🏡 House Price Prediction

📜 Description

• This project is a house price prediction system that estimates property prices based on various features, with a focus on properties in Bangalore, Karnataka, India.
• It combines machine learning and data science techniques to provide accurate price predictions.

🎥 Demo

Watch the demo video of the project: Demo Video

🌟 Features

• Predict house prices based on features such as area, location, number of rooms, and more.
• Interactive web interface for user input and displaying results.
• Backend server implemented with Flask to handle predictions.

🛠️ Tech Stack

• Programming Language: Python
• Machine Learning Library: Scikit-Learn
• Data Science Libraries: Pandas, NumPy
• Web Framework: Flask
• Frontend: HTML5, CSS3, JavaScript
• Data Storage: CSV files for dataset

🚀 Installation and Usage

1. Clone the Repository:

   git clone https://github.com/user/house-price-prediction.git

2. Navigate to the Project Directory:

   cd house-price-prediction

3. Create a Virtual Environment (if not already created):

   python -m venv venv

4. Activate the Virtual Environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

5. Install Dependencies:

   pip install -r requirements.txt

6. Navigate to the Server Directory:

   cd server

7. Run the Server:

   python server.py

8. In a new terminal, navigate to the Client Directory:

   cd house-price-prediction/client

9. Run the HTML file:

   • On Windows:

     start index.html

   • On macOS:

     open index.html

   • On Linux:

     xdg-open index.html

🌐 Web Page

📚 Additional Resources

• Flask Documentation
• NumPy Documentation
• Pandas Documentation
• Scikit-Learn Documentation