🚖 Ola Booking Data Analysis using SQL

A comprehensive SQL project designed to analyze Ola booking data and answer real-world business questions using SQL queries. This project demonstrates proficiency in data retrieval, aggregation, filtering, grouping, and analytical SQL techniques.

📂 Dataset: 49 bookings × 20 columns · 🛢️ Engine: MySQL · ✅ 10 business questions answered

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📌 Project Overview

This project uses SQL to analyze Ola ride booking data and generate actionable business insights. The queries cover booking performance, customer behavior, payment methods, vehicle analysis, ride cancellations, ratings, and booking value.

The project showcases SQL skills commonly required for Data Analyst, Business Analyst, and SQL Developer roles.

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🎯 Objectives

• Analyze ride booking performance
• Identify customer booking trends
• Measure vehicle performance
• Evaluate cancellation reasons
• Analyze customer and driver ratings
• Calculate revenue-related metrics
• Practice real-world SQL queries

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🛠️ Technologies Used

• MySQL
• SQL
• Relational Database

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📁 Repository Structure

Ola-SQL-Analysis/
│
├── Ola project.sql            # All 10 analysis queries
├── Dataset/
│   └── ola_bookings.csv       # Source dataset (49 rows)
├── README.md
└── LICENSE

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📊 Database

Database Name

ola

Main Table

bookings

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📋 SQL Queries & Results

The queries in Ola project.sql solve the following business problems. Results shown were produced by running the queries on the sample dataset.

1. Retrieve all successful bookings

Returns every completed booking → 33 successful bookings.

2. Average ride distance for each vehicle type

Vehicle Type	Avg Distance (km)
Mini	24.14
eBike	21.63
Prime Sedan	20.00
Prime Plus	16.80
Bike	16.71
Prime SUV	15.57
Auto	1.67

3. Count rides cancelled by customers

3 bookings were cancelled by customers.

4. Identify the Top 5 customers

Retrieves the customers with the highest number of rides (ranked with ORDER BY … LIMIT 5).

5. Driver cancellations due to personal or vehicle issues

Counts rides cancelled with the reason "Personal & Car related issue".

6. Maximum and minimum driver ratings (Prime Sedan)

Max = 4.3, Min = 3.2

7. Retrieve rides paid using UPI

13 rides were paid via UPI.

8. Average customer rating by vehicle type

Vehicle Type	Avg Customer Rating
Auto	4.65
Prime SUV	4.18
eBike	4.16
Bike	3.90
Prime Plus	3.73
Mini	3.64
Prime Sedan	3.43

9. Total booking value of successful rides

₹18,208 total revenue from successful bookings.

10. Retrieve incomplete rides and their reasons

Lists each incomplete ride with its Incomplete_Rides_Reason.

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💡 Key Insights

• 67% of bookings were completed successfully (33 of 49).
• Mini covers the longest average trips; Auto the shortest.
• Auto and Prime SUV earn the highest customer ratings.
• UPI is a widely used digital payment method (13 rides).
• Successful rides generated ₹18,208 in booking value.

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📈 SQL Concepts Used

SELECT · WHERE · GROUP BY · ORDER BY · COUNT() · SUM() · AVG() · MAX() · MIN() · LIMIT · Aggregate Functions · Filtering · Sorting · Data Analysis

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📌 Business Questions Answered

• How many bookings were successfully completed?
• What is the average ride distance for each vehicle type?
• How many rides were cancelled by customers?
• Which customers book the most rides?
• Why are drivers cancelling rides?
• Which vehicle type has the highest customer ratings?
• How much revenue was generated from successful bookings?
• Which rides were paid through UPI?
• Which rides remained incomplete, and why?

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▶️ How to Run

1. Create the database and table:

   CREATE DATABASE ola;
   USE ola;

2. Import Dataset/ola_bookings.csv into a table named bookings (MySQL Workbench → Table Data Import Wizard).
3. Open and run Ola project.sql — each query is labelled with the business question it answers.

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📚 Learning Outcomes

Through this project, I practiced:

• Writing analytical SQL queries
• Using aggregate functions
• Grouping and summarizing data
• Solving real-world business problems
• Extracting insights from structured datasets
• Working with relational databases

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🚀 Future Improvements

• Advanced SQL using Window Functions
• Common Table Expressions (CTEs)
• Stored Procedures & SQL Views
• Index optimization
• A Power BI dashboard on the same dataset
• Exploratory Data Analysis (EDA)

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👨‍💻 Author

Tejus Pandey

• 💼 LinkedIn: https://www.linkedin.com/in/tejuspandey
• 🐙 GitHub: https://github.com/tejus23

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