Restaurant-Sales-SQL-Project

Overview

This project was part of a guided exercise from Maven Analytics, where I worked with an imaginary restaurant sales dataset. The exercise provided the dataset and database schema, and I focused on solving business-related questions by writing SQL queries.

The project helped me practice:

1. Data Analysis: Writing SQL queries to gain insights into menu performance, order trends, and customer preferences.
2. Problem Solving: Answering real-world questions through advanced SQL techniques like joins, aggregations, and subqueries.

By completing this project, I gained hands-on experience with SQL and strengthened my ability to analyze data effectively.

Database Schema

Tables

1. menu_items

   • Contains details about menu items, including their name, category, and price.
   • Columns:
     • menu_item_id (Primary Key): Unique identifier for each menu item.
     • item_name: Name of the menu item.
     • category: Type of cuisine or dish.
     • price: Cost of the menu item.

2. order_details

   • Contains details about customer orders, including items ordered and their timestamps.
   • Columns:
     • order_details_id (Primary Key): Unique identifier for each order detail.
     • order_id: Identifier for a specific order.
     • order_date: Date of the order.
     • order_time: Time of the order.
     • item_id: Identifier for the ordered menu item (Foreign Key linked to menu_items.menu_item_id).

Skills Demonstrated

• Database Design: I worked on creating and populating tables with structured data.
• Data Manipulation: I wrote and executed SQL queries to extract meaningful insights from the data.
• Joins and Aggregations: I combined data from multiple tables and used aggregation techniques to analyze trends.
• Advanced Querying: I applied techniques like subqueries, filters, and grouping to answer complex questions.

Questions Answered

Menu Analysis

• How many items are on the menu?
• What are the least and most expensive items on the menu?
• How many Italian dishes are on the menu? What are the least and most expensive Italian dishes?
• How many dishes are in each category?
• What is the average price of dishes in each category?

Order Trends

• What is the date range of the orders in the dataset?
• How many orders and items were made within this date range?
• Which orders had the most number of items?
• How many orders had more than 12 items?

Combined Analysis (Menu + Orders)

• What were the least and most ordered items? What categories were they in?
• What were the top 5 orders that spent the most money?
• What specific items were purchased in the highest spending order?
• Which categories were most popular in the top 5 highest spending orders?