Swiggy Restaurant Data Analysis — MySQL

⭐ Project Highlights

• Analyzed 81,777 Swiggy restaurant records using MySQL.
• Cleaned and transformed raw restaurant data.
• Explored restaurant locations, cuisine trends, ratings, and pricing.
• Identified data quality issues and potential cost anomalies.
• Documented findings with SQL queries and visual evidence.

Project Overview

This project analyzes 81,777 Swiggy restaurant records using MySQL to identify patterns in restaurant locations, cuisine categories, ratings, pricing, and the relationship between cost and rating.

The project demonstrates practical SQL skills including data cleaning, transformation, aggregation, filtering, grouping, and business-oriented analysis.

Dataset

• Records: 81,777 restaurant records
• Database: MySQL
• Raw table: swiggy
• Cleaned table: swiggy_cleaned

Main Columns

• Restaurant ID
• Restaurant Name
• Location
• Cuisine
• Rating
• Rating Count
• Cost
• License Number
• Restaurant Link
• Address

Data Cleaning

The raw restaurant data contained rating and cost values stored as text.

The swiggy_cleaned table was created to:

• Convert restaurant ratings into numeric values
• Convert currency-formatted cost values into numeric values
• Handle missing or placeholder rating values
• Preserve the original raw dataset for reference

SQL Analysis

The project includes analysis of:

1. Restaurant distribution by location
2. Popular cuisine categories and combinations
3. Location-wise average restaurant ratings
4. Highest-cost restaurant records
5. Overall rating distribution
6. Cost ranges compared with average ratings
7. Business insights from the analyzed data

Key Findings

• Bikaner had the highest number of restaurant records among the analyzed locations, with 1,440 records.
• Indian was the most frequently occurring individual cuisine category, with 3,595 records.
• The highest average location rating in the analysis was 4.22.
• The largest rating group was 4.0–4.4, containing 15,679 records.
• The ₹500+ cost group had the highest average rating among the analyzed cost ranges at 4.06.
• A recorded cost of ₹300,350 for KOHINOOR HOTEL was identified as a potential data anomaly requiring validation.

The cost-versus-rating analysis identifies differences between price groups but does not establish that higher cost directly causes higher ratings.

Results & Visual Evidence

Restaurant Distribution by Location

Rating Distribution

Cost vs Rating Analysis

Project Structure

Swiggy-SQL-Analysis/
│
├── README.md
├── swiggy_analysis.sql
├── findings.md
├── 01-location-analysis.png
├── 02-rating-distribution.png
└── 03-cost-vs-rating.png

Tools & Technologies

• MySQL
• MySQL Workbench
• SQL
• Git
• GitHub

Skills Demonstrated

• SQL Data Cleaning
• Data Transformation
• Aggregate Functions
• GROUP BY and HAVING
• Filtering with WHERE
• Sorting and Ranking
• CASE Statements
• Regular Expressions
• Business Data Analysis
• Data Quality Checking

How to Run

1. Install MySQL and MySQL Workbench.
2. Create a database named swiggy_analysis.
3. Import the original Swiggy CSV dataset into a table named swiggy.
4. Open swiggy_analysis.sql in MySQL Workbench.
5. Run the data-cleaning query to create swiggy_cleaned.
6. Run the analysis queries to reproduce the findings.

📂 Explore Project Files

File	Description
SQL Analysis	Data cleaning and analysis queries
Detailed Findings	Analysis results and observations
Location Analysis	Restaurant distribution by location
Rating Distribution	Restaurant rating analysis
Cost vs Rating	Pricing and rating comparison

Author

Harshitha C. Software Developer | Aspiring Data Analyst