pizzas.price) AS total_revenue,
RANK() OVER (PARTITION BY pizza_types.category ORDER BY SUM(order_details.quantity * pizzas.price) DESC) AS rnk
FROM order_details
JOIN pizzas ON order_details.pizza_id = pizzas.pizza_id
JOIN pizza_types ON pizza_types.pizza_type_id = pizzas.pizza_type_id
GROUP BY pizza_types.name, pizza_types.category
)
SELECT name, category, total_revenue, rnk
FROM ranked_pizza_types
WHERE rnk <= 3;
```
---
## 📌 Conclusion
This project utilizes **SQL queries** to analyze pizza sales data effectively. By leveraging **JOINs, GROUP BY, aggregate functions, window functions, and ranking**, we uncover valuable insights that can help optimize sales and inventory management.
---
## 📜 Author
**Project by:** Ayonika Dutta