Savoring Success using MySQL Workbench: A Data-Driven Analysis of Restaurant Order Trends

About this Project:

As a Business Intelligence Analyst, I spearheaded the transformation of data to deep dive into the sales performance of an international restaurant. The aim of this Restaurant Order Analysis project was to extract actionable insights from menu and order data using SQL. By addressing key business needs—such as optimizing menu offerings, improving inventory management, and enhancing customer satisfaction—this analysis provides valuable recommendations to drive business success.

1. Data Sources:

• Menu Items Data: Contains detailed information about each menu item offered by the restaurant.
• Order Details Data: Provides comprehensive details about each order placed at the restaurant.

2. Tools Used:

• MySQL: For ETL (Extract, Transform, Load) processes and Exploratory Data Analysis (EDA).

Data Sources:

1. Menu Items Data: Contains detailed information about each menu item offered by the restaurant.
2. Order Details Data: Provides comprehensive details about each order placed at the restaurant.

3. Tools Used:

• MySQL: For ETL (Extract, Transform, Load) processes and Exploratory Data Analysis (EDA).

4. Data Cleaning & Preparation:

During the initial data preparation phase, the following tasks were completed:

• Data loading and inspection
• Handling missing values
• Data cleaning and formatting

5. Key Areas of Focus:

1. Menu Analysis:

   • Understanding the menu composition by determining the number of items.
   • Identifying the least and most expensive items.
   • Counting the number of dishes and identifying the least and most expensive among them.

2. Category Analysis:

   • Analyzing the distribution of dishes across categories to understand menu diversity.
   • Determining the number of dishes in each category and calculating the average dish price.

3. Order Analysis:

   • Gaining insights into customer ordering behavior by examining the date range of orders.
   • Counting total orders and items within this date range.
   • Identifying orders with the most items and determining the number of orders with more than 12 items.

4. Combining Tables:

   • Merging the menu_items and order_details tables for a comprehensive analysis of menu item popularity and customer preferences.
   • Identifying the least and most ordered items and their respective categories.
   • Analyzing the top 5 orders with the highest total spending.

5. Top Spending Orders:

   • Exploring the details of high-spending orders to gain insights into customer preferences and purchasing behavior.
   • Analyzing the highest-spending order and, optionally, the top 5 highest-spending orders.

6. Results & Findings:

• Most Expensive Item: The most Expensive Item on the menu is Shrimp Scampi (Italian)
• The Least Expensive Item: The Least Expensive Item on the menu is Edamame (Asian)
• Most Ordered Item: The most Ordered Item on the menu is American Hamburger
• Least Ordered Item: The least Ordered Item on the menu is Mexican Chicken Tacos