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📈 Insightify Dashboard

A powerful and interactive sales analytics dashboard built with Streamlit, offering comprehensive insights into superstore sales data.

🌟 Features

  • Real-time KPI Metrics: Track sales, profit, and order volumes with year-over-year changes
  • Interactive Filtering: Filter data by year for targeted analysis
  • Rich Visualizations:
    • Category Performance Scatter Plot
    • Order Distribution Sunburst Chart
    • Monthly Sales Trends Line Chart
    • Top Products Analysis
    • Shipping Performance Gauge
    • Category Sales Trends

🛠️ Tech Stack

  • Streamlit: Core framework for building the web application
  • Pandas: Data manipulation and analysis
  • Plotly: Interactive plotting (scatter plots, sunburst charts, gauges)
  • Altair: Declarative statistical visualizations
  • NumPy: Numerical computations
  • Millify: Number formatting

📋 Prerequisites

  • Python 3.12+
  • Required Python packages: 
    streamlit
pandas
numpy
plotly
altair
millify
  • Excel dataset: superstore_enriched.xlsx

🚀 Installation & Running

  1. Clone the repository
  2. Install dependencies: 
    pip install -r requirements.txt
  3. Run the dashboard: 
    streamlit run app.py

📊 Dashboard Sections

1. KPI Metrics

  • Sales total with YoY change
  • Profit total with YoY change
  • Order count with YoY change

2. Category Performance Analysis

  • Scatter plot showing Sales vs Profit by Category
  • Sunburst chart displaying order distribution by Region and Segment

3. Sales Trends

  • Interactive line chart showing monthly sales trends by year

4. Product Performance

  • Top 10 selling products
  • Top 10 most profitable products

5. Shipping and Category Trends

  • Gauge chart showing average shipping days
  • Stacked bar chart displaying sales trends by product category

🎨 Customization

The dashboard uses a custom color palette:

CUSTOM_COLORS = {
    'Furniture': '#005C53',
    'Office Supplies': '#9FC131',
    'Technology': '#042940'
}

🔧 Performance Optimizations

  • Data loading is cached using @st.cache_data
  • Efficient data filtering and aggregation
  • Responsive layout using Streamlit's column system

📝 Code Structure

  • Constants: Configuration and color definitions
  • Data Loading: Cached data loading and preprocessing
  • UI Styling: Custom styling for metrics and layout
  • Dashboard Components: Modular functions for each visualization
  • Main Function: Orchestrates the dashboard assembly

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.