https://github.com/mdanwarulkarim/supershop_sales_analysis_sql

This project examines customer demographics, sales trends, and high-value transactions, uncovering patterns, top customers, and category performance. The insights provide actionable perspectives to understand sales, customer behavior, and product performance for informed decisions.
https://github.com/mdanwarulkarim/supershop_sales_analysis_sql

eda etl sql

Last synced: about 1 month ago
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This project examines customer demographics, sales trends, and high-value transactions, uncovering patterns, top customers, and category performance. The insights provide actionable perspectives to understand sales, customer behavior, and product performance for informed decisions.

• Host: GitHub
• URL: https://github.com/mdanwarulkarim/supershop_sales_analysis_sql
• Owner: mdanwarulkarim
• Created: 2024-11-28T11:50:04.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-11-28T13:57:39.000Z (about 1 month ago)
• Last Synced: 2024-11-30T21:15:08.385Z (about 1 month ago)
• Topics: eda, etl, sql
• Homepage:
• Size: 1.98 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Supershop_Sales_Analysis

## Project Overview

**Project Title**: Supershop_Sales_Analysis 

**Database Name**: Supershop

This project showcases foundational SQL skills applied to retail sales data. It involves database creation, exploratory data analysis (EDA), and business problem-solving. The primary goal is to clean, analyze, and derive meaningful insights from sales data using SQL queries.

## Objectives

**Database Setup**: Create and populate a database with retail sales data.

Data Cleaning: Identify and address null or missing values.

**Exploratory Analysis**: Understand data distribution and key metrics.

**Business Insights**: Solve specific business queries to guide decision-making.

Project Structure

1. Database Setup

Database Creation: Initialize a database named Supershop.

Table Creation: Create a retail_sales table with the following fields:

Transaction ID

Sale Date & Time

Customer ID, Gender, Age

Product Category, Quantity Sold, Price Per Unit

Cost of Goods Sold (COGS), Total Sale Amount

This structured approach ensures a comprehensive understanding of SQL applied to real-world retail data.

## Findings

**Customer Demographics**: The data reveals diverse customer age groups with sales spread across categories such as Clothing and Beauty.

High-Value Transactions: A significant number of transactions exceeded 1000 in total sales, highlighting premium purchases.

Sales Trends: Monthly sales analysis uncovers seasonal patterns and peak periods.

Customer Insights: Key insights include identifying top-spending customers and the most popular product categories.

## Reports

**Sales Overview**: A summary of total sales, customer demographics, and category-wise performance.

Trend Analysis: Insights into monthly sales trends and performance across different time shifts.

Customer Insights: Reports highlighting top customers and unique customer counts per category.

## Conclusion

This project provides a thorough introduction to SQL for data analysis, encompassing database creation, data cleaning, exploratory analysis, and actionable business insights. The findings offer valuable perspectives on sales patterns, customer behavior, and product performance to support data-driven decision-making.