Hello Everyone, I’m excited to share my recent SQL project titled “Pizza Sales Analysis”.

This project uses SQL to analyze the dataset of a Pizza sales.

Dive into the world of pizza sales to analyze customer behavior and study sales data to identify key metrics and patterns. Understand how menu items and promotions influence customer choices, and provide insights to help Pizza Hut enhance its sales strategies. Join me on an exciting journey to decode pizza sales behavior and drive business growth.

Pizza Hut provided the primary data source for this project, focusing on pizza sales analysis. The dataset was imported into the pgAdmin database management system, ensuring reliable and efficient data storage. During data preparation, specific columns were restructured or modified to facilitate easier analysis and querying, resulting in clean, relevant, and user-friendly data.

The methodology involved solving 15 problem statements related to pizza sales using SQL queries. These problem statements, provided by Pizza Hut, guided the analysis and uncovered key insights into customer behavior and sales patterns. By leveraging SQL and the pgAdmin database system, we performed robust data manipulation and querying, enabling a comprehensive exploration of the dataset.

This systematic approach ensured a thorough analysis of Pizza Hut's sales data, leading to valuable insights and conclusions that can inform sales strategies and enhance the overall pizza ordering experience.

The dataset consist of 4 tables which are:

The order_details table consist of the order_details_id, order_id, pizza_id, quantity and its also contain 48,620 records.

The orders table consist of the order_id, date, time. Its also contain 21,350 records.

The pizza_types table consist of the pizza_type_id, name, category, ingredients and its also contains 32 different pizza types.

The pizzas table consist of the pizza_id, pizza_type_id, size, price and its also contains 96 records of pizzas.

Q1: The total number of order place

Q2: The total revenue generated from pizza sales

Q3: The highest priced pizza.

Q4: The most common pizza size ordered.

Q5: The top 5 most ordered pizza types along their quantities.

Q6: The quantity of each pizza categories ordered.

Q7: The distribution of orders by hours of the day.

Q8: The category-wise distribution of pizzas.

Q9: The average number of pizzas ordered per day.

Q10: Top 3 most ordered pizza type base on revenue.

Q11: The percentage contribution of each pizza type to revenue.

Q12: The cumulative revenue generated over time.

Q13: The top 3 most ordered pizza type based on revenue for each pizza category.