🍕 Online Food Delivery Analysis — SQL Project 1

A beginner-level SQL analysis project exploring core business metrics of an online food delivery platform — covering orders, revenue, customer behaviour, and restaurant performance.

Level: 🟢 Beginner | Status: ✅ Complete | Part: 1 of 3

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🔗 Demo Link

Screenshots of query results are available in the /Screenshots folder of this repository.

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📑 Table of Contents

• Business Understanding
• Data Understanding
• Screenshots
• Technologies
• Setup
• Approach
• Status
• Credits

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💼 Business Understanding

Online food delivery platforms handle thousands of orders daily across multiple cities and restaurants. To make smart operational and strategic decisions, businesses need to understand where orders are coming from, which items generate the most revenue, and which restaurants are performing well.

Goal: This project performs foundational SQL-based exploratory analysis on an online food delivery dataset to answer key business questions around orders, revenue, customers, and restaurant activity.

Why this project? This is the first project in a 3-part SQL portfolio series, designed to build a strong foundation in SQL querying before progressing to advanced analytics and full business systems. It demonstrates proficiency in:

• Writing structured SQL queries for real-world business questions
• Aggregating and summarising transactional data
• Extracting actionable insights from relational datasets

Challenges faced:

• Joining multiple related tables (Orders, Customers, Restaurants, Menu Items) to produce meaningful aggregations
• Handling date-based trend analysis using monthly grouping
• Filtering and ranking results to surface the most relevant business insights

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📊 Data Understanding

Datasets Used

This project uses 5 relational tables representing a food delivery platform's core data:

Table	Description
Customers	Customer details including signup dates and city
Orders	Transaction records with order date and customer ID
Menu_Items	List of food items offered by restaurants with pricing
Order_Details	Line-item breakdown of items ordered per transaction
Restaurants	Restaurant information including city and menu offerings

Dataset Relationships

Customers ──< Orders ──< Order_Details >── Menu_Items
                │
            Restaurants

Key Metrics Available

• Total order volume by city and restaurant
• Revenue per food item and per city
• Customer spending patterns
• Monthly order trends over time

Future enhancements:

• Add delivery time analysis once delivery agent data is available (covered in Project 3)
• Incorporate customer segmentation based on spending tiers (covered in Project 2)
• Build a Power BI / Excel dashboard on top of these queries

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📸 Screenshots

Query result screenshots are available in the /Screenshots folder.

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🛠️ Technologies

Tool	Purpose
MySQL 8.x	Relational database engine for data storage and querying
VS Code	Primary development IDE
SQLTools Extension	MySQL connection and query execution within VS Code
SQLTools MySQL/MariaDB Driver	VS Code driver to connect to MySQL server
MySQL Workbench	Backend MySQL server (connected via SQLTools)
Git & GitHub	Version control and project showcase

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⚙️ Setup

Prerequisites

• MySQL Server installed and running (port 3306)
• VS Code installed
• SQLTools extension installed in VS Code
• SQLTools MySQL/MariaDB Driver installed

Step 1 — Clone the Repository

git clone https://github.com/BuragapalliKamachari/online-food-delivery-sql-project-1.git
cd online-food-delivery-sql-project-1

Step 2 — Configure SQLTools in VS Code

1. Open VS Code → Click the SQLTools icon in the Activity Bar
2. Select Add New Connection → Choose MySQL
3. Enter connection details:
   • Host: localhost
   • Port: 3306
   • Username: root
   • Password: (your MySQL root password)
4. Click Test Connection → Save

Step 3 — Run SQL Scripts in Order

Open each file from the Sql_Queries/ folder and execute using Ctrl+E+E:

1. Database_Creation.sql     ← Create the project database
2. Table_Creation.sql        ← Create all 5 tables
3. Data_Insertion.sql        ← Load sample data
4. Analysis_Queries.sql      ← Run all 10 business queries

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🔍 Approach

This project follows the Data Analysis Lifecycle: Define → Collect → Clean → Analyse → Report.

Phase 1 — Database & Table Setup

• Created a dedicated MySQL database for the project
• Defined all 5 tables with appropriate data types and relationships
• Loaded the dataset into MySQL via CSV import or INSERT statements

Phase 2 — Exploratory Analysis (10 Queries)

#	Business Question	SQL Technique
1	Total orders per city	GROUP BY, COUNT
2	Revenue generated by each food item	JOIN, SUM
3	Top 5 spending customers	ORDER BY, LIMIT
4	Restaurant-wise order count	GROUP BY, COUNT
5	Average order value by city	AVG, GROUP BY
6	Monthly order trends	MONTH(), GROUP BY
7	Top 3 cities by revenue	SUM, ORDER BY, LIMIT
8	Number of unique customers per city	COUNT(DISTINCT)
9	Most frequently ordered items	COUNT, ORDER BY DESC
10	Restaurants with low order count (<30)	HAVING, COUNT

Phase 3 — Insights & Reporting

• Results exported and documented
• Screenshots captured for each key query result
• Insights summarised for progression into Project 2

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📌 Status

✅ Complete — Sept 2025

This is Part 1 of 3 in the Online Food Delivery SQL Portfolio Series:

• ✅ Project 1 — Beginner Analysis ← You are here
• ✅ Project 2 — Advanced Analytics
• 🔄 Project 3 — Business Analytics System (In Progress)

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🙏 Credits

Contributor	Role
Buragapalli Kamachari	Project Author — SQL Development & Analysis
MySQL Documentation	Reference for SQL functions and syntax
SQLTools by Matheus Teixeira	VS Code MySQL integration extension
GitHub	Repository hosting and portfolio showcase

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Online Food Delivery SQL Project 1 of 3 | Buragapalli Kamachari | Sept 2025