dhanashrisangle/swiggy-data-analysis-using-mysql

Swiggy-Data-Analysis-using-MySql

• Project Overview:

This repository contains SQL queries for analyzing a dataset from Swiggy, a popular food delivery platform. The Swiggy Dataset Analysis project involves analyzing a dataset obtained from Swiggy, a popular food delivery platform. Using SQL queries, various aspects of the dataset are explored to gain insights into restaurant name, restaurant address, ratings, city, food items, menu catagory, cuisine, price per person, veg or nonveg, restaurant performance and more.

• Purpose of the Project:

  The primary purpose of the project is to demonstrate proficiency in SQL by performing
  data analysis tasks on a real-world dataset.By analyzing the Swiggy dataset, the project
  aims to showcase the ability to extract meaningful insights and make data-driven decisions.
  

• Data Source:

  The dataset was obtained from google. The dataset contains information about restaurant name, restaurant address,
  ratings, city, food items, menu catagory, cuisine, price per person, veg or nonveg on Swiggy platform.
  

• Project Steps:

• Data Import:

   Imported Swiggy data from csv file contaning all the information about restaurants and all.
  

• SQL Querying:

   Utilize MySQL to read and manipulate the dataset.
  

• Data Manipulation:

   Used SQL functions and clauses (e.g., DISTINCT, COUNT, WHERE, GROUP BY, ORDER BY, HAVING)
   to filter, aggregate, and analyze data.
  

• Skills Demonstrated:

   SQL, Data Analysis, Query Optimization.
  

Key Achievements:

                * Developed complex SQL queries incorporating DISTINCT, COUNT, WHERE, GROUP BY, ORDER BY, HAVING clauses.  
                
                * Leveraged SQL functions like AVG, MAX for calculating averages, maximum values, etc.                                                         
                
                * Implemented self-joins for correlating data within the same table.
              
                * Optimized query performance for efficient data retrieval.

Outputs:

     The project outputs include SQL queries, analysis results. These outputs provide insights into various aspects of 
     the Swiggy dataset and can be used for decision-making purposes by stakeholders.

Conclusion:

      Through this project, the ability to manipulate and analyze data using SQL is demonstrated, along with the capability
      to derive actionable insights from real-world datasets. The project showcases valuable skills in data analysis, 
      query optimization, and data-driven decision-making, which are essential in various industries and domains.