📊 Sales Dashboard - Power BI Project

This project features an interactive and dynamic Sales Dashboard built using Power BI. The underlying data was sourced from a MySQL database, queried using MySQL Workbench, and then modeled, cleaned, and visualized in Power BI using DAX measures and calculated columns.

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📁 Dataset

The data was imported from a structured MySQL database (see db_dump.sql). It includes information related to:

• Sales transactions
• Products and categories
• Customer details
• Dates and order timelines

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📚 Data Source

This project's data was sourced from the Sales Insights Project by Codebasics.
The original database dump (db_dump.sql) is publicly available for educational and non-commercial use.

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📌 Key Features

• 📅 Monthly Sales Trends – Time-series breakdown of revenue across months
• 🛒 Top Performing Products – Based on total sales and order frequency
• 🌍 Geographical Sales Insights (if included in your data)
• 📈 Category-wise Revenue Analysis
• 🎯 KPIs – Total Sales, Number of Orders, Average Order Value, Repeat Customers, etc.
• 📊 Interactive Filters – Slicers by category, product, date range, and customer

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🛠 Tools & Technologies Used

• Power BI Desktop
• MySQL Workbench (for querying the database)
• DAX (Data Analysis Expressions) for calculated fields and measures

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🧠 Challenges Faced

• Exploring and mastering Power BI’s canvas layout for professional storytelling
• Writing efficient and accurate DAX formulas to generate metrics
• Designing a dashboard that is visually intuitive and analytically rich

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💡 Learnings

This project enhanced my understanding of data visualization best practices, efficient data modeling, and the power of combining SQL with BI tools to extract interactive business insights.

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📂 Project Structure

├── Sales-Dashboard.pbix           # Power BI file
├── db_dump.sql                    # MySQL database dump file
└── README.md                      # Project overview

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📊 Dashboard Overview

📌 Page 1 – Overall Performance and Distribution

📌 Page 2 – Top Performing Products and Category Sold

📌 Page 3 – Revenue & Sales Distribution Over Time

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