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:
- Data Analysis: Writing SQL queries to gain insights into menu performance, order trends, and customer preferences.
- 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.
-
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.
-
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 tomenu_items.menu_item_id).
- 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.
- 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?
- 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?
- 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?