Corporate Financial Analysis & Machine Learning Dashboard

A Streamlit-based dashboard for analyzing corporate financial data with machine learning predictions, featuring a professional dull black and off-white theme.

Features

• 📊 Interactive financial data visualization
• 🤖 Machine learning EPS prediction model
• 📈 Real-time financial metrics analysis
• 🎨 Professional dark theme design
• 📱 Responsive layout for all devices

Demo

Watch the demo video on YouTube

Click the button above to watch the interactive dashboard demo

Local Development

Prerequisites

• Python 3.11+
• pip or poetry

Setup with Virtual Environment

1. Clone or download the project
2. Run the setup script:

   ./setup.sh

3. Activate the virtual environment:

   source venv/bin/activate

4. Run the dashboard:

   streamlit run app.py

Manual Setup

1. Create virtual environment:

   python3 -m venv venv
   source venv/bin/activate

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   streamlit run app.py

The dashboard will be available at http://localhost:8501

Deployment on Railway

Option 1: Direct Deploy Button

Option 2: Manual Deployment

1. Create a new Railway project
2. Connect your GitHub repository
3. Railway will automatically detect the configuration files and deploy

Environment Variables

No additional environment variables are required for basic functionality.

Project Structure

cooperate-Analysis and Machine Learning/
├── app.py                          # Main Streamlit application
├── requirements.txt                # Python dependencies
├── pyproject.toml                 # Poetry configuration
├── Procfile                       # Process file for deployment
├── runtime.txt                    # Python version specification
├── railway.json                   # Railway deployment config
├── nixpacks.toml                  # Nixpacks build configuration
├── setup.sh                       # Environment setup script
├── .env                          # Environment variables
├── .streamlit/
│   └── config.toml               # Streamlit configuration
└── README.md                     # This file

Key Components

Dashboard Features

• Financial Metrics: Revenue, Net Income, EPS, ROE analysis
• Interactive Charts: Time series, scatter plots, box plots
• Company Comparison: Multi-company analysis capabilities
• Industry Analysis: Cross-industry performance comparison

Machine Learning Model

• Algorithm: Random Forest Classifier
• Purpose: EPS performance prediction (above/below median)
• Features: Automated feature selection
• Accuracy: Real-time model performance metrics

Theme Customization

• Colors: Dull black (#1a1a1a) background, off-white (#f5f5f5) text
• Professional: Designed for business presentations
• Responsive: Optimized for desktop and mobile viewing

Data Source

The dashboard currently uses synthetic financial data for demonstration purposes. To use real data:

1. Replace the load_data() function in app.py
2. Connect to your financial data source (CSV, database, API)
3. Ensure data follows the expected schema

Technology Stack

• Frontend: Streamlit
• Data Processing: Pandas, NumPy
• Visualization: Plotly, Seaborn, Matplotlib
• Machine Learning: Scikit-learn
• Deployment: Railway, Nixpacks

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test locally
5. Submit a pull request

License

This project is licensed under the MIT License.

Support

For issues and questions:

1. Check the documentation
2. Review existing issues
3. Create a new issue with detailed description

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Note: This dashboard is designed for educational and demonstration purposes. For production use with real financial data, ensure proper data validation, security measures, and compliance with financial regulations.