https://github.com/harshbisht27/walmart-sales-data-analysis--sql-project

This project employs SQL to perform an in-depth analysis of Walmart sales data, drawing valuable insights from Kaggle's Walmart Sales Forecasting Competition dataset to uncover trends and patterns in retail performance.
https://github.com/harshbisht27/walmart-sales-data-analysis--sql-project

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This project employs SQL to perform an in-depth analysis of Walmart sales data, drawing valuable insights from Kaggle's Walmart Sales Forecasting Competition dataset to uncover trends and patterns in retail performance.

• Host: GitHub
• URL: https://github.com/harshbisht27/walmart-sales-data-analysis--sql-project
• Owner: harshbisht27
• Created: 2024-12-05T08:55:43.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-05T09:11:18.000Z (about 1 month ago)
• Last Synced: 2024-12-05T10:20:22.396Z (about 1 month ago)
• Topics: sql
• Homepage:
• Size: 40 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# 📊 Walmart Sales Data Analysis (SQL Project)

This project aims to analyze Walmart's sales data to derive actionable insights into branch performance, customer behavior, and product sales trends. The findings can be used to optimize sales strategies and improve decision-making processes.

## 🛠️ Technologies Used

🗄️ Database: MySQL

📊 Tools: SQL Workbench

💻 Language: SQL

## 📂 Dataset

The data is sourced from the Kaggle Walmart Sales Forecasting Competition. It contains transactional data from three Walmart branches located in Mandalay, Yangon, and Naypyitaw.

## 🎯Project Objectives

Wallpapers sourced from Pinterest 🖼️.

📊 Analyze sales performance across branches.

🏆 Identify high-performing product lines.

🛍️ Understand customer behavior based on type, gender, and purchase patterns.

📅 Explore seasonal and time-based trends in sales.

##  📌Conclusion

This project provides a comprehensive understanding of Walmart’s sales data, highlighting key trends and actionable insights. The findings can help in optimizing inventory management, improving marketing strategies, and enhancing customer experiences.