{"id":22072826,"url":"https://github.com/mdanwarulkarim/supershop_sales_analysis_sql","last_synced_at":"2026-01-31T14:32:58.089Z","repository":{"id":265250474,"uuid":"895536682","full_name":"mdanwarulkarim/Supershop_Sales_Analysis_SQL","owner":"mdanwarulkarim","description":" This project examines customer demographics, sales trends, and high-value transactions, uncovering patterns, top customers, and category performance. 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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.\n\n## Objectives\n**Database Setup**: Create and populate a database with retail sales data.\nData Cleaning: Identify and address null or missing values.\n**Exploratory Analysis**: Understand data distribution and key metrics.\n**Business Insights**: Solve specific business queries to guide decision-making.\nProject Structure\n1. Database Setup\nDatabase Creation: Initialize a database named Supershop.\nTable Creation: Create a retail_sales table with the following fields:\nTransaction ID\nSale Date \u0026 Time\nCustomer ID, Gender, Age\nProduct Category, Quantity Sold, Price Per Unit\nCost of Goods Sold (COGS), Total Sale Amount\nThis structured approach ensures a comprehensive understanding of SQL applied to real-world retail data.\n## Findings\n**Customer Demographics**: The data reveals diverse customer age groups with sales spread across categories such as Clothing and Beauty.\nHigh-Value Transactions: A significant number of transactions exceeded 1000 in total sales, highlighting premium purchases.\nSales Trends: Monthly sales analysis uncovers seasonal patterns and peak periods.\nCustomer Insights: Key insights include identifying top-spending customers and the most popular product categories.\n## Reports\n**Sales Overview**: A summary of total sales, customer demographics, and category-wise performance.\nTrend Analysis: Insights into monthly sales trends and performance across different time shifts.\nCustomer Insights: Reports highlighting top customers and unique customer counts per category.\n## Conclusion\nThis 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmdanwarulkarim%2Fsupershop_sales_analysis_sql","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmdanwarulkarim%2Fsupershop_sales_analysis_sql","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmdanwarulkarim%2Fsupershop_sales_analysis_sql/lists"}