https://github.com/syed-amjad-ali/restaurant-sales-sql-project
This was a simple SQL project where I analyzed restaurant sales data, showcasing skills in data creation and querying. The project explores menu performance, order trends, and customer insights.
https://github.com/syed-amjad-ali/restaurant-sales-sql-project
aggregations business-intelligence data-analysis guided-project joins maven-analytics querying restaurant-sales sales-data sql subqueries
Last synced: 7 months ago
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This was a simple SQL project where I analyzed restaurant sales data, showcasing skills in data creation and querying. The project explores menu performance, order trends, and customer insights.
- Host: GitHub
- URL: https://github.com/syed-amjad-ali/restaurant-sales-sql-project
- Owner: Syed-Amjad-Ali
- Created: 2024-12-24T02:51:18.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-09T00:54:57.000Z (over 1 year ago)
- Last Synced: 2025-01-09T01:55:22.652Z (over 1 year ago)
- Topics: aggregations, business-intelligence, data-analysis, guided-project, joins, maven-analytics, querying, restaurant-sales, sales-data, sql, subqueries
- Language: SQL
- Homepage:
- Size: 200 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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?