Propalytic

🏠 Propalytic - Professional House Price Prediction App

🏠 Overview

Propalytic is a professional-grade house price prediction application built with Streamlit, featuring advanced machine learning models trained on comprehensive real estate data. The application provides accurate price estimates with user-friendly interfaces and comprehensive analytics.

✨ Features

🚀 Quick Start

Prerequisites

Installation & Setup

  1. Download or clone this project
  2. Run the application:
    • Windows: Double-click run_app.bat
    • Command line: python -m streamlit run src/app.py
  3. First run: Dependencies will be automatically installed
  4. Open your browser to http://localhost:8501

Simple One-Click Launch

The run_app.bat file handles everything automatically:

📁 Project Structure

Propalytic/
├── src/
│   ├── app.py                    # Main Streamlit application
│   ├── components/               # UI components
│   │   ├── modern_cards.py      # Modern card components
│   │   ├── option_menu_navbar.py # Navigation menu
│   │   ├── prediction_display.py # Prediction display components
│   │   └── team.py              # Team information
│   ├── models/                  # ML models and predictors
│   │   ├── predictor.py         # Main predictor class
│   │   ├── house_price_model.joblib # Trained model file
│   │   └── scaler.joblib        # Feature scaler
│   └── utils/                   # Utility functions
│       ├── utils.py            # General utilities
│       ├── data_utils.py       # Data processing utilities
│       └── feature_mapping.py  # Feature name mapping
├── assets/
│   └── style.css               # Propalytic custom CSS styles
├── data/                       # Dataset files
│   ├── train.csv              # Training dataset
│   ├── test.csv               # Test dataset
│   ├── X_train.csv            # Processed training features
│   └── selected_features.csv  # Selected feature list
├── ML/                         # Machine learning notebooks
│   └── House price.ipynb      # Model development notebook
├── .streamlit/                 # Streamlit configuration
│   └── config.toml            # App configuration
├── pages/                      # Additional Streamlit pages
├── requirements.txt            # Python dependencies
├── run_app.bat                # One-click Windows launcher
└── README.md                  # This file

🎯 Application Features

🏡 Price Prediction

📊 Analytics & Insights

🎨 Modern User Experience

🤖 Model Information

Training Data

Model Performance

Key Features

🎨 Propalytic Design System

Professional Branding

User Interface Features

Technical Features

🔧 Customization Guide

Styling

Edit assets/style.css to customize:

Features

Edit src/utils/feature_mapping.py to:

Model Integration

Edit src/models/predictor.py to:

🚀 Deployment Options

Local Development

# Simple command line launch
python -m streamlit run src/app.py

# Or use the automated launcher
run_app.bat  # Windows - handles all setup automatically

The run_app.bat launcher provides a complete automated experience:

  1. Python Detection: Automatically checks for Python 3.8+
  2. Dependency Management: Installs required packages if missing
  3. Error Handling: Clear error messages and troubleshooting
  4. One-Click Launch: Just double-click to start Propalytic

Cloud Deployment

Streamlit Cloud

  1. Push to GitHub repository
  2. Connect to Streamlit Cloud
  3. Deploy automatically from main branch
  4. Uses requirements.txt for dependencies

Docker Deployment

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8501
HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
CMD ["streamlit", "run", "src/app.py", "--server.port=8501", "--server.address=0.0.0.0"]

Cloud Platforms

Production Configuration

# For production deployment
streamlit run src/app.py --server.port=8501 --server.address=0.0.0.0

📈 Performance & Optimization

Automated Setup Features

Application Performance

Error Handling

Security Features

🤝 Contributing

Getting Started

  1. Fork the repository
  2. Set up development environment: 
    # Clone your fork
git clone <your-fork-url>
   
# Run the app to test setup
run_app.bat  # or python -m streamlit run src/app.py
  3. Create a feature branch
  4. Make your changes following the Propalytic design system
  5. Test thoroughly with the automated launcher
  6. Submit a pull request

Development Workflow

Code Standards

Design Guidelines

Deployment Testing

Before submitting changes, test with:

# Test automated setup
run_app.bat

# Test manual setup
python -m streamlit run src/app.py

# Verify all dependencies are in requirements.txt
pip install -r requirements.txt

🏢 About Propalytic

Propalytic is a professional real estate analytics platform that leverages advanced machine learning to provide accurate property valuations and market insights. This application demonstrates the power of combining sophisticated data science with intuitive user experience design.

Key Features

🚀 Getting Started Summary

For End Users

  1. Download the Propalytic application
  2. Double-click run_app.bat (Windows)
  3. Wait for automatic setup to complete
  4. Open your browser to http://localhost:8501
  5. Start predicting house prices!

For Developers

  1. Clone the repository
  2. Run run_app.bat to test the setup
  3. Edit files using your preferred IDE
  4. Test changes with the automated launcher
  5. Deploy using the provided deployment guides

🙏 Acknowledgments

📞 Support & Contact

For support, feature requests, or bug reports:

Troubleshooting

If you encounter issues:

  1. Python: Ensure Python 3.8+ is installed and in your PATH
  2. Dependencies: Let run_app.bat handle automatic installation
  3. Permissions: Run as administrator if needed on Windows
  4. Firewall: Allow Python/Streamlit through your firewall for localhost access

🏠 Built with ❤️ by the Propalytic Team Real Estate Analytics - Powered by Machine Learning

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