README.md

metadata

title: Business Intelligence Dashboard
emoji: 📊
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 3.50.2
app_file: app.py
pinned: false

📊 Business Intelligence Dashboard

A professional, interactive Business Intelligence dashboard built with Gradio that enables non-technical stakeholders to explore and analyze business data.

🌟 Features

📂 Data Management

• Pre-loaded Datasets: Online Retail and Airbnb datasets included
• Custom Upload: Support for CSV, Excel (.xlsx, .xls), JSON, and Parquet files (max 50MB)
• Automatic Data Cleaning: Handles missing values, type conversions, and duplicate removal
• Data Validation: Comprehensive error handling and user-friendly error messages

📈 Statistics & Profiling

• Automated Data Profiling: Get instant insights into your dataset
• Numerical Summary: Mean, median, std deviation, quartiles, min/max
• Categorical Analysis: Unique values, value counts, mode
• Missing Values Report: Identify data quality issues
• Correlation Matrix: Visual correlation heatmap for numerical features

🔍 Interactive Filtering

• Dynamic Filters: Filter by numerical ranges, categorical values, or date ranges
• Real-time Updates: See row counts update as you apply filters
• Multiple Filters: Combine multiple filters for precise data exploration
• Filter Management: Easy to add, view, and clear filters

📉 Smart Visualizations

• AI-Powered Recommendations: Get intelligent visualization suggestions based on your data
• One-Click Creation: Create recommended visualizations with a single click
• 5 Visualization Types:
  • Time Series Plots (with aggregation: sum, mean, count, median)
  • Distribution Plots (histogram, box plot)
  • Category Analysis (bar chart, pie chart)
  • Scatter Plots (with color coding and trend lines)
  • Correlation Heatmap
• Dual Backend: Supports both Matplotlib and Plotly
• Customization: Full control over columns, aggregations, and visual parameters

💡 Automated Insights

• Top/Bottom Performers: Identify highest and lowest values
• Trend Analysis: Detect patterns over time with growth rate and volatility
• Anomaly Detection: Find outliers using Z-score or IQR methods
• Distribution Analysis: Understand data distributions with skewness and kurtosis
• Correlation Insights: Discover strong relationships between variables

💾 Export Capabilities

• Data Export: Export filtered data as CSV or Excel
• Visualization Export: Save charts as PNG images

🏗️ Architecture & Design

SOLID Principles Implementation

• Single Responsibility: Each class has one clear purpose
• Open/Closed: Extensible through Strategy Pattern without modifying existing code
• Liskov Substitution: All strategies are interchangeable
• Interface Segregation: Specific interfaces for different operations
• Dependency Inversion: Depends on abstractions, not concrete implementations

Design Patterns

• Strategy Pattern: Used for data loading, visualizations, and insights
• Facade Pattern: DataProcessor provides simple interface to complex operations
• Factory Pattern: Dynamic strategy selection based on file type

Project Structure

Business-Intelligence-Dashboard/
├── app.py                      # Main Gradio application with 6 tabs
├── data_processor.py           # Data loading, cleaning, filtering (Strategy Pattern)
├── visualizations.py           # Chart creation with multiple strategies
├── insights.py                 # Automated insight generation
├── utils.py                    # Utility functions and validators
├── requirements.txt            # Python dependencies
├── README.md                   # This file
├── data/                       # Sample datasets
│   ├── Online_Retail.xlsx
│   └── Airbnb.csv
└── tests/                      # Comprehensive test suite
    ├── init.py
    ├── conftest.py
    ├── test_utils.py
    ├── test_data_processor.py
    ├── test_visualizations.py
    └── test_insights.py

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Clone the repository

git clone https://github.com/CR1502/Business-Intelligence-Dashboard.git
cd Business-Intelligence-Dashboard

2. Create a virtual environment

# On macOS/Linux
python3 -m venv venv
source venv/bin/activate

# On Windows
python -m venv venv
venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run the application

python app.py

The dashboard will launch and open in your default browser at http://localhost:7860

📖 Usage Guide

1. Loading Data

• Option A: Select "Online Retail" or "Airbnb" from the dropdown
• Option B: Upload your own dataset (CSV, Excel, JSON, or Parquet)

2. Exploring Statistics

• Navigate to "Statistics & Profiling" tab
• Click "Generate Data Profile" to see comprehensive statistics
• View missing values, numerical summaries, and correlation matrix

3. Filtering Data

• Go to "Filter & Explore" tab
• Select filter type (Numerical, Categorical, or Date)
• Choose column and set filter criteria
• Click "Add Filter" and see real-time updates

4. Creating Visualizations

• Navigate to "Visualizations" tab
• Smart Recommendations: Click "Get Visualization Recommendations" for AI-powered suggestions
• Custom Visualizations: Select visualization type and configure parameters
• Supported charts: Time Series, Distribution, Category, Scatter, Correlation

5. Generating Insights

• Go to "Insights" tab
• Click "Generate All Insights" for automated analysis
• Or select specific insight type for targeted analysis

6. Exporting Results

• Navigate to "Export" tab
• Choose format (CSV or Excel)
• Click "Export Data" to download filtered dataset

🧪 Testing

Run the comprehensive test suite:

# Run all tests
pytest tests/ -v

# Run specific test file
pytest tests/test_utils.py -v

# Run with coverage
pytest tests/ --cov=. --cov-report=html

Test coverage includes:

• 180+ test cases across all modules
• Unit tests for all functions and classes
• Strategy Pattern implementation tests
• Edge case and error handling tests

🛠️ Technologies Used

• Gradio: Web interface and interactive components
• Pandas: Data manipulation and analysis
• NumPy: Numerical computations
• Matplotlib/Seaborn: Static visualizations
• Plotly: Interactive visualizations
• Python 3.10+: Core programming language

📊 Sample Datasets

Online Retail Dataset

• 8 columns: InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country
• Use case: E-commerce sales analysis, product trends, customer analysis

Airbnb Dataset

• 26 columns: Including price, location, room type, reviews, availability
• Use case: Pricing analysis, location trends, booking patterns

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Development Guidelines

• Follow PEP 8 style guidelines
• Add docstrings to all functions
• Include unit tests for new features
• Update README.md for significant changes

👨‍💻 Author

Craig Roberts

🙏 Acknowledgments

• Northeastern University - CS5130 Course (Prof Lino)
• Dataset sources: UCI ML Repository, Kaggle

⚡ Performance Notes

• Handles datasets up to 50MB efficiently
• Optimized for 1,000-10,000 rows
• Tested with datasets containing 100+ columns
• Real-time filtering with sub-second response times

🐛 Known Issues

• Large datasets (>100MB) may cause memory issues
• Some complex visualizations may take time to render
• Browser storage not available (by design for security)

---