https://github.com/thariniselvakumar/retailsalesanalysisbysql

This project can help drive business decisions by understanding sales patterns, customer behavior, and product performance.
https://github.com/thariniselvakumar/retailsalesanalysisbysql

business microsoftsql sql sqlserver-2022 ssms

Last synced: 5 days ago
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This project can help drive business decisions by understanding sales patterns, customer behavior, and product performance.

• Host: GitHub
• URL: https://github.com/thariniselvakumar/retailsalesanalysisbysql
• Owner: thariniselvakumar
• Created: 2024-12-28T09:19:15.000Z (11 days ago)
• Default Branch: main
• Last Pushed: 2024-12-28T18:09:20.000Z (11 days ago)
• Last Synced: 2024-12-28T19:18:25.737Z (11 days ago)
• Topics: business, microsoftsql, sql, sqlserver-2022, ssms
• Homepage:
• Size: 4.88 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# RetailSalesAnalysisbySQL

## Project Overview

This project is designed to demonstrate SQL skills and techniques typically used by data analysts to explore, clean, and analyze retail sales data. The project involves setting up a retail sales database, performing exploratory data analysis (EDA), and answering specific business questions through SQL queries. This project is ideal for those who are starting their journey in data analysis and want to build a solid foundation in SQL.

## Objectives

1.Set up a retail sales database: Create and populate a retail sales database with the provided sales data.

2.Data Cleaning: Identify and remove any records with missing or null values.

3.Exploratory Data Analysis (EDA): Perform basic exploratory data analysis to understand the dataset.

4.Business Analysis: Use SQL to answer specific business questions and derive insights from the sales data.

## SQL Queries in separate FILE( https://github.com/thariniselvakumar/RetailSalesAnalysisbySQL/blob/main/RetailSalesSQL.sql)

## Findings

Customer Demographics: The dataset includes customers from various age groups, with sales distributed across different categories such as Clothing and Beauty.

High-Value Transactions: Several transactions had a total sale amount greater than 1000, indicating premium purchases.

Sales Trends: Monthly analysis shows variations in sales, helping identify peak seasons.

Customer Insights: The analysis identifies the top-spending customers and the most popular product categories.

The findings from this project can help drive business decisions by understanding sales patterns, customer behavior, and product performance.