https://github.com/nagar2nd/swiggy-analysis-using-mysql

This project analyzes Swiggy's SQL dataset to derive insights on customer behavior, restaurant performance, and order patterns. By implementing sophisticated SQL queries with intricate joins, the analysis aims to facilitate strategic decision-making for improving service quality and customer satisfaction.
https://github.com/nagar2nd/swiggy-analysis-using-mysql

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This project analyzes Swiggy's SQL dataset to derive insights on customer behavior, restaurant performance, and order patterns. By implementing sophisticated SQL queries with intricate joins, the analysis aims to facilitate strategic decision-making for improving service quality and customer satisfaction.

• Host: GitHub
• URL: https://github.com/nagar2nd/swiggy-analysis-using-mysql
• Owner: Nagar2nd
• Created: 2024-09-11T10:43:29.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-10-22T06:54:52.000Z (3 months ago)
• Last Synced: 2024-10-23T09:53:10.140Z (3 months ago)
• Topics: sql
• Homepage:
• Size: 565 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Swiggy SQL Analysis Project

## Overview

This project aims to extract insights from Swiggy's SQL dataset using sophisticated SQL queries with intricate joins for in-depth analysis and strategic decision-making.

## Dataset

[Swiggy SQL Dataset](https://drive.google.com/file/d/1S32wPjwNUlhi2G5xiW3_wZvGVKipJq-b/view)

The dataset includes information about customers, restaurants, orders, ratings, and delivery partners.

## Queries

The analysis involves executing the following SQL queries:

1. **Display all customers who live in 'Delhi'.**

2. **Find the average rating of all restaurants in 'Mumbai'.**

3. **List all customers who have placed at least one order.**

4. **Display the total number of orders placed by each customer.**

5. **Find the total revenue generated by each restaurant.**

6. **Find the top 5 restaurants with the highest average rating.**

7. **Display all customers who have never placed an order.**

8. **Find the number of orders placed by each customer in 'Mumbai'.**

9. **Display all orders placed in the last 30 days.**

10. **List all delivery partners who have completed more than 1 delivery.**

11. **Find the customers who have placed orders on exactly three different days.**

12. **Find the delivery partner who has worked with the most different customers.**

13. **Identify customers who have the same city and have placed orders at the same restaurants, but on different dates.**

*For the complete SQL queries, please refer to the attached PDF document in the project repository.*

## MySQL Features Used

The following MySQL features were utilized to execute the queries:

- **SELECT Statement**: To retrieve specific columns and data from the database.

- **WHERE Clause**: To filter records based on specified conditions.

- **JOIN Operations**: To combine rows from two or more tables based on a related column, enabling comprehensive analysis.

- **GROUP BY**: To aggregate data across multiple records, facilitating calculations like averages and totals.

- **HAVING Clause**: To filter groups based on aggregate functions, ensuring accurate analysis of grouped data.

- **COUNT() Function**: To count the number of rows that match a specified condition.

- **AVG() Function**: To calculate the average of a specified column.

- **DATE Functions**: To manipulate and filter date values for time-based queries.

- **DISTINCT Keyword**: To return unique values from a column, useful for identifying unique customers or orders.

## Technologies Used

- SQL for data querying and analysis

- Google Drive for dataset storage

- GitHub for version control and collaboration

## Installation

To run the SQL queries locally, follow these steps:

1. Download the dataset from the provided link.

2. Import the dataset into your SQL database management system.

3. Execute the SQL queries to perform the analysis.

## Contribution

Contributions are welcome! If you have suggestions for improvements or additional queries, please fork the repository and submit a pull request.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.