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📊 End-to-End Sales Data Analytics

An interactive sales analytics dashboard built with Python and Streamlit, using the classic Superstore dataset. This project demonstrates a complete data analytics pipeline — from raw data ingestion and cleaning to KPI calculation and interactive visualizations.

🚀 Features

  • Interactive Sidebar Filters — Filter the entire dashboard by Year, Region, and Category in real time
  • 5 Key Performance Indicators — Total Sales, Total Profit, Total Orders, Avg. Order Value, and Profit Margin
  • 5 Visualizations:
    • Monthly Sales Trend (area line chart)
    • Sales & Profit by Category (grouped bar chart)
    • Sales by Region (bar chart)
    • Profit by Sub-Category (horizontal bar chart with green/red coloring)
    • Sales Share by Customer Segment (pie chart)
  • Raw Data Explorer — Browse filtered data in an interactive table and download it as a CSV

🗂️ Project Structure

├── app.py              # Main Streamlit application
├── Superstore.csv      # Source dataset
├── requirements.txt    # Python dependencies
├── .gitignore          # Git exclusion patterns
└── README.md           # Project documentation

🛠️ Tech Stack

Tool Purpose
Python 3.x Core programming language
Pandas Data loading, cleaning, and aggregation
Streamlit Interactive web dashboard framework
Matplotlib Chart rendering
Seaborn Chart styling and higher-level plots

⚙️ Setup & Installation

Prerequisites

  • Python 3.8 or higher
  • pip (Python package manager)

Steps

  1. Clone or download this repository to your local machine.

  2. Navigate to the project directory:

    cd Python-Project

  3. Install dependencies:

    pip install -r requirements.txt

  4. Run the dashboard:

    python -m streamlit run app.py

  5. The app will open automatically in your browser at http://localhost:8501

📦 Dataset

The project uses the Sample Superstore dataset — a widely-used retail sales dataset containing order details across categories like Furniture, Office Supplies, and Technology across regions of the United States.

Key columns used:

Column Description
Order Date Date the order was placed
Sales Revenue generated per order line
Profit Net profit per order line
Quantity Units sold
Discount Discount applied
Category Product category
Sub-Category Product sub-category
Region Geographic region (East, West, Central, South)
Segment Customer segment (Consumer, Corporate, Home Office)

📈 Analytics Pipeline

Raw CSV Data
     │
     ▼
Data Loading (with encoding fallback)
     │
     ▼
Data Cleaning (datetime parsing, derived columns)
     │
     ▼
Interactive Filtering (sidebar)
     │
     ▼
KPI Calculation
     │
     ▼
Visualizations & Dashboard
     │
     ▼
Export (CSV download)

📸 Dashboard Sections

  1. KPI Metrics Row — High-level business health at a glance
  2. Monthly Sales Trend — Identify seasonality and growth patterns
  3. Category & Region Charts — Understand where revenue comes from
  4. Sub-Category Profitability — Spot which products lose money (shown in red)
  5. Customer Segment Breakdown — See which customer type drives the most sales
  6. Data Explorer — Drill into the raw filtered data

👨‍💻 Author

Built as part of the Data Analytics with Python subject project.