House Price Prediction Application for Armenia

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Welcome to the House Price Prediction application repository, designed to work seamlessly with my ML model tailored for predicting house prices in Armenia!

Overview

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This repository hosts the codebase for our House Price Prediction application, which integrates with our ML model to provide users with the ability to forecast house prices that can be sold within Armenia's real estate market landscape. The application offers a user-friendly interface for interacting with the model, making it easy for users to obtain accurate house price predictions.

Features

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• Seamlessly integrates with our ML model for accurate house price predictions.
• User-friendly interface for ease of use.
• Tailored specifically for Armenia's real estate market landscape.

Getting Started

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To get started with using our House Price Prediction application, follow these steps:

1. Clone this repository to your local machine.
2. Install the required dependencies by running:
   pip install -r requirements.txt
   This will install all the necessary packages listed in the requirements.txt file.
3. Run house.parse_link() in parse_logic.py to obtain the necessary data. (Note: The data file is over 100MB and cannot be included in this repository. Your data should look like this after parsing Example Data).
4. Run Analyse.ipynb to model the ML model. (Note: The model file is over 600MB and cannot be included in this repository).
5. Run the application using app.py.

Usage

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After running app.py on http://127.0.0.1:5000 you will see this page.

After clicking the Predict Price button, the price will appear below the button.

Note! You can find house latitude and longtitude here

Documentation

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For detailed documentation and usage instructions, please refer to our Sphinx documentation: Project Documentation

Model Information

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For price prediction is used RandomForest model that fits best for needs of accurate prediction. Screenshot of model's Mean Squared Error, Mean Absolute Error, and R^2 score (R-squared).

Contributing

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I welcome contributions from the community! If you'd like to contribute to this project, please follow these guidelines:

1. Fork this repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and ensure tests pass.
4. Submit a pull request with a clear description of your changes.

License

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This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. See the LICENSE file for details.

Contact

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If you have any questions or feedback, feel free to reach out to me at [email protected].