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README.md

Swiggy Food Delivery Analysis Using SQL

Project Title:

Optimizing Food Delivery Analysis Using MySQL

Project Overview:

This SQL-based analysis project focuses on understanding and improving food delivery operations using a sample dataset from Swiggy — a leading online food delivery platform in India. It includes advanced SQL queries to derive business insights from data related to restaurants, delivery time, ratings, food types, and customer feedback.

Objective:

To enhance business decision-making by analyzing key metrics such as:

  • Delivery time

  • Food type preferences

  • Customer ratings

  • Restaurant pricing

  • Area and city-wise performance

This project leverages MySQL to write clean, optimized queries for data retrieval, manipulation, and creation of new feedback data structures.

Dataset Overview:

Column Name Description
ID Unique identifier for each record
Area Area/locality of the restaurant
City City in which the restaurant is located
Restaurant Name of the restaurant
Price Average price per order
Avg_ratings Average customer rating
Total_ratings Total number of ratings received
Food_type Types of cuisines served
Address Detailed address of the restaurant
Delivery_time Time taken to deliver the order (in minutes)

Key SQL Tasks Performed:

Data Exploration & Aggregation:

  1. City-wise restaurant count where delivery time > 60 minutes.

  2. All unique food types offered.

  3. Filtered restaurant listings from Koramangala with price between 200–400 and ratings > 4.

  4. List of restaurants serving Chinese cuisine.

  5. Total number of restaurants per city.

Advanced Filtering & Ranking:

  1. Restaurants in Bangalore with more than 100 total ratings.

  2. Top 5 restaurants with the highest average ratings.

  3. Restaurants in either Koramangala or Indiranagar.

Aggregation Metrics:

  1. Average delivery time for restaurants serving Biryani.

Data Manipulation:

  1. Delete all restaurants where price < 200.

  2. Update delivery time to 45 mins for restaurants with more than 500 ratings.

  3. Create and populate a new customer_feedback table with constraints and sample data.

Technologies Used:

  • SQL (MySQL)

  • DDL & DML operations

  • Filtering, Aggregation, Joins, and Subqueries

Outcome:

This project demonstrates practical SQL skills used in real-world data analytics:

  • Identifying performance bottlenecks

  • Filtering restaurant options by multiple criteria

  • Manipulating data and creating new tables

  • Delivering insights that can guide operational improvements for food delivery platforms