• Features
• Demo
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Project Structure
• Machine Learning Model
• Web Application
• Deployment
• Contributing
• License

• Real-time real estate price prediction based on user-provided property details.
• Basic web interface for user interaction.
• Integration with a pre-trained machine learning model.
• Clean and user-friendly design.

Before running the application, you need to have the following dependencies installed:

• Python 3.x
• Flask (install with pip install Flask)

1. Clone this repository:

   git clone https://github.com/sahilshukla3003/RealEstatePricePrediction.git
   cd RealEstatePricePrediction

2. Install the required Python packages:

   pip install -r requirements.txt

Start the Flask application:

python app.py

Open a web browser and navigate to http://localhost:5000 to access the web application.

Fill out the property details form, and click the "Predict" button to get a real estate price estimate.

• app.py: The main Flask application.
• templates/: HTML templates for the web interface.
• static/: Static files such as CSS and JavaScript.
• model/: Contains the pre-trained machine learning model.

The real estate price prediction model is trained on a dataset of real estate properties and used to make predictions.

The web application is built with Flask, a Python web framework. It provides a user-friendly interface for users to input property details and receive price predictions.

You can deploy this application to a production environment using platforms like Heroku, AWS, or Azure. Be sure to set up a production-ready web server like Gunicorn for serving the Flask app.

Feel free to contribute to this project. You can add any specific feature and kindly request a pull request.

This project is licensed under the MIT License.