https://github.com/mdanwarulkarim/pizzahut_sales_report_sql

The analysis of pizza sales using SQL involes the various sales trends, month wise sales analysis, category wise analysis and many more. Undercover different insights into ccustomer preferences and choices.
https://github.com/mdanwarulkarim/pizzahut_sales_report_sql

sql

Last synced: about 2 months ago
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The analysis of pizza sales using SQL involes the various sales trends, month wise sales analysis, category wise analysis and many more. Undercover different insights into ccustomer preferences and choices.

• Host: GitHub
• URL: https://github.com/mdanwarulkarim/pizzahut_sales_report_sql
• Owner: mdanwarulkarim
• Created: 2024-11-07T17:33:58.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-07T17:45:40.000Z (2 months ago)
• Last Synced: 2024-11-07T18:34:44.651Z (2 months ago)
• Topics: sql
• Homepage:
• Size: 9.32 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Pizzahut_Sales_Report_SQL

Project Overview

This project analyzes Pizza Hut sales data using SQL to gain insights into sales performance, customer preferences, and revenue trends. The report covers:

## Introduction to deep note

This project is done using Deepnote which is like an SQL program and makes an analysis out of it. The best part is you can make an app out of our analysis and share it in two to three steps.

## Top-selling pizzas and item popularity

Sales trends over time and by season

Customer demographics and order preferences

Revenue analysis by order type

These insights help in making data-driven decisions to optimize sales strategies and improve customer satisfaction.

## Project Structure

SQL Queries: All SQL queries are used to extract, filter, and aggregate data.

Data Files: Sample data used for the analysis.

Report PDF: Download the Full Pizza Hut Sales Report

Getting Started

Clone this repository.

Run the SQL scripts on your preferred SQL database to explore the data and analysis.

View the final report in the PDF to see the compiled insights and visualizations.

Tools & Technologies

## SQL for data extraction and analysis

Reporting tools like Tableau/Excel for visualization (optional)