https://github.com/devlm7/2_data_analysis

In this Power BI project, we analyzed the Superstore dataset to uncover meaningful business insights through interactive data visualizations. The data was first cleaned and prepared by removing unnecessary columns, fixing the date format, and correcting data types to ensure accuracy.
https://github.com/devlm7/2_data_analysis

Last synced: 5 months ago
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In this Power BI project, we analyzed the Superstore dataset to uncover meaningful business insights through interactive data visualizations. The data was first cleaned and prepared by removing unnecessary columns, fixing the date format, and correcting data types to ensure accuracy.

• Host: GitHub
• URL: https://github.com/devlm7/2_data_analysis
• Owner: DevLM7
• Created: 2025-04-09T16:26:08.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-09T16:34:28.000Z (about 1 year ago)
• Last Synced: 2025-04-30T22:55:21.144Z (about 1 year ago)
• Homepage:
• Size: 1.81 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          

# 📊 Superstore Sales Dashboard – Power BI Project

Welcome to the **Superstore Sales Analysis** project — a sleek and insightful Power BI dashboard designed to transform raw retail data into clear business intelligence.

This project was created as part of a hands-on learning experience in **Power BI**, where I explored everything from data cleaning to advanced visualization.

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## 🔍 What This Dashboard Covers

This dashboard provides a comprehensive breakdown of sales performance using the **Superstore dataset**, focusing on:

- 📍 **Region-wise Sales & Profit**

- 📈 **Monthly Sales Trends**

- 💰 **Profit Margins**

- 📦 **Top-level KPIs (Sales, Profit, Orders)**

- 🧭 **Interactive Filters (Slicers)**

- 🎯 **Key Insights and Business Recommendations**

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## 🧹 Data Preparation Highlights

- Removed unnecessary columns (e.g., Row ID)

- Fixed **Order Date** format (MM-DD-YYYY → proper Date format)

- Set correct data types for sales figures, dates, and categorical fields

- Cleaned and standardized fields to ensure accuracy in reporting

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## 💡 Key Takeaways

- **West** region has the highest overall sales

- **South** region shows strong profitability despite lower sales

- Month-on-month sales trend reveals seasonal patterns

- Filtering by Region enables targeted performance analysis

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## 🛠️ Tools Used

- **Power BI Desktop**

- **Superstore Dataset** (from Kaggle)

- DAX for calculated metrics like **Profit Margin**

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## 👨‍💼 Purpose

This dashboard was created as a **Power BI learning task** using a real-world dataset, as part of my internship with **Elevate Labs**.  

It showcases how data visualization can reveal business insights that spreadsheets often hide.

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