Hello Dear User, This Streamlit application provides predictions on real estate prices based on user-inputted property details. Leveraging a trained machine learning model(GradientBoostingRegressor), it interprets factors such as type, size, amenities, and location to estimate market values effectively.
- User Inputs: Users can input various property details including type, number of rooms, size, and amenities through an intuitive interface.
- Dynamic Predictions: Provides real-time property price predictions as users input or modify the details.
- Styled Components: Custom HTML and CSS are used for styling, enhancing the visual presentation of the app.
# Clone the repository
git clone [email protected]:mstfgul/Immo_Eliza_Deployment.git
cd myStreamlit
# Set up a virtual environment
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
# Install required packages
pip install -r requirements.txt
### How to Run the App
With the dependencies installed, you can start the app using Streamlit:
streamlit run app.py
The app will be live at http://localhost:8501 in your web browser.
-
streamlit: For creating the web interface and handling app logic.
-
pandas: For managing data structures.
-
pickle: To load pre-trained models and some features data.
app/
│
├── app.py # Main application script
├── model.pkl # Serialized machine learning model
├── commune_encoder.pkl # Serialized label encoder for communes
├── requirements.txt # Python dependencies
└── README.md # Project documentationMustafa Gul - Initial Work - https://github.com/mstfgul