Real Estate Value Predictor

A machine learning–powered web application for predicting real estate property values using regression models and engineered housing features.

Description

This project implements an end-to-end workflow for training and deploying a house price prediction system using the Ames Housing dataset. It applies Linear Regression with scaling, one-hot encoding, and correlation-based feature selection. The system includes user authentication, data visualization (feature summary table and pie chart), and stores reusable model artifacts as .pkl files. A Streamlit web application provides an interactive interface where users can input property attributes and receive price predictions in USD.

Getting Started

Dependencies

• Python 3.x
• Required libraries listed in requirements.txt (including Streamlit, Scikit-learn, Pandas, NumPy, Matplotlib)
• Compatible with Windows, macOS, and Linux
• Streamlit for running the web interface

Installing

1. Download or clone the repository:

   bash git clone https://github.com/Droid-DevX/HousePricePrediction cd RealEstateValuePredictor

2. Install required dependencies:

   bash pip install -r requirements.txt

3. Ensure the following serialized model/data files, generated during the training phase, exist in the project directory:

   • model.pkl
   • scaler.pkl
   • columns.pkl

Executing program

1. Launch the Streamlit application:

   bash streamlit run app.py # Assuming your main application file is named app.py

2. Use the default login credentials:

   • Username: Ayush
   • Password: lolipop123

3. After login:

   • Enter property attributes using the input fields.
   • View the predicted price in USD.
   • Review the feature summary table and pie chart visualization.

Help

Common issues:

• Missing model/scaler/column files: Ensure .pkl files are placed in the project directory.
• Module import errors: Reinstall dependencies: bash pip install -r requirements.txt

Authors

Contributors names and contact info

• Ayush Tandon (Droid-DevX)
  • GitHub: https://github.com/Droid-DevX/Droid-DevX

Version History

• 0.2
  • UI improvements
  • Authentication added
  • Visualization features added
  • Model optimization using Ridge Regression
• 0.1
  • Initial regression model and basic Streamlit interface

License

This project is licensed under the MIT License. See the LICENSE file for full details.

Acknowledgments

Inspiration, code snippets, and data sources:

• Ames Housing Dataset
• Scikit-learn
• Streamlit
• Pandas & NumPy
• Matplotlib