📊 Global Superstore Sales Dashboard

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A fully interactive, professional Streamlit dashboard built for analyzing the Global Superstore dataset. Designed for business intelligence insights across sales, profit, customer behavior, and regional trends.

📌 Ideal for showcasing in portfolios, BI case studies, or analytics interviews.

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🚀 Live Demo

🌐 [Click here to view the deployed app] (https://your-streamlit-cloud-link)

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🎯 Key Features

✅ Interactive Filters: • Year, Region, Category, Sub-Category

📌 Dynamic KPIs: • Total Sales, Profit, Orders, Unique Customers • 📈 Profit Margin (%)

📊 Charts & Visualizations:

• Sales by Category (Bar Chart)
• Profit by Region (Pie Chart)
• Monthly Sales Trend (Line Chart)
• Product-Level Drill Down (Top 10 Products)
• Top Customers by Profit & Sales
• Customer Segmentation by Sales (Pie Chart)
• Monthly Sales Heatmap (by Category)
• City-Wise Sales Map (Geo Scatter Plot)

📋 Data Preview & Export: • Preview filtered dataset • Download CSV with one click

🖥️ Responsive & Clean UI: • Wide layout, clear color usage, and tooltips

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🧠 Skills Demonstrated

• ✅ Business Intelligence & KPI Dashboards
• ✅ Data Storytelling with Visual Insights
• ✅ Streamlit for Interactive Web Apps
• ✅ Plotly for Rich Data Visualizations
• ✅ Customer Segmentation & Drilldowns
• ✅ Data Cleaning & Preprocessing (Pandas)

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📁 Repository Structure

📦 superstore-streamlit-dashboard
├── 📄 app.py                   # Main Streamlit dashboard app
├── 📄 Global_Superstore.csv    # Dataset used for visualizations
├── 📄 LICENSE                  # MIT License file
├── 📄 README.md                # Project documentation
├── 📄 requirements.txt         # Python dependencies
├── 📄 dataset-readme.md        # Description of dataset fields
├── 📁 screenshots/             # App screenshots used in README
└── 📁 notebook/                # Jupyter notebooks 

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🛠️ Installation & Setup

✅ Requirements

pip install streamlit pandas plotly

Or using requirements.txt:

pip install -r requirements.txt

▶️ Running the App

streamlit run app.py

Ensure Global_Superstore.csv is in the same folder as app.py.

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🧾 Dataset Overview

• Dataset Name: Global Superstore

• Source: Kaggle – Global Superstore Dataset

• Rows: ~10,000+ transactions

• Columns Used:

  • Order Date, Sales, Profit, Region, Country, City, State
  • Category, Sub-Category, Segment
  • Customer Name, Product Name

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📸 Dashboard Screenshots

Explore the key features and visualizations of the Global Superstore Dashboard in a logical flow—from high-level overview to detailed analysis.

📊 Main KPIs and Filters

📋 Preview Data by Year

📅 Monthly Sales

📈 Monthly Sales Trend

🌆 City-Wise Sales Distribution

🧮 Sales by Categories

📦 Product-Level Sales Analysis

💰 Profit Distribution

👥 Customer Segmentation

🏆 Top Customers by Sales

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👨‍💻 Developer

Muhammad Zain Mushtaq

📍 AI/ML & Data Science Enthusiast | Researcher

📧 m.zainmushtaq74@gmail.com 🔗 GitHub LinkedIn · Portfolio

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💬 Contact

If you have any questions, suggestions, or would like to collaborate, feel free to reach out:

• 📧 Email: m.zainmushtaq74@gmail.com
• 🔗 LinkedIn: Muhammad Zain Mushtaq
• 🔗 GitHub: @M-Z-5474

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⭐ Show Your Support

If you found this project helpful:

• 🌟 Star the repo
• 🔁 Fork it
• 🐛 Report issues or suggest improvements
• 🤝 Share it with others

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📝 License

This project is licensed under the MIT License.

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🙌 Thank You for Visiting!

Your support motivates future improvements and contributions.