✈️ Airline Booking Data Analysis (SQL Project)

👩‍💻 Author

Vallabha Supraja
Fresher | Aspiring Data Analyst | SQL Enthusiast

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📋 Project Overview

This project focuses on analyzing airline booking data using MySQL.
It explores customer behavior, flight performance, and revenue patterns through SQL queries.

You’ll find a mix of JOINS, CTEs, and Window Functions used to answer real business questions.

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🧮 Dataset Description

The project is based on four key tables:

1️⃣ Passengers

Column	Description
PassengerID	Unique ID of each passenger
Name	Passenger name
Age	Passenger age
Gender	Passenger gender
Nationality	Passenger nationality

2️⃣ Flights

Column	Description
FlightID	Unique ID of each flight
Airline	Airline name
Source	Departure city
Destination	Arrival city
DepartureDate	Flight departure time
ArrivalDate	Flight arrival time
Status	Flight status (On-time, Delayed, etc.)

3️⃣ Bookings

Column	Description
BookingID	Unique booking ID
FlightID	Associated flight ID
PassengerID	Passenger who booked
BookingDate	Date of booking
Class	Flight class (Economy, Business, etc.)
Price	Ticket price

4️⃣ Cancellations

Column	Description
CancelID	Unique cancellation ID
BookingID	Related booking ID
CancelDate	Date of cancellation
Reason	Cancellation reason

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🧠 SQL Queries Solved

Each .sql file represents a different problem statement:

File	Problem Statement
1.sql	Passengers who booked more than one flight
2.sql	Revenue per airline
3.sql	Youngest passenger who booked a flight
4.sql	Top 3 passengers who spent the most
5.sql	Rank airlines by total revenue
6.sql	Average ticket price for each flight class
7.sql	Most recent booking per passenger
8.sql	View all confirmed bookings
9.sql	List all cancelled bookings with no reason
10.sql	Top 5 most frequently booked destinations

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🧰 Concepts Used

• SQL JOINs
• CTEs (Common Table Expressions)
• Window Functions
• Aggregation & Ranking
• Filtering & Subqueries

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⚙️ Tools Used

• MySQL Workbench
• GitHub (for version control)
• Excel / CSV (for data storage)

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📈 Insights Example

• Airlines with the highest total revenue
• Top 3 frequent flyers
• Most popular destinations
• Average ticket price per class

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🏁 How to Run

1. Import all .csv files into MySQL database.
2. Open each .sql file and run it one by one in MySQL Workbench.
3. View results for each query.

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🏆 Project Summary

This project demonstrates:

• Strong SQL querying and data analysis skills
• Use of advanced SQL concepts (CTEs, window functions)
• Real-world business problem-solving approach

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⭐ Feel free to fork or star this repository if you found it useful!